hypothesis in marketing research example

How to write a hypothesis for marketing experimentation

Apr 11, 2021
5 minute read
Creating your strongest marketing hypothesis

The potential for your marketing improvement depends on the strength of your testing hypotheses.

But where are you getting your test ideas from? Have you been scouring competitor sites, or perhaps pulling from previous designs on your site? The web is full of ideas and you’re full of ideas – there is no shortage of inspiration, that’s for sure.

Coming up with something you want to test isn’t hard to do. Coming up with something you should test can be hard to do.

Hard – yes. Impossible? No. Which is good news, because if you can’t create hypotheses for things that should be tested, your test results won’t mean mean much, and you probably shouldn’t be spending your time testing.

Taking the time to write your hypotheses correctly will help you structure your ideas, get better results, and avoid wasting traffic on poor test designs.

With this post, we’re getting advanced with marketing hypotheses, showing you how to write and structure your hypotheses to gain both business results and marketing insights!

By the time you finish reading, you’ll be able to:

Distinguish a solid hypothesis from a time-waster, and
Structure your solid hypothesis to get results and insights

To make this whole experience a bit more tangible, let’s track a sample idea from…well…idea to hypothesis.

Let’s say you identified a call-to-action (CTA)* while browsing the web, and you were inspired to test something similar on your own lead generation landing page. You think it might work for your users! Your idea is:

“My page needs a new CTA.”

*A call-to-action is the point where you, as a marketer, ask your prospect to do something on your page. It often includes a button or link to an action like “Buy”, “Sign up”, or “Request a quote”.

The basics: The correct marketing hypothesis format

Level up: moving from a good to great hypothesis, it’s based on a science, building marketing hypotheses to create insights, what makes a great hypothesis.

A well-structured hypothesis provides insights whether it is proved, disproved, or results are inconclusive.

You should never phrase a marketing hypothesis as a question. It should be written as a statement that can be rejected or confirmed.

Further, it should be a statement geared toward revealing insights – with this in mind, it helps to imagine each statement followed by a reason :

Changing _______ into ______ will increase [conversion goal], because:
Changing _______ into ______ will decrease [conversion goal], because:
Changing _______ into ______ will not affect [conversion goal], because:

Each of the above sentences ends with ‘because’ to set the expectation that there will be an explanation behind the results of whatever you’re testing.

It’s important to remember to plan ahead when you create a test, and think about explaining why the test turned out the way it did when the results come in.

Understanding what makes an idea worth testing is necessary for your optimization team.

If your tests are based on random ideas you googled or were suggested by a consultant, your testing process still has its training wheels on. Great hypotheses aren’t random. They’re based on rationale and aim for learning.

Hypotheses should be based on themes and analysis that show potential conversion barriers.

At Conversion, we call this investigation phase the “Explore Phase” where we use frameworks like the LIFT Model to understand the prospect’s unique perspective. (You can read more on the the full optimization process here).

A well-founded marketing hypothesis should also provide you with new, testable clues about your users regardless of whether or not the test wins, loses or yields inconclusive results.

These new insights should inform future testing: a solid hypothesis can help you quickly separate worthwhile ideas from the rest when planning follow-up tests.

“Ultimately, what matters most is that you have a hypothesis going into each experiment and you design each experiment to address that hypothesis.” – Nick So, VP of Delivery

Here’s a quick tip :

If you’re about to run a test that isn’t going to tell you anything new about your users and their motivations, it’s probably not worth investing your time in.

Let’s take this opportunity to refer back to your original idea:

Ok, but what now ? To get actionable insights from ‘a new CTA’, you need to know why it behaved the way it did. You need to ask the right question.

To test the waters, maybe you changed the copy of the CTA button on your lead generation form from “Submit” to “Send demo request”. If this change leads to an increase in conversions, it could mean that your users require more clarity about what their information is being used for.

That’s a potential insight.

Based on this insight, you could follow up with another test that adds copy around the CTA about next steps: what the user should anticipate after they have submitted their information.

For example, will they be speaking to a specialist via email? Will something be waiting for them the next time they visit your site? You can test providing more information, and see if your users are interested in knowing it!

That’s the cool thing about a good hypothesis: the results of the test, while important (of course) aren’t the only component driving your future test ideas. The insights gleaned lead to further hypotheses and insights in a virtuous cycle.

The term “hypothesis” probably isn’t foreign to you. In fact, it may bring up memories of grade-school science class; it’s a critical part of the scientific method .

The scientific method in testing follows a systematic routine that sets ideation up to predict the results of experiments via:

Collecting data and information through observation
Creating tentative descriptions of what is being observed
Forming hypotheses that predict different outcomes based on these observations
Testing your hypotheses
Analyzing the data, drawing conclusions and insights from the results

Don’t worry! Hypothesizing may seem ‘sciency’, but it doesn’t have to be complicated in practice.

Hypothesizing simply helps ensure the results from your tests are quantifiable, and is necessary if you want to understand how the results reflect the change made in your test.

A strong marketing hypothesis allows testers to use a structured approach in order to discover what works, why it works, how it works, where it works, and who it works on.

“My page needs a new CTA.” Is this idea in its current state clear enough to help you understand what works? Maybe. Why it works? No. Where it works? Maybe. Who it works on? No.

Your idea needs refining.

Let’s pull back and take a broader look at the lead generation landing page we want to test.

Imagine the situation: you’ve been diligent in your data collection and you notice several recurrences of Clarity pain points – meaning that there are many unclear instances throughout the page’s messaging.

Rather than focusing on the CTA right off the bat, it may be more beneficial to deal with the bigger clarity issue.

Now you’re starting to think about solving your prospects conversion barriers rather than just testing random ideas!

If you believe the overall page is unclear, your overarching theme of inquiry might be positioned as:

“Improving the clarity of the page will reduce confusion and improve [conversion goal].”

By testing a hypothesis that supports this clarity theme, you can gain confidence in the validity of it as an actionable marketing insight over time.

If the test results are negative : It may not be worth investigating this motivational barrier any further on this page. In this case, you could return to the data and look at the other motivational barriers that might be affecting user behavior.

If the test results are positive : You might want to continue to refine the clarity of the page’s message with further testing.

Typically, a test will start with a broad idea — you identify the changes to make, predict how those changes will impact your conversion goal, and write it out as a broad theme as shown above. Then, repeated tests aimed at that theme will confirm or undermine the strength of the underlying insight.

You believe you’ve identified an overall problem on your landing page (there’s a problem with clarity). Now you want to understand how individual elements contribute to the problem, and the effect these individual elements have on your users.

It’s game time – now you can start designing a hypothesis that will generate insights.

You believe your users need more clarity. You’re ready to dig deeper to find out if that’s true!

If a specific question needs answering, you should structure your test to make a single change. This isolation might ask: “What element are users most sensitive to when it comes to the lack of clarity?” and “What changes do I believe will support increasing clarity?”

At this point, you’ll want to boil down your overarching theme…

Improving the clarity of the page will reduce confusion and improve [conversion goal].

…into a quantifiable hypothesis that isolates key sections:

Changing the wording of this CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.

Does this answer what works? Yes: changing the wording on your CTA.

Does this answer why it works? Yes: reducing confusion about the next steps in the funnel.

Does this answer where it works? Yes: on this page, before the user enters this theoretical funnel.

Does this answer who it works on? No, this question demands another isolation. You might structure your hypothesis more like this:

Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion for visitors coming from my email campaign about the next steps in the funnel and improve order completions.

Now we’ve got a clear hypothesis. And one worth testing!

1. It’s testable.

2. It addresses conversion barriers.

3. It aims at gaining marketing insights.

Let’s compare:

The original idea : “My page needs a new CTA.”

Following the hypothesis structure : “A new CTA on my page will increase [conversion goal]”

The first test implied a problem with clarity, provides a potential theme : “Improving the clarity of the page will reduce confusion and improve [conversion goal].”

The potential clarity theme leads to a new hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.”

Final refined hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion for visitors coming from my email campaign about the next steps in the funnel and improve order completions.”

Which test would you rather your team invest in?

Before you start your next test, take the time to do a proper analysis of the page you want to focus on. Do preliminary testing to define bigger issues, and use that information to refine and pinpoint your marketing hypothesis to give you forward-looking insights.

Doing this will help you avoid time-wasting tests, and enable you to start getting some insights for your team to keep testing!

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .

Table of Contents

What is a hypothesis ?

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.

What is a research hypothesis ?

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.

Characteristics of a good hypothesis

Here are the characteristics of a good hypothesis :

Clearly formulated and free of language errors and ambiguity
Concise and not unnecessarily verbose
Has clearly defined variables
Testable and stated in a way that allows for it to be disproven
Can be tested using a research design that is feasible, ethical, and practical
Specific and relevant to the research problem
Rooted in a thorough literature search
Can generate new knowledge or understanding.

How to create an effective research hypothesis

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.

Let’s look at each step for creating an effective, testable, and good research hypothesis :

Identify a research problem or question: Start by identifying a specific research problem.
Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.
Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.
State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.
Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.
Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.

How to write a research hypothesis

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.

An example of a research hypothesis in this format is as follows:

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”

Population: athletes

Independent variable: daily cold water showers

Dependent variable: endurance

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.

Research hypothesis checklist

Following from above, here is a 10-point checklist for a good research hypothesis :

Testable: A research hypothesis should be able to be tested via experimentation or observation.
Specific: A research hypothesis should clearly state the relationship between the variables being studied.
Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.
Falsifiable: A research hypothesis should be able to be disproven through testing.
Clear and concise: A research hypothesis should be stated in a clear and concise manner.
Logical: A research hypothesis should be logical and consistent with current understanding of the subject.
Relevant: A research hypothesis should be relevant to the research question and objectives.
Feasible: A research hypothesis should be feasible to test within the scope of the study.
Reflects the population: A research hypothesis should consider the population or sample being studied.
Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.

Research hypothesis: What it is, how to write it, types, and examples

Types of research hypothesis

Different types of research hypothesis are used in scientific research:

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.

Example: “ The newly identified virus is not zoonotic .”

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.

Example: “ The newly identified virus is zoonotic .”

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.

Example, “ Cats and dogs differ in the amount of affection they express .”

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.

Example: “ Applying sunscreen every day slows skin aging .”

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)

7. Associative hypothesis:

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.

Example: “ There is a positive association between physical activity levels and overall health .”

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.

Example: “ Long-term alcohol use causes liver damage .”

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.

Research hypothesis examples

Here are some good research hypothesis examples :

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)

Importance of testable hypothesis

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.

Frequently Asked Questions (FAQs) on research hypothesis

1. What is the difference between research question and research hypothesis ?

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.

3. How can I be sure my hypothesis is testable?

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:

Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.
The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.
You should be able to collect the necessary data within the constraints of your study.
It should be possible for other researchers to replicate your study, using the same methods and variables.
Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.
The hypothesis should be able to be disproven or rejected through the collection of data.

4. How do I revise my research hypothesis if my data does not support it?

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.

5. I am performing exploratory research. Do I need to formulate a research hypothesis?

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Methodology

How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

An independent variable is something the researcher changes or controls.
A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

The relevant variables
The specific group being studied
The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

Research question	Hypothesis	Null hypothesis
What are the health benefits of eating an apple a day?	Increasing apple consumption in over-60s will result in decreasing frequency of doctor’s visits.	Increasing apple consumption in over-60s will have no effect on frequency of doctor’s visits.
Which airlines have the most delays?	Low-cost airlines are more likely to have delays than premium airlines.	Low-cost and premium airlines are equally likely to have delays.
Can flexible work arrangements improve job satisfaction?	Employees who have flexible working hours will report greater job satisfaction than employees who work fixed hours.	There is no relationship between working hour flexibility and job satisfaction.
How effective is high school sex education at reducing teen pregnancies?	Teenagers who received sex education lessons throughout high school will have lower rates of unplanned pregnancy teenagers who did not receive any sex education.	High school sex education has no effect on teen pregnancy rates.
What effect does daily use of social media have on the attention span of under-16s?	There is a negative between time spent on social media and attention span in under-16s.	There is no relationship between social media use and attention span in under-16s.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Sampling methods
Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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A Beginner’s Guide to Hypothesis Testing in Business

Business professionals performing hypothesis testing

30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

Business Analytics | Become a data-driven leader | Learn More

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

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Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

About the Author

Marketing Analytics Lab

Hypothesis Testing in Marketing Research

Introduction to Hypothesis Testing in Marketing Research

Hypothesis testing is a critical component of marketing research that allows marketers to draw conclusions about the effectiveness of their strategies. In essence, hypothesis testing involves making an educated guess about a population parameter and then using data to determine if the hypothesis is supported or rejected. In the context of marketing, hypotheses can be formulated about consumer behavior, product preferences, advertising effectiveness, and many other aspects of the marketing mix. By conducting hypothesis tests, marketers can make informed decisions based on empirical evidence rather than intuition or guesswork.

A hypothesis test in marketing research typically follows a structured process that involves defining a null hypothesis (H0) and an alternative hypothesis (HA), collecting and analyzing data, determining the appropriate statistical test to use, setting a significance level, and interpreting the results to either accept or reject the null hypothesis. The null hypothesis represents the status quo or the assumption that there is no significant difference or relationship between variables, while the alternative hypothesis suggests that there is a significant effect or relationship. By rigorously testing hypotheses, marketers can evaluate the impact of their marketing strategies and make data-driven decisions to optimize their campaigns and initiatives.

The results of hypothesis testing in marketing research provide valuable insights that can inform strategic decision-making and help marketers achieve their business objectives. Whether testing the effectiveness of a new product launch, evaluating the impact of a promotional campaign, or analyzing consumer preferences, hypothesis testing enables marketers to quantify the impact of their actions and make evidence-based recommendations. By employing statistical techniques and hypothesis testing in marketing research, organizations can gain a deeper understanding of consumer behavior, identify market trends, and refine their marketing strategies to drive business growth and success.

Key Steps and Considerations for Hypothesis Testing in Marketing Analysis

When conducting hypothesis testing in marketing research, there are several key steps and considerations that marketers should keep in mind to ensure the validity and reliability of their findings. Firstly, it is essential to clearly define the research question and formulate testable hypotheses that are specific, measurable, and relevant to the marketing objectives. By articulating clear hypotheses, marketers can establish a framework for data collection and analysis that aligns with the research objectives.

Once the hypotheses have been formulated, the next step is to determine the appropriate research design and methodology for data collection. Depending on the nature of the research question and the variables involved, marketers may choose to conduct experiments, surveys, observational studies, or other research methods to gather data. It is crucial to ensure that the data collected is representative of the target population and is collected in a systematic and unbiased manner to generate reliable results.

After collecting the data, marketers can perform statistical analysis to test the hypotheses using techniques such as t-tests, ANOVA, regression analysis, or chi-square tests, among others. It is important to select the appropriate statistical test based on the type of data and the research question being investigated. Additionally, setting a significance level (alpha) is crucial for determining the threshold for accepting or rejecting the null hypothesis. By interpreting the results in the context of the significance level, marketers can make informed decisions about the implications of the findings and their impact on marketing strategies.

9 Key stages in your marketing research process

You can conduct your own marketing research. Follow these steps, add your own flair, knowledge and creativity, and you’ll have bespoke research to be proud of.

Marketing research is the term used to cover the concept, development, placement and evolution of your product or service, its growing customer base and its branding – starting with brand awareness , and progressing to (everyone hopes) brand equity . Like any research, it needs a robust process to be credible and useful.

Marketing research uses four essential key factors known as the ‘marketing mix’ , or the Four Ps of Marketing :

Product (goods or service)
Price ( how much the customer pays )
Place (where the product is marketed)
Promotion (such as advertising and PR)

These four factors need to work in harmony for a product or service to be successful in its marketplace.

The marketing research process – an overview

A typical marketing research process is as follows:

Identify an issue, discuss alternatives and set out research objectives
Develop a research program
Choose a sample
Gather information
Gather data
Organize and analyze information and data
Present findings
Make research-based decisions
Take action based on insights

Step 1: Defining the marketing research problem

Defining a problem is the first step in the research process. In many ways, research starts with a problem facing management. This problem needs to be understood, the cause diagnosed, and solutions developed.

However, most management problems are not always easy to research, so they must first be translated into research problems. Once you approach the problem from a research angle, you can find a solution. For example, “sales are not growing” is a management problem, but translated into a research problem, it becomes “ why are sales not growing?” We can look at the expectations and experiences of several groups : potential customers, first-time buyers, and repeat purchasers. We can question whether the lack of sales is due to:

Poor expectations that lead to a general lack of desire to buy, or
Poor performance experience and a lack of desire to repurchase.

This, then, is the difference between a management problem and a research problem. Solving management problems focuses on actions: Do we advertise more? Do we change our advertising message? Do we change an under-performing product configuration? And if so, how?

Defining research problems, on the other hand, focus on the whys and hows, providing the insights you need to solve your management problem.

Step 2: Developing a research program: method of inquiry

The scientific method is the standard for investigation. It provides an opportunity for you to use existing knowledge as a starting point, and proceed impartially.

The scientific method includes the following steps:

Define a problem
Develop a hypothesis
Make predictions based on the hypothesis
Devise a test of the hypothesis
Conduct the test
Analyze the results

This terminology is similar to the stages in the research process. However, there are subtle differences in the way the steps are performed:

the scientific research method is objective and fact-based, using quantitative research and impartial analysis
the marketing research process can be subjective, using opinion and qualitative research, as well as personal judgment as you collect and analyze data

Step 3: Developing a research program: research method

As well as selecting a method of inquiry (objective or subjective), you must select a research method . There are two primary methodologies that can be used to answer any research question:

Experimental research : gives you the advantage of controlling extraneous variables and manipulating one or more variables that influence the process being implemented.
Non-experimental research : allows observation but not intervention – all you do is observe and report on your findings.

Step 4: Developing a research program: research design

Research design is a plan or framework for conducting marketing research and collecting data. It is defined as the specific methods and procedures you use to get the information you need.

There are three core types of marketing research designs: exploratory, descriptive, and causal . A thorough marketing research process incorporates elements of all of them.

Exploratory marketing research

This is a starting point for research. It’s used to reveal facts and opinions about a particular topic, and gain insight into the main points of an issue. Exploratory research is too much of a blunt instrument to base conclusive business decisions on, but it gives the foundation for more targeted study. You can use secondary research materials such as trade publications, books, journals and magazines and primary research using qualitative metrics, that can include open text surveys, interviews and focus groups.

Descriptive marketing research

This helps define the business problem or issue so that companies can make decisions, take action and monitor progress. Descriptive research is naturally quantitative – it needs to be measured and analyzed statistically , using more targeted surveys and questionnaires. You can use it to capture demographic information , evaluate a product or service for market, and monitor a target audience’s opinion and behaviors. Insights from descriptive research can inform conclusions about the market landscape and the product’s place in it.

Causal marketing research

This is useful to explore the cause and effect relationship between two or more variables. Like descriptive research , it uses quantitative methods, but it doesn’t merely report findings; it uses experiments to predict and test theories about a product or market. For example, researchers may change product packaging design or material, and measure what happens to sales as a result.

Step 5: Choose your sample

Your marketing research project will rarely examine an entire population. It’s more practical to use a sample - a smaller but accurate representation of the greater population. To design your sample, you’ll need to answer these questions:

Which base population is the sample to be selected from? Once you’ve established who your relevant population is (your research design process will have revealed this), you have a base for your sample. This will allow you to make inferences about a larger population.
What is the method (process) for sample selection? There are two methods of selecting a sample from a population:

1. Probability sampling : This relies on a random sampling of everyone within the larger population.

2. Non-probability sampling : This is based in part on the investigator’s judgment, and often uses convenience samples, or by other sampling methods that do not rely on probability.

What is your sample size? This important step involves cost and accuracy decisions. Larger samples generally reduce sampling error and increase accuracy, but also increase costs. Find out your perfect sample size with our calculator .

Step 6: Gather data

Your research design will develop as you select techniques to use. There are many channels for collecting data, and it’s helpful to differentiate it into O-data (Operational) and X-data (Experience):

O-data is your business’s hard numbers like costs, accounting, and sales. It tells you what has happened, but not why.
X-data gives you insights into the thoughts and emotions of the people involved: employees, customers, brand advocates.

When you combine O-data with X-data, you’ll be able to build a more complete picture about success and failure - you’ll know why. Maybe you’ve seen a drop in sales (O-data) for a particular product. Maybe customer service was lacking, the product was out of stock, or advertisements weren’t impactful or different enough: X-data will reveal the reason why those sales dropped. So, while differentiating these two data sets is important, when they are combined, and work with each other, the insights become powerful.

With mobile technology, it has become easier than ever to collect data. Survey research has come a long way since market researchers conducted face-to-face, postal, or telephone surveys. You can run research through:

Social media ( polls and listening )

Another way to collect data is by observation. Observing a customer’s or company’s past or present behavior can predict future purchasing decisions. Data collection techniques for predicting past behavior can include market segmentation , customer journey mapping and brand tracking .

Regardless of how you collect data, the process introduces another essential element to your research project: the importance of clear and constant communication .

And of course, to analyze information from survey or observation techniques, you must record your results . Gone are the days of spreadsheets. Feedback from surveys and listening channels can automatically feed into AI-powered analytics engines and produce results, in real-time, on dashboards.

Step 7: Analysis and interpretation

The words ‘ statistical analysis methods ’ aren’t usually guaranteed to set a room alight with excitement, but when you understand what they can do, the problems they can solve and the insights they can uncover, they seem a whole lot more compelling.

Statistical tests and data processing tools can reveal:

Whether data trends you see are meaningful or are just chance results
Your results in the context of other information you have
Whether one thing affecting your business is more significant than others
What your next research area should be
Insights that lead to meaningful changes

There are several types of statistical analysis tools used for surveys. You should make sure that the ones you choose:

Work on any platform - mobile, desktop, tablet etc.
Integrate with your existing systems
Are easy to use with user-friendly interfaces, straightforward menus, and automated data analysis
Incorporate statistical analysis so you don’t just process and present your data, but refine it, and generate insights and predictions.

Here are some of the most common tools:

Benchmarking : a way of taking outside factors into account so that you can adjust the parameters of your research. It ‘levels the playing field’ – so that your data and results are more meaningful in context. And gives you a more precise understanding of what’s happening.
Regression analysis : this is used for working out the relationship between two (or more) variables. It is useful for identifying the precise impact of a change in an independent variable.
T-test is used for comparing two data groups which have different mean values. For example, do women and men have different mean heights?
Analysis of variance (ANOVA) Similar to the T-test, ANOVA is a way of testing the differences between three or more independent groups to see if they’re statistically significant.
Cluster analysis : This organizes items into groups, or clusters, based on how closely associated they are.
Factor analysis: This is a way of condensing many variables into just a few, so that your research data is less unwieldy to work with.
Conjoint analysis : this will help you understand and predict why people make the choices they do. It asks people to make trade-offs when making decisions, just as they do in the real world, then analyzes the results to give the most popular outcome.
Crosstab analysis : this is a quantitative market research tool used to analyze ‘categorical data’ - variables that are different and mutually exclusive, such as: ‘men’ and ‘women’, or ‘under 30’ and ‘over 30’.
Text analysis and sentiment analysis : Analyzing human language and emotions is a rapidly-developing form of data processing, assigning positive, negative or neutral sentiment to customer messages and feedback.

Stats IQ can perform the most complicated statistical tests at the touch of a button using our online survey software , or data from other sources. Learn more about Stats iQ now .

Step 8: The marketing research results

Your marketing research process culminates in the research results. These should provide all the information the stakeholders and decision-makers need to understand the project.

The results will include:

all your information
a description of your research process
the results
conclusions
recommended courses of action

They should also be presented in a form, language and graphics that are easy to understand, with a balance between completeness and conciseness, neither leaving important information out or allowing it to get so technical that it overwhelms the readers.

Traditionally, you would prepare two written reports:

a technical report , discussing the methods, underlying assumptions and the detailed findings of the research project
a summary report , that summarizes the research process and presents the findings and conclusions simply.

There are now more engaging ways to present your findings than the traditional PowerPoint presentations, graphs, and face-to-face reports:

Live, interactive dashboards for sharing the most important information, as well as tracking a project in real time.
Results-reports visualizations – tables or graphs with data visuals on a shareable slide deck
Online presentation technology, such as Prezi
Visual storytelling with infographics
A single-page executive summary with key insights
A single-page stat sheet with the top-line stats

You can also make these results shareable so that decision-makers have all the information at their fingertips.

Step 9 Turn your insights into action

Insights are one thing, but they’re worth very little unless they inform immediate, positive action. Here are a few examples of how you can do this:

Stop customers leaving – negative sentiment among VIP customers gets picked up; the customer service team contacts the customers, resolves their issues, and avoids churn .
Act on important employee concerns – you can set certain topics, such as safety, or diversity and inclusion to trigger an automated notification or Slack message to HR. They can rapidly act to rectify the issue.
Address product issues – maybe deliveries are late, maybe too many products are faulty. When product feedback gets picked up through Smart Conversations, messages can be triggered to the delivery or product teams to jump on the problems immediately.
Improve your marketing effectiveness - Understand how your marketing is being received by potential customers, so you can find ways to better meet their needs
Grow your brand - Understand exactly what consumers are looking for, so you can make sure that you’re meeting their expectations

Free eBook: Quantitative and qualitative research design

Scott Smith

Scott Smith, Ph.D. is a contributor to the Qualtrics blog.

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How to Conduct the Perfect Marketing Experiment [+ Examples]

Updated: January 11, 2022

Published: August 30, 2021

After months of hard work, multiple coffee runs, and navigation of the latest industry changes, you've finally finished your next big marketing campaign.

Team meets to discuss upcoming marketing experiment

Complete with social media posts, PPC ads, and a sparkly new logo, it's the campaign of a lifetime.

But how do you know it will be effective?

While there's no sure way to know if your campaign will turn heads, there is a way to gauge whether those new aspects of your strategy will be effective.

If you want to know if certain components of your campaign are worth the effort, consider conducting a marketing experiment.

Marketing experiments give you a projection of how well marketing methods will perform before you implement them. Keep reading to learn how to conduct an experiment and discover the types of experiments you can run.

What are marketing experiments?

A marketing experiment is a form of market research in which your goal is to discover new strategies for future campaigns or validate existing ones.

For instance, a marketing team might create and send emails to a small segment of their readership to gauge engagement rates, before adding them to a campaign.

It's important to note that a marketing experiment isn't synonymous with a marketing test. Marketing experiments are done for discovery, while a test confirms theories.

Why should you run a marketing experiment?

Think of running a marketing experiment as taking out an insurance policy on future marketing efforts. It’s a way to minimize your risk and ensure that your efforts are in line with your desired results.

Imagine spending hours searching for the perfect gift. You think you’ve found the right one, only to realize later that it doesn’t align with your recipient’s taste or interests. Gifts come with receipts but there’s no money-back guarantee when it comes to marketing campaigns.

An experiment will help you better understand your audience, which in turn will enable you to optimize your strategy for a stronger performance.

How to Conduct Marketing Experiments

Brainstorm and prioritize experiment ideas.
Find one idea to focus on.
Make a hypothesis.
Collect research.
Select your metrics.
Execute the experiment.
Analyze the results.

Performing a marketing experiment involves doing research, structuring the experiment, and analyzing the results. Let's go through the seven steps necessary to conduct a marketing experiment.

1. Brainstorm and prioritize experiment ideas.

The first thing you should do when running a marketing experiment is start with a list of ideas.

Don’t know where to start? Look at your current priorities. What goals are you focusing on for the next quarter or the next year?

From there, analyze historical data. Were your past strategies worked in the past and what were your low performers?

As you dig into your data, you may find that you still have unanswered questions about which strategies may be most effective. From there, you can identify potential reasons behind low performance and start brainstorming some ideas for future experiments.

Then, you can rank your ideas by relevance, timeliness, and return on investment so that you know which ones to tackle first.

Keep a log of your ideas online, like Google Sheets , for easy access and collaboration.

2. Find one idea to focus on.

Now that you have a log of ideas, you can pick one to focus on.

Ideally, you organize your list based on current priorities. As such, as the business evolves, your priorities may change and affect how you rank your ideas.

Say you want to increase your subscriber count by 1,000 over the next quarter. You’re several weeks away from the start of the quarter and after looking through your data, you notice that users don’t convert once they land on your landing page.

Your landing page would be a great place to start your experiment. It’s relevant to your current goals and will yield a large return on your investment.

Even unsuccessful experiments, meaning those that do not yield expected results, are incredibly valuable as they help you to better understand your audience.

3. Make a hypothesis.

Hypotheses aren't just for science projects. When conducting a marketing experiment, the first step is to make a hypothesis you're curious to test.

A good hypothesis for your landing page can be any of the following:

Changing the CTA copy from "Get Started" to "Join Our Community" will increase sign-ups by 5%.
Removing the phone number field from the landing page form will increase the form completion rate by 25%.
Adding a security badge on the landing page will increase the conversion rate by 10%.

This is a good hypothesis because you can prove or disprove it, it isn't subjective, and has a clear measurement of achievement.

A not-so-good hypothesis will tackle several elements at once, be unspecific and difficult to measure. For example: "By updating the photos, CTA, and copy on the landing page, we should get more sign-ups.

Here’s why this doesn’t work: Testing several variables at once is a no-go when it comes to experimenting because it will be unclear which change(s) impacted the results. The hypothesis also doesn’t mention how the elements would be changed nor what would constitute a win.

Formulating a hypothesis takes some practice, but it’s the key to building a robust experiment.

4. Collect research.

After creating your hypothesis, begin to gather research. Doing this will give you background knowledge about experiments that have already been conducted and get an idea of possible outcomes.

Researching your experiment can help you modify your hypothesis if needed.

Say your hypothesis is, "Changing the CTA copy from "Get Started" to "Join Our Community" will increase sign-ups by 5%." You may conduct more market research to validate your ideas surrounding your user persona and if they will resonate better with a community-focused approach.

It would be helpful to look at your competitors’ landing pages and see which strategies they’re using during your research.

5. Select your metrics.

Once you've collected the research, you can choose which avenue you will take and what metrics to measure.

For instance, if you’re running an email subject line experiment, the open rate is the right metric to track.

For a landing page, you’ll likely be tracking the number of submissions during the testing period. If you’re experimenting on a blog, you might focus on the average time on page.

It all depends on what you’re tracking and the question you want to answer with your experiment.

6. Execute the experiment.

Now it's time to create and perform the experiment.

Depending on what you’re testing, this may be a cross-functional project that requires collaborating with other teams.

For instance, if you’re testing a new landing page CTA, you’ll likely need a copywriter or UX writer.

Everyone involved in this experiment should know:

The hypothesis and goal of the experiment
The timeline and duration
The metrics you’ll track

7. Analyze the results.

Once you've run the experiment, collect and analyze the results.

You want to gather enough data for statistical significance .

Use the metrics you've decided upon in the second step and conclude if your hypothesis was correct or not.

The prime indicators for success will be the metrics you chose to focus on.

For instance, for the landing page example, did sign-ups increase as a result of the new copy? If the conversion rate met or went above the goal, the experiment would be considered successful and one you should implement.

If it’s unsuccessful, your team should discuss the potential reasons why and go back to the drawing board. This experiment may spark ideas of new elements to test.

Now that you know how to conduct a marketing experiment, let's go over a few different ways to run them.

Marketing Experiment Examples

There are many types of marketing experiments you can conduct with your team. These tests will help you determine how aspects of your campaign will perform before you roll out the campaign as a whole.

A/B testing is one of the popular ways to marketing in which two versions of a webpage, email, or social post are presented to an audience (randomly divided in half). This test determines which version performs better with your audience.

This method is useful because you can better understand the preferences of users who will be using your product.

Find below the types of experiments you can run.

Your website is arguably your most important digital asset. As such, you’ll want to make sure it’s performing well.

If your bounce rate is high, the average time on page is low, or your visitors aren’t navigating your site in the way you’d like, it may be time to run an experiment.

2. Landing Pages

Landing pages are used to convert visitors into leads. If your landing page is underperforming, running an experiment can yield high returns.

The great thing about running a test on a landing page is that there are typically only a few elements to test: your background image, your copy, form, and CTA .

Experimenting with different CTAs can improve the number of people who engage with your content.

For instance, instead of using "Buy Now!" to pull customers in, why not try, "Learn more."

You can also test different colors of CTAs as opposed to the copy.

4. Paid Media Campaigns

There are so many different ways to experiment with ads.

Not only can you test ads on various platforms to see which ones reach your audience the best, but you can also experiment with the type of ad you create.

As a big purveyor of GIFs in the workplace, animating ads are a great way to catch the attention of potential customers. Those may work great for your brand.

You may also find that short videos or static images work better.

Additionally, you might run different types of copy with your ads to see which language compels your audience to click.

To maximize your return on ad spend (ROAS), run experiments on your paid media campaigns.

4. Social Media Platforms

Is there a social media site you're not using? For instance, lifestyle brands might prioritize Twitter and Instagram, but implementing Pinterest opens the door for an untapped audience.

You might consider testing which hashtags or visuals you use on certain social media sites to see how well they perform.

The more you use certain social platforms, the more iterations you can create based on what your audience responds to.

You might even use your social media analytics to determine which countries or regions you should focus on — for instance, my Twitter Analytics , below, demonstrates where most of my audience resides.

If alternatively, I saw most of my audience came from India, I might need to alter my social strategy to ensure I catered to India's time zone.

When experimenting with different time zones, consider making content specific to the audience you're trying to reach.

Your copy — the text used in marketing campaigns to persuade, inform, or entertain an audience — can make or break your marketing strategy .

If you’re not in touch with your audience, your message may not resonate. Perhaps you haven’t fleshed out your user persona or you’ve conducted limited research.

As such, it may be helpful to test what tone and concepts your audience enjoys. A/B testing is a great way to do this, you can also run surveys and focus groups to better understand your audience.

Email marketing continues to be one of the best digital channels to grow and nurture your leads.

If you have low open or high unsubscribe rates, it's worth running experiments to see what your audience will respond best to.

Perhaps your subject lines are too impersonal or unspecific. Or the content in your email is too long.

By playing around with various elements in your email, you can figure out the right strategy to reach your audience.

Ultimately, marketing experiments are a cost-effective way to get a picture of how new content ideas will work in your next campaign, which is critical for ensuring you continue to delight your audience.

Editor's Note: This post was originally published in December 2019 and has been updated for comprehensiveness.

Learn how to run effective A/B experimentation in 2018 here.

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Tips to Create and Test a Value Hypothesis: A Step-by-Step Guide

Developing a robust value hypothesis is crucial as you bring a new product to market, guiding your startup toward answering a genuine market need. Constructing a verifiable value hypothesis anchors your product's development process in customer feedback and data-driven insight rather than assumptions.

This framework enables you to clarify the potential value your product offers and provides a foundation for testing and refining your approach, significantly reducing the risk of misalignment with your target market. To set the stage for success, employ logical structures and objective measures, such as creating a minimum viable product, to effectively validate your product's value proposition.

What Is a Verifiable Value Hypothesis?

A verifiable value hypothesis articulates your belief about how your product will deliver value to customers. It is a testable prediction aimed at demonstrating the expected outcomes for your target market.

To ensure that your value hypothesis is verifiable, it should adhere to the following conditions:

Specific : Clearly defines the value proposition and the customer segment.
Measurable : Includes metrics by which you can assess success or failure.
Achievable : Realistic based on your resources and market conditions.
Relevant : Directly addresses a significant customer need or desire.
Time-Bound : Has a defined period for testing and validation.

When you create a value hypothesis, you're essentially forming the backbone of your business model. It goes beyond a mere assumption and relies on customer feedback data to inform its development. You also safeguard it with objective measures, such as a minimum viable product, to test the hypothesis in real life.

By articulating and examining a verifiable value hypothesis, you understand your product's potential impact and reduce the risk associated with new product development. It's about making informed decisions that increase your confidence in the product's potential success before committing significant resources.

Value Hypotheses vs. Growth Hypotheses

Value hypotheses and growth hypotheses are two distinct concepts often used in business, especially in the context of startups and product development.

Value Hypotheses : A value hypothesis is centered around the product itself. It focuses on whether the product truly delivers customer value. Key questions include whether the product meets a real need, how it compares to alternatives, and if customers are willing to pay for it. Valuing a value hypothesis is crucial before a business scales its operations.

Growth Hypotheses : A growth hypothesis, on the other hand, deals with the scalability and marketing aspects of the business. It involves strategies and channels used to acquire new customers. The focus is on how to grow the customer base, the cost-effectiveness of growth strategies, and the sustainability of growth. Validating a growth hypothesis is typically the next step after confirming that the product has value to the customers.

In practice, both hypotheses are crucial for the success of a business. A value hypothesis ensures the product is desirable and needed, while a growth hypothesis ensures that the product can reach a larger market effectively.

Tips to Create and Test a Verifiable Value Hypothesis

Creating a value hypothesis is crucial for understanding what drives customer interest in your product. It's an educated guess that requires rigor to define and clarity to test. When developing a value hypothesis, you're attempting to validate assumptions about your product's value to customers. Here are concise tips to help you with this process:

1. Understanding Your Market and Customers

Before formulating a hypothesis, you need a deep understanding of your market and potential customers. You're looking to uncover their pain points and needs which your product aims to address.

Begin with thorough market research and collect customer feedback to ensure your idea is built upon a solid foundation of real-world insights. This understanding is pivotal as it sets the tone for a relevant and testable hypothesis.

Define Your Value Proposition Clearly: Articulate your product's value to the user. What problem does it solve? How does it improve the user's life or work?
Identify Your Target Audience. Determine who your ideal customers are. Understand their needs, pain points, and how they currently address the problem your product intends to solve.

2. Defining Clear Assumptions

The next step is to outline clear assumptions based on your idea that you believe will bring value to your customers. Each assumption should be an assertion that directly relates to how your customers will find your product valuable.

For example, if your product is a task management app, you might assume that the ability to share task lists with team members is a pain point for your potential customers. Remember, assumptions are not facts—they are educated guesses that need verification.

3. Identify Key Metrics for Your Hypothesis Test

Once you've defined your assumptions, delineate the framework for testing your value hypothesis. This involves designing experiments that validate or invalidate your assumptions with measurable outcomes. Ensure that your hypothesis can be tested with measurable outcomes. This could be in the form of user engagement metrics, conversion rates, or customer satisfaction scores.

Determine what success looks like and define objective metrics that will prove your product's value. This could be user engagement, conversion rates, or revenue. Choosing the right metrics is essential for an accurate test. For instance, in your test, you might measure the increase in customer retention or the decrease in time spent on task organization with your app. Construct your test so that the results are unequivocal and actionable.

4. Construct a Testable Proposition

Formulate your hypothesis in a way that can be tested empirically. Use qualitative research methods such as interviews, surveys, and observation to gather data about your potential users. Formulate your value hypothesis based on insights from this research. Plan experiments that can validate or invalidate your value hypothesis. This might involve A/B testing, user testing sessions, or pilot programs.

A good example is to posit that "Introducing feature X will increase user onboarding by Y%." Avoid complexity by testing one variable simultaneously. This helps you identify which changes are actually making a difference.

5. Applying Evidence to Innovation

When your data indicates a promising avenue for product development , it's imperative that you validate your growth hypothesis through experimentation. Align your value proposition with the evidence at hand.

Develop a simplified version of your product that allows you to test the core value proposition with real users without investing in full-scale production. Start by crafting a minimum viable product ( MVP ) to begin testing in the market. This approach helps mitigate risk by not investing heavily in unproven ideas. Use analytics tools to collect data on how users interact with your MVP. Look for patterns that either support or contradict your value hypothesis.

If the data suggests that your value hypothesis is wrong, be prepared to revise your hypothesis or pivot your product strategy accordingly.

6. Gather Customer Feedback

Integrating customer feedback into your product development process can create a more tailored value proposition. This step is crucial in refining your product to meet user needs and validate your hypotheses.

Use customer feedback tools to collect data on how users interact with your MVP. Look for patterns that either support or contradict your value hypothesis. Here are some ways to collect feedback effectively :

Feedback portals
User testing sessions
In-app feedback
Website widgets
Direct interviews
Focus groups
Feedback forums

Create a centralized place for product feedback to keep track of different types of customer feedback and improve SaaS products while listening to their customers. Rapidr helps companies be more customer-centric by consolidating feedback across different apps, prioritizing requests, having a discourse with customers, and closing the feedback loop.

7. Analyze and Iterate Quickly

Review the data and analyze customer feedback to see if it supports your hypothesis. If your hypothesis is not supported, iterate on your assumptions, and test again. Keep a detailed record of your hypotheses, experiments, and findings. This documentation will help you understand the evolution of your product and guide future decision-making.

Use the feedback and data from your tests to make quick iterations of your product and drive product development . This allows you to refine your value proposition and improve the fit with your target audience. Engage with your users throughout the process. Real-world feedback is invaluable and can provide insights that data alone cannot.

Identify Patterns : What commonalities are present in the feedback?
Implement Changes : Prioritize and make adjustments based on customer insights.

9. Align with Business Goals and Stay Customer-Focused

Ensure that your value hypothesis aligns with the broader goals of your business. The value provided should ultimately contribute to the success of the company. Remember that the ultimate goal of your value hypothesis is to deliver something that customers find valuable. Maintain a strong focus on customer needs and satisfaction throughout the process.

10. Communicate with Stakeholders and Update them

Keep all stakeholders informed about your findings and the implications for the product. Clear communication helps ensure everyone is aligned and understands the rationale behind product decisions. Communicate and close the feedback loop with the help of a product changelog through which you can announce new changes and engage with customers.

Understanding and validating a value hypothesis is essential for any business, particularly startups. It involves deeply exploring whether a product or service meets customer needs and offers real value. This process ensures that resources are invested in desirable and useful products, and it's a critical step before considering scalability and growth.

By focusing on the value hypothesis, businesses can better align their offerings with market demand, leading to more sustainable success. Placing customer feedback at the center of the process of testing a value hypothesis helps you develop a product that meets your customers' needs and stands out in the market.

Rapidr helps companies be more customer-centric by consolidating feedback across different apps, prioritizing requests, having a discourse with customers, and closing the feedback loop.

Build better products with user feedback

Rapidr helps SaaS companies understand what customers need through feedback, prioritize what to build next, inform the roadmap, and notify customers on product releases

From Hypothesis to Results: Mastering the Art of Marketing Experiments

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From Hypothesis to Results: Mastering the Art of Marketing Experiments

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Suppose you’re trying to convince your friend to watch your favorite movie. You could either tell them about the intriguing plot or show them the exciting trailer.

To find out which approach works best, you try both methods with different friends and see which one gets more people to watch the movie.

Marketing experiments work in much the same way, allowing businesses to test different marketing strategies, gather feedback from their target audience, and make data-driven decisions that lead to improved outcomes and growth.

By testing different approaches and measuring their outcomes, companies can identify what works best for their unique target audience and adapt their marketing strategies accordingly. This leads to more efficient use of marketing resources and results in higher conversion rates, increased customer satisfaction, and, ultimately, business growth.

Marketing experiments are the backbone of building an organization’s culture of learning and curiosity, encouraging employees to think outside the box and challenge the status quo.

In this article, we will delve into the fundamentals of marketing experiments, discussing their key elements and various types. By the end, you’ll be in a position to start running these tests and securing better marketing campaigns with explosive results.

Why Digital Marketing Experiments Matter

One of the most effective ways to drive growth and optimize marketing strategies is through digital marketing experiments. These experiments provide invaluable insights into customer preferences, behaviors, and the overall effectiveness of marketing efforts, making them an essential component of any digital marketing strategy.

Digital marketing experiments matter for several reasons:

Customer-centric approach: By conducting experiments, businesses can gain a deeper understanding of their target audience’s preferences and behaviors. This enables them to tailor their marketing efforts to better align with customer needs, resulting in more effective and engaging campaigns.
Data-driven decision-making: Marketing experiments provide quantitative data on the performance of different marketing strategies and tactics. This empowers businesses to make informed decisions based on actual results rather than relying on intuition or guesswork. Ultimately, this data-driven approach leads to more efficient allocation of resources and improved marketing outcomes.
Agility and adaptability: Businesses must be agile and adaptable to keep up with emerging trends and technologies. Digital marketing experiments allow businesses to test new ideas, platforms, and strategies in a controlled environment, helping them stay ahead of the curve and quickly respond to changing market conditions.
Continuous improvement: Digital marketing experiments facilitate an iterative process of testing, learning, and refining marketing strategies. This ongoing cycle of improvement enables businesses to optimize their marketing efforts, drive better results, and maintain a competitive edge in the digital marketplace.
ROI and profitability: By identifying which marketing tactics are most effective, businesses can allocate their marketing budget more efficiently and maximize their return on investment. This increased profitability can be reinvested into the business, fueling further growth and success.

Developing a culture of experimentation allows businesses to continuously improve their marketing strategies, maximize their ROI, and avoid being left behind by the competition.

The Fundamentals of Digital Marketing Experiments

Marketing experiments are structured tests that compare different marketing strategies, tactics, or assets to determine which one performs better in achieving specific objectives.

These experiments use a scientific approach, which involves formulating hypotheses, controlling variables, gathering data, and analyzing the results to make informed decisions.

Marketing experiments provide valuable insights into customer preferences and behaviors, enabling businesses to optimize their marketing efforts and maximize returns on investment (ROI).

There are several types of marketing experiments that businesses can use, depending on their objectives and available resources.

The most common types include:

A/B testing

A/B testing, also known as split testing, is a simple yet powerful technique that compares two variations of a single variable to determine which one performs better.

In an A/B test, the target audience is randomly divided into two groups: one group is exposed to version A (the control). In contrast, the other group is exposed to version B (the treatment). The performance of both versions is then measured and compared to identify the one that yields better results.

A/B testing can be applied to various marketing elements, such as headlines, calls-to-action, email subject lines, landing page designs, and ad copy. The primary advantage of A/B testing is its simplicity, making it easy for businesses to implement and analyze.

Multivariate testing

Multivariate testing is a more advanced technique that allows businesses to test multiple variables simultaneously.

In a multivariate test, several elements of a marketing asset are modified and combined to create different versions. These versions are then shown to different segments of the target audience, and their performance is measured and compared to determine the most effective combination of variables.

Multivariate testing is beneficial when optimizing complex marketing assets, such as websites or email templates, with multiple elements that may interact with one another. However, this method requires a larger sample size and more advanced analytical tools compared to A/B testing.

Pre-post analysis

Pre-post analysis involves comparing the performance of a marketing strategy before and after implementing a change.

This type of experiment is often used when it is not feasible to conduct an A/B or multivariate test, such as when the change affects the entire customer base or when there are external factors that cannot be controlled.

While pre-post analysis can provide useful insights, it is less reliable than A/B or multivariate testing because it does not account for potential confounding factors. To obtain accurate results from a pre-post analysis, businesses must carefully control for external influences and ensure that the observed changes are indeed due to the implemented modifications.

How To Start Growth Marketing Experiments

To conduct effective marketing experiments, businesses must pay attention to the following key elements:

Clear objectives

Having clear objectives is crucial for a successful marketing experiment. Before starting an experiment, businesses must identify the specific goals they want to achieve, such as increasing conversions, boosting engagement, or improving click-through rates. Clear objectives help guide the experimental design and ensure the results are relevant and actionable.

Hypothesis-driven approach

A marketing experiment should be based on a well-formulated hypothesis that predicts the expected outcome. A reasonable hypothesis is specific, testable, and grounded in existing knowledge or data. It serves as the foundation for experimental design and helps businesses focus on the most relevant variables and outcomes.

Proper experimental design

A marketing experiment requires a well-designed test that controls for potential confounding factors and ensures the reliability and validity of the results. This includes the random assignment of participants, controlling for external influences, and selecting appropriate variables to test. Proper experimental design increases the likelihood that observed differences are due to the tested variables and not other factors.

Adequate sample size

A successful marketing experiment requires an adequate sample size to ensure the results are statistically significant and generalizable to the broader target audience. The required sample size depends on the type of experiment, the expected effect size, and the desired level of confidence. In general, larger sample sizes provide more reliable and accurate results but may also require more resources to conduct the experiment.

Data-driven analysis

Marketing experiments rely on a data-driven analysis of the results. This involves using statistical techniques to determine whether the observed differences between the tested variations are significant and meaningful. Data-driven analysis helps businesses make informed decisions based on empirical evidence rather than intuition or gut feelings.

By understanding the fundamentals of marketing experiments and following best practices, businesses can gain valuable insights into customer preferences and behaviors, ultimately leading to improved outcomes and growth.

Setting up Your First Marketing Experiment

Embarking on your first marketing experiment can be both exciting and challenging. Following a systematic approach, you can set yourself up for success and gain valuable insights to improve your marketing efforts.

Here’s a step-by-step guide to help you set up your first marketing experiment.

Identifying your marketing objectives

Before diving into your experiment, it’s essential to establish clear marketing objectives. These objectives will guide your entire experiment, from hypothesis formulation to data analysis.

Consider what you want to achieve with your marketing efforts, such as increasing website conversions, improving open email rates, or boosting social media engagement.

Make sure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART) to ensure that they are actionable and provide meaningful insights.

Formulating a hypothesis

With your marketing objectives in mind, the next step is formulating a hypothesis for your experiment. A hypothesis is a testable prediction that outlines the expected outcome of your experiment. It should be based on existing knowledge, data, or observations and provide a clear direction for your experimental design.

For example, suppose your objective is to increase email open rates. In that case, your hypothesis might be, “Adding the recipient’s first name to the email subject line will increase the open rate by 10%.” This hypothesis is specific, testable, and clearly linked to your marketing objective.

Designing the experiment

Once you have a hypothesis in place, you can move on to designing your experiment. This involves several key decisions:

Choosing the right testing method:

Select the most appropriate testing method for your experiment based on your objectives, hypothesis, and available resources.

As discussed earlier, common testing methods include A/B, multivariate, and pre-post analyses. Choose the method that best aligns with your goals and allows you to effectively test your hypothesis.

Selecting the variables to test:

Identify the specific variables you will test in your experiment. These should be directly related to your hypothesis and marketing objectives. In the email open rate example, the variable to test would be the subject line, specifically the presence or absence of the recipient’s first name.

When selecting variables, consider their potential impact on your marketing objectives and prioritize those with the greatest potential for improvement. Also, ensure that the variables are easily measurable and can be manipulated in your experiment.

Identifying the target audience:

Determine the target audience for your experiment, considering factors such as demographics, interests, and behaviors. Your target audience should be representative of the larger population you aim to reach with your marketing efforts.

When segmenting your audience for the experiment, ensure that the groups are as similar as possible to minimize potential confounding factors.

In A/B or multivariate testing, this can be achieved through random assignment, which helps control for external influences and ensures a fair comparison between the tested variations.

Executing the experiment

With your experiment designed, it’s time to put it into action.

This involves several key considerations:

Timing and duration:

Choose the right timing and duration for your experiment based on factors such as the marketing channel, target audience, and the nature of the tested variables.

The duration of the experiment should be long enough to gather a sufficient amount of data for meaningful analysis but not so long that it negatively affects your marketing efforts or causes fatigue among your target audience.

In general, aim for a duration that allows you to reach a predetermined sample size or achieve statistical significance. This may vary depending on the specific experiment and the desired level of confidence.

Monitoring the experiment:

During the experiment, monitor its progress and performance regularly to ensure that everything is running smoothly and according to plan. This includes checking for technical issues, tracking key metrics, and watching for any unexpected patterns or trends.

If any issues arise during the experiment, address them promptly to prevent potential biases or inaccuracies in the results. Additionally, avoid making changes to the experimental design or variables during the experiment, as this can compromise the integrity of the results.

Analyzing the results

Once your experiment has concluded, it’s time to analyze the data and draw conclusions.

This involves two key aspects:

Statistical significance:

Statistical significance is a measure of the likelihood that the observed differences between the tested variations are due to the variables being tested rather than random chance. To determine statistical significance, you will need to perform a statistical test, such as a t-test or chi-squared test, depending on the nature of your data.

Generally, a result is considered statistically significant if the probability of the observed difference occurring by chance (the p-value) is less than a predetermined threshold, often set at 0.05 or 5%. This means there is a 95% confidence level that the observed difference is due to the tested variables and not random chance.

Practical significance:

While statistical significance is crucial, it’s also essential to consider the practical significance of your results. This refers to the real-world impact of the observed differences on your marketing objectives and business goals.

To assess practical significance, consider the effect size of the observed difference (e.g., the percentage increase in email open rates) and the potential return on investment (ROI) of implementing the winning variation. This will help you determine whether the experiment results are worth acting upon and inform your marketing decisions moving forward.

A systematic approach to designing growth marketing experiments helps you to design, execute, and analyze your experiment effectively, ultimately leading to better marketing outcomes and business growth.

Examples of Successful Marketing Experiments

In this section, we will explore three fictional case studies of successful marketing experiments that led to improved marketing outcomes. These examples will demonstrate the practical application of marketing experiments across different channels and provide valuable lessons that can be applied to your own marketing efforts.

Example 1: Redesigning a website for increased conversions

AcmeWidgets, an online store selling innovative widgets, noticed that its website conversion rate had plateaued.

They conducted a marketing experiment to test whether a redesigned landing page could improve conversions. They hypothesized that a more visually appealing and user-friendly design would increase conversion rates by 15%.

AcmeWidgets used A/B testing to compare their existing landing page (the control) with a new, redesigned version (the treatment). They randomly assigned website visitors to one of the two landing pages. They tracked conversions over a period of four weeks.

At the end of the experiment, AcmeWidgets found that the redesigned landing page had a conversion rate 18% higher than the control. The results were statistically significant, and the company decided to implement the new design across its entire website.

As a result, AcmeWidgets experienced a substantial increase in sales and revenue.

Example 2: Optimizing email marketing campaigns

EcoTravel, a sustainable travel agency, wanted to improve the open rates of their monthly newsletter. They hypothesized that adding a sense of urgency to the subject line would increase open rates by 10%.

To test this hypothesis, EcoTravel used A/B testing to compare two different subject lines for their newsletter:

“Discover the world’s most beautiful eco-friendly destinations” (control)
“Last chance to book: Explore the world’s most beautiful eco-friendly destinations” (treatment)

EcoTravel sent the newsletter to a random sample of their subscribers. Half received the control subject line, and the other half received the treatment. They then tracked the open rates for both groups over one week.

The results of the experiment showed that the treatment subject line, which included a sense of urgency, led to a 12% increase in open rates compared to the control.

Based on these findings, EcoTravel incorporated a sense of urgency in their future email subject lines to boost newsletter engagement.

Example 3: Improving social media ad performance

FitFuel, a meal delivery service for fitness enthusiasts, was looking to improve its Facebook ad campaign’s click-through rate (CTR). They hypothesized that using an image of a satisfied customer enjoying a FitFuel meal would increase CTR by 8% compared to their current ad featuring a meal image alone.

FitFuel conducted an A/B test on their Facebook ad campaign, comparing the performance of the control ad (meal image only) with the treatment ad (customer enjoying a meal). They targeted a similar audience with both ad variations and measured the CTR over two weeks. The experiment revealed that the treatment ad, featuring the customer enjoying a meal, led to a 10% increase in CTR compared to the control ad. FitFuel decided to update its

Facebook ad campaign with the new image, resulting in a more cost-effective campaign and higher return on investment.

Lessons learned from these examples

These fictional examples of successful marketing experiments highlight several key takeaways:

Clearly defined objectives and hypotheses: In each example, the companies had specific marketing objectives and well-formulated hypotheses, which helped guide their experiments and ensure relevant and actionable results.
Proper experimental design: Each company used the appropriate testing method for their experiment and carefully controlled variables, ensuring accurate and reliable results.
Data-driven decision-making: The companies analyzed the data from their experiments to make informed decisions about implementing changes to their marketing strategies, ultimately leading to improved outcomes.
Continuous improvement: These examples demonstrate that marketing experiments can improve marketing efforts continuously. By regularly conducting experiments and applying the lessons learned, businesses can optimize their marketing strategies and stay ahead of the competition.
Relevance across channels: Marketing experiments can be applied across various marketing channels, such as website design, email campaigns, and social media advertising. Regardless of the channel, the principles of marketing experimentation remain the same, making them a valuable tool for marketers in diverse industries.

By learning from these fictional examples and applying the principles of marketing experimentation to your own marketing efforts, you can unlock valuable insights, optimize your marketing strategies, and achieve better results for your business.

Common Pitfalls of Marketing Experiments and How to Avoid Them

Conducting marketing experiments can be a powerful way to optimize your marketing strategies and drive better results.

However, it’s important to be aware of common pitfalls that can undermine the effectiveness of your experiments. In this section, we will discuss some of these pitfalls and provide tips on how to avoid them.

Insufficient sample size

An insufficient sample size can lead to unreliable results and limit the generalizability of your findings. When your sample size is too small, you run the risk of not detecting meaningful differences between the tested variations or incorrectly attributing the observed differences to random chance.

To avoid this pitfall, calculate the required sample size for your experiment based on factors such as the expected effect size, the desired level of confidence, and the type of statistical test you will use.

In general, larger sample sizes provide more reliable and accurate results but may require more resources to conduct the experiment. Consider adjusting your experimental design or testing methods to accommodate a larger sample size if necessary.

Lack of clear objectives

Your marketing experiment may not provide meaningful or actionable insights without clear objectives. Unclear objectives can lead to poorly designed experiments, irrelevant variables, or difficulty interpreting the results.

To prevent this issue, establish specific, measurable, achievable, relevant, and time-bound (SMART) objectives before starting your experiment. These objectives should guide your entire experiment, from hypothesis formulation to data analysis, and ensure that your findings are relevant and useful for your marketing efforts.

Confirmation bias

Confirmation bias occurs when you interpret the results of your experiment in a way that supports your pre-existing beliefs or expectations. This can lead to inaccurate conclusions and suboptimal marketing decisions.

To minimize confirmation bias, approach your experiments with an open mind and be willing to accept results that challenge your assumptions.

Additionally, involve multiple team members in the data analysis process to ensure diverse perspectives and reduce the risk of individual biases influencing the interpretation of the results.

Overlooking external factors

External factors, such as changes in market conditions, seasonal fluctuations, or competitor actions, can influence the results of your marketing experiment and potentially confound your findings. Ignoring these factors may lead to inaccurate conclusions about the effectiveness of your marketing strategies.

To account for external factors, carefully control for potential confounding variables during the experimental design process. This might involve using random assignment, testing during stable periods, or controlling for known external influences.

Consider running follow-up experiments or analyzing historical data to confirm your findings and rule out the impact of external factors.

Tips for avoiding these pitfalls

By being aware of these common pitfalls and following best practices, you can ensure the success of your marketing experiments and obtain valuable insights for your marketing efforts. Here are some tips to help you avoid these pitfalls:

Plan your experiment carefully: Invest time in the planning stage to establish clear objectives, calculate an adequate sample size, and design a robust experiment that controls for potential confounding factors.
Use a hypothesis-driven approach: Formulate a specific, testable hypothesis based on existing knowledge or data to guide your experiment and focus on the most relevant variables and outcomes.
Monitor your experiment closely: Regularly check the progress of your experiment, address any issues that arise, and ensure that your experiment is running smoothly and according to plan.
Analyze your data objectively: Use statistical techniques to determine the significance of your results and consider the practical implications of your findings before making marketing decisions.
Learn from your experiments: Apply the lessons learned from your experiments to continuously improve your marketing strategies and stay ahead of the competition.

By avoiding these common pitfalls and following best practices, you can increase the effectiveness of your marketing experiments, gain valuable insights into customer preferences and behaviors, and ultimately drive better results for your business.

Building a Culture of Experimentation

To truly reap the benefits of marketing experiments, it’s essential to build a culture of experimentation within your organization. This means fostering an environment where curiosity, learning, data-driven decision-making, and collaboration are valued and encouraged.

Encouraging curiosity and learning within your organization

Cultivating curiosity and learning starts with leadership. Encourage your team to ask questions, explore new ideas, and embrace a growth mindset.

Promote ongoing learning by providing resources, such as training programs, workshops, or access to industry events, that help your team stay up-to-date with the latest marketing trends and techniques.

Create a safe environment where employees feel comfortable sharing their ideas and taking calculated risks. Emphasize the importance of learning from both successes and failures and treat every experiment as an opportunity to grow and improve.

Adopting a data-driven mindset

A data-driven mindset is crucial for successful marketing experimentation. Encourage your team to make decisions based on data rather than relying on intuition or guesswork. This means analyzing the results of your experiments objectively, using statistical techniques to determine the significance of your findings, and considering the practical implications of your results before making marketing decisions.

To foster a data-driven culture, invest in the necessary tools and technologies to collect, analyze, and visualize data effectively. Train your team on how to use these tools and interpret the data to make informed marketing decisions.

Regularly review your data-driven efforts and adjust your strategies as needed to continuously improve and optimize your marketing efforts.

Integrating experimentation into your marketing strategy

Establish a systematic approach to conducting marketing experiments to fully integrate experimentation into your marketing strategy. This might involve setting up a dedicated team or working group responsible for planning, executing, and analyzing experiments or incorporating experimentation as a standard part of your marketing processes.

Create a roadmap for your marketing experiments that outlines each project’s objectives, hypotheses, and experimental designs. Monitor the progress of your experiments and adjust your roadmap as needed based on the results and lessons learned.

Ensure that your marketing team has the necessary resources, such as time, budget, and tools, to conduct experiments effectively. Set clear expectations for the role of experimentation in your marketing efforts and emphasize its importance in driving better results and continuous improvement.

Collaborating across teams for a holistic approach

Marketing experiments often involve multiple teams within an organization, such as design, product, sales, and customer support. Encourage cross-functional collaboration to ensure a holistic approach to experimentation and leverage each team’s unique insights and expertise.

Establish clear communication channels and processes for sharing information and results from your experiments. This might involve regular meetings, shared documentation, or internal presentations to keep all stakeholders informed and engaged.

Collaboration also extends beyond your organization. Connect with other marketing professionals, industry experts, and thought leaders to learn from their experiences, share your own insights, and stay informed about the latest trends and best practices in marketing experimentation.

By building a culture of experimentation within your organization, you can unlock valuable insights, optimize your marketing strategies, and drive better results for your business.

Encourage curiosity and learning, adopt a data-driven mindset, integrate experimentation into your marketing strategy, and collaborate across teams to create a strong foundation for marketing success.

If you’re new to marketing experiments, don’t be intimidated—start small and gradually expand your efforts as your confidence grows. By embracing a curious and data-driven mindset, even small-scale experiments can lead to meaningful insights and improvements.

As you gain experience, you can tackle more complex experiments and further refine your marketing strategies.

Remember, continuous learning and improvement is the key to success in marketing experimentation. By regularly conducting experiments, analyzing the results, and applying the lessons learned, you can stay ahead of the competition and drive better results for your business.

So, take the plunge and start experimenting today—your marketing efforts will be all the better.

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A/B Testing: Example of a good hypothesis

Want to know the secret to always running successful tests?

The answer is to formulate a hypothesis .

Now when I say it’s always successful, I’m not talking about always increasing your Key Performance Indicator (KPI). You can “lose” a test, but still be successful.

That sounds like an oxymoron, but it’s not. If you set up your test strategically, even if the test decreases your KPI, you gain a learning , which is a success! And, if you win, you simultaneously achieve a lift and a learning. Double win!

The way you ensure you have a strategic test that will produce a learning is by centering it around a strong hypothesis.

So, what is a hypothesis?

By definition, a hypothesis is a proposed statement made on the basis of limited evidence that can be proved or disproved and is used as a starting point for further investigation.

Let’s break that down:

It is a proposed statement.

A hypothesis is not fact, and should not be argued as right or wrong until it is tested and proven one way or the other.

It is made on the basis of limited (but hopefully some ) evidence.

Your hypothesis should be informed by as much knowledge as you have. This should include data that you have gathered, any research you have done, and the analysis of the current problems you have performed.

It can be proved or disproved.

A hypothesis pretty much says, “I think by making this change , it will cause this effect .” So, based on your results, you should be able to say “this is true” or “this is false.”

It is used as a starting point for further investigation.

The key word here is starting point . Your hypothesis should be formed and agreed upon before you make any wireframes or designs as it is what guides the design of your test. It helps you focus on what elements to change, how to change them, and which to leave alone.

How do I write a hypothesis?

The structure of your basic hypothesis follows a CHANGE: EFFECT framework.

While this is a truly scientific and testable template, it is very open-ended. Even though this hypothesis, “Changing an English headline into a Spanish headline will increase clickthrough rate,” is perfectly valid and testable, if your visitors are English-speaking, it probably doesn’t make much sense.

So now the question is …

How do I write a GOOD hypothesis?

To quote my boss Tony Doty , “This isn’t Mad Libs.”

We can’t just start plugging in nouns and verbs and conclude that we have a good hypothesis. Your hypothesis needs to be backed by a strategy. And, your strategy needs to be rooted in a solution to a problem .

So, a more complete version of the above template would be something like this:

In order to have a good hypothesis, you don’t necessarily have to follow this exact sentence structure, as long as it is centered around three main things:

Presumed problem

Proposed solution

Anticipated result

After you’ve completed your analysis and research, identify the problem that you will address. While we need to be very clear about what we think the problem is, you should leave it out of the hypothesis since it is harder to prove or disprove. You may want to come up with both a problem statement and a hypothesis .

For example:

Problem Statement: “The lead generation form is too long, causing unnecessary friction .”

Hypothesis: “By changing the amount of form fields from 20 to 10, we will increase number of leads.”

When you are thinking about the solution you want to implement, you need to think about the psychology of the customer. What psychological impact is your proposed problem causing in the mind of the customer?

For example, if your proposed problem is “There is a lack of clarity in the sign-up process,” the psychological impact may be that the user is confused.

Now think about what solution is going to address the problem in the customer’s mind. If they are confused, we need to explain something better, or provide them with more information. For this example, we will say our proposed solution is to “Add a progress bar to the sign-up process.” This leads straight into the anticipated result.

If we reduce the confusion in the visitor’s mind (psychological impact) by adding the progress bar, what do we foresee to be the result? We are anticipating that it would be more people completing the sign-up process. Your proposed solution and your KPI need to be directly correlated.

Note: Some people will include the psychological impact in their hypothesis. This isn’t necessarily wrong, but we do have to be careful with assumptions. If we say that the effect will be “Reduced confusion and therefore increase in conversion rate,” we are assuming the reduced confusion is what made the impact. While this may be correct, it is not measureable and it is hard to prove or disprove.

To summarize, your hypothesis should follow a structure of: “If I change this, it will have this effect,” but should always be informed by an analysis of the problems and rooted in the solution you deemed appropriate.

Related Resources:

A/B Testing 101: How to get real results from optimization

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15 Years of Marketing Research in 11 Minutes

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Thanks for the article. I’ve been trying to wrap my head around this type of testing because I’d like to use it to see the effectiveness on some ads. This article really helped. Thanks Again!

Hey Lauren, I am just getting to the point that I have something to perform A-B testing on. This post led me to this site which will and already has become a help in what to test and how to test .

Again, thanks for getting me here .

Good article. I have been researching different approaches to writing testing hypotheses and this has been a help. The only thing I would add is that it can be useful to capture the insight/justification within the hypothesis statement. IF i do this, THEN I expect this result BECAUSE I have this insight.

@Kaya Great!

Good article – but technically you can never prove an hypothesis, according to the principle of falsification (Popper), only fail to disprove the null hypothesis.

Hypotheses in Marketing Science: Literature Review and Publication Audit

Published: May 2001
Volume 12 , pages 171–187, ( 2001 )

Cite this article

J. Scott Armstrong 1 ,
Roderick J. Brodie 2 &
Andrew G. Parsons 2

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We examined three approaches to research in marketing: exploratory hypotheses, dominant hypothesis, and competing hypotheses. Our review of empirical studies on scientific methodology suggests that the use of a single dominant hypothesis lacks objectivity relative to the use of exploratory and competing hypotheses approaches. We then conducted a publication audit of over 1,700 empirical papers in six leading marketing journals during 1984–1999. Of these, 74% used the dominant hypothesis approach, while 13% used multiple competing hypotheses, and 13% were exploratory. Competing hypotheses were more commonly used for studying methods (25%) than models (17%) and phenomena (7%). Changes in the approach to hypotheses since 1984 have been modest; there was a slight decrease in the percentage of competing hypotheses to 11%, which is explained primarily by an increasing proportion of papers on phenomena. Of the studies based on hypothesis testing, only 11% described the conditions under which the hypotheses would apply, and dominant hypotheses were below competing hypotheses in this regard. Marketing scientists differed substantially in their opinions about what types of studies should be published and what was published. On average, they did not think dominant hypotheses should be used as often as they were, and they underestimated their use.

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Normative Criteria for the Development and Appraisal of Marketing Theory

Research Method Topics and Issues that Reduce the Value of Reported Empirical Insights in the Marketing Literatures: An Abstract

Marketing Theory: The Present Stage Of Development

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Armstrong, J.S., Brodie, R.J. & Parsons, A.G. Hypotheses in Marketing Science: Literature Review and Publication Audit. Marketing Letters 12 , 171–187 (2001). https://doi.org/10.1023/A:1011169104290

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Examples of a hypothesis in market research.

Home » Examples of a hypothesis in market research

In the fast-paced world of market research, the ability to formulate a precise market hypothesis can significantly influence the success of a study. Market Hypothesis Formulation serves as a foundational element, allowing researchers to navigate complex data and generate meaningful insights. By clearly defining what you aim to prove or disprove, you set a focused path for data collection and analysis.

Understanding how to develop effective hypotheses is essential for drawing relevant conclusions that can guide business strategies. Through exploring various examples of hypotheses in market research, one can better appreciate how targeted questions lead to valuable customer insights. This knowledge not only aids in decision-making but also enhances the overall effectiveness of marketing efforts.

Significance of a Market Hypothesis Formulation

Market Hypothesis Formulation is crucial for navigating the complexities of market research. It serves as a guiding framework, helping researchers focus their efforts on specific questions and objectives. By formulating a hypothesis, researchers can articulate their expectations about market behaviors or consumer preferences. This targeted approach streamlines data collection and analysis, allowing for more effective interpretations of results.

Additionally, a well-structured hypothesis encourages systematic investigation and fosters critical thinking. This process ultimately leads to more insightful conclusions, enabling businesses to make informed decisions. By understanding the significance of a market hypothesis formulation, organizations can better identify opportunities, respond to challenges, and enhance their strategic positioning. Whether it’s refining marketing messages or assessing product-market fit, the formulation of hypotheses is an indispensable first step in successful market research.

Why Hypotheses Matter in Market Research

Formulating a market hypothesis lays the groundwork for focused research and exploration. By establishing a clear statement that predicts a relationship between variables, researchers can concentrate their efforts on specific questions. This allows them to gather relevant data, ultimately leading to more accurate insights and informed decisions.

Additionally, a well-defined hypothesis aids in identifying what data to collect and how to analyze it. For example, if a hypothesis states that increasing product visibility will boost sales, researchers know to track marketing efforts and their corresponding sales figures. Testing this hypothesis can reveal not only whether the initial assumption holds true but also deeper insights into customer behavior and preferences. Thus, understanding the importance of market hypothesis formulation creates a framework for effective market research, promoting actionable insights and strategic growth.

How Hypotheses Drive Business Decisions

Formulating a market hypothesis is essential for guiding effective business decisions. A well-structured hypothesis provides a foundation for research and analysis, driving the direction of marketing strategies. Businesses can test these hypotheses through surveys, interviews, or focus groups, refining their products or services based on the findings. This targeted approach ensures that decisions are backed by solid data rather than assumptions, leading to more accurate outcomes.

One key aspect is that hypotheses encourage exploration and innovation. When teams construct hypotheses, they set clear expectations about market trends or customer behavior. Testing these hypotheses allows companies to validate ideas or uncover new insights. Additionally, successful hypothesis testing not only helps validate current strategies but also informs future initiatives. By continuously adapting based on results, organizations can stay ahead of the competitive curve and better meet customer needs.

Examples of Market Hypothesis Formulation in Action

Formulating a market hypothesis effectively transforms theoretical ideas into actionable insights. For example, a company may hypothesize that increasing social media presence will lead to a higher customer engagement rate. This hypothesis can be tested through targeted campaigns and measured by tracking engagement metrics before and after implementation. By analyzing the data, researchers can verify or refute the initial assumption, guiding future marketing strategies.

Another scenario involves a new product launch. A business might propose that consumers will prefer an eco-friendly version of an existing product over the traditional option. Conducting surveys or focus groups helps gather consumer reactions and preferences. The insights gained from these methods can significantly influence product development and marketing techniques, demonstrating the power of market hypothesis formulation in driving effective decision-making and strategy optimization.

Customer Behavior Predictions

Understanding Customer Behavior Predictions remains crucial for refining market strategies. The formulation of effective market hypotheses can shine a light on customer preferences and buying patterns. These predictions enable businesses to proactively respond to changing market dynamics and enhance customer satisfaction.

Focusing on behavioral insights leads to a more personalized shopping experience. For example, we can explore how pricing influences customer decisions—higher perceived value often results in increased purchases. Additionally, mapping customer journeys and developing detailed personas allows for tailored marketing options. This approach not only boosts customer retention but also aids in identifying new opportunities for growth. Exploring these facets is central to formulating a robust market hypothesis that aligns closely with the evolving landscape of customer behavior and expectations.

Product Market Fit Assessment

Product Market Fit Assessment focuses on evaluating how well your product meets market needs. At the core of this assessment is the Market Hypothesis Formulation, which helps define the assumptions about product fit and target audience. Understanding customers' expectations and pain points is essential for shaping these hypotheses. This insight allows businesses to identify potential gaps in the market that their products can fill.

To effectively conduct a Product Market Fit Assessment, consider three key elements. First, customer feedback is crucial in refining product features based on real user experiences. Second, competitive analysis helps clarify how your product stands against others, revealing unique selling propositions. Lastly, iterative testing enables businesses to validate or adjust their market hypotheses based on continuous learning and adaptation. Through these steps, you can create robust assumptions that facilitate product development and positioning strategy, driving sustainable growth in your market.

Conclusion: The Vital Role of Market Hypothesis Formulation in Research

Market hypothesis formulation is essential in guiding effective market research. By articulating clear hypotheses, researchers can focus their inquiries, directing efforts toward specific questions about consumer behavior and market trends. This targeted approach enhances the reliability of insights gained, ensuring that conclusions drawn from data collection are actionable and relevant.

Furthermore, well-defined hypotheses help in identifying the right metrics for evaluation. As researchers analyze data, they can assess whether their initial assumptions hold true, allowing for refined strategies in response to market demands. Ultimately, the process of hypothesis formulation serves as a critical foundation for informed decision-making, driving successful business outcomes.

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14 Market Research Examples

Curiosity.

At the heart of every successful marketing campaign is a curious marketer who learned how to better serve a customer.

In this industry, we scratch that curiosity itch with market research.

To help give you ideas to learn about your customer, in this article we bring you examples from Consumer Reports, Intel, Visa USA, Hallmark, Levi Strauss, John Deere, LeapFrog, Spiceworks Ziff Davis and more.

This article was originally published in the MarketingSherpa email newsletter .

Example #1: National bank’s A/B testing

You can learn what customers want by conducting experiments on real-life customer decisions using A/B testing. When you ensure your tests do not have any validity threats, the information you garner can offer very reliable insights into customer behavior.

Here’s an example from Flint McGlaughlin, CEO of MarketingSherpa and MECLABS Institute, and the creator of its online marketing course .

A national bank was working with MECLABS to discover how to increase the number of sign-ups for new checking accounts.

Customers who were interested in checking accounts could click on an “Open in Minutes” link on the bank’s homepage.

Creative Sample #1: Anonymized bank homepage

After clicking on the homepage link, visitors were taken to a four-question checking account selector tool.

Creative Sample #2: Original checking account landing page — account recommendation selector tool

After filling out the selector tool, visitors were taken to a results page that included a suggested package (“Best Choice”) along with a secondary option (“Second Choice”). The results page had several calls to action (CTAs). Website visitors were able to select an account and begin pre-registration (“Open Now”) or find out more information about the account (“Learn More”), go back and change their answers (“Go back and change answers”), or manually browse other checking options (“Other Checking Options”).

Creative Sample #3: Original checking account landing page — account recommendation selector tool results page

After going through the experience, the MECLABS team hypothesized that the selector tool wasn’t really delivering on the expectation the customer had after clicking on the “Open in Minutes” CTA. They created two treatments (new versions) and tested them against the control experience.

In the first treatment, the checking selector tool was removed, and instead, customers were directly presented with three account options in tabs from which customers could select.

Creative Sample #4: Checking account landing page Treatment #1

The second treatment’s landing page focused on a single product and had only one CTA. The call-to-action was similar to the CTA customers clicked on the homepage to get to this page — “Open Now.”

Creative Sample #5: Checking account landing page Treatment #2

Both treatments increased account applications compared to the control landing page experience, with Treatment #2 generating 65% more applicants at a 98% level of confidence.

Creative Sample #6: Results of bank experiment that used A/B testing

You’ll note the Level of Confidence in the results. With any research tactic or tool you use to learn about customers, you have to consider whether the information you’re getting really represents most customers, or if you’re just seeing outliers or random chance.

With a high Level of Confidence like this, it is more likely the results actually represent a true difference between the control and treatment landing pages and that the results aren’t just a random event.

The other factor to consider is — testing in and of itself will not produce results. You have to use testing as research to actually learn about the customer and then make changes to better serve the customer.

In the video How to Discover Exactly What the Customer Wants to See on the Next Click: 3 critical skills every marketer must master , McGlaughlin discussed this national bank experiment and explained how to use prioritization, identification and deduction to discover what your customers want.

This example was originally published in Marketing Research: 5 examples of discovering what customers want .

Example #2: Consumer Reports’ market intelligence research from third-party sources

The first example covers A/B testing. But keep in mind, ill-informed A/B testing isn’t market research, it’s just hoping for insights from random guesses.

In other words, A/B testing in a vacuum does not provide valuable information about customers. What you are testing is crucial, and then A/B testing is a means to help better understand whether insights you have about the customer are either validated or refuted by actual customer behavior. So it’s important to start with some research into potential customers and competitors to inform your A/B tests.

For example, when MECLABS and MarketingExperiments (sister publisher to MarketingSherpa) worked with Consumer Reports on a public, crowdsourced A/B test, we provided a market intelligence report to our audience to help inform their test suggestions.

Every successful marketing test should confirm or deny an assumption about the customer. You need enough knowledge about the customer to create marketing messages you think will be effective.

For this public experiment to help marketers improve their split testing abilities, we had a real customer to work with — donors to Consumer Reports.

To help our audience better understand the customer, the MECLABS Marketing Intelligence team created the 26-page ConsumerReports Market Intelligence Research document (which you can see for yourself at that link).

This example was originally published in Calling All Writers and Marketers: Write the most effective copy for this Consumer Reports email and win a MarketingSherpa Summit package and Consumer Reports Value Proposition Test: What you can learn from a 29% drop in clickthrough .

Example #3: Virtual event company’s conversation

What if you don’t have the budget for A/B testing? Or any of the other tactics in this article?

Well, if you’re like most people you likely have some relationships with other human beings. A significant other, friends, family, neighbors, co-workers, customers, a nemesis (“Newman!”). While conducting market research by talking to these people has several validity threats, it at least helps you get out of your own head and identify some of your blind spots.

WebBabyShower.com’s lead magnet is a PDF download of a baby shower thank you card ‘swipe file’ plus some extras. “Women want to print it out and have it where they are writing cards, not have a laptop open constantly,” said Kurt Perschke, owner, WebBabyShower.com.

That is not a throwaway quote from Perschke. That is a brilliant insight, so I want to make sure we don’t overlook it. By better understanding customer behavior, you can better serve customers and increase results.

However, you are not your customer. So you must bridge the gap between you and them.

Often you hear marketers or business leaders review an ad or discuss a marketing campaign and say, “Well, I would never read that entire ad” or “I would not be interested in that promotion.” To which I say … who cares? Who cares what you would do? If you are not in the ideal customer set, sorry to dent your ego, but you really don’t matter. Only the customer does.

Perschke is one step ahead of many marketers and business leaders because he readily understands this. “Owning a business whose customers are 95% women has been a great education for me,” he said.

So I had to ask him, how did he get this insight into his customers’ behavior? Frankly, it didn’t take complex market research. He was just aware of this disconnect he had with the customer, and he was alert for ways to bridge the gap. “To be honest, I first saw that with my wife. Then we asked a few customers, and they confirmed it’s what they did also. Writing notes by hand is viewed as a ‘non-digital’ activity and reading from a laptop kinda spoils the mood apparently,” he said.

Back to WebBabyShower. “We've seen a [more than] 100% increase in email signups using this method, which was both inexpensive and evergreen,” Perschke said.

This example was originally published in Digital Marketing: Six specific examples of incentives that worked .

Example #4: Spiceworks Ziff Davis’ research-informed content marketing

Marketing research isn’t just to inform products and advertising messages. Market research can also give your brand a leg up in another highly competitive space – content marketing.

Don’t just jump in and create content expecting it to be successful just because it’s “free.” Conducting research beforehand can help you understand what your potential audience already receives and where they might need help but are currently being served.

When Spiceworks Ziff Davis (SWZD) published its annual State of IT report, it invested months in conducting primary market research, analyzing year-over-year trends, and finally producing the actual report.

“Before getting into the nuts and bolts of writing an asset, look at market shifts and gaps that complement your business and marketing objectives. Then, you can begin to plan, research, write, review and finalize an asset,” said Priscilla Meisel, Content Marketing Director, SWZD.

This example was originally published in Marketing Writing: 3 simple tips that can help any marketer improve results (even if you’re not a copywriter) .

Example #5: Business travel company’s guerilla research

There are many established, expensive tactics you can use to better understand customers.

But if you don’t have the budget for those tactics, and don’t know any potential customers, you might want to brainstorm creative ways you can get valuable information from the right customer target set.

Here’s an example from a former client of Mitch McCasland, Founding Partner and Director, Brand Inquiry Partners. The company sold a product related to frequent business flyers and was interested in finding out information on people who travel for a living. They needed consumer feedback right away.

“I suggested that they go out to the airport with a bunch of 20-dollar bills and wait outside a gate for passengers to come off their flight,” McCasland said. When people came off the flight, they were politely asked if they would answer a few questions in exchange for the incentive (the $20). By targeting the first people off the flight they had a high likelihood of reaching the first-class passengers.

This example was originally published in Guerrilla Market Research Expert Mitch McCasland Tells How You Can Conduct Quick (and Cheap) Research .

Example #6: Intel’s market research database

When conducting market research, it is crucial to organize your data in a way that allows you to easily and quickly report on it. This is especially important for qualitative studies where you are trying to do more than just quantify the data, but need to manage it so it is easier to analyze.

Anne McClard, Senior Researcher, Doxus worked with Shauna Pettit-Brown of Intel on a research project to understand the needs of mobile application developers throughout the world.

Intel needed to be able to analyze the data from several different angles, including segment and geography, a daunting task complicated by the number of interviews, interviewers, and world languages.

“The interviews were about an hour long, and pretty substantial,” McClard says. So, she needed to build a database to organize the transcripts in a way that made sense.

Different types of data are useful for different departments within a company; once your database is organized you can sort it by various threads.

The Intel study had three different internal sponsors. "When it came to doing the analysis, we ended up creating multiple versions of the presentation targeted to individual audiences," Pettit-Brown says.

The organized database enabled her to go back into the data set to answer questions specific to the interests of the three different groups.

This example was originally published in 4 Steps to Building a Qualitative Market Research Database That Works Better .

Example #7: National security survey’s priming

When conducting market research surveys, the way you word your questions can affect customers’ response. Even the way you word previous questions can put customers in a certain mindset that will skew their answers.

For example, when people were asked if they thought the U.S. government should spend money on an anti-missile shield, the results appeared fairly conclusive. Sixty-four percent of those surveyed thought the country should and only six percent were unsure, according to Opinion Makers: An Insider Exposes the Truth Behind the Polls .

But when pollsters added the option, "...or are you unsure?" the level of uncertainty leaped from six percent to 33 percent. When they asked whether respondents would be upset if the government took the opposite course of action from their selection, 59 percent either didn’t have an opinion or didn’t mind if the government did something differently.

This is an example of how the way you word questions can change a survey’s results. You want survey answers to reflect customer’s actual sentiments that are as free of your company’s previously held biases as possible.

This example was originally published in Are Surveys Misleading? 7 Questions for Better Market Research .

Example #8: Visa USA’s approach to getting an accurate answer

As mentioned in the previous example, the way you ask customers questions can skew their responses with your own biases.

However, the way you ask questions to potential customers can also illuminate your understanding of them. Which is why companies field surveys to begin with.

“One thing you learn over time is how to structure questions so you have a greater likelihood of getting an accurate answer. For example, when we want to find out if people are paying off their bills, we'll ask them to think about the card they use most often. We then ask what the balance was on their last bill after they paid it,” said Michael Marx, VP Research Services, Visa USA.

This example was originally published in Tips from Visa USA's Market Research Expert Michael Marx .

Example #9: Hallmark’s private members-only community

Online communities are a way to interact with and learn from customers. Hallmark created a private members-only community called Idea Exchange (an idea you could replicate with a Facebook or LinkedIn Group).

The community helped the greeting cards company learn the customer’s language.

“Communities…let consumers describe issues in their own terms,” explained Tom Brailsford, Manager of Advancing Capabilities, Hallmark Cards. “Lots of times companies use jargon internally.”

At Hallmark they used to talk internally about “channels” of distribution. But consumers talk about stores, not channels. It is much clearer to ask consumers about the stores they shop in than what channels they shop.

For example, Brailsford clarified, “We say we want to nurture, inspire, and lift one’s spirits. We use those terms, and the communities have defined those terms for us. So we have learned how those things play out in their lives. It gives us a much richer vocabulary to talk about these things.”

This example was originally published in Third Year Results from Hallmark's Online Market Research Experiment .

Example #10: L'Oréal’s social media listening

If you don’t want the long-term responsibility that comes with creating an online community, you can use social media listening to understand how customers talking about your products and industry in their own language.

In 2019, L'Oréal felt the need to upgrade one of its top makeup products – L'Oréal Paris Alliance Perfect foundation. Both the formula and the product communication were outdated – multiple ingredients had emerged on the market along with competitive products made from those ingredients.

These new ingredients and products were overwhelming consumers. After implementing new formulas, the competitor brands would advertise their ingredients as the best on the market, providing almost magical results.

So the team at L'Oréal decided to research their consumers’ expectations instead of simply crafting a new formula on their own. The idea was to understand not only which active ingredients are credible among the audience, but also which particular words they use while speaking about foundations in general.

The marketing team decided to combine two research methods: social media listening and traditional questionnaires.

“For the most part, we conduct social media listening research when we need to find out what our customers say about our brand/product/topic and which words they use to do it. We do conduct traditional research as well and ask questions directly. These surveys are different because we provide a variety of readymade answers that respondents choose from. Thus, we limit them in terms of statements and their wording,” says Marina Tarandiuk, marketing research specialist, L'Oréal Ukraine.

“The key value of social media listening (SML) for us is the opportunity to collect people’s opinions that are as ‘natural’ as possible. When someone leaves a review online, they are in a comfortable environment, they use their ‘own’ language to express themselves, there is no interviewer standing next to them and potentially causing shame for their answer. The analytics of ‘natural’ and honest opinions of our customers enables us to implement the results in our communication and use the same language as them,” Tarandiuk said.

The team worked with a social media listening tool vendor to identify the most popular, in-demand ingredients discussed online and detect the most commonly used words and phrases to create a “consumer glossary.”

Questionnaires had to confirm all the hypotheses and insights found while monitoring social media. This part was performed in-house with the dedicated team. They created custom questionnaires aiming to narrow down all the data to a maximum of three variants that could become the base for the whole product line.

“One of our recent studies had a goal to find out which words our clients used to describe positive and negative qualities of [the] foundation. Due to a change in [the] product’s formula, we also decided to change its communication. Based on the opinions of our customers, we can consolidate the existing positive ideas that our clients have about the product,” Tarandiuk said.

To find the related mentions, the team monitored not only the products made by L'Oréal but also the overall category. “The search query contained both brand names and general words like foundation, texture, smell, skin, pores, etc. The problem was that this approach ended up collecting thousands of mentions, not all of which were relevant to the topic,” said Elena Teselko, content marketing manager, YouScan (L'Oréal’s social media listening tool).

So the team used artificial intelligence-based tagging that divided mentions according to the category, features, or product type.

This approach helped the team discover that customers valued such foundation features as not clogging pores, a light texture, and not spreading. Meanwhile, the most discussed and appreciated cosmetics component was hyaluronic acid.

These exact phrases, found with the help of social media monitoring, were later used for marketing communication.

Creative Sample #7: Marketing communicating for personal care company with messaging based on discoveries from market research

“Doing research and detecting audience’s interests BEFORE starting a campaign is an approach that dramatically lowers any risks and increases chances that the campaign would be appreciated by customers,” Teselko said.

This example was originally published in B2C Branding: 3 quick case studies of enhancing the brand with a better customer experience .

Example #11: Levi’s ethnographic research

In a focus group or survey, you are asking customers to explain something they may not even truly understand. Could be why they bought a product. Or what they think of your competitor.

Ethnographic research is a type of anthropology in which you go into customers’ homes or places of business and observe their actual behavior, behavior they may not understand well enough to explain to you.

While cost prohibitive to many brands, and simply unfeasible for others, it can elicit new insights into your customers.

Michael Perman, Senior Director Cultural Insights, Levi Strauss & Co. uses both quantitative and qualitative research on a broad spectrum, but when it comes to gathering consumer insight, he focuses on in-depth ethnographic research provided by partners who specialize in getting deep into the “nooks and crannies of consumer life in America and around the world.” For example, his team spends time in consumers’ homes and in their closets. They shop with consumers, looking for the reality of a consumer’s life and identifying themes that will enable designers and merchandisers to better understand and anticipate consumer needs.

Perman then puts together multi-sensory presentations that illustrate the findings of research. For example, “we might recreate a teenager’s bedroom and show what a teenage girl might have on her dresser.”

This example was originally published in How to Get Your Company to Pay Attention to Market Research Results: Tips from Levi Strauss .

Example #12: eBags’ ethnographic research

Ethnographic research isn’t confined to a physical goods brand like Levi’s. Digital brands can engage in this form of anthropology as well.

While usability testing in a lab is useful, it does miss some of the real-world environmental factors that play a part in the success of a website. Usability testing alone didn’t create a clear enough picture for Gregory Casey, User Experience Designer and Architect, eBags.

“After we had designed our mobile and tablet experience, I wanted to run some contextual user research, which basically meant seeing how people used it in the wild, seeing how people are using it in their homes. So that’s exactly what I did,” Gregory said.

He found consumers willing to open their home to him and be tested in their normal environment. This meant factors like the television, phone calls and other family members played a part in how they experienced the eBags mobile site.

“During these interview sessions, a lot of times we were interrupted by, say, a child coming over and the mother having to do something for the kid … The experience isn’t sovereign. It’s not something where they just sit down, work through a particular user flow and complete their interaction,” Gregory said.

By watching users work through the site as they would in their everyday life, Gregory got to see what parts of the site they actually use.

This example was originally published in Mobile Marketing: 4 takeaways on how to improve your mobile shopping experience beyond just responsive design .

Example #13: John Deere’s shift from product-centric market research to consumer-centric research

One of the major benefits of market research is to overcome company blind spots. However, if you start with your blind spots – i.e., a product focus – you will blunt the effectiveness of your market research.

In the past, “they’d say, Here’s the product, find out how people feel about it,” explained David van Nostrand, Manager, John Deere's Global Market Research. “A lot of companies do that.” Instead, they should be saying, “Let's start with the customers: what do they want, what do they need?”

The solution? A new in-house program called “Category Experts” brings the product-group employees over as full team members working on specific research projects with van Nostrand’s team.

These staffers handle items that don’t require a research background: scheduling, meetings, logistics, communication and vendor management. The actual task they handle is less important than the fact that they serve as human cross-pollinators, bringing consumer-centric sensibility back to their product- focused groups.

For example, if van Nostrand’s team is doing research about a vehicle, they bring in staffers from the Vehicles product groups. “The information about vehicle consumers needs to be out there in the vehicle marketing groups, not locked in here in the heads of the researchers.”

This example was originally published in How John Deere Increased Mass Consumer Market Share by Revamping its Market Research Tactics .

Example #14: LeapFrog’s market research involvement throughout product development (not just at the beginning and the end)

Market research is sometimes thought of as a practice that can either inform the development of a product, or research consumer attitudes about developed products. But what about the middle?

Once the creative people begin working on product designs, the LeapFrog research department stays involved.

They have a lab onsite where they bring moms and kids from the San Francisco Bay area to test preliminary versions of the products. “We do a lot of hands-on, informal qualitative work with kids,” said Craig Spitzer, VP Marketing Research, LeapFrog. “Can they do what they need to do to work the product? Do they go from step A to B to C, or do they go from A to C to B?”

When designing the LeapPad Learning System, for example, the prototype went through the lab “a dozen times or so,” he says.

A key challenge for the research department is keeping and building the list of thousands of families who have agreed to be on call for testing. “We've done everything from recruiting on the Internet to putting out fliers in local schools, working through employees whose kids are in schools, and milking every connection we have,” Spitzer says.

Kids who test products at the lab are compensated with a free, existing product rather than a promise of the getting the product they're testing when it is released in the future.

This example was originally published in How LeapFrog Uses Marketing Research to Launch New Products .

Related resources

The Marketer’s Blind Spot: 3 ways to overcome the marketer’s greatest obstacle to effective messaging

Get Your Free Test Discovery Tool to Help Log all the Results and Discoveries from Your Company’s Marketing Tests

Marketing Research: 5 examples of discovering what customers want

Online Marketing Tests: How do you know you’re really learning anything?

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How to Generate and Validate Product Hypotheses

Every product owner knows that it takes effort to build something that'll cater to user needs. You'll have to make many tough calls if you wish to grow the company and evolve the product so it delivers more value. But how do you decide what to change in the product, your marketing strategy, or the overall direction to succeed? And how do you make a product that truly resonates with your target audience?

There are many unknowns in business, so many fundamental decisions start from a simple "what if?". But they can't be based on guesses, as you need some proof to fill in the blanks reasonably.

Because there's no universal recipe for successfully building a product, teams collect data, do research, study the dynamics, and generate hypotheses according to the given facts. They then take corresponding actions to find out whether they were right or wrong, make conclusions, and most likely restart the process again.

On this page, we thoroughly inspect product hypotheses. We'll go over what they are, how to create hypothesis statements and validate them, and what goes after this step.

What Is a Hypothesis in Product Management?

A hypothesis in product development and product management is a statement or assumption about the product, planned feature, market, or customer (e.g., their needs, behavior, or expectations) that you can put to the test, evaluate, and base your further decisions on . This may, for instance, regard the upcoming product changes as well as the impact they can result in.

A hypothesis implies that there is limited knowledge. Hence, the teams need to undergo testing activities to validate their ideas and confirm whether they are true or false.

Hypotheses guide the product development process and may point at important findings to help build a better product that'll serve user needs. In essence, teams create hypothesis statements in an attempt to improve the offering, boost engagement, increase revenue, find product-market fit quicker, or for other business-related reasons.

It's sort of like an experiment with trial and error, yet, it is data-driven and should be unbiased . This means that teams don't make assumptions out of the blue. Instead, they turn to the collected data, conducted market research , and factual information, which helps avoid completely missing the mark. The obtained results are then carefully analyzed and may influence decision-making.

Such experiments backed by data and analysis are an integral aspect of successful product development and allow startups or businesses to dodge costly startup mistakes .

‍ When do teams create hypothesis statements and validate them? To some extent, hypothesis testing is an ongoing process to work on constantly. It may occur during various product development life cycle stages, from early phases like initiation to late ones like scaling.

In any event, the key here is learning how to generate hypothesis statements and validate them effectively. We'll go over this in more detail later on.

Idea vs. Hypothesis Compared

You might be wondering whether ideas and hypotheses are the same thing. Well, there are a few distinctions.

What's the difference between an idea and a hypothesis?

An idea is simply a suggested proposal. Say, a teammate comes up with something you can bring to life during a brainstorming session or pitches in a suggestion like "How about we shorten the checkout process?". You can jot down such ideas and then consider working on them if they'll truly make a difference and improve the product, strategy, or result in other business benefits. Ideas may thus be used as the hypothesis foundation when you decide to prove a concept.

A hypothesis is the next step, when an idea gets wrapped with specifics to become an assumption that may be tested. As such, you can refine the idea by adding details to it. The previously mentioned idea can be worded into a product hypothesis statement like: "The cart abandonment rate is high, and many users flee at checkout. But if we shorten the checkout process by cutting down the number of steps to only two and get rid of four excessive fields, we'll simplify the user journey, boost satisfaction, and may get up to 15% more completed orders".

A hypothesis is something you can test in an attempt to reach a certain goal. Testing isn't obligatory in this scenario, of course, but the idea may be tested if you weigh the pros and cons and decide that the required effort is worth a try. We'll explain how to create hypothesis statements next.

How to Generate a Hypothesis for a Product

The last thing those developing a product want is to invest time and effort into something that won't bring any visible results, fall short of customer expectations, or won't live up to their needs. Therefore, to increase the chances of achieving a successful outcome and product-led growth , teams may need to revisit their product development approach by optimizing one of the starting points of the process: learning to make reasonable product hypotheses.

If the entire procedure is structured, this may assist you during such stages as the discovery phase and raise the odds of reaching your product goals and setting your business up for success. Yet, what's the entire process like?

How hypothesis generation and validation works

It all starts with identifying an existing problem . Is there a product area that's experiencing a downfall, a visible trend, or a market gap? Are users often complaining about something in their feedback? Or is there something you're willing to change (say, if you aim to get more profit, increase engagement, optimize a process, expand to a new market, or reach your OKRs and KPIs faster)?
Teams then need to work on formulating a hypothesis . They put the statement into concise and short wording that describes what is expected to achieve. Importantly, it has to be relevant, actionable, backed by data, and without generalizations.
Next, they have to test the hypothesis by running experiments to validate it (for instance, via A/B or multivariate testing, prototyping, feedback collection, or other ways).
Then, the obtained results of the test must be analyzed . Did one element or page version outperform the other? Depending on what you're testing, you can look into various merits or product performance metrics (such as the click rate, bounce rate, or the number of sign-ups) to assess whether your prediction was correct.
Finally, the teams can make conclusions that could lead to data-driven decisions. For example, they can make corresponding changes or roll back a step.

How Else Can You Generate Product Hypotheses?

Such processes imply sharing ideas when a problem is spotted by digging deep into facts and studying the possible risks, goals, benefits, and outcomes. You may apply various MVP tools like (FigJam, Notion, or Miro) that were designed to simplify brainstorming sessions, systemize pitched suggestions, and keep everyone organized without losing any ideas.

Predictive product analysis can also be integrated into this process, leveraging data and insights to anticipate market trends and consumer preferences, thus enhancing decision-making and product development strategies. This approach fosters a more proactive and informed approach to innovation, ensuring products are not only relevant but also resonate with the target audience, ultimately increasing their chances of success in the market.

Besides, you can settle on one of the many frameworks that facilitate decision-making processes , ideation phases, or feature prioritization . Such frameworks are best applicable if you need to test your assumptions and structure the validation process. These are a few common ones if you're looking toward a systematic approach:

Business Model Canvas (used to establish the foundation of the business model and helps find answers to vitals like your value proposition, finding the right customer segment, or the ways to make revenue);
Lean Startup framework (the lean startup framework uses a diagram-like format for capturing major processes and can be handy for testing various hypotheses like how much value a product brings or assumptions on personas, the problem, growth, etc.);
Design Thinking Process (is all about interactive learning and involves getting an in-depth understanding of the customer needs and pain points, which can be formulated into hypotheses followed by simple prototypes and tests).

Need a hand with product development?

Upsilon's team of pros is ready to share our expertise in building tech products.

How to Make a Hypothesis Statement for a Product

Once you've indicated the addressable problem or opportunity and broken down the issue in focus, you need to work on formulating the hypotheses and associated tasks. By the way, it works the same way if you want to prove that something will be false (a.k.a null hypothesis).

If you're unsure how to write a hypothesis statement, let's explore the essential steps that'll set you on the right track.

Step 1: Allocate the Variable Components

Product hypotheses are generally different for each case, so begin by pinpointing the major variables, i.e., the cause and effect . You'll need to outline what you think is supposed to happen if a change or action gets implemented.

Put simply, the "cause" is what you're planning to change, and the "effect" is what will indicate whether the change is bringing in the expected results. Falling back on the example we brought up earlier, the ineffective checkout process can be the cause, while the increased percentage of completed orders is the metric that'll show the effect.

Make sure to also note such vital points as:

what the problem and solution are;
what are the benefits or the expected impact/successful outcome;
which user group is affected;
what are the risks;
what kind of experiments can help test the hypothesis;
what can measure whether you were right or wrong.

Step 2: Ensure the Connection Is Specific and Logical

Mind that generic connections that lack specifics will get you nowhere. So if you're thinking about how to word a hypothesis statement, make sure that the cause and effect include clear reasons and a logical dependency .

Think about what can be the precise and link showing why A affects B. In our checkout example, it could be: fewer steps in the checkout and the removed excessive fields will speed up the process, help avoid confusion, irritate users less, and lead to more completed orders. That's much more explicit than just stating the fact that the checkout needs to be changed to get more completed orders.

Step 3: Decide on the Data You'll Collect

Certainly, multiple things can be used to measure the effect. Therefore, you need to choose the optimal metrics and validation criteria that'll best envision if you're moving in the right direction.

If you need a tip on how to create hypothesis statements that won't result in a waste of time, try to avoid vagueness and be as specific as you can when selecting what can best measure and assess the results of your hypothesis test. The criteria must be measurable and tied to the hypotheses . This can be a realistic percentage or number (say, you expect a 15% increase in completed orders or 2x fewer cart abandonment cases during the checkout phase).

Once again, if you're not realistic, then you might end up misinterpreting the results. Remember that sometimes an increase that's even as little as 2% can make a huge difference, so why make 50% the merit if it's not achievable in the first place?

Step 4: Settle on the Sequence

It's quite common that you'll end up with multiple product hypotheses. Some are more important than others, of course, and some will require more effort and input.

Therefore, just as with the features on your product development roadmap , prioritize your hypotheses according to their impact and importance. Then, group and order them, especially if the results of some hypotheses influence others on your list.

Product Hypothesis Examples

To demonstrate how to formulate your assumptions clearly, here are several more apart from the example of a hypothesis statement given above:

Adding a wishlist feature to the cart with the possibility to send a gift hint to friends via email will increase the likelihood of making a sale and bring in additional sign-ups.
Placing a limited-time promo code banner stripe on the home page will increase the number of sales in March.
Moving up the call to action element on the landing page and changing the button text will increase the click-through rate twice.
By highlighting a new way to use the product, we'll target a niche customer segment (i.e., single parents under 30) and acquire 5% more leads.

How to Validate Hypothesis Statements: The Process Explained

There are multiple options when it comes to validating hypothesis statements. To get appropriate results, you have to come up with the right experiment that'll help you test the hypothesis. You'll need a control group or people who represent your target audience segments or groups to participate (otherwise, your results might not be accurate).

‍ What can serve as the experiment you may run? Experiments may take tons of different forms, and you'll need to choose the one that clicks best with your hypothesis goals (and your available resources, of course). The same goes for how long you'll have to carry out the test (say, a time period of two months or as little as two weeks). Here are several to get you started.

Experiments for product hypothesis validation

Feedback and User Testing

Talking to users, potential customers, or members of your own online startup community can be another way to test your hypotheses. You may use surveys, questionnaires, or opt for more extensive interviews to validate hypothesis statements and find out what people think. This assumption validation approach involves your existing or potential users and might require some additional time, but can bring you many insights.

Conduct A/B or Multivariate Tests

One of the experiments you may develop involves making more than one version of an element or page to see which option resonates with the users more. As such, you can have a call to action block with different wording or play around with the colors, imagery, visuals, and other things.

To run such split experiments, you can apply tools like VWO that allows to easily construct alternative designs and split what your users see (e.g., one half of the users will see version one, while the other half will see version two). You can track various metrics and apply heatmaps, click maps, and screen recordings to learn more about user response and behavior. Mind, though, that the key to such tests is to get as many users as you can give the tests time. Don't jump to conclusions too soon or if very few people participated in your experiment.

Build Prototypes and Fake Doors

Demos and clickable prototypes can be a great way to save time and money on costly feature or product development. A prototype also allows you to refine the design. However, they can also serve as experiments for validating hypotheses, collecting data, and getting feedback.

For instance, if you have a new feature in mind and want to ensure there is interest, you can utilize such MVP types as fake doors . Make a short demo recording of the feature and place it on your landing page to track interest or test how many people sign up.

Usability Testing

Similarly, you can run experiments to observe how users interact with the feature, page, product, etc. Usually, such experiments are held on prototype testing platforms with a focus group representing your target visitors. By showing a prototype or early version of the design to users, you can view how people use the solution, where they face problems, or what they don't understand. This may be very helpful if you have hypotheses regarding redesigns and user experience improvements before you move on from prototype to MVP development.

You can even take it a few steps further and build a barebone feature version that people can really interact with, yet you'll be the one behind the curtain to make it happen. There were many MVP examples when companies applied Wizard of Oz or concierge MVPs to validate their hypotheses.

Or you can actually develop some functionality but release it for only a limited number of people to see. This is referred to as a feature flag , which can show really specific results but is effort-intensive.

What Comes After Hypothesis Validation?

Analysis is what you move on to once you've run the experiment. This is the time to review the collected data, metrics, and feedback to validate (or invalidate) the hypothesis.

You have to evaluate the experiment's results to determine whether your product hypotheses were valid or not. For example, if you were testing two versions of an element design, color scheme, or copy, look into which one performed best.

It is crucial to be certain that you have enough data to draw conclusions, though, and that it's accurate and unbiased . Because if you don't, this may be a sign that your experiment needs to be run for some additional time, be altered, or held once again. You won't want to make a solid decision based on uncertain or misleading results, right?

What happens after hypothesis validation

If the hypothesis was supported , proceed to making corresponding changes (such as implementing a new feature, changing the design, rephrasing your copy, etc.). Remember that your aim was to learn and iterate to improve.
If your hypothesis was proven false , think of it as a valuable learning experience. The main goal is to learn from the results and be able to adjust your processes accordingly. Dig deep to find out what went wrong, look for patterns and things that may have skewed the results. But if all signs show that you were wrong with your hypothesis, accept this outcome as a fact, and move on. This can help you make conclusions on how to better formulate your product hypotheses next time. Don't be too judgemental, though, as a failed experiment might only mean that you need to improve the current hypothesis, revise it, or create a new one based on the results of this experiment, and run the process once more.

On another note, make sure to record your hypotheses and experiment results . Some companies use CRMs to jot down the key findings, while others use something as simple as Google Docs. Either way, this can be your single source of truth that can help you avoid running the same experiments or allow you to compare results over time.

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Final Thoughts on Product Hypotheses

The hypothesis-driven approach in product development is a great way to avoid uncalled-for risks and pricey mistakes. You can back up your assumptions with facts, observe your target audience's reactions, and be more certain that this move will deliver value.

However, this only makes sense if the validation of hypothesis statements is backed by relevant data that'll allow you to determine whether the hypothesis is valid or not. By doing so, you can be certain that you're developing and testing hypotheses to accelerate your product management and avoiding decisions based on guesswork.

Certainly, a failed experiment may bring you just as much knowledge and findings as one that succeeds. Teams have to learn from their mistakes, boost their hypothesis generation and testing knowledge , and make improvements according to the results of their experiments. This is an ongoing process, of course, as no product can grow if it isn't iterated and improved.

If you're only planning to or are currently building a product, Upsilon can lend you a helping hand. Our team has years of experience providing product development services for growth-stage startups and building MVPs for early-stage businesses , so you can use our expertise and knowledge to dodge many mistakes. Don't be shy to contact us to discuss your needs!

Product Charter: Purpose, Writing Tips, and Examples

How to Make an MVP Roadmap

How Much Does It Cost to Build an AI Solution in 2024?

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J Korean Med Sci
v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Quantitative research questions	Quantitative research hypotheses
Descriptive research questions	Simple hypothesis
Comparative research questions	Complex hypothesis
Relationship research questions	Directional hypothesis
	Non-directional hypothesis
	Associative hypothesis
	Causal hypothesis
	Null hypothesis
	Alternative hypothesis
	Working hypothesis
	Statistical hypothesis
	Logical hypothesis
	Hypothesis-testing
Qualitative research questions	Qualitative research hypotheses
Contextual research questions	Hypothesis-generating
Descriptive research questions
Evaluation research questions
Explanatory research questions
Exploratory research questions
Generative research questions
Ideological research questions
Ethnographic research questions
Phenomenological research questions
Grounded theory questions
Qualitative case study questions

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Quantitative research questions
Descriptive research question
	- Measures responses of subjects to variables
	- Presents variables to measure, analyze, or assess
	What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training?
Comparative research question
	- Clarifies difference between one group with outcome variable and another group without outcome variable
	Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)?
	- Compares the effects of variables
	How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells?
Relationship research question
	- Defines trends, association, relationships, or interactions between dependent variable and independent variable
	Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic?

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Quantitative research hypotheses
Simple hypothesis
	- Predicts relationship between single dependent variable and single independent variable
	If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered.
Complex hypothesis
	- Foretells relationship between two or more independent and dependent variables
	The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable).
Directional hypothesis
	- Identifies study direction based on theory towards particular outcome to clarify relationship between variables
	Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects.
Non-directional hypothesis
	- Nature of relationship between two variables or exact study direction is not identified
	- Does not involve a theory
	Women and men are different in terms of helpfulness. (Exact study direction is not identified)
Associative hypothesis
	- Describes variable interdependency
	- Change in one variable causes change in another variable
	A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable).
Causal hypothesis
	- An effect on dependent variable is predicted from manipulation of independent variable
	A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient.
Null hypothesis
	- A negative statement indicating no relationship or difference between 2 variables
	There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2).
Alternative hypothesis
	- Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables
	The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2).
Working hypothesis
	- A hypothesis that is initially accepted for further research to produce a feasible theory
	Dairy cows fed with concentrates of different formulations will produce different amounts of milk.
Statistical hypothesis
	- Assumption about the value of population parameter or relationship among several population characteristics
	- Validity tested by a statistical experiment or analysis
	The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2.
	There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan.
Logical hypothesis
	- Offers or proposes an explanation with limited or no extensive evidence
	If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less.
Hypothesis-testing (Quantitative hypothesis-testing research)
	- Quantitative research uses deductive reasoning.
	- This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses.

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative research questions
Contextual research question
	- Ask the nature of what already exists
	- Individuals or groups function to further clarify and understand the natural context of real-world problems
	What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems)
Descriptive research question
	- Aims to describe a phenomenon
	What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities?
Evaluation research question
	- Examines the effectiveness of existing practice or accepted frameworks
	How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility?
Explanatory research question
	- Clarifies a previously studied phenomenon and explains why it occurs
	Why is there an increase in teenage pregnancy (phenomenon) in Tanzania?
Exploratory research question
	- Explores areas that have not been fully investigated to have a deeper understanding of the research problem
	What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic?
Generative research question
	- Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions
	How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative?
Ideological research question
	- Aims to advance specific ideas or ideologies of a position
	Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care?
Ethnographic research question
	- Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings
	What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis?
Phenomenological research question
	- Knows more about the phenomena that have impacted an individual
	What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual)
Grounded theory question
	- Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups
	What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed?
Qualitative case study question
	- Assesses a phenomenon using different sources of data to answer “why” and “how” questions
	- Considers how the phenomenon is influenced by its contextual situation.
	How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan?

Qualitative research hypotheses
Hypothesis-generating (Qualitative hypothesis-generating research)
	- Qualitative research uses inductive reasoning.
	- This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis.
	- Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach.

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

Variables	Unclear and weak statement (Statement 1)	Clear and good statement (Statement 2)	Points to avoid
Research question	Which is more effective between smoke moxibustion and smokeless moxibustion?	“Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.”	1) Vague and unfocused questions
			2) Closed questions simply answerable by yes or no
			3) Questions requiring a simple choice
Hypothesis	The smoke moxibustion group will have higher cephalic presentation.	“Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group.	1) Unverifiable hypotheses
		Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group.	2) Incompletely stated groups of comparison
		Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.”	3) Insufficiently described variables or outcomes
Research objective	To determine which is more effective between smoke moxibustion and smokeless moxibustion.	“The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.”	1) Poor understanding of the research question and hypotheses
Research objective			2) Insufficient description of population, variables, or study outcomes

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

Variables	Unclear and weak statement (Statement 1)	Clear and good statement (Statement 2)	Points to avoid
Research question	Does disrespect and abuse (D&A) occur in childbirth in Tanzania?	How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania?	1) Ambiguous or oversimplistic questions
Research question			2) Questions unverifiable by data collection and analysis
Hypothesis	Disrespect and abuse (D&A) occur in childbirth in Tanzania.	Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania.	1) Statements simply expressing facts
Hypothesis	Disrespect and abuse (D&A) occur in childbirth in Tanzania.	Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania.	2) Insufficiently described concepts or variables
Research objective	To describe disrespect and abuse (D&A) in childbirth in Tanzania.	“This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.”	1) Statements unrelated to the research question and hypotheses
Research objective			2) Unattainable or unexplorable objectives

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

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Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

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EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

EXAMPLE 1. Descriptive research question (quantitative research)
- Presents research variables to be assessed (distinct phenotypes and subphenotypes)
“BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
EXAMPLE 2. Relationship research question (quantitative research)
- Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
“Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
EXAMPLE 3. Comparative research question (quantitative research)
- Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
“BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
EXAMPLE 4. Exploratory research question (qualitative research)
- Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
“Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
EXAMPLE 5. Relationship research question (quantitative research)
- Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
“Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

EXAMPLE 1. Working hypothesis (quantitative research)
- A hypothesis that is initially accepted for further research to produce a feasible theory
“As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
“In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
EXAMPLE 2. Exploratory hypothesis (qualitative research)
- Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
“We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
“Conclusion
Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
“We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
EXAMPLE 4. Statistical hypothesis (quantitative research)
- An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
“Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
“Statistical Analysis
( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

EXAMPLE 1. Background, hypotheses, and aims are provided
“Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
“ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
“This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
EXAMPLE 2. Background, hypotheses, and aims are provided
“We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
“ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
EXAMPLE 3. Background, aim, and hypothesis are provided
“In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
“This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
“ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

Conceptualization: Barroga E, Matanguihan GJ.
Methodology: Barroga E, Matanguihan GJ.
Writing - original draft: Barroga E, Matanguihan GJ.
Writing - review & editing: Barroga E, Matanguihan GJ.

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What is a Research Hypothesis And How to Write it?
A/B Testing in Digital Marketing: Example of four-step hypothesis
Marketing Research Hypothesis Examples : Research questions hypotheses
Expert Advice on Developing a Hypothesis for Marketing Experimentation
Business Research Hypothesis Examples / (PDF) Null hypothesis
13 Different Types of Hypothesis (2024)

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Intro to hypothesis testing for quantitative variables (pg 104-105)
THE RESEARCH HYPOTHESIS-ACADEMIC RESEARCH WRITING BASIC GUIDELINES
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NEGATIVE RESEARCH HYPOTHESIS STATEMENTS l 3 EXAMPLES l RESEARCH PAPER WRITING GUIDE l THESIS TIPS
NEED AND IMPORTANCE OF HYPOTHESIS
Hypothesis Tests| Some Concepts

COMMENTS

How to write a hypothesis for marketing experimentation
The original idea: "My page needs a new CTA.". Following the hypothesis structure: "A new CTA on my page will increase [conversion goal]". The first test implied a problem with clarity, provides a potential theme: "Improving the clarity of the page will reduce confusion and improve [conversion goal].".
Research Hypothesis: Definition, Types, Examples and Quick Tips
3. Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
Expert Advice on Developing a Hypothesis for Marketing Experimentation
The Basics: Marketing Experimentation Hypothesis. A hypothesis is a research-based statement that aims to explain an observed trend and create a solution that will improve the result. This statement is an educated, testable prediction about what will happen. It has to be stated in declarative form and not as a question.
What is a Research Hypothesis: How to Write it, Types, and Examples
It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis. 7.
What is a Hypothesis
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
How to Write a Strong Hypothesis
5. Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
A Beginner's Guide to Hypothesis Testing in Business
3. One-Sided vs. Two-Sided Testing. When it's time to test your hypothesis, it's important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests, or one-tailed and two-tailed tests, respectively. Typically, you'd leverage a one-sided test when you have a strong conviction ...
A/B Testing in Digital Marketing: Example of four-step hypothesis
Developing a hypothesis is an essential part of marketing experimentation. Qualitative-based research should inform hypotheses that you test with real-world behavior. The hypotheses help you discover how accurate those insights from qualitative research are. If you engage in hypothesis-driven testing, then you ensure your tests are strategic ...
Hypothesis Testing in Marketing Research
In essence, hypothesis testing involves making an educated guess about a population parameter and then using data to determine if the hypothesis is supported or rejected. In the context of marketing, hypotheses can be formulated about consumer behavior, product preferences, advertising effectiveness, and many other aspects of the marketing mix.
Designing Hypotheses that Win: A four-step framework for gaining
A/B Testing Summit free online conference - Research your seat to see Flint McGlaughlin's keynote Design Hypotheses that Win: A 4-step framework for gaining customer wisdom and generating significant results. The Hypothesis and the Modern-Day Marketer. Customer Theory: How we learned from a previous test to drive a 40% increase in CTR
9 Key Stages in the Marketing Research Process
The marketing research process - an overview. A typical marketing research process is as follows: Identify an issue, discuss alternatives and set out research objectives. Develop a research program. Choose a sample. Gather information. Gather data. Organize and analyze information and data. Present findings.
How to Conduct the Perfect Marketing Experiment [+ Examples]
Make a hypothesis. Collect research. Select your metrics. Execute the experiment. Analyze the results. Performing a marketing experiment involves doing research, structuring the experiment, and analyzing the results. Let's go through the seven steps necessary to conduct a marketing experiment. 1.
Tips to Create and Test a Value Hypothesis: A Step-by-Step Guide
Before formulating a hypothesis, you need a deep understanding of your market and potential customers. You're looking to uncover their pain points and needs which your product aims to address. Begin with thorough market research and collect customer feedback to ensure your idea is built upon a solid foundation of real-world insights.
How McKinsey uses Hypotheses in Business & Strategy by McKinsey Alum
Running with our example, you could prove or disprove your hypothesis on the ideas you think will drive the most impact by executing: 1. An analysis of previous research and the performance of the different ideas 2. A survey where customers rank order the ideas 3. An actual test of the ten ideas to create a fact base on click-through rates and cost
Marketing Experiments: From Hypothesis to Results
With your marketing objectives in mind, the next step is formulating a hypothesis for your experiment. A hypothesis is a testable prediction that outlines the expected outcome of your experiment. It should be based on existing knowledge, data, or observations and provide a clear direction for your experimental design.
A/B Testing: Example of a good hypothesis
For example: Problem Statement: "The lead generation form is too long, causing unnecessary friction.". Hypothesis: "By changing the amount of form fields from 20 to 10, we will increase number of leads.". Proposed solution. When you are thinking about the solution you want to implement, you need to think about the psychology of the ...
5 Hypothesis testing
This can be formally expressed as follows: ˉx − μ0 = zσˉx. In this equation, z will tell us how many standard deviations the sample mean ˉx¯x is away from the null hypothesis μ0μ0. Solving for z gives us: z = ˉx − μ0 σˉx = ˉx − μ0 σ / √n. This standardized value (or "z-score") is also referred to as a test statistic.
Hypotheses in Marketing Science: Literature Review and ...
We examined three approaches to research in marketing: exploratory hypotheses, dominant hypothesis, and competing hypotheses. Our review of empirical studies on scientific methodology suggests that the use of a single dominant hypothesis lacks objectivity relative to the use of exploratory and competing hypotheses approaches. We then conducted a publication audit of over 1,700 empirical papers ...
Examples of a hypothesis in market research
Whether it's refining marketing messages or assessing product-market fit, the formulation of hypotheses is an indispensable first step in successful market research. Why Hypotheses Matter in Market Research. Formulating a market hypothesis lays the groundwork for focused research and exploration.
14 Market Research Examples
Curiosity. At the heart of every successful marketing campaign is a curious marketer who learned how to better serve a customer. In this industry, we scratch that curiosity itch with market research. To help give you ideas to learn about your customer, in this article we bring you examples from Consumer Reports, Intel, Visa USA, Hallmark, Levi Strauss, John Deere, LeapFrog, Spiceworks Ziff ...
How to Generate and Validate Product Hypotheses
A hypothesis in product development and product management is a statement or assumption about the product, planned feature, market, or customer (e.g., their needs, behavior, or expectations) that you can put to the test, evaluate, and base your further decisions on. This may, for instance, regard the upcoming product changes as well as the ...
A Practical Guide to Writing Quantitative and Qualitative Research
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...