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Showing the editorial vision

Showing the, editorial vision.

Uber Eats tasked us to create a presentation for their editorial team. The deck was a high-stakes pitch to their leadership, and the pitch was to get buy-in on the business value of editorial content.

The team had already done great work in outlining their story, but they needed help in making the message crystal clear with good typography and strong visuals. We created a clean design, aligned with the strong Uber brand, where the message and visuals made an immediate impact.

"Damon and his team are fantastic to work with! They're responsive, honest, and have a great eye."

Vidya Rao, Former Global Editorial Lead Uber Eats

The key to good presentation design is clear messaging. By changing the structure of a slide, we can create clear visual hierarchy that essentially guides the attention of the audience to where we want it.

"They not only made it pop visually, but working with them helped give me so much more clarity about what I wanted to say. Definitely worth the investment!"

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Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations

The Uber Eats app serves as a portal to more than 320,000 restaurant-partners in over 500 cities globally across 36 countries. In order to make the user experience more seamless and easy-to-navigate, we show users the dishes, restaurants, and cuisines they might like up front. To this end, we previously developed ML models to better understand queries and for multi-objective optimization in Uber Eats search and recommender system in Uber Eats searches and surfaced food options. 

Existing research [1] has shown the efficacy of graph learning methods for recommendation tasks. Applying this idea to Uber Eats, we developed graph learning techniques to surface the foods that are most likely to appeal to an individual user. Productionizing this method improves the quality and relevance of our food and restaurant recommendations on the platform.

Graph learning in a nutshell

To best understand how we made our Uber Eats recommendations more accurate, it helps to know the basics of how graph learning works. Many machine learning tasks can be performed on data structured as graphs by learning representations of the nodes. The representations that we learn from graphs can encode properties of the structure of the graph and be easily used for the above-mentioned machine learning tasks. For example, to represent an eater in our Uber Eats model we don’t only use order history to inform order suggestions, but also information about what food items are connected to past Uber Eats orders and insights about similar users.

Specifically, in order to obtain representations with such properties, we calculate a vector for each node in the graph (users, restaurants and food items in this case) such that node vector similarity approximates the strength of the connection between two nodes in the graph. Our objective is to find a that maps from a node to its vector representation (an encoding function) such that nodes that are structurally similar in the graph have similar representations.

For our Uber Eats use case, we opted for a graph neural network (GNN)-based approach to obtain an encoding function. This type of approach, although being initially proposed in the late 1990s and early 2000s [2, 3] have recently been adopted extensively by the research community for a variety of tasks [6,7,8] and has been shown particularly effective for recommendation problems [1].

The basic idea behind GNNs consists of using a neural network to obtain a representation for a node by aggregating the representations of neighboring nodes in a recursive fashion limited to a certain depth, as shown in Figure 1, below:

Assuming we limit the depth of the recursion to two to obtain the representation of the node A in Figure 1, we first perform a breadth-first search starting from A . Next, we obtain the features x of the nodes at two steps removed from A . Features are aggregated by an aggregation/pooling function, for instance, by taking the average and projected by a matrix multiplication with a learned weight matrix W (PROJ W in the figure) to obtain a representation of the neighborhood of the nodes at one hop distance from A . 

This neighborhood representation is combined with information about the node itself projected by a matrix multiplication with a learned weight matrix B (PROJ B in Figure 1), and this combination forms the representation h of nodes at a distance one from the node A . This representation is recursively aggregated and projected to obtain the representation of node A , and at each step of the recursion, new matrices W and B are used ( W1 and B1 for Layer 1 and W2 and B2 for Layer 2 in Figure 1). The main advantage of obtaining a representation in this manner is that it captures both the properties of node A and the structural information about its neighborhood, aggregating information about the nodes that node A connects to, as depicted in Figure 2, below:

We then use the node representations to predict the probability that a connection between two nodes exists and optimize a loss that maximizes the probability of two nodes that are actually connected in the graphs and minimizes the probability of disconnected nodes.

GNNs require only a fixed amount of parameters that do not depend on the size of the graph, making learning scalable to large graphs, especially if the neighboring nodes are sampled to be a certain fixed amount when obtaining the representation of a specific node. Moreover, a representation can be induced for a newly added node by virtue of its basic features and connections. These features of GNNs support recommendations at scale on Uber Eats,  which adds new users, restaurants, and dishes daily. 

Graph learning for dish and restaurant recommendation at Uber

There are several recommendation surfaces within the Uber Eats app, depicted in Figure 3, below:  

Figure 3: The Uber Eats UI surfaces a wealth of options for hungry users informed by past orders and previously specified user preferences.

On the main feed, we generate recommendation carousels for both restaurants and menu items based on user preferences. When browsing the menu of a restaurant, we also generate personalized recommendations of items within that restaurant to suit a user’s tastes. These suggestions are made by recommender systems trained on past orders and user preferences.

The Uber Eats recommendation system can be broken down into two phases: candidate generation and personalized ranking.

The candidate generation component generates relevant candidates, in other words, dishes and restaurants, in a scalable fashion. We needed to make this phase highly scalable to enable pre-filtering of the huge and ever-growing number of dish and restaurant options on the platform. Pre-filtering can be based on factors such as geographical location, so we do not recommend a restaurant to a user that is out of its delivery range. Dish and restaurant candidates also need to be relevant to the given user to ensure we are not filtering out items they would like. 

The second component of this system, the personalized ranker, is a fully-fledged ML model that ranks the pre-filtered dish and restaurant candidates based on additional contextual information, such as the day, time, and current location of the user when they open the Uber Eats app. An example of a recurring order pattern the model can learn to capture includes ordering certain types of food on specific days of the week or different types of dishes for lunch and dinner. 

In order to use GNNs to improve Uber Eats recommendations, we create two bipartite graphs: one that represents users and dishes as nodes with edges representing the number of times a user ordered a specific dish, and a second graph which represents users and restaurants as nodes, and edges represent how many times a user ordered from a specific restaurant.

We chose GraphSAGE [4], a specific flavor of GNN in which the aggregation function is a max or mean pooling after a projection, for our modeling starting point because of its strong scalability. In this GNN, the combination of node information and neighbor information is obtained through concatenation. Additionally, GraphSAGE adopts a sampling strategy to constrain the number of nodes sampled at one and two-hop distance from the node of which we want to obtain the representation, making it possible to scale learning to graphs with billions of nodes and providing even better suggestions.

In order to apply GraphSAGE to our bipartite graphs, we had to modify it in a few ways. First, since each node type may have different features, we needed to add an additional projection layer to the GNN. This layer projects the input features into a vector of the same size depending on the type of input node (user, restaurant, or dish). For instance, since dishes can be represented by the word embeddings from their descriptions or features of their associated images, and restaurants can have basic features related to their menu and cuisine offerings, their feature size is different, but the projection layer needs to project them in a space of the same size. 

Moreover, GraphSAGE only considers graphs with binary edges, but in our case the edges need to be weighted to include information about the number of times a user orders from a restaurant or a specific dish and the rating given by a user to a dish, as these are very important signals. For this issue, we introduced a few new concepts to add weights on the edges. The most impactful change was adopting a hinge loss, a type of loss that fits the ranking of items with respect to the user better than using binary edges.

Given a user u ordering a dish v at least one time, a weighted edge between them exists in the graph. If we want to predict a score for this pair of nodes that is higher than the score that we predict for the same node u and a randomly selected node n that is not connected to it (a dish the user never ordered), the difference between the scores should be greater than a margin .

The problem with this loss is that edges with a high weight and edges with a low weight are treated interchangeably, which doesn’t work well given the difference between a dish a user ordered once and a dish a user ordered ten times. For this reason, we introduced the concept of low-rank positives in the loss.

Finally, we also used the weights in the aggregation and sampling functions.

Once we obtain the representations of the nodes using the trained GNN, we can use the distance between the node representations to approximate the similarity between them. Specifically, we added the dot product and cosine similarity of user and items to both our dish and restaurant recommender systems as features, and tested them both offline and online to determine their accuracy.

To evaluate how useful the embeddings are for our recommending task, we trained the model on four months of historical data up to a specific split date. We then tested the model performance on recommending dishes and restaurants using order data from the ten days following the split date. Specifically, we computed the cosine similarity between a user and all the dish and restaurant embeddings in the city and computed the rank of the dish and restaurant that the user ordered. During the experiment we observed a performance boost of over ~20 percent compared to the existing production model on metrics like Mean Reciprocal Rank , Precision@K , and NDCG .

The improved performance obtained from the embeddings trained with graph learning convinced us to add them as features in our Uber Eats recommendation system’s personalized ranking model. When we trained the personalized ranking model with the graph learned embeddings similarity feature, we saw a 12 percent boost in AUC compared to the existing productionized baseline model, leading to improved recommendations for users. 

Moreover, analyzing the impact of the feature on our predictions, we saw that the graph learning similarity feature was by far the most influential feature in the recommendation model. This gave us confidence that the graph learned embeddings captured more information than any existing feature in our system, as depicted in Figure 5, below: 

Given the offline results, we felt comfortable rolling out the new model in an online experiment. We conducted an A/B test in San Francisco and observed a substantial improvement in engagement and click-through rate when leveraging the graph learning feature compared to the previous production model, demonstrating that the surfaced dishes predicted by our model appealed more to Uber Eats users.

Data and training pipeline

Once we determined the positive impact of graph learning on our recommendation system, we built a scalable data pipeline to both train models and obtain predictions in a real-time production environment.

We train separate models for each city, as their graphs are only loosely connected.

In order to do this, we used anonymized, aggregated order data from the past several months available and designed a four-step data pipeline to transform the data into the networkx graph format that is required to train our models. The pipeline also extracts aggregated features not directly available in the raw order data, like the total number of times users ordered dishes, which determines the weight of the graph’s edges.

Additionally, the pipeline is also capable of creating graphs for older time frames, which can be used for offline analysis. The overall pipeline is depicted in Figure 6, below: 

In the first step of the pipeline, multiple jobs pull data from Apache Hive tables, ingesting it into HDFS as Parquet files containing nodes and edges information respectively. Each node and edge has properties that are versioned by timestamp, which is needed for constructing back-dated graphs.

In the second step, we retain the most recent properties of each node and edge given a specific date and store them in HDFS using Cypher format. When training production models, the specified date is the current one, but the process is the same also if past dates are specified for obtaining back-dated graphs.

The third step involves using the Cypher query language in an Apache Spark execution engine to produce multiple graphs partitioned by city.

Finally, in the fourth step we convert the city graphs into the networkx graph format, which is consumed during the model training and embedding generation process, which are implemented as TensorFlow processes and executed on GPUs.

The generated embeddings are stored in a lookup table from which they can be retrieved by the ranking model when the app is opened and a request for suggestions is issued.

Visualizing learned embeddings

In order to provide an example capable of characterizing what is learned by our graph representation learning algorithm, we show how the representation of a hypothetical user changes over time.

Assuming we have a new user on Uber Eats who ordered a Chicken Tandoori and a Vegetable Biryani (both Indian dishes), we obtain a representation for such user at this moment in time.

The same user later orders a few other dishes, including: Half Pizza, Cobb Salad, Half Dozen Donuts, Ma Po Tofu ( a Chinese dish), Chicken Tikka Masala and Garlic Naan (three Indian dishes). We obtain a representation of the user after these additional orders and we compute the distance of those two representations with respect to the most popular dishes from different cuisine types and display it in Figure 7 below using the explicit axes technique introduced in Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae .

In the bottom left section of Figure 7, clear patterns emerge. The first pattern is highlighted in the green box in the bottom right: the dishes closest to the user representation before the additional orders are almost all Indian dishes (green dots) as expected given the fact that the initial orders were both of Indian food, but also some Chinese dishes end up ranked high on the x-axis, suggesting a second order correlation between these cuisine types (i.e., users who ordered many Indian dishes also ordered Chinese ones). Chinese dishes also rank pretty high on the y-axis, suggesting that ordering Ma Po Tofu influenced the model to suggest more Chinese dishes. 

In the top right section of Figure 7, a second pattern is highlighted in the orange box: American, Italian, Thai, and Korean dishes are selected, showing how they are much closer to the user representation after the user ordered additional dishes. This is due to both ordering Pizza, Doughnuts, and the Cobb Salad, but also due to second order effects from the increase of Chinese suggestions, as users ordering Chinese dishes are also more likely to order Thai and Korean. 

Finally, in the top left section of the image, a third pattern is highlighted in the blue box: all the cuisines that are not among the top three closest to both user representations ended up increasing their similarity substantially after their subsequent orders, which suggests that the model learned that this specific user might like for new cuisine suggestions to be surfaced.

Future directions

As discussed, graph learning is not just a compelling research direction, but is already a compelling option for recommendation systems deployed at scale.

While graph learning has led to significant improvements in recommendation quality and relevancy, we still have more work to do to enhance our deployed system. In particular, we are exploring ways to merge our dish and restaurant recommendation tasks, which are currently separate, because we believe they could reinforce each other. Over time, we plan to move from two bipartite graphs to one single graph that contains nodes of all the entities. This will require additional work on the loss and aggregation function to work properly, but we believe it will provide additional information to both tasks leveraging common information.

Another limitation we want to tackle is the problem of recommending reasonable items to users even in situations with data scarcity, such as in cities that are new to the Uber Eats platform. We are conducting research in this direction through the use of meta graph learning [5] with encouraging results.

Interested in how Uber leverages AI to personalize order suggestions on Uber Eats? Learn more about our Uber Eats recommendation system through our Food Discovery with Uber Eats series: 

• Food Discovery with Uber Eats: Recommending for the Marketplace
• Food Discovery with Uber Eats: Building a Query Understanding Language

Acknowledgments

We are grateful for the contributions of Jimin Jia, Alex Danilychev, Long Tao, Santosh Golecha, Nathan Barrebbi, Xiaoting Yin, Jan Pedersen, and Ramit Hora to this research.

The icons in the header are obtained from icons8.com . 

• Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton and Jure Leskovec: Graph Convolutional Neural Networks for Web-Scale Recommender Systems , KDD 2018
• Alessandro Sperduti and Antonina Starita: Supervised neural networks for the classification of structures . IEEE Transactions on Neural Networks, 1997
• Marco Gori, Gabriele Monfardini and Franco Scarselli: A new model for learning in graph domains . IJCNN 2005
• William L. Hamilton, Rex Ying and Jure Leskovec: Inductive Representation Learning on Large Graphs . NIPS 2017
• Joey Bose, Ankit Jain, Piero Molino and William L. Hamilton: Meta-Graph: Few shot Link Prediction via Meta-Learning . Graph Representation Learning Workshop @ NeurIPS 2019
• Thomas N. Kipf and Max Welling: Semi Supervised Classification With Graph Convolutional Networks . ICLR 2017
• Bing Yu, Haoteng Yin and Zhanxing Zhu : Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting . IJCAI 2018
• X. Geng, Y. Li, L. Wang, L. Zhang, Q. Yang, J. Ye, and Y. Liu, Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting , AAAI 2019.

Ankit Jain is a former research scientist of Uber AI.

Isaac Liu was an engineer at Uber focused on search and recommendations within the Uber Eats app. He holds a Ph.D. in Electrical Engineering and Computer Science from UC Berkeley.

Ankur Sarda

Ankur is a former software engineer at the Uber Risk engineering team focussing on graph applications at Uber.

Piero Molino

Piero is a Staff Research Scientist in the Hazy research group at Stanford University. He is a former founding member of Uber AI where he created Ludwig, worked on applied projects (COTA, Graph Learning for Uber Eats, Uber’s Dialogue System) and published research on NLP, Dialogue, Visualization, Graph Learning, Reinforcement Learning and Computer Vision.

Posted by Ankit Jain, Isaac Liu, Ankur Sarda, Piero Molino

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The following Uber pitch deck template follows the traditional order of slides recommended by accelerators and successful startups. But first things first: what must a pitch deck contain? Is there a standard? Yes, actually there is. There's really no need to reinvent the wheel here. So, before we dig deep in our Uber pitch deck template, let's review the pitch deck outline.

What does a Pitch Deck contain?

• Problem slide
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The Uber pitch deck template

Nine years after founding Uber, Garret Camp (co-founder) shared the pitch via Medium. We took the liberty of redesigning (using our AI button ) the original Uber pitch deck to make it look better. As we are all aware of how big Uber became, their pitch deck has become a major reference for anyone building a startup.

The Uber pitch deck template can be divided in three sections.

The slides teardown

Don't forget that the initial part of your presentation is critical, as it is your chance to catch your audience's attention. Make sure to add the following information in your slides:

• Problem: summarize the problem you are tackling. Use short and to-the-point and down to earth statements.
• Proposed solution: you and your company. Mention how you will address the problem. Again, less is more. Get to the point quickly using words everyone will understand.
• Product demo: be careful when presenting live. If anything goes wrong, then the whole meeting goes down the drain. Stick to a video and to the safest solutions.
• Market Size: How large can your company be? In this Uber Pitch Deck template, you can see that they decided to start in San Francisco and New York, before expanding to other big cities. This is a market that could result in $1.3 Billion. This allowed them to visualize a best, a realistic and a worst case scenario. Who would have thought that their prediction fell incredibly short from what really happened?

Wrapping it up

• Business Model.
• Competition slide: inspired in Steve Job's keynote, we recommend using a grid-approach to compare yourself to your competitors.
• Underlying magic: talk about the technology you have developed to create your product. You can get more technical here. Specifically on Uber: this is were they talk about their route optimization, their tracking technology and their not-so-loved surges.
• Go-to-market: your company's marketing ideas.
• Team slide: mention your founders and a short bio.
• Traction slides: add your sales and revenue in a clear and straight forward chart.
• Fundraising slide: Uber added this slide where they specify how much money they were asking at the time.

You can download a PDF version of the redesigned Uber pitch deck template here, or you can use create a free Slidebean account here to use this template online and edit it as wished.

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Deliver this complete deck to your team members and other collaborators. Encompassed with stylized slides presenting various concepts, this Uber Company Profile Powerpoint Presentation Slides CP CD is the best tool you can utilize. Personalize its content and graphics to make it unique and thought-provoking. All the fifty nine slides are editable and modifiable, so feel free to adjust them to your business setting. The font, color, and other components also come in an editable format making this PPT design the best choice for your next presentation. So, download now.

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The slide showcases the total revenue gained by Uber company for five consecutive years from 2018 to 2022. It outlines the overall revenue attained by all the products and service offerings. Present the topic in a bit more detail with this Net Revenue Uber Company Profile CP SS Use it as a tool for discussion and navigation on Net Revenue This template is free to edit as deemed fit for your organization. Therefore download it now.

The slide showcases operating profit loss of Uber company for five consecutive years from 2018 to 2022 which is recognized as income derived from business core operational activities. Present the topic in a bit more detail with this Operating Income Uber Company Profile CP SS Use it as a tool for discussion and navigation on Operating Income This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide showcases the ownership pattern of Uber company which consists of types of investors by number of shares. It categorizes name of directors and executive officers having more than 5 percent of holding and less. Deliver an outstanding presentation on the topic using this Ownership Structure Uber Company Profile CP SS Dispense information and present a thorough explanation of Beneficial Owner, Executive Officers, Shareholding Pattern using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.

This slide outlines the total revenue gained by Uber company for five consecutive years, i.e., from 2018 to 2022. It presents revenue gained in various geographies such as United States and Canada, Latin America, Europe, Middle East and Africa and Asia Pacific. Present the topic in a bit more detail with this Revenue By Geography Uber Company Profile CP SS Use it as a tool for discussion and navigation on Revenue By Geography This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide outlines total revenue gained by delivery business segment of Uber company for five consecutive years i.e. from 2018 to 2022. Its offerings allows consumers to search for and discover the best of local commerce. Deliver an outstanding presentation on the topic using this Revenue Delivery Segment Uber Company Profile CP SS Dispense information and present a thorough explanation of Revenue By Segment Delivery using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.

This slide outlines total revenue gained by freight business segment of Uber company for five consecutive years i.e. from 2018 to 2022. Its offerings connect shippers and carriers in a digital marketplace to move shipments. Present the topic in a bit more detail with this Revenue Freight Segment Uber Company Profile CP SS Use it as a tool for discussion and navigation on Revenue By Segment Freight This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide outlines total revenue gained by mobility business segment of Uber company for five consecutive years i.e. from 2018 to 2022. Its offerings consist of connecting consumers with a wide range of transportation modalities. Deliver an outstanding presentation on the topic using this Revenue Mobility Segment Uber Company Profile CP SS Dispense information and present a thorough explanation of Revenue By Segment Mobility using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.

The following slide outlines encouraging sustainability at Uber by using zero-emission vehicles that do not emit exhaust gas and other pollutants from the onboard power source. It highlights key statistics and performance of ZEV drivers, ZEV trips and ZEV uptake. Present the topic in a bit more detail with this Sustainability Through Zev Uber Company Profile CP SS Use it as a tool for discussion and navigation on Sustainability Through This template is free to edit as deemed fit for your organization. Therefore download it now.

The following slide outlines the number of completed rides or journeys taken by passengers that have been completed by the Uber platform. It outlines journey for five consecutive years i.e. from 2018 to 2022. Present the topic in a bit more detail with this Total Trips Uber Company Profile CP SS Use it as a tool for discussion and navigation on Total Trips This template is free to edit as deemed fit for your organization. Therefore download it now.

The following slide outlines key facts about diversity, equity and inclusion at Uber companys workplace. It outlines male and female employees in different departments and by different geographical regions. Present the topic in a bit more detail with this Workforce Diversity Uber Company Profile CP SS Use it as a tool for discussion and navigation on Workforce Diversity This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide presents the financial projections showcasing company anticipated growth, net income and revenue. The projections are based on comprehensive market analysis and historical performance. Present the topic in a bit more detail with this Business Model Of Uber Financial Projection Ppt File Templates BMC SS. Use it as a tool for discussion and navigation on Financial Projection, Market Analysis, Historical Performance, Income And Revenue. This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide showcases the plan how company earns revenue from the business. It include three segments and streams such as car rides, surge pricing, service divisions, and advertising and marketing. Deliver an outstanding presentation on the topic using this Business Model Of Uber Revenue Model Ppt File Graphics Design BMC SS. Dispense information and present a thorough explanation of Earns Revenue, Surge Pricing, Service Divisions, Advertising And Marketing using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.

The following slide showcases Uber micromarketing strategy to offer more specific marketing results. It includes elements such as leveraged social media, encourage app utilization, launched localized online advertisements, offered promos, referral advantages etc. Increase audience engagement and knowledge by dispensing information using Challenges And Solutions Of Uber Strategic Micromarketing Adoption Guide MKT SS V. This template helps you present information on three stages. You can also present information on Brand, Challenges, Transportation using this PPT design. This layout is completely editable so personaize it now to meet your audiences expectations.

The following slide showcases Uber micromarketing strategy to offer more specific marketing results. It includes elements such as leveraged social media, encourage app utilization, launched localized online advertisements, offered promos, referral advantages etc. Increase audience engagement and knowledge by dispensing information using Challenges And Solutions Of Uber Implementing Micromarketing To Minimize MKT SS V. This template helps you present information on two stages. You can also present information on Services, Unique, Referral using this PPT design. This layout is completely editable so personaize it now to meet your audiences expectations.

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This slide covers referral program strategies used by Uber company to increase customer retention in the company. It includes elements such as company name, referral program strategies used by company, impact of referral program such as increase in ROI, customer LTV, customer acquisition cost, etc.Present the topic in a bit more detail with this Uber Referral Program To Increase Referral Marketing Solutions MKT SS V. Use it as a tool for discussion and navigation on Rental Rebates, Increase Customer, Customer Acquisition. This template is free to edit as deemed fit for your organization. Therefore download it now.

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This slide covers referral program strategies used by Uber company to increase customer retention in the company. It includes elements such as company name, referral program strategies used by company, impact of referral program such as increase in ROI, customer LTV, customer acquisition cost, etc.Present the topic in a bit more detail with this Uber Referral Program To Increase Customer Referral Marketing Promotional Techniques MKT SS V. Use it as a tool for discussion and navigation on Transportation Company, Referral Program Impact, Rental Rebates. This template is free to edit as deemed fit for your organization. Therefore download it now.

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This slide showcases recent awards and recognition received by Uber company from external institutions for company initiatives and extraordinary performance. It outlines recognitions by World Economic Forum, Fast Company, CNBC, etc. Present the topic in a bit more detail with this Awards And Achievements Uber Company Profile CP SS Use it as a tool for discussion and navigation on Date Of Achievement, Technology Pioneer, Innovative Companies This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide focuses on the business model of Uber, which covers elements like key partners, key activities, value proposition, customer relationships, customer segments, key resources, channels, cost structure and revenue streams. Deliver an outstanding presentation on the topic using this Business Model Canvas Uber Company Profile CP SS Dispense information and present a thorough explanation of Value Propositions, Customer Relations, Customer Segments using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.

The slide outlines mapping of business model of Uber company that shows operational workflows of business across globe. It outlines operations of all the segments such as rides, air, eats and freight. Present the topic in a bit more detail with this Business Model Map Uber Company Profile CP SS Use it as a tool for discussion and navigation on Availability Usage Status, Consumers, Business Model This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide shows the company introduction which includes the company name, founder, founded year, headquarters and ticker symbol of Uber company which is a global transportation services providing company. Introducing Company Overview Uber Company Profile CP SS to increase your presentation threshold. Encompassed with one stage, this template is a great option to educate and entice your audience. Dispence information on Company, Headquarters, Ticker Symbol using this template. Grab it now to reap its full benefits.

The following slide highlights the various platforms to follow and contact Uber company. It includes links for social media profile pages on Instagram, Twitter, Facebook, LinkedIn and YouTube. Introducing Contact Us Uber Company Profile CP SS to increase your presentation threshold. Encompassed with five stages, this template is a great option to educate and entice your audience. Dispence information on Contact Us using this template. Grab it now to reap its full benefits.

The slide outlines various statistics highlighting the impact of business on environmental and social factors. It showcases statistics related to zero-emission trips, riders who experienced an EV, growth in active EVs, anti-racism commitments, etc. Introducing Environmental And Social Highlights Uber Company Profile CP SS to increase your presentation threshold. Encompassed with four stages, this template is a great option to educate and entice your audience. Dispence information on Environmental Highlights, Social Highlights, Mobility Business using this template. Grab it now to reap its full benefits.

The slide showcases the executive summary to provide brief insights into Uber Technologies company, including offerings, business segments, regions served, performance highlights and team overview. Introducing Executive Summary Uber Company Profile CP SS to increase your presentation threshold. Encompassed with four stages, this template is a great option to educate and entice your audience. Dispence information on Executive Summary, Performance Highlights using this template. Grab it now to reap its full benefits.

The following slide consists of members of the executive team at Uber company that are responsible to provide strategic and operational leadership to the business. It consists of CEO, CFO, chief legal officer and corporate secretary, chief people officer, chief product officer and SVP engineering, etc. Increase audience engagement and knowledge by dispensing information using Executive Team Uber Company Profile CP SS This template helps you present information on nine stages. You can also present information on Executive Team, Business Operations, Chief Product Officer using this PPT design. This layout is completely editable so personaize it now to meet your audiences expectations.

The following slide showcases various strategies and goals that Uber company is likely to achieve in future years. It consists of elements such as achieve GAAP profitability, improve customer experience, advancement of IC plus model, executive compensation and technology innovation. Introducing Future Goals Strategy Uber Company Profile CP SS to increase your presentation threshold. Encompassed with five stages, this template is a great option to educate and entice your audience. Dispence information on Consumer Experience, Executive Compensation, Technological Innovation using this template. Grab it now to reap its full benefits.

The slide showcases the Uber companys presence in international regions across all continents. It depicts regions such as United States and Canada, Latin America, EMEA Europe, Middle East, and Africa and APAC Asia Pacific. Increase audience engagement and knowledge by dispensing information using Global Presence Uber Company Profile CP SS This template helps you present information on one stage. You can also present information on Global Presence using this PPT design. This layout is completely editable so personaize it now to meet your audiences expectations.

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The following slide outlines competitor businesses that operate in similar markets and serves similar customer base as Uber does. It classifies its competitors based on business segments such as mobility, delivery and freight. Introducing Key Competitors Uber Company Profile CP SS to increase your presentation threshold. Encompassed with three stages, this template is a great option to educate and entice your audience. Dispence information on Mobility, Delivery, Freight using this template. Grab it now to reap its full benefits.

The following slide showcases various statistics outlining an overview of Uber company to gain an understanding of business performance. It consists of annualized run-rate gross bookings, annualized run-rate gross bookings, monthly active platform customers, global presence and active drivers and couriers globally. Increase audience engagement and knowledge by dispensing information using Key Stats Uber Company Profile CP SS This template helps you present information on five stages. You can also present information on Key Stats using this PPT design. This layout is completely editable so personaize it now to meet your audiences expectations.

The slide showcases the mission statement and core values of Uber company which consists of go get it, trip obsessed, build with heart, stand for safety, see the forest and the trees and great mind dont think alike. Present the topic in a bit more detail with this Mission And Core Values Uber Company Profile CP SS Use it as a tool for discussion and navigation on Trip Obsessed, Stand For Safety, Forest And The Trees This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide showcases details and benefits of food delivery options as offerings by uber company that make it easy and convenient for customers and restaurants to order and deliver eatables. It consists of uber eats which provides services such as ordering from your favorite restaurants, boosting visibility, etc. Deliver an outstanding presentation on the topic using this Offerings Food Delivery Uber Company Profile CP SS Dispense information and present a thorough explanation of Favorite Restaurants, Boost Visibility, Connect With Customers using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.

This slide showcases the details and benefits of Uber Connect as an offering provided by Uber company that is an on-demand package delivery service and allows people to send commodities from one place to another. It provides benefits such as availability, speed, tracking and flexibility. Introducing Offerings Uber Connect Uber Company Profile CP SS to increase your presentation threshold. Encompassed with four stages, this template is a great option to educate and entice your audience. Dispence information on Availability, Tracking, Flexibility using this template. Grab it now to reap its full benefits.

This slide showcases the details and benefits of delivery and driving options provided by Uber to people looking for part-time income options. It assists people to earn money by delivering food from restaurants to customers and by driving people by providing rides. Present the topic in a bit more detail with this Offerings Uber Drive Uber Company Profile CP SS Use it as a tool for discussion and navigation on Feature, Offerings Uber Drive This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide showcases the details and benefits of Uber Freight and Uber for Business as offerings provided by Uber company to other businesses and customers. It provides service types such as freight marketplace and ground transportation management platform. Deliver an outstanding presentation on the topic using this Offerings Uber Freight Uber Company Profile CP SS Dispense information and present a thorough explanation of Offerings Uber Freight, Feature using the slides given. This template can be altered and personalized to fit your needs. It is also available for immediate download. So grab it now.

This slide showcases details and benefits of Ride options as offerings by Uber company that makes it easy and convenient for customers to travel. Its offerings consist of UberX, UberX Share, Uber Comfort, Safety and Cities. Introducing Offerings Uber Ride Uber Company Profile CP SS to increase your presentation threshold. Encompassed with three stages, this template is a great option to educate and entice your audience. Dispence information on Offerings Uber Ride using this template. Grab it now to reap its full benefits.

The following slide showcases the opportunities of Uber company and showcases external factors that can lead to business growth and development. It showcases elements such as new market expansion, new product development and merger, acquisitions and partnerships. Introducing Opportunities Uber Company Profile CP SS to increase your presentation threshold. Encompassed with three stages, this template is a great option to educate and entice your audience. Dispence information on Strengths, Weaknesses, Opportunities, Threats using this template. Grab it now to reap its full benefits.

This slide outlines business structure of Uber company that outlines a hierarchy of designations. It depicts position levels such as CEO, CFO, chief legal officer and corporate secretary, chief people officer, chief product officer and SVP engineering, SVP mobility and business operations, SVP delivery, etc. Present the topic in a bit more detail with this Organizational Structure Uber Company Profile CP SS Use it as a tool for discussion and navigation on Corporate Secretary, Chief Product, Business Operations This template is free to edit as deemed fit for your organization. Therefore download it now.

This slide highlights the Uber company journey from 2008 to the present, which showcases information on the foundation of business, expansion, partnerships, new launches, campaigns, milestones achieved, etc. Introducing Our History Uber Company Profile CP SS to increase your presentation threshold. Encompassed with one stage, this template is a great option to educate and entice your audience. Dispence information on Our History using this template. Grab it now to reap its full benefits.

The slide showcases Uber company unique selling points which differentiates the business from its competitors and establishes the brand as a global leader in its industry. It consists of convenience, on demand service, cashless payments, real time tracking, safety features and rewards. Present the topic in a bit more detail with this Our USP Uber Company Profile CP SS Use it as a tool for discussion and navigation on Convenience, Cashless Payments, Safety Features This template is free to edit as deemed fit for your organization. Therefore download it now.

The following slide showcases various business combinations made by Uber company to expand into different markets and maximize its market share globally. It outlines its strategic business combinations with Careem, Cornorshop, Postmates, Drizly and Transplace. Present the topic in a bit more detail with this Strategic Acquisitions Uber Company Profile CP SS Use it as a tool for discussion and navigation on Acquired Business, Operating Region, Total Consideration This template is free to edit as deemed fit for your organization. Therefore download it now.

How the UberEATS Team uses Design Thinking

To Uber Eats Design Thinking has been a powerful tool to create higher customer satisfaction. Creating the future of food delivery takes empathy, innovation, and an appetite for complex logistical challenges. Using Design Thinking, UberEATS is on a mission to make eating well effortless for everyone, everywhere. The UberEats service connects customers to Uber-speed delivery from restaurants in over 80 cities around the world. UberEats give people more options when choosing how to eat.

Designers on the UberEATS team love food culture, logistical challenges, and the demands of a fast-growth startup. They take pride in their ability to move quickly, build empathy with their customers, and make complex services run smoothly. The UberEats teams designs reach many different types of people with very different needs, but the same design thinking approach guides their mission to make eating well effortless at any time, for anyone.

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