manage lead assignment rules

• AppAssessor

Articles by role:

• Consultants
• Business Analysts

By Lucy Mazalon

By Ben McCarthy

• Courses Login

What’s trending

The Future of Salesforce: Is the Cloud Giant Really Losing Its Momentum?

Breaking News: Elton John Pulls Out of Dreamfest

10 Hottest Salesforce Winter ’25 Features For Admins

Do Salesforce Admins Have Too Much on Their Plate?

10 Salesforce Winter ’25 Features for Developers

UPCOMING EVENTS

Salesforce devops made simple: essential git insights, everything you need to know before dreamforce 2024, mile high dreamin’, adhd & neurodiversity: what you need to know as a salesforce professional, how to build a successful salesforce devops process, salesforce lead assignment rules best practices and tricks.

By Stacy O’Leary

I confess: I love Salesforce Lead Assignment Rules almost as much as I love the Approval Process . A good set of Lead Assignment Rules will buy you endless friends in both sales and marketing, and will make your incoming data sparkle and look perfect (even if it is not!) In this guide, I’ll be talking about the initial Lead sort, upon creation.

Salesforce Lead Assignment Rule Example

• Criteria #1: If State = California, assign to Stacy
• Criteria #2: If Country = United Kingdom, assign to Ben
• Criteria #3: If Country = France, assign to Lucy
• Criteria #4: If Annual Revenue is greater than $500,000,000 USD, assign to “High Roller Queue”

Planning Lead Assignment Rules

Discovery: questions to ask.

• Where are the new Leads coming from? Marketo? HubSpot? Other integrated systems? Web forms? Are there any examples you look at? Make friends with the people who run these systems, you need to have a good relationship because you’re going to need their help.
• What fields are populated on these newly created Leads? What fields are required? If it’s minimal, can you get more information? Generally, the more information you have, the easier it is to sort.
• What if a Lead comes in from one of your Partners? What if a Lead comes in from one of your competitors? From one of your employees? Are there any kinds of Leads that should never be distributed out to your team, like students or media inquiries? (Remember – ANYONE with access to the internet can fill out your form! They do not have to be a legitimate prospect!)
• Who is covering what territories? Do you have any territories that don’t have a sales rep yet? Do all new Leads have enough data to determine territories?
• What about the Leads that don’t meet any criteria at all? Where will they go? Who will work them?

Refining the requirements

• Our new Leads, almost always, come from Marketo . They could come from a Marketo form, or a list imported from a trade show, but Marketo is the system that pushes them to Salesforce. If a person creates their own Lead, we do not want to take it away from them.
• We always have: first name, last name, lead source, email, company, state and country. We sometimes have # of Employees, but that’s pretty much all we know about them at the moment of creation.
• Any Lead that comes in from a Partner should be directed to our channel team. We don’t want to market to competitors, employees, or students.
• We have a territory plan defined by Sales, and we’d also like to separate prospects for the UK and France, though we do not have a sales rep for those areas yet.
• If something comes in that we cannot otherwise sort, let’s put it in a holding place and let marketing send out generic nurture emails. If a person in this holding place takes interest, we can always give it to the sales team later.

• Partners (any Lead that comes in from a Partner company)
• Disqualified (any Lead that comes in from a competitor, is an employee, or is a student)
• UK + France (any Lead where Country = United Kingdom, or France)
• Unsorted (any Lead that does not meet any criteria)

Creating Lead Assignment Criteria

Leads that shouldn’t be distributed, next criteria.

The Final Empty Criteria

Activate the Lead Assignment Rules

• Leads can only be sorted by a field value at the moment it was sorted.
• The Lead Router does not auto-convert Leads to Contacts
• You cannot deactivate a User license if that person is part of the Lead Assignment Rules (even if the Lead Assignment Rules have been deactivated.)
• Create a report for yourself, for that last criteria – Leads that are unsorted. This way you can review them periodically and see if there’s enough volume to justify sorting them in a certain way.

Stacy O'Leary

More like this:

Free ebook: a comprehensive backup guide for ai, data, and salesforce.

By Lauren Metcalf

Your Certs Might Be Running Out: Spring ’24 Maintenance Due

By Mariel Domingo

Get Strategic with Salesforce Account Plans: New Winter ‘25 Release Feature

By Andreea Doroftei

Leave a Reply Cancel reply

Save my name and email in this browser for the next time I comment.

Salesforce Lead Assignment Rules: How To Manage The Salesforce Lead

You're busy, and managing leads in Salesforce is a pain. You want to use the system but there's just too many hoops to jump through. This blog post will help you understand how salesforce lead assignment rules work and show you how they can make your life easier when assigning leads in Salesforce.

Lead assignment is a critical function in any sales organization and it has been our experience that many companies are not following these simple rules due to lack of understanding or poor training. 

We hope to help you avoid these mistakes by sharing some of the most common mistakes made when assigning leads in Salesforce with you.

What Are Salesforce Lead Assignment Rules

Salesforce lead assignment rules allow a user to specify rules that pick up a specific value from a custom object and assign it to the Contact Manager field of the Lead.

Lead assignment rules are created based on your business requirements, for example: If a prospect fills out an interest form or email marketing survey , you can automatically add their information as Leads in Salesforce, which makes them available for sales reps to follow up with.

Or maybe your company is growing rapidly and you need opportunities to be auto-assigned every time one is updated by an internal team member. In all these cases, lead assignment rules come in handy .

A lead assignment rule can be executed when a custom object record has been created, updated or deleted. It starts with a trigger and ends with an assignment rule.

For example: whenever a user updates the 'Lead Status' field on the 'Opportunity' object, update or delete (depending upon whether Lead Status is set to Closed/Lost or Reopened) the associated Contact record on the 'Cust Team' custom object.

To create this Lead Assignment Rule

• Navigate to Setup
• Leads  and click on New Assignment Rule. Give your lead assignment rule a name and select Sub Object as Opportunity then choose Trigger as Updated Opportunity Status.
• Leave Conditions as Blank and click Save.

The moment you save the lead assignment rules, Salesforce creates a picklist of Available Values with two options – Yes and No.

This is because a lead assignment rule can either create or update a record on another object, depending on the condition that you set.

Now, let's say you want to automatically create a new Lead record whenever the 'Lead Status' field on the 'Opportunity' object is set to Closed/Lost. To do this, we'll need to add an Assignment Rule. Select Assignment Rule as New Lead and choose the action as Create.

Under Object Type , select Lead and under Field Name , select Status . Leave Conditions blank and click Save .

You've now created a lead assignment rule that will automatically create a new Lead record whenever the 'Lead Status' field on the 'Opportunity' object is set to Closed/Lost.

But wait, this rule will create Leads for Closed/Lost Opportunities only. What if you also wanted to automatically create a lead whenever the Status is set to Reopened? 

Well, all you need to do is add another Assignment Rule and choose both rules as New Lead . Select the action Create under the object Salesforce creates a picklist with three options – Yes , No , and No Match. This signifies that it will either update or create records depending upon its conditions.

To edit an existing assignment rule, follow these steps:

• ‍ Navigate to Setup | Customize | Leads | Assignment Rules. The Assignment Rules page displays all of your existing rules together. You can filter this list by selecting a particular object, such as Accounts or Contacts, and then clicking on the Edit button.
• ‍ The Rule Details page for the selected rule displays. This page enables you to edit the rule's name, description, and conditions. You can also specify which objects the rule applies to, what type of action to take (such as Create or Update), and which fields on those objects to use.
• When you're finished editing, click Save.

You can also delete a lead assignment rule by following these steps:

• ‍ Navigate to Setup | Customize | Leads | Assignment Rules. The Assignment Rules page displays all of your existing rules together. You can filter this list by selecting a particular object, such as Accounts or Contacts, and then clicking on the Delete button.
• ‍ The Rule Details page for the selected rule displays . This page provides information about the rule, including the rule's name, description, and conditions.
• ‍ Click Delete to remove the rule from Salesforce. There are many different ways to use lead assignment rules in Salesforce. By creating rules that automatically update or create records on other objects, you can save time and ensure that your data is always up-to-date.

The Seven Rules Of Compliance: Salesforce Lead Assignment Policy

Now that we have covered the basics of Lead Assignment Policy, it's time to get into the specifics. Below are the seven rules of compliance for Salesforce Lead Assignment rules:

Rule 1: All leads must be assigned to a user. 

This is a basic rule and should go without saying. All leads must be assigned to a user in order for them to be worked. If you're not sure who should receive a lead, consult your company's sales process and procedures.

Rule 2: Leads must be assigned in a timely manner.

Leads should be assigned as soon as they are created in order to ensure that they are worked promptly. 

However, there may be cases where a lead expires before it has any activity. In these cases, you can either let the lead expire or reassign it to another user who may work it later on.

Rule 3: A user cannot belong to more than one owner-recipient pair in a single Salesforce organization.

A lead must be owned by one person, but that same lead can be assigned to one or more users within your team using owner-recipient pairs. 

However, no single user should appear in multiple owner-recipient relationship fields for different leads in your database. If this happens, then all recipients will receive every opportunity created from every new lead that's assigned to the problematic user! 

To avoid this, create a new lead assignment policy with the problematic user excluded. Then, create a new lead assignment policy without the problematic user and re-assign all of your leads to the appropriate users using owner-recipient pairs.

Rule 4: A user cannot be a recipient in more than one owner-recipient pair within a single Salesforce organization.

As shown by Rule 3 above, you can have an owner for a lead as well as multiple recipients on that same lead record. However, no single user should appear as both an owner and a recipient on different leads that are under the same qualifying rules. 

If this happens, then all of those leads will route directly to your Salesforce Inbox ! To avoid this, simply create a new lead assignment policy with the problematic user excluded and re-assign all of your leads to the appropriate users using owner-recipient pairs.

Rule 5: A recipient cannot be a member of more than one owner-recipient pair in a single Salesforce organization.

This is another rule that should go without saying, but just like Rules 3 and 4 above, it's important to ensure that you're not setting up multiple recipient relationships for the same user within your Salesforce instance. 

To avoid this problem, simply create a new lead assignment policy with the problematic user excluded and re-assign all of your leads to the appropriate users using owner-recipient pairs.

Rule 6: Recipients must own at least one opportunity to be considered valid.

If you're looking for the most basic rule to begin with, this is it. If recipients are not set up to own one or more opportunities, then they won't have any activities in Salesforce automatically created for them when a lead is assigned. 

This means that Leads will need to route directly into your Salesforce Inbox until you either create some activities for your team or manually assign the lead back to the original owner. 

To avoid this problem, simply create a new lead assignment policy with all of your existing recipients included and re-assign all of your leads accordingly!

Rule 7: You cannot add existing users as recipients if their user records contain fields that are incompatible with assignment policy criteria.

There may be one or more cases where you have users who are already in your system but are not set up to receive leads through your lead assignment policy. 

If you try to add these users as recipients for this newly created rule, then their accounts will show up in red because they won't meet the criteria defined in the assignment policy's qualifications field. 

To fix this problem, simply create a new lead assignment policy by including existing recipients in your Salesforce instance. Then, re-assign all of your leads accordingly!

What Are The Drawbacks Of Using Salesforce Lead Assignment Rules?

Lead Assignment rules are an extremely powerful tool inside Salesforce. They enable business users to control who can view and respond to leads, while also streamlining workflows and ensuring that important leads aren't being ignored.

However, as with all features in Salesforce, there are several drawbacks or limitations that need to be taken into account when a lead assignment rule is put into use:

• A lead has to meet the criteria of all lead assignment rules assigned to it, which might not always be possible. Imagine a Business Development Manager using a Lead Assignment Rule so they receive all high priority leads from two queues - one for new accounts and one for existing customers. But what if the lead doesn't have an account? In this case both queues would be evaluated, even though the lead should only go to one of them.
• Rules can be time consuming and complex to set up. The business user has to have a good understanding of how the rule works in order to create it.
• Rules can be overridden by other users if they have access to the lead. This means that although the Lead Assignment Rule was supposed to ensure that a specific person received the lead, it could end up going to someone else if they're faster at responding or have more seniority.

Overall, Lead Assignment Rules are an extremely valuable tool for controlling workflows and ensuring that important leads don't fall through the cracks. 

However, it's important to be aware of the drawbacks and limitations when using them in order to make sure they're being implemented in the most effective way possible.

Salesforce Lead Assignment Rules are an extremely powerful tool inside Salesforce. They enable business users to control who can view and respond to leads, while also streamlining workflows and ensuring that important leads aren't being ignored.

Himangi Lohar

Get paid for your time, join the marketplace for b2b pitches, meetings, and connections, see cliently in action, start engaging with your prospects, start engaging with your users and clients today, don't miss out, sign up for our newsletter.

Guide to lead assignment rules in Salesforce

Use SFDC lead assignment rules to get more done, create a better experience, and close deals faster.

Rachel Burns Jul 24, 2023

15 min read

Table of contents

Experience scheduling automation for yourself!

Create a Calendly account in seconds.

What are Salesforce lead assignment rules?

What if your sales team could spend their valuable time connecting with prospects and closing deals — instead of losing time doing admin work like assigning and organizing leads?

When you automate lead assignment and routing, your sales team can:

Boost sales team productivity and efficiency

Prevent high-quality leads from slipping through the cracks

Create a better experience for potential customers

Speed up your entire sales pipeline to close more deals, faster

In this blog post, we'll discuss the ins and outs of Salesforce lead assignment. We'll cover the benefits, how to plan your lead assignment strategy, and a step-by-step walkthrough of adding lead assignment rules in Salesforce. We'll also explore the power of scheduling automation to simplify and speed up lead assignment, routing, and qualification.

Key takeaways:

Lead assignment rules help sales teams boost productivity, respond to leads faster, and make better data-driven decisions. 

Matching leads with the right sales reps and teams creates a better customer experience by responding to leads faster and giving them personalized attention.

Before you set up your lead assignment rules, work with your sales, marketing, and RevOps teams to understand your lead generation processes and sales team structure.

Within Salesforce lead management settings, rule entries are the individual criteria and actions. A “lead assignment rule” refers to a set of rule entries. 

Automating lead routing , qualification, and booking with Calendly helps your team be more efficient and organized while creating a better experience for prospective customers.

6 benefits of creating lead assignment rules in Salesforce

Why should your team take the time to set up lead assignment rules in Salesforce? Here are six great reasons:

Ensure leads are assigned to the right reps and teams: Lead assignment rules mean each incoming lead is directed to the salesperson or team who has the relevant expertise and skills to engage and convert that lead. Automated lead assignment also prevents leads from falling through the cracks by making sure each lead is assigned to a rep or team, rather than relying on manual assignment.

Respond to leads faster: With lead assignment rules, leads are automatically assigned to the right salesperson, reducing response time and increasing the chances of converting leads into customers .

Boost sales team productivity: Automating lead assignment reduces manual work for RevOps teams and sales managers. Lead assignment rules also help identify and prioritize leads more likely to convert, saving time and resources that would otherwise be wasted on pursuing poor-fit leads. These time savings let sales teams focus on nurturing leads and closing deals.

Create a better customer experience: Leads can be assigned to sales reps who have relevant industry or product expertise, understand their unique needs, and can provide personalized solutions. This tailored approach creates a better experience for leads, which results in more conversions and higher customer satisfaction.

Improve sales forecasting: With well-defined lead assignment rules, you can gather more accurate data on lead distribution and conversion rates. This data can be used for sales forecasting, data driven decision-making, and resource allocation.

How to create lead assignment rules in Salesforce

Step 1: build your lead assignment strategy.

Before you go into your Salesforce instance and set up lead assignment rules, you need to figure out what exactly those rules will be. The options are limitless — where should you start?

It’s time to bring RevOps, sales, and marketing together to answer some questions:

Lead sources: Where do leads come from? Do we use marketing forms through Salesforce web-to-lead forms or a third-party integration? Are we importing leads via the data import wizard?

Sales team structure: How is the sales team structured? Are different teams or individuals specialized in specific products, industries, use cases, or regions?  

Lead data: What info do we request from new leads? Which standard and custom fields do we require?

Sales territories: How are sales territories defined? Are there specific regions, countries, or territories we should take into account for lead assignment?

Integrations : Do we have any third-party integrations with lead assignment or distribution features? Are we using those features?

Special circumstances: Are there any priority levels or tiers for leads that require special attention? For example, do we have a designated rep or queue for leads with complex needs and use cases?

Poor fits: What should we do with leads who don’t meet any of our criteria?

It’s a lot of information to gather and organize, but it’s important to learn as much as possible up front to cover every scenario and equip your sales team with accurate data. Putting this time and effort in now will pay off tenfold in productivity once your lead rules are in place!

Step 2: Set up lead assignment rules in Salesforce

You’re almost ready to enter your lead assignment rules in SFDC . First, let’s go over some terminology. We’ve been talking about lead assignment rules as individual directives: “If the lead matches X, then do Y.” Within Salesforce lead management settings, a “lead assignment rule” refers to a set of rule entries. Rule entries are the individual criteria and actions (“If X, then do Y”). An assignment rule can consist of up to 3,000 rule entries, and you can only have one active assignment rule at a time.

For example, a rule entry can assign all leads interested in a particular product to a queue of reps who are experts on that product. In Salesforce, a lead queue is essentially a bucket for unassigned leads, and you can choose which sales reps can pull leads from each queue.

Another rule entry can assign all leads from companies with over 5,000 employees to your top enterprise sales rep.

To create a lead assignment rule in Salesforce: 

From Setup, enter “Assignment Rules” in the Quick Find box, then select Lead Assignment Rules.

Enter the rule name. (Example: 2023 Standard Lead Rules)

Select “Set this as the active lead assignment rule” to activate the rule immediately.

Click Save.

Click the name of the rule you just created.

Click New in the Rule Entries section.

Enter an order number that tells Salesforce when to run this rule entry in relation to other rule entries. For example, if you want this to be the first criteria Salesforce looks at when assigning a lead, enter number one.

Select the rule criteria. What attributes must the lead have before Salesforce applies the rule entry? You can use any standard or custom field in the lead record for your criteria. For example, you want to assign leads to your U.S.-based enterprise sales team, so the company size field must be equal to or greater than 5,000 and the country field must equal the United States. You can include up to 25 filter criteria.

Choose the user or queue to be the assignee if the lead meets the criteria. For example, assign to the U.S.-based enterprise sales team queue.

Optional: Choose an email template to use when notifying the new lead owner. After you set up your lead rules, you can also use Salesforce Flow automations to notify lead owners via other channels. For example, at Calendly, we integrate Salesforce with Slack, and a workflow automatically notifies sales reps via Slack when a lead is assigned to them.

Salesforce goes through the rule entries in order until it finds one that matches the lead's info, then routes the lead accordingly. 

Let's say you have small business, mid-market, and enterprise sales team queues. Your first three rule entries would match company size to each of those three queues. If they don't have a company size listed, or the company size doesn't match any of the values in your rule entries, Salesforce will move on to the industry rule entries.

To make sure no leads fall through the cracks, you also need to set a default lead owner. If the assignment rules fail to locate an owner, or you don’t set up assignment rules, web-generated leads are assigned to the default lead owner.

To select a default lead owner:

From Setup, enter “Lead Settings” in the Quick Find box, then select Lead Settings and click Edit.

Define the Default Lead Owner. The Default Lead Owner can be a specific user or a queue.

Save your settings.

Salesforce lead assignment rule examples

As we mentioned earlier, your rule entries can include up to 25 filter criteria.

Simple rules include just one filter criteria:

By country or state/province: Route leads from specific states or countries to sales representatives who understand the regional market. You need this rule if your team uses sales territories to divide leads. For example, if the state/province equals Alaska, Arizona, California, Hawaii, Nevada, Oregon, or Washington, assign the lead to the West Coast queue.

By language: Assign leads to sales reps who speak the same language.

By industry: Assign leads from different industries to salespeople who have experience working with those industries.

By company size: Assign leads based on the size of the company, assigning larger companies to a dedicated enterprise sales team.

Complex rules use two or more filter criteria. For example, you could route leads from specific states or provinces to salespeople based on their sales territory and the company size. If you have a particular rep (Bob) working enterprise leads on the West Coast, your filter criteria could say: If the state/province equals Alaska, Arizona, California, Hawaii, Nevada, Oregon, or Washington, and the company size equals greater than 5,000, assign the lead to Bob.

These are just a few examples. Lead assignment rules can be customized to fit your team’s and customers’ needs. Review your strategy to choose the right combination of criteria for your sales processes, products, and customers.

What does the built-in Salesforce lead process look like in action?

A website visitor named Nora fills out a contact form to learn more about your product. She shares her name, email address, company name (Acme Inc.), and company size. You use Salesforce’s built-in web-to-lead forms , so Nora’s form submission automatically creates a lead record.

Your team has set up lead assignment rules that assign leads to sales queues based on their company size. Acme Inc. has 5,000 employees, so Nora is automatically assigned to the enterprise sales team queue.

Enterprise sales team queue members receive an email notification that a new lead has been added to the queue. Taylor, an enterprise sales rep in Acme Inc.’s territory, assigns Nora’s lead record to themself.

Taylor emails Nora to set up a qualification call.

Nora, who has been waiting to hear back from your team, agrees to meet with Taylor. After some email back-and-forth, they find a time that works.

What are the limitations of Salesforce’s built-in lead assignment rules?

Salesforce’s built-in lead assignment rules are a great place to start, but there are a few critical limitations, especially for enterprise sales teams:

Single level of evaluation: Salesforce assignment rules operate based on a single level of evaluation, meaning that once a rule matches the criteria and assigns a lead, the evaluation process stops. Your team might miss out on important info, like a complex use case or unique industry, when matching the lead with a rep.

No built-in round robin distribution: Round robin lead distribution is the process of assigning leads to reps based on factors like team member availability or equal distribution for a balanced workload. Salesforce lead assignment rules don't include an easy way to set up round robin distribution — you need an additional tool like Pardot, one of the round robin apps on AppExchange , complex Apex code , or a third-party lead routing platform .

No lead escalation settings: Lead escalation is the process of flagging a lead to higher levels of management or specialized teams for further assistance or action. This process comes into play when a lead requires additional attention or intervention beyond the assigned salesperson or team's capabilities. Unfortunately, Salesforce doesn’t have built-in settings for lead escalation rules. If your customer success team uses Service Cloud, you can set up escalation rules for customer support case escalations, but this feature isn’t included in Sales Cloud.

High maintenance for large organizations: Managing and maintaining a comprehensive set of assignment rules can become challenging and time-consuming in large organizations with complex sales structures and multiple teams or regions. Sure, you can include up to 3,000 rule entries in a single lead assignment rule, but that’s a lot to set up and keep up to date — especially if you’re trying to save your team time, not add to their workload.

Built-in Salesforce lead assignment rules and automations are a solid starting point, but what about automating lead qualification and booking? If you use Salesforce on its own, your reps might still spend a ton of time on lead reassignment to balance their workload, manual lead qualification, and email back-and-forths to schedule sales calls.

That’s where Calendly comes in.

How to automate lead assignment, qualification, and booking with Calendly

Your scheduling automation platform can be an excellent lead generation, qualification, and routing tool — especially when it integrates with Salesforce. Calendly’s Salesforce integration helps your team be more efficient and organized while creating a better experience for prospective customers.

When a lead books a meeting via a sales rep or team’s Calendly booking page, Salesforce automatically creates a new lead, contact, or opportunity. If the lead already exists in your Salesforce instance, the event is added to the lead’s existing record, so you don’t end up with duplicate lead records or time-consuming manual reassignment.

What if you don’t want to let just anyone book a meeting with your team? When you add Calendly Routing to your marketing forms, you can show scheduling pages only to leads who meet your qualifications, like prospects from specific industries or companies of a certain size. That way, your busy team can spend time on the most valuable deals.

Calendly Routing works with HubSpot , Marketo , Pardot , and Calendly forms and is built for your Salesforce CRM. You can use any form field (email, domain, company name) in any Salesforce standard object to match visitors with their account owner. Account lookups let you send known leads or customers from your website form directly to their account owner’s booking page, without needing to manually reassign leads to the right rep.

Remember the lead assignment example we walked through earlier featuring Nora from Acme Inc.? Here's what that process looks like when you add Calendly:

Nora fills out your “contact sales” form, which is already built in HubSpot, connected to Calendly Routing , and enriched with Clearbit .

She enters her email address in the form, and Clearbit fills in the company name, size, and industry. This shortens the form, so Nora only has to input her name and job title.

Calendly checks to see if Acme Inc. has an account in your Salesforce instance. They don’t, so the next step is lead qualification .

Based on Nora’s information — company size, industry, job title — she’s a highly qualified lead, so she’s automatically routed to the booking page for your enterprise sales team.

Nora is happy about that, and immediately books a meeting time that works for her, with the exact team she needs to talk to.

On the backend, Calendly’s Round Robin meeting distribution is set to optimize for availability, so it assigns the meeting to the first available sales rep — in this case, Taylor. This automation helps your team respond to meeting requests faster, hold initial sales calls sooner, and balance the workload across reps.

Calendly creates a lead record in Salesforce with the info Nora entered into your website form (including the data from Clearbit) and an activity log of any meetings she books with your team via Calendly. Salesforce automatically makes Taylor the lead owner.

If you were relying on Salesforce’s built-in lead assignment rules, Nora’s lead record would have gone to an enterprise sales queue, and she would have had to wait for a rep to pick up the lead and reach out to her to book a meeting.

“ A good tool is one that’s so simple, sales reps can basically forget about it and let the meetings roll in. That’s essentially what happened when we implemented Calendly. ”

Sales Enablement Manager at SignPost

What happens if a lead doesn’t qualify for a meeting? Instead of sending them to a booking page, you can display a custom message with next steps, ask them for more information, or redirect them to a specific URL, like a piece of gated content or a webinar signup page.

Automating lead assignment with Calendly Routing has been a game changer for RCReports , a compensation analysis solution for accountants and business valuators. Before connecting Calendly Routing with their Salesforce instance, RCReports’ AEs spent at least five hours a month reassigning leads booked on the wrong calendar. This created a disjointed customer experience and frustration for the sales and marketing teams.

“ Now that we’ve implemented Calendly’s routing feature with Salesforce integration, demos are always booked with the correct AE, reducing friction for both our team and the customer. ”

Abbie Deaver

Director of Marketing at RCReports

Users on Calendly’s Teams plan and above can connect Calendly to Salesforce. The full suite of Salesforce routing features , including routing by Salesforce ownership, is available on Calendly’s Enterprise plan.

To learn more about Calendly Routing, get in touch with our sales team .

Spend less time on manual lead assignment and more time closing deals

When you automate Salesforce lead assignment and routing, high-value leads stop slipping through the cracks, the workload is balanced across the team, leads are matched with the sales reps best equipped to help them, and team members have more time to focus on connecting with prospects and closing deals. 

The results? A more productive team, faster sales cycle, higher conversion rates, and better customer experience.

Webinar: How Calendly Uses Calendly to Close More Deals

Rachel Burns

Rachel is a Content Marketing Manager at Calendly. When she’s not writing, you can find her rescuing dogs, baking something, or extolling the virtue of the Oxford comma.

Related Articles

Read Time: 15 minutes

How to write sales follow-up emails that keep deals moving

Connect with prospects and close deals faster with these follow-up email tips and templates.

How to prep for a sales demo with confidence

Real-world tips to tackle discovery, create agendas, and manage pre-demo anxiety.

Help! What do I do after a sales demo?

A timely follow-up keeps sales momentum high and closes deals faster.

Don't leave your prospects, customers, and candidates waiting

Calendly eliminates the scheduling back and forth and helps you hit goals faster. Get started in seconds.

We surveyed hundreds of B2B companies about their top go-to-market challenges...

• Intelligent Lead Delivery
• Convert Signals to Revenue
• Enterprise Salesforce Orchestration
• Products Overview Discover modern Revenue Orchestration
• Integrations Connect signals to plays
• Pricing Plans for every company
• Why LeanData

Snowflake Scales Account Based Plays with LeanData Revenue Orchestration

Clockwise Supports PLG Motion with LeanData Revenue Orchestration

• Become a Partner Team up with LeanData
• Technology Partner Directory Discover ISV solutions to fit your needs
• Solutions Partner Directory Connect with our network of authorized Service Integrators

LeanData’s integration with Salesloft allows a user to route prospects to the right reps.

Slack has transformed business communication– it’s the platform where work can happen.

Expertly target and engage high-value accounts when they’re ready to buy.

LeanData’s integration with Outreach allows a user to route prospects to the right reps.

• Learning Center
• Certification
• Tips & Tricks
• Help Center
• About Us Learn more about us and our mission
• Newsroom Keep up with what’s new at LeanData
• Events Stay up to date and network with industry professionals at our upcoming events
• Careers Join the LeanData team
• Contact Us Get your questions answered - contact us now

G2 Recognizes Lead-to-Account Matching and Routing as Newest Tech Category, with LeanData the #1 Vendor

Leandata showcases power of modern revenue orchestration at opsstars 2022, leandata announces winners of the 2022 opsstars awards, what are lead assignment rules in salesforce.

Lead assignment rules are a powerful feature within Salesforce to assist your team’s automation of its lead generation and customer support processes. Assignment rules in Salesforce are used to define to whom your Leads and Cases (customer questions, issues or feedback) are assigned based on any one of a number of specified criteria you determine. 

Organizations typically develop lead assignment rules for their GTM processes or flows:

• Rules for inbound Leads
• Rules for website-generated Leads
• Rules for importing Leads from an event

For case assignments, a company might establish one case assignment rule for weekdays and another assignment rule for weekends and holidays. 

A lead or case assignment rule often consists of multiple rule entries to specify exactly how leads and cases are assigned throughout your go-to-market teams. For example, related to customer service inquiries, a standard case assignment rule might have multiple entries. Cases with “Type equals Gold” are assigned to the Gold Level service queue, cases with “Type equals Silver” are assigned to the Silver Level service” queue, and so on. 

As organizations grow and scale, they operationalize multiple GTM motions: inbound, outbound, account-based, upsell/cross-sell, and hybrid. However, many are limited to having just one rule in Salesforce.

As a work-around, many organizations create one massive lead assignment ruleset. They then wedge all of their rule entries into that one big ruleset, regardless of how many different motions that represents. Over time, Salesforce lead assignment rules can quickly become unmanageable .

This post covers the best practices for Salesforce lead and case assignment rules. The ultimate goal is to fully engaging your hard-won leads and speed up your organization’s time-to-revenue.

How to Define Assignment Rules

Your Salesforce administrator can only have one rule in effect at any particular moment in your go-to-market motions, and that assignment rule is intended to both automate lead generation processes and other customer-facing processes routed through your CRM. 

Lead assignment rules specify how leads are assigned to users or queues as they are created manually, captured from your website, or imported via SFDC’s Data Import Wizard.

Case assignment rules determine how cases are assigned to users or put into queues as they are created, either manually or through the use of Web-to-Case, Email-to-Case, On-Demand Email-to-Case, the Self-Service portal, the Customer Portal, Outlook, or other data generation applications.

Criteria for Lead Assignment Rules

Okay, so you’ve decided that lead assignment rules in Salesforce make sense for your revenue operations team – now what?

Well, first, you’ll need to determine the edition of your Salesforce instance. Lead assignment rules are available in the Group, Essentials, Professional, Enterprise, Performance, Unlimited, and Developer Editions of SFDC. Case assignment rules, conversely, are available only in the Professional, Enterprise, Performance, Unlimited, and Developer editions.

With regard to User Permissions, to view assignment rules, you’ll need View Setup and Configuration permissions. However, to create or change assignment rules, you’ll need Customize Application. If you are not your organization’s Salesforce administrator, you should check with them before attempting to head off on your own.

How to Create Salesforce Lead Assignment Rules

Creating lead and case assignment rules in Salesforce is a relatively straightforward process. 

• Login to Salesforce and select Setup in the upper right corner of the horizontal navigation bar.
• In the Setup search box , type “assignment rules” and then select either Lead Assignment Rules or Case Assignment Rules.
• Select New to create a new assignment rule.
• In the Rule Name box, type a name and specify whether it should be active for leads or cases created manually and by those created automatically by web and email. When done, click Save .
• Click open your newly created rule and select New in the Rule Entries to specify your rule criteria.
• Step 1 in the “Enter the rule entry” window requires you to enter an Order for your new rule (the Order is the order in which the entry is processed, like a queue).
• In Step 2, you determine whether your new rule is based on meeting a set of criteria or a formula. In the Run this rule if the dropdown box, select either “criteria are met” or “formula evaluates to true.”
• Lastly, in Step 3, select the user or queue to whom your rule will assign your new lead or case (use the lookup feature to find specific users or a queue). After completing Step 3, select Save .

Why Are Your Salesforce Lead Assignment Rules Not Working?

If you discover your lead or case assignment rules are not working, here are a few tips to quickly troubleshoot the root cause.

First, check to ensure the assignment rule is active. Remember, only one case or lead assignment rule can be active at one time. Secondly, ensure the record is assigned to the correct user or queue.

Make certain to select the checkbox Assign using active assignment rule . In support of this step, enable field History tracking on case or lead owner, as well as add object History (case or lead) in your page layout. 

One common problem is overlapping rule entries, or rule entries in the wrong order. With dozens of rule entries, many will overlap, causing records to get assigned unpredictably. For example, if entry #1 assigns California leads to John, and entry #2 assigns Demo Request leads to Jane, then John might wonder why he’s receiving Demo Requests leads who are supposed to go to Jane. 

Assignment Rule Examples

The image, below, shows sample rule entries being entered into Salesforce for a variety of “what if” situations:

• Junk leads containing “test” are sent to an unassigned queue
• Demo requests are routed directly to SDR 3
• Leads at accounts with over $100 million in annual revenue are routed to AE 1
• Leads in certain states are sent to their respective representatives

How LeanData Simplifies Salesforce Lead Assignment

Creating lead and assignment rules in Salesforce is relatively straightforward. However, as your GTM motions become more and more complex, it becomes necessary to populate that one rule with multiple defining rule entries. As you grow and scale, your rule threatens to become unwieldy. Then these problems arise:

• Difficulty in both comprehending and managing
• Poor visibility, making it difficult to troubleshoot and validate
• Restrictions allowing only the criteria on the routed record

LeanData’s lead routing flow and assignment solution is a native Salesforce application that allows users to create flows in an easy-to-understand visual graph. Its visible representation of an organization’s desired lead flow affords many benefits to users, including:

• Easier ability to visualize and understand complex flows
• Real-time visibility of the routing of leads and the ability to quickly troubleshoot and make adjustments
• At-a-glance ability to use information on matched records for routing decisions and actions

Assignment rules in Salesforce are a relatively easy-to-learn feature that can be very quickly implemented, delivering a flexible and powerful logic to your CRM processes. Automating your lead and customer processes will accelerate your GTM motions and deliver your organization a sustainable competitive advantage.

For more best practices, read the eBook, “ Best Practices for a Winning B2B Marketing Data Strategy .”

• lead assignment rules
• lead assignment rules salesforce

Ray Hartjen

Ray Hartjen is an experienced writer for the tech industry and published author. You can connect with Ray on both LinkedIn  &  Twitter .

More Related Content

How to Automate Lead Routing in Salesforce

How assignment rules work in Salesforce When leads come into your Salesforce instance, a rep needs to reach out to...

Salesforce Lead-to-Account Matching, the Easy Way

Salesforce lead-to-account matching is an important consideration in better aligning Sales with Marketing and increasing the efficiency and productivity of...

10 Best Lead Assignment Tools for Revenue Teams (2024)

Lead assignment tools optimize sales processes by building efficiency into lead distribution. Here are the top 10 lead assignment tools worth investigating.

Lead Assignment: Strategies to Optimize Your Sales Team's Performance

Your marketing team works hard to generate leads for your company.

As a salesperson, it's your job to capitalize on these leads and produce as many paying customers as possible. Thankfully, this burden doesn't rest on your shoulders alone.

In all likelihood, you're one of several sales reps who've been tasked with implementing your department's sales process . This is good news—as long as you have a lead assignment plan in place, of course. If you don't, you and your colleagues will waste time fighting over leads.

In this article, I'll explain what lead assignment is and why it's beneficial. Then I'll share a few best practices you can use to make sure leads are always assigned to the right rep.

What Is Lead Assignment?

Lead assignment , sometimes referred to as lead routing , is the act of distributing leads amongst the sales reps in your organization. It's an important part of lead management.

Here's how the process normally works:

• Your marketing team implements various marketing strategies to drive leads
• Leads enter your CRM of choice and are automatically assigned to specific reps
• Said reps access incoming lead records, then use the information there to connect with, nurture, and eventually sell to the individual leads assigned to them

A strong lead routing process will improve your sales team's productivity , increase the trust your reps have in your organization, and ultimately, result in more sales.

Benefits of Having a Clear, Transparent Lead Routing Process

Effective lead distribution seems simple…

Leads come in, your CRM routes them to certain reps based on the specific lead assignment rules you've created, then each rep works to turn their leads into long-term customers.

Simple? Yes. But the truth is, your lead routing process can make or break your sales department. Get it right, and your sales team will succeed. Get it wrong, and your reps will lose productivity, become distrustful of their superiors, and drive less revenue.

With that in mind, let's explore the benefits of a proper lead assignment in more detail:

Productivity

A rock-solid lead routing workflow will help your sales reps get more done faster.

Think about it: when your reps don't have to fight each other for leads, they'll have more time (and energy!) to spend nurturing prospects . This will almost always result in more deals.

Want to optimize your sales strategies? Uncover the hidden potential of sales productivity tools in our detailed guide.

What about trust? Transparent case assignment rules will help you build it.

How so? When your reps know how the sausage is made, so to speak, they won't ask themselves why so-and-so was assigned X lead and not them. They'll just know.

When your reps trust your organization, they'll enjoy their work more. When they enjoy their work, they'll put in more effort. And when they put in more effort, they'll make more sales. They'll probably stick around for longer, too, which will improve your team's turnover rate.

Lastly, a proper lead assignment process will generate more revenue for your company.

As mentioned earlier, your reps will focus on closing deals , not wrestling leads away from their colleagues. This will allow them to pour more effort into their sales processes, helping them connect with and sell to more customers. More customers generally means more revenue.

I should also mention response time. When leads are automatically assigned to reps, they'll reach out to them faster. Since 78 percent of customers buy from the company that responds to their inquiry first, your team's response time has a huge impact on the revenue it brings in.

Lead Assignment Rules: 5 Ways Top Sales Teams Assign New Leads to Their Team

A strong lead assignment process is essential to the success of your sales department. The question is, how do you distribute leads to reps in a way that's logical and fair?

It's pretty easy: just implement a few lead assignment rules, a.k.a. a set of criteria that determines where and when incoming leads are routed in real-time.

Every CRM software handles this process differently. In Close, for example, you can assign leads manually. You can also assign them automatically based on information such as the location and/or priority of the lead, as well as the qualifications of your reps.

Let's look at the most popular lead assignment rules available to your team:

1. Manual Assignment

The manual assignment rule is exactly what it sounds like: sales reps manually assign leads to themselves in their CRMs. This approach is best when sales reps do their own prospecting .

In Close , sales reps can manually assign themselves leads by simply adding their name to the corresponding custom field inside the Close CRM platform. Or, sales managers can manually assign new leads to their team.

One of the best things about this lead assignment rule is how easy it is to set up.

The downside? It won't work for every team—especially large teams that employ multiple kinds of reps, i.e. sales development reps (SDRs), inside sales reps , account executives, etc.

2. Bulk Assignment

The bulk assignment rule is pretty self-explanatory, too. Leads are uploaded to a CRM, then assigned in bulk to specific users, which allows for peak productivity levels.

In Close, sales managers can bulk assign leads by first creating a "lead owner" column in the document they plan to upload. That way, when said file is integrated into their Close account, leads will automatically be routed to the proper sales rep.

Why should you use this lead assignment role? Because it will save you a ton of time!

Unfortunately, it only works for sales teams that either store leads outside their CRMs (why would you do that?) or purchase leads lists from third-party companies (also not recommended).

3. Round Robin Lead Assignment

The round robin assignment rule will automatically assign leads to specified sales reps in your organization in a circular manner, hence the name "round robin."

In Close, sales managers can implement a round-robin lead assignment process by connecting their Close, Python, and Zapier accounts . Once they do, inbound leads will get sent to their sales reps in order. When the last rep receives a lead, the cycle will start over.

A round-robin lead assignment approach will allow you to automate the lead routing process. Just know that it's more complicated to set up than other lead assignment use cases.

Check out this template to use a round-robin lead assignment approach for your team.

4. Rule-based Assignment

The rule-based assignment process will help you to assign inbound leads to reps based on specific criteria, such as where the lead lives and/or the product/service they're interested in.

In Close, use Smart Views to identify leads that fall within your specified criteria. Then assign them to the most qualified rep on your team.

Rule-based assignments are perfect for global companies that serve customers in many different locations around the world, as it allows them to route leads based on geographical location or time zone, the specialties of their sales reps, and the priority level of their leads.

5. Random Lead Assignment

True to its name, the random lead assignment rule assigns leads to sales rep at random. The best part is, this process happens automatically, so you don't have to worry about it!

In Close, you can also set up Sequences and have a random user assigned to each new lead in that sequence. You can also do this in groups, which means you can separate groups of reps and assign leads randomly within that group.

To randomly assign all leads continuously, sales managers can integrate their Close accounts with Zapier. When this happens, inbound leads will automatically get sent to one of your sales reps, but there won't be any rhyme or reason to the assignments.

Random lead assignment is easier to set up than round robin lead assignment (mentioned above) but will still help you evenly distribute leads across your sales team . When that happens, fewer leads will fall through the cracks, which will help you drive more revenue .

Check out this template to quickly build a random lead assignment approach for your team.

Best Practices for Lead Assignment

There are a bunch of ways to assign leads. No matter which approach you choose, make sure you keep these five best practices in mind. Doing so will help you find more success.

Keep It Simple

First, do your best to simplify your lead assignment rule entries.

Ask yourself, "Do we really need to have 17 criteria to route leads?" Unless you're a mega, enterprise-level organization, the answer is probably no. So why do it?

Trust me, most companies don't need to route the leads that come from LinkedIn and the leads that come from Facebook to different sales reps. There's no point in complicating processes that don't need to be complicated. Sales is already hard enough.

Be Transparent about How Leads Are Assigned

Trust is important in sales. As we discussed earlier, reps that trust their organizations work harder, which almost always translates to more sales and revenue.

Trust is especially important when it comes to lead routing. Your reps need to believe that they get a fair amount of leads. The easiest way to ensure this is to be perfectly transparent about your lead routing processes. That way each rep knows exactly how leads are split up.

If you aren't transparent, your sales reps will question your lead assignment approach, which is less than ideal when most companies pay reps on commission . Basically, your reps might feel like you're "stealing" money from them if they don't understand how leads are divided.

Handle Lead Scoring and Qualification Before Assigning Leads

Next, take care of your lead scoring and qualification procedures before you assign leads.

This will ensure you only send quality prospects to your sales reps, saving them valuable time. One of the worst things you can do in sales is spend an afternoon nurturing a lead that will never buy from you. It's completely unproductive and, honestly, kind of demoralizing.

Fortunately, this isn't that hard to do. Simply use an inbound form to learn more about your leads. Then give each lead a score based on the information you receive. (Note: You can use Close to help automatically score your leads, which will save you boatloads of time.)

Once your leads have been vetted, send them to your reps. Trust me, this simple process will boost team productivity to new heights and help you close way more deals.

Develop a Process to Reassign Leads When Necessary

Guess what—leads can be reassigned to new sales reps if and when necessary.

Maybe a lead is first routed to Dwight. Uh oh, Dwight is on vacation and won't be able to follow up with the lead in a timely manner. No problem, just send that bad boy to Jim instead.

You can do this manually, of course. But I suggest building reassignment rules into your CRM. That way, the tech at your disposal will handle this tedious task for you. Doing so will help your entire team reach the apex of their potential, which is exactly what you want.

Analyze and Optimize Your Lead Assignment Protocols

Last but not least, always look for ways to improve your lead routing approach.

What can you do better? Is there a way to eliminate complexity from your process? Maybe you need to go the opposite way, get a little fancy, and use automation to your advantage. (Just don't get too fancy. Like I said earlier, simple is generally best for lead assignment.)

The point is you might not build the perfect lead routing system on the first try. Even if you do, your sales team might grow in the future. Or adopt a new sales strategy. Either way, you'll want to develop a new process to assign leads. Just to make sure everything works right.

Remember, lead routing isn't a one-time checkbox. You can't set it and forget it. You need to continually analyze your approach and look for ways to optimize it. That's how you win.

Close More Deals with the Right Lead Assignment Process

Lead assignment is critical to the ultimate success of your sales department.

Once you implement a few active assignment rules, your reps will become more productive, trust your organization more, and, most importantly, drive more revenue. Win!

You just need to learn how to create new rules in your CRM of choice. That way the robots can handle the lead distribution process for you, while you focus on other tasks. Win, Win!

If you're a Close user, check out this tutorial . In it, Matt Bonde, one of our esteemed product managers, will show you how to assign leads to an automated sequence. Hello, productivity!

If you're not a Close user, what's stopping you? Sign up for a free 14-day trial today to see everything our platform offers, including its lead assignment capabilities.

START YOUR FREE TRIAL→

More articles from The Close Blog

Discover our latest free sales tools powered by AI

Learn from the sales pros with our free sales guides.

Getting Started with Lead Assignment Rules in Salesforce

Lead Assignment Rules in Salesforce are a powerful tool that allow your GTM (Go-To-Market) teams to automate the process of generating leads and assigning them to the most suitable sales representatives based on specific criteria. Managing and assigning leads becomes key, particularly when dealing with high daily volumes and considering the priority of leads based on various factors. And, as we all know, speed to lead is crucial : if you waste even one minute, your conversion plummets by a shocking 391%.

Ideally, your organization should employ different lead assignment rules tailored to your go-to-market strategies and motions. For instance, the assignment rules for leads generated from a demo request on your website may differ from those for leads obtained through downloading an ebook.

A lead assignment rule consists of a prioritized set of rule entries that dictate how leads are assigned throughout your go-to-market motions, either to a specific user or to a Salesforce Queue. These rules are automatically triggered when leads are created and can also be applied to existing records.

However, there is one significant constraint with Salesforce assignment rules: only one can be active at any given time. This means that as your business scales and you implement multiple business processes, you'll need to consolidate all the complex logic into a single comprehensive assignment rule with multiple rule entries. As you can imagine, managing, maintaining, and updating such an operation can become increasingly challenging over time.

Salesforce Lead Assignment Rules Examples

• Leads from Enterprise Accounts with ARR greater than $200M are assigned to Enterprise SDR Queue.
• Leads from accounts with ARR greater than $50M (and less than $200M) are assigned to Mid Market SDR Queue.
• Leads reaching Nurture Lead Status are directed to SDR 1.
• Irrelevant leads with a ‘gmail.com’ domain are routed to a dummy user and disregarded.
• Distribute leads to reps based on their respective sales territories.

Salesforce Lead Assignment Rules: A step-by-step guide

Here is a step-by-step guide to help you set up Salesforce Lead Assignment Rules in keeping with your business needs and sales strategies.

Step 1: Log in to Salesforce:

Access your Salesforce account using your credentials.

Step 2: Navigate to Setup:

Click on the gear icon in the screen's upper-right corner to access the Setup menu.

Step 3: Access Lead Assignment Rules:

In the Setup menu, search for "Lead Assignment Rules" in the Quick Find box. Click on "Lead Assignment Rules" under the "Leads" section.

Step 4: Create a New Rule:

Click on the "New Lead Assignment Rule" button to create a new rule.

Step 5: Define Rule Entry Criteria:

Enter a suitable name for the rule and specify the rule's entry criteria.

You can decide whether to write a formula or build a prioritized set of rule entry criteria. The logic you define determines the conditions that need to be met for the rule to trigger.

Step 6: Specify Rule Assignments:

Choose the desired assignment method for your leads. Salesforce provides different assignment options, such as assigning leads to Queues or specific users. Select the appropriate option based on your business requirements, and (optionally) choose an email template to be sent to the new owner of the lead.

Step 7: Save and Activate the Rule:

Once you have configured all the necessary settings, click the "Save" button to save the lead assignment rule. After saving, activate the rule to make it operational.

Step 8: Test and Validate:

It is essential to test the rule and ensure that leads are being assigned as expected. Create test leads and verify their assignments based on the defined criteria. Make any necessary adjustments or refinements to the rule if required.

Salesforce editions and permissions for Lead Assignment Rules

After gaining an understanding of how lead assignment rules can benefit your business and determining their suitability, you may wonder, "Do I have the appropriate Salesforce edition?" The short answer is, “Yes.” Salesforce has now made this feature available in every edition.

In terms of user permissions, certain requirements must be met to view and edit lead assignment rules in Salesforce. Users must have the "Manage Leads" permission and the ability to "View Setup and Configuration" in order to access lead assignment rules. For editing purposes, users need the "Customize Application" permission along with either the "Modify All Data" or "Modify Leads" permission. If you are unable to locate this section in Salesforce or encounter difficulties with viewing or editing assignment rules, reach out to your Salesforce administrator to verify that you have the necessary permissions in place.

Assignment rules limits and challenges

Salesforce Lead Assignment Rules, while suitable for small teams with straightforward rules, come with significant limitations that can hinder efficiency for larger teams. These limitations become more apparent as the volume of leads increases and more complex use cases arise.

One major drawback is the stringent limits imposed by Salesforce on the number of rules, entries, and actions allowed per rule. These limits are illustrated in the following table:

However, there are additional factors to consider when utilizing assignment rules in Salesforce. It is crucial to be aware of the following challenges before embarking on building your entire logic using Salesforce's native functionality:

• Limited to Leads and Cases: Assignment rules only apply to the lead and case objects. While this may suffice for simple and initial use cases, it falls short when implementing ABM (Account Based Marketing) strategy or when the need to route Opportunities arises.
• Complex QA and Audit: Salesforce's native functionality lacks versioning or debugging tools, making testing and troubleshooting assignments a laborious task. Additionally, there is no built-in record of why a lead was assigned in a particular way, further complicating the auditing process.
• Limited Assignment Options: When using assignment rules, you can only assign a Lead record to a specific user or a Salesforce Queue. Managing more intricate assignments, such as round-robin distribution, becomes challenging and costly to implement within the confines of assignment rules.
• Single Active Rule: As mentioned previously, Salesforce restricts the use of only one active lead assignment rule at a time. This means that despite having multiple processes with distinct underlying logic, you are forced to consolidate them into a single, busy rule, making management and updates cumbersome.

Considering these challenges, it becomes evident that relying solely on assignment rules may not provide the flexibility and functionality required for more complex lead management scenarios.

How Sweep Simplifies Salesforce Lead Assignment

Although assignment rules in Salesforce offer a powerful solution with a short learning curve, we recognize that the drawbacks can become a significant pain point as businesses grow and the native Salesforce tools may not fully support your team's needs.

To address these challenges, we have developed Sweep's Assignment tool, a visual no-code solution designed to simplify the implementation of assignment logic within Salesforce. This empowers organizations to build their go-to-market motions while maximizing assignment efficiency. The benefits of using Sweep's Assignment capabilities include:

• Assigning any object: From Opportunities to Accounts & Contacts and custom objects, you can apply assignment logic across various Salesforce objects.
• Achieving full visibility: Gain a comprehensive understanding of your business processes and easily track where assignments are taking place.
• Managing round-robin groups: Distribute leads equally among sales representatives using round-robin assignment to ensure fairness, or add weights to better control assignments between reps.
• Simplifying territory management and assignment: Streamline the process of managing territories and assigning leads based on specific territories.
• End-to-end lead routing capabilities: Enhance the lead routing process with advanced features such as Lead Deduplication and Lead-to-Account Matching, ensuring that leads always receive a seamless buying experience.

If you have any questions or would like to learn more about how Sweep Assignment can assist your business as it scales, please don't hesitate to contact me at [email protected]. I would be delighted to show you around or provide further insights into Salesforce Lead Assignment Rules.

Interested in scheduling a demo or free trial? Contact us today.

Salesforce Assignment Rules Deep Dive

• July 7, 2022

What Are Salesforce Assignment Rules

Assignment rules are a standard feature in Salesforce used to automate the assignment of leads and cases. They can be a great alternative to manually assigning records. However, there are more than a few limitations you’ll want to be aware of. In this article we’ll discuss the benefits and limitations of Salesforce assignment rules so you can decide if they make sense for your organization. We’ll also share advice and guidance on how to effectively configure assignment rules.

The Benefits of Assignment Rules

Salesforce assignment rules are a powerful tool designed to streamline the distribution and management of leads and cases within an organization. By automating the assignment process, these rules ensure that leads and cases are instantly assigned to the most appropriate team members based on specific criteria such as product interest, priority, and geographic location. This target approach helps to accelerate response times, balance workload, improve team performance, and increase customer satisfaction. The use of assignment rules in Salesforce, therefore, represents a strategic advantage for businesses looking to optimize their sales and support workflows, ultimately driving growth and customer loyalty.

Limitations of Assignment Rules

While Salesforce assignment rules offer significant advantages, they also have limitations that organizations should be aware of:

• Limited to leads and cases : One of the most significant limitations of Salesforce assignment rules is the inability to assign standard or custom objects beyond leads and cases. This restriction often prompts organizations to look for an alternative solution that can assign any object .
• Lack of round robin assignment : They do not support round robin assignment, which is essential for most modern sales and support teams. Instead, each rule assigns records to a specific user or queue you designate.
• Lack of workload-based assignment : They don’t consider the existing workload of team members, potentially leading to an uneven distribution of leads and cases. This can result in slow response times and employee burnout.
• Lack of availability-based assignment : They don’t consider the availability of team members, resulting in leads and cases being assigned to team members that are away from work or otherwise unavailable.
• Difficult to maintain : Assignment rules can quickly become difficult to manage—even for small teams with simple assignment logic. Here’s an example of what a small portion of a typical assignment rule looks like:

Assignment rules can still be very useful despite these limitations. Continue reading to learn how assignment rules can be used to optimize your lead and case routing process.

How Assignment Rules Work

An assignment rule is a collection of conditional statements known as assignment rule entries. Each assignment rule entry contains one or more conditions and a user or queue to whom matching records will be assigned.

The Sort Order field can be used to change the order in which assignment rules are executed. Leads and cases will be evaluated against assignment rule entries in order and assigned by the first assignment rule entry that matches.

In the example above, we’ve prioritized our rules for Canada provinces (e.g. Ontario) higher than our country-wide Canada rule entry to ensure that leads from specific provinces don’t get assigned to the wrong person.

Next we’ll step you through how to actually create an assignment rule. 

How to Create Assignment Rules

You’ll need the “Customize Application” permission in order to manage assignment rules. If you don’t have this permission, contact your Salesforce administrator.

Ready to create your first assignment rule? Follow these steps:

• Login to Salesforce.
• Navigate to Setup .
• Search for “assignment rules” in Quick Find and click either Lead Assignment Rules or Case Assignment Rules .
• Click New to create a new rule.
• Name your rule and then click Save . We recommend leaving the Active box unchecked for now. 

Now you’re ready to specify how leads or cases will be assigned.

• Click on the rule you created.
• Click New to create a rule entry.
• Sort Order : this controls the order in which rules are executed.
• Criteria : you can enter one or more filters to define which records should be assigned by this rule.
• Owner : choose a user or queue to which records should be assigned. Alternatively you can check the Do Not Reassign Owner checkbox if this rule should not assign records.
• (Optional) Select an email template for notifying users of assignments.
• Click “ Save. ”
• Repeat the above steps for any additional rule entries.

Activate Your Assignment Rule

You can follow these steps to activate your assignment rule:

• Navigate to your assignment rule.
• Click the Edit
• Check the Active
• Click Save .

Keep in mind that only one assignment rule can be active at a time. We’ll discuss how your active assignment rule can be used to assign records in the next section.

What Triggers Assignment Rules in Salesforce

There is often some confusion about how and when assignment rules run. There are a few different ways these rules can be triggered:

• Creating a New Record : When a new lead or case is created, either manually or through an automated process, assignment rules can be triggered to assign the record to the appropriate user or queue.
• Updating a Record : If a record is updated and meets certain criteria set in the assignment rules, this can also trigger the reassignment of the lead or case.
• Web-to-Lead or Web-to-Case Submission : When leads or cases are generated through Salesforce’s web-to-lead or web-to-case features, assignment rules can automatically assign these incoming records.
• Data Import : When importing data into Salesforce, you can opt to apply assignment rules to the imported records, ensuring they are assigned according to the established criteria.
• API Creation or Update : Records created or updated via Salesforce’s API can also trigger assignment rules, depending on the configuration.
• Manual Triggering : Users with the appropriate permissions can manually apply assignment rules to leads or cases, either individually or in bulk.

Understanding these triggers is essential to effectively utilizing assignment rules in Salesforce, ensuring that leads and cases are assigned to the right team members promptly and efficiently.

Tips and Tricks

• It’s always a good idea to include a final rule entry with no conditions. This will be used to catch anything that didn’t match your rule criteria and assign it to a user or queue for review.
• It’s also a good idea to include a rule entry that assigns junk (e.g. spam, test records, etc.) to a queue for review and deletion.
• We recommend you test assignment rules in a sandbox before you add to your production org. However, keep in mind that assignment rules cannot be deployed from a sandbox to a production org.
• Custom formula fields can help to simplify complex assignment rules. For example, rather than entering lengthy criteria (e.g. lists of states by region) you could create a formula field instead. This would reduce your criteria from “STATE/PROVINCE EQUALS IL,IN,IA,KS,MI,MN,MO,NE,ND,OH,SD,WI” to “REGION EQUALS Midwest”.
• You can enable field history tracking on the owner field to track assignments made by your assignment rules.

Frequently Asked Questions

What happens to records that don’t meet salesforce assignment rule criteria.

These records will be assigned to whomever is designated as the default lead owner or case owner.

What are the different types of assignment rules in Salesforce?

Salesforce currently support lead and case assignment rules. Additionally, account assignment rules can be created as part of enterprise territory management.

What is the order of execution for assignment rules?

It’s important to understand exactly when assignment rules are run in relation to other events. For example, assignment rules are run after apex triggers and before workflow rules. See Salesforce’s Triggers and Order of Execution article for a comprehensive list of events and the order in which they’re executed.

How do you run assignment rules when creating or editing records using the REST API?

You can use the Sforce-Auto-Assign header when making REST API calls to control whether or not assignment rules run.

Salesforce assignment rules can be a valuables tool for many organizations. However, it’s important to understand the limitations. If you’re struggling with assignment rules it may be time to look at alternative solutions. Kubaru is a powerful automated assignment application for Salesforce. Check us out on the Salesforce AppExchange or contact us to schedule a demo.  

How to Calculate (and Reduce) Your Customer Acquisition Cost

Have you ever considered how much your company spends to obtain a single customer? If you’re not calculating customer acquisition cost (CAC) you may be reading the signals all wrong. Even when business seems to be booming, if it’s costing

Net Revenue Retention Explained: Increase Your Revenue Growth in 2024

Imagine you’re sailing the vast ocean of subscription-based business, where the winds of customer churn and revenue expansion constantly shift. In this unpredictable sea, there’s one compass that guides you: Net Revenue Retention (NRR). It’s not just a metric–it’s your

A Comprehensive Guide to Data Enrichment for B2B Sales Success

Imagine you’re at a party, and you spot someone you’d like to get to know. You have their name and a vague idea of what they do, but that’s about it. Now, what if you had a magic lens that

Contact us to schedule a demo today!

Schedule Demo

Fill out the form below and we’ll respond in a few minutes

* We take privacy seriously. We will never sell or share your personal information with anyone.

September 30, 2021

Speed Up and Sell More: Salesforce Lead Assignment Rules Best Practices

Get the latest RevOps insights

When a lead comes in, an opportunity should come knocking.

But there’s a lot more under the hood. You need solid lead assignment rules in place, and one key variable to keep in mind.

Time. According to LeadSimple, responding to a lead in the first 5 minutes is 21x more effective.

No surprises here. If you’re a scaling business, you know that responding first to a lead is mission-critical.

If you’re manually triaging leads or waiting for IT to make business-critical changes to your lead assignment rules, it’s not scalable. Nor fast.

As an operations leader, you feel this pain across your entire organization. 

Demand teams work hard to generate incoming leads, so it doesn’t make sense to abandon them just because they’re not getting to the right rep in real-time. Your leads, after all, are directly tied to sales revenue.

Automating the process doesn’t solve the problem alone, either. It’s an important piece to speeding up, but not the only piece to the lead assignment puzzle.

You’re inundated with the notion often – speed is everything!

Well, we’re here to tell you:

Respond right is the new respond first.

Shotgun responses don’t help if your lead happens to work for a target enterprise account of yours. You definitely want your Enterprise sales rep, Rachael putting her best foot forward.

Setting the right lead assignment rules also helps with what ‘future you’ couldn’t know ahead of time.  Say a lead comes in from a territory that doesn’t have a rep assigned – It’s going to sit in a queue. A potential quality lead slipped through the cracks of time because there’s no accountability or rule in place.

Complex business processes and go-to-market efforts add additional layers of friction. How can you get it right if you’re constantly evolving at scale?

Your lead assignment process could be stunting your growth potential.

It’s time to speed up, starting with smarter lead assignment rules. 

Give your operations teams their sanity back, and set your sales reps up for speed-to-lead success.

Go ahead and skip the next section if you’re already aware of the challenges to overcome as a scaling business, and want to get right to Salesforce lead assignment rules for success.

Businesses Quickly Outgrow Native Salesforce Lead Assignment Rules

Asana, a project management platform, was scaling fast.

They were grappling with increasing volumes of leads, lagging response times, and complex assignment rules that became impossible to keep up within Salesforce.

As more leads came in from a variety of sources, and with complex territory assignments and hundreds of sales reps that change frequently, lead assignment became a nightmare to manage in native Salesforce.

That’s because creating and changing lead assignment rules can quickly become very complex:

Only a dozen or so lead assignment rules are implemented here, primarily basic rule sets like location, company size, industry, or lead quality. You can imagine how cluttered your rules would get as you continued to add more criteria.

Asana knew that not having a more sophisticated Salesforce workflow automation process meant they didn’t have the flexibility to adapt at scale.

There were two problems Asana needed to overcome:

1. Complex, evolving go-to-market rules

You wouldn’t want sales reps responding to a lead that’s not in their sales territory. You also wouldn’t want junior reps following up with your largest target accounts.

But it happens.

Typical go-to-market (GTM) models are unique by company and can vary by:

• Named account
• Role or product focus
• Partner channels and more

How a company sets up their go-to-market strategy informs how they need to route or assign leads to reps. SaaS sales teams are regularly selling into different territories, market segments (SMB, mid-market, enterprise), verticals, and industries.

What’s more, lead assignment rules often require changing daily with large enterprise businesses. 

Asana, for example, consistently had leads assigned to reps that no longer worked with them.

Imagine juggling complex territory assignment rules and hundreds of sales reps that change frequently?

It can take weeks or months for IT to get involved whenever a Salesforce lead assignment rule needs to be changed:

• IT has to define the required changes, scope them, slot those into a sprint, which may occur weeks or months later
• During the sprint, the team will make the changes, validate them, test them
• Push them from the development environment to the QA environment, and perhaps a staging environment, and then finally into production

Doing things manually, or not at all, is not a scalable alternative.

You can automate to help you move faster, but speed is sidelined when you don’t have the flexibility to adapt to your changing assignment or routing environment.

And it only gets worse as your lead volume climbs.

2. Massive volumes of incoming leads and lagging response times

When too many cars are trying to get to various destinations, traffic jams occur, with some drivers giving up and going somewhere else altogether.

If companies are slow to respond, the chances of those leads sticking around drops with every. passing. minute. Someone else will hop on a plane instead and get facetime sooner.

You need to move faster.

On average, it took companies 42 hours, or almost two days, to respond to a lead .

That’s basically a lifetime:

Dramatics aside, it means most B2B companies are still falling behind and not responding to leads within the five-minute-or-less sweet spot. But it’s there for the taking.

In the past, we had people manage catchall queues, trying to figure out who should own each lead. – Jim Maddison, Veracode

In Xant’s Lead Response Study 2021 of 5.7 million inbound leads at 400 plus companies, they found that 57.1% of first call attempts occurred after more than a week of receiving a lead.

So why are most companies lagging behind? They need to automate and create more adaptable lead assignment rules that actually reflect their go-to-market.

Speed might be serving up the silver platter, but you’re only going to get the deal if you implement effective salesforce lead assignment rules.

Here are some best practices to help set yourself up for success.

Lead Assignment Rules Best Practices For High-Growth Companies

You’ve got massive volumes of incoming leads and ultra-complex go-to-market rules. You’re in the right place.

First things first.

Automate, automate, automate. 

Let’s get to that golden window of 5-minutes. Picture Tesla’s “Come to Me” app (it comes to you and eliminates a long trek to your parking spot).

It requires one tap.

Once a lead enters Salesforce, they follow the defined rule roadmap according to lead assignment rules that you set and ultimately land with the correct salesperson in record time.

You’re giving back those precious minutes to your revenue and sales operations teams.

Now about those rules.

Define Your Go-To-Market Rule Baseline

Carving out territories based on geography, segments, verticals, industries, named accounts, or whatever your go-to-market strategy is, is the first step. This is your baseline.

Any lead that falls into a sales rep’s territory should be assigned to them based on these defined rules, but that’s easier said than done. They’re constantly changing based on several factors.

You need to define your criteria  

In other words, the set of criteria that you will be implementing – you know the drill. To do so, you ask all the necessary questions:

• Which rep will take on what territories?
• What happens if new reps are hired and old ones leave?
• What happens if someone goes on vacation? Or doesn’t work on Fridays?
• A lead comes in from a partner, where do you want this to go?

The beauty is that the sky is the limit.

But how do you get there with native Salesforce constraints lacking the required sophistication?

We had about 800 or 900 rule criteria. We needed something flexible and something that could change, or help us change as we change our business a year to year. – Jim Maddison, Veracode

The next step:

Create customer rule criteria 

Veracode, a security company, had incredibly complex criteria. They had to hire a developer to manually code changes to lead assignment rules. Things changed daily for them, and they grappled with how to adapt.

Ditching the code for the intuitive drag and drop Complete Lead’s interface gave Veracode more flexibility to create assignment rules on the go.

Remember when we said the sky’s the limit?

Implement Nested Flows to Tackle Ultra-Complex Rule Sets

If there were a way to make it easier, you do it right?

Nested flows keep your rules organized. 

At a high level, think of it like nesting dolls: each “nested” or child assignment flow sits within a bigger, or parent assignment flow.

These parent-child relationships can span far beyond just one or two levels, giving you the freedom to allow each business unit to oversee their own GTM processes and territories.  This is a huge win for Rev Ops organizations looking to simplify and speed up ultra-complex lead management.

Department Managers can even set and keep track of rules for their own set of assignment flows, for different GTM teams and within different nested flows. That means lines are drawn in the sand but teams still have visibility and control of how a lead is tracked for their particular team.

Your business depends on data getting where it needs to go, fast. That’s why no matter how complex, your assignment rules should never feel out of hand.

Leverage Powerful Account-Based Assignment

Account-based strategies should be a cornerstone to your go-to-market strategy, and you want to know that your strategic investments are being implemented successfully.

• In a survey conducted by ITSMA , 87% of B2B marketers said that ABM initiatives outperform their other marketing investments.
• COVID-19 caused companies t o rush to create ABM strategies to respond to an increased need for a strong digital presence.
• 56% of the 800 B2B marketers that LinkedIn surveyed said that they are using ABM. Over 80% said that they plan to increase their ABM budget over the next year.

Use account-based assignment.

Account-based marketing targets specific companies, so setting up account-based rules in your lead flow process allows you to route leads from these target accounts to your most experienced reps quickly and easily.

The rule of thumb is that leads from target accounts need to go to the account rep that owns the account. The account owner has the deepest knowledge of the account and the highest chance to convert. Simply put, account based routing  has a positive impact on your bottom line.

With a more robust lead assignment solution to align with their account-based selling and marketing strategies, Alfresco was able to increase their close/won rate by 10%!

Enterprise hierarchy assignment is a no-brainer for account selling.

Imagine if you could automatically visualize all the related customer accounts including subsidiaries, and assign one strategic rep to the parent enterprise account?

You can and you should. Complete Hierarchies gives you the ability to automatically build and visualize complex account hierarchies, so that you’re able to route leads to the right rep no matter how complex the account structure.

Let’s say a new lead comes in from Hulu, but you’ve no idea that it’s a subsidiary of The Walt Disney Company. Chances are the same rep won’t be assigned the account if other go-to-market rules are in place.

Also, you’ve already given a discount to The Walt Disney Company, and this information is not available to the rep who gets the new lead. Account Hierarchies can be a trick up the sleeve when it comes to account-based assignment.

But what happens if a lead comes in and it’s not associated with any account or go-to-market territory?

Set Up A Sophisticated Round Robin

You’re leaving revenue on the table when you let leads sit and die without a timely response.

Native Salesforce just isn’t sophisticated enough to handle more complex round-robin criteria that’s required to keep your leads flowing when they hit a snag.  It’s limited and cumbersome to manage – major setbacks when it comes to your speed-to-lead.

For certain territories or situations, you may have multiple reps covering the territory or a catch-all queue for leads that don’t have enough information to assign properly.

In those cases, businesses often have someone dedicated to manually triaging and assigning leads. This is an incredibly time-consuming operational nightmare and a good way to tank your response times.

And with a lack of accountability, reps often cherry-pick the ideal leads and leave others to the crows.

To avoid these assignment pitfalls you need to push leads to a chosen pool of sales reps and evenly distributed to your sales team, giving everyone an equal opportunity to generate a sale. But you also need more flexible options.

Use sophisticated dynamic round-robin assignment to:

• Set sophisticated criteria like rep speciality or languages
• Use availability settings to ensure leads can be responded to immediately (e.g. office hours)
• Automatically notify reps when new leads are pushed through
• Enforce SLAs on response times to make sure leads are responded to as quickly as possible
• Pair with a rep response dashboard that gives you a complete view to help you monitor how fast a rep is following up with their round-robin leads

Weighted round-robin:

Give your best-performing reps more leads, and improve your overall chance at generating more pipeline. Based on:

• Performance
• On their speciality
• Any desired field

If you’ve found that reps have hit their max capacity for being able to manage any more leads, you can cap the number of records assigned to your team members in the round-robin.

Hit a snag? Re-route your leads:

If reps aren’t responding within their SLA, you can reroute the lead and assign it to someone who will respond. This helps prevent further roadblocks and keeps data flowing, even when there’s a bottleneck.

It’s typical for our team to get four to five requests a week to change territories for a user. Onboarding and offboarding now takes just a few minutes to run all our leads back through the system and automatically get reassigned. – Jim Maddison, Veracode

The ultimate speed-to-lead tactic to keep in your back pocket.

Go Beyond Leads, Assign Any Object

Just imagine that feeling you get if you could create assignment flow, beyond leads. It’s a whole new world.

Assign any object

Go beyond leads and create any assignment flow across any object. You can assign any record, update any field, and trigger any action.

It works similarly to the assignment flow you create for leads, so define your goals and determine your set of criteria for each particular object.

No more manual effort!

This presents endless opportunities to customize your assignment flows, resulting in streamlined processes and less manual administrative time spent manually sifting through information.

What Are You Waiting For?

It’s time to speed up and sell more. 

Speed is crucial, but there’s so much more than that underpinning your speed-to-lead. You need the flexibility to handle your go-to-market complexity and to keep your leads flowing to the right reps in real time.

When you’re scaling fast, you can’t afford to let good leads slip through the cracks.

Interested in hearing more?

We’re happy to talk you through how you can elevate your lead assignment rules in Salesforce, and dramatically improve your speed-to-lead game.

Book a personalized demo with one of our experienced team members today.

Don’t let data hold you back. Build a better way with Complete Leads.

Related Posts

Data in motion: Complete RevOps data management for Salesforce

Data management for RevOps has never been more important. Today, we set in motion a recommitment to you, your data, and your business.

3 lead routing plays to boost pipeline for marketing operations

Why we built this: Multi-SLA timers

Traction Complete Launches "The RevOps Data Management Suite for Salesforce"

Reforming Data Management for Revenue Operations Professionals

• Salesforce Admin

Salesforce Lead Assignment rules

• Dec 28, 2023

Salesforce Lead Assignment rules automate your org lead generation and support processes. Lead Assignment Rules are a powerful tool that enables the automatic assignment of leads to the right sales representatives based on specific criteria.

What is Salesforce Lead Assignment rules?

Lead assignment rules are used to specify how leads are assigned to users or queues. This is used to assign the owner of leads based on lead generation, like leads created from the web or imported from a data loader.

Type of Assignment Rule 

There are two types of assignment rules we have in Salesforce.

• Lead Assignment Rules
• Case Assignment Rule

How to create lead Assignment Rules

Let’s see the step-by-step guide to set up the lead assignment rules.

• Creating Lead Assignment Rule
• Creating Lead Assignment Criteria
• Specify the lead assignment method
• Activate the lead assignment rules
• Test the lead assignment rules

Consideration for Lead Assignment Rules

• We can create as many assignment rules as possible, but only one can be active.

Lead Assignment Rules can help streamline your lead management process, improve lead conversion rates, and increase sales productivity by ensuring that the right leads are assigned to the right representatives.

Related Posts

How to Sync Data from Microsoft Excel to Salesforce: Use Cases

Revenue Lifecycle Management (RLM) Training

Einstein Copilot Standard and Custom Actions

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Name  *

Email  *

Add Comment  *

Notify me of follow-up comments by email.

Notify me of new posts by email.

Post Comment

Automation Champion

Automating Salesforce One Click at a Time

Running Lead Assignment Rules From Salesforce Flow

Last Updated on February 14, 2022 by Rakesh Gupta

To understand how to solve the same business use case using Process Builder . Check out this article Getting Started with Process Builder – Part 49 (Running Lead Assignment Rules From Process Builder) .

Big Idea or Enduring Question:

How do you run the lead assignment rule from the Salesforce flow? Lead assignment rules allow us to automatically assign Leads to the appropriate queue or user. A Lead assignment rule consists of multiple rule entries that define the conditions and order for assigning cases. From a Salesforce User interface, a user can trigger assignment rules by simply checking the Assign using the active assignment rules checkbox under the optional section.

The problem arises when you need to insert or update the Leads from Salesforce Flow and wants to trigger assignment rules. Using the Salesforce Flow a Lead will be inserted or updated but the assignment rule will not be triggered as there is no check box to use the organization’s assignment rule or a prompt to assign using the active assignment rule.

Let’s start with a business use case.

Objectives:

After reading this blog post, the reader will be able to:

• Running the lead assignment rules from Salesforce Flow
• Understand @InvocableMethod Annotation
• How to call an Apex method using Salesforce Flow

Business Use Case

Pamela Kline is working as a System administrator at Universal Containers (UC) . She has received a requirement from the management to update the following Lead fields when Lead Source changed to Partner Referra l .

• Status = Working – Contacted
• Rating = Hot

As data changed by the process, she wants to fire the assignment rule as soon as the process updates the lead record.

Automation Champion Approach (I-do):

Guided Practice (We-do):

There are 4 steps to solve Pamela’s business requirement using Salesforce Flow and Apex. We must:

• Setup a lead assignment rule
• Create Apex class & Test class
• Define flow properties for record-triggered flow
• Add a decision element to check the lead source
• Add an assignment element to update status & rating
• Add a scheduled path
• Add a decision element to check if lead source changed
• Add action – call an Apex class to invoke lead assignment rule

Step 1: Setting Up Lead assignment Rule

• Click Setup .
• In the Quick Find box, type Lead Assignment Rules .
• Click on the Lead Assignment Rules | New button .
• Now create an assignment rule, as shown in the following screenshot:

Step 2: Create an Apex class and Test class

Now, we have to understand a new Apex annotation i.e . @InvocableMethod . This annotation lets us use an Apex method as being something that can be called from somewhere other than Apex . The AssignLeadsUsingAssignmentRules class contains a single method that is passing the ids of the Leads whose Lead Source changed to Partner Referral . Create the following class in your organization.

• In the Quick Find box, type Apex Classes .
• Click on the New button .
• Copy code from GitHub and paste it into your Apex Class.
• Click Save.

Step 3.1: Salesforce Flow – Define Flow Properties for Before-Save Flow

• In the Quick Find box, type Flows .
• Select Flows then click on the New Flow .
• How do you want to start building : Freeform
• Object : Lead
• Trigger the Flow When : A record is created or updated
• Condition Requirements: None
• Optimize the Flow For : Fast Field Updates
• Click Done .

Step 3.2: Salesforce Flow – Using Decision Element to Check the Lead Source

Now we will use the Decision element to check the lead source to ensure that it is equal to Partner Referral.

• Under Toolbox , select Element .
• Drag-and-drop Decision element onto the Flow designer.
• Enter a name in the Label field; the API Name will auto-populate.
• Under Outcome Details , enter the Label the API Name will auto-populate.
• Resource: {!$Record.LeadSource}
• Operator: Equals
• Value: Partner Referral
• When to Execute Outcome : Only if the record that triggered the flow to run is updated to meet the condition requirements

Step 3.3: Salesforce Flow – Adding an Assignment Element to Update Rating and Status

• Drag-and-drop the Assignment Element element onto the Flow designer.
• Enter a name in the Label field- the API Name will auto-populate.
• Field: {!$Record.Rating}
• Add Condition
• Field: {!$Record.Status}
• Value: Working – Contacted

• Click Save .
• Enter Flow Label the API Name will auto-populate.
• Click Show Advanced .
• API Version for Running the Flow : 53
• Interview Label : Record-Trigger: Lead Before Save {!$Flow.CurrentDateTime}

Step 4.1: Salesforce Flow – Define Flow Properties for After-Save Flow

• Field : Lead Source
• Operator: Euqals
• Optimize the Flow For : Action and Related Records

Step 4.2: Salesforce Flow – Add Scheduled Paths

• Under SCHEDULED PATHS , click on the New Scheduled Path .
• Under Scheduled Path Details , enter the Label the API Name will auto-populate.
• Time Source : Lead: Last Modified Date
• Offset Number : 1
• Offset Options : Minutes After

Step 4.3: Salesforce Flow – Adding an Action to Call Apex class to Trigger Lead Assignment Rule

• Drag-and-drop the Actions element onto the Flow designer.
• Select the AssignLeadsUsingAssignmentRules Apex class.
• Field: LeadIds
• Value: {!$Record.Id}

• Interview Label : Record-Trigger: Lead After Save {!$Flow.CurrentDateTime}

Proof of Concept

Now onward, if a business user updates the Lead Source to Partner Referral , Process Builder will automatically update Status , Type , and Assign it to the right user or queue based on the lead assignment rule.

Monitor Your Schedule Flow

To monitor Flows that are scheduled, navigate to the following path:

• Navigate to Setup (Gear Icon) | Environments | Monitoring | Time-Based Workflow .

• Use the Delete button to delete the time-based Flow job from the queue.

Formative Assessment:

I want to hear from you! What is one thing you learned from this post?  How do you envision applying this new knowledge in the real world? Let me know by Tweeting me at @automationchamp , or find me on LinkedIn.

Submit Query!

Similar Posts

Elevate Your Screen Flows with a Customizable Counter Component

Dynamic Progress Bar for Form Completion in Salesforce Flow

Launch a Flow from CRM Analytics Dashboard Text Widget

9 thoughts on “ running lead assignment rules from salesforce flow ”.

It ran repeatedly, every minute, over and over again. I was getting notification email every minute when testing. I did the same steps as you mentionned, with a record triggered flow containing the apex action.

I found that this ran repeatedly, every minute, over and over again. Was easy to spot because I modified the Apex to include sending the user notification email as well – so I was getting notification email every minute when testing.

When I updated the ‘Time Source’ in the flow scheduled path from ‘Time Source: Lead: Last Modified Date’ to ‘Time Source: When Lead is Created or Updated’ that seems to have solved the problem.

Was curious if you had the same experience or if there was some other nuance happening.

It also looks like you had originally intended to use a decision element in step 4.3 but changed that to flow entry requirements, likely because the scheduled path can’t assess the prior and current values the same way the starting node can.

Thank you for sharing your valuable feedback. I have a quick question for you: When executing the Apex class, do you utilize a Record-triggered Flow or a Scheduled-triggered Flow?

after the apex class fires, noticed the lead owner is assigned to default lead owner, instead of using lead assignment rule. Any clue?

Thank you for an excellent tutorial 🙂 you solved my problem! Very much appreciated

Anyone getting issues with an error on mass updates “Apex error occurred: System.QueryException: List has more than 1 row for assignment to SObject “? if each one is called individually, I don’t understand how there is more than 1 row for assignment. Sometimes I get an email with this error only to see that the trigger actually worked for the specified record so a bit odd. Thanks!

Thank you for the great tutorial. Why add the 1 minute wait? Is that just to take avoid too much synchronous automation? Or is it required for another reason?

You’re right Kevin (to make the process asynchronous).

• Pingback: Getting Started with Process Builder – Part 49 (Running Lead Assignment Rules From Process Builder) - Automation Champion

Leave a Reply Cancel reply

This site uses Akismet to reduce spam. Learn how your comment data is processed .

Discover more from Automation Champion

Subscribe now to keep reading and get access to the full archive.

Type your email…

Continue reading

• May 12, 2020

How I Manage Lead Assignment in Salesforce For a Global Sales Development Team

Part of my role as a Marketing Operations Manager is to manage the lead assignment process within Salesforce for our 25+ Sales Development Representatives from around the world. A crucial piece to our success is getting the leads to the right person without any delay and ensure a smooth handoff from marketing to sales. In this post, I will walk you through exactly how I manage the lead assignment and lead reassignment process within Salesforce and share some lessons learned along the way.

High-level overview of the lead assignment process

• Manage lead assignment for 25+ Sales Developement Representives around the world
• Manage lead assignment for 10+ channel partners around the world
• Leads are only assigned to SDRs or partners (no AEs or ADMs, for example)
• Leads in target countries are scored and given a rating (A-D).
• Leads that meet our MQL rating are assigned to an SDR for follow up
• Leads that do not meet our MQL rating are assigned to a lead queue in Salesforce

Using standard Salesforce lead assignment rules

When the Sales Development team was smaller and the business rules were not as complex, we used the standard Salesforce lead assignment rules . Now, we only use the standard assignment rules to assign leads to channel partners. These assignment rules are only based on Country and are rarely updated.

Using advanced lead assignment rules in Salesforce

The lead assignment for the Sales Development team is more complex. Leads are assigned based on a number of different criteria including industry and company size. With 25+ Sales Development Reps, territories constantly shifting and the team growing, using the standard Salesforce lead assignment rules were difficult to manage. We purchased Traction Complete an app available on the Salesforce AppExchange to manage the lead assignment for our SDR team. Not only does Traction Complete give us an easier, more visual way of managing our lead assignment rules, it also gives us the ability to:

• Assign leads via round robin
• Automatically match leads to accounts
• Filter records by advanced criteria (using fields from the leads or matched account)
• Account based lead assignment
• Update fields
• Automatically convert leads from a matched account into a contact
• Auto-merge duplicate leads

The lead assignment process

Here’s a basic overview of what the lead assignment process looks like.

Leads are created in our Marketing Automation platform and are scored. The leads are then synced with Salesforce where the Complete assignment rules run to determine:

• Is the lead in one of our target countries? If the lead is not in one of the target countries, the lead is assigned via the standard Salesforce assignment rules.
• Is the lead an MQL? Leads are only assigned to an SDR once they reach them MQL threshold. If they are not an MQL, they will be assigned to the “Unqualified Leads” queue until they reach the MQL threshold.

Assigning leads via round robin

We also have the ability to assign leads via round robin. We occasionally use this feature if an SDR is going on vacation for an extended period of time.

Triggering lead assignment rules when a lead is an MQL

Leads are only assigned once they are marketing qualified. To trigger the lead assignment process in Complete, the checkbox field called “Re-run Traction Complete” must be set to TRUE. To update this field, we have MQL flows set up in our Marketing Automation system to do this automatically.

Here’s what the smart campaign looks like in Marketo to trigger lead assignment when a lead becomes an MQL

Leads can only run through the smart campaign every 60 days because we do not want leads to continuosly become an MQL if the SDR has had a conversation with them recently.

Here’s what the automation rule looks like in Pardot to trigger lead assignment when a lead becomes an MQL

Tips for managing lead assignments

• Limit the number of times a lead can become an MQL . If a lead is considered an MQL more than once, it can impact your conversion rates and skew your reporting. Limiting the number of times a lead can become an MQL can help with this, and it can prevent SDRs from constantly updating the lead to “recycle” or something similar.
• Make sure you have all countries, states and provinces assigned . This might seem obvious, but when you are territory planning, make sure that all states, provinces, and countries you do business in are accounted for. Otherwise, some leads might slip through the cracks and not be assigned to anyone or assigned to the wrong person.
• Determine the criteria for lead reassignment . When should the lead be reassigned? Do you have an SLA the SDR needs to meet before the lead is reassigned to someone else? If you shift territories, does the current SDR get to keep anything they are currently working on? Are there certain leads that should be excluded from reassignment? All these questions are something you should work with your Sales Manager to determine before making any changes in Salesforce.
• Allow SDRs to update lead ownership . Sometimes the lead is assigned to the wrong person, usually due to incorrect demographic data. Allow SDRs to be able to update the lead ownership to the correct person instead of pinging an admin to do it.
• Determine if lead alerts need to be set up . Do SDRs need to be alerted when a lead is assigned to them? If not, then you need to create views where they can easily see leads assigned to them. We use Outreach.io as our Sales Engagement Platform, which allows our SDRs to easily see what leads are assigned to them and need to be actioned on immediately. For that reason, we do not have lead alerts set up for MQLs, but we do have alerts sent out if a high-value form (pricing, contact, etc) is filled out.
• Enable field history tracking for lead owner . Enabling field history tracking for lead owner will allow you to see when lead owner updates occur. This is important for troubleshooting to see if your automated lead assignment process assigned the lead to the incorrect owner, or if someone updated the lead owner manually. It will also allow you to easily reverse owner updates if leads are assigned to someone by mistake.

Lead reassignment due to territory changes

Step 1: update the lead assignment rules in traction complete.

The first step to do a territory change is to update the flow in Traction Complete. This update will only apply to new leads and new MQLs.

Step 2: Pull Salesforce report of the leads that need to be updated

Next, pull a Salesforce report of all the leads that need to be reassigned. The report should at minimum include the fields Lead ID or 18-Digit Lead ID and Re-Run Traction Complete. I also add the following filters to the report:

• Converted = False (to remove any leads that are already converted into contacts)
• Lead Owner contains _______ (to pull leads owned by a specific person)
• Lead Status not equal to Working (to exclude leads that the SDR is already working)

Run the report and export it as a CSV.

Step 3: Update the field “Re-run Traction Complete” to TRUE

Open the file in Excel and set the “Re-run Traction Complete field to TRUE. Save the file.

Step 4: Upload the file to trigger the re-assignment

I use Salesforce Data Loader to upload the CSV into Salesforce.

Lessons learned from completing many territory reassignments

• Updating via my Marketing Automation platform is MUCH slower . I used to run data updates to trigger reassignment or lead ownership in Marketo. However, it takes a lot longer to complete then exporting a report in Salesforce and importing it via Data Loader.
• If you use a Sales Automation tool, make sure ownership is updated there as well . We use Outreach.io for our Sales Automation tool. I find that 90% of the time lead ownership is synced from Salesforce to Outreach immediately, but sometimes you need to force the sync.
• Create a “Lead Territory Change Template” report in Salesforce . I have a report saved that I reuse each time I do a territory reassignment. The template includes all the filters that I would typically use as well as all the columns I need.
• Always double check that the lead assignment is correct . Run a report in Salesforce and group by the lead owner to ensure that everything is assigned to the new owner.
• Ensure you don’t have any lead alerts setup . Check your Marketing Automation platform and processes in Salesforce to make sure nothing will trigger an email alert or something similar when the lead owner is updated. You don’t want to trigger hundreds, or maybe even thousands of alerts when a lead is reassigned.

How do you currently manage lead assignment in Salesforce?

• I use the standard lead assignment rules
• I use an app from the AppExchange
• I have my own custom process set up in Salesforce
• I have my own custom process set up in my Marketing Automation System
• Other (specify in comments below)

View Results

Send me a tweet @jennamolby , leave a comment below, or book a Peer Chat .

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

You are a truly rock star! Thank you for sharing a helpful work and tools on lead assignment!

@brandy_chi

Related Posts

Account Scoring in Salesforce: How to Summarize Lead Score for Accounts

• September 29, 2020

8 Noteworthy Features in the Salesforce Summer ’19 Release

• April 22, 2019

How to Capture Every UTM Touchpoint in Salesforce

• September 10, 2021

• Dynamics 365 Sales
• IT Professionals

Route leads with dynamic assignment rules

• By Deepshikha Kumari, Software Engineer, Dynamics 365 Sales

Lead routing—the process of distributing incoming leads among sales reps—can be simple or complex. If you have sophisticated requirements for making those lead assignments, review these tips for using the standard seller information and dynamic matching to streamline rule configuration in the Sales Premium for Dynamics 365 Sales.

A simple approach to lead routing is to make a list of all your sales reps, and then assign each new lead to the next seller in sequence (round robin) or based on availability (load balancing). This can be achieved by using the assignment rule shown in the following screenshot. This configuration will assign all the leads that are part of the segment Leads from web to sellers in a round-robin way.

More sophisticated requirements  can also be configured by using the lead assignment rules. The rules can identify the most appropriate seller based on the fields of incoming lead s. Seller availability and capacity can also be considered in the rule. The following two options are available to select sellers:  

• Use existing fields from seller records in Dynamics 365.  
• Use seller attributes defined for assignment rules. More information:  Manage seller attributes  

Using the first option to manage lead distribution is simpler in terms of onboarding and management because it uses the seller information that already exists in Dynamics 365 .  The following example shows a rule to assign leads to sellers who are based out of Seattle :  

Dynamic matching eliminates manual rule setting

Dynamic matching reduces the effort of having to write and maintain multiple static rules for each permutation and combination of values. Suppose we want to distribute leads based on country. For example, we can have leads from the United States assigned to any seller focused on US clients and leads from India assigned to any seller focused on India.

If we try to create static rules for each assignment by country, we’d need as many rules as countries. If the organization is serving 150 countries, we might need to create 150 static rules. If we wanted to use more attributes, such as Zip Code and currency, the rule count would multiply exponentially. A simpler approach is to use the dynamic match capability. In the scenario described above, where leads are assigned to sellers based on country, we can have a single rule as follows:  

Here, the rule will check the country of the lead ( Lead.Country ) and match it with seller’s country. When there is an exact match, the lead is assigned to the appropriate seller.  

Note: Country as used here is a global option set defined for both lead and system user entities. You could instead use the look up mechanism to find a match. In this way, you can use any string type of field (a single line of text). However, the string type of lead field will need a logical name on the right side as shown in the screenshot below:  

ZIP/Postal Code as selected for the first field, on the left side, refers to a system user  field that needs to match with the  lead field called address_1_postalcode , which is the logical name for the field ZIP/Postal Code in Lead.  

Bul k import  of user  fields for lead assignment  

Once the admin identifies the relevant lead and user fields for lead assignment, the  challenge can be to populate  the user fields that will be used in lead routing.   This can be  done by the sales manager using a CSV file. The file is reviewed, and then imported  in bulk as shown in How to import data .

Rule management  

Here’s  a scenario  in which  an organization would like to route leads based on a few parameters :   country ( option set) , Z ip Code   (string), and currency ( look up) . If all the three parameters  match  with seller , lead should be routed to that seller, otherwise  it should try to match the country only.   In t his scenario , we can have two assignment rules in the following evaluation order (rules get evaluated in the order specified):

• The first rule matches on all three parameters (country, Zip Code, and currency).
• The second rule matches based on country. 

Configuration for the rule to match country, Z ip Code , and currency:  

Configuration for the rule that finds a match only based on country:  

Note that these scenarios simply depend on the user information that is entered into the system whenever a new seller is added. The new seller can begin to get leads right away, as long as all the routing-relevant fields are populated as part of onboarding .  

For more information on how to manage assignment rules for lead routing, review the documentation .

Related posts

Leveraging Copilot in Dynamics 365 Sales to prepare for sales meeting  

Elevating email efficiency using Copilot in Dynamics 365 Sales and the rich text editor   

Transform seller effectiveness with Dynamics 365 Sales Copilot  

Copilot in D365 Sales boosts efficiency with content recommendations and Q&A    

Salesforce is closed for new business in your area.

Getting Started

Learn through 101 guides and easy solutions.

Dive into the weeds with advanced tips.

New Features

Evolve with the Salesforce platform feature updates.

Get to know our community and join the action.

6 Ways to Manage Lead Assignment in Pardot

• Marketing Automations , Pro Tips
• January 13, 2022

min. reading

Pardot lead assignment routing can trip up even the most experienced sales and marketing teams. 

There are so many different ways to assign your prospects within Marketing Cloud Account Engagement (Pardot). Whether you want to manually assign prospects or utilize automation rules or completion actions to assign prospects based on their activity or information, the options can seem overwhelming.  

In this post, I’ll dive into 6 different ways to manage lead assignment in Pardot and how to determine what the best option is for you. After all, cultivating interest, inspiring action, and responding to people who raise their hands is why we’re here, right?

Six Pardot Lead Routing Methods

Choosing the right lead management strategy and tools to automate the process is challenging, but it’s super important. Read on to understand what you should know to make the right choice for your organization.

 There are multiple ways to assign new leads in Pardot ranging from manual assignments to complex and automated assignments based on lead criteria or activity. 

• Assign to User
• Assign Prospect Via Salesforce Active Assignment Rule
• Assign to Group
• Assign to Queue in Salesforce
• Assignment via Prospect list actions
• Assign via advanced lead assignment rules in Salesforce

1. Assign to User

Assigns a prospect to the specified user when the requirements of the rule are fulfilled.

How to use it

Completion action:.

Assign to user can be set up as a completion action for your forms, form handlers, files, custom redirects, emails and page actions, by selecting “Assign to user” and selecting the user’s name from the dropdown

Automation Rule:

Assign prospect to user can be set up as an action within an Automation Rule. This is a great way to assign leads if you have criteria the lead needs to meet before assigning the prospect to Sales. 

For example, if you want to only assign leads to sales that meet a certain scoring threshold, use an automation rule.

Engagement Studio Program:

Assign to user can also be set up as an action with an Engagement Studio Program, by selecting add element > action > assign to user .

2. Assign Prospect via Salesforce Active Assignment Rule

Pushes a prospect to your Salesforce active assignment rule when the rule’s criteria are met (see more information on this process below). The prospect syncs to Salesforce without an owner. 

The assigned user is synced to Pardot when the active assignment rule determines who to assign it to.

To get started, review this Pardot help article: Assigning Prospects

First, you will need an active lead assignment rule setup in Salesforce. This should include all the criteria for your lead assignment as well as a default user to assign the lead to if the lead does not meet any of the criteria.

Assign via active Salesforce assignment rules can be set up as a completion action for your forms, form handlers, files, custom redirects, emails and page actions, by selecting “Assign prospect via Salesforce active assignment rule”.

Assign prospect via the Salesforce active assignment rule can be set up as an action within an Automation Rule.

Engagement Studio Program

Assign prospect via Salesforce active assignment rule can also be set up as an action with an Engagement Program, by selecting add element > action > assign prospect via Salesforce active assignment rule .

3. Assign to Group

Assigns a prospect to a user in a specific group (round robin lead assignment) when the requirements of the rule are met.

First, create a group in Pardot (Admin > User Management > Groups > Add User Group) .

Navigate to the user you should belong to the group (Admin > User Management > Users) and click Edit Groups . Select the group to add the user.

Assign to user can be set up as a completion action for your forms, form handlers, files, custom redirects, emails and page actions, by selecting “Assign to group” and selecting the user’s name from the dropdown

Assign prospect to user in group can be set up as an action within an Automation Rule.

Assign to group can also be set up as an action with an Engagement Program, by selecting add element > action > assign prospect to group .

4.  Assign to Queue in Salesforce

Assigns a prospect to the Sales Cloud lead queue when the rule’s criteria are met. Prospects assigned to a queue are considered assigned to an owner and aren’t reassigned. You can assign only leads to Sales Cloud lead queues.

First, you must have a lead queue set up in Salesforce.

Assign to queue can be set up as an action within an Automation Rule.

Assign to a queue can also be set up as an action with an Engagement Program, by selecting add element > action > assign to Salesforce queue .

5. Assignment via Prospect list actions

Prospects can be assigned to a User or Group via prospect list actions.

Navigate to the prospect table, select the prospects you want to assign and select one of the options from the drop down menu.

6.  Assign via advanced lead assignment rules in Salesforce

If you have more complex business rules, assigning leads via an app on the Salesforce AppExchange might be a good option. Most apps offer a visual way to view lead assignment rules and some advanced functionality, including auto-merging duplicate leads and lead to account matching. 

Traction Complete is a good option we’ve used before.

Lead routing in Salesforce

Here are some additional ways Sales Cloud handles lead routing.

Lead assignment rules

In Sales Cloud, you can set up lead assignment rules to automate the assignment of new leads to your sales team or queues based on the specified rule criteria. 

The rules will execute and evaluate in the numbered order as leads enter Sales Cloud. Each lead assignment rule consists of multiple rule entries that specify exactly how the leads or cases are assigned. When a lead matches the rule, it will be assigned to the correct person or queue. Only one lead assignment rule can be active at a time.

To get started, review this Salesforce help article: Set Up Assignment Rules

Round-robin lead assignment

The round-robin lead assignment rule in Sales Cloud helps you automate the assignment of new leads in an even way to your sales team or queues.

Here’s how the repeating round robin assignment looks if your sales team for lead assignment has 3 members:

• Lead 1 is assigned to sales person 1
• Lead 2 is assigned to sales person 2
• Lead 3 is assigned to sales person 3
• Lead 4 is assigned to sales person 1
• Lead 5 is assigned to sales person 2
• Lead 6 is assigned to sales person 3

To get started, review this Salesforce help article: Create a Round-Robin Lead Assignment Rule . 

Still not sure what lead assignment method is best? Run through this flow chart to see what the recommended option is for you.

Additional Resources

• Salesforce Lead Assignment Rules Best Practices and Tricks
• Lead Routing in Salesforce
• Trailhead: Qualify and Route Leads to Your Reps
• Trailhead: Grow Your Business with Sales Cloud
• Trailhead: Convert and Assign Leads
• Trailhead: Sell as a Team
• ParDreamin’ on-demand video: Now You See Me, Now You Don’t! Lead Routing Basics for Pardot Admins
• The Three L’s in Pardot: Lists, Location, and Lead Assignment
• 6 Ways to Manage Lead Assignment in Pardot and How to Determine The Best Option

Complex lead routing with third-party tools

Third-party tools enable you to create complex lead routing rules that go beyond out-of-the-box Salesforce and Pardot lead routing features. Different tools serve specific purposes that may be the right fit depending on what you’re trying to accomplish.

Here are the most common ones.

LeanData is a native Salesforce lead matching and routing platform that is easy to use and customize with their visual drag-and-drop interface functionality and complex rule building.

RingLead is a native Salesforce lead routing, data normalization, enrichment, and duplicate managing platform.

DemandTools from Validity is a data management platform to help with importing, assigning, standardizing, manipulating, and the deduplication of your Salesforce data quickly.

Feel the Relief of Knowing Your Leads Are In Good Hands

Think you’re a Pardot lead routing pro now? Still have no idea where to start? Either way, tell us about it in the comments! And reach out to Sercante when you need Pardot consultant expertise for extra help and customization.

Let's Get You Unstuck

We’re here to support your growth journey. Let’s connect and find the right solution for you. Fill out the form, and our experts will be in touch!

Subscribe to The Spot

Upcoming Salesforce Events

Salesforce Training Courses

Top 5 Recent Posts

Salesforce Winter ‘25 Highlights: Marketing Cloud Growth Edition

The Salesforce Winter ‘25 Release Notes are out! Let’s dive into what’s new in...

Salesforce Winter ‘25 Highlights: Marketing Cloud Account Engagement

The Salesforce Winter ‘25 release notes are out! Let’s dive into Winter ’25 highlights...

Using Account Engagement for Event Marketing: From Planning to Follow-Up

Event marketing is a fantastic way to connect with your audience, build brand awareness,...

Big News! Account Engagement Users Can Access Marketing Cloud Growth Edition

In February 2024, Salesforce released Marketing Cloud Growth Edition, its first marketing automation platform...

Combating Headwinds in Higher Ed: Part 1 – Real-Time Interaction Management

Student expectations today are vastly different than they were a decade ago. It’s causing...

Jenna is a Marketing Operations leader with over 10 years of experience working with both enterprise organizations and start-ups. She started her career as a consultant, helping B2B and B2C clients get the most out of Marketo, Pardot, Marketing Cloud and Salesforce. She then moved in-house, working with B2B SaaS companies, helping them build their sales and marketing technology stacks and processes from scratch.

Leave Your Comment Cancel Reply

You must be logged in to post a comment.

Related Articles

Event marketing is a fantastic way to connect with your audience, build brand awareness, and generate leads. However, managing event marketing activities can take time and effort. That’s where...

Student expectations today are vastly different than they were a decade ago. It’s causing technology leaders at the most successful educational institutions to rethink their data infrastructure and student...

How to Customize Primary Source on Opportunities with Salesforce Flow

I recently ran into a situation where a client needed to exclude select campaign types from being populated as the Primary Campaign Source field on opportunities. While this might...

Bot Single Post

Martech Resources

Shopping consultants or jobs? Wanna join the action? Here’s everything you need to solve all the marketing technology things.

Consulting Services

Work with certified experts to transform your tech stack.

Training Courses

Develop your Salesforce & Pardot skills from anywhere.

Community Event

Learn from the only virtual Pardot-focused conference.

Virtual Networking

Connect with people in the Pardashian Slack community.

Quick Links

Get The Spot

The Spot for Pardot newsletter delivers a healthy dose of martech to your inbox — and nothing more.

© 2024 The Spot For Pardot | All Rights Reserved Sercante LLC.4

This website uses cookies to improve your browsing experience. By clicking “Accept,” you consent to the use of all cookies.

You can customize your preferences or learn more about our use of cookies in our Privacy Policy .

Your browser appears to have JavaScript disabled or does not support JavaScript. Please refer to your browser's help file to determine how to enable JavaScript.

• HubSpot Community
• Marketing & Content

Lists, Lead Scoring & Workflows

Lead assignment rules.

• Subscribe to RSS Feed
• Mark Topic as New
• Mark Topic as Read
• Float this Topic for Current User
• Printer Friendly Page

‎Dec 20, 2017 6:29 PM

Solved! Go to Solution.

• Mark as New
• Report Inappropriate Content

‎Dec 21, 2017 12:52 PM

View solution in original post

• View all posts
• Previous post

‎Nov 23, 2020 11:08 PM

‎Nov 24, 2020 11:54 AM

‎Feb 22, 2018 9:12 AM

‎Jan 8, 2019 11:54 PM

‎Feb 22, 2018 9:22 AM

‎Feb 22, 2018 9:29 AM

‎Feb 22, 2018 9:37 AM

‎Feb 22, 2018 2:38 PM

‎Feb 22, 2018 2:42 PM

‎Feb 22, 2018 2:52 PM

‎Feb 22, 2018 2:53 PM - edited ‎Feb 22, 2018 2:53 PM

Sign up for the Community Newsletter

Receive Community updates and events in your inbox every Monday morning.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 26 August 2024

Modelling the effectiveness of an isolation strategy for managing mpox outbreaks with variable infectiousness profiles

• Yong Dam Jeong   ORCID: orcid.org/0000-0002-3970-7690 1 , 2 ,
• William S. Hart   ORCID: orcid.org/0000-0002-2504-6860 1 , 3   na1 ,
• Robin N. Thompson   ORCID: orcid.org/0000-0001-8545-5212 3   na1 ,
• Masahiro Ishikane 4   na1 ,
• Takara Nishiyama 1 ,
• Hyeongki Park 1 ,
• Noriko Iwamoto 4 ,
• Ayana Sakurai 4 ,
• Michiyo Suzuki 4 ,
• Kazuyuki Aihara   ORCID: orcid.org/0000-0002-4602-9816 5 ,
• Koichi Watashi   ORCID: orcid.org/0000-0002-4536-9966 6 ,
• Eline Op de Coul   ORCID: orcid.org/0000-0002-8263-9187 7 ,
• Norio Ohmagari   ORCID: orcid.org/0000-0002-4622-8970 4 ,
• Jacco Wallinga   ORCID: orcid.org/0000-0003-1725-5627 7 , 8 ,
• Shingo Iwami   ORCID: orcid.org/0000-0002-1780-350X 1 , 5 , 9 , 10 , 11 , 12 , 13   na2 &
• Fuminari Miura   ORCID: orcid.org/0000-0001-8235-757X 7 , 14   na2  

Nature Communications volume  15 , Article number:  7112 ( 2024 ) Cite this article

Metrics details

• Computational models
• Epidemiology
• Viral infection

The global outbreak of mpox in 2022 and subsequent sporadic outbreaks in 2023 highlighted the importance of nonpharmaceutical interventions such as case isolation. Individual variations in viral shedding dynamics may lead to either premature ending of isolation for infectious individuals, or unnecessarily prolonged isolation for those who are no longer infectious. Here, we developed a modeling framework to characterize heterogeneous mpox infectiousness profiles – specifically, when infected individuals cease to be infectious – based on viral load data. We examined the potential effectiveness of three different isolation rules: a symptom-based rule (the current guideline in many countries) and rules permitting individuals to stop isolating after either a fixed duration or following tests that indicate that they are no longer likely to be infectious. Our analysis suggests that the duration of viral shedding ranges from 23 to 50 days between individuals. The risk of infected individuals ending isolation too early was estimated to be 8.8% (95% CI: 6.7–10.5) after symptom clearance and 5.4% (95% CI: 4.1–6.7) after 3 weeks of isolation. While these results suggest that the current standard practice for ending isolation is effective, we found that unnecessary isolation following the infectious period could be reduced by adopting a testing-based rule.

Introduction

Since May 2022, a global outbreak of mpox (formerly monkeypox) has spread primarily among men who have sex with men (MSM), first in European and North American countries, and later in other regions 1 . Although growth of the outbreak was initially rapid, the global trend in reported cases changed around the summer of 2022 and has been declining ever since 2 . Recent studies have suggested that the case saturation in many countries may be explained largely by infection-derived immunity accumulated among individuals who have many sexual partners 3 , and the following decline in cases may have been accelerated by vaccination campaigns and/or behavioral changes 4 , 5 . However, since the beginning of 2023, sporadic outbreaks have been reported, mainly in Asian countries, which were less affected by the 2022 outbreak and where vaccination campaigns had not yet been initiated, leading to a substantial number of individuals remaining at risk of infection 6 . There are also reports of some breakthrough infections and reinfections in European countries 7 , 8 . These findings warrant caution against a resurgence of mpox and highlight the importance of maintaining nonpharmaceutical interventions (NPIs).

One essential NPI is case isolation. The effectiveness of isolation has been extensively studied in the context of COVID-19 9 , 10 and other diseases 11 , 12 . In general, if cases are detected earlier (e.g., via contact tracing) and isolation begins sooner, a larger proportion of onward transmissions can be prevented 13 . When determining the end of isolation, later is always safer, as a longer isolation period minimizes the risk of exposure by individuals who remain infectious. However, case identification may require a substantial public health investigation effort, and redundant isolation induces societal cost and burden for individuals 14 , 15 . Furthermore, more stringent control strategies may also lead to reduced efficacy due to non-adherence 16 . To mitigate these burdens, testing-based rules were applied during the COVID-19 pandemic 17 , 18 , 19 , which typically involved ending isolation following a specified number of successive negative PCR or antigen test results. Individual-level viral load data have been used to balance the effectiveness and cost of isolation rules 20 , which is key to sustainable implementation.

The duration of the infectious period for mpox has yet to be quantitatively characterized. Several studies have investigated serial intervals (i.e., the time interval between symptom onset dates of primary and secondary cases in transmission pairs) as a proxy, and have suggested that more than 90% of transmission occurs within two weeks of symptom onset 21 , 22 . However, factors such as self-reporting may induce biases in serial interval estimates, and moreover, the serial interval distribution may be different to that of the infectious period. Recent findings have also indicated that there might be considerable heterogeneity in the infectious period among individuals with mpox; for example, there is substantial variation in observed serial intervals 21 , and some confirmed cases in Europe have exhibited prolonged viral shedding in their bodily fluids 23 , 24 . Consequently, isolation rules that do not account for heterogeneity between individuals (e.g. rules based on isolating for a fixed period following symptom onset) may lead to either a risk of ending isolation too early for those who are infectious for long periods, or an unnecessarily long isolation period for those who are only infectious for short periods.

Current guidelines for mpox generally suggest quarantine of individuals exposed to mpox virus for about 3 weeks 18 , 25 . The three-week monitoring period is based on the estimated incubation period 18 , 25 , in which more than 98% of people with mpox show symptoms within 21 days of exposure, to check if the exposed individual becomes symptomatic. However, the incubation period does not reflect the generation time of the virus, with onward transmission often occurring after symptom onset. Consequently, individuals whose symptoms have developed are recommended to isolate until their skin lesions have resolved (typically 2–4 weeks) 18 , 19 , 25 , 26 , 27 , 28 and are advised to refrain from sexual contact for 12 weeks after the end of isolation 25 , 27 , 28 . It should be noted that judging the clearance of symptoms for mpox is often difficult and quite subjective, as mild lesions can be undetected even at clinics 29 , 30 . This may result in misleading evaluations of individuals’ risk of further onward transmission.

In this study, we first characterize individual infectiousness profiles among mpox cases by analyzing longitudinal viral load data. We describe the time course of virus shedding using a mathematical model that captures individual heterogeneity in the duration of viral clearance. We then stratify the population according to the characterized shedding profiles and evaluate the effectiveness of three different isolation rule types: a symptom-based rule (i.e., the typical current approach), a fixed-duration rule (i.e., a one-size-fits-all approach), and a testing-based rule (i.e., a personalized approach in which individuals end their isolation upon test results suggesting the individual is no longer infectious). Our study provides an approach to quantify both the risk of ending isolation too early and the period for which infected individuals are isolated but do not pose an infection risk. Our model can be used by policy makers to inform decision-making, allowing isolation strategies to be determined that balance cost and effectiveness appropriately.

Analyzed data and model fitting

We identified 7 publications including at least one case with lesion samples meeting the inclusion criteria, and a total of 90 mpox cases (see section “Methods”). All cases were symptomatic, and most of them were reported in Europe. To standardize the collected data, we converted the reported cycle threshold values to viral load (copies/ml) using the conversion formula proposed in a previous study 31 (Supplementary Table  1 ). We then fitted a viral clearance model to the longitudinal viral load data from lesion samples (Supplementary Fig.  1a and Supplementary Fig.  2 ). Estimated parameters suggested a median viral load of 7.7 log10 copies/ml (95% CI: 7.3–8.2) at symptom onset and a median viral clearance rate of 0.36 day −1 (95% CI: 0.24–0.44), respectively. Using these parameters, duration of infectiousness was estimated: we first assumed a threshold value for infectiousness as 6.0 log10 copies/ml based on data on viral replication in cell culture 23 , 32 , 33 , and the duration of infectiousness was estimated to be 10.9 days (95% CI: 7.3–21.6) following the onset of symptoms. Additionally, a prolonged duration of viral shedding was estimated: the viral load dropped below the limit of detection of a PCR test (2.9 log10 copies/ml) 30.9 days (95% CI: 23.4–50.6) after symptom onset (Supplementary Fig.  1b ). This finding is consistent with previous studies suggesting the persistent presence of mpox viruses in clinical specimens 23 , 34 .

Stratification for mpox cases

The 90 analyzed mpox cases were stratified into two groups Group 1 and Group 2 (Fig.  1a–c ), using the K-means clustering algorithm based on three estimated individual-level parameters: the viral load at symptom onset, the total amount of virus excreted between symptom onset and the end of shedding, and the duration of viral shedding (see Supplementary Note  2 ). Group 1 had a lower viral load at symptom onset and faster viral clearance, whereas Group 2 showed a higher viral load at symptom onset and slower viral clearance. As a result, the estimated duration of infectiousness in Group 2 was longer than in Group 1 (Fig.  1c and Supplementary Fig.  1b ). To compare the viral dynamics between the two groups, we conducted statistical tests: Individuals in Group 2 had significantly higher viral loads at symptom onset than individuals in Group 1 ( \(p=5.7\times {10}^{-10}\) from the Mann–Whitney test). Viral clearance was significantly slower in Group 2 than in Group 1 ( \(p=2.1\times {10}^{-9}\) from the Mann–Whitney test). Also, individuals in Group 2 had a larger area under the viral load curve (AUC) ( \(p=4.6\times {10}^{-11}\) from the Mann–Whitney test). Thus, Groups 1 and 2 were characterized as groups with low and high transmission potential, respectively (Fig.  1d ). To describe the difference in timing of viral clearance, we also reconstructed the probability of virus being detectable over time by using the model with estimated parameters for each group (Fig.  1e ). In both stratified groups, the probability was greater than 90% at 3 weeks after symptom onset. However, in the total group (i.e., a group of all analyzed cases), the probability dropped to 69.9% (95% CI: 67.0–73.2) at 4 weeks after symptom onset, which is the upper bound of the isolation period recommended by the CDC and ECDC 19 , 25 . The probability in Group 1 at 4 weeks after symptom onset was 61.6% (95% CI: 58.2–64.8), whereas the corresponding probability in Group 2 was 94.6% (95% CI: 93.1–96.0).

a Results of K-means clustering of mpox cases based on viral load at symptom onset, area under the viral load curve (AUC), i.e., the total amount of virus shed over time, and duration of viral shedding using estimated individual parameters. Data points indicate individuals and are colored based on the group that each individual is in. Principal component analysis (PCA) was used to visualize the clusters in two dimensions. Groups 1 and 2 comprise 71 and 19 mpox cases, respectively. b Stratified viral load data points measured in lesion samples. The cross represents data points where the viral load was below the limit of detection. c Reconstructed individual viral load trajectories in each group. The horizontal dashed line means the assumed infectiousness threshold. d Comparison between groups of: viral load at symptom onset (left panel); duration of viral shedding (middle panel); and area under viral load curve (right panel), respectively. The box-and-whisker plots show the medians (50th percentile; bold lines), interquartile ranges (25th and 75th percentiles; boxes), and 2.5th to 97.5th percentile ranges (whiskers). The sizes of Group 1 and Group 2 are 71 and 19 cases, respectively. Using the two-sided Mann–Whitney test, statistically significant differences between the two groups were found for viral load at symptom onset ( \({p}\,{\mbox{value}}=5.7\times {10}^{-10}\) ), duration of viral shedding ( \({p}\,{\mbox{value}}=2.1\times {10}^{-9}\) ), and area under viral load curve ( \({p}\,{\mbox{value}}=4.6\times {10}^{-11}\) ). Group 1 and Group 2 represent cases with low and high risk of transmission, respectively. e Viral clearance in each group. Probability of detectable virus after symptom onset for each group (left panel). The solid lines and shaded regions indicate means and 95% confidence intervals, respectively. The dashed lines and dotted lines stand for probabilities at 3 and 4 weeks after symptom onset, respectively. Bar plots represent the probabilities for 3 weeks (right upper panel) and 4 weeks (right lower panel) after symptom onset, respectively. The centers and error bars indicate means and 95% confidence intervals, respectively. Note that the estimated probabilities are based on 100 independent simulations.

Symptom-based rule

Under the estimated viral dynamics, we compared three types of rules for ending the isolation of individuals with mpox: a symptom-based rule, a fixed-duration rule, and a testing-based rule. To assess the effectiveness of the three rules, we considered three metrics: (1) the risk of prematurely ending isolation, (2) the average estimated infectious period after ending isolation (where this period was defined to be zero for individuals who are no longer infectious at the time of ending isolation), and (3) the average estimated duration for which individuals were isolated unnecessarily after the end of their infectious period (which could be positive or negative). Whether an individual was infectious or not was ascertained based on an assumed threshold viral load value (see section “Methods”).

With these metrics, we first evaluated the current symptom-based isolation guideline (i.e., patients remain isolated until their skin lesions have cleared), accounting for variations in the timing of lesion clearance between individuals. We estimated distributions of the timing of lesion clearance using data from 43 mpox patients describing the duration of lesion presence (see section “Methods”). As a result, the mean duration from symptom onset to lesion clearance was estimated to be 25.2 days (95% CI: 21.6–29.7). The median and interquartile range (IQR) were 23.2 days and 17.6–30.7 days, respectively. The estimated values were consistent with typical current isolation periods of 2–4 weeks 18 , 19 , 25 , 26 , 27 , 28 (Supplementary Fig.  3 and Supplementary Table.  4 ). In the total group, the risk of prematurely ending isolation was estimated to be 8.8% (95% CI: 6.7–10.5) and Group 1 had a lower risk of 4.9% (95% CI: 3.8–6.1). In addition, both stratified groups yielded an average estimated infectious period after ending isolation of lower than 1 day. However, in Group 2, the risk of prematurely ending isolation was 25.7% (95% CI: 23.8–28.0) with a longer estimated infectious period after ending isolation of 1.6 days (95% CI: 1.4–1.8). The mean estimated duration for which individuals in the total group were isolated unnecessarily after the end of their infectious period was 12.1 days (95% CI: 11.6–12.8), whereas Groups 1 and 2 had unnecessary isolation periods of 13.5 days (95% CI: 13.0–14.1) and 6.6 days (95% CI: 5.9–7.3), respectively (Supplementary Fig.  4 ).

Furthermore, to ensure isolation is ended safely, we considered an additional isolation period beyond the time of lesion clearance. To lower the risk of prematurely ending isolation below 5% and the estimated infectious period after ending isolation below 1 day, the total group and Group 2 required additional isolation periods of 3 and 10 days on average, respectively. However, no additional isolation periods were necessary for Group 1. The resulting unnecessarily prolonged isolation periods in the total group, Group 1, and Group 2 were estimated to be 15.1, 13.5, and 16.6 days on average, respectively (Fig.  2a ).

a Symptom-based rules. The vertical dotted lines mean the current symptom-based isolation guideline. The x-axis represents the additional isolation period from the current guideline. b Fixed-duration rules. The x-axis represents the fixed period of isolation. Left panels in both a and b show the risk of prematurely ending isolation for different isolation periods. The horizontal lines correspond to 5%. Estimated infectious period after ending isolation for different isolation periods (middle panels). The horizontal lines correspond to 1 day. The estimated period for which individuals are isolated unnecessarily after the end of their infectious period for different isolation periods (right panels). The squares and circles indicate the points with the lowest unnecessarily prolonged isolation period for which the following conditions are satisfied: i) the risk of prematurely ending isolation is lower than 5% and ii) the estimated infectious period after ending isolation is shorter than 1 day. The vertical dashed lines correspond to the optimal additional isolation period and the optimal fixed duration of isolation in the total group for symptom-based rules and fixed-duration rules, respectively. The solid lines and shaded regions in each panel indicate means and 95% confidence intervals, respectively. c Testing-based rules. The risk of prematurely ending isolation (first row of panels), the estimated infectious period after ending isolation (second row of panels), the estimated isolation period following the end of infectiousness (third row of panels), and the overall isolation period (fourth row of panels) are shown for different intervals between tests and numbers of consecutive tests indicating loss of infectiousness necessary to end isolation. PCR (polymerase chian reaction) tests (limit of detection \({{\boldsymbol{=}}}\) 2.9 log10 copies/ml) were used to measure viral load. The areas surrounded by solid lines are those with 5% or lower risk of prematurely ending isolation and with 1 day or shorter estimated infectious period after ending isolation, respectively. The triangles correspond to the points with the shortest estimated isolation period following the end of infectiousness for which both conditions noted above are satisfied. Color keys and symbols apply to all panels. Note that the estimated values are based on 100 independent simulations.

In our main analyses, we assumed that the presence or absence of symptoms was independent of viral dynamics. However, as a sensitivity analysis, we evaluated the current symptom-based rule for the total group under different assumed relationships between individual viral dynamics and the duration of lesion presence (see section “Methods”). The risk of ending isolation prematurely was lower when increased and/or prolonged viral shedding was assumed to be more strongly correlated with slower lesion clearance—the estimated risk under our baseline assumption (i.e., that lesion clearance is independent of viral shedding) can therefore be considered as an upper bound. This is because, if the presence of lesions is shown to be positively correlated with viral shedding, patients with fast lesion clearance could end isolation safely, and thus the estimated risk under such conditions would be lower than the baseline assumption. The unnecessarily prolonged isolation periods in this supplementary analysis were found to be comparable to those under the baseline setting and were lower than 2 weeks on average (Supplementary Fig.  5 ).

Fixed-duration rule

Under a fixed-duration rule of ending isolation 3 weeks after symptom onset, the risk of ending isolation prematurely in the total group was estimated to be 5.4% (95% CI: 4.1–6.7). The average estimated duration for which individuals were isolated unnecessarily after the end of their infectious period was 8.3 days (95% CI: 8.0–8.6). Group 1 had a lower risk of ending isolation prematurely of 1.9% (95% CI: 1.0–2.9), and a longer unnecessary isolation period of 9.7 days (95% CI: 9.4–9.9). However, in Group 2, a higher risk of 25.7% (95% CI: 23.2–28.0) was estimated, with a shorter unnecessary isolation period of 2.8 days (95% CI: 2.4–3.1). To guarantee a risk of prematurely ending isolation below 5% and an estimated infectious period after ending isolation shorter than 1 day, we found that the total group, Group 1, and Group 2 needed to be isolated for 22, 19, and 29 days, respectively. In this case, the estimated duration for which individuals were isolated unnecessarily after the end of their infectious period was estimated to be 9.4, 7.7, and 10.8 days for the total group, Group 1, and Group 2, respectively (Fig.  2b ).

Testing-based rule

For symptom-based and fixed-duration rules, isolation of individuals with mpox ends after lesion clearance or fixed-time period following symptom onset, so the three metrics considered here are determined by the mean symptom duration or the predefined isolation period (Fig.  2a, b ). By contrast, a testing-based rule is dependent on both the time interval between tests and the exact criterion used for ending isolation (see section “Methods”). Under a criterion in which isolation ends following two consecutive PCR test results indicating loss of infectiousness with daily testing (similar to a criterion widely used for COVID-19) 17 , the total group had a risk of prematurely ending isolation of 52.2% (95% CI: 49.7–54.6), and the estimated infectious period after ending isolation was calculated to be 2.3 days (95% CI: 2.1–2.5). Similarly, high risks of prematurely ending isolation, accompanied with an estimated infectious period after ending isolation longer than 1 day, were estimated in the stratified groups (first-row and second-row panels in Fig.  2c ).

By varying the criteria (i.e., the required number of consecutive test results indicating loss of infectiousness and the time interval between tests), different testing-based isolation rules can be tested in terms of their effects on the three metrics. The risk of prematurely ending isolation and the estimated infectious period after ending isolation decreased with a longer interval between tests and with a larger number of consecutive test results indicating loss of infectiousness (first-row and second-row panels in Fig.  2c ), whereas the estimated duration for which individuals were isolated unnecessarily after the end of their infectious period increased (third-row panels in Fig.  2c ). Under the conditions that the risk of prematurely ending isolation is lower than 5% and the estimated infectious period after ending isolation is shorter than 1 day, the minimum value of the unnecessary isolation period in the total group was 7.4 days (95% CI: 7.1–7.7) with three consecutive test results indicating loss of infectiousness and an interval of 5 days between tests (purple triangles in Fig.  2c ). Correspondingly, an isolation period of 20.1 days (95% CI: 19.7–20.5) was required on average. On the other hand, under the same conditions, stricter rules were needed for Group 2: four consecutive test results indicating loss of infectiousness and an interval of 2 days between tests were needed to minimize the estimated duration for which individuals were isolated unnecessarily after the end of their infectious period to 8.4 days (95% CI: 8.0–8.7), with a mean isolation period of 26.6 days (95% CI: 26.0–27.0) (red triangles in Fig.  2c ).

To further evaluate the uncertainty in the test-based rule, we considered a different type of measurement error model (i.e., a proportional error model), where the error variance increases in proportional to the predicted mean viral load, and examined the corresponding difference in the total group as a sensitivity analysis (see section “Methods”). While the measurement error was constant in the main analysis (i.e., constant error model), the proportional error model described higher error variance near the assumed infectiousness threshold. As a result, a stricter optimal isolation rule was needed to lower the risk and the estimated infectious period after ending isolation below 5% and 1 day, respectively: four consecutive test results indicating loss of infectiousness and an interval of 3 days between tests. However, the minimized unnecessary period and corresponding optimal isolation period under the proportional error model were comparable to the constant error model (Supplementary Fig.  6 ).

Additionally, whereas we focused on PCR testing in our main analyses, we conducted further analyses considering rapid antigen tests (RATs) to evaluate the effectiveness of using different test types when applying the testing-based rule. Under the testing-based rules using RATs, test results correspond to negative results (i.e., measured viral loads below a limit of detection) (see section “Methods”). When RATs with either high or low sensitivity were utilized in the testing-based rule, the optimal isolation periods on average were comparable with those under PCR testing. However, the optimal testing rules for ending isolation differed depending on RAT sensitivity: 2 consecutive negative results with 3-day intervals between tests were optimal for the high sensitivity RAT and 5 consecutive negative results with 3-day intervals between tests were optimal for the low sensitivity RAT. In both scenarios, a higher number of total tests was required to meet the specified conditions (i.e., the risk of prematurely ending isolation \(\le\) 5% and the estimated infectious period after ending isolation \(\le\) 1 day) (Supplementary Fig.  7 ) than for PCR testing, since even high sensitivity RATs are typically much less sensitive than PCR tests (see Supplementary Note  5 and Supplementary Fig.  8 ). Additionally, RATs only provide qualitative test results (i.e. positive or negative), making it impossible to determine whether an individual who tests positive has a viral load that has fallen below the assumed infectiousness threshold.

Comparison between three different isolation rules for ending isolation

To highlight the difference between the three isolation rules for each group, we compared the three types of rule by computing the optimal rules in which the estimated isolation period following the end of infectiousness is minimized while ensuring that the risk of prematurely ending isolation is less than 5% and the estimated infectious period after ending isolation is less than 1 day (squares, circles, and triangles in Fig.  2 ). In the total group, the optimized symptom-based (i.e., current guideline \(+\) 3 days), fixed-duration, and testing-based rules gave isolation periods of 28.3, 22, and 20.1 days, resulting in minimized unnecessary isolation periods of 15.1, 9.4, and 7.4 days on average, respectively (Fig.  3 ). In particular, compared to the current (non-optimized) symptom-based rule, the other two optimized rules involved shorter isolation periods and reduced unnecessarily prolonged isolation periods. In Group 2, the testing-based rule led to an unnecessary isolation period that was 8.2 and 2.4 days shorter than the symptom-based and fixed-duration rules, respectively. The testing-based rule in Group 1 yielded an unnecessary isolation period of 7.1 days shorter than the symptom-based rule, whereas it was comparable to the fixed-duration rule. However, compared with the other two rules in the total group, the testing-based rule in Group 1 could reduce the unnecessary isolation period to 17.7 days, with the optimal isolation period of 6.4 days.

The filled squares, circles, and triangles represent symptom-based, fixed-duration, and testing-based rules, respectively. Each symbol represents the mean length of isolation using the rule that minimizes unnecessarily prolonged isolation under the conditions that the risk of prematurely ending isolation is less than 5% and the estimated infectious period after ending isolation is less than 1 day. Note that for testing-based rules, the interval between tests and the number of consecutive tests indicating loss of infectiousness necessary to end isolation were chosen to minimize the unnecessarily prolonged isolation period. The vertical lines indicate the optimized isolation periods of three isolation rules for the total group. The unfilled square with outline indicates the current (non-optimized) symptom-based rule for the total group.

In addition, to assess the effectiveness of the testing-based rule, we examined the difference between the testing-based rule and the other two rules in the total group. The considered conditions for this assessment were; three consecutive test results indicating loss of infectiousness and a 5-day interval between tests for the testing-based rule (Fig.  2c ), the current isolation guideline (i.e., the estimated duration of lesion presence) for the symptom-based rule, and the 22-day isolation period for the fixed-duration rule (Fig.  2a, b ). Compared with symptom-based and fixed-duration rules, 63.2% (95% CI: 60.8–66.0) and 63.5% (95% CI: 60.6–65.4) of the total group could reduce their isolation periods using the testing-based rule, respectively (Supplementary Fig.  9a ). The mean isolation period was shortened by 10.8 and 5.5 days compared to symptom-based and fixed-duration rules, respectively, and the average unnecessarily prolonged isolation period was also effectively reduced (Supplementary Fig.  9b, c ). In these cases, the total number of tests required for ending isolation in the testing-based rule was estimated to be from 3 to 7 times (Supplementary Fig.  9d ).

As a sensitivity analysis, we varied the assumed infectiousness threshold and investigated the corresponding difference in the estimated period for which individuals were isolated unnecessarily after the end of their infectious period between the three rules (Supplementary Fig.  10 ). When the assumed infectiousness threshold is higher, the corresponding estimated infectious period becomes shorter, leading to a shorter required isolation period and shorter period of unnecessary isolation given the same acceptable risk. Our analysis showed that a higher assumed infectiousness threshold resulted in smaller differences between the fixed-duration and testing-based rules for each stratified group, which was consistent with our previous findings for COVID-19 9 . On the other hand, simulations for the symptom-based rule in the total group suggested that the current guideline would lead to safer ending isolation but longer unnecessary isolation periods if the assumed infectiousness threshold value was increased.

We used an assumed infectiousness threshold of the viral load (6.0 log10 copies/ml) as a cut-off value for assessing isolation rules in the main analysis; however, other metrics for the risk of prematurely ending isolation could also be considered. To demonstrate this, we estimated the average area under the viral load curve (AUC) following the end of isolation for the total group under the different isolation rules, and compared the estimated values with the AUC without isolation (see Supplementary Note  6 ). Without any isolation, the average AUC was estimated to be 10 8.5 copies/ml·days (95% CI: 10 8.4 –10 8.6 ). On the other hand, the estimates of average AUC after ending isolation were 10 6.3 copies/ml·days (95% CI: 10 6.1 –10 6.5 ), 10 6.0 copies/ml·days (95% CI: 10 5.8 –10 6.1 ), and 10 5.8 copies/ml·days (95% CI: 10 5.7 –10 5.9 ) under the current isolation guideline (i.e., the estimated duration of symptoms) for the symptom-based rule, the 22-day isolation period for the fixed-duration rule, and three consecutive test results indicating loss of infectiousness and a 5-day interval between tests for the testing-based rule, respectively. Compared to the case of no isolation, the average AUC was reduced by more than 95% under all three isolation rules, with the optimized testing-based rule giving the greatest reduction in the AUC (Supplementary Fig.  11 ). This indicates that the risk of prematurely ending isolation can be limited through those isolation rules.

Additionally, to highlight the necessity of the optimal isolation strategy (i.e., three consecutive test results indicating loss of infectiousness and a 5-day interval between tests) for the testing-based rules, we compared its average AUC to one under the other testing strategy. Since the mpox viral load continuously decreases over time since symptom onset, only one test result may be considered sufficient to guarantee the loss of infectiousness. However, this strategy may miss the ongoing infectiousness due to the measurement error (Supplementary Fig.  12a ), whereas the optimal isolation strategy may end isolation more safely (Supplementary Fig.  12b ). The average AUC under the strategy with one test result indicating loss of infectiousness and a 5-day interval between tests was estimated to be 10 7.8 copies/ml·days (95% CI: 10 7.7 \(-\) 10 7.9 ), much higher than one under the optimal testing strategy (Supplementary Fig.  12c ).

As a sensitivity analysis, we considered an alternative two-phase exponential decay model 35 , estimated the viral dynamics, and assessed the effectiveness of different isolation rules. In this additional analysis, to ensure that the parameters were identifiable, we used data from 30 cases (out of 90 cases with lesion samples) for which the viral load in lesion samples was recorded at four or more time points (see Supplementary Note  7 ). Compared to the baseline model (one-phase exponential decay model), the two-phase model indicated a higher viral load at symptom onset with a faster clearance in the first phase but with a slower clearance in the second phase (Supplementary Fig.  13a , Supplementary Fig.  14 , and Supplementary Table  5 ), resulting in a longer estimated duration of infectiousness ( p = 2.1 × 10 −4 from the Mann-Whitney test) (Supplementary Fig.  13b ). However, there was no significant difference in the duration of viral shedding between the baseline model and the two-phase exponential decay model (Supplementary Fig.  13c ). For symptom-based and fixed-duration isolation rules under the two-phase exponential decay model, longer isolation periods on average were needed for ending isolation than using the baseline model. On the other hand, under the testing-based rules, the required isolation period was comparable with the baseline model (Supplementary Fig.  13d ). Consequently, in the two-phase exponential decay model, the testing-based rules again substantially reduced the unnecessary duration of isolation, with shorter required isolation periods than under the symptom-based and fixed-duration rules.

Comparison between lesion and other samples for infectiousness after ending isolation

To demonstrate that lesion samples are suitable for designing isolation rules, we compared the viral dynamics that we inferred using lesion samples to analogous results obtained using other samples. Specifically, we used longitudinal viral load data measured in upper respiratory tract, blood, and semen samples from the same mpox cases to estimate mpox virus dynamics in those samples (Supplementary Table  1 , Supplementary Table  2 , Supplementary Fig.  15a , and Supplementary Fig.  16 ). Following symptom onset, other samples exhibited lower viral loads compared with lesion samples. For example, at the optimal ending isolation period of 22 days under fixed-duration rules, the viral load in lesion samples was substantially higher than in other samples (Supplementary Fig.  15b ). Moreover, we compared the predicted infectiousness in lesion samples and other samples by estimating the proportion of individuals who remained infectious on day 22 after symptom onset. Around 3% of individuals were estimated to be infectious when lesion samples were used, whereas the viral load never exceeded the assumed infectiousness threshold for the other samples (Supplementary Fig.  15c ). This suggests that infectious individuals with mpox may be missed if we implement a testing-based rule with samples other than lesion samples.

In this study, we have compared the effectiveness of different rules that can be implemented to determine when individuals with mpox stop isolating. For the current symptom-based rule, our results showed that after skin lesions have disappeared, an additional 3-day isolation ensures the safe end of isolation. If instead a single population-wide fixed-duration rule is used, then we found that allowing individuals to end their isolation after a period of three weeks following symptom onset is a reasonable threshold. Under these rules, our transmission model suggests that more than 95% of post-diagnosis transmissions would be prevented. Our modelling analysis showed that there is individual heterogeneity in viral shedding kinetics, indicating that the use of testing-based rules may reduce the period for which infected individuals with a shorter duration of virus shedding are required to isolate.

Because we observed different shedding kinetics between mpox cases in the analyzed data, we stratified the cases based on their viral load during the decay phase of infection (Fig.  1 ). Variations in virus shedding may lead to substantial heterogeneity in infectiousness between individuals. For mpox, existing studies have focused on individual variations in the number of sexual contacts or partners 4 , 36 , because higher contact rates generally result in a larger number of secondary cases. In contrast, variations in viral shedding have received limited attention to date. Our study found that about 32% of mpox cases exhibit a 5-10-day shorter (or longer) duration of virus shedding than the average. Such individuals may thus contribute to virus transmission for shorter (or longer) periods, resulting in a lower (or higher) number of secondary cases. When designing tailored interventions to ensure that the time-dependent reproduction number, R (the average number of secondary cases generated by each infected individual) 37 , is below one (i.e., the outbreak is declining), our approach provides a way to incorporate such heterogeneity in the infectious period by using individual viral load as a proxy.

For evaluating the risk of transmission following the end of isolation, the use of longitudinal viral load data may be advantageous over symptom-based rules. One difficulty lies in the inherent uncertainty in self-reported symptoms; many confirmed mpox cases have been found to be not fully aware of their symptoms at the time of reporting 38 . Also, the precise identification of visible clearance of skin lesions poses a challenge in determining the end of isolation. Viral load data have the potential to provide more objective and quantitative criteria for ending isolation 39 . If viral load data are used, measuring viral load in lesion samples (rather than the other sample types that we considered) is the safest choice, as lesion samples showed the highest viral load and the longest detectable period (Supplementary Fig.  15 ).

Isolation rules need to balance the risk of exposure by individuals who remain infectious and the societal burden of extended isolation. Our results indicate that optimized symptom-based, fixed-duration, and testing-based rules can result in comparable risk levels if the same rule is applied to all cases. By contrast, the total duration of unnecessary isolation can be reduced using a testing-based rule. It is possible to shorten the isolation period for those with faster viral clearance, leading to a reduced burden at the population level. Furthermore, our simulation results showed that about 3–7 total tests were required by most individuals under the optimized testing-based rule (Supplementary Fig.  9 ). The estimated reduced isolation duration and testing frequency could be used to assess the cost-effectiveness of different strategies in future studies if additional information (e.g. the costs of prolonged isolation or PCR testing) can be obtained or estimated. A testing-based rule may also be beneficial for evaluating the times at which individuals can resume sexual activities. Despite the current recommendation of using a condom for 12 weeks after scabs have fallen off as a precaution 25 , 27 , 28 , having additional information about patients’ infectiousness would offer reassurance to them and help to prevent discrimination and stigma related to sexual behaviors. As outlined in Supplementary Fig.  15 , our assessment revealed that viral load in semen had fallen below the assumed infectiousness threshold by the endpoint of an optimized fixed-duration isolation period of 22 days. However, this result requires careful interpretation, since the threshold viral load above which transmission can occur is likely to depend on the precise route of transmission, in addition to other factors (e.g. scars on the skin).

As with any modelling analysis, there are limitations to our study. For example, our analysis relied on the patient data collected, which may not be representative of all mpox cases in the affected population. The estimated parameters were obtained using data from untreated patients, but in outbreak settings antiviral drugs such as tecovirimat may be provided to individuals with mpox and used prophylactically. Further investigations into the association between clinical characteristics of patients (e.g., medication history, smallpox vaccination status, and symptom severity) and viral load would be needed for a more granular understanding of heterogeneous infectiousness profiles. This may enable, for example, different isolation periods to be specified for individuals with different characteristics. Another limitation of this work is that the duration of lesion presence was estimated independently of the mpox viral load data. Since there were no available data to analyze the relationship between lesion clearance and mpox viral kinetics, we assumed these features to be independent in our main analysis. Accordingly, we conducted sensitivity analyses for the symptom-based rule by incorporating a dependency between the two features (Supplementary Fig.  5 ), demonstrating that our main estimates of the risk of premature release from isolation under the current rule may be upper bounds in practice.

Of course, the relationships between viral dynamics, symptoms and transmission are likely to be complex. For example, individuals would be more likely to refrain from sexual activities when lesions are still present, resulting in a lower risk of transmission. In addition, mpox virus shedding may not be exactly concurrent with the presence of symptoms, as has been demonstrated for other viral infections. For example, viral shedding of SARS-CoV-2 does not coincide with COVID-19 symptoms 40 , and in genital herpes caused by HSV (Herpes Simplex Virus), viral elimination typically occurs when lesions are still present and visible 41 . The presence of lesions may not always be a good indicator of transmission risk, potentially leading to even more unnecessary isolation in some scenarios. If coupled viral load and lesion clearance data become available, the relationship between viral dynamics and symptoms could be analyzed more clearly; this is an essential target for future work. Similarly, the association between mpox viral load and infectiousness needs to be understood more deeply. Based on experimental data on viral culturability, we used 6.0 log10 copies/ml as an assumed threshold value for infectiousness in our main analysis; however, this estimate is uncertain 23 , 32 , 33 . We therefore performed sensitivity analyses (Supplementary Fig.  10 ) and found similar qualitative results about the relative effectiveness of different strategies regardless of the assumed threshold value. Also, since the in vitro-in vivo relationship on the culturability of mpox is not well understood, the assumed infectiousness threshold has not been validated in vivo for human sexual transmission. If coupling epidemiological data and viral load data at the individual level becomes available, we could estimate the infectiousness threshold in vivo in future studies. Furthermore, our analysis considered observation error in the testing-based rules but did not explicitly capture external sources of uncertainty that affect the false-negative rate of testing. In practical settings, imperfect swab sampling, especially with self-collection, could occur (leading to different test specificity/sensitivity). There may also be who repeatedly test until they obtain a negative result, as occurred during the COVID-19 pandemic 42 . Such practical challenges need to be considered when implementing testing-based rules. Finally, our literature search was not completely systematic. The estimated viral dynamics may be biased towards those of patients who showed clear symptoms and were recorded in the collected datasets. Our analysis showed the substantial variability of viral dynamics across individuals; however, this result should be interpreted as the variation within the analyzed patient data, and there may be even larger individual variation if additional data of milder cases are incorporated.

In conclusion, despite the necessary simplifications of the modelling framework, this study provides empirical evidence for heterogeneity in virus shedding kinetics and infectiousness among mpox cases and describes the impact of such heterogeneity on the effectiveness of different isolation rules. Rules that recommend isolating until lesion clearance or following a fixed period after being exposed to the virus are straightforward to apply, and can be effective at preventing transmission. Rules that are instead based on obtaining test results indicating loss of infectiousness prior to ending isolation are more nuanced and can have advantages, for example by reducing the period for which some individuals are required to isolate after they are no longer infectious. Careful consideration of the benefits and drawbacks of different rules for ending isolation is essential. Ensuring sustainable implementation of NPIs remains key to responding effectively to future outbreaks of mpox and other diseases.

Longitudinal viral load data from mpox cases were obtained through literature searches using PubMed and Google Scholar. Specifically, the following query was used: (“Mpox” or “Monkeypox”) and (“viral load” or “viral concentration” or “Ct value” or “cycle threshold”) and (“skin lesion” or “lesion”) and (“dynamics” or “kinetics” or “clinical course” or “symptom onset”). Further, we investigated articles from 2022 and 2023 identified in the search, reviewing each to extract the relevant data based on the following inclusion criteria: (1) time of symptom onset was recorded; (2) viral load was measured at least at two different time points (i.e., positive test results); (3) viral load was measured in different samples including lesion samples; and (4) patients did not receive any antivirals (as our model does not consider antiviral treatment). A total of 7 publications met those criteria, and 90 mpox cases were identified 23 , 24 , 29 , 43 , 44 , 45 , 46 .

Additionally, we collected data on lesion clearance on mpox patients from the National Center for Global Health and Medicine (NCGM), Tokyo, Japan from July 2022 to November 2023. All mpox patients were diagnosed by polymerase chain reaction (PCR) for mpox using patient specimens. Data were collected on the following patient characteristics: date of birth, age, sex, race, clinical symptoms such as fever, skin lesion and annal pain, severe illness, treatment such as tecovirimat and vaccinia immune globulin intravenous. Severe illness was defined according to the mpox treatment guideline provided by the Disease Control and Prevention Center, National Center for Global Health and Medicine 47 , and the Guidance for Tecovirimat Use by the Centers for Disease Control and Prevention of the United States of America 48 .

Ethics declarations

For the longitudinal viral load data of mpox patients, we used only de-identified data from published studies and thus ethics approval was not required. For the lesion clearance data of mpox patients, this study was approved by the ethics committee of the National Center for Global Health and Medicine (NCGM) (approval no: NCGM-S-004748-00) and was implemented in accordance with the Declaration of Helsinki. Patient data were anonymized prior to the analysis. Due to the retrospective nature of the study, the requirement of patient consent was waived.

Modelling mpox viral clearance and parameter estimation

Using the viral load data, we parameterized a mathematical model of temporal viral clearance dynamics in each infected individual. The collected viral load showed a decreasing trend over time since symptom onset (Supplementary Fig.  17 ), which was observed in other literature 23 , 49 , 50 . Thus, we employed an exponential decay model, which was previously utilized in a mpox study 23 :

where the variable \(V(t)\) is the viral load (copies/ml) at time \(t\) and parameter \(\delta\) represents the viral clearance rate. Note that the timescale is time after symptom onset; \(t=0\) is thus the date on which symptoms of mpox first began. \(V(0)\) is the initial viral load at symptom onset.

A nonlinear mixed-effect model was used to estimate the parameters \(\delta\) and \(V(0)\) 9 , 10 . This approach captures the heterogeneity in the viral dynamics by including both a fixed effect (the shared effect among individuals, i.e., population parameter) and a random effect (the individual-level effect) in each parameter. Population parameters and the standard deviation of random effects were estimated by using the Stochastic Approximation Expectation Maximization algorithm to compute the maximum likelihood estimator of the parameters, assuming a Gaussian distribution (mean \(0\) and variance \({\sigma }^{2}\) ) for the residuals (i.e., differences between predicted log viral load and measured log viral load) to quantify the error used in our simulations 51 . Individual parameters were subsequently computed using Markov Chain Monte Carlo (MCMC), and then best-fit estimates for each individual were calculated as Empirical Bayes Estimates (EBEs) (see Supplementary Note  1 ). The estimation procedures were performed using MONOLIX 2023R1 ( www.lixoft.com ).

Clustering algorithm to stratify mpox cases

We stratified the mpox cases using the K-means clustering algorithm 52 , which finds cluster assignments by minimizing the sum of squared Euclidean distances between three estimated features: viral load at symptom onset, area under the viral load curve (AUC), and duration of viral shedding (see Supplementary Note  2 ). We first standardized the estimated features to ensure well-separated clusters, since they have different units. Then, the algorithm partitioned the set of estimated features into \(k\) clusters. The optimal number of clusters was determined by the Silhouette method 53 . To visualize the result of K-means clustering in a two-dimensional plane, we conducted a dimensionality reduction using principal component analysis (PCA) 54 .

Simulation of viral dynamics and different rules for ending isolation

To account for individual variability in viral dynamics when determining the optimal duration of isolation, we simulated the predicted viral load, \(V(t)\) , for \(N\) virtual patients by running the viral clearance model. Parameter sets for each virtual patient were sampled from distributions of estimated model parameters. The measured viral load, \(\hat{V}(t)\) , was obtained from the following observation error model: \({\log }_{10}\hat{V}\left(t\right)={\log }_{10}V(t)+\varepsilon,\varepsilon \sim N(0,{\sigma }^{2})\) , where \(\varepsilon\) is the error term. For each individual, the residuals were calculated at all measurement time points, and by fitting a Gaussian distribution to all computed residuals, the variance of error, \({\sigma }^{2}\) , was estimated. Note that the variance for each virtual patient was assumed to be constant over time in the main analysis (i.e., constant error model). For parameter sets for virtual patients in each stratified group, we used individual parameters drawn from conditional distributions (i.e., distributions conditioned on the observed data and the estimated population parameters) characterizing the estimated parameters for individuals in each group, as estimated in the MCMC procedure. Additionally, as a sensitivity analysis, we considered a different observation error model whose variance is proportional to the predicted viral load (i.e., proportional error model): \({\log }_{10}\widehat{V}\left(t\right)={\log }_{10}V(t)+\varepsilon,\, \varepsilon \sim N(0,{\left(\eta {\log }_{10}V(t)\right)}^{2})\) , where \(\eta\) is the proportional constant. The estimated parameter values in observation error models are summarized in Supplementary Table  3 .

Under a symptom-based rule, as a current standard, patients would be released from isolation after the removal of lesions. For the timing of lesion clearance, maximum likelihood estimation (MLE) was used to fit parametric distributions to data on duration between symptom onset and lesion clearance for 43 mpox patients from the National Center for Global Health and Medicine (NCGM), Tokyo, Japan (see Supplementary Note  3 ). The timing of lesion removal for each patient was sampled from the best-fitted lognormal distribution (see Supplementary Fig.  3b and Supplementary Table  4 ). Additionally, as a sensitivity analysis, various relationships between individual viral dynamics and lesion clearance were considered. Specifically, we generated virtual mpox patient data under a range of possible values of the correlations between viral dynamics model parameters and the duration of lesion presence (see Supplementary Note  4 ). Under a fixed-duration rule, it was assumed that isolation would end at a specified time following symptom onset. On the other hand, under testing-based rules using tests, isolation ended when a given number of consecutive test results indicating loss of infectiousness was met with a given interval between tests. Here, we assumed that patients began to take tests immediately following symptom onset. To simulate various situations, we varied the interval between tests (from 1 to 5 days) and the number of consecutive test results indicating loss of infectiousness (from 1 to 5 times). To ascertain individuals’ infectious periods, a viral load threshold of infectiousness was considered. If the viral load of a patient was above the threshold, the patient was considered as being infectious (see Supplementary Fig.  18 ). The threshold values considered were obtained from studies on viral replication in cell culture 23 , 32 , 33 . In this study, we set 6.0 log10 copies/ml as the main assumed infectiousness threshold value. However, this value is still uncertain and thus we also considered different threshold values from 5.0 to 7.0 log10 copies/ml as sensitivity analyses.

In particular, in the main analysis of testing-based rules, we considered PCR tests (limit of detection = 2.9 log10 copies/ml) 23 , which can quantitatively measure viral load above the limit of detection to directly compare the measured viral load against the assumed infectiousness threshold. In this scenario, measured viral load can be used to determine whether an infected individual may no longer be infectious. Additionally, we considered rapid antigen tests (RATs) with lesion swabs, with either: 1) high sensitivity (i.e., limit of detection = 5.0 log10 copies/ml, lower than the assumed infectiousness threshold), or 2) low sensitivity (i.e., limit of detection = 7.0 log10 copies/ml, higher than the assumed infectiousness threshold). Under the testing-based rules using RATs, negative results correspond to a measured viral load below the limit of detection.

For the evaluation of different rules, three metrics were computed: (1) risk of prematurely ending isolation, (2) estimated infectious period after ending isolation, and (3) unnecessarily prolonged isolation period. The first metric gives the probability of releasing patients while they are still infectious. The second metric is defined as the mean number of days for which patients remain infectious after they are released from isolation (defined to be zero for an individual who is no longer infectious when released from isolation). The third metric gives the mean difference between the time when patients are no longer infectious and the time at which their isolation ends 9 , 10 (which is negative for individuals who are infectious beyond the end of isolation). Specifically, the risk of prematurely ending isolation was computed as

where \(I\) is the indicator function, \({V}_{i}\) is the predicted viral load of patient \(i\) , \({\tau }_{i}\) is the time when isolation of patient \(i\) ends, and \({IT}\) is the assumed infectiousness threshold. Note that \(N\) stands for the number of virtual mpox patients and is set as \(1000\) in our analysis. We computed the infectious period after ending isolation by use of the following formula:

where \({\bar{\tau }}_{i}\) indicates the time when the predicted viral load of patient \(i\) drops below the assumed infectiousness threshold. Finally, the unnecessarily prolonged isolation period was calculated as

By running \(100\) simulations ( \(N\) patients for each simulation), we reported the mean and 95% confidence intervals for distributions of those three metrics, respectively. All analyses were conducted using the statistical computing software R (version 4.2.3).

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The mpox viral load data used in this study are publicly available and are also available via Zenodo 55 . Using WebPlotDigitizer-4.6, those data were extracted from the following publications: Suner et al. ( https://doi.org/10.1016/S1473-3099(22)00794-0 ), Norz et al. ( https://doi.org/10.1016/j.jcv.2022.105254 ), Adler et al. ( https://doi.org/10.1016/j.jcv.2022.105254 ), Gaspari et al. ( https://doi.org/10.1128/jcm.01365-22 ), Relhan et al. ( https://doi.org/10.1002/jmv.28249 ), Hornuss et al. ( https://doi.org/10.1016/j.cmi.2022.09.012 ), and Antinori et al. ( https://doi.org/10.2807/1560-7917.ES.2022.27.22.2200421 ). The timing of lesion clearance data for mpox patients used in this study are publicly available. Those data were obtained from National Center for Global Health and Medicine (NCGM), Tokyo, Japan.  Source data are provided with this paper.

Code availability

All analyses were performed with the statistical computing software R (version 4.2.3). The analysis using nonlinear mixed-effects model was performed on MONOLIX 2023R1 ( www.lixoft.com ). Our code is publicly available via Zenodo 55 .

Vaughan, A. M. et al. A large multi-country outbreak of monkeypox across 41 countries in the WHO European Region, 7 March to 23 August 2022. Euro Surveill. 27 . https://doi.org/10.2807/1560-7917.ES.2022.27.36.2200620 (2022).

World Health Organization. 2022-23 Mpox Outbreak: global trends. https://worldhealthorg.shinyapps.io/mpx_global/ (2023).

Murayama, H. et al. Accumulation of immunity in heavy-tailed sexual contact networks shapes mpox outbreak sizes. J. Infect. Dis . https://doi.org/10.1093/infdis/jiad254 (2023).

Xiridou, M. et al. The fading of the mpox outbreak among men who have sex with men: a mathematical modelling study. medRxiv , 2023.2001.2031.23285294. https://doi.org/10.1101/2023.01.31.23285294 (2023).

Brand, S. P. C. et al. The role of vaccination and public awareness in forecasts of Mpox incidence in the United Kingdom. Nat. Commun. 14 , 4100 (2023).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Endo, A., Jung, S. M. & Miura, F. Mpox emergence in Japan: ongoing risk of establishment in Asia. Lancet 401 , 1923–1924 (2023).

Article   PubMed   Google Scholar  

Thy, M. et al. Breakthrough Infections after Postexposure Vaccination against Mpox. N. Engl. J. Med. 387 , 2477–2479 (2022).

Raccagni, A. R. et al. Two individuals with potential monkeypox virus reinfection. Lancet Infect. Dis. 23 , 522–524 (2023).

Article   PubMed   PubMed Central   Google Scholar  

Jeong, Y. D. et al. Revisiting the guidelines for ending isolation for COVID-19 patients. Elife 10 , e69340 (2021).

Jeong, Y. D. et al. Designing isolation guidelines for COVID-19 patients with rapid antigen tests. Nat. Commun. 13 , 4910 (2022).

Muller, J. & Kretzschmar, M. Contact tracing - old models and new challenges. Infect. Dis. Model 6 , 222–231 (2021).

PubMed   Google Scholar  

Alpalhao, M. & Filipe, P. The impacts of isolation measures against SARS-CoV-2 infection on sexual health. AIDS Behav. 24 , 2258–2259 (2020).

Kretzschmar, M. E. et al. Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study. Lancet Public Health 5 , e452–e459 (2020).

Ash, T., Bento, A. M., Kaffine, D., Rao, A. & Bento, A. I. Disease-economy trade-offs under alternative epidemic control strategies. Nat. Commun. 13 , 3319 (2022).

Hossain, M. M., Sultana, A. & Purohit, N. Mental health outcomes of quarantine and isolation for infection prevention: a systematic umbrella review of the global evidence. Epidemiol. Health 42 , e2020038 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Leng, T., Hill, E. M., Keeling, M. J., Tildesley, M. J. & Thompson, R. N. The effect of notification window length on the epidemiological impact of COVID-19 contact tracing mobile applications. Commun. Med. 2 , 74 (2022).

Discontinuation of transmission-based precautions and disposition of patients with COVID-19 in healthcare settings (interim guidance). https://stacks.cdc.gov/view/cdc/88538 (2020).

Infection prevention and control of mpox in healthcare settings. https://www.cdc.gov/poxvirus/mpox/clinicians/infection-control-healthcare.html (2022).

Centers for Disease Control and Prevention (CDC). Considerations for reducing mpox transmission in congregate living settings, https://www.cdc.gov/poxvirus/mpox/community/congregate.html (2022).

Maya, S. & Kahn, J. G. COVID-19 testing protocols to guide duration of isolation: a cost-effectiveness analysis. BMC Public Health 23 , 864 (2023).

Miura, F. et al. Time scales of human mpox transmission in the Netherlands. J. Infect. Dis . https://doi.org/10.1093/infdis/jiad091 (2023).

Madewell, Z. J. et al. Serial interval and incubation period estimates of monkeypox virus infection in 12 jurisdictions, United States, May-August 2022. Emerg. Infect. Dis. 29 , 818–821 (2023).

Suner, C. et al. Viral dynamics in patients with monkeypox infection: a prospective cohort study in Spain. Lancet Infect. Dis. 23 , 445–453 (2023).

Gaspari, V. et al. Monkeypox Outbreak 2022: clinical and virological features of 30 patients at the sexually transmitted diseases centre of Sant’ Orsola Hospital, Bologna, Northeastern Italy. J. Clin. Microbiol. 61 , e0136522 (2023).

European Centre for Disease Prevention and Control. Factsheet for health professionals on mpox (monkeypox). https://www.ecdc.europa.eu/en/all-topics-z/monkeypox/factsheet-health-professionals (2023).

Centers for Disease Control and Prevention (CDC). Isolation and prevention practices for people with mpox. https://www.cdc.gov/poxvirus/mpox/clinicians/isolation-procedures.html (2023).

UK Health Security Agency. Mpox (monkeypox): people who are isolating at home. https://www.gov.uk/guidance/guidance-for-people-with-monkeypox-infection-who-are-isolating-at-home (2022).

National Institute for Public Health and the Environment. Information letter for a person with monkeypox. https://lci.rivm.nl/information-letter-person-monkeypox (2022).

Adler, H. et al. Clinical features and management of human monkeypox: a retrospective observational study in the UK. Lancet Infect. Dis. 22 , 1153–1162 (2022).

Jones, B. et al. Variability in clinical assessment of clade IIb mpox lesions. Int. J. Infect. Dis. 137 , 60–62 (2023).

Luciani, L. et al. A novel and sensitive real-time PCR system for universal detection of poxviruses. Sci. Rep. 11 , 1798 (2021).

Saijo, M. et al. Diagnosis and assessment of monkeypox virus (MPXV) infection by quantitative PCR assay: differentiation of Congo Basin and West African MPXV strains. Jpn. J. Infect. Dis. 61 , 140–142 (2008).

Article   CAS   PubMed   Google Scholar  

Norz, D. et al. Evidence of surface contamination in hospital rooms occupied by patients infected with monkeypox, Germany, June 2022. Euro Surveill. 27 , 2200477 (2022).

Kapmaz, M. et al. A complicated case of monkeypox and viral shedding characteristics. J. Infect. 86 , 66–117 (2023).

White, J. A., et al. Complex decay dynamics of HIV virions, intact and defective proviruses, and 2LTR circles following initiation of antiretroviral therapy. Proc. Natl Acad. Sci. USA 119 (2022).

Endo, A. et al. Heavy-tailed sexual contact networks and monkeypox epidemiology in the global outbreak, 2022. Science 378 , 90–94 (2022).

Article   ADS   CAS   PubMed   Google Scholar  

Thompson, R. N. et al. Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics 29 , 100356 (2019).

De Baetselier, I. et al. Retrospective detection of asymptomatic monkeypox virus infections among male sexual health clinic attendees in Belgium. Nat. Med. 28 , 2288–2292 (2022).

Ejima, K. et al. Estimation of the incubation period of COVID-19 using viral load data. Epidemics 35 , 100454 (2021).

Chen, P. Z. et al. SARS-CoV-2 shedding dynamics across the respiratory tract, sex, and disease severity for adult and pediatric COVID-19. Elife 10 . https://doi.org/10.7554/eLife.70458 (2021).

Tronstein, E. et al. Genital shedding of herpes simplex virus among symptomatic and asymptomatic persons with HSV-2 infection. JAMA 305 , 1441–1449 (2011).

Mouliou, D. S. & Gourgoulianis, K. I. False-positive and false-negative COVID-19 cases: respiratory prevention and management strategies, vaccination, and further perspectives. Expert Rev. Respir. Med. 15 , 993–1002 (2021).

Norz, D. et al. Clinical characteristics and comparison of longitudinal qPCR results from different specimen types in a cohort of ambulatory and hospitalized patients infected with monkeypox virus. J. Clin. Virol. 155 , 105254 (2022).

Relhan, V. et al. Clinical presentation, viral kinetics, and management of human monkeypox cases from New Delhi, India 2022. J. Med Virol. 95 , e28249 (2023).

Hornuss, D. et al. Transmission characteristics, replication patterns and clinical manifestations of human monkeypox virus-an in-depth analysis of four cases from Germany. Clin. Microbiol Infect. 29 , 112 e115–112 e119 (2023).

Article   Google Scholar  

Antinori, A. et al. Epidemiological, clinical and virological characteristics of four cases of monkeypox support transmission through sexual contact, Italy, May 2022. Euro Surveill. 27 . https://doi.org/10.2807/1560-7917.ES.2022.27.22.2200421 (2022)

Ministry of Health, Labour and Welfare, Japan. Mpox treatment guideline. https://www.mhlw.go.jp/content/001183682.pdf (2023).

Centers for Disease Control and Prevention (CDC). Mpox. Guidance for tecovirimat use. https://www.cdc.gov/poxvirus/mpox/clinicians/Tecovirimat.html (2023).

Palich, R. et al. Viral loads in clinical samples of men with monkeypox virus infection: a French case series. Lancet Infect. Dis. 23 , 74–80 (2023).

Kim, H. et al. Viral load dynamics and shedding kinetics of mpox infection: a systematic review and meta-analysis. J. Travel Med. 30 . https://doi.org/10.1093/jtm/taad111 (2023).

Kuhn, E. & Lavielle, M. Maximum likelihood estimation in nonlinear mixed effects models. Comput. Stat. Data Anal. 49 , 1020–1038 (2005).

Article   MathSciNet   Google Scholar  

Sammut, C. & Webb, G. I. Encyclopedia of Machine Learning (Springer Science & Business Media, 2011).

Rousseeuw, P. J. & Kaufman, L. Finding Groups in Data: an Introduction to Cluster Analysis (John Wiley & Sons Hoboken, New Jersey, 2009).

Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Trans. A Math. Phys. Eng. Sci. 374 , 20150202 (2016).

ADS   MathSciNet   PubMed   PubMed Central   Google Scholar  

Jeong, Y. D. Modelling the effectiveness of an isolation strategy for managing mpox outbreaks with variable infectiousness profiles. https://doi.org/10.5281/zenodo.12561102 (2024).

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) RS-2024-00345478 (to Y.D.J). This study was supported in part by Scientific Research (KAKENHI) B 23H03497 (to S.I.); Grant-in-Aid for Transformative Research Areas 22H05215 (to S.I.); Grant-in-Aid for Challenging Research (Exploratory) 22K19829 (to S.I.); AMED CREST 19gm1310002 (to S.I.); AMED Research Program on Emerging and Re-emerging Infectious Diseases 22fk0108509 (to S.I.), 23fk0108684 (to S.I.), 23fk0108685 (to S.I.); AMED Research Program on HIV/AIDS 22fk0410052 (to S.I.); AMED Program for Basic and Clinical Research on Hepatitis 22fk0210094 (to S.I.); AMED Program on the Innovative Development and the Application of New Drugs for Hepatitis B 22fk0310504h0501 (to S.I.); AMED Strategic Research Program for Brain Sciences 22wm0425011s0302; AMED JP22dm0307009 (to K.A.); JST MIRAI JPMJMI22G1 (to S.I.); Moonshot R&D JPMJMS2021 (to K.A. and S.I.) and JPMJMS2025 (to S.I.); Institute of AI and Beyond at the University of Tokyo (to K.A.); Shin-Nihon of Advanced Medical Research (to S.I.); SECOM Science and Technology Foundation (to S.I.); The Japan Prize Foundation (to S.I.); Japan Society for the Promotion of Science (JSPS) Grants-in-Aid KAKENHI JP20J00793 and JST JPMJPR23RA (to F.M.); Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) to a project on Joint Usage/Research Center– Leading Academia in Marine and Environmet Pollution Research (LaMer) (to F.M.). The collaboration between R.N.T. and S.I. was supported by a Royal Society International Exchange award (grant number IES-R3-193037).

Author information

These authors contributed equally: William S. Hart, Robin N. Thompson, Masahiro Ishikane.

These authors jointly supervised this work: Shingo Iwami, Fuminari Miura.

Authors and Affiliations

Interdisciplinary Biology Laboratory (iBLab), Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya, Japan

Yong Dam Jeong, William S. Hart, Takara Nishiyama, Hyeongki Park & Shingo Iwami

Department of Mathematics, Pusan National University, Busan, 46241, South Korea

Yong Dam Jeong

Mathematical Institute, University of Oxford, Oxford, OX2 6GG, UK

William S. Hart & Robin N. Thompson

Disease Control and Prevention Centre, National Centre for Global Health and Medicine, Tokyo, Japan

Masahiro Ishikane, Noriko Iwamoto, Ayana Sakurai, Michiyo Suzuki & Norio Ohmagari

International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo, Japan

Kazuyuki Aihara & Shingo Iwami

Research Center for Drug and Vaccine Development, National Institute of Infectious Diseases, Tokyo, Japan

Koichi Watashi

Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands

Eline Op de Coul, Jacco Wallinga & Fuminari Miura

Department of Biomedical Data Sciences, Leiden University Medical Center (LUMC), Leiden, The Netherlands

Jacco Wallinga

Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan

Shingo Iwami

Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan

Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS), RIKEN, Saitama, Japan

NEXT-Ganken Program, Japanese Foundation for Cancer Research (JFCR), Tokyo, Japan

Science Groove Inc., Fukuoka, Japan

Center for Marine Environmental Studies (CMES), Ehime University, Matsuyama, Ehime, Japan

Fuminari Miura

You can also search for this author in PubMed   Google Scholar

Contributions

S.I. and F.M. designed the research. Y.D.J., W.S.H., and R.N.T. carried out the computational analysis. S.I., R.N.T., M.I., and F.M. supervised the project. Y.D.J., W.S.H., R.N.T., M.I., T.N., H.P., N.I., A.S., M.S., K.A., K.W., E.O.d.C., N.O., J.W., S.I., and F.M. wrote the paper. All authors discussed the research and approved the final manuscript.

Corresponding authors

Correspondence to Shingo Iwami or Fuminari Miura .

Competing interests

The authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Joshua Schiffer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, reporting summary, source data, source data, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Jeong, Y.D., Hart, W.S., Thompson, R.N. et al. Modelling the effectiveness of an isolation strategy for managing mpox outbreaks with variable infectiousness profiles. Nat Commun 15 , 7112 (2024). https://doi.org/10.1038/s41467-024-51143-w

Download citation

Received : 28 September 2023

Accepted : 31 July 2024

Published : 26 August 2024

DOI : https://doi.org/10.1038/s41467-024-51143-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.