How to Set Up Lead Scoring in Salesforce: Tools, Tips, & Alternatives

Salesforce can (hypothetically) store up to 30 million contact records per account. But even if you have a fraction of that—say just 30,000 leads—that’s still more than even a large sales team can handle. Salesforce lead scoring can help prioritize those records so reps only spend time on leads highly likely to close. 

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However, Salesforce’s built-in lead scoring functionality has limitations. As a result, many users adopt separate lead scoring software and integrate it with Salesforce. Let’s walk through the best way to configure and supplement Salesforce lead scoring to get seamless, lightning-fast functionality. 

Key takeaways

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• Brief overview of what lead scoring is and why should be part of your sales workflow software
• Rundown of Salesforce’s native lead scoring functionality—including pros, cons, and missing features
• How to compensate for Salesforce’s limited lead scoring capabilities without major integration headaches (e.g. broken flows, excessive developer support, lost data, revenue leakage)

What is lead scoring?

Lead scoring is an automated process that determines a contact’s likelihood of becoming a paying customer. There are two broad approaches to lead scoring: rules-based and predictive. 

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Rules-based involves pre-assigning a certain number of points to designated attributes, actions, or intent signals. For example, a lead can receive 20 points for an Industry and Job Title that match your ICP, 10 points for every comment they leave on a social media post, and 30 points for visiting a product page on your website. 

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Those points are then aggregated, and all leads whose aggregate score exceeds a certain threshold are considered qualified. 

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Predictive lead scoring uses existing customer data, rather than predetermined criteria, to score leads. A machine learning model will use your data to train a predictive algorithm, then run all new inbound leads through that algorithm. Those who more closely align with your existing customers will receive a higher score than those who do not. 

Learn more: How to build a lead scoring model

What are the key benefits of lead scoring?

Lead scoring, whether in Salesforce or any other platform, should be part of every RevOps tech stack. Without it, you would have to manually qualify every lead that comes. This is slow and painstaking work. And as you start to scale up your inbound flows, it becomes impossible to keep up. 

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With automated ai lead scoring, you can qualify, route, schedule, and pipeline leads fast. Here are some of the benefits this can offer your sales organization. 

Faster speed-to-lead

The faster you book a meeting with an inbound lead, the less opportunity you’re giving competitors to contact and close them. Responding to an inbound request in five minutes or less increases your odds of booking a meeting by 100X. 

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With automated lead scoring, you know instantly whether an inbound lead is qualified and, if you have automated lead routing rules in place, who should reach out. This speeds up your response time significantly and, by extension, your conversion rates. 

More effective sales teams

Most sales reps spend 60-70% of their time on non-selling tasks. These tasks can include everything from prospect research, data management, CRM recordkeeping, and more. 

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Lead scoring can remove the need to extensively research and qualify leads before reaching out—because your lead scoring system does it automatically. Your reps, in turn, can then spend more time doing what they do best: selling!

Improved win rates

Higher win rates mean that your sales organization is operating efficiently and you’re not wasting time on opportunities that will never close. Lead scoring helps to filter out bad-fit leads so your reps can spend more time on high-value opportunities. 

Better marketing-sales alignment

A major point of friction between marketing and sales teams is the definition of what constitutes a “qualified” lead (whether it’s an MQL or SQL). Lead scoring provides an objective way to determine whether a lead is a good fit and ready to buy. If both teams get buy-in on your lead scoring framework, it can provide a solid foundation for improved marketing-sales alignment. 

An overview of lead scoring in Salesforce

Salesforce’s lead scoring system uses a tool called Einstein Lead Scoring. This tool uses machine learning and data science to predict and prioritize leads that are most likely to convert into customers. 

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Einstein analyzes Salesforce account data through a machine learning algorithm that can predict lead conversion patterns. This provides several advantages over traditional, rules-based lead scoring, as it reduces the human error and bias that can creep in when establishing individual scoring criteria. 

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Here’s a breakdown of how Einstein Lead Scoring works: 

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• Leads are scored at least every six hours. If an attribute changes, Einstein resources the lead within the hour—which can be helpful for time-sensitive record changes, like intent data
• Salesforce refreshes Einstein models every 10 days based on new customer data—so you don’t miss a single trend
• Einstein creates internal categories for text fields that have a single meaning, but variable values. For example, a VP of Revenue and VP of Sales often have the same responsibilities and authority at different companies, so Einstein will have a common category for both
• Einstein adds a Lead Score field to your contact records, which you can use to trigger automations once its value reaches or exceeds a certain threshold
• If you don’t have enough data to create a custom lead scoring model, Einstein uses a global model based on anonymous data from other Salesforce users

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Additionally, your Salesforce admin can choose whether to score all leads together, or segment them based on specific field criteria. For example, leads associated with larger companies will likely exhibit different attributes from SMBs, so it’s a good idea to score them separately. You can also omit certain fields from the lead scoring model if you believe they don’t impact lead quality (positively or negatively). 

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Einstein Lead Scoring is only available in Lightning Experience and Salesforce Classic, as well as an add-on for the Enterprise, Performance, and Unlimited Editions. 

How to create a Salesforce lead scoring model

Setting up a lead scoring model in Salesforce isn’t complicated, but it requires some detailed knowledge of the application to do correctly. Here are the steps you need to take to get started. 

1. Enable user permissions

Before you can enable Einstein to start scoring leads in Salesforce, your admin needs to grant you appropriate user permissions. Specifically:

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• Customize Application. This is a very powerful set of permissions that should only be given to trained and experienced Salesforce users. These include the ability to create, edit, and delete customs; set field security; modify the Salesforce customer portal; and more. 
• Modify All Data. This permission set gives you control over all the data within your Salesforce account, including the ability to create, edit, and modify custom objects; convert leads; override forecasts; view setup and configuration; and more. 
• View All Profiles. When you enable the Profile Filtering option in User Management Settings, you can control users’ ability to view the settings and permissions that determine their access to Salesforce data and features. 

2. Turn on Einstein Lead Scoring

Once those permissions are enabled for your account, enabling Einstein Lead Scoring is pretty straightforward:

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1. Go to Setup. 
2. Select the Quick Find box.
3. Enter “Einstein Lead Scoring” 
4. Select Einstein Lead Scoring under Einstein Sales
5. Turn on Einstein Lead Scoring

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3. Choose default or custom settings

Next you need to decide whether you want to move forward with Einstein’s default settings or customize them to match your own needs. 

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By default, Einstein uses all fields in leads converted to accounts and contacts to identify conversion patterns. What’s more, it considers all these leads together without segmentation. If you want to use these parameters, you simply need to select Score Leads after turning on Einstein Lead Scoring.

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If you choose custom settings, you need to navigate to the Conversion Milestone page to choose which milestone you want to use (account, contact, or opportunity creation). Then select Save & Next. 

4. Segment your leads

If you work with multiple ICPs, industries, or other lead segments, you’ll want to select the Segments of Leads option. Otherwise, simply select Save & Next. 

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Here’s the process for building a lead scoring segment:

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1. Select Add Segment. 
2. Name the segment (up to 80 characters)
3. Select Add Condition, then choose a field, operator, type, and value to define that criterion for inclusion into the segment. You can specify up to 100 field filters; however, the following are excluded: Address, Date, Datetime, Encrypted String, Geolocation, Multipicklist, Reference, Text Area, Time
4. Select Save & Next. 

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You can repeat this process and create additional lead segments. Salesforce allows up to 35 segments. 

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It’s not uncommon for a lead to fall into multiple segments. You can then create a priority order for your segments, and Einstein will only save a lead to the segment with the highest priority. 

5. Exclude irrelevant fields from lead scoring

Einstein’s default approach is to use all fields when making lead scoring decisions. If you create a new field, it automatically is included in Einstein’s scoring algorithm. 

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However, there are situations where you may want to exclude a field in a contact record from lead scoring. Some fields have a neutral or negative impact on lead scoring accuracy and can confuse the model. Other fields can create negative feedback loops—for example, you don’t want a field that indicates why a lead didn’t convert to end up informing the model.

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The Einstein Lead Scoring dashboard shows you which fields have the greatest and least impact on your scores. This information can help you drop irrelevant fields, as well as prevent you from excluding a field that will actually improve your scoring accuracy. 

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Here’s how to exclude irrelevant fields from lead scoring:

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1. Go to the Included Fields page.
2. Select Include Fields.
3. Deselect all fields you don’t want Einstein to include.
4. Select Next. 

6. Score your leads

Once you finish all the steps listed above, the next step is to select Score Leads. Once you select this option, Einstein will analyze converted leads and build a lead scoring model for each segment. 

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Note that it can take up to 48 hours to analyze your data, build the model, and score your leads. The Einstein Lead Scoring setup page should give you a window into your progress throughout this process. 

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If you don’t have enough lead conversion data to build a custom predictive model, Einstein will use a global model built with anonymous data from various Salesforce customers. Once your data passes the threshold necessary to build a custom model, you can rescore your leads for better accuracy. 

Learn more about Salesforce lead conversion.

7. Add lead scoring to pages

If you use Lightning Experience, you’ll want to use the Lightning App Builder to add Einstein Lead Scoring to Lightning pages. That way, users can see the lead score when they pull up a Lead record. 

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A similar approach works in Salesforce Classic, where you simply add the newly created Lead Score field to the lead page layout. Note: you can’t add the Lead Score field to the same page layout as the following report components: Lead Score Distribution, Conversion Rate by Lead Score.  

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In both cases, users need read access to Company, Phone and Email fields to view lead scores. 

8. Add lead scoring to list views

Once Lead Scores are available in Salesforce, you’ll want to add them to your custom lead list views. This enables sales reps to filter their lead lists by score, enabling them to prioritize their outreach to the highest-value leads. 

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Salesforce will automatically add Lead Scores to default list views. 

9. Update your lead scoring setup

After you set up Einstein Lead Scoring, there will undoubtedly be times where you’ll want to make changes or updates. If you do, you’ll want to follow these best practices to avoid unintended consequences to the rest of your database:

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• Update your lead scoring setup as little as possible. Salesforce enables you to save updates as draft settings and tweak them until you deploy them to score leads. 
• Note that CRM Analytics Data Sync, if enabled, will show errors during error sync while the model is updating. These should resolve once updated scores are distributed across lead records. 

Salesforce lead scoring limitations

Despite Einstein’s powerful, ML-based lead scoring approach, it does come with some limitations. Here are a few of the most impactful. 

Data quality dependencies

Einstein Lead Scoring’s accuracy is dependent solely on the quality of your database. If your data is accurate and up to date, then it will yield good results. If not, you may find its results lacking. 

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Another way to improve the quality of your lead scoring model is by increasing the data available to it. Because Salesforce has no built-in enrichment capabilities, you’ll need to rely on a third-party lead enrichment platform to source, integrate, and continually update those data. 

Irrelevant data

Einstein Lead Scoring, by default, uses all available data in your CRM to score leads. While the model does weight certain data based on its usefulness in making predictions, it won’t omit irrelevant or counterproductive fields unless you manually tell it to. 

Limited insights

Although Salesforce lead scoring gives you a number that indicates a lead’s likelihood to close, that’s about all it provides. You can look at which fields were relied on most heavily to provide that score, but ultimately there’s a lot of contextual information you’re missing. 

Resource-intensive

Salesforce CRM is complex and constantly being updated. It takes specific training and expertise to be able to effectively manage it, not to mention time. The cost spent configuring and deploying lead scoring models, incorporating them into pages, integrating them into Flows, etc. are high. 

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What’s more, Lead Scoring is only available in the more expensive Salesforce plan. So you’re already at a high spending threshold just to access this feature. 

Lack of cross-cloud data

Salesforce lead scoring only considers the data contained within its own CRM records. Modern revenue tech stacks, however, are continually generating new customer data. These data can be critical in improving lead scoring accuracy. 

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For example, web analytics data, marketing automation insights, product usage, support ticketing, and other key indicators of customer buying intent are excluded from Salesforce lead scoring. 

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This is part of the reason why modern lead qualification and lead distribution software needs to be able to orchestrate its functionality across various platforms. Otherwise, you risk losing valuable data that is critical to accurate, timely engagement with potential customers. 

How to score your leads with Default

If you want a lead scoring platform that overcomes the challenges inherent in Salesforce, here are some capabilities you want to see: 

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• Ability to easily enrich records with verifiable, up-to-date lead data to improve lead scoring accuracy
• Cross-stack orchestration capabilities to pull in and manage data from a range of sources
• Easy to use, set up, and integrate into your overall tech stack
• Automated instant action on lead scores—qualification, routing, scheduling, and nurture

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Default is the only platform that not only scores and qualifies leads, but also takes instant action on them in real time. What’s more, Default’s batteries-included enrichment and automation capabilities serve as the perfect complement to Salesforce. 

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Default’s integrations

Setting up integrations between Salesforce and the other tools in your stack is easier said than done. While Salesforce does come with built-in integrations, going beyond simple data transfer and trigger-based actions often requires custom development. 

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If you want to avoid using significant developer resources to solve this problem, you need a user-friendly RevOps orchestration platform like Default. This platform provides a layer of automation and data management that serves as a single source of truth for your stack.

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Using Default’s built-in integrations with Salesforce, HubSpot, Slack, Webhooks, Google Calendar, Salesloft, Outreach, and more to achieve the following benefits:

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• Ensure seamless management of your data across all platforms—no more broken integrations and data loss
• Enable instant action on changes in lead score, including automated qualification, routing, scheduling, and nurture
• Integrate alternatives to Einstein lead scoring—for example, HubSpot’s predictive AI lead scoring or Marketo’s built-in lead scoring—with the Salesforce CRM

You can see our guide for information about Hubspot lead scoring if that's your sales tool of choice.

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Simply put, Default offers a more robust approach and integrated approach to lead scoring than Salesforce does. If you want to avoid broken integrations, missed opportunities, and revenue leakage, talk to our sales team to see how we can help you today. 

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