Lead Scoring

What is Lead Scoring?

Lead scoring is a process marketing and sales teams use to numerically rank potential customers based on how likely they are to purchase a product or service. It uses a variety of criteria, including job title, company size, and industry, to assign a numerical score to each potential customer.

The lead score — which indicates probabilistic purchase intent and qualification for each sales lead — is determined by implicit and explicit attributes.

  • Implicit attributes — the lead’s behavior on certain channels like email or webpage visits.
  • Explicit attributes — demographic and firmographic data like job title, company revenue, headcount, location, and industry.

Within these attributes, positive actions (such as clicking on and saving a pricing page) add points to a prospect’s score. Low probabilistic indicators like a prospect hitting “Unsubscribe” would subtract from the total.


  • Lead scoring marketing automation
  • Predictive lead scoring

Importance of Lead Scoring in Marketing and Sales

Without lead scoring, there’s a lot of guesswork involved in a company’s marketing initiatives and sales process.

How would marketers know whether their campaigns are hitting the right audience? And how would sellers know whether they’re engaging the right prospects?

They wouldn’t.

Lead scoring is essential to marketing and sales for the following reasons:

Knowing Where to Focus Marketing and Sales Activities

Perhaps the most important benefit of a lead scoring system is that it gives marketing and sales teams insight into where they should focus their resources.

By assigning a numerical value to each prospect based on specific, pre-identified positive and negative behaviors, they can determine which prospects are most likely to close and prioritize them accordingly.

When marketing teams understand the scores of the leads their campaigns bring in, they can:

  • focus their efforts on leads with higher scores.
  • double down on tactics that bring in hotter leads.
  • eliminate or change messaging and tactics that bring in low-quality leads.
  • optimize marketing channels that drive the highest lead volumes.
  • ditch the channels that miss the mark.

For sellers, the clear benefit here is fewer marketing qualified leads (MQLs) that won’t benefit from, be able to afford, or actually show long-term interest in the company’s offerings.

It also enables sales reps to:

  • spend less time selling to MQLs that have little chance of closing a deal.
  • respond to leads with the highest scores first.
  • tailor outbound strategies that pull in more leads from the “best” categories.
  • refine sales engagement based on the buyer persona and win potential.
  • know when to ditch a lead that has been sitting in the pipeline for a while.

In that sense, lead scoring also helps to bridge the communication gap between marketing and sales teams, allowing both departments to have a better understanding of each other’s processes and goals.

Conversion Rate Improvements

The bottom line is a well-executed lead scoring process dramatically increases the frequency that deals make it to closed won (with higher sales efficiency, might we add).

But within the deal cycle, there are more conversions taking place:

  • Leads converting to MQLs
  • An MQL requesting a sales call
  • An MQL coming a sales qualified lead (SQL)
  • An SQL becoming an opportunity

And that’s just a few sections of the sales process.

The point is, a lead scoring system allows companies to focus their efforts on leads they know are most likely to convert and make it all the way through the funnel. And every step of the way, they can optimize the lead management process.

Impacts on Pipeline and Opportunity Management

A huge part of sales efficiency revolves around how well reps move their opportunities through the sales pipeline.

Lead scoring helps them spot which opportunities are most likely to turn into closed deals, enabling them to focus their efforts on the right leads at the right time. It also helps sales teams measure their progress and manage deals across every stage of the pipeline by telling them:

  • how quickly leads move through each stage.
  • which leads get stuck in each stage and require nurturing or further qualification.
  • which leads are the most profitable and have the highest ROI.

Using data points like sales velocity on a per-lead basis, they can shift their opportunity management procedures to accommodate their ideal lead types. And when they see leads with higher scores come through the pipeline, they can immediately allocate more resources to them.

Helps Grow Revenue

It almost goes without saying that better lead management practices and more efficient sales and marketing efforts directly benefit top-line revenue growth for the company.

With lead scoring, marketers continuously learn how to attract more high-quality leads, nurture them, and eventually get them into the sales funnel.

Once it’s time for sales to take over, reps use the lead score to tailor their approach and maximize their conversion potential.

More conversions = more revenue.

Saves Time and Money

Understanding concepts like whether or not a prospect clicked on a pricing document or unsubscribed from an email list would be near impossible without a system in place to take care of it. Documenting these instances and assigning them to each new sales lead would be error-prone at best.

Most marketers spend about half of their budget on lead generation — money that goes to waste if they’re targeting poor-quality leads. Lead scoring allows companies to target leads that make them the most potential revenue, which extends their budget significantly.

Plus, there’s no need to manually track each lead or their corresponding attributes — sales and marketing automation software take care of it all.

Reduces Customer Acquisition Costs

Consider all the costs of customer acquisition. Initial demand generation, content marketing, lead nurturing, and sales rep time all add up. Between in-house sales reps and marketers, the software they use, and the cost of the campaigns themselves, these costs can be substantial.

Lead scoring allows companies to reduce the number of leads they need to acquire and nurture. When sales reps can spend more of their time with high-quality leads, the overall acquisition cost per lead goes down.

Lead Scoring Models

To build a lead scoring model, it’s important to first understand what factors drive a prospect’s behavior. There are several sources of lead scoring data — most businesses use many or all of the following data types:


Intent data shows how prospects interact with content. It uses activities like what web pages they look at, how often they visit, and the time they spend on each page to understand their likelihood of being interested in those services.

There are four main types of intent data:

  • Known first-party intent data — data customers willingly input, such as signing up for an email newsletter or attending an event.
  • Anonymous first-party intent data — data from visits to the company website, picked up by Google Analytics and other analytics tools.
  • Known third-party intent data — voluntary activities like filling in a web form gathered and tracked by a partner or third-party website.
  • Anonymous third-party intent data — browsing activities tracked on third-party websites via the lead’s IP address, such as when a customer visits the website and engages content but doesn’t sign up for anything or provide any personal information.

First-party intent data is the easiest to access — it’s available right in the company’s CRM or Google Analytics account. Using these platforms, it’s easy to create a list of targets based on the lead’s activities and score them accordingly.

Demographics for B2C

Demographics are the characteristics that make up a company’s ideal customer profile (ICP). Every product is built with someone in mind. Demographic data defines the attributes that make a person more likely to purchase that product.

There are several types of demographic data:

  • Age
  • Gender
  • Race
  • Ethnicity
  • Location
  • Income level
  • Marital status
  • Education level
  • Employment status and occupation
  • Hobbies and interests

A makeup brand’s marketing initiatives, for instance, wouldn’t be well-received by an audience of young men. And home office equipment wouldn’t have the same success if it were marketed to construction workers.

To score leads based on demographic data, a lead scoring system would look at the above attributes of each potential customer. Then, it would assign it a score based on how many of the boxes that individual ticks. Based on the data and predefined ICP, marketers would then know who to target their ads towards (and waste less money on exposure to the wrong audience).

For example, a solar company evaluating potential new customers for solar might look at age, location, and income to quickly determine eligibility. For customers above 40 who live in sunny areas and earn $150,000+, the system would assign a higher score.

Firmographics for B2B

Firmographics are like demographics but for businesses. They’re the attributes that make up an ICP for a B2B business.

Firmographic data includes:

Some software companies, for instance, sell solutions for small businesses while others focus on enterprise-level customers. Understanding the right firmographic targets for a given product helps sales and marketing organizations target the most promising leads.

Lead scoring works similar for firmographics as it does for demographics, but the stakes are higher because B2B companies typically have robust sales and marketing departments that work with qualified leads separately and differently.

Suppose an enterprise CRM software vendor offers a platform with features purpose-built for manufacturing and industrial companies, such as native ERP integration and automated order tracking for the users’ customers.

Their lead scoring system might adjust scores based on firmographic information like size, industry, company location, and revenue. An organization with fewer than 250 employees, for example, might be scored lower because they don’t typically have the budget or scale to benefit from all the platform has to offer.

Website User Behavior

When a new lead comes into the marketing funnel (e.g., they use the website, read a blog, or convert from an ad), the website’s backend immediately starts to track their behavior, including:

How long they spend on the website after clicking

  • Which content they look at
  • Whether or not they download or share anything
  • Which products they add to their cart
  • How often they return to the site after their first visit

This data integrates with other company software to score leads based on intent. High-intent behavior like saving a pricing document or signing up for a webinar would add to the lead’s score, whereas low-intent or non-interaction would lower it.

Social or Email Engagement 

Lead scoring systems can identify potential customers that are highly engaged with a company’s email or social media marketing. B2C customers might comment on a brand’s Instagram post, click on an online store after receiving a special offer, or sign up for deals. B2B customers may read email snippets of the company blog and click on to read more or watch a video posted to LinkedIn.

Each of these activities adds to the lead’s score. Over time, marketers can get a better picture of who their best customers are and how they interact with the brand.

Predictive Lead Scoring

Predictive lead scoring is the culmination of multiple data points compiled over time. By combining historical intent data with demographics and firmographics, predictive scoring algorithmically determines whether or not a lead fits the mold of previous customers that have successfully converted (and generated revenue).

In addition to pre-sale factors like the ones above, predictive lead scoring also considers the following factors from similar leads:

All of this data — which can be gathered from the customer’s online behavior, surveys, and data from company software — is compiled and sorted to determine how likely a lead is to purchase.

A customer that may not tick all the boxes based on demographics/firmographics or intent, for instance, might receive a higher score if similar customers have historically been more valuable.

Negative Scoring

To a lead scoring algorithm, some activities are detractors. Examples include:

  • A customer who recently unsubscribed from emails or marked them as “spam”
  • Users who click on social media ads and immediately leave the website
  • An organization with firmographic data that is complete opposite of the target customer (e.g., small business requesting a demo from an enterprise software company)

When a potential customer takes any of these actions, the lead scoring system assigns negative scores. To ensure they aren’t ignored, every company should have a system for responding to and managing these types of leads. 

Since a lead score is just a numerical value, it doesn’t necessarily mean the lead isn’t valuable. It just means they probably aren’t worth responding to over one with a higher score.

How to Determine the Lead Scoring Method to Use

How a company builds its lead scoring model largely depends on how much data it can access. Those with more resources can tap into a larger pool of customer data to create a more intricate scoring system.

Those with limited data can still assign scores based on demographics, firmographics, website behaviors, or engagement levels. But it may be partially based on assumptions and internal opinions about who should buy the product rather than compiled data.

To find the right model, it helps to answer these questions:

  • What sales/marketing channels are we using? Companies relying primarily on social media for customer acquisition, for example, should consider post engagement over website visits, which may not be as targeted.
  • What data is available? If customer data is limited, scoring start with demographics and firmographics. Companies with better data infrastructure can move to anonymous intent data, which is available for purchase through third-party resellers.
  • How well-defined is our ICP? Early-stage companies don’t usually know exactly where to look, while scaled businesses can take a more straightforward approach to scoring their leads.

When building a lead scoring model, it’s important to define SQL criteria beforehand. This includes the factors that contribute to lead conversion. As an example, a business that focuses on enterprise customers might say, “We won’t take to any customers that have fewer than 250 employees.”

It’s also important to consider the conversion process. Consider what SQLs do before they book a sales demo to understand where a rep should come into the picture.

Indicators of Effective Lead Scoring

Businesses that successfully score leads have something to show for it. These indicators typically include:

Lower Unsubscribe Rates

Leads that receive valuable content are less likely to unsubscribe from emails. When leads deem the content they receive “valuable,” they’re probably more likely (or, at the very least, qualified) to buy a product.

Better MQL:SQL Ratios

Most of the leads who request to talk with a sales rep aren’t actually qualified. Salesforce data estimates this conversion rate to be as low as 13% on average. If more MQLs become SQLs, it’s a sign that marketing efforts are garnering interest and intent from the right kinds of prospects.

High Engagement Scores Across the Board

Businesses score engagement in several ways. Net Promoter Score (NPS), customer satisfaction ratings, and Google Analytics data can all measure it effectively. In general, a highly engaged audience will be loyal and likely to buy products/services.

Higher Revenue Per Lead

The ultimate goal of any lead scoring system is high revenue per lead. It takes time to accurately track the ROI of predictive lead scoring, but once it’s in place, companies should start seeing a spike in the number of leads converted into paying customers.

Shorter Sales Cycle Time

It shouldn’t take forever to convert leads that fit the scoring criteria and are likely to buy. Sales cycle length depends significantly on the type of business, product complexity, and other factors. But companies with successful scoring algorithms should see shorter sales cycles relative to their current company dynamics.

What to Look for in Lead Scoring Tools in a CRM

As the heart of any customer-oriented operation, lead scoring is characteristic of CRM software. All CRMs offer different tools to cater to specific markets. Still, every CRM’s lead scoring arm should have the following capabilities:

Pipeline and Opportunity Management

Lead scoring tools should offer pipeline and opportunity management, which allows sales reps to track progress on deals they’re working on. This is important for understanding what kind of customer is buying a product or service.


Automated reporting is the logical next step for evaluating lead scoring performance. Customizable reports provide a comprehensive view of the sales funnel and outcomes, so companies can assess what works and where improvements need to be made.

Email Marketing Automation

Most companies manage their email marketing through Mailchimp, Klaviyo, or a similar platform. All of these systems integrate with CRM’s native features to create an efficient lead scoring process.

Contact Data Synchronization

Customer contact data should sync with the CRM in real-time. Since it includes demographic and firmographic information, it’s essential to the lead scoring process.


Not every CRM will offer predictive analytics out of the box — larger companies purchase this feature as an add-on. It might also require greater IT infrastructure, which some companies don’t have. But it’s necessary for successful lead scoring, since it helps companies anticipate the probability of a potential sale.

People Also Ask

What’s the difference between lead grading and lead scoring?

Lead scoring primarily focuses on interest from the prospective customer’s point of view. Lead grading is more concerned with assessing the quality of leads from the company’s perspective. It’s largely based on target markets and buyer personas.

What is predictive lead scoring?

Predictive lead scoring is a machine learning-based system that uses customer data to state the probability of a future sale. It’s based on the idea that customers who have similar buying patterns are likely to behave in similar ways.