What Is Lead Scoring? Benefits, Types, and Best Practices

Suppose you have two leads, A & B. B is your best shot, but you’re too busy pleasing A because, of course, A arrived first. This means you might be spending valuable time chasing the wrong lead while missing out on the one with the highest potential to convert.

If you still rely on this FCFS approach to manage and convert leads, it’s time you switch to a lead scoring system. 

A lead scoring system helps you identify and prioritize the most promising leads. It uses a point-based system to score leads based on their likeliness to convert, allowing your sales and marketing teams to direct efforts toward high-value, quality leads. 

In this article, I’ve covered the benefits, types, and best lead scoring practices. I’ve also provided a step-by-step guide on creating a lead scoring system from scratch. 

Here is a quick overview of what you’ll learn in this article.

• Lead scoring is a point-based system that helps you organize and prioritize leads based on their likeliness to convert
• A lead scoring model has three key components – Explicit, Implicit, and Negative scoring
• Different types of lead scoring models: Traditional, Predictive, and Hybrid
• Learn to develop a lead scoring system in just three steps
• The best practices of lead scoring involve defining your ICP, identifying key scoring criteria, defining scores, and regularly reviewing your approach

Let’s dive in!

What is Lead Scoring?

Lead scoring is a method to rank or score leads based on their readiness to buy. It generally involves assigning a numeric value between 1 and 100—the higher the value, the better they align with your ideal customer profile (ICP), and the more likely they are to convert. 

Ideally, leads are scored using two approaches: Explicit and Implicit scoring. 

• In Explicit scoring, we use demographic (for individuals) or firmographic (for businesses) data, such as a lead’s age, gender, location, job title, company size, and more, to help you determine whether the lead is a good fit.

• Implicit scoring is based on a lead’s behavioral data (online body language), such as interactions with your website, emails, or content–that helps you understand their interest level. 

When you merge the two scoring systems, you build a clear picture of the prospect’s value to your business based on their attributes (explicit data) and behaviors (implicit data). 

Why is Lead Scoring Important?

Here’s why lead scoring is important:

• It focuses your sales and marketing team efforts on the right account and leads
• It gives sales and marketing teams more context on how a lead interacts
• Acts as a foundation for marketing and sales service-level agreements (SLAs) for lead follow-up
• Generates relevant data and insights to deliver more personalized experiences
• Gives a deeper understanding of leads with the highest intent and fit for your ideal customer profile, hence increasing conversion rates

Key Components of a Lead Scoring System

Generally, in B2B companies, lead scoring is a combination of 3 components.

1. Explicit Data

Explicit data refers to the demographic (personal) and firmographic (company) information you have about your leads.

Demographic information includes personal details about the individual, such as age, location, job title, gender, and education level. 

Why is it important?

If your target audience lives in a specific region, you’ll want to create a model based on relevant attributes to exclude outliers.  

For example,

• If you sell in the United States, anyone outside that gate will score less than someone from within the territory. 
• Similarly, if you’re targeting millennials, a 21-year-old lead may score higher against a 50-year-old veteran.
• If you have a product for Android users, Apple users won’t be the right fit.

Here’s an example of how demographic scoring works 👇

Firmographic information includes the ‘Firm’ or company information related to the lead, such as job title, industry, company size, location, or revenue. 

Why is it important?

Most B2B companies rely on firmographic data. It indicates whether the lead (individual) is a decision-maker and whether the lead (business) fits their product or service offerings. 

Think of this: If I sell a small business software, my target audience isn’t an enterprise with 1000+ employees.

This is exactly why firmographic data is essential. 

• A lead with the title “CEO” or “Marketing Director” might be more valuable than someone with the title “Intern.”
• A lead working at a company with 1000+ employees might score higher for a B2B enterprise solution. 
• Or, a lead in the tech industry may be more relevant to a software solution than a lead in the healthcare industry.

Here's an example of how firmographic scoring works 👇

2. Implicit Data

Implicit data includes behavioral and engagement signals (online body language) of your prospects to measure their level of interest in your product or service.

Behavioral signals consist of how a lead behaves across your website.

For example:

• How many pages did they visit? How long did they stay on each?
• Did they view any case studies or testimony?
• Did they visit the pricing or demo page?
• Did they fill out a form? Did they fill out every form field?

Each of these signals is scored based on their relevance. For example, visitors to a product page exhibit better buying behavior than visitors to your careers page. 

Engagement signals, as the name says, refer to solid actions performed by the lead.

For example,

• Did they download any materials? If so, how many?
• Did they open your email and CTA links?
• Did they interact with your social media posts?
• Are you mentioned in a user-generated content (UGC)?

A combination of behavioral and engagement data creates Implicit data. 

Example of how Implicit data works:

A lead who frequently visits your pricing page or registers for your webinar may score higher than someone who only browses a blog.

Here's an example of lead scoring based on implicit data:

3. Negative Scoring

Also known as data quality scoring, negative scoring involves assigning a minus score to prospects when their actions suggest a disengagement or disinterest.

For example, if the email address comes from a common domain (e.g., gmail.com, yahoo.com, etc.)

Or if the first or last name contains numbers or the geolocation based on the IP address is outside your service area. There can be many actions for negative scoring, such as:

• Unsubscribing from emails
• Prolonged inactivity
• Ghosting/no replies
• Unqualified company size or industry

Types of Lead Scoring Models

All in all, there are 3 lead scoring models: traditional, AI-driven (predictive), and hybrid.

1. Traditional Lead Scoring

The traditional scoring model uses predefined rules and criteria to score leads. Your marketing and sales teams mutually assign weightage or values to leads based on how well their demographic and engagement data align with your ICP.

For example, If a lead is a CEO (+9), visits your pricing page (+7), registers for a free trial (+10), unsubscribes from emails (-4), and has not replied in a while (-7).

It’s an old-school method that requires manual calculations. But it is helpful for small businesses that neatly understand their ideal customer profile and want to focus ONLY on those who meet specific criteria.

However, traditional lead scoring is time-consuming and falls short of capturing complex relationships between different lead attributes. 

2. Predictive Lead Scoring

Predictive lead scoring leverages artificial intelligence (AI) and machine learning (ML) to identify high-value leads.

For example, CRM software like Freshsales, Zendesk Service, and Salesforce delivers rich predictive scoring models. 

These tools can analyze years of historical data within minutes (nearly impossible for humans) and extract behavioral patterns. Based on these patterns, the technology can predict a lead's likelihood of converting before you assign a sales rep to it. 

However, technology's reliance on humans seems inevitable. You still need to feed these systems with your scoring criteria. For the rest, they do all the heavy lifting, including capturing, analyzing, and scoring leads.

Predictive scoring has some key advantages over traditional scoring:

• You can analyze oceans of data within minutes
• Use augmented analytics to deliver insights hidden within the prospect's data 
• Capture complex buyer journeys and lead attributes 
• Spend roughly half the time compared to traditional scoring

3. Hybrid Lead Scoring

Hybrid lead scoring integrates explicit, implicit, and predictive models to evaluate and prioritize leads more effectively. By combining these approaches, you can ensure all-around, accurate lead predictions. 

For example, I can stay put to my defined criteria to score leads but also use predictive scoring to come to a conclusion. 

Hybrid scoring models have three key benefits:

• You get a holistic understanding and assessment of each lead
• This combination leads to a robust lead qualification process
• The synergy results in well-targeted marketing campaigns and sales strategies
• There’s always a human-in-the-loop to avoid AI bias

Now, let’s check which attributes and characteristics are popular in lead scoring.

Popular Lead Scoring Metrics

Below are the 10 most popular lead-scoring metrics.

1. Location: A demographic metric that indicates a lead’s relevance to your target ICP. If you sell in a specific region, a lead from within the zip code, state, or country will score more points than from other locations.
2. Job title: The lead's role provides insights into the lead’s authority and relevance within the organization. Titles like "CEO" or "Marketing Manager" might suggest a higher likelihood of influencing purchasing decisions.
3. Industry: A qualitative metric that promotes leads from your targeted industries, helping you eliminate outliers and focus efforts on more promising prospects.
4. Company size: A quantitative metric that indicates a prospect’s purchasing capacity. Larger enterprises might have bigger budgets, while smaller companies might be more agile in decision-making.
5. Unsubscribe rates on emails: A negative scoring metric that indicates the rate at which prospects are unsubscribing from your emails, highlighting improvement in email campaigns
6. Lead engagement rate: Measures a prospect’s engagement level with your brand. Higher engagement rates often correlate with conversion.
7. The source of information: Linking leads back to the source (e.g., organic search, paid ads, referrals) can help determine their quality and effectiveness. About 62% of marketers believe LinkedIn brings them 2X B2B leads than the next highest social channel.
8. Social media interaction: Reflects brand awareness levels but doesn’t immediately highlight a purchase intent. Most marketers use this metric to create relevant content and run targeted campaigns.
9. Demo requests: Leads who request product or service demos indicate higher purchasing intent and are most likely to convert
10. Purchase history: Your prospect’s past buying decisions impact your predictive lead scoring model. Additionally, it helps identify opportunities for upselling or cross-selling.

How To Create a Lead Scoring Model?

You might have already gone down the rabbit hole of figuring out how to calculate a lead score.

And let’s face it, it is no rocket science. At the same time, it’s confusing to choose from different attributes and models.

If you’re not ready to invest in a predictive lead scoring tool just yet, you can still develop a manual calculation to empower cross-functional teams.

Let’s see how you can create a lead scoring model step-by-step with an example. 

Scenario: I run an enterprise software company, and my ideal customer profile (ICP) includes enterprise-level organizations with 1000+ employees located in the U.S.

Suppose I got two leads:

• Lead A is the CEO of a fabric manufacturing company with 1000+ employees. They browse a few random website pages, sign up for your newsletter, like a few social posts, open three emails once on your email list, and submit an inquiry.
• Lead B is a project manager from Seattle working in the software industry at a company with 100 employees. They view five pages of your website, visit your pricing page, download an eBook, join a webinar, and sign up for a free trial.  

Step 1: Identify Key Scoring Criteria

To begin with, take your implicit and explicit data sets and jot down common attributes based on your lead behavior or demographics. 

You might want to look into your customer data and analytics and discuss with your sales and marketing teams who they believe is the ideal target audience. 

The tricky thing about designing a lead scoring system is that there's no single playbook that works for every business. You'll need to decide which attributes make a prospect more or less valuable for your business.

For this example, I defined seven key attributes that signal the likeness to convert. 

Attributes	Data Category
Location	Demographic
Industry	Firmographic
Job Title	Demographic
Company size	Firmographic
Website engagement	Behavioral
Social media engagement	Engagement
Visiting the product pricing page	Behavioral
Free trial	Engagement

Step 2: Assign Weights and Points to Each Criterion

The value assigned to these attributes depends on the target ICP and the likeliness of an action leading to a conversion. The more closely relevant the attributes are to your ideal buyer persona, the higher the point value should be.

In my example, my ideal buyer is an enterprise-level organization with 1000+ employees in the U.S.

Here's how my lead scores might look on a scale of 1-10:

Attributes	Lead A	Lead B
Location	+2	+10
Industry	+3	+9
Job title	+9	+5
Company size	+10	+4
Website engagement	+4	+7
Social media engagement	+5	0
Lead magnet	0	+10
Visiting the product pricing page	0	+10
Webinar	0	+10
Free trial	0	+10

Step 3: Incorporate Score Degradation for Inactive Leads

We’ll now consider attributes that signal disinterest or disengagement from leads.

Suppose Lead A unsubscribes from your email list while Lead B has an unqualified company size. After discussing this with the team, you assign points -6 and -10, respectively.

Here’s how the score will be adjusted: 

Attributes	Lead A	Lead B
Location	+2	+10
Industry	+3	+9
Job title	+9	+5
Company size	+10	+4
Website engagement	+4	+7
Social media engagement	+5	0
Lead magnet	0	+10
Visiting the product pricing page	0	+10
Webinar	0	+10
Free trial	0	+10
Unsubscribe email	-6	0
Unqualified company size	0	-10
Lead Score	27	65

While the traditional lead scoring method offers complete transparency and flexibility, it’s labor-intensive—especially determining the value of your prospects' activity—like engaging with emails, filling out web forms, and attending webinars—can be trickier.

And for that, today’s CRMs use marketing automation and artificial intelligence to generate predictive lead scores.

Bonus Step: Use Automation Tools and CRM Systems To Automate Lead Scoring

You can always rely on AI to do the job if all this feels overwhelming. It won't be quite as nuanced—nor will it understand your ideal customer as well as you do—but AI has large-scale number-crunching on its side.

Larger businesses typically run on enterprise software like Salesforce or HubSpot. A more purpose-built option for small and medium-sized companies is Pipedrive or Freshsales, which include advanced lead scoring and AI features.

Here's what Freshsales looks like (the Score and Customer Fit are at the top, and the details of how the score was developed are provided).

👉 Read our detailed review of Freshsales sales CRM software

Best Practices for Effective Lead Scoring

Below are 7 best practices you should stick to, when scoring leads: 

1. Define Your Ideal Customer Profile (ICP)

An ideal customer profile is a hypothetical description of the lead or prospect who would most benefit from your product or service.

Think this way: an ICP is what you put as a benchmark for your leads to see if they are a right fit for your product.

A typical ICP includes demographic and firmographic attributes of an account. For example, if you sell in a particular area, is your lead within that zip code, state, or country? Are they from the industry you want to sell in?

However, in a B2B setting, you need to consider the below traits:

• Industry fit
• Ability to purchase (Revenue)
• Company size
• Demographic fit

...and more, based on your business requirements.

Key Takeaway: I understand defining your ICP can be overwhelming at first. If you’re unsure where to start, use Figma’s ICP generator to create your ideal customer profiles in seconds without much scooby-doo! 

2. Identify Key Engagement Criteria

Focus on the touchpoints and activities that push the lead ahead in the sale cycle. Actions such as email opens, content downloads, free trial sign-ups, social media interactions, demo requests, etc.

An ideal way to get around this is to interview your existing customers and get answers to questions like:

• What actions did you take before making a purchase?
• Which content/video/landing page paced up your buying journey? 
• How did you hear about us?

These answers allow you to find common patterns, trends, or actions your leads undertake during their buying journey. 

For example, if most customers consider emails as a deciding factor in their purchase decision, you could assign more value to leads that engage with emails. 

Key Takeaway: There can be multiple touchpoints in today’s digitized selling process. I’d suggest using CRM software to process and analyze knee-deep historical data and predict perfect lead scores within minutes.

3. Develop a Scoring System

Once you’ve identified the key criteria, develop a clear and consistent scoring system. This system should be based on both explicit data (e.g., job title and company size) and implicit data (e.g., website visits and content downloads).

Search for common patterns and touchpoints in your conversion cycle. Those data represent a crucial element in the buyer process, and the leads who currently surpass those actions get a higher score.

Key Takeaway: Ensure your scoring system is flexible enough to accommodate changes in strategy and market conditions. Try HubSpot’s free Lead Scoring Template. It’s simple to set up and has a built-in calculator to input lead data and calculate a final lead score.

4. Set Clear Thresholds

A threshold ensures that only high-value leads are passed to the sales team.

You see, once a lead crosses a defined score threshold based on a combination of demographic and engagement scores, the lead goes to a sales rep for follow-up. 

For example, for a marketing-qualified lead (MQL), the final lead score may be around 40 to 50, while for a sales-qualified lead (SQL), this could be above 80 points. 

Key Takeaway: Yet again, investing in lead scoring software is your best bet to automate this process. It helps to set up clear thresholds for handoffs between marketing and sales.

5. Use Negative Scoring

Incorporating negative scoring into your lead scoring model is the same as adding lemon to spaghetti sauce. Bitter but brighten the flavor!

Negative scoring helps account for disinterest or disengagement.

For example, a lead unsubscribing from your emails signals a low interest. By penalizing negative actions, you keep your team focused on active leads with the highest potential to convert.

6. Regularly Review and Refine

Lead scoring is not a ‘once and for all’ system. Traditional or manual scoring, especially, requires continuous adjustments and iterations. You see, as you learn more about a prospect’s sales cycle, you stumble into more tricks and turns.

Therefore, it’s essential to regularly review and refine your system to ensure it stays aligned with your business goals and market changes. Analyze the performance of past leads and adjust scoring parameters accordingly to improve accuracy and efficiency.

Key Takeaway: To gather insights on your lead scoring model's effectiveness, set up quarterly reviews involving both marketing and sales teams.

7. Align Sales and Marketing

Lead scoring is effective when the sales and marketing teams collaborate. I’m sure both teams would have contradicting ideas about what counts as an attribute and which should be weighted more. 

Align your marketing and sales teams to create an ideal customer profile and define what constitutes a marketing-qualified lead (MQL) and a sales-qualified lead (SQL).

Once you’ve done that, your sales team can review and approve your information and buyer personas. 

Key Takeaway: Set up weekly or monthly marketing and sales team meetings. Brainstorm and discuss lead quality. Focus on the most relevant and common findings. Make adjustments needed to the scoring system 

Common Challenges of Lead Scoring (How to Overcome Them)

Although highly effective, lead scoring systems have their share of challenges:

1. Difficulty in Collecting Accurate Data

Lead scoring relies heavily on data, but collecting accurate and up-to-date data can be challenging. 

How to overcome?

Establish truthful sources for your data:

• Collect explicit data like demographic and firmographic information using web forms
• Website tracking software, such as Google Analytics, can gather precise data. You can use it to track lead behavior and interactions and gather profile information.
• Use automation tools or CRMs to do it for you. For example, Freshsales has an auto profile enrichment feature that automatically imports all of the above information once you add an email address and website.

2. Over-Reliance on Predictive Models Leads to Missed Opportunities

While predictive models can be powerful, over-relying on them may cause you to miss opportunities with leads that don't fit the exact patterns the model identifies.

These models tend to favor leads that closely resemble traits from past successful customers, potentially overlooking fresh or unconventional prospects.

How to overcome?

• Use Hybrid Scoring: Combine traditional and predictive lead scoring. This way, you can take a more holistic approach and prevent completely ignoring leads that fall outside your typical patterns but still show potential.
• Monitor and Adjust the Model: Regularly assess your predictive model’s performance and adjust it based on new insights and market changes. Test new variables or tweak the model to capture leads with different characteristics.

Ultimately, don't ignore leads that don’t fit the predictive model but show strong engagement. Unconventional leads may convert at a higher rate. You never know!

Key Takeaways

1. Lead Scoring Prioritizes High-Value Leads – A lead scoring system helps businesses focus on leads with the highest potential to convert, replacing the outdated "first-come, first-served" approach.
2. Three Key Components of Lead Scoring – Effective lead scoring combines Explicit Data (demographic/firmographic details), Implicit Data (behavioral and engagement signals), and Negative Scoring (factors indicating disinterest).
3. Different Types of Lead Scoring Models – Businesses can choose between Traditional (manual rule-based), Predictive (AI-driven), or Hybrid (a combination of both) lead scoring models based on their needs.
4. Steps to Create a Lead Scoring System – The process involves identifying key scoring criteria, assigning weighted scores to attributes, and incorporating negative scoring to refine lead prioritization.
5. Best Practices for Lead Scoring – Defining an Ideal Customer Profile (ICP), setting clear scoring thresholds, incorporating negative scoring, regularly refining the system, and aligning sales and marketing teams ensure effective lead scoring.