Without a proper lead scoring process, your sales reps are wasting valuable time on leads that are unlikely to convert — meaning your company is losing money just like that. But how do you determine which leads are most likely to become paying customers?

The struggle to distinguish high-value leads from low-value is all too familiar for sales teams, but it’s time to turn the tide.

Let’s get started!

B2B lead scoring: definition and specifics

B2B lead scoring is a system used by revenue teams to determine the potential monetary value of a lead. It’s based on predefined criteria and displayed as scores, indicating the likelihood of the lead making a purchase and whether they require further lead nurturing.

Let’s now delve into the 2 types of lead scoring data sources.

Explicit data to evaluate external lead characteristics

Explicit data refers to the objective criteria used to evaluate a lead’s potential value. It is often easier to collect and evaluate than implicit data (we’ll touch upon it below), making it a popular choice for businesses just starting with lead scoring. 

Explicit lead scoring includes:

• Company size: Use firmographic data to evaluate the size of a lead’s company. You can assign higher scores to leads that work for larger companies or have higher revenue potential.
• Geographic location: Depending on your product or service, it might be more valuable to target leads in specific geographic regions. Assigning points based on location can help you prioritize leads that are in your target market.
• Industry: Leads from certain industries might be more likely to convert than others.
• Job title/position/time on position: Assign points based on a lead’s job title or position. This factor can help you identify decision-makers and key influencers within a company.
• Technologies used: You might want to target only the companies that use specific tools, tech, or marketing instruments if, for example, your product or service should enhance their effectiveness.

Implicit data to understand your leads’ behavior

Implicit data provides insight into underlying leads’ behaviors and interest in your product or service. It helps with understanding a lead on a more granular level and spotting behavioral patterns that indicate the readiness for a purchase.

Implicit lead scoring includes:

• Website behavior: Look for web page visits, content downloads, demo requests, webinar registrations, and other website interactions to identify leads that are actively engaging with your brand.
• Social media behavior: Monitor how leads interact with your social media profiles. Do they follow your accounts, like and comment on your posts, and engage with your brand? Assign points accordingly.
• Email engagement: Keep track of how leads interact with your email campaigns. If a lead consistently opens and clicks on your emails, that could indicate higher interest and engagement with your brand.
• Purchase intent: Use intent data to evaluate a lead’s likelihood to purchase. This data can come from a pricing page visit, high social media activity, and other online behaviors.

Knowing the basics, let’s dig deeper and explore how to set up a lead scoring model.

How to create an effective lead scoring process in B2B

1. Start with an ICP

Before you can identify and prioritize leads, you need to define the characteristics of your target customers. That’s why crafting an ideal customer profile (ICP) is a crucial step in lead evaluation.

Here are some questions to consider when crafting your ICP:

• What industry does your ideal customer operate in?
• What is the size of the company you are targeting?
• Are they startups, small businesses, or large enterprises?
• Where are they located?
• What pain points does your product or service solve for them?
• What factors influence their decision-making process?

Once you’ve got a good grasp of your ICP, you can start targeting companies that perfectly match the explicit criteria. This way, you’re not just randomly chasing leads but rather actively pursuing and organizing qualified ones.

2. Predict future outcomes by leveraging historical data 

Historical data is crucial in the lead scoring process because it allows you to identify patterns and trends that can help you predict future outcomes. By analyzing historical data, you can gain insights into the characteristics and behaviors of leads that have already converted.

Where can you find this data? Your CRM! Delve into reporting and try to locate patterns. Also, you can carry out and benefit from a 360-degree view of your customer.

3. Establish your scoring criteria

To rank all of your leads objectively, determine the main criteria or characteristics for scoring. This involves considering two groups of lead scoring parameters we’ve mentioned earlier:

1. External
2. Internal

External criteria involve listing all the demographic criteria needed to assess leads based on your ICP.

On the other hand, an internal criterion is a list of all the lead activities you need to assess how well they fit the ICP. This may include factors like website visits, content downloads, social media engagement, and email opens and clicks.

Here’s how to put this knowledge into action and create a lead scoring model surfacing both implicit and explicit data points.

4. Decide on a point value threshold

Once you’ve identified the implicit and explicit criteria for scoring, the next step is to prioritize them by grouping them into levels of importance.

For instance, you could use the following point system:

• Critical criteria — 10 to 15 points
• Important criteria — 5 to 9 points
• Influencing criteria — 1 to 4 points
• Negative criteria — negative points

Let’s say a lead completes multiple actions and the sum of their points is 12, indicating that they fall under the critical criteria. This means that they are highly qualified and should be prioritized for sales outreach or passed on to the account managers.

That’s it. In 4 simple steps, you can draft your lead scoring system and improve it as you begin receiving more lead data. Analyze what values translate into higher conversions and shorter sales cycles. 

And before outlining your lead scoring model, learn how we at Belkins score leads. If the approach applies to your pipeline, replicate and tweak it for a better fit.

Belkins lead scoring system breakdown

At Belkins, we take lead scoring seriously. Our business development representatives (BDRs), sales development representatives (SDRs), and account managers (AMs) all use HubSpot to score leads for us and our clients alike.

Belkins’ scoring scheme ranges from -10 to 10, with -10 representing the most irrelevant and ill-fitted leads, and 9–10 being our ideal prospects. Here are 3 categories we break them into:

• <0 — low-potential
• 1-5 — medium potential
• 6-10 — high potential

Prospects that are qualified with a score of 3 and higher go to our sales team.

We use numerous qualification parameters and questions to score leads, including:

• Channels they’ve come to us through (e.g., referrals, direct traffic, organic search, etc.)
• Decision-making authority based on their titles
• Company size
• Industry
• B2B/B2C focus
• Online presence (we work exclusively in the digital domain, so companies that focus on cold calling or other non-digital outbound channels won’t benefit from working with us)

Below, you can see an example of Belkins’ deal scoring according to the channels they came from.

5 lead scoring models

Merodio’s lead scoring matrix

Let’s start with the lead scoring process created by Juan Merodio. He uses 2 scores: 

• PAIN measures the level of difficulty or discomfort a potential client is experiencing, with scores from 0 to 10. 
• FIT measures how closely aligned the lead is with the company’s ideal customer, considering factors like financial resources and the ability to apply the solution effectively.

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Example

Let’s say you run a software development company that creates custom software solutions for businesses. A potential lead fills out a contact form on your website, expressing interest in your services.

Your team assigns a PAIN score of 8 because the lead mentions that their current software is outdated and causes problems in their day-to-day operations. They’re actively seeking a solution, and the problem is causing a significant impact on their business.

Your team also assigns a FIT score of 7 because the lead has a budget to invest in new software, and their business needs align with your company’s ideal client.

The lead’s final score would be 15 (PAIN score of 8 + FIT score of 7).

Based on the lead score, your team categorizes the lead as a “hot” lead, indicating that they’re actively seeking a solution and are a good fit for your business.

Adobe Marketo Engage lead scoring

Michael Guanci explained the basics of lead scoring and how to calculate scores in Adobe Marketo Engage.

The process consists of 2 components:

1. Demographics or qualifications
2. Behavior or engagement

And here’s their step-by-step process:

1. Create programs to update the score fields regularly. 
2. Decide how much to increase behavior scores for different actions and the minimum needed for other actions. 
3. Use demographic data to set standards for what makes a good fit.
4. Run campaigns to score your entire database once so people don’t get scored twice.

To qualify leads further, use a matrix that combines behavior and demographic scores to create a lead grade.

The grade has a letter grade for demographics (A–D) and a number for behavior (1–4+). To focus your efforts better, try demographic gating. This approach shows the percentage of people who are a good fit based on demographics.

Dealfront lead scoring system

Dealfront uses buyer intent data to create a lead scoring system, says Alicia Murphy, PLG Sales USA Team Lead. They track a lead’s behavior and traffic source to determine their level of qualification and how best to convert them.

Their scoring system is based on points, taking into account website visits, page views, and form fills.

For example, a lead who visits your website twice a week will receive 200 points, and a lead who also checks your pricing page will earn an additional 450 points. Once a lead accumulates 1,000 points, they’re considered ready to be handed over to the sales team.

Negative scoring attributes play a crucial role in Dealfront’s lead scoring system too. These attributes help to filter out unqualified leads.

For example, if a lead unsubscribes from Dealfront’s email newsletter, they will receive a deduction of 300 points. Additionally, if a lead doesn’t visit your website within 2 weeks, they’ll receive further deductions.

Here’s how it looks:

ProPair predictive lead scoring

ProPair’s AI/ML software offers predictive lead scoring through its product RANK, which allows sales teams to prioritize leads, guide follow-up activities, and revisit abandoned opportunities.

Ladera Lending, a home loan and mortgage refinance provider in California, implemented ProPair’s technology to enhance its lead scoring and conversion system. 

Here are the benefits they got with ProPair’s technology:

• Optimized lead and transfer assignments
• Prioritized prospect scoring
• Continued lead handling optimization
• Leveraged mortgage industry expertise

Kevin Smith, Senior Technology Manager for Ladera Lending, explained that having data insights is only the beginning. The real value lies in how you apply the insights to drive business growth. ProPair’s software engine bridged the gap between raw lead profiles and optimal handling to maximize business results.

With ProPair’s custom-ranked leads and actionable insights, Ladera Lending was able to enhance its lead conversion efforts and boost its closing rates.

Qwilr lead scoring model

According to Brendan Connaughton, Head of Growth Marketing at Qwilr, their company takes into account both firmographic and demographic factors and uses MadKudu to get all necessary data.

Qwilr captures free trials and demo bookings from a range of individuals, including new founders, CEOs, and sales leaders.

It has also built robust marketing automation to support smaller accounts and sole traders. They classify all leads into 4 categorical buckets (Low, Medium, Good, and Very Good) and numerical scores. Good and Very Good leads are given more attention, while Low and Medium leads receive greater focus for marketing automation and lifecycle marketing.

Since incorporating lead scoring, Qwilr has been able to shift its focus toward higher-value opportunities, which has led to greater sales velocity and outcomes.

Best lead scoring software

Lead scoring is a daunting task, especially when you’re dealing with a colossal amount of data to analyze. Trying to score leads manually can be overwhelming and time-consuming. So, make your life easier and utilize automation tools that will simplify the whole process of collecting data and assigning scores.

HubSpot

Best for: Businesses of any size and need

HubSpot has a lead scoring system that uses machine learning to find the best potential leads for your business by analyzing large amounts of pipeline data. What’s more, the automated system will continually optimize its lead scoring process over time. And of course, you can customize it as you like to meet your business specifications.

In fact, you can create up to 25 unique lead scoring models to effectively target different audiences and maximize your lead generation efforts.

Salesforce

Best for: Enterprise-level companies and corporations with complex infrastructure.

Salesforce offers a highly flexible API, making it a great choice for businesses that require advanced lead scoring customizations. It also provides out-of-the-box tools to effectively score, grade, and qualify inbound leads.

Pipedrive

Best for: SMBs who prioritize user-friendly UX and great pipeline and reporting visualization.

The lead scoring system of Pipedrive is based on customized pipeline stages, and it allows businesses to score their leads based on various criteria such as lead source, company size, industry, and engagement level.

From prospects to profits

And that’s it! We’ve covered a lot of ground on lead scoring and how to turn those prospects into profits.

Let’s recap some key takeaways:

• Lead scoring isn’t one-size-fits-all. Assess your business needs before using it. Based on that, create a customized lead scoring model that works for you.
• Make data your best friend. The key to nailing your lead scoring game is data, data, data! The more you have, the more accurate your scores will be. So, invest in tools that’ll provide you with valuable intel on your leads and their behavior.
• Experiment. The world of sales is constantly changing; what works today may not work tomorrow. So, be open to trying new things and adapting your lead scoring strategies as needed.