Understanding Lead Scoring: How to Build a Scoring Framework (2024)

If you’ve ever swiped through a dating app, you know quantity isn’t everything. It’s nice to have a lot of options, but they’re not all going to be life-partner material. 

Finding your best match in a pile of dating app profiles is sort of like spotting a loyal ecommerce customer in a sea of casual online shoppers. Dating might always be hard, but when it comes to business, there’s an alternative to manual sorting: Lead scoring is a way for companies to identify prospects with the highest chance of converting. In essence, it’s a way to make sure the best prospects sort to the top.

Small business owners can’t afford to waste time on bad leads, so here’s how to use lead scoring to focus your marketing efforts on the highest quality leads.

What is lead scoring?

Lead scoring is the process of ranking potential customers, or “leads,” based on the probability they will convert to paying customers. In the lead scoring process, a business identifies the behaviors and attributes that indicate a potential customer is likely to make a purchase, then assigns a point value to each characteristic or signal. The business combines these points to form a potential customer’s lead score, which represents the overall lead quality. 

Calculating a potential customer’s lead score helps you determine next steps. For example, you might prioritize leads with strong conversion potential by asking your sales and marketing teams to reach out to them personally. You might decide that approaching low scoring leads, on the other hand, is a waste of time.

Lead scoring models

Your lead scoring model encompasses the types of data you’ll collect and analyze in order to assign points to leads. These models can include explicit customer data (data the customer enters themselves, like an email address) or implicit data (insights the business draws by observing customer behavior, like engagement with a brand’s social media account). 

Companies often use multiple lead scoring models at the same time. These are some common ones:

Demographic data

Demographic data includes basic information like age, gender, location, and job title. Companies can add demographic fields to lead capture forms to collect some of this data. Analyzing demographic data can reveal how well a user aligns with your ideal customer, and it can help you identify ineligible customers. 

If your locally sourced food product is only available for delivery within the Pacific Northwest, for example, you could assign a negative point value to respondents who live outside of this region. 

Company requirements 

A company requirements model scores leads based on how well they align with your customer base. This model involves identifying your minimum requirements for consumer eligibility and collecting data to verify users satisfy these conditions. This system aims to reduce the number of irrelevant leads passed to your marketing and sales teams. 

For example, if you run a B2B company focused on small businesses, you create a lead capture form with fields for intended product use and company size. To score leads, you could assign a higher point value to responses indicating professional use at a company with fewer than 100 employees. You might also assign a negative point value to users who indicated they were interested in your product for personal use. 

Engagements

This model uses engagement data to evaluate consumer interest. Engagement models assign higher point values to customers who regularly interact with your content by evaluating data points like email open rates, click-through rates, and social media engagement. 

Website behavior patterns

Website behavioral data reveals how a user interacts with your website. This model involves tracking online behavior patterns such as how many pages a user visits and how long they spend on each page. It can also consider a user’s visits to high-value pages like a checkout or pricing page. With this model, a business might assign a high point value to users who have visited the website more than twice and spent more than 30 seconds on a product page. 

Spam contact detection 

The spam detection model prevents you from passing along low-quality leads to your sales team. This model involves identifying and assigning a negative point value to fake leads. Spam detection tools flag suspicious behaviors like entering form responses with multiple consecutive letters or submitting invalid email addresses. 

Spam detection can also detect dubious keywords and flag submissions that include profanity, celebrity names, or names of fictional characters. For example, a business might use this model to automatically assign a negative point value to leads with email addresses like timothee@chalamet.plumbing or fake12345@notreal.psych. 

How to score leads manually

1. Identify key lead attributes
2. Assign point values
3. Create tiers and determine next steps
4. Refine your scoring process

From pop-ups asking for an email address to referral programs to in-person sign-up sheets, there are multiple ways to capture leads, and you can even use lead generation tools to expedite the process. Once you have data on potential customers, you can use a scoring framework to help you identify the individuals with the strongest lead conversion potential. Here’s how to do it:

1. Identify key lead attributes 

Using data like past conversions and market research, identify both the traits and behaviors that might signify a quality lead. Determine the characteristics associated with your ideal customer profile (like age, gender, and profession), and consider any requirements that limit your eligible customer base. 

Make sure to identify customer behaviors, too: If a customer signed up for SMS marketing, for example, they might be a quality lead.

2. Assign point values

Develop a scoring system and assign values to each attribute. Businesses often choose a 100-point scoring system. Assign high point values to behaviors that indicate serious interest, and consider using negative scoring by applying negative points to disqualifying actions. 

This practice helps to weed out low-quality leads. The goal is to generate scores that reflect how likely a customer is to convert. For example, if your ideal customer is a mom who likes DIY car repairs, you might assign a higher numeric value to female-identified leads and a negative value to users who don’t own automobiles. 

Make sure to use past conversion rates. For example, if you know that 50% of leads who add a product to their cart end up making a purchase, and 20% of leads who open your marketing emails convert, you’ll assign a higher number to leads who’ve added a product to their cart than you will to leads who’ve clicked through your emails. 

3. Create tiers and determine next steps

Create a system to categorize your leads. For example, a company scoring leads using a 100-point scale might classify leads with a score of 40 or less as cold, 40 to 70 as warm, and above 70 as hot. 

These categories will determine your next steps and help you decide how to approach the leads. For example, a business might have sales reps call the best leads, add warm leads to an email list so that the marketing team can continue to nurture the relationship, and decide to ignore the cold leads for the time being. 

4. Refine your lead scoring process

Lead scoring requires you to choose and rank several data points, but you might not always select and score those data points correctly the first time around. Maybe leads with a low-scoring attribute ended up converting, or vice versa. Tweak your lead scoring process to ensure the best results.

Lead scoring tools

Developing, using, and updating a manual lead scoring system can be time-consuming and complicated, so a business might use a lead scoring tool to simplify the process and ensure more accurate results. Predictive lead scoring tools use machine learning technology to collect data throughout the customer journey, parse through that data, and qualify leads. 

You can also use lead scoring tools to automate certain actions based on a potential customer’s lead score. For example, a lead scoring tool could send a conversion-focused email to high-scoring leads. 

Here are a few popular tools that can help with lead scoring:

• Hubspot. Hubspot lets you toggle between predictive and manual lead scoring, letting you harness the power of predictive lead scoring while retaining the ability to customize your system. Hubspot’s marketing software suite also has lead generation functionality for seamless integration across your sales pipeline.

• Pipedrive. Pipedrive is a sales management and CRM (customer relationship management) software that can autofill information about leads by searching the web. It can then qualify those leads and automate tasks like sending marketing emails to promising prospects.

• Leadsquared. Leadsquared ranks leads with three scores: lead quality (fit with ideal customer), overall lead score, and engagement score. Lead score is cumulative, adding up all of a lead’s interest-showing activities, while engagement measures recent activities, based on a timeline you specify. 

• Apollo.ai. Apollo.ai uses your business’s historical customer data to create lead scoring models with AI. You can still customize your ranking criteria, and, if you’re curious about how the AI determined a lead’s score, you can view a full breakdown of how the score was calculated.