How to do Lead Scoring at your Company

 23 May 2024
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Having too many leads — it’s not something you’d normally complain about. But it can actually lead to fewer sales if you pursue the wrong ones, and let the right ones go cold. That’s where lead scoring comes in.

Lead scoring can help you decide which leads are hot (most likely to buy) —in other words, which leads you should be going after. In this article, we’ll take you through lead scoring, how to do it, and which model might fit your company best.

What is lead scoring?

To simplify it, lead scoring is when you assign a numerical value or number of points to each lead in your CRM, to help your sales team prioritise prospects based on their likelihood to make a purchase.

By implementing this scoring system, your sales and marketing teams can work closer together, passing leads between each other more smoothly, and gain clarity on which leads to prioritise and how to engage with them effectively.

Lead scoring vs lead grading

Lead scoring and lead grading, while they sound the same, focus on different aspects of lead qualification.

Lead scoring

Lead scoring is a method used to rank prospects based on their likelihood of becoming customers, as we described above.

Lead grading

Lead grading is a qualitative assessment of leads based on predefined criteria that align with the company’s ideal customer profile.

In summary, lead scoring quantitatively ranks leads based on their behaviour and engagement, while lead grading qualitatively evaluates leads based on their fit with the company’s ideal customer profile.

Both techniques are valuable for prioritising leads and improving sales effectiveness, and which one you use is up to you.

How to find new companies

Whether you’re an investor, local authority or looking for your client, we show you how to find new companies in our guide.

What companies can use lead scoring?

A lead scoring system can benefit many different businesses, not just the obvious ones, like B2B sales companies and marketing agencies.


Lead scoring isn’t just for B2B. Leading B2C or even D2C e-commerce companies use it to find customers with the highest likelihood of making a purchase. This can be determined by factors such as browsing behaviour, purchase history, or demographic characteristics.

Real estate

Real estate agencies can use lead scoring to avoid wasting time on casual browsers and concentrate their efforts on genuine prospects.

Financial services

In financial services, you’ll find insurance firms, banks, credit card providers, investment platforms, accountancy services, and various others. Lead scoring proves invaluable for assessing the eligibility and interest level of incoming contacts, whether they’re seeking coverage, credit, or aiming to become a customer.


Colleges and universities can use lead scoring to gauge the probability of applicants attending, as well as their compatibility with the institution’s programs.

How to do lead scoring

There’s no set way to do lead scoring. These are just options you can try, and each one will better suit a different type of businesses. The first thing you’ll want to look at is different lead scoring models.

Lead scoring models

Here are some of the most common lead scoring models.

Demographic lead scoring
Scores leads based on demographic information such as:

This model assumes that leads with certain characteristics are more likely to convert, so it relies on you having a good overview of your target market. For example, if you’re a tech provider and you want to sell to senior members of a business within a certain market, then you would need to know the seniority level and team size of your ideal customer.

Behavioural lead scoring
This model scores leads based on their online behaviour, such as website visits, content downloads, email opens, and click-through rates. It focuses on how engaged leads are with your brand and content, helping you to understand if your leads are expressing buying signals. Ultimately, helping you to understand—are they already ready to buy?

Firmographic lead scoring
This model is similar to demographic scoring but focuses specifically on attributes of the lead’s organisation, such as industry, revenue, number of employees, and technology stack. This model helps identify leads that are a good fit for your product or service.

Lead fit scoring
Lead fit scoring scores leads based on how well they match your ideal customer profile or buyer persona. This model considers factors such as job title, company role, responsibilities, and pain points to determine lead fit.

Predictive lead scoring
This model uses machine learning algorithms to analyse historical data and predict which leads are most likely to convert, and automatically adjusts scoring criteria based on real-time data and performance. We’ll cover more on this later.

Combined lead scoring
This combines multiple scoring models (e.g. demographic, behavioural, firmographic) to create a more comprehensive lead scoring approach. This is a popular approach, as it means you can get the best picture possible. This model takes into account a wide range of factors to accurately assess lead readiness and quality.

There are more lead scoring models out there, but usually a combination of the above will enable your business to focus on the right leads to pursue.

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Lead scoring best practices

As you can see there are a lot of ways to do lead scoring, and while there isn’t necessarily a right way, there are some best practices you can adhere to.

1. Define clear criteria
Establish precise criteria for what constitutes a qualified lead, including demographic information (such as location within the UK), firmographics (industry, company size), and behavioural data (website interactions, content downloads).

2. Align sales and marketing
It’s important to ensure that your sales and marketing teams are aligned on the lead scoring criteria. Regular meetings and feedback loops can help refine the scoring process to better reflect the quality of leads.

3. Use data and analytics
Use CRM systems such as HubSpot, Salesforce, and Pipedrive, as well as any other marketing automation tools you use, to track interactions with these leads, and update scores in real-time.

4. Segment and personalise
Tailor your lead scoring model to different segments within the UK market. For example, leads from London might have different criteria than those from rural areas. Personalisation helps accurately assess each lead’s potential.

5. Incorporate multiple touchpoints
Consider using and tracking various touchpoints such as email engagement, social media interactions, and attendance at webinars or events. A holistic view of a lead’s journey provides a more accurate score.

6. Regularly review and update
The UK market is dynamic, so regularly review and adjust your lead scoring model to reflect changing buyer behaviours and market conditions. It’s also encouraged to use feedback from your sales team to fine-tune the process.

7. Use predictive scoring
Implement predictive lead scoring using AI and machine learning to analyse patterns and predict which leads are most likely to convert. We’ll discuss predictive lead scoring further in the article.

8. Training and education
Provide ongoing training for your sales and marketing teams on the importance of lead scoring and how to use the scoring system effectively.

9. Measure and optimise
Even once you have your lead scoring system in place, you need to make sure you continuously measure its performance against conversion rates and sales outcomes.

And most importantly, you must make sure your lead scoring practices comply with UK data protection laws, such as GDPR. Obtain explicit consent for data collection and clearly communicate how the data will be used.

Lead scoring example

Here’s an example of lead scoring for a fictional UK-based B2B software company. In this example, we’re using a combined lead scoring model.

Lead scoring criteria and points allocation

Demographic information

UK-based leads: +10 points
Outside the UK: 0 points

Job title
Senior Management (CEO, CTO, etc.): +15 points
Middle Management (Department Heads, Managers): +10 points
Entry Level: +5 points


Company size
Large Enterprises (500+ employees): +20 points
Medium-sized Businesses (50-499 employees): +10 points
Small Businesses (1-49 employees): +5 points

Technology sector: +15 points
Other sectors: +5 points

Behavioural data

Website activity
Visits the pricing page: +10 points
Downloads a whitepaper or eBook: +10 points
Attends a webinar: +15 points
Signs up for a newsletter: +5 points
Visits the website multiple times (e.g. 3 or more visits in a week): +10 points

Email engagement
Opens emails: +2 points per email
Clicks on links within emails: +5 points per click

Engagement with sales
Requests a demo or meeting: +20 points
Responds to sales outreach: +10 points
No response to outreach: -5 points

Example lead profile and score calculation

Lead details

Location: London, UK
Job title: Head of IT
Company size: 200 employees
Industry: Technology


Visited the pricing page twice
Downloaded an eBook
Attended a webinar
Opened three marketing emails and clicked on links in two of them

Requested a demo

Scoring calculation

Location: UK-based (+10 points)

Job title: Head of IT (Middle Management) (+10 points)

Company size: 200 employees (medium-sized business) (+10 points)
Industry: Technology (+15 points)

Visits the pricing page: 2 visits (+10 points)
Downloads an eBook: (+10 points)
Attends a webinar: (+15 points)
Opens emails: 3 opens (+2 points each = +6 points)
Clicks on links within emails: 2 clicks (+5 points each = +10 points)

Requests a demo: (+20 points)

Total lead score
10 (Location) + 10 (Job Title) + 10 (Company Size) + 15 (Industry) + 10 (Pricing Page Visits) + 10 (eBook Download) + 15 (Webinar Attendance) + 6 (Email Opens) + 10 (Email Clicks) + 20 (Demo Request) = 116 points

Based on this score, the lead would be classified as highly qualified and ready for sales follow-up, as they have demonstrated significant interest and engagement with the company’s offerings.

Predictive AI lead scoring

With the rise of AI in the past few years, it’s no surprise that people started using it for lead scoring — it’s a handy and effective way to make lead scoring work for you.

What is predictive lead scoring?

Predictive lead scoring with AI involves using machine learning algorithms to analyse historical data and predict the likelihood of a lead converting into a customer.

Here’s an overview of how to implement predictive lead scoring:

1. Data collection

Collect data from various sources such as CRM systems, marketing automation platforms, and customer databases (like us). This data should include information on past leads, including both those which converted and those which did not.

2. Data preparation

Ensure the data is clean and well-organised. Handle missing values, remove duplicates, and standardise formats.

Create relevant features (attributes) from raw data. For example, calculate the number of website visits in a month, the frequency of email clicks, and the average time spent on the site and what score you want to attribute to those.

From here you can upload that data into your CRM or machine learning lead scoring tool, such as HubSpot.

How to do lead scoring in HubSpot

One example of predictive lead scoring is to use the Lead Scoring feature in HubSpot. Here’s a step-by-step guide on how to set up lead scoring in HubSpot:

1. Access lead scoring

Navigate to settings:

Find properties:

Lead scoring property:

2. Setting up scoring criteria

Edit scoring property:

Add positive attributes:

Add negative attributes:

Once you’ve added all your criteria, you can fine-tune them by analysing and monitoring the data coming in.

If you need more guidance, HubSpot offers a free lead scoring template.

How your Business Development team can find prospects and implement lead scoring with Beauhurst

Finding prospects is generally the biggest challenge in sales. With Beauhurst, you can find high-quality leads easily and efficiently. Let’s take a look at an example.

Imagine you’re on the business development team at a B2B IT management company (based in London) looking for companies to offer your IT services to, namely small tech or finance companies that will need a lot of IT support.

You’d need to find local tech and finance startups — and then find the contact details of key decision-makers.

First, you’d navigate to our search function — helpfully called Advanced Search — where you can build a search using detailed criteria. We’re looking for fintech startups, so we’d want to look for fintech companies in London at seed stage that have recently raised funding.

So, add the criteria:

This list will put up all companies in the UK that match these criteria.

You can then add them to a Beauhurst collection, which is a saved group of companies on the platform. This means you’ll get alerted when companies in the Collection experience an event, such as new hires, new investments, or mentions in the news. You’ll also be alerted any time a new company fulfils the criteria of the search.

Once that’s done, you can then pick one of the companies that you want to prospect.
Take the top one, for example (1fs Wealth). Click through to the company page and you will have all of the information that you need, including company details, recent activity, transactions, and financials.

You can also go to the People tab to see all of the company’s key people to find who to contact.

Watch our video below for a step-by-step tutorial.

Then you can feed that data into a CRM that’s fully integrated with Beauhurst, such as HubSpot.


At first glance, lead scoring can seem like a huge task, with so many options. However, the most important thing is finding the right lead scoring system for your company. Hopefully, this article has helped you discover what might work best for you and show you how you can find even more prospects using our platform.

Find prospects with Beauhurst, score those leads in HubSpot, make more sales.

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