Set up a lead scoring model: how to qualify the right leads

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Generating leads is one thing. But knowing which leads to prioritise? For that you need a smart lead-scoring model. It helps your marketing and sales teams focus on the most promising prospects. In this blog you’ll learn how to set up such a model successfully and what to watch out for to avoid mistakes.

What is lead scoring?

Lead scoring is a method of assigning points to leads based on their profile (fit) and behaviour (engagement). Think of job title, company size, number of web pages visited or requesting a demo. The higher the score, the greater the chance someone will become a customer. 

Such a score makes it easier to: 

• qualify leads,
• choose the right timing for follow-up,
• and align marketing and sales more effectively.

Why is lead scoring so important?

Companies that use lead scoring achieve demonstrably better results. For example, the ROI on lead generation is on average 77% higher at organisations with a scoring model, according to research by MarketingSherpa. The conversion from lead to customer also increases significantly and sales teams can move faster and more efficiently.

But the real difference lies in alignment: marketing and sales speak the same language. What constitutes a “good lead” becomes measurable instead of a gut feeling.

7 steps to an effective lead-scoring model

A lead-scoring model is more than just handing out a few points. It forms a strategic foundation under your marketing and sales process. With the right approach you can qualify better leads, shorten sales cycles and improve internal collaboration. These are the 7 crucial steps to get it right:

1. Collect and structure your data 
A model is only as good as the data you put into it. Start by mapping which data you already have in your CRM and marketing automation platform. Think of: 

• Company data: industry, company size, location.
• Demographic data: job function, seniority level, role in the buying process. 
• Behavioural data: email interactions, website visits, downloads, webinars. 
• Sales data: previous conversations, status in the sales funnel. 

Make sure you have tools that automatically capture and enrich this data. Platforms like HubSpot or Salesforce can do much of this for you, especially when combined with integrations such as LinkedIn Sales Navigator or enrichment tools like Clearbit.

2. Develop the model together with marketing and sales 
Lead scoring only works if both teams understand and support the model. Organise a joint workshop and answer these questions together: 

• Which leads became customers in the past?
• Which characteristics or behaviours did they have in common?
• What challenges did Sales face with “bad” leads?

Use customer data as a starting point, but supplement it with the real-world knowledge of your sales team. And keep monitoring together whether the highest-scoring leads actually convert.

3. Choose your lead-score criteria and give them the right weight 
Not every behaviour is equally valuable. Downloading a white paper indicates early interest, while requesting a demo usually signals buying intent. Make sure you: 

• weight behavioural data logically based on the buyer journey;
• link profile data (such as job title or industry) to your ICP; 
• include negative signals (such as a bounced email address or inactivity). 

Start with 5 to 10 core criteria and expand later. A concise model is easier to test and maintain. 

4. Set clear thresholds and follow-up actions 
When is a lead ready for Sales? Define a threshold—say 60 points—and make sure a clear action is linked to it: 

• the lead is marked as an SQL and assigned to a salesperson;
• a personalised email is sent automatically; 
• leads below the threshold remain in a nurture campaign. 

Automation is essential here. Use workflows in your marketing automation platform to arrange this follow-up smartly. 

5. Combine fit and engagement 
A good lead is not only interested but also fits your ideal customer profile. That’s why it’s smart to use two scores: 

• Fit score: does this person’s profile fit your target audience?
• Engagement score: is this person showing buying behaviour?

Work with a 2D matrix: high fit and high interest means top priority. A CEO who visited your homepage once is very different from a student who opened your pricing page five times. 

6. Test, learn and improve continuously 
Lead scoring is not a “set-and-forget” job. Your market changes, behaviour changes, your product changes. Therefore analyse periodically: 

• how many MQLs actually become SQLs or customers;
• which signals prove predictive; 
• which criteria create noise. 

Use dashboards in tools like HubSpot or Klipfolio to make scoring performance visible. Adjust scores and add new signals when necessary. 

7. Ensure internal buy-in 
A model without buy-in will not deliver. Explain to salespeople why the model exists. Show that it saves time and helps them work with better leads. 

• Provide training on how the score is generated.
• Actively ask for feedback on forwarded leads. 
• Share success stories: “This lead had 78 points — and signed within 3 weeks.”

If lead scoring is seen as a joint tool (and not as a marketing thing), you’ll get the most out of it. 

Also look at customer scoring and renurture scores

A good lead-scoring model doesn’t stop at the SQL threshold. Two additional dimensions make your model stronger: customer scoring and a renurture score. 

What is customer scoring?

While lead scoring focuses on the likelihood that someone will become a customer, customer scoring looks at existing customers instead. How valuable are they? How active? How much potential is left in the relationship? 

Think of signals such as: 

• frequency of product use;
• support requests or NPS scores;
• expansion of usage (upsell/cross-sell);
• participation in customer programmes or events.

By scoring customers as well, you can work proactively on retention, upsell and customer satisfaction. Combine this score with your CRM and marketing-automation data to target existing customers with relevant content or personal follow-ups. 

What is a renurture score?

Not every lead that drops out is lost. Many leads drop off simply because it isn’t the right time yet. A renurture score helps you assess whether an old lead is showing new potential. 

Typical indicators: 

• after months, suddenly visiting the website again;
• interaction with a new newsletter; 
• downloading a current white paper. 

By valuing this behaviour again, you prevent warm opportunities from going unnoticed. Use workflows in HubSpot or Act-On, for example, to detect and follow up on this renewed interest automatically. 

Why do these scores add value?

Both customer and renurture scoring give you a more complete picture of your funnel. They make your marketing and sales process more cyclical than linear: 

• Lead → MQL → customer → valuable customer.
• Lead → dropout moment → renurture → second chance.

By integrating these scores into your dashboard or automation workflows, you work smarter and seize more commercial opportunities. 

Want to talk more about lead scoring?

Would you like to brainstorm about how to implement lead scoring effectively in, for example, HubSpot or Act-On? Or are you curious about how to smartly add customer scoring and renurture flows to your existing model? 

Then feel free to schedule a strategy session with one of our experts.

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