Lead scoring, MQL and SQL: when is a lead ready for sales?

Lead scoring is assigning points to leads based on their profile and behaviour, so you know who is ready for sales and who is not. It solves the classic conflict between marketing, which delivers volume, and sales, which wants quality. In this article you will learn the difference between an MQL and an SQL, how scoring works, and how to get both teams aligned.

Do the maths yourself: calculate what a single lead is worth with our free value-per-lead calculator.

The problem scoring solves

Marketing says “we deliver plenty of leads”, sales says “they are bad leads”. Both are partly right, because they measure different things. Without a shared definition of a good lead, that argument runs forever.

In B2B, “more leads” is not a goal in itself anyway. A form that gets filled in a hundred times feels good, but if not a single customer comes out of it, you have mostly kept your sales team busy with noise. That is exactly the role of scoring: separating noise from pipeline.

Lead scoring and the terms MQL and SQL give both teams the same language. That way everyone knows when a lead is ready to hand over.

MQL versus SQL

Two milestones in a lead’s journey:

• MQL (Marketing Qualified Lead). Someone who shows enough interest to be relevant, but is not ready to buy yet. Marketing keeps nurturing them.
• SQL (Sales Qualified Lead). Someone far enough along that a salesperson can justify spending time on them. Sales takes over.

The full explanation is in our glossary: MQL and SQL. The art lies in agreeing clearly on when an MQL becomes an SQL.

How does lead scoring work?

You assign points along two axes:

• Fit (who is this?). Does the lead match your ideal customer? Sector, size, job title. A decision-maker at a suitable company scores high.
• Behaviour (what are they doing?). Do they open your emails, visit your pricing page, submit a request? Buying signals push the score up.

A lead with a high behaviour score but a low fit score is enthusiastic but wrong. Only when both axes add up is a lead truly sales-ready. Salesforce on lead scoring uses that same split between profile and behaviour. Above an agreed threshold, a lead becomes an SQL and goes to sales. That way your sales team spends time on the right people, and good leads no longer disappear into the noise. This ties in with qualified leads.

A concrete example scoring model

A workable starter model gives every action a point value according to how much it reveals about how serious someone is. An illustrative example:

• Newsletter sign-up: 2 points
• Beginner’s guide downloaded: 3 points
• Brochure requested: 5 points
• Webinar attended: 8 points
• Free trial started: 10 points

Next you pick a threshold above which sales makes contact. Set it at, say, 10 or 12 points: someone who only reads your newsletter will not reach it, but someone who attended a webinar and then requested a brochure will. The threshold is not a law, you adjust it as you see which scores actually lead to customers. Not every company weighs actions the same way, by the way: for a software company a free trial is the strongest signal, for a consultancy a requested conversation carries more weight.

Scoring without over-engineering

You do not need a complicated system to get started. Start simple:

1. Agree on what characterises a good customer (fit).
2. Pick a few clear buying signals (behaviour).
3. Set the threshold for “ready for sales” together.
4. Document the handover, so nothing falls through the cracks. See lead follow-up.

Then refine based on what you learn. A simple model that gets used beats a perfect model nobody follows.

Good data is the foundation of every score

A scoring model is only as good as the data feeding it: wrong or incomplete data leads to leads that score high but would never sign. It is no coincidence that 35% of companies name a lack of quality data as the biggest barrier to lead generation success (Ascend2). And research estimates that poor data quality costs companies 15 to 25% of their revenue (MIT Sloan Management Review).

Good data feeds not just your scoring model, but also the conversation that follows it. 42% of sales reps feel they do not have the right information before they call a prospect (CSO Insight). Call without context and you lose the deal before it starts.

When may sales make contact?

The difference between interest and buying intent determines the right moment. Someone reading your content is interested, but not necessarily ready to buy. Call too early and you scare off a prospect who is still exploring. Wait too long and your competitor gets there first.

The good news: prospects tell you themselves when they want to talk. 60% of prospects want to speak to a sales manager during the consideration phase, a signal that matches what HubSpot describes about the sales qualified lead. That is exactly the moment your scoring model proves its worth: it flags when someone shifts from “browsing” to “seriously considering”, so sales steps in at the right time and not sooner.

What about the leads that do not score yet?

You do not throw them away. The bulk of your leads are not ready to buy yet. You nurture them with lead nurturing until their score rises and they are ready.

At the same time, actively removing a lead who will never fit from your pipeline is not a failure but a good call. Sometimes your product simply is not the best solution for that person. Every call attempt to a lead without buying intent is time you are not spending on a real opportunity, and an inflated pipeline hides where your sales should really be. Daring to say honestly “we are not the right fit for you” earns respect more often than a half-hearted sales attempt.

Which metric do you steer on?

Steer on cost per qualified lead instead of cost per lead. Cost per lead counts every form submission, including the leads that never stood a chance. Cost per qualified lead measures what a lead with a real chance of closing costs you. That metric separates noise from real pipeline.

The metric that ultimately counts is lead-to-close: how many of your leads become customers. Yet that is precisely the number most companies fly blind on. In practice we see that the vast majority of companies cannot reliably track that lead-to-customer conversion, and a considerable share cannot measure it at all. As a result, a large share has no view of the very number that justifies their lead generation.

AI and lead scoring: tool or miracle cure?

AI can weigh more variables per lead than a human can by hand, and that potentially makes scoring sharper. But it only works if your data is right: AI on bad data mostly amplifies your mistakes. And practice shows how early we still are: most companies barely use AI and automation today to distribute their leads and budget more intelligently.

Our honest advice: for most B2B companies, a simple model that works matters more than the tool. A clear points model your team understands and uses delivers results faster than an expensive AI solution running on messy data.

From scoring to more customers

Lead scoring is not a goal in itself. The goal is more customers, by getting sales and marketing aligned and investing time in the right leads. That is exactly what our lead generation steers on: pipeline, not noise. For a Belgian SME with a small sales team, that is not a luxury but a necessity: your sales time is limited, so every wrongly followed-up lead is a real opportunity missed.

In the programmes we roll out, we often see a doubling to tripling of the number of enquiries. Our approach for Get Driven, for instance, delivered 400% more conversions.

Want to know which leads are ready?

Tell us how you hand over leads today, and we will help you set up a simple, workable scoring model.

We are a small team, so we move fast and do more than you expect. Schedule your free intake and you will hear within 24 hours where your opportunities are.