Term
Lead scoring
Scoring prospects by how well they match the ideal customer and how they behave, such as whether they open emails or have visited the pricing page. Salespeople can then focus first on those closest to buying.
Updated
What it means
Lead scoring is a system that gives every prospect, or lead, points according to how likely it is to become a customer. Points are added for characteristics that match the ideal client and for behaviour that shows interest.
Characteristics come from the buyer persona: industry, company size, the contact’s role or location. Behaviour includes visiting the pricing page, downloading a lead magnet, opening emails repeatedly, attending a webinar or filling in a form. Negative points may be deducted, for example for a personal email domain in a B2B service.
When a lead crosses an agreed threshold, the system flags it as sales-ready and passes it to a salesperson. Those below the threshold keep receiving content and marketing attention. Salespeople thus spend their time on people with whom a conversation has the best chance.
How it works in practice
Lead scoring is set up in the CRM or a marketing automation tool. We recommend starting with a simple model of a few rules and regularly comparing it with reality: which leads actually became customers and what their scores were.
Scoring also helps with planning. If you know how many high-scoring leads arrive each month and what share close, you can estimate future sales and set the marketing budget according to what a quality lead costs to acquire.
When people are scored automatically, the GDPR must be considered. If lead scoring is used only to decide the order of follow-up, it is ordinary processing based on legitimate interest. If it led to an automated decision with significant effects, such as refusing a service, stricter profiling rules would apply.
What to watch out for
The most common mistake is a model designed by marketing without sales. The points then reflect what is easy to measure, such as email opens, rather than what genuinely predicts a purchase. The criteria should be agreed together and adjusted according to results.
The second mistake is a model that is never reviewed. Markets, products and customer behaviour change, and a model that worked two years ago may now send salespeople the wrong people. At least every six months it is worth checking whether a high score still means a better chance of a deal.
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