Lead scoring that sales actually trusts
Most lead scores are ignored because they're arbitrary. Build on fit plus behaviour, set thresholds with sales, review misses monthly.
Every marketing platform offers lead scoring, and in most companies the sales team quietly ignores it. Ten points for a whitepaper, five for a page view, minus two for a webinar no-show — nobody remembers why, and the “hot” leads it produces keep going nowhere. Scoring earns trust the same way people do: by being right often enough to bet on.
Score two things, never one
A useful score combines fit — does this person look like your customer (role, company size, industry) — and behaviour — are they acting interested (pricing visits, repeated returns, quote starts). High fit with no behaviour is a future campaign target. High behaviour with no fit is a student writing a thesis. Only both together deserve a salesperson's morning.
Weight actions by intent, not availability
Platforms score what's easy to track, which is why email opens — a weak, inflated signal — dominate default models. Rank behaviours by the intent they actually reveal: a pricing page visit or a quote request outweighs fifty opens. If you did the maths honestly, one form-fill would outrank a year of newsletter engagement.
Set thresholds with sales, not for them
The number where a lead becomes “sales-ready” is an agreement, not a setting. Define it in one meeting with sales, with real recent examples on the screen: would you have wanted this lead? Then write down the service-level promise on both sides — marketing sends only above-threshold leads; sales touches them within an agreed time. This is where scoring connects to a humane automation programme rather than replacing judgement.
Let scores decay
Interest is perishable. A burst of activity in March means little in September, yet decay is the most commonly skipped setting in scoring models. Reduce behavioural points over time so the score reflects current interest — recycled leads can re-earn their way up through lifecycle nurturing.
Review the misses monthly
Two lists tell you everything: high scores sales rejected, and closed deals that never scored high. Each mismatch is a weight to adjust. Expect to be embarrassed for the first quarter — that's the model learning. A score that's never revised is astrology with an API.
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