Lead Scoring That Sales Actually Trusts

Why Lead Scoring Fails in Practice

The majority of lead scoring implementations fail not because the model is inaccurate but because sales doesn't trust it. Marketing builds a model based on attributes they can measure. Sales ignores it because it doesn't match their intuition about what a good lead looks like. The model scores leads that don't convert and misses leads that do — or so the sales team perceives.

The root cause is process, not technology. Sales wasn't involved in defining what a good lead looks like. The scoring criteria reflect marketing's data availability, not sales' conversion experience. The model produces scores that don't align with ground truth — because sales' ground truth wasn't part of the model design.

Building Scoring Criteria With Sales

Involve sales in the scoring design before the model is built. Run structured interviews with high-performing sales reps: what characteristics do your best-converting leads share? What are the signals that tell you a lead is worth calling immediately? What makes you deprioritise a lead regardless of what marketing says?

Translate these qualitative criteria into measurable signals. 'They seem serious' becomes: multiple high-intent page visits, pricing page viewed, demo requested, company size above threshold, industry in target vertical. Build the scoring model around criteria that sales identified, not criteria that were convenient to measure.

• Firmographic fit: company size, industry, geography, technology stack
• Behavioural intent: pricing page visits, demo requests, content consumption depth
• Engagement velocity: how rapidly engagement is increasing or decreasing
• Sales intelligence signals: job change alerts, company funding events, competitor contract expiry

Score Decay: The Missing Element

Most lead scoring models assign scores based on cumulative engagement — every positive action adds points, nothing subtracts. A contact who was highly engaged six months ago and has gone completely silent still carries a high score in many implementations.

Score decay addresses this by reducing scores for contacts whose engagement has decreased. A contact who hasn't opened an email in 90 days, visited the website in 60 days, or engaged with any content in 45 days should have their score reduced — they may have evaluated your product and moved on, their circumstances may have changed, or their intent may have been misread.

Validating the Model

Validate lead scoring models by measuring outcomes, not correlations. The relevant question is: do leads in the top score band convert at a meaningfully higher rate than leads in the middle band, which convert at a meaningfully higher rate than leads in the bottom band?

Run this analysis monthly for the first six months post-launch. A well-functioning scoring model should show clear and consistent conversion rate differentiation across score bands. If it doesn't, the scoring criteria need revision — and the sales team almost certainly already knows why.