Lead Scoring

Lead Scoring

Design, validate, and maintain a model that ranks leads by likelihood to become revenue. The practitioner standard splits scoring into two separate axes:

• Fit (can they buy): firmographic, demographic, technographic.
• Engagement (are they about to buy): behavioral, in-product.

This two-axis model appears under several names: Marketo's A-D x 1-4 grade-and-score grid, MadKudu's Customer Fit x Likelihood to Buy, and OpenView's product-qualified lead (PQL) in PLG.

Scoring fails far more often from organizational neglect - no sales sign-off, no recalibration, MQL count treated as the goal - than from bad math, so validation and governance are part of the design here, not an afterthought. The finished score is an input to assignment: routing, territories, and rep matching belong to mbfinotti/revops-skills@lead-routing. Defining the account-fit criteria this skill's fit axis weights - firmographic and technographic ICP tiers - belongs to mbfinotti/sales-skills@sales-icp-definition; this skill owns turning that definition, plus behavioral engagement, into a working score.

The capture-score-route-nurture mechanics are structurally identical for B2B and B2C/PLG. What genuinely differs between B2C/PLG and B2B:

• Dominant signal source is in-product behavior, not forms and content.
• No buying committee - one user's behavior can qualify.
• Cycles run in days, not quarters.

Because of these differences, decay, thresholds, rescoring frequency, and recalibration all run faster in B2C/PLG.

Interview

Ask before designing anything. One question per message; offer multiple-choice answers when possible. Skip a question only when the user already gave the answer.

• Which motion? (a) sales-led B2B, (b) PLG / self-serve with sales assist, (c) B2C transactional.
• Roughly how many new leads per month, and how many closed-won and closed-lost outcomes exist from the last 12-24 months? (Decides rules-based vs predictive, and how much backtesting is possible.)
• What is actually on a lead record at creation time - enrichment coverage per fit field, form fields, product events? (A score is downstream of data quality.)
• Does a model already exist? If yes: redesign, or diagnose first? (Diagnosis path: see Diagnosing a Broken Model.)
• How many leads can sales actually work per week? (The honest threshold constraint.)
• Who signs off - which sales leader, and what acceptance-rate floor will they hold the model to?
• Any EU or consent constraints? Behavioral scoring is profiling under GDPR; the lawful basis must be documented.
• By what date must the model be scoring live, and what is waiting on it? (A date inside two weeks rules out both enrichment procurement and the conversion-band threshold method; a quarter of runway makes both affordable.)
• One-off win or compounding asset? (A single campaign list to work stops at v1 weights and a capacity threshold; a compounding mandate is what justifies enrichment, the full backtest, and the recalibration cadence that keep the model honest after launch.)
• What is the effort ceiling - analyst hours, CRM/marketing-automation admin access, a procurement and legal path for a data contract, and how much of sales' attention you can spend? (No analyst rules out re-deriving weights from won/lost data and the conversion-band method; no procurement path deletes the enrichment rung outright rather than ranking it last.)

Carry these last three answers into every ranking in this skill and in its references; they are the only inputs that reorder the defaults for this user. Every ranking here is a default, not a law: it shifts with context and with who executes it. Re-rank before recommending a rung, for example:

• An enrichment contract already in place collapses that rung's effort to a field mapping and promotes it to the top.
• A CRM whose fit fields arrive self-reported on the form at high coverage removes the coverage menu entirely.
• No analyst promotes every rung a CRM admin can ship alone and demotes every rung that needs a cohort analysis.

Workflow

1. Run the Interview; collect every answer before designing.
2. Audit the data first. Measure enrichment coverage per fit field over the last 90 days of leads. A fit field below roughly 70% coverage means most of the database can never reach the threshold - the enrichment-ceiling failure. Three ways out, ranked by value per unit of effort, where effort is analyst hours, admin work, procurement, and reversibility:

• efficiency: score unknowns 0 > enrich the field > drop the field
• value (fit signal the model can actually act on): enrich the field > score unknowns 0 > drop the field
• effort: enrich the field > drop the field > score unknowns 0
• compliance cost: enrich the field > score unknowns 0 == drop the field

Effort in magnitudes:

• Scoring unknowns 0: near-zero, a scoring-config change reversible in a click.
• Dropping the field: an hour, and the least reversible option in this menu - the signal stops being collected and the next recalibration cannot re-derive its weight without first rebuilding the history.
• Enrichment: a procurement and contracting step before a single lead scores, then a standing per-record dependency and a coverage number to monitor forever.

Default to scoring unknowns 0: it keeps every thin record scoreable on engagement and ships this week. Justify the compliance tie: neither the 0 rule nor the deletion introduces a new data source, so neither triggers a consent or provenance review. Enrichment does - third-party fit data is profiling input under GDPR, and its lawful basis and provenance have to be documented before it scores anyone.

Name the bias that default buys: scoring unknowns 0 demotes records that are thin for reasons other than fit - SMB, non-US, privacy-conscious buyers - so track their conversion as a separate cohort and re-derive the weight if they convert anyway. What this order starves is enrichment: first on value, first on effort, so a ratio never buys it, and it is the only rung that restores the signal instead of routing around it.

Promote enrichment when:

• The field is the ICP gate itself (employee count at enterprise ACV).
• Coverage is low across the whole database rather than one segment.
• A contract already exists.

Delete the enrichment rung outright, and say it is deleted, where the Interview gave no procurement path - a rung parked at the bottom of the list returns as a quarter of vendor calls nobody approved.

1. Derive signals from outcomes, not intuition.

• Pull 12-24 months of closed-won and closed-lost records.
• Compare signal frequency between the two groups.
• Keep signals with a real conversion delta over baseline.

The gut list and the evidenced list are usually different lists. Trap: fields that look complete on won deals because sales filled them right before close - verify each signal existed at scoring time.

1. Build the signal catalogue from references/signal-taxonomy.md: fit and engagement as two separate scores, hard disqualifiers as exclusions (never negative points), soft demotions as negative points, decay on engagement only.
2. Weight per references/weighting-and-thresholds.md: start near 60/40 fit/engagement for sales-led, 50/50 hybrid, 30-40 fit / 60-70 engagement for PLG and B2C where behavior dominates. Cap each signal category so no single channel can cross the threshold alone.
3. Set the threshold by sales capacity first, conversion band second, percentile as sanity check - that is the efficiency ranking of the three methods, not just a sequence, and the same reference states each method's value and effort before choosing. Define tiers where each tier maps to a distinct play.