Lead Scoring Model Template
Four documents and four sheets that fit every attribute weight to your own conversion history, then validate the score by conversion within band.
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Assigned Points Against Fitted Weights
Duffield, 12,400 leads, 405 closed-won, base rate 3.27%
One joint logistic fit across every candidate attribute on 18 months of leads with outcomes. Joint, so two signals that fire for the same person do not both take full credit. Illustrative, for a fictional warehouse management software company.
| Attribute | Leads | Won | Rate | Lift | Old | Fitted |
|---|---|---|---|---|---|---|
| Integration docs visit | 916 | 112 | 12.23% | 3.74x | 0 | +25 |
| Demo requested, under 30d | 701 | 79 | 11.27% | 3.45x | +30 | +21 |
| 200 to 999 employees | 3,730 | 217 | 5.82% | 1.78x | +5 | +17 |
| Industry: distribution or 3PL | 6,471 | 291 | 4.50% | 1.38x | +8 | +17 |
| More than one warehouse | 4,939 | 211 | 4.27% | 1.31x | 0 | +10 |
| Whitepaper or ebook download | 3,505 | 91 | 2.60% | 0.79x | +10 | -6 |
| 1,000 or more employees | 2,075 | 33 | 1.59% | 0.49x | +10 | -8 |
| Title: C-level | 1,313 | 16 | 1.22% | 0.37x | +15 | -13 |
| Demo requested, over 30d | 762 | 35 | 4.59% | 1.41x | +30 | dropped |
| Opened 3+ emails in 7 days | 4,970 | 146 | 2.94% | 0.90x | +5 | dropped |
Then the validation, which is the part no points table has
Conversion by score band on the same leads. Top-decile lift goes from 2.84x to 4.44x. The old model's top band held 40 of 12,400 leads, so it was never a group anyone could staff against, and its middle band held 51% of the book at the base rate. Zero out its single largest weight and its lift falls to 1.56x against a 1.00x floor for scoring at random, so sixteen of its seventeen attributes were carrying almost none of the ranking between them.
And the threshold gets priced. Of the 405 closed-won deals, 262 scored below the old MQL line of 45 and went to the nurture stream. Hold the volume handed to sales constant and the fitted model's line at 51 passes the same 2,048 leads while holding 230 of those deals instead of 143.
Every lead scoring template is a points table somebody assigned. Adobe's own shipped behaviour scoring program is a fair example of the genre: a contact form worth +30, a webinar worth +20, a PDF download worth +5, no activity worth -10. Its authoring guide tells you to set those numbers relative to the importance of each action. Starting out wrong is unavoidable. The problem is that nothing in the table ever tells you it is wrong, so the tuning never converges.
So this pack fits the weights instead. The worked example runs on 12,400 leads and 405 closed-won deals from a warehouse management software company. Three attributes flip sign there: C-level titles convert at 1.22% against 3.51% for everything else, employers over a thousand people at 1.59%, gated content downloads at 2.60%. Five of twenty-one candidate terms come out entirely for lack of evidence. Two attributes nobody had ever scored carry real weight, and one is now the largest term in the model.
Then the threshold gets priced in the only currency that matters, which is the closed-won deals it held back. On the same history, 262 of the 405 wins scored below the old MQL line and were routed to nurture. Holding the volume handed to sales constant, the fitted model's line carries 230 of them instead of 143. Pair it with lead routing rules, which decides which owner a qualified lead reaches, and the SLA that governs what happens next.
What is in the pack
Attribute Weights
Every term with its lead count, closed-won count, conversion rate, lift, old points, fitted points and Wald statistic
Score Distribution
Decile by decile, with the cumulative share of all wins at or above each line and the route that decile gets
Conversion by Score Band
The validation, both models on the same leads and bands, ending in lift at the top decile
Model Change Log
One row per change with the evidence for it in the next column, plus open items dated to the next recalibration
Scoring Methodology
Why the fit is joint, the two evidence rules, what the fit disagreed with the old table about and by how much
Signal Definitions
Every scored term, the field it reads from, and the four kinds of change that void its weight until a refit
Threshold and Rollout Note
The threshold priced in won deals, the volume-matched replacement, and the shadow month before routing changes
Recalibration Procedure
The refit schedule, five triggers that pull one forward, and what gets re-tested rather than just refitted
How it works
- 1
Send the leads with outcomes on them
One row per lead, with the closed-won flag on the same row as the attribute values, plus the date each behavioural signal fired. A year works, eighteen months is better. Bring the current points table too, because the comparison column is the most persuasive thing in the finished sheet.
- 2
Fit every weight at once
One joint model across every candidate attribute, including the ones nobody scores. Joint matters: 61% of the leads that read integration documentation also visited pricing, so a table scoring both at full value pays twice for one person's afternoon.
- 3
Drop what the outcomes cannot support
Two rules, written down before the fit runs. A term needs twelve closed-won deals behind it, and a coefficient inside plus or minus 1.96 standard errors scores zero rather than a small number. Then refit without the dropped terms and publish the refit.
- 4
Validate, then price the line
Conversion by band on both models, the share of the book in each band, and lift with each model's largest weight zeroed. Then the closed-won deals below the current threshold, and the volume-matched line that recovers most of them.
Frequently asked questions
What do I need before this is useful?
A lead export where the outcome and the attribute values sit on the same row, covering at least a year. The closed-won count matters more than the lead count: forty deals cannot support twenty weights, and the honest output there is a five-term model with a note about volume rather than twenty coefficients with nothing behind them.
Our CRM already has predictive lead scoring
Then use it, and read what it needs. Salesforce documents that Einstein Lead Scoring uses a global model trained on other customers' anonymised data until your org has 1,000 leads and 120 conversions in six months. Below that line you are scoring your leads with somebody else's history, and the vendor says so.
Why fit jointly instead of one attribute at a time?
Because the signals travel together and a per-attribute table double counts them. On the worked example 61% of the leads that read integration documentation also visited pricing and 40% also requested a demo. Scoring all three at full value ranks the people who clicked around most at the top, rather than the people most likely to buy.
Some of the fitted weights are negative on attributes we like
Publish them. A C-level title converting at 1.22% against 3.51% for everything else is a result, and suppressing it is the same act as assigning the points by opinion. If the real reason is that enterprise deals are slow rather than bad, that is a cycle-length finding and it belongs in its own field, not in a fudged coefficient.
Should behavioural points decay after 30 days?
Test it per signal instead of applying one curve. Two of nine behavioural signals earned a freshness window on the worked example. Integration documentation did not: 12.58% inside thirty days against 11.88% outside it. A uniform half-life would have discounted the strongest term in the model for being three months old.
How do we pick the new threshold?
Do not pick it. Solve for it by holding the lead volume handed to sales constant, then report the share of the routed book that changes hands. On the worked example that share is 60%, which is what the conversation with sales is actually about. It pairs with lifecycle stage definitions, since the threshold is a stage boundary.
Does this cover routing, hygiene or the handover agreement?
No, and the boundary is deliberate. Which owner a scored lead reaches is a routing question, the score depends on deduplicated records to be computable at all, and what the two teams owe each other around the handoff belongs in the SLA. An account ownership overlap is a separate, earlier problem: which single owner an account should have at all.
Find out what your points table is worth
Send the lead export with outcomes on it. The first thing back is conversion by score band on your current model, next to the fitted one.
Fit my weights to my own history