Most scoring models were configured, not designed. Someone accepted the defaults, adjusted a few point values, and set the threshold at whatever produced a comfortable monthly volume. There is no statement anywhere of what the number is supposed to mean — which is precisely why sales ignores it.
What this package fixes
- Score and grade separated: interest and fit answer different questions and need different mechanics.
- Score decay, so your highest scores are people who are interested now, not eighteen months ago.
- Disqualification as hard suppression, not negative points that enough activity can out-vote.
- A threshold set from sales capacity and observed conversion, not from a round number.
- A disposition loop so rejection reasons come back and actually change the model.
The four faults, in order of how often I find them
- No decay. Scores only ever rise, so your top-scoring prospects are whoever was most curious last year. The single most common fault.
- Grade unused or invented. Score without grade sends sales highly-engaged students and competitors. Where a grading profile exists it was usually built from an aspirational ICP slide rather than from closed-won data.
- Negative points instead of suppression. A determined competitor browsing your pricing page will eventually cross any threshold.
- No return path. MQLs go out, nothing comes back, and the model never learns. This is a conveyor belt, not a process.
Grading needs data you may not collect
If your forms capture only name and email, grading has nothing to work with and every prospect sits at the default. Fixing that — progressive profiling, enrichment, or pulling account-level data from the CRM — is part of this package, because model design without input data is theatre.
What you get
| Deliverable | Detail |
|---|---|
| The written promise | One sentence both teams sign: what marketing is asserting when it sends an MQL |
| Grading profile | Built from your last 50–100 closed-won deals, not from a workshop |
| Scoring model | Intent hierarchy, weighted by signal strength, with decay tuned to your sales cycle |
| Suppression rules | Competitors, students, employees, existing customers, out-of-territory |
| Routing and SLA | Named owner within a defined window, with a fallback for when no rule matches |
| Record-page context | Why this person, what they looked at, and the score trend — not just the number |
| Disposition list | Short, controlled, and reported back monthly |
| Two workshops with sales | Designed with them, not for them. This is what makes it stick. |
Show the trend, not the value
“72, up 40 this week” is an actionable sales signal. “72” is trivia. Surfacing score change on the lead record changes how reps use the model more than any tuning of point values — and it is a half-day of work.
Best for
- Teams where sales has visibly stopped working the MQL queue
- Orgs still running the default scoring model
- Anyone who cannot produce a breakdown of why MQLs were rejected last month
- Marketing leaders who need to defend lead quality with evidence rather than volume
Price and duration
From €5,900. Four to six weeks. Fixed scope, fixed price.
Not sure it is the model rather than the data? The free 12-point checklist covers the four scoring items in ten minutes, and you keep the findings either way.
Sound like your org?
Thirty minutes, free, no slides. Bring your questions about scope, timing or whether this package is even the right one — you will get straight answers.