Working patterns for behavior scoring and fit scoring in B2B marketing automation — for Marketo, Pardot/Marketing Cloud Account Engagement, HubSpot, and platforms that follow the same general model.
Most published guides on lead scoring are written by vendors. Vendor guides optimize for a recommendation that sounds defensible in a sales conversation. This repo optimizes for a model that survives contact with a real sales team for more than one quarter.
| File | What it does |
|---|---|
01-what-scoring-is-actually-for.md |
The misconception that breaks most scoring models. What scoring is genuinely useful for, and what it is asked to do but cannot. |
02-behavior-scoring-patterns.md |
Signal selection, weighting, decay rules, anti-decay exemptions, and the boring math of keeping a behavior model calibrated. |
03-fit-scoring-patterns.md |
Firmographic and demographic fit. How to score it without making the model brittle when the ICP shifts. |
04-anti-patterns.md |
The eight ways scoring models go bad. What they look like, why they fail, how to fix or retire them. |
templates/scoring-model-canvas.md |
A one-page canvas for designing a scoring model with marketing and sales in the room. |
Scoring models do not fail at the math. They fail at agreement on what the model is supposed to do, and at maintenance.
- Before any model design: run the canvas in
templates/scoring-model-canvas.mdwith marketing and sales together. Half a working session, not async. - Read
01together: agree on what scoring is for before debating weights. Most teams skip this and end up arguing about thresholds for a model that does not match what they actually need. - Design behavior and fit separately (
02and03). Combine them only as the final step. A two-dimensional model (separate behavior score, separate fit score) is more interpretable and easier to debug than a single combined number. - Quarterly: run the model against
04-anti-patterns.md. The patterns there are the dominant failure modes I have audited in real instances.
Lead scoring is the most rewritten and least maintained piece of marketing automation. Most instances have a scoring model that someone built two years ago, that nobody trusts, that everybody works around, and that nobody has the political mandate to retire.
A working scoring model is not a sophisticated one. It is a model both teams understand, can explain, and are willing to act on. The sophistication comes second — and only after the first version has proven the team can agree on something.
Companion repos:
- marketo-audit-checklist — surfaces scoring problems during an audit.
- marketo-naming-conventions — naming for scoring fields and campaigns.
Field notes on lifecycle and scoring as one system: vadim-koenen.github.io/marketo
MIT. Fork it, adapt the weights to your data, PR back patterns that have held up in a real instance for at least two quarters.
Maintained by Vadim Koenen — marketing automation, RevOps, and GTM systems consultant.
- Website: https://vadimkoenen.com
- LinkedIn: https://www.linkedin.com/in/vadim-koenen-mba/
- Field notes & case studies: https://vadim-koenen.github.io/