There's plenty of go-to-market content about what you should do. Less about what the config actually looked like, what it cost, and where it fell over. These are the ones I wished existed while I was building them.
A companion to the fit model: a time-decayed buying-signal score built in Python, layering champion moves, hiring, and pricing-page visits on top of a stable fit tier — so reps get both who to call and when.
Fixed weights instead of machine learning, disqualifiers that override rather than subtract, and an enrichment waterfall that kept per-account cost low enough to run on the whole database. Plus the mistake of treating fit as intent.
No vendor sells the field I needed, but federal filings publish it quarterly for free. Resolving account codes at runtime, finding the column without hardcoding filenames, and the fuzzy anti-join that turns a raw universe into a usable prospect list.
Best fit: Head of GTM Ops / Engineering, or a RevOps manager role where the mandate is the whole revenue system — not just the CRM.