Thomas Lopez — Writing https://thomas-lopez-gtm.com/blog/ Build write-ups on revenue operations, GTM engineering, Salesforce architecture and public-data pipelines — including the parts that broke. en-us Wed, 29 Jul 2026 00:00:00 GMT The fit score tells you who to call. The surge score tells you when. https://thomas-lopez-gtm.com/blog/surge-score-buying-signals-python/ https://thomas-lopez-gtm.com/blog/surge-score-buying-signals-python/ Wed, 29 Jul 2026 00:00:00 GMT 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. A five-tier account fit score that routes each lead to a different motion https://thomas-lopez-gtm.com/blog/account-fit-scoring-model-clay/ https://thomas-lopez-gtm.com/blog/account-fit-scoring-model-clay/ Thu, 24 Jul 2026 00:00:00 GMT 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. How to pull any account balance for every US credit union with Python https://thomas-lopez-gtm.com/blog/credit-union-call-report-data-python/ https://thomas-lopez-gtm.com/blog/credit-union-call-report-data-python/ Thu, 24 Jul 2026 00:00:00 GMT 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.