The situation
A lending-software platform selling to private lenders found that standard firmographics (employee count, company revenue) didn’t predict which prospects would buy or how big they would be. Their best customers were defined by lending volume and the size of their active loan book.
What we built- A lead scoring model workbook built from a structured scoring survey with the client team, separating ICP fit, behavior and disqualifiers.
- Fit scored on lending volume and active loan count instead of headcount or revenue.
- Explicit disqualifiers for lender types outside the ICP, so reps stop working leads that look big but never buy.
- A competitive intelligence brief to arm sales and marketing on the alternatives prospects evaluate.
- An embedded RevOps pod on a weekly sprint cadence, continuing alongside the client’s new in-house RevOps hire.
Results
- An ICP definition the whole GTM team can apply: volume and book size first, firmographics second.
- A scoring model that ranks high-volume lean lenders above large low-fit firms.
- Clear disqualifiers that keep out-of-ICP lender types out of the sales queue.
- A pod model that complements, rather than competes with, an in-house RevOps hire.
In lending and proptech, the account that looks smallest on a firmographic report is often the best one. Scoring, routing and forecasting all get better once the CRM speaks in volume.
