Overview
The company had zero GTM engineering: all sales were manual and rolodex-based. Large pharma sponsors, the obvious buyers, were locked into incumbents like Körber and Veeva, so the wedge had to be early-stage sponsors and the CROs serving them. Tidalstead built the target list by joining two databases, one paid and one federal and free, and in about a week produced ~150 accounts that were both right-sized and demonstrably in need, before a dollar of outreach.
The obvious market was already taken
Large sponsors run on Körber and Veeva and do not move. Selling into them meant a displacement fight the company was not positioned to win.
No GTM engineering at all
Sales was a rolodex. There was no list, no qualification logic, and no system to inherit. The motion had to be built from nothing.
Right-sized is not the same as in need
Firmographics can find a small pharma company. They cannot tell you whether that company is actually running trials, and a company not running trials cannot use a manufacturing analytics engine at all.
Tidalstead’s Approach
We always start from first principles thinking. Intent data is usually something you rent, and it is usually a guess. Here the buyer’s need was a matter of public record: trials are registered federally. So the qualification was not inferred from behaviour, it was read directly off a registry, and the join between the two databases became the entire filter.
Size the market with the paid database
Apollo gave US pharma companies and CROs in the 0-100 employee band: the wedge segment, right-sized by definition. About 350 accounts came out.
Prove the need with the free one
clinicaltrials.gov, the free federal registry, says which of them actually run trials. Filtering the Apollo list against live trial activity turned firmographic fit into demonstrated need.
Qualify before spending anything
The join ran before outreach, not after it. Every account that survived had already proven, in public, that it had the problem the product solves.
Results
- ~350 Apollo-sourced accounts filtered down to ~150 real targets
- Each target right-sized and demonstrably running trials, before any outreach spend
- The whole process took about a week, end to end
- $0 spent on intent-data vendors
Conclusion
The company went from a rolodex to a qualified, evidence-backed account list in a week, using one paid database and one free one. The expensive part of most GTM stacks, buying intent, turned out to be a public registry nobody had thought to join against.