How to Find Adjacent Use Cases Using Your Own Data
Your next best expansion idea is probably already in your product data—it’s hiding in friction, drop-offs, and support noise.
Thesis: Use internal signals to find complementary or adapted use cases with the highest probability of adoption.
Signal 1: Search and zero-results
If users search for something and get nothing, you’ve found intent.
Track: top search terms, failed searches, repeated queries.
Signal 2: Repeated exports and workarounds
Exports are ‘manual integrations.’ If users export the same thing repeatedly, they’re telling you what workflow you don’t cover.
Track: export frequency, destinations, formats, and follow-up actions.
Signal 3: Support taxonomy
Tag tickets by job-to-be-done:
- Setup/onboarding
- Data correctness/trust
- Workflow gaps
- Governance/permissions
- Reporting/activation
Look for the categories that correlate with churn or stalled expansions.
Signal 4: Power-user behavior
Power users create patterns you can productize:
- templates
- saved views
- custom calculations
- automation rules
Interview 5 power users; extract the repeatable pattern.
Turn signals into candidate wedges
For each signal cluster, write:
- Who has this problem?
- When does it occur?
- What workaround do they use?
- What would ‘one-click’ look like?
- What metric proves success?
Key takeaways
- Zero-results search and repeated exports reveal real intent.
- Support tickets should be tagged by job-to-be-done, not by feature.
- Power users show repeatable patterns worth productizing.
- Convert signals into wedges with who/when/workaround/one-click/metric.