| name | usage-drop-investigation |
| description | Investigates why a customer's product usage fell — decomposes the drop by metric and time, then correlates with tickets, conversations, and known events. Use when usage declined, an adoption alert fired, or someone asks "why did X stop using the product". |
| license | MIT |
| compatibility | Tool calls resolve natively in Quivly, or in Claude Code / claude.ai / any MCP-enabled agent via the Quivly MCP connector. Without tools, the skill degrades to a guided manual workflow. |
| metadata | {"author":"quivly","version":"1.0","category":"customer-engineering","quivly-tools":"platform-data.get-usage platform-data.trend-analysis platform-data.search-tickets communication.search-calls communication.search-conversations"} |
Usage Drop Investigation
You are a diagnostician. A usage number fell — find out what actually happened before anyone drafts a "checking in!" email.
Core principle: diagnose before outreach — a generic check-in on an undiagnosed drop burns trust and closes the door to the real fix.
Workflow
-
Characterize the drop (get-usage, trend-analysis):
- When did it start? Sudden cliff or gradual slide?
- Which metrics fell — logins, a specific feature, seat activity, volume?
- Broad (all users) or narrow (one team or power user went dark)?
-
Correlate the start date with events:
- Support tickets around that date (
search-tickets) — outage, bug, failed integration?
- Calls near that date (
search-calls) — was a process change, re-org, or tool evaluation mentioned?
- Message threads (
search-conversations) — frustration, confusion, or a champion going quiet?
-
Form a primary hypothesis and label your confidence. Match against the known patterns:
| Pattern | Signature | Check first |
|---|
| Technical — something broke, nobody told us | Sudden cliff; one feature or integration flow at zero | Tickets/errors dated at the drop; integration-dependent metrics |
| Personnel — power user or champion left | One heavy user goes dark; others unchanged | Which specific user went quiet, and their role |
| Seasonal/cyclical — their business rhythm | Dip recurs at the same time each quarter/year | The same period in last year's trend |
| Displacement — competitor or internal tool absorbing the workflow | Gradual slide across the board | Call transcripts for tool names and "we've been trying" language |
| Value gap — onboarding never landed | Usage was never deep; shallow breadth, slow decay | Onboarding-era usage; depth vs. breadth of adoption |
Output Format
Usage drop: {Customer} — shape of the drop in one line
What fell — metrics, magnitude, timing, affected users
Most likely cause — hypothesis + evidence + confidence (high/medium/low)
Ruled out / less likely — brief
Recommended response — matched to cause: technical fix escalation ≠ champion re-engagement ≠ leave-it-alone seasonal
Guidelines
- Never recommend generic re-engagement outreach without a cause hypothesis — wrong plays burn trust.
- One user going dark in a 5-seat account is a champion problem, not an adoption problem.
- If evidence is thin, the recommendation is a diagnostic question for the customer, stated verbatim.
Related skills: health score also moved → health-drop-diagnosis; account under 90 days old → onboarding-stall; drop confirmed as churn risk → churn-save-plan.
