usage-drop-investigation

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

1. 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)?

2. 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?

3. 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.