Usage Monitoring & Proactive Support
Watching usage and reaching out before it dips.
Workflow
Step 1Baseline Usage Definition
ObjectiveEstablish expected usage patterns by user type and company size
InputProduct telemetry, customer data, industry benchmarks
OutputUsage baseline by customer segment
Key activities
Define usage metrics (logins, features used, actions per session), establish benchmarks by segment, identify normal variation, document baseline methodology
Decision points
Metrics meaningful? Benchmarks accurate? Variation understood?
Tools: Product analytics, benchmarking dataRoles: Analytics (definition), CSM (validation), Product (metrics)
Success: Usage baseline defined for 3-5 customer segments
Quality gate: Usage baseline approved, documented
Why it matters
- Usage decline precedes churn. Proactive support at the first dip is cheaper than a save later.
Best practices
- Set baselines before you monitor.
- Configure alerts that mean something.
- Reach out with help, not surveillance.
- Track rebound after outreach.
Common mistakes
- Monitoring everything, acting on nothing.
- Alerts nobody owns.
- Outreach that reads as nagging.
Key questions
- What usage pattern predicts churn?
- Who responds when an alert fires?
Agents that drive this
Health Monitor
Tools we use · alternates in [ ]
Metabase[ Looker Studio, Power BI, Grafana ]Supabase[ Postgres, Firebase ]n8n[ Zapier, Make, Airflow ]