See what each customer company does in your product — act before churn or upsell is missed.
Old way
- Unit of analysis: Account health score in a CS tool nobody opens
- Output: Charts, cohorts, and red/yellow/green widgets
- Operator workflow: Export to spreadsheet, chase CSMs in Slack
- AI role: Generic “insights” sidebar
- Time to first action: Weeks configuring health models and playbooks
- Platform fit: Another silo next to CRM and product analytics
Wavly
- Unit of analysis: Per-company product behavior — who is active, who went quiet
- Output: Governed plays — save, activate, expand — with evidence on each row
- Operator workflow: Intervention queue — approve, execute, dismiss from one command surface
- AI role: Scout explains why each intervention is proposed — tied to live usage
- Time to first action: Conversational boot — outcome, metric, motion, data — command staged in minutes
- Platform fit: Module 3 on the Revenue Graph — same orchestration, governance, attribution substrate
Six questions the runtime answers
- Who is using?
- Who stopped using?
- Which team is adopting?
- Which feature is sticky?
- Which behavior predicts expansion?
- Which behavior predicts churn?
- Who is active vs quiet inside each customer company
- Which teams adopt and which product behaviors look sticky
- Which signals predict expansion vs churn risk
- Recommended save, activation, and expansion plays — not another chart
Teach the runtime — not another setup form dump
01
Calibrate
Pick the outcome you are optimizing for
02
Measure
Choose the 90-day metric your team already reports
03
Motion
Pilot scope and how customers start
04
Data
Connect PostHog or import companies — or skip honestly
05
Command
Preview command, open to #needs-attention
Who is using — and who stopped
Roll up first-party events by customer company: active users, quiet teams, and adoption depth. Behavior change surfaces in the intervention queue before renewal calls — not a health score widget reps ignore.
Expansion vs churn risk
Sticky features, repeated high-value actions, and drop-offs rank into plays — save motions for slipping accounts, expansion when usage crosses thresholds you define during onboarding.
Plays, not charts
Growth Command queues governed interventions on the same orchestration substrate as enterprise account motion. Approve from the queue, execute inline, dismiss with audit — keyboard path included.
Scout reads behavior — not dashboards
Scout is framed as your product behavior intelligence analyst: explains stage, play type, SLA, and heat per row. Ask why an intervention is proposed before you approve it.
Frequently asked
Is this a customer success platform?
Not a customer success dashboard — product behavior that drives approved plays. Wavly ranks companies by product behavior and proposes plays — it does not replace your CS team with another dashboard.
What data do I need on day one?
PostHog or first-party events identified with work email is ideal. You can import company domains or open with an honest empty state until events connect — the command center still boots.
How is this different from product analytics?
Analytics tells you what happened. Product Behavior Intelligence tells operators which companies need a play right now — with Scout rationale, inline approve/execute, and shared GTM governance.
Does it work with Scout discovery and ABM orchestration?
Yes — three runtime cognition modes on one Revenue Graph. Scout elevates Pipeline opportunity; Keeper and Grower run behavior intelligence after signup; Closer handles named enterprise motion — all on shared Revenue Memory.