customer-analytics

Customer Analytics (LENS Framework)

Design a customer analytics architecture that answers which customers, doing what, are driving (or breaking) the business - instead of dashboards full of vanity counts. LENS produces a defensible segmentation, a retention model, an engagement score, and a behavioral diagnostic loop that PMs and CS can act on weekly.

Core Principle

Customer analytics fails when it stops at "users went up." LENS forces decomposition into who, what, when, and why - the four axes a dashboard usually collapses into one number.

The LENS Framework

Letter	Stage	The Question
L	Lifecycle Mapping	What are the named lifecycle stages and what does each one's "good" look like?
E	Engagement Scoring	What weighted score combines depth, breadth, and recency of value events?
N	Net Retention Decomposition	Where exactly is NRR coming from - new logo, expansion, contraction, churn?
S	Segment Behavior	Which segments behave differently, and which behavioral cohorts predict outcomes?

Lifecycle Stages

Stage	"Good" Signal	Diagnostic
New	First value event within target window	Activation rate by cohort
Activated	≥ N value events / week within 30 days	Stickiness (DAU/WAU or analog)
Habituated	Multi-workflow + multi-user adoption	Workflow coverage %
Expanding	New seats / modules / use cases attached	Expansion lead indicators
At-risk	Engagement decay + stakeholder loss	Churn-risk score
Churned / Contracted	Logo or ARR loss	Reason-coded post-mortems

Engagement Scoring

Engagement is depth × breadth × recency, not raw event counts.

Dimension	Definition	Example
Depth	Frequency of core value events per active user	Core actions / week
Breadth	% of paid seats active + # of distinct workflows used	Seat activation, workflow coverage
Recency	Time since last value event, weighted exponentially	Decay half-life of 14-30 days

Combine into a 0-100 score; bucket into Engaged / Mixed / Disengaged for routing into CS plays.

Net Retention Decomposition

A single NRR number hides the truth. Always decompose:

Component	Formula	What It Tells You
GRR	(Starting ARR − Churn − Contraction) / Starting ARR	Floor on the business
Expansion %	Expansion ARR / Starting ARR	Upside from existing book
NRR	GRR + Expansion %	Compound growth signal
Churn drivers	Reason-coded, % of churned ARR by reason	Where to fix the leak
Contraction drivers	Seat reductions vs price reductions vs downgrades	Where pricing/packaging is misaligned

Segment Behavior

Segments must be decision-driving, not decorative. Two segmentation lenses:

Lens	Example	Use For
Firmographic	Industry × Size × Region	GTM motion design
Behavioral	Activation pattern, workflow mix, usage intensity	Lifecycle interventions, expansion targeting

The behavioral lens almost always predicts retention better than the firmographic one - most teams underuse it.

Output

Save to outputs/customer-analytics-[scope]-[YYYY-MM-DD].md

Artifact	Description
Lifecycle Model	Named stages with entry/exit criteria + "good" definitions
Engagement Score Spec	Dimensions, weights, decay, bucket thresholds
NRR Decomposition	Waterfall: starting → expansion → contraction → churn → ending
Segment Behavior Matrix	Behavior cohorts × outcome (retention, expansion, time-to-value)
Diagnostic Loop	Weekly review template: anomaly → hypothesis → action → owner
Cohort Retention Curves	M0-M12 retention by acquisition cohort and segment

Process

1. Map lifecycle stages with entry/exit criteria - agree with PM and CS before instrumenting
2. Define engagement score with explicit weights; validate against historical churn
3. Decompose NRR into a waterfall - every component reason-coded
4. Build behavioral cohorts that predict outcomes better than firmographics
5. Stand up the diagnostic loop - weekly anomaly review, owner assigned, action tracked
6. Wire cohort retention curves into the executive cadence, not buried in a tool

Tips

1. Avoid vanity engagement metrics - logins, page views, and DAU rarely predict retention
2. Validate the score against churn before deploying - score that doesn't correlate is decoration
3. Segment behavior beats firmographic targeting for retention plays
4. Treat contraction separately from churn - different root causes, different fixes
5. Anomaly without action is noise - every diagnostic must end with an owner

Pairs With

• journey-architect - Lifecycle stages map directly to journey gates
• customer-success - Health score consumes the engagement score
• growth-loop - Retention curves feed loop-strength analysis
• revenue-analytics - NRR decomposition rolls into revenue diagnostics