Customer cohort analysis + behavioral cohorts for multi-location operators
Customer-graph-native cohort computation with cohort membership emitted as a runtime signal every AI content and decisioning agent consumes — not a dashboard view your analyst checks weekly.
The problem
Your analytics team computes a cohort once a quarter for the board deck. By the time it ships, the cohort is six weeks old. Your lifecycle email flow has no idea who is in the at-risk cohort this week. Your save-flow team manually pulls a churn-risk CSV every Tuesday.
Mixpanel ($25-$2,000+/month), Amplitude ($61-$2,000+/month), Heap ($3,600-$25,000+/year), and PostHog expose cohort VIEWS inside their product. Klaviyo flows, GBP agents, and paid creative do not read from them. CDPs (Segment $120-$1,800+/month, Tealium $50k+/year, mParticle $35k+/year, Klaviyo CDP, Bloomreach) sync events and let you author cohort definitions; cohort math then runs in the underlying warehouse with nightly refresh at best. Specialized customer-analytics (Optimove $50k+/year, BlueShift $30-$150k/year, Insider, Iterable Insights) build RFM and cohort modules but tie to a single-tenant graph — cross-location and cross-brand cohorts manual. Data-warehouse plus BI (Snowflake / BigQuery / Redshift plus Looker / Tableau / Power BI / Sigma Computing) requires $5,000-$50,000+/year per data-analyst FTE to maintain.
The gap is cohort COMPUTATION on the canonical customer graph with composable RFM, behavioral-signal, and LTV-math inputs — emitting cohort membership as a real-time signal every content and decisioning agent in the catalog consumes.
What success looks like
Cohort definitions compose RFM + behavioral-signal-ingestion + LTV-math-primitives into operator-defined multi-axis cohorts: recency × frequency × monetary × behavioral signal × per-vertical engagement × per-location activity. Computation operates on the canonical customer graph from identity-resolution-deterministic-probabilistic, so cross-device and cross-channel customers count once.
Multi-brand portfolios get per-brand cohort sets; multi-vertical operators get per-vertical cohort axes; multi-location operators get per-location cohort variance with cross-location roll-up. When a customer enters or exits a cohort, customer-change-event-emission fires a typed event. Lifecycle-flow-architecture, save-flow-propensity-scoring, predictive-tier-transition, churn-prediction-per-subscriber, loyalty member-journey-decisioning, and every other downstream agent consume cohort membership as a runtime signal.
Versioned-customer-history captures every cohort-membership transition for audit-defensible regulator-inquiry response. Mixpanel and Amplitude dashboards remain useful for analyst exploration; the operational cohort-membership state lives in this skill.
How most operators solve this today
Five tiers of incumbent tools — none provide cohort COMPUTATION on the canonical customer graph with cross-agent runtime signal emission.
Product analytics with cohort features (Mixpanel, Amplitude, Heap, PostHog)
$25-$25,000+/month
Cohort VIEWS inside the product analytics tool. Klaviyo flows, GBP agents, and paid creative do not read from them. Multi-location and multi-brand operators get coarse cohorts.
Customer-data platforms (Segment, Tealium, mParticle, Klaviyo CDP, Bloomreach)
$120-$50,000+/year
Sync events between systems and let you author cohort definitions; cohort math runs in the underlying warehouse with nightly refresh at best. Not a real-time signal layer.
Specialized customer-analytics (Optimove, BlueShift, Insider, Iterable Insights, Pendo)
$30,000-$150,000+/year
RFM and cohort modules built-in but tied to a single-tenant customer graph. Cross-location and cross-brand cohorts manual.
Data warehouse plus BI (Snowflake / BigQuery / Redshift plus Looker, Tableau, Power BI, Sigma)
$5,000-$50,000+/year per data-analyst FTE
Custom SQL plus dbt models plus dashboards. Cohort outputs live in dashboards; not consumed by content or decisioning agents at runtime.
DIY SQL plus Excel
Free
Analyst writes SQL or exports CSV; pandas / Excel computes RFM bands or behavioral splits. Falls apart past 20-50 location operators or 5+ brand portfolios.
What changes when this is an agent skill
The Completions behavioral-cohort-computation skill runs cohort math on the canonical customer graph (identity-resolution-deterministic-probabilistic) so cross-device and cross-channel customers count once. RFM math + behavioral-signal-ingestion + LTV-math-primitives compose into operator-defined multi-axis cohort definitions.
Multi-brand portfolios get per-brand cohort sets; multi-vertical operators get per-vertical cohort axes; multi-location operators get per-location cohort variance with cross-location roll-up.
When a customer enters or exits a cohort, customer-change-event-emission fires a typed event. Lifecycle-flow-architecture, save-flow-propensity-scoring, predictive-tier-transition, churn-prediction-per-subscriber, loyalty member-journey-decisioning, and per-location-list-segmentation consume cohort membership as a real-time signal.
Versioned-customer-history captures every cohort-membership transition for audit-defensible regulator-inquiry response. Mixpanel and Amplitude dashboards remain useful for analyst exploration; the operational cohort-membership state lives in this skill — every AI agent in the catalog reads it at runtime, not nightly.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Customer Data Graph Foundation Agent
Resolves DTC subscriber identity, computes LTV math, and emits the canonical customer-data-graph downstream subscription agents consume.
Early-adopter
$2,500–$4,500/mo
FAQ
- What is customer cohort analysis?
- Grouping customers by shared characteristics (acquisition date, behavior pattern, RFM band, vertical engagement) and analyzing how each cohort behaves over time. This skill makes cohort membership a runtime signal every AI content and decisioning agent consumes — not just a dashboard view your analyst checks weekly.
- How is this different from Mixpanel or Amplitude cohort features?
- Mixpanel and Amplitude expose cohort VIEWS inside their product. Klaviyo flows, GBP agents, and paid creative do not read from them. This skill exposes cohort MEMBERSHIP as a runtime signal every agent in the catalog consumes.
- How is this different from a CDP (Segment, Tealium, mParticle)?
- CDPs sync customer events between systems and let you author cohort definitions; cohort math runs in the underlying warehouse with nightly refresh at best. This skill operates on the canonical customer graph with resolved identity, composes RFM plus behavioral-signal plus LTV primitives into operator-defined cohorts, and emits change events.
- What customer cohorts can be computed?
- RFM bands, behavioral signal cohorts (high-frequency engagers, lapsed, at-risk-churn, high-LTV, new), per-vertical engagement cohorts (regulated vertical sub-cohorts), per-location cohorts, multi-brand portfolio cohorts, and custom operator-defined combinations.
- How does this compose with identity-resolution?
- Identity-resolution (deterministic plus probabilistic) is the input — it resolves the canonical customer graph. Cohort computation runs on that resolved graph so a cross-device customer counts once.
- What downstream agents consume cohort membership?
- Lifecycle-flow-architecture, save-flow-propensity-scoring, predictive-tier-transition, churn-prediction-per-subscriber, loyalty member-journey-decisioning, lifecycle-stage-cadence, per-location-list-segmentation, and any other content or decisioning agent that needs runtime cohort signal.
- How does this compose with RFM analysis and LTV math?
- RFM math plus LTV-math primitives are inputs. This skill composes them with behavioral-signal-ingestion into operator-defined cohort definitions. RFM by itself is a static band; this skill makes it dynamic and multi-axis.