Sports
The original use case, and the reference model for every other vertical
Reference Organization
Metro Athletics
A multi-property sports organization operating multiple franchises across professional and minor-league tiers, sharing an arena, a fan database, and an operating staff.
Core Customer Identity Problem
A fan who buys tickets, engages with the mobile app, attends a concert in the same arena, and responds to a renewal email appears as four separate records across four disconnected systems. The marketing team targets demographics, the CRM team targets lists, and the analytics team reports on what happened last season.
Lifecycle Model
Stage assignment is driven by behavioral signals: ticket purchase velocity, game attendance cadence, bot interaction intent, and in-venue NPS. A fan moves from Engaged to At Risk well before the renewal window closes, not after it does.
Engagement Signals
- →Ticket purchase frequency and advance lead time
- →Game-day attendance rate vs. purchased games
- →Mobile app session frequency and feature usage
- →In-venue per-cap spend and dwell time
- →Fan Bot interaction intent classification
- →Email open and click-through rates by journey
- →Social engagement depth and advocacy index
- →Renewal cadence and upsell acceptance rate
Relevant Platform Modules
Multi-stage lifecycle model with churn risk scoring that surfaces before the renewal window. The canonical model that every other module reads from.
Behavioral segments with LTV trajectories: from the Season Ticket Core to the Lapsed Prospect. Each drives a different CRM investment thesis.
Cross-property revenue, attendance, and retention KPIs in a single executive frame. Period-over-period rollups without manual SQL.
Signal-driven journeys with enrollment triggered by lifecycle stage transitions, not calendar dates. Attributed revenue with full source tracing.
Structured intent classification at every interaction: upsell, retention, service, and information all feed back into the lifecycle model in real time.
In-venue signals such as per-cap spend, NPS, and section behavior are integrated with the same fan identity layer as CRM and ticketing.
Executive Dashboard Examples
- 01Cross-property renewal rate and period-over-period delta for Q2 vs. Q1
- 02Churn risk count by segment: how many fans are in 'At Risk' stage 90 days before renewal
- 03CRM journey attribution summary: revenue per journey and per enrolled fan with traceable source signals
- 04Sponsor renewal risk matrix: partner activation rate vs. save probability score
- 05Attendance trend by section and game type: home opener vs. late-season differential
AI Recommendation Examples
- Fan in Engaged stage with declining attendance rate and declining app sessions: recommend win-back touchpoint before next home game
- Season Ticket Holder with missed games and a service bot interaction flagged for churn risk: route to high-touch retention journey
- Lapsed fan re-engaged through email open after extended dormancy: re-enroll in onboarding cadence at Casual stage
- New Prospect who attended one game and purchased merch: recommend next-best-action: game plan upgrade offer
Business Outcomes
- →Churn signals surfacing well before the renewal window: time to act instead of time to react
- →Attributed CRM revenue across all journeys with traceable source signals
- →Fans in active retention and renewal enrollment tracks simultaneously, at scale
- →Sponsor renewal risk score with save probability: partner value protected through fan behavior data
- →Cross-property allocation decisions made from a single executive dashboard instead of siloed per-property reports
Strategic Framing
- 01“Sports is where the lifecycle model was proven: every other vertical is an adaptation, not a rebuild.”
- 02“The operating loop (Signal → Intelligence → Action → Measurement → Decision) was designed in a sports context because sports has the richest signal density of any consumer vertical.”
- 03“Multi-property portfolio management is the hardest version of this problem. If the architecture handles six franchises sharing an arena, it handles anything.”
- 04“The synthetic data strategy was validated here: same schema as production ticketing and CRM data, zero PII surface area, full architectural fidelity.”
Coming Soon: data pipeline in progress · FanSignal OS