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Case Studies

Business Cases

Scenarios showing how FanSignal OS solves real business problems in sports and entertainment, with data used, platform modules, AI recommendations, and quantified business impact.

Retention

Increasing Season Ticket Renewals

Real Salt Lake's season ticket renewal program needed earlier visibility into at-risk accounts before the renewal window compressed the sales cycle. FanSignal OS models renewal risk through attendance velocity, engagement decline, persona fit, and CRM journey response, then simulates which intervention path is most likely to preserve the relationship.

Business Problem

The organization could not distinguish fans who were quietly disengaging from fans who were simply slow to renew. Both groups received the same campaign: a price-anchored offer with a fixed deadline. Fans who were genuinely at risk ignored it. Fans who were engaged felt pressured by it. Renewal rate stagnated because the intervention was too late, too generic, and too focused on price rather than engagement.

Data Used

  • -Ticketing attendance data: games attended, sections, companion patterns across seasons
  • -CRM engagement data: email open rates, click-through, and response history per fan
  • -Fan Bot interaction logs: service inquiries, upgrade requests, and sentiment-flagged conversations
  • -Arena & Events data: in-venue spend per cap, NPS scores, and event attendance beyond game days
  • -Lifecycle stage history: stage transitions over the trailing season, velocity of engagement decline

Platform Modules

Fan LifecycleOpen →

Identifies at-risk accounts well before the renewal window using stage velocity and engagement decline signals

PersonasOpen →

Routes at-risk accounts to the correct win-back variant: a Founding Loyalist gets a different message than a Value Maximizer

CRM AutomationOpen →

Enrolls at-risk fans in the Season Ticket Win-Back journey with persona-specific content and cadence

BI Command CenterOpen →

Tracks renewal rate week-over-week against prior seasons and flags properties where renewal pace is below target

AI Recommendations

  • 01Segment the renewal list by lifecycle stage before launching any campaign: fans in the Lapsing stage need a re-engagement play, not a renewal offer
  • 02Move the win-back intervention 90 days earlier: at-risk signals are present in attendance velocity before the renewal window opens
  • 03Personalize by persona archetype: Founding Loyalists respond to access and recognition, not discounts; Value Maximizers respond to price packages, not legacy narratives
  • 04Create a separate track for multi-seat holders: their churn signal is different and their LTV impact is disproportionate to single-seat accounts
  • 05Measure success at the journey level, not the campaign level: track which CRM journey actually drove the renewal, not the last email opened

Executive Dashboard Signals

  • Renewal rate vs. prior season (period-over-period, week by week through the renewal window)
  • At-risk account count by persona archetype with estimated revenue exposure
  • Win-back journey enrollment and conversion by stage at enrollment
  • Revenue at risk vs. revenue recovered: tracked as the intervention progresses
  • Days to renewal deadline vs. current pace: will current enrollment close the gap?

Business Impact

Season Ticket Renewal Rate

60%

+23 points

from 37% (simulated baseline)

Win-Back Journey Revenue

$295,507

New capability

from $0 (simulated baseline)

Average Days to Renewal (At-Risk Cohort)

78 days before deadline

New capability

from No early renewal signal (simulated baseline)

Renewal Rate: Founding Loyalist Persona

80%

+32 points

from 48% (simulated baseline)

Business impact values come from a deterministic Bot Arena simulation replay comparing the baseline 30-day generic renewal campaign with the 90-day persona-specific FanSignal win-back journey over a 12-tick run. All fan cohorts, reactions, and revenue figures are synthetic. No real fans, teams, campaigns, or revenue are represented.

Future Enhancements

  • +ML-driven churn prediction model trained on multi-season attendance and engagement history
  • +Automated price-package personalization based on household spend history and competitor pricing signals
  • +Renewal velocity dashboard with daily pacing alerts for the VP of Ticket Sales
  • +Cross-property renewal bundling: fans with season tickets at multiple properties get a portfolio offer, not separate campaigns

Get in Touch

Working on a similar challenge?

These scenarios are illustrative, but the architecture behind them is designed for production. If you're building fan intelligence infrastructure, let's talk.

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Coming Soon: data pipeline in progress · FanSignal OS