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Fan Experience

Every signal is an experience opportunity

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Primary Business Challenges

  • 01Fan experience is reactive: something goes wrong and the team responds, but nobody captures why it went right
  • 02Survey NPS data sits in a standalone tool that nobody connects to retention or renewal outcomes
  • 03In-venue behavior is observed but not measured: per-cap spend and section data exist in spreadsheets
  • 04Next-best-action recommendations ignore the fan's history across touchpoints
  • 05The gap between what a fan experiences and what the organization knows is measured in seasons, not days

Typical Executive Questions

  • Q1How do you connect an in-venue experience signal to a downstream CRM action?
  • Q2Describe a time you used fan feedback data to change an operational decision, not just validate one.
  • Q3How do you differentiate the experience for a season ticket holder versus a single-game attendee?
  • Q4What does 'proactive' fan experience mean to you at the operational level?
  • Q5How do you build a personalization model when you have fans across a wide loyalty spectrum?

Discovery Questions

  • 01Where does in-venue experience data currently live, in a venue ops tool, a survey platform, or something else?
  • 02How does a fan complaint today travel from the point of feedback to a CRM action?
  • 03Is there a current definition of what 'good experience' looks like by fan segment, or is it treated as uniform?
  • 04How is AI being used in fan-facing interactions today, and does it connect to a shared fan data model?
  • 05What's the most common experience signal you wish you were capturing but currently aren't?

Suggested Demonstration Flow

  1. 01Fan Bot: show intent classification on a service interaction and trace it to a lifecycle stage update
  2. 02Arena & Events: walk per-cap spend by section, NPS trends, and arrival pattern data by segment
  3. 03Fan Lifecycle: show how a game-day NPS signal triggers a churn risk flag in the lifecycle model
  4. 04Personas: show how experience benchmarks differ across behavioral segments

Key Takeaways

  • “Every interaction is a signal. The bot doesn't just respond. It classifies, routes, and feeds the lifecycle model with structured data.”
  • “Experience data should flow upstream into the CRM, not sit in a survey tool that nobody opens during the season.”
  • “Fan experience and fan retention are the same problem. A bad game-day experience is a churn signal. The question is whether your systems connect those two facts.”
  • “Personalization at scale means knowing which experience moments matter most for each segment, not applying the same treatment to everyone.”

Relevant Architecture Decisions

  • →Intent classification producing structured output at every bot interaction: feeds lifecycle, not a ticketing queue
  • →In-venue signals modeled against the same fan identity layer as CRM and ticketing data
  • →NPS and satisfaction scores linked to lifecycle stage and churn risk, not stored in isolation
  • →Next-best-action routing that reads current lifecycle stage before recommending any action
  • →Persona-differentiated experience benchmarks defined in the data model, not applied at the report level

Recommended Follow-Up Demo

Open Fan Bot and walk through a retention interaction: show the intent classification output and the lifecycle signal it generates. Switch to Arena & Events to show an in-venue NPS trend linked to fan segment. Then open Fan Lifecycle to show how that NPS signal becomes a churn risk flag. End on Personas to show why the recommended follow-up action differs by segment.

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