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Solution Guide

Customer Insights

Behavioral truth over survey opinion

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Primary Business Challenges

  • 01Insights reports describe what happened last season, not what's likely to happen next quarter
  • 02Segmentation is demographic rather than behavioral. It describes who fans are, not what they'll do
  • 03Churn is measured after it happens, not predicted while there's still time to intervene
  • 04Survey data and behavioral data live in separate tools with no reconciliation layer
  • 05Insights findings don't make it into CRM logic: the loop from research to action is broken

Typical Executive Questions

  • Q1How do you build a segmentation model that's useful for both a marketing team and a data science team?
  • Q2Describe the last time an insight you surfaced changed an investment decision.
  • Q3How do you measure the impact of an insights program on business outcomes?
  • Q4Walk me through your approach to churn prediction: what signals do you weight and why?
  • Q5How do you distinguish a leading indicator from a lagging indicator in fan behavior data?

Discovery Questions

  • 01How does the insights team currently deliver findings to the CRM team: a report, a meeting, or a data feed?
  • 02What's the lag time between a behavioral signal appearing in the data and informing a campaign decision?
  • 03How does the organization define fan segments today: demographics, purchase history, or behavioral clustering?
  • 04What's the most important question the insights team can't currently answer because the data doesn't support it?
  • 05Is there a churn model in place, and if so, how does it feed into retention operations?

Suggested Demonstration Flow

  1. 01Personas: walk one persona's behavioral signal profile, LTV arc, and the investment thesis it generates
  2. 02Fan Lifecycle: show churn risk scoring and the leading indicators that trigger a flag before the window closes
  3. 03Fan Bot: show intent classification as a behavioral data source feeding the insights model
  4. 04BI Command Center: show how behavioral insights surface as executive KPIs with business consequence

Key Takeaways

  • “Segmentation that stops at demographics is just a mailing list. Behavioral segmentation tells you what the fan will do next.”
  • “Churn prediction is only useful if it surfaces early enough to act on. This platform flags risk 90 days before the renewal window, not 30 days after it closes.”
  • “The insights loop has to close. If a finding doesn't make it into CRM logic, it was research, not intelligence.”
  • “LTV trajectory matters more than current spend. The question isn't who's spending the most today. It's who's worth investing in over the next three years.”

Relevant Architecture Decisions

  • →Behavioral clustering as the foundation of segmentation, not demographic proxies
  • →Lifecycle stage as a leading indicator: stage assignment changes when signals change, not on a monthly batch schedule
  • →LTV projection modeled at the segment level, not only at the individual fan level
  • →Intent classification from all touchpoints flowing into a unified behavioral data layer
  • →Insights output designed to be consumed by CRM enrollment logic, not just by report readers

Recommended Follow-Up Demo

Open Personas and walk through one persona in depth: the behavioral signals that define it, the LTV arc it follows, and the CRM thesis it generates. Then switch to Fan Lifecycle and show the churn risk model: which signals move a fan toward 'At Risk' and how far in advance the flag appears. End on the BI Command Center to show how those behavioral insights become the executive retention KPI.

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