Business Intelligence
From data warehouse to decision engine
Primary Business Challenges
- 01Fan data lives across ticketing, CRM, venue ops, and loyalty, with no unified model connecting them
- 02Executive dashboards answer 'what happened' but not 'what to do next'
- 03Data pipelines are brittle because schema contracts were never defined upfront
- 04Attribution between marketing investment and revenue outcome is reverse-engineered, not designed
- 05Cross-property visibility requires custom SQL from an analyst, not a shared reporting layer
Typical Executive Questions
- Q1How do you define data quality at the pipeline level, not just in reports?
- Q2Walk me through how you'd build a fan retention KPI that a CMO and a data engineer can both trust.
- Q3How do you handle attribution when the conversion happens weeks after the initial touchpoint?
- Q4Describe a time you built a BI layer that changed a strategic decision.
- Q5What's your philosophy on self-serve analytics vs. curated executive dashboards?
Discovery Questions
- 01What's the current state of your fan data model, is there a canonical identity layer, or are systems still siloed?
- 02How does the executive team consume analytics today: live dashboards, scheduled reports, or ad hoc requests?
- 03Where does attribution live right now, in the CRM, in a BI tool, or in someone's spreadsheet?
- 04What's the biggest question leadership can't answer today because the data doesn't support it?
- 05How mature is your data governance practice, are schema contracts a norm or an aspiration?
Relevant FanSignal OS Modules
Cross-property executive dashboard aggregating revenue, attendance, retention, and activation KPIs with period-over-period rollups across six synthetic properties.
Multi-stage lifecycle model with churn risk and opportunity scores: demonstrates how intelligence surfaces as actionable flags, not just historical metrics.
LTV trajectory modeling by behavioral segment: the answer to 'which fans are worth investing in' at the segment level, not just the individual.
Attributed revenue traced to named journeys and lifecycle signals: demonstrates attribution as a design constraint, not an afterthought.
Partner ROI tied to fan behavior signals rather than impressions: shows how BI architecture connects activation to measurable outcomes.
Suggested Demonstration Flow
- 01BI Command Center: open the cross-property KPI summary, walk period-over-period trend on any metric
- 02Fan Lifecycle: show how churn risk scores surface before the renewal window closes
- 03Personas: walk one segment's LTV arc and explain the investment thesis it generates
- 04CRM Automation: open the attribution summary and trace one dollar of revenue to its source signal
- 05Sponsorship: show partner renewal risk score and connect it to fan behavior data
Key Takeaways
- “The intelligence layer is built before the dashboards. The question isn't what the data says. It's what decision it enables.”
- “Schema contracts are defined before data is generated. If the contract is wrong, the platform is wrong, not the downstream report.”
- “Attribution isn't a reporting layer. It's a structural decision made when you design the journey, not when you run the query.”
- “The BI architecture here isn't sitting on top of the CRM. It's the decision layer the CRM feeds into. That inversion matters.”
Relevant Architecture Decisions
- →Contract-first data design: JSON Schema defined before any data record is generated or ingested
- →Schema validation at the pipeline boundary: invalid records do not reach the reporting layer
- →Lifecycle model as the decision layer: the CRM executes what lifecycle intelligence recommends
- →Period-over-period rollup baked into the data model, not computed at query time
- →Attribution tracing built into the journey structure from day one, not added as a reporting tag
Recommended Follow-Up Demo
Open the BI Command Center and drill into a single KPI trend. Then switch to CRM Automation and show how the revenue number in that dashboard traces back to a specific lifecycle signal and journey name. End on the Sponsorship renewal risk matrix to show how BI intelligence extends to partner relationships.
Coming Soon: data pipeline in progress · FanSignal OS