AI USE CASE
Fan Engagement Scoring with ML
Score every fan's engagement level to personalize outreach and boost loyalty for sports venues.
What it is
By combining ticket purchase history, merchandise transactions, social media signals, and mobile app activity into a unified ML-driven engagement score, venues can segment fans and trigger targeted campaigns. High-scoring fans can be prioritised for loyalty rewards, while at-risk fans receive re-engagement nudges before key fixtures. The system continuously retrains as new behavioural data flows in, keeping scores current throughout the season.
Data you need
At least 2 seasons of fan transaction data (tickets, merchandise), app interaction logs, and social media activity linked to individual fan profiles.
Required systems
- crm
- marketing automation
- data warehouse
- ecommerce platform
Why it works
- Establish a single fan identity layer that links all data sources (ticketing, app, CRM, social) before model training begins.
- Automate weekly or event-triggered model retraining so scores reflect recent behaviour throughout the season.
- Define clear action playbooks for each score tier (top fans, mid-tier, at-risk) before launch so marketing can execute immediately.
- Start with a single high-impact channel (e.g. email) to prove ROI before expanding personalisation across all touchpoints.
How this goes wrong
- Fan data is siloed across ticketing, merchandise, and app platforms with no unified identity resolution, making scoring impossible.
- Model scores become stale mid-season because retraining pipelines are not automated, reducing personalisation accuracy.
- Marketing team lacks processes to act on scores, so high-value segments receive the same generic communications as everyone else.
- Low app adoption rates mean behavioural data is too sparse for reliable scoring for the majority of the fanbase.
When NOT to do this
Don't build a fan engagement scorer if your ticketing, app, and CRM data live in separate systems with no shared fan identifier, you'll spend the entire budget on data plumbing before any ML work begins.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.