Predicting who walks away when the whistle blows.
Sports subscribers don't churn evenly. They churn on a schedule — the off-season, the team elimination, the final match. I built a seasonal retention model that treats the football calendar as a feature, not a nuisance. Outputs feed a tiered save-the-sale playbook that kicks in before the cliff, not after.
Churn spiked every May when the Premier League ended — and again after team eliminations in knockout rounds. Retention ran flat dashboards that surfaced the drop after it happened, not before.
Built a gradient-boosted model on Redshift with features keyed to the football calendar: match density, team-of-interest signal, off-season decay, renewal history. Scored nightly into a 4-segment risk tier.
Save-the-sale triggered on risk score, not on the calendar. Diehards got early renewal perks. Casuals got non-sports content discovery nudges. Seasonal churn dropped 8 points year-over-year.
Churn follows the calendar. So does the model.
What actually predicts walk-away.
save-the-sale call queue · CX priority flag.
Four fans, four curves.
Diehards barely flinch when the season ends. Bundled subs lose more than half of their base by March. Each segment got its own save playbook — and its own trigger window.
Astro Sports
Four tiers. Four triggers.
The model scores nightly. Risk tier routes the subscriber to the right channel at the right cost — before the churn event, not after.
No intervention. Measure baseline retention. Feeds the control group.
Non-sports content discovery push. Free mini-bundle trial. CleverTap in-app.
Early-renewal discount, match-pack bundle, loyalty-point accelerator.
Outbound save-the-sale. Human CX. Custom offer authority to level-2 agents.