A churn forecast that knows when the football stops.
Sports OTT lives or dies on fixtures. A subscriber who joined for matchweek 32 has a different shelf-life than one who joined in October — and a flat retention model treats them the same. This forecast layers the fixture calendar, key-match flags, telco bundle expiries and Ramadan overlap onto the trial-to-VIP cohort, so retention numbers reflect the season the subscribers actually live in.
A linear churn curve assumes every month is equal. In sports OTT they are not. The trial cohort that joined for Liverpool–Utd does not behave like the cohort that joined in pre-season — and a single retention number averages them into uselessness.
For every join-month cohort, build a feature vector: fixture density over the next 90 days, count of top-6 derbies, telco bundle overlap, Ramadan overlap, key tournaments. Refit a survival model per segment, not a global one.
Marketing buys the offseason media in advance, not after retention has already crashed. Bundle teams renegotiate Celcom and Maxis windows knowing exactly which weeks are exposed. Programming pulls forward Euros marketing into March.
The naive line says climb. The fixture list says cliff.
Score one cohort yourself.
Pick a cohort dossier — or paste your own (4 KB cap) — and watch the agent score 6-month churn, name the dominant driver, recommend one retention play, and surface the calendar signals that drive the score. Live model call. Cached replay if the rate-limit fires.
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What the season layer actually reads.
When matches per week fall below 1.5, freemium-to-VIP retention drops 8–12pp within 3 weeks. The trial cohorts joining for end-of-season fixtures churn through the dry month.