Churn prediction (events)

Events feel churn later than most businesses: an exhibitor decides not to return in the weeks after the show, but you find out at re-booking, months too late to act. Churn prediction closes that gap by reading the signals that precede the decision — a shrinking stand year over year, a thin meeting diary, poor lead volume, declining session engagement, a key contact who left the company. Scored and ranked, those signals give your sales team a call list ordered by risk, which turns retention from anecdote into routine. The commercial case is blunt: replacing a lost exhibitor costs far more sales effort than saving one, and renewal rates compound across editions. In practice, useful churn models need connected data — registration, matchmaking activity, lead counts, booking history — tied to the same account across years, which is where most attempts stall. The common mistake is admiring the risk scores instead of acting on them; a churn model without an owned intervention playbook (who calls, with what offer, how success is logged) is a dashboard, not a retention programme. One honest nuance: the model tells you who's likely to leave, not why. The reasons still come from conversations, and sometimes the honest answer is that the event underdelivered — which no model fixes.