“we can try to build ML models and then use that to predict users willing to pay and then we can surface the best product they might most likely to buy”

ML predicts willingness to pay and serves each user their best-fit SKU

Like Netflix's top picks eliminating scroll paralysis, Tinder's ML dynamically matched each user to the subscription tier most likely to convert. The team replaced one universal paywall with personalized offers — the resulting revenue lift was measured in the multi-millions annually. Shawn frames this as the industry shift from stale to dynamic pricing.