Dynamic Paywalls That Drove Millions in New Revenue – Shawn Gong, Tinder

Problem: many tiers (Plus/Gold/Platinum) x plans (weekly/monthly) plus a la carte items caused decision overload; some users bought nothing, others bought Platinum but only used Gold features. The ML team built models predicting willingness to pay; at paywall time the paywall asks the model which paywall/product to recommend for that user, instead of showing everyone the same static paywall. Rolled out small first (a couple of features/paywalls, not the whole scope) to limit cost and risk. A/B test: control = historical static paywall, treatment = model-chosen paywall; measured conversion and total revenue. Counter-metrics: repurchase, renewal and cancellation of users nudged to higher tiers; several model variants compared against each other and against no-model paywall.

Passport (travel mode: swipe anywhere in the world) was previously only in the subscription. Chosen for unbundling because only some users need it, occasionally (when travelling or meeting people outside their city) and they don't want a subscription commitment. Offered as a standalone a la carte purchase for 1 day, 3 days or a week to capture non-subscribers. Criteria used: features whose use is situational (like Boost, Super Like) suit a la carte.

After the standalone Passport cannibalized Plus, tested a higher a la carte price and measured conversion, subscription cannibalization and total revenue. Price amounts not given.

Raised the a la carte Passport price again so the 7-day Passport costs the same as a 7-day Plus subscription (which includes Passport and more), and added an upsell on the paywall showing both: 'you can buy Passport, standalone a la carte, but also you can buy Plus subscription'. The subscription becomes the obvious better deal.

Using the paywall ML model, users are shown the subscription first; those who don't buy are then shown the a la carte Passport as a 'second chance'.

Tinder Select: a higher-end tier above Platinum targeting 'whales' who already spend heavily on top subscriptions and a la carte, offering better service and product. Guest's diagnosis: identity/brand fit; users of a mass-market app don't feel the environment is exclusive (like an expensive table in a regular club vs an exclusive club), so the luxury benefits don't meet expectations.