Dynamic Paywalls That Drove Millions in New Revenue – Shawn Gong, Tinder
Sub Club by RevenueCat
A/B Testing the Dynamic Model
Shawn outlines how Tinder validated the ML paywall via controlled experiments and measured conversion and revenue lift.
On the podcast: how Tinder's ML-powered paywalls drove millions in new revenue, the art of selling features à la carte without killing subscription revenue, and why Tinder Select flopped despite users saying they'd pay for it.
This conversation is shorter than usual and will be featured in RevenueCat’s State of Subscription Apps report. Each episode in this series will explore one crucial topic and share actionable insights from top subscription app operators.
Top Takeaways:
🤖Users need fewer options, not more
Decision overload kills conversion. Tinder saw multimillion-dollar annual revenue gains by using ML to predict and surface the single best product for each user instead of showing every tier and plan at once.
🎯Anchor a la carte prices to subscriptions to prevent cannibalization
Unbundling features can capture non-subscribers, but pricing too low steals from subscription revenue. Tinder priced its standalone Passport feature equal to the weekly equivalent of a full-featured subscription, making the subscription the obvious better deal.
🧠 Design for emotional decisions, not logical ones
Users don't read every feature comparison and weigh their options rationally. They decide in seconds based on feeling. Observe how users actually behave, not how you assume they should, and build your purchase flows around that.
About Shawn Gong:
🚀 Product Growth & Monetization at Tinder, the world's most popular dating app, with over 55 billion matches made across 190+ countries since launching in 2012.
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- David Barnard - @drbarnard
- Jacob Eiting - @jeiting
- RevenueCat - @RevenueCat
- SubClub - @SubClubHQ
Episode Highlights:
[0:00] Introduction to Shawn Gong, Product Leader in Monetization & Growth at Tinder
[1:05] The challenge of decision overload and how Tinder tackled it with dynamic pricing
[2:47] How machine learning helps Tinder predict and serve the right product for each user
[4:25] Simplifying user choices: Reducing overwhelming options for better conversion
[5:48] Shifting from static to dynamic pricing: The role of AI in optimizing Tinder’s paywall
[7:06] A/B testing the dynamic pricing model: How Tinder validated the ML model's effectiveness
[8:12] Unbundling features like Passport mode: Meeting specific user needs without subscriptions
[9:33] The impact of pricing changes on conversion rates and subscription cannibalization
[10:57] Long-term retention metrics: Measuring the success of dynamic pricing beyond just revenue
[12:00] Tinder Select: Lessons from launching a high-end tier and why it didn’t work
[13:18] The importance of aligning product offerings with user emotions for better decision-making
[14:25] How Tinder continues to optimize pricing strategies through iterative testing and learning
[15:48] Shawn’s advice for startup founders: Focus on retention and building better product decision design
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