Founder Playbook · Sub Club by RevenueCat
12 tactics from Shawn Gong
Dynamic Paywalls That Drove Millions in New Revenue
Watch the full episode“if we only offer one product so your decision is should I buy or not but if a day will show you three products now you are thinking which one should I get so you bypass the yes or no and then more likely to convert”
Three pricing tiers bypass the buy/no-buy decision and shift users to "which one?"
Presenting a single product forces a binary yes/no decision. Presenting three tiers reframes the choice entirely — the user now asks which one rather than whether to buy at all. Shawn's simplest advice to founders without ML teams: design three tiers with a clear low, middle, and anchor option.
“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.
“we couldn't just like start it from all the pay walls right because we have a lot of different paywall and then the same times that's very expensive to and it takes a lot of time and then effort to train the model to test the model so we start with something small so we test with couple features”
Roll out ML paywalls on a small feature subset first — full rollout is expensive and slow
Tinder scoped the initial ML paywall rollout to a handful of features rather than the full product catalog, reducing training cost and risk. The incremental approach let the team validate the model quality before expanding. The eventual multi-million-dollar lift came after proving the concept at small scale first.
“don't wait to think about your monetization strategy you know you don't feel bad to charge your users they wanted to pay you and they deserve it because you're going to provide better experience for them”
Don't defer monetization — charge early and reinvest revenue into product quality
Shawn consistently advises early-stage founders to stop treating monetization as something to earn later. Revenue is the fuel for building a better product — charging users early creates a virtuous cycle where investment back into experience keeps subscribers. The reluctance to charge is usually a founder bias, not a user one.
“you cannot only focus on one area for example for this case we cannot just only focus on the first time conversion and then only the revenue amount right so we have to measure hey is David going to come back is David going to buy platinum again it's going to cancel”
Track contra-metrics: did the upsold user re-subscribe or churn?
Tinder required every monetization test to track contra-metrics — not just first-time conversion and revenue, but downstream retention and renewal of upsold users. When ML nudged a user from Plus to Platinum, the team watched whether they came back. A conversion win that causes long-term churn is a net loss; tracking it prevented over-optimizing for short-term revenue.
“boost I think it's perfect allocar product why because you don't it depends on when you want to use it right when you don't receive enough likes so you want to boost yourself when let's say you swipe during the peak hours”
Sell boosts a-la-carte because the use case is situational, not habitual
Gong's framework for what should be unbundled: features whose demand is moment-driven — boosts needed when likes dry up, or during peak swipe hours — should not be locked inside a subscription. Selling them a-la-carte aligns the purchase event with the felt need and reduces churn from subscribers who rarely use intermittent features.
“the travel mode also aka passport mode it's allowed any users to see anyone globally... that feature is a great candidate as like a unbundled feature stand alone so that's how we think about what features make sense to be unbundled”
Unbundle features that have standalone value not all users need all the time
Tinder's Passport Mode — browse profiles in any city globally — was locked in the subscription but used only when users planned to travel. The framework: if a feature has a self-contained use case with variable demand, it is a candidate for unbundling. Selling it standalone captures non-subscriber revenue without cannibalizing the core bundle.
“for our 7day passport feature allocart the price is the same as 7-day plus subscription so that case David you think like oh duh then the 7-day plus subscription is a better deal right so that also helped reduce the cannibalization also increase the conversion or revenue”
Price standalone features at parity with short subscriptions to make subscription the obvious deal
When a la carte Passport caused subscription cannibalization, Tinder set the 7-day Passport price equal to the 7-day Plus subscription. Users immediately saw the subscription as the better deal — more features for the same price — and cannibalization dropped while total revenue rose. Price parity is a behavioral nudge that works without removing the standalone option.
“we will show users subscription first if you don't want to buy subscription we show you all our car so we give you a second chance”
Show subscription first; use a-la-carte as a sequential fallback to capture non-subscribers
Tinder's ML layer sequences paywall exposure: subscription offer first, then a-la-carte products for users who skip it. This prevents non-subscribers from churning off the monetization path entirely and creates a second revenue layer. The fallback captures users who would never subscribe but will pay for one feature at the moment they need it.
“hey you're going to pay $499 a month for subscription and then when you come to Tinder you see people you might be able to see even just use a platinum subscription now you might think like okay I don't feel that special here”
$499/mo Tinder Select failed because the environment couldn't match the price signal
Tinder Select at $499/month tested whether a luxury tier would capture whales. It did not — because users who paid for exclusivity still saw the same pool as Platinum subscribers. The lesson: a premium tier needs the product environment to signal the status, not just the listed perks and the price. Exclusivity must be felt in the experience, not only promised on the paywall.
“it's not like a fail is basically like we learned what we initially wanted to and then we realized it is not worth the continue investment”
Winding down a failed experiment that answered the question is not failure
Tinder Select was gradually scaled down after the team confirmed it was not the right fit for the product and brand. Shawn reframes this as disciplined validation: once the test answered the original hypothesis and further investment could not be justified, the right move was a clean exit. Sunsets that are triggered by learning rather than inertia are a product management skill.
“don't treat your users as they are logical human beings... they don't follow your user experience they don't just read everything to make decision so they are like us you know we make emotional decisions”
Design paywalls for emotional, not logical, users — they skip your features list
The most common paywall design mistake is assuming users will read a feature comparison and reason their way to a purchase. Shawn's framing: real users scan, feel, and decide in a moment. The real unlock at Tinder was not better pricing tiers — it was better decision design, structuring the choice architecture so users landed on the right product without cognitive work.