Sub Club by RevenueCat

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.
👋 LinkedIn

Follow us on X: 
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

On the podcast: testing prices from $5 all the way to $120 per year, why rising CACs forced a pricing rethink, and how raising the price allows them to discount more aggressively.

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:
💰 Retest prices you've already ruled out 
Market conditions shift constantly. A price point that couldn't beat the control for years can suddenly break even as competitors raise prices and consumer expectations change.
📈A higher base price unlocks more aggressive discounting Going from $40 to $80 creates room for steeper percentage discounts that drive higher conversion, even when the absolute dollar price is still higher.
🔒Rising CACs demand pricing that funds acquisition At $40/year, paid UA math barely worked. Doubling the price gave the marketing team room to compete on acquisition channels where costs keep climbing.

About Patrick Rills: 

🚀 Chief Product & Technology Officer at Lose It!, the app-based weight loss program mobilizing the world to achieve a healthy weight.
👋 LinkedIn

Follow us on X: 
David Barnard - @drbarnard
Jacob Eiting - @jeiting
RevenueCat - @RevenueCat
SubClub - @SubClubHQ

Episode Highlights:
[0:00] Introduction to Patrick Rills, Chief Product & Technology Officer at Lose It!
[1:05] How pricing changes unlocked new growth opportunities at Lose It!
[2:12] Balancing customer acquisition costs (CAC) with retention through pricing strategy
[3:25] Key insights from years of price testing, ranging from $5 to $120 per year
[4:42] Raising prices to enable deeper discounting and improve conversions
[5:58] Aligning product value with pricing to retain loyal users
[7:06] The role of the freemium model in keeping users engaged after price increases
[8:02] Using smart pricing and AI to drive growth
[9:14] Leveraging data to fine-tune pricing decisions
[10:27] How customer feedback and product data shape pricing strategies
[11:38] Challenges and benefits of raising prices for an established product
[12:33] Future plans for pricing tiers and new monetization strategies
[13:18] Patrick shares iOS developer hiring opportunities at Lose It!
[13:41] Final thoughts on driving sustainable growth and user value

On the podcast: how Duolingo prioritizes clarity over persuasion on their paywalls, why they offer users multiple free trials instead of just one, and how adding friction to their trial reminder flow actually boosted conversions.
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:

🎯 Prioritize clarity over persuasion on your paywalls
Show users a timeline of exactly what happens during their trial, when they'll be charged, and how refunds work. Duolingo found that removing uncertainty about the purchase process drives more conversions than trying to sell harder.

⚡Shorter trials compound experimentation velocity
Cutting their free trial from 14 days to 7 doubled Duolingo's experimentation velocity. Faster feedback loops let the team kill losing tests sooner and run significantly more experiments per quarter.
🔬Adding friction to trial reminders can boost conversions 
Duolingo tested letting users pick which day they get their expiration reminder. The extra step signaled transparency, built trust that they wouldn't be surprised by a charge, and gave them time to experience real value before deciding.

About Anmol Tiwari:

🚀 Director of Product Management at Duolingo, the world's most popular way to learn a language.
👋 LinkedIn

🖥️Carers at Duolingo
Follow us on X: 
David Barnard - @drbarnard
Jacob Eiting - @jeiting
RevenueCat - @RevenueCat
SubClub - @SubClubHQ

Episode Highlights:
[0:00] Introduction to Anmol Tiwari, Director of Product at Duolingo
 [1:05] Anmol discusses Duolingo’s focus on clarity in free trials and product offerings
 [2:10] How transparency in trial terms and refunds builds confidence and boosts conversions
 [3:45] The benefits of shorter trials for faster experimentation and better user engagement
 [5:10] Why Duolingo offers multiple trials to cater to different user life stages
 [6:30] Using machine learning to personalize subscription offers and in-app ads
 [7:40] How Duolingo uses contextual paywalls to increase conversions
 [8:50] Regional differences in trial strategies, especially in markets like China
 [10:10] The impact of "free tastes" and trial-like experiences in premium tiers
 [11:30] Using timers and reward-based copy to clearly communicate trial benefits
 [13:00] How Duolingo reduces day-zero cancellations and builds trust with users
 [14:15] Personalizing paywalls based on individual user behavior
 [15:30] Experimenting with new creative strategies to promote subscriptions
 [16:40] The role of AI in optimizing engagement and conversions
 [17:50] Understanding global trial preferences, particularly in China
 [19:10] How trust drives conversions and prevents cancellations
 [19:55] Closing thoughts on how transparency, experimentation, and personalization fuel growth

On the podcast: the experiments behind Mojo's 60% lift in ARPU, why a winning paywall in Japan completely failed in the US, and why not relying on day one for most of your revenue is actually a strength.

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:

🌍Show free users a paywall every week after onboarding
Triggering a paywall on app open once per week for free users drove 15% of new revenue with no backlash. The more generous your free tier, the more users tolerate the ask.

💪A winning paywall in one region can completely fail in another
A long, detail-rich paywall lifted revenue 20% in Japan but flopped in the US, where cleaner designs with punchy copy outperformed. Always retest winners in each market before rolling out globally.
⚡Experiment velocity is a huge unlock for revenue optimization
Running parallel paywall tests across geo segments on a weekly cadence compounds gains fast. More iterations mean shorter feedback loops, faster learning, and fewer months leaving revenue on the table.

About Michal Parizek

🚀 Senior Growth Product Manager at Mojo, a mobile-first content creation platform that empowers businesses and creators to produce professional, animated social media content in minutes.

👋 LinkedIn
Episode Highlights:
[0:00] Introduction to Michal Parizek, Senior Growth Product Manager at Mojo
[1:02] How Mojo achieved a 60% increase in average revenue per user
[2:16] The impact of paywall design experiments on Mojo's revenue
[3:31] Why the same paywall design worked in Japan but failed in the US
[4:45] Mojo’s global pricing strategies and the role of regional differences
[5:45] How Mojo optimized early revenue with the 7-day ARPU metric
[7:02] The role of customer feedback in shaping Mojo’s growth strategies
[8:15] Testing different pricing models: How Mojo decided on the $79 price point
[9:30] Why focusing on new revenue, rather than renewals, was crucial for Mojo’s growth
[10:45] The benefits of running paywall campaigns for existing users
[12:02] How Mojo balances customer experience with aggressive monetization strategies
[13:15] The importance of experiment velocity and fast iteration in scaling Mojo
[14:34] Surprising results: Mojo’s success with paywall strategies for existing users
[15:41] Closing thoughts on scaling an app with data-driven experimentation and customer focus

On the podcast: about the cost of not tracking your experiments and decisions, how refunds and chargebacks quietly erase your paywall wins, and why stacking A/B test wins should compound your growth, but almost never does.

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:

💸 Map your revenue history before running new experiments
Chart revenue across new subscribers, upgrades, renewals, and win-backs over time. Matching spikes and dips to past decisions reveals what actually moved the business and prevents you from re-learning expensive lessons.
🤫 Refunds and chargebacks are silent killers
A paywall “win” can quickly become a net negative if you aren’t tracking the downstream effects of cancellations, refunds, and chargebacks, which often hide the true cost of a seemingly successful experiment.
📈If your A/B test wins aren't showing up in top-line growth, something is wrong
Stacking 5% and 10% experiment wins should compound, but many teams see modest growth despite a long list of "winners". Set calendar reminders to recheck winning cohorts at 3 and 6 months, especially for price changes, to catch lifts that don't hold.

About Sara Grana: 
 🚀 Revenue Strategy Lead at Yousician, a revolutionary music platform for anyone to learn, play, create, and teach music. 
👋 LinkedIn

Follow us on X: 
David Barnard - @drbarnard
Jacob Eiting - @jeiting
RevenueCat - @RevenueCat
SubClub - @SubClubHQ

Episode Highlights:
[0:00] Introduction to Sara Grana, Revenue Strategy Lead at Yousician
[1:05] The importance of tracking experiments and business decisions in subscription apps
[2:19] Mapping revenue and understanding its evolution across different user segments
[3:06] Tracking revenue changes and connecting them to business decisions
[4:34] The pitfalls of focusing too much on early funnel metrics and ignoring long-term impacts
[5:26] The impact of chargebacks and refunds on paywall performance and customer retention
[7:20] Why understanding downstream effects is crucial for making smart pricing decisions
[8:44] The challenges and opportunities of introducing new subscription plans (e.g., lifetime subscriptions)
[9:43] How commercial strategy influences churn rates and renewals
[13:13] The importance of rechecking experiments after months to measure long-term impact
[14:52] Sara's advice on when to revisit experiments based on their impact on pricing and user behavior
[15:49] Tracking cohort data for subscription retention and understanding renewal trends
[16:21] Why surprising lifts in experiments may require deeper investigation
[17:13] The mismatch between short-term experiment results and long-term growth expectations
[18:02] Final thoughts on driving sustainable growth, tracking, and adapting strategies over time