Founder Playbook · Sub Club by RevenueCat
10 tactics from Michal Parizek
How Mojo Increased ARPU 60% In Just Five Months
Watch the full episode“we've tried putting the monthly plan under sort of like a view all plans button so it wasn't really visible on the first glance and that really helped drive yearly plan adoption a lot like I think like a 15 or 20% percentage point”
Hide the monthly plan to push annual adoption
Mojo showed only the annual plan on the main paywall screen and buried the monthly option behind a 'view all plans' tap. This single layout change drove a 15-20 percentage-point shift toward annual subscriptions, materially lifting ARPU without any price change. If your paywall shows annual and monthly side-by-side, you are giving every user an easy escape valve to the lower-revenue option.
“what we tried and succeeded was actually sort of like a monthly plan anchoring So we essentially added like a small line next to our sort of yearly plan which says like that that price is equivalent of I like 10 $10 $10 a month”
Show the per-month equivalent to anchor annual as a deal
Adding a single line showing the annual plan as its monthly equivalent next to a higher standalone monthly price drove a 10% lift in the US and 30-40% in Latin America. Michal attributes the outsized LatAm effect to lower purchasing power: when users see a monthly cost comparison, it feels far more affordable. The same anchoring mechanism works everywhere, but its magnitude scales with price sensitivity in the market.
“our sort of like a umbrella metric for all those monetization experiments was the average revenue per user in the first seven days So this kind of RPU 7D”
Use ARPU-7D as your monetization north star
Mojo chose 7-day ARPU as their single unifying experiment metric because it covers the full 3-day trial window and directly feeds the unit economics needed to scale paid acquisition. Optimizing for trial conversion alone can miss revenue quality signals; ARPU-7D captures plan mix, price, and conversion simultaneously. It also shortened the feedback loop so each experiment cycle concluded in 1-2 weeks per geo segment.
“the price actually mostly like a higher price actually turned out to be the winner on the new revenue But when we actually modeled having that new price for a year long we saw that we would actually sacrifice in a long term mainly because the renewal rates just dropped”
Model long-term LTV before shipping a higher-price winner
A higher price point won on ARPU-7D, but when Mojo modeled annual revenue using 7-day cancellation rate as a renewal proxy, it was a long-term loser. They rejected the winning variant and kept the original price to preserve renewal quality. Before shipping any price increase, project the renewal curve: short-term revenue gains can mask downstream churn that wipes out the lift within 6-12 months.
“we mostly run experiments We typically kind of broke down the audience to sort of three buckets according to GIO It was like a US or kind of US Australia Canada Then there was the Europeans and then there was Latin America and we typically had like this streams of tests for each”
Run three geo-segmented experiment streams simultaneously
Mojo ran three parallel experiment pipelines covering US/Canada/AU, Europe, and Latin America so each test reached statistical significance in 1-2 weeks per segment. A winner in Europe sometimes failed in LatAm and rolled out only where it worked, preventing revenue dilution in mismatched markets. Segmenting experiments by purchasing-power region rather than language avoids misleading blended results.
“running a paywall campaign for existing users triggering the paywall in certain behavior for existing users either as an app open like we did in Mojo or after some key behavior event that paywall campaign actually drove I think about 15% of new revenue from the existing user base”
Run a weekly paywall for free users to unlock 15% incremental revenue
Mojo showed a paywall to existing free users once per week on app open and generated 15% of new revenue from that untouched segment. The frequency cap was key: users did not complain, and app store reviews showed no negative reaction. Most apps skip this entirely; triggering one weekly paywall for free users is a low-effort, low-risk revenue unlock that compounds with your existing UA spend.
“the number like 50% is actually it's actually a good signal that if the app actually does that I think it's a very good signal for the app that it can actually drive a good revenue also from existing users It's likely have a good retention and essentially a lot of existing users”
Day-1 revenue below 80% is a retention signal, not a problem
Industry average sees 80% of paying subscribers convert on day 1; Mojo's was only 50%, which Michal reframes as a strength indicating a generous free tier, high retention, and a large monetizable existing user base. If nearly all your revenue hits on day 1, you have very little pricing surface area for future campaigns. A healthy spread across the user lifecycle is not a conversion problem: it is evidence of long-term subscription health.
“the number one thing which allowed me to do that was having kind of a third party payable platform allowed me to being autonomous and and iterate fast and and really kind of shorten the cycle of actually like coming up with experiment developing it launching it from yeah months or weeks to to essentially days”
A paywall platform compresses experiment cycles from weeks to days
Using a third-party paywall platform let Michal run experiments without engineering dependency, cutting the cycle from months or weeks down to days. This autonomy was the enabling factor behind the 60% ARPU increase: without it, the same experiments would have taken far too long to compound in five months. Experiment velocity is a growth asset; if your paywall requires a sprint to change, your testing cadence is artificially capped.
“a long scrolling paywall with a lot of information lots of social proof reviews a good comparison between the free tier and the pro tier And that design actually worked incredibly well in Japan driving I think 20% lift in revenue But actually the same design kind of failed in the US”
Japan prefers info-dense paywalls; the US wants clean and punchy
An information-dense, long-scroll paywall with social proof and feature comparisons drove a 20% revenue lift in Japan but underperformed baseline in the US, where a clean slider with punchy text won instead. Market-level paywall design is not just translation: it is a fundamentally different visual contract. Mojo rolled out region-specific designs rather than a global default, letting each market's UX preferences drive conversion independently.
“we've actually lowered the price we didn't have the equivalent of the US prices that's something what I think usually app store sort of suggests they basically just do the exchange trade right and then calculate So no no we didn't”
Test below-exchange-rate prices in emerging markets
App stores default to currency-equivalent pricing, but Mojo found that testing below-exchange-rate prices in Brazil and Mexico outperformed the default on total revenue: lower prices drove enough additional volume to more than offset the margin reduction per user. Price testing in emerging markets is not just localization; it requires questioning the entire price floor. Markets with lower purchasing power often have dramatically higher elasticity than US or EU equivalents.