Founder Playbook · Sub Club by RevenueCat
10 tactics from Sara Grana
Stop Celebrating Conversion Wins Before Checking Renewals
Watch the full episode“You look a bit more deep and you realize oh a lot of people are putting the auto-renew off at the beginning and what they're doing is they're asking for a refund and then they're getting the offer — so you are actually like net negative.”
Winback campaigns can go net-negative when users exploit the cancellation offer to grab a refund
A winback campaign that looks great on surface metrics can be destroying value underneath. When users learn that cancelling auto-renewal triggers a discount offer, some deliberately cancel, claim the offer, then request a refund — netting you nothing (or less). Always check refund and chargeback rates alongside win-back conversion before calling the campaign a success.
“We have a price increase — oh great you know we did amazing, we rolled this — and then six months later you look at the cohort and say oh actually the control group is performing better than the test group because they renew more.”
Don't call a paywall win until the cohort has had time to renew — or not
Conversion lifts are misleading on their own. A price increase that boosts short-term revenue can quietly suppress renewal rates for months. The winning move is to set a calendar reminder — three to six months out — to revisit any experiment that changed price, discount, or plan structure before declaring it permanently rolled out.
“Every time you do a test like lifetime versus something else the something else is going to lose because people are not looking at what is the LTV of that something else — if they would have bought a yearly subscription they are bringing you €300, not €250.”
Lifetime plans always "win" A/B tests when teams forget to compare against projected LTV
A lifetime plan generates immediate revenue that makes any alternative look like a loser in a naive A/B test. The fix is to model the projected LTV of the competing plan — annual subscribers who renew 3-4 times are often worth more in net present value than a one-time lifetime purchase. Never compare a lump-sum option against a recurring one without doing the LTV math first.
“Sometimes you do a discount — you bring a lot of revenue but then those users are not going to renew. Or sometimes you do a price increase — you bring more revenue because your conversion goes down but not as much, but then you have a smaller pool to upgrade.”
Discounted cohorts renew less — price-increase cohorts leave you fewer users to upgrade later
Every pricing intervention creates a downstream trade-off that only becomes visible months later. Discounting accelerates short-term revenue but damages renewal cohort quality. Raising prices improves ARPU but shrinks the base eligible for future upgrades. The only way to make an informed call is to model the full cohort lifecycle before deciding which trade-off is acceptable.
“The commercial strategy that you have — what people buy, when they buy it, and which price they buy it — is going to have such an enormous impact on your renewals and extensions.”
Churn is not just a product problem — the plan, price, and timing of the sale drives renewals
Product teams own churn in most companies, but the actual driver is often the sales funnel above them. Which plan a user picked, whether they came in on a discount, and whether they found the app through paid or organic all predict renewal rate better than in-app engagement metrics alone. Revenue strategy and product need to be looking at the same cohort slice.
“A lot of product teams are going to be like wow our product is amazing, we have increased our renewals by whatever 20% — but if they were to slice it: what is the cohort that bought natively in-app versus the cohort that bought through web, maybe the renewal rates are flat.”
Web checkout's higher renewal rates can be a mix-shift illusion, not a product win
Web-purchased subscriptions renew at significantly higher rates than in-app purchases in many apps — not because of product improvements, but because web attracts a different, higher-intent user. When aggregate renewal metrics improve after enabling web checkout, product teams need to segment by acquisition channel before claiming credit for a retention win.
“We have all these experiments — all positive — but then when you're looking at the numbers you're still flat. I look at the list of wins with 5% and 10% and I'm like — but wait, why are we not growing 30% year-on-year if we have all these wins?”
Stacked A/B wins rarely compound into real revenue growth — investigate the gap
It's common to log a string of positive A/B test results — 5% here, 10% there — yet see flat or slow revenue growth when you zoom out. This mismatch is a signal to go back and recheck whether the individual wins actually held in the real product. Either the wins cancelled each other out, the measurement was flawed, or the gains didn't translate to downstream subscription value.
“My Google Calendar has this experiment recheck with my data analyst. Anything that is price changes I really care about — every time there's a price or discounting thing I would always recheck after, depending on what we are selling, 3 months, 6 months.”
Schedule a calendar recheck for every price or discount experiment — 3 to 6 months out
The discipline of rechecking experiments on a calendar cadence is what separates operators who learn from those who repeat mistakes. For pricing and discount experiments, a 3-6 month delay is usually needed for annual-plan cohorts to mature enough to show renewal behaviour. Book the follow-up at the same time you ship the test.
“From your subscription business you can get money from people that never had a subscription and start new, people that upgraded, people that renew, or people that used to have a subscription, churned, and then came back. Having the history of how these four buckets evolve can tell you a lot.”
Map revenue in four buckets — new, upgrade, renewal, winback — to spot anomalies instantly
Rather than watching a single revenue line, decompose it into four cohort flows: new subscribers, upgrades, renewals, and reactivations. When a single bucket moves, it narrows the cause immediately — a spike in new subscriber revenue points to a pricing or acquisition change, not a product problem. This framework makes historical diagnosis fast and makes gaps in your experiment log visible.
“Refunds and chargebacks are something that people tend to forget about — like they never happen — and you might be messing up with your system. You really need to understand your whole set of metrics and how they all work together.”
Refunds and chargebacks quietly erase paywall wins — include them in every experiment read-out
Most subscription teams look at conversion, trial rate, and renewal — but skip refunds and chargebacks in their standard dashboards. A new payment method, a promotional offer, or a paywall change can suppress your refund rate or spike it, materially changing whether the experiment was actually positive. Treat refund rate as a mandatory metric in every paywall read-out.