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25: 13 Onboarding Principles for High-Converting Subscription Apps
01 oct. 2026
00:25:15
Jacob Rushfinn, CEO of Botsi, has spent a decade in consumer apps figuring out how to convert users. He compiled 13 principles for your onboarding flow that will make a difference. These are all from experimentation and research from top apps like Opal, Finch, Headway, Tiimo, Tolan, Zing, and more.
What you'll learn:
• Why the first screen is a drop-off point most teams never measure • How Opal, Rise Science, and Headway hook users before they tap anything • Why story-style intro screens beat any UI pattern you invent • Why simple string personalization beats complex personalization almost every time • How to make quiz questions feel like a conversation instead of a form • Why letting people build something (a pet, a name, a journal entry) raises commitment • How to show users the math on the value they'll get • How to build trust across the whole flow: answer the biggest fear, lead with emotion, back it with numbers • When to prime permission prompts and when to just fire the native one • Why there is no excuse left to skip video, with examples from Lingokids, BoldVoice, and Ladder • How to turn loading screens into selling screens • How to make the free trial impossible to miss, and what to do when users close the paywall anyway
Key Takeaways: - Your first screen is a drop-off point. It can lose 10 to 30 percent of new users before they tap anything. A plain welcome, get started, log in screen works for big brands; everyone else needs a hook that's interesting and easy, like Opal's glowing rock or Finch's "Hatch a new pet" button.
- Ask for the account when intent is highest. Tiimo moved account creation from right before the paywall to right after the welcome screen and lifted both account creation and trial starts. It also stops you from asking for an email and a payment back to back. Test it, or skip account creation until after the paywall.
- Say the user's goal back to them. Ask the goal, ask why it matters, then repeat their words on the next screen, in the plan recap, and in the paywall headline. Zing, Tiimo, and Impulse all do this, and it rarely fails to lift conversion.
- Let people build something. People value what they put work into. Finch has you design a bird, which is fun, low-effort, and raises no privacy concerns. Add presets and shuffle buttons so nobody stalls on a blank text field.
- Show them the math. Turn inputs into a concrete number: Opal's five years of your life, Rise's sleep debt, Headway's 38 book summaries a month. When users hit the paywall, they should already know what they're getting.
- Build trust before you ask for money. Most apps cram trust into the paywall. Better apps spread it across the flow: answer the user's biggest fear (privacy, for Flo), lead with emotion, back it with a number, and repeat your strongest social proof.
- Make the trial impossible to miss. People don't read paywalls. They see a price and leave, even when the trial is visible. Trial reminders, choose-your-trial screens, and multi-page paywalls force users to notice it. When they close the paywall, follow up with a second offer.
- Onboarding doesn't end at the paywall. Don't drop a new subscriber onto an empty home screen. Imprint sends you straight into your first lesson; Balance auto-starts a sleep meditation for people who picked sleep. This is what separates good trial starts from good trial conversion.
Links & Resources - Price Power Podcast: https://pricepowerpodcast.com - Botsi onboarding flow library: https://botsi.com/resources/flows - Retention.blog: https://retention.blog - Jacob on LinkedIn: https://www.linkedin.com/in/jacob-rushfinn/
Timestamps 00:00 Why onboarding decides whether your app is worth paying for 00:45 1. Hook them on the first screen 03:00 2. Borrow UI patterns people already know 04:00 3. Ask for account creation when intent is highest 05:30 4. Ask the user's goal, then say it back to them 07:05 5. Make onboarding questions feel like a conversation 08:15 6. Let people build something 09:30 7. Show them the math 10:30 8. Build trust before you ask for money 12:48 9. Earn the permission opt-in 14:15 10. Use video, because it's cheap now 16:05 11. Turn loading screens into selling screens 18:00 12. Make the trial impossible to miss 20:20 13. Onboarding doesn't end at the paywall 22:13 Recap of all 13 24:15 What to test first and which metrics to watch
24: Failed Payments are Free Money w/ Philip Pages
17 sept. 2026
01:04:17
Philip Pages, founder of Redux Payments, explains why moving off Apple's in-app purchases quietly strips away a retention machine most founders never knew they had, how failed payments actually work, and when payment orchestration is worth the extra complexity.
Philip has looked inside hundreds of app payment accounts, and most of them are leaving real money on the table. He walks through the difference between a customer who wants to churn and a card that simply had no funds that morning; why recovered subscribers stick around for another three to six billing cycles; and the single setting that quietly blows up Stripe accounts. Jacob and Philip also get tactical on the app-to-web paywall pattern that converts, the retry schedule that recovers cards without triggering the card networks, and a meditation app that got back six figures by changing nothing but the timing of its retries.
What you'll learn
• Why Apple's in-app purchase system silently recovers failed payments, and exactly what you give up when you leave it • Why most failed payments have nothing to do with a customer wanting to cancel • How recovered subscribers behave once you win them back (they stay another three to six billing cycles) • Why the "30% down to 5%" margin pitch for web billing is misleading • How to tell whether your app is even ready to test web billing • Why older, Facebook-native audiences convert better on web-to-app flows than younger ones • How to set up web payments without blowing up your Stripe or Paddle account • What chargebacks, early fraud warnings, and approval rates actually do to your account health • How merchant-of-record providers take ownership of your transactions, and what that costs you later • What payment orchestration is, and the revenue point where it starts paying for itself • How one meditation app recovered over $150K by fixing retry timing by locale • How long you should really retry a failed card, and why spamming it backfires • The compliant two-button app-to-web paywall pattern that didn't exist a year ago
Key takeaways
• Apple runs a retention machine you never see. Behind the scenes, Apple retries cards near paydays, fixes bank issues, and serves in-app prompts for up to 60 days. It feels like magic, but it is a system. Move to web and that system is now your job.
• Most failed payments are not people trying to leave. Insufficient funds at one moment in time and random bank rejections make up the bulk of failures. Treating every failure as a lost cause writes off customers who already paid you for months.
• The margin math is messier than the pitch. Providers love to say you'll save 25 to 27 percent. Philip says heavily discount that. Even so, saving 15 percent and reinvesting it in growth can compound fast, so the channel is still worth testing.
• Don't touch web billing before product-market fit. Under a million in ARR with no PMF, the only job is finding PMF. Optimizing payments early is a distraction. Post-PMF, commit real budget or don't bother.
• Cancel your subscriptions. The number one way apps wreck their Stripe account is leaving failed subscriptions open. They stack, they collect chargebacks, and one subscription that should have had a single chargeback ends up with seven.
• Retry timing beats retry volume. One app had 64 percent of failures come from insufficient funds. Retrying by local payday instead of at random recovered over $150K. Brute-forcing the card just gets you flagged by the networks.
• Orchestration is where big brands quietly win. Netflix, Spotify, and other large subscription companies route payments across multiple processors to lift approval rates. Most smaller apps have never even considered it.
• The app-to-web playbook is new, and it works. Two CTA buttons, a rebuilt mini-onboarding before the paywall, and Apple Pay on by default. Philip's word for the pattern Jacob describes: gold. It did not exist a year ago.
Links & resources • Philip Pages on LinkedIn: https://www.linkedin.com/in/philip-pages-a881b5139/ • Redux Payments: https://www.reduxpayments.com/
0:00 Intro 02:00 Intro and Philip's first company (peaked near $3.5M ARR, then shrank) 04:11 Apple's hidden failed-payment machine, and what you lose moving to web 07:00 What a failed payment actually is, and why most aren't churn 11:05 The real margin math behind leaving the App Store 12:16 Why web billing is hitting critical mass now 17:20 Matching the right user to the right payment method 21:33 Getting started: providers, merchant of record, subscription management 25:47 How to not blow up your Stripe account 28:43 Chargeback alerts and fighting chargebacks 30:40 Merchant of record: owning the transaction, and getting out 34:27 Payment orchestration and cascading across processors 39:06 When web billing is a mistake (pre product-market fit) 44:47 App-to-web compliance and the two-button paywall playbook 52:32 A failed-payment recovery teardown (the six-figure meditation app) 59:02 How long to retry a failed card 01:03:00 The scariest account story, and what Redux Payments does
23: Lifecycle Marketing Will Be Autonomous w/ CEO of OneSignal
03 sept. 2026
00:56:06
George Deglin, CEO and co-founder of OneSignal, explains why calendar-based lifecycle marketing holds subscription apps back, why SaaS dashboards are losing their place as the primary interface, and what autonomous lifecycle marketing looks like in practice.
George walks through OneSignal's autonomy ladder, from L0 (a traditional SaaS dashboard) to L4 (a self-improving agent). He explains how each level builds customer trust, why behavioral triggers outperform scheduled messages, why email remains overlooked by consumer apps, and why companies worried about token costs should build features that cost more—not less.
What you'll learn:
• Why lifecycle messaging should respond to product behavior instead of a marketing calendar
• Why behavioral triggers outperform scheduled sends by 4x to 9x
• How product and lifecycle-team silos weaken message quality
• Why email is an underused, inexpensive retention and win-back channel
• What George means when he says "dashboards are dying"
• What happened when OneSignal asked 20 customers whether they preferred its dashboard or Claude, Gemini, or ChatGPT
How the L0-to-L4 autonomy ladder moves from assistance to recommendations and independent execution
• Why most companies pursuing ML personalization are optimizing the final 5% before mastering the basics
• Whether AI could mean fewer lifecycle-marketing hires
• How AI inbox filtering will raise the bar for message relevance
• Where app-to-web billing and RCS payment flows could go next
• Why OneSignal won't charge separately for AI—and why George favors expensive AI use cases today
Key takeaways:
• Calendar sends are the default—and the problem. Moving from scheduled messages to behavioral triggers is a larger opportunity than adding AI-generated personalization to a weak foundation.
• Email is inexpensive and still reaches users after they uninstall. The main obstacle is organizational coordination, not technology.
• Dashboards may fade, but product intelligence remains valuable. Users will increasingly operate software through agents while vendors expose more configuration through APIs.
• The autonomy ladder is also a trust ladder: assistance, proactive recommendations, delegated execution, and finally guarded autonomy that knows when to ask for help.
• Complex personalization can move a mature program from 95% to 100%, but many teams are overlooking the fundamentals.
• AI may eliminate tedious lifecycle work and reduce some hiring, while also making effective lifecycle marketing affordable for more companies.
• Today's expensive AI capability may become inexpensive as model costs fall, so George argues for building ahead of the cost curve.
Links & resources
• OneSignal: https://onesignal.com • George Deglin on LinkedIn: https://linkedin.com/in/gdeglin • George's essay on autonomous lifecycle marketing: https://onesignal.com/blog/the-future-of-lifecycle-marketing-is-autonomous/
Chapters
00:00 Cold open and episode introduction 01:00 What separates apps that win at lifecycle marketing 02:25 The most underused channel in consumer apps 03:15 Why teams default to paid media over email 04:30 Lifecycle marketing as an extension of the product 05:35 Retention, LTV, and who owns it 06:55 Change, provocative claims, and pushback 07:55 "Dashboards are dying" 08:05 Where the idea started with browser and computer use tools 10:10 Asking 20 customers: dashboard or agent? 11:15 Does every tool become a transactional layer? 12:40 Headless software and configuration APIs 14:50 The visual feedback loop problem 16:40 Previewing an email campaign inside Claude 18:05 The autonomy ladder, L0 to L4 20:00 Moving from assistance to recommendations 23:50 What customers are doing with L1 25:00 Saving customers hours on reports 26:00 L2: the recommendation engine 27:30 Democratizing customer engagement 28:40 Will competitors learn from my data? 30:30 AI and one to one personalization 32:20 Why triggered messages perform 4x to 9x better 34:15 Personalization versus segmentation 35:00 The 95% to 100% problem 36:25 Will AI replace marketers? 40:05 Why cheaper lifecycle marketing creates more companies 41:25 Does easier sending lower quality? 43:00 AI filtering on the receiving end 44:50 Pricing, packaging, and subscriptions 46:20 App to web billing 49:10 RCS payments inside the conversation 51:30 Token costs and why OneSignal won't charge for AI 54:30 Wrap
22: 9x ARR, +47% ARPU, the real tests that won with Michael Bardin | Price Power Podcast Ep. 21
20 août 2026
00:51:16
Michal, product growth lead at Applica Agency, explains why moving a paywall to the point of peak anticipation beat waiting for the aha moment, how trial length changes trial starts rather than trial conversion, and why the same paywall test produced opposite results on paid and organic traffic.
Michal walks through three client teardowns. A client went from a buried feature-gate paywall to an onboarding paywall and grew ARR 9x; trial start rate moved from roughly 3% to 16%. Beducated, a sex education web funnel, lifted revenue per user 47% on Meta traffic and 27% on organic after Applica discovered the two channels wanted completely different plan structures. Alux, a wealth and finance content app charging $149 a year, improved day one retention 22% and install-to-paid conversion 26% by mining users' own free-text answers out of Mixpanel and rewriting the onboarding in their words.
What you'll learn:
Why anticipation beats the "aha moment" as a buying trigger
What a paywall exit-intent survey revealed about price objections
Why device buyers felt they were paying twice for a subscription
How trial duration moves trial starts but leaves conversion untouched
Why a 14-day trial beat every discount as an exit-intent downsell
How that downsell came to drive 10-15% of total revenue
When per-placement paywalls are worth building, and when they're not
How often to paywall a retained free user base
Why one A/B test can land positive and negative at the same time
Why plan length is really about how far ahead users can picture themselves
How a weekly plan works as the web's version of a free trial
Why "Start learning" beat "Subscribe now" for conversion in 2026
How to cheaply validate a post-purchase upsell before building one
How to turn Mixpanel free-text answers into paywall copy
Why fewer options in session one improved day one and day seven retention
Key takeaways:
Anticipation peaks before first use, not after. Users had just paid $300 for a device and were hopeful — more hopeful than after their first session, which feels like a mild shock. Waiting for the aha moment meant waiting for intent to decay.
Trial length moves trial starts, not trial conversion. A longer window lowers cancel-anxiety and gets more people to begin; conversion rate holds steady. Trials went 3 days to 7, then added 14 days as an exit-intent downsell at the same conversion rate.
Survey the people who say no. Fewer than 10% of people who closed the paywall cited price. The top reasons were not feeling ready and feeling they'd already paid via the hardware, which redirected the roadmap from discounting to trial duration.
Discounting isn't the only downsell. The winning offer was more time, not less money, priced identically to the original, converting at the same rate.
A single test can produce two opposite results. Beducated's first test looked ambiguous until Applica split it by campaign ID: Meta and organic-influencer traffic needed completely different plan structures. Cold traffic needed a cheap entry point; warm traffic didn't.
Plan length reflects how far ahead users can picture themselves. Removing Beducated's one-month plan for organic traffic didn't hurt conversion — rare. Trusting the influencer, users moved straight to three-month and annual plans.
Settled best practices are worth retesting. A CTA test Michal almost skipped — swapping "Subscribe now" for "Start learning" — lifted conversion 20-50%, a reminder that even settled wins deserve a rerun.
Your users already wrote your best copy. Alux asked users what they wanted to achieve and by when; Applica pulled every Mixpanel answer, split by converters vs. non-converters, and rewrote onboarding in converters' own words.
Focus beats choice in a first session. Alux's home screen offered too many paths; Applica cut it to one goal-based block until 50% completion. Day one and day seven retention both improved.
Links and resources Applica: https://applica.agency Applica case studies: https://applica.agency/case-studies Michal on LinkedIn: https://www.linkedin.com/in/michael-bardin-60b224291/ Pulsetto: https://pulsetto.tech Beducated: https://beducated.com Alux: https://alux.com Botsi: https://botsi.com
Timestamps 01:00 Intro and what Pulsetto actually is 03:30 The paywall was buried behind feature gates 04:30 Moving the paywall to the end of the onboarding quiz 07:00 Why the industry fetishizes the aha moment 08:00 The math nobody runs: if they never see it, they can't buy 09:00 The result: 9x ARR 09:30 Trial start rate was 3%, trial conversion was 80% 10:30 The exit-intent survey and what people actually said 11:45 Trial length moves starts, not conversion 14:00 RevenueCat Paywall Builder and getting independent of developers 15:00 The downsell that was more time instead of less money 17:30 When unique per-placement paywalls are worth building 20:00 How often to show a paywall to free users 22:30 Trial start rate goes from 3% to 16% 23:30 Beducated and the web-to-web funnel 24:30 +47% RPU on paid, +27% on organic 25:00 One test, two completely different results 27:00 Splitting the analysis by campaign ID 28:00 Removing the monthly plan and nothing happened 29:45 The weekly plan as the web version of a trial 32:30 The CTA test Michal did not want to run 35:00 Post-purchase upsells: PDF first, then Beducated Duo 37:00 Why stacking two upsells kills the second one 39:20 Alux, a $149 content app that could not lower prices 41:00 Mining Mixpanel for what users wrote in their own words 43:30 Converter language is the best marketing copy you have 45:00 Too many options in the first session 47:00 Anything completed beats no completion 50:45 +22% day one retention, +26% install to paid 51:00 Retention problems are activation problems 52:00 Wrap and credits
Jonathan Parra on 4,700 Paywall Tests | Price Power Podcast Ep. 21
06 août 2026
00:55:11
Jonathan Parra, founder of Tapas Growth, explains why app category predicts test results better than the app itself, how to sequence design, packaging, and price tests, and why the ugly paywall keeps winning.
Jonathan has designed close to 4,700 paywalls. He walks through the testing order he uses with clients, the five paywall placements every new app should ship before optimizing anything, and the exit questionnaire that replaced his old discount ladder. He also gets specific on numbers: a healthy app loses half its trial starts, win-back campaigns aimed at those cancelers convert at 5 to 6 percent, and removing a free plan can push conversion from 2 percent to 12 percent while gutting your traffic.
What you'll learn: • Why app category, not app quality, is the first thing Jonathan looks at when predicting a test outcome • How product polish and a clear ICP change the size of the win you can expect • Why he turns down clients he doesn't think he can make money for • How to decide between freemium and a hard paywall using your marginal cost per free user • Why AI apps with real inference costs should start with a hard paywall and a 3 to 7 day trial • How to gate the expensive part of your product and leave the cheap part free • Why design tests come before packaging tests, and packaging before price • How a design winner sets up a price increase that doubles ARPU • What changed in his testing workflow now that LLMs can crunch the data • How device signals like battery level and network type get used as demand scores
Key Takeaways:
• Marginal cost decides your monetization model. If a free user costs you nothing, keep them and monetize later. If every action fires an LLM call or streams video, a hard paywall with a short trial is the honest answer. The middle path is gating the expensive feature and leaving the cheap one open, like charging for photo-to-macros and giving away water logging.
• Design, then packaging, then price. A design winner can double conversion rate. Once you have it, raising price walks conversion back toward where it started while ARPU stays doubled. Price testing first just trades conversion for revenue with no ceiling raised.
• The ugly paywall wins and you have to accept it. Jonathan is a trained UX designer and says CRO is a different game entirely. Dense, loud, in-your-face layouts beat minimal ones often enough that he stopped arguing with the data, especially in the companionship and character AI space.
• Ask instead of guessing. His old exit flow was a fixed ladder: extended trial, then 33 percent off. It cannibalized revenue from people who would have paid more. Now an exit questionnaire asks why they bailed, and the offer matches the answer. Price complaint gets a discount. Trial complaint gets a longer trial.
• Half your trials cancel, and nobody markets to them. Jonathan targets users with an active entitlement and auto-renewal switched off. Those campaigns convert at 5 to 6 percent, which adds 2.5 to 3 points to overall conversion. It's the largest high-intent audience most apps ignore.
• Discount depth is a sequencing decision. Don't open with 80 percent off. Save the steep offers for expired users and Black Friday. A downgrade to a cheaper tier often keeps the customer without cheapening the brand, and a first-year-only discount lets you rebill at full price later.
• Weekly-only pricing is a speed run. ARPU looks great and churn is brutal. Jonathan will use weekly plans as paid intro offers or for genuinely short-use ICPs, but apps that sell nothing else ride viral traffic until the cohorts stop stacking.
Links & Resources • Tapas Growth: https://tapasgrowth.com/ • Jonathan Parra on X: https://x.com/jondeparra • Jonathan Parra on LinkedIn: https://www.linkedin.com/in/jondeparra/ • Jonathan's guest post on Retention.blog: https://www.retention.blog/p/expert-paywall-tips
Timestamps 00:00 Intro: 4,700 paywalls and counting 01:00 What Jonathan got wrong early at Superwall 03:30 Predicting test results before you run them 05:30 Using category benchmarks to diagnose an app 07:00 The two times he was wrong, and working for free 09:30 Freemium vs hard paywall, decided by cost 13:00 Gating the expensive feature, freeing the cheap one 14:00 Test order: design, packaging, price 17:30 Demand scores from device attributes 18:30 Age-based price testing and why it's risky 20:30 What changed post-AI in the testing workflow 23:30 Why the ugly paywall wins 27:30 Building a real exit flow 28:30 The questionnaire that replaced the discount ladder 31:00 The exact questions he asks 34:30 The five paywalls every new app should ship 38:30 Trial cancelers: the 5 to 6 percent win-back 40:30 Downgrades, discount depth, and brand 42:00 Transaction abandon tactics 44:00 Winning back expired subscribers 48:30 Email, push, SMS, and where the ceiling is 51:30 Weekly plans and the TikTok wall 54:00 Biggest packaging win: multi-page paywalls
20: Opal Killed the Quiz Funnel. What's up next?
14 juil. 2026
00:26:31
Opal rebuilt their onboarding to work like a chat thread instead of a quiz, and Jacob walks through the whole thing screen by screen. The rock you crack open on the first tap, the sign-in question that replaced the sign-in buttons, the moment they tell you you'll spend eighteen years of your life looking at your phone, the paywall, and the monthly plan they only offer you if you try to leave.
This is a solo episode, so it goes deeper on the screens than a conversation usually allows. An app at Opal's scale doesn't ship an onboarding redesign without testing it hard first, which makes it a useful thing to study. The question isn't whether it works. It's which pieces of it would work for you.
What you'll learn:
• Why the first screen of your app is probably leaking more users than your paywall is • How Opal replaced the Log in / Sign up wall with a question • Why more login options usually pay for themselves once you scale • The phrasing trick that gets people to answer a demographic question honestly • Why Opal asks about your screen time before asking for screen time permissions • How they split their permission requests apart, and what they put in between • The three-beat setup: 91 days this year, 18 years of your life, then the rescue • How paywall copy pays off a goal the user selected five screens earlier • Why the trial reminder screen has almost nothing to do with reminders • What "design your trial" is really doing to the user's decision • The math behind pulling monthly off your first paywall • The exit-intent monthly offer that almost nobody runs • Why "no payment due now" keeps showing up next to the CTA • What the Law of Shitty Clickthroughs says about the future of quiz onboarding • The screen-count test for whether chat onboarding fits your app
Links & resources
• Retention.blog full written breakdown: https://retention.blog • Opal: https://opalapp.com/ • Andrew Chen, "The Law of Shitty Clickthroughs": https://andrewchen.com/the-law-of-shitty-clickthroughs/ • Botsi: https://botsi.com
00:00 Intro: tired of quiz-style onboarding? 01:05 Opal's chat-style redesign 01:28 The rock you crack open 02:34 Why first-screen drop-off compounds 03:15 "Have we met before?" instead of Log in / Sign up 03:58 Login options and the data reassurance copy 04:22 The hybrid quiz/chat question style 05:31 "What best describes you?" 06:21 Easing into the screen time permission 07:44 91 days, 18 years, and the aha moment 09:07 Splitting the permission asks apart 10:00 The personalized pre-paywall screen 11:38 The fist bump commitment prompt 12:15 "Two plus hours" and a copy critique 13:23 Social proof and the "Reclaim my time" CTA 13:55 The trial reminder screen 15:47 Design your trial 16:13 The math on removing monthly 18:06 "Not ready for a year?" 18:27 The "no payment due now" checkbox 20:08 Post-paywall onboarding and gamification 22:15 The Law of Shitty Clickthroughs 23:27 Why chat UX works right now 23:54 Which apps should actually test this 25:19 Teaser: the onboarding and paywall library
19: Lessons From Reviewing 100+ Web Funnels w/ FunnelFox CEO
25 juin 2026
01:03:52
Andrey Shakhtin, founder and CEO of FunnelFox, explains why web subscriptions convert and monetize better than the app store, how to stand up a minimum viable web-to-app test, and the payment risks that can freeze your revenue once you scale.
What you'll learn:
• Why full-funnel conversion (impression to purchase) runs roughly 2x higher on web than in-app, and how the app store install step explains the gap • Why web LTV is about 2x in-app on annual plans and at least 50% higher on monthly • The two structural reasons web LTV is higher: quiz funnels skew toward older, higher-willingness-to-pay buyers, and you control retention end to end • How owning the payment stack lets you run custom cancellation flows and dunning (failed-payment recovery) that Apple and Google never expose • Why deterministic post-ATT attribution makes web the fastest creative-testing loop you have • Why a web funnel is the cheapest way to validate a product, sometimes before the app exists • Why free trials quietly poison your Meta optimization, and how a $1-$5 paid trial fixes the signal • The unit-economics benchmarks that matter: roughly 40-60% day-zero ROAS and a 6-month payback • Why most "amazing-looking" funnels still fail at the paywall and checkout • How to structure a paywall: outcome-based value, visualization, FOMO, price, then social proof • The real difference between refunds, disputes, and chargebacks, and why only chargebacks threaten your account • How dispute-rate and VAMP (Visa's acquirer monitoring) thresholds can get your PSP to freeze recurring revenue • Why chargeback-alert services and external billing (payment orchestration) are close to mandatory at scale • How an easy refund path plus a 50% save offer lowers chargebacks and protects net revenue • How post-purchase upsells add roughly 20% LTV without triggering disputes
Key Takeaways:
A web funnel is the cheapest validation you have. Test demand, pricing, even a niche for a few hundred dollars a day before committing engineering. Some teams launch with no product at all, then build the app for the niche that converts.
Web's edge is deterministic measurement. With no ATT loss, every purchase ties to an exact creative, so you iterate on message and audience by real numbers instead of inferring from installs.
The full funnel converts about 2x on web. You skip the app store install decision, a friction point where lukewarm users drop before they ever see the offer. Most teams miss it because they optimize to installs.
Paid trials protect your ad signal. A $1-$5 charge proves the card works and sends Meta a real purchase event, so it optimizes toward payers instead of trial-tourists who never convert.
The paywall is where funnels die. After ~100 funnel reviews, it's the most under-built step. "Unlock all features" is not value. Lead with a specific outcome and date, and build it like a landing page.
Chargebacks can freeze everything. Refunds are harmless, but cross a provider's dispute or VAMP threshold and it can lock all stored recurring revenue. Make refunds easy and offer a 50% save to keep subscribers.
Links & Resources
FunnelFox: https://funnelfox.com State of Web-to-App Subscriptions report: https://funnelfox.com/state-of-web2app-2026 Andrey Shakhtin on LinkedIn: https://www.linkedin.com/in/andrey-shakhtin/
Timestamps
00:00 Andrey's path: 16 years in mobile, from code to growth to FunnelFox 04:30 The minimum viable web-to-app experiment 08:00 What scale you need first, and validating a niche with no product 10:00 Why web should complement, not replace, your other channels 13:30 How web monetization differs from in-app 16:00 The report: web LTV and conversion roughly 2x in-app 16:40 Why web LTV is higher: older buyers and full retention control 19:00 Why the app store install step kills conversion 21:00 Organic vs. paid traffic funnels 22:30 Free trials vs. paid trials, and the Meta signal 25:30 Why web-to-app projects fail 28:30 Unit economics: day-zero ROAS and the 6-month payback 29:30 Diagnosing a broken funnel against benchmarks 36:00 How to structure a paywall that converts 41:30 Intro offers and the telecom playbook 44:30 Refunds, disputes, chargebacks, and VAMP explained 51:00 Defending your payment account: alerts and external billing 55:30 The refund hack: offer 50% instead of losing the subscriber 57:30 Upsells and lifting LTV ~20% 59:30 Biggest pricing win: GLP-1 and $800 order values 01:00:30 What FunnelFox does
18: Hybrid Monetization: When and where to start w/ Cristian Rotari
11 juin 2026
00:59:14
Cristian Rotari, Monetization Lead at Zing Coach, explains why hybrid monetization is more than bolting ads onto a subscription app, how to layer in-app purchases, affiliates, physical products and partnerships without cannibalizing your core revenue, and when an app is actually ready to start.
He walks through the demand curve idea he picked up from Thomas Petit at Lingokids: a single subscription price treats willingness to pay as binary when it really runs across a wide spectrum, from whales who will buy anything to plankton who will never convert. He covers why ads are a volume business that loses money for most small apps, why AI apps have to think about credits and token costs from day zero, and the cannibalization rule he uses at Zing Coach: promote subscriptions to free users, promote upsells only to people who have already paid.
What you'll learn:
Why hybrid monetization is hard to get real guidance on, even though everyone talks about it
How the demand curve reframes pricing from one number to a spectrum of willingness to pay
Why whales and plankton need completely different monetization strategies
Why freemium, not a hard paywall, is what unlocks both hybrid revenue and organic growth
When an app is actually ready to add a second monetization model (hint: not day one)
Why most apps should start with in-app purchases, not ads
How AI apps break the subscription math when one power user burns thousands in tokens overnight
Why ads only pay off with high daily usage and long sessions
How to add affiliate revenue with nothing more than an Amazon link
How Zing Coach structures partnerships like the New York Sports Clubs white-label deal
Why you should sell more to subscribers, not to the free users who already said no
How Zing Coach gets 40% of yearly-plan buyers to take at least one upsell
Why subscription tiers can clash with hybrid and how Spotify avoids the trap
Cristian's hot take on the trials debate
Key Takeaways:
Willingness to pay is a spectrum, not a yes/no. A single subscription price leaves money on the table at both ends. Whales would pay more if you let them; plankton will never subscribe but might buy a one-off. Hybrid monetization exists to capture both.
Freemium is the foundation. A hard paywall caps your user base, which caps both hybrid revenue and word-of-mouth growth. When Zing Coach eased its paywall and added a trial, it grew the top of the funnel without losing much subscription conversion.
Hybrid is not a day-one move. Nail product market fit and one monetization model first, usually subscriptions. Once you understand and can segment your users, add a second layer, and start with in-app purchases rather than ads.
Ads are a volume business. They need high daily active users and long sessions to pay off. Most subscription apps, used once or twice a week, do not have the volume, so ads usually lose to in-app purchases for small and mid-size apps.
AI apps are the exception to "go slow." Token costs mean a single power user can spend thousands overnight. These apps need credits and usage limits from the start, so it is the status quo, not an add-on.
Stop the cannibalization with a simple rule. Promote subscriptions to free users; promote upsells only to people who already subscribed. Someone who paid has shown intent, so that is who you upsell. Free users who declined have low willingness to pay, and stacking offers on them just lowers subscription conversion.
Sell to intent. At Zing Coach, about 40% of yearly-plan buyers take at least one upsell, and conversion drops as plan length and intent drop. The wallet is already out, so monetize that moment instead of leaving it unattended.
Tiers need a clear value ladder. Spotify segments by use (individual, family, student) rather than piling on pro and premium feature tiers. Add tiers as extra value on top, never by removing things users expect in the base plan.
Links & Resources
Cristian Rotari on LinkedIn: https://www.linkedin.com/in/cristianrotari/
Alice Muir on AI app pricing (referenced in episode): https://pricepowerpodcast.com/episodes/16-how-to-build-a-subscription-app-in-the-ai-era-w-alice-muir
Timestamps
00:00 Intro 01:34 What hybrid monetization actually means (and why it is more than ads) 05:34 The demand curve: whales, plankton, and willingness to pay 08:34 Why freemium unlocks both revenue and organic growth 14:34 When an app is ready to add a second monetization model 17:04 AI apps and the token-cost problem 21:04 Why ads are a volume business most apps lose at 28:04 Affiliate revenue and the Amazon link shortcut 33:04 Physical products and brand extensions 35:34 Partnerships and white-label deals 41:04 Avoiding cannibalization: sell to intent 48:34 Subscription tiers and the value ladder 54:04 Hot take on the trials debate 56:04 Biggest win: the Body Scan upsell
Best of Price Power Podcast from the Last 6 Months | Price Power Podcast Ep. 17
28 mai 2026
01:29:44
12 guests. 16 clips. One hour of the most tactical advice from the past six months of the Price Power Podcast.
This is a best-of episode — no fluff, just the insights that stuck with me the most from conversations with world-class growth leaders. You'll hear frameworks for strategic friction, activation, pricing, signal engineering, Meta and Google campaign architecture, creative strategy, and referral programs.
Guests featured: Alice Muir, Daphne Tideman, Ekaterina Gamsriegler, Michal Parizek, Barbara Galiza, Ashley Black, Shumel Lais, Marcus Burke, Lucas Moscon, Gabe Kwakyi, Xavier De Baillenx, and Anthony Scarpaci.
00:00 Introduction 01:10 Alice Muir — Strategic Friction & the MyFitnessPal Lesson 04:19 Daphne Tideman — Time to First Value vs. Time to Core Value 10:39 Ekaterina Gamsriegler — When Lowering Your Price Makes Sense 15:45 Michal Parizek — 7-Day Cancellation Rate Predicts Revenue 25:46 Barbara Galiza — Send Predicted Value or Get Garbage Installs 32:03 Ashley Black — Optimize for Deeper Engagement Events 38:42 Shumel Lais — Signal Engineering Explained Simply 47:24 Shumel Lais — The 10-Conversions-Per-Day Rule 51:09 Marcus Burke — Signal Engineering & the Trial Signal Problem 1:01:27 Lucas Moscon — Move Away from ROAS, Focus on Blended ROI 1:06:18 Marcus Burke — Blended CPA Is Irrelevant, Break Down by Placement 1:11:35 Gabe Kwakyi — Creative Hits Drive Paid Social 1:16:10 Xavier Baez — How Many Creatives You Actually Need 1:20:23 Anthony Scarpaci — The RIGHT Framework for Referral Programs
16: How to Build a Subscription App in the AI Era w/ Alice Muir
06 mai 2026
01:04:30
Alice Muir, independent subscription consultant who's worked with Headspace, VSCO, Adobe, SoundCloud, and MyFitnessPal, explains why "higher engagement equals lower profit" is the new reality for AI apps, how to use strategic friction without choking activation, and why most consumers don't actually care that your app is AI-powered.
What you'll learn:
How strategic friction worked for MyFitnessPal, and what they got wrong by gating barcode scanning too late
Why "protect the learning actions, charge for the outcomes" beats free-vs-paid debates
How to handle the 90% of installs that never subscribe when free users now cost real money
When hybrid monetization actually makes sense and when it just adds complexity
Why weekly subscriptions are a proxy for usage-based pricing on novelty AI apps
Why margin-qualified acquisition matters more than CAC alone for AI products
How Flibbo used persona tiers on the paywall to get users to self-identify their willingness to pay
Why a fitness app got a 6x paywall lift by removing strikethroughs, countdown timers, and stacked offers
What the Subscription Stack framework needs to add for the AI era
The back-of-the-napkin math founders should run before shipping any AI feature
Why consumers may actually be turned off by "AI-powered" positioning
Key Takeaways:
Protect learning actions, charge for outcomes. Users should learn what your product does for free. The thing that completes their job is what they pay for.
GPU cost is a CAC line item, not a margin problem. If everyone you acquire gets one free generation, that compute cost belongs in your acquisition budget. Run worst-case-scenario math before you ship.
Highest engagement is now lowest profit. Traditional subscription thinking inverts when each interaction has a real cost. The Subscription Stack engagement layer needs a full revision for AI apps.
Weekly subscriptions are a usage proxy. AI apps see strong initial conversion and terrible retention because users have high intent for short bursts. Annual plans for a song generator are a fantasy.
Self-identification at the paywall beats the questionnaire. Flibbo put basic, pro, and max personas on the paywall. Users picked the one that matched their use case.
Simpler paywalls outperform copycat paywalls. Stripping countdown timers, strikethroughs, and stacked plan tiles from a fitness web-to-app funnel produced a 6x lift.
Consumers don't care about AI as a feature. They care about the outcome. "AI-powered coach" can read as cheap, not premium. Lead with benefit, not technology.
Don't add AI just to add AI. If a feature doesn't measurably improve retention or activation, you're paying GPU costs to compress your margin.
Links & Resources
Alice Muir on LinkedIn: https://www.linkedin.com/in/alicemuir/
Andrew Chen on consumer reactions to AI: linkedin.com/posts/andrewchen_when-consumers-dont-care-that-youre-building-activity-7358342997639360512-wqKM
Thomas Petit RevenueCat article on hybrid monetization: https://www.revenuecat.com/blog/growth/ai-hybrid-monetization/
Timestamps
00:00 – Intro 01:25 – Baby raves in Berlin and the new May Day 02:58 – How the playbook changed: from acquisition-first to retention-first 07:25 – Strategic friction and the MyFitnessPal example 10:43 – Hard paywalls vs letting users discover value 11:55 – Protect learning actions, charge for outcomes 17:25 – The 90% problem: monetizing low-intent users 21:36 – When hybrid monetization actually makes sense 26:15 – Apple tax, GPU costs, and the AI app profitability squeeze 28:55 – Why weekly pricing fits novelty AI apps 33:53 – Margin-qualified acquisition for AI apps 39:08 – Flibbo's self-identifying paywall personas 43:55 – The 6x paywall win: stripping out the fluff 47:56 – Revisiting the Subscription Stack for the AI era 51:55 – Switching models to protect margin 53:38 – What founders should get right before adding AI 56:58 – Hot take: consumers don't care about AI
15: How to start with Signal Engineering w/ Shumel Lais
22 avr. 2026
00:54:30
Shumel Lais, co-founder of Day30 and previously founded Appsumer (acquired by InMobi), explains why most subscription apps feed ad platforms the wrong goal, how precision and recall reshape signal selection, and what a realistic measurement maturity ladder looks like in 2026.
Shumel walks Jacob through the five stages of measurement maturity, from apps that just compare App Store Connect revenue to ad spend, through MMP attribution and cohorted reporting, up to incrementality testing for the largest spenders. He breaks down why signal engineering only makes sense once you have the right foundation in place, shares the 10-conversions-per-campaign-per-day rule of thumb for when to go further down funnel, and unpacks the restaurant booking app mistake that first put him onto the precision/recall framework.
What you'll learn:
Why optimizing to cost-per-trial leaves money on the table for most subscription apps
How Meta's 7-day visibility window forces the signal engineering problem
Why recall, not precision, is the metric most marketers overlook
Why the restaurant booking app example was Shumel's own mistake, and what it taught him
How Meta's event-day reporting can hide renewals inside new purchase counts
Why server-side events struggle more with matching than client-side events
How to decide between revenue-value signals and binary convert/no-convert signals
Why subscription apps are years behind gaming on analytics maturity
The 10 conversions per campaign per day floor before attempting signal engineering
When LTV curves become reliable enough to extend payback from 30 days to 6+ months
Key Takeaways:
Signal engineering is closing the gap between what the platform can see and what you actually care about. Meta sees 7 days. You care about month 3 revenue.
Recall is the metric most teams forget to measure. Precision tells you if the users firing your signal convert. Recall tells you what share of your actual converters it captures. A signal with 90% precision and 40% recall tells the algorithm that 60% of your good users are bad.
There are five levels of measurement maturity, and most apps skip steps. ASC comparison → platform attribution → MMP → cohorted reporting → incrementality. Signal engineering is a level 3 or 4 exercise. Attempting it earlier wastes the effort.
The 10-conversions-per-campaign-per-day rule. Below that, Meta cannot learn from a more selective signal. Above 30 to 40 per day, you are leaving performance on the table by not going further down funnel.
Meta reports on event day, not install day. Renewals fire as purchase events, so Meta can claim credit for users who were already paying. Without install-cohorted MMP visibility, you are paying to acquire users you already had.
Speed of signal affects matching quality and algorithm learning. Events sent within 24 hours have more matching parameters, and they let Meta decide if a user is good without waiting 7 days for the purchase to come through.
The restaurant booking app was Shumel's own mistake. Before Day30, he optimized toward behaviors that correlated with bookings but were not causal. Performance did not move. The fix was cohorts, observation windows, and a binary prediction statement.
Measurement problems are not an excuse anymore. In 2026, the tools exist and the playbooks exist. Hiding behind attribution gaps is a choice, as is hiding behind blended CAC when direct CAC is uncomfortable.
Timestamps 00:00 Shumel's background and early mobile agency days 00:56 The signal engineering framing and how Day30 landed on it 03:30 A basic example: trials vs trials plus behavior 05:56 Why signal engineering exists (attribution gap, not just subscriptions) 08:45 Signal volume as the second dimension after precision 09:30 Defining recall and the photo storage app example 15:58 When to send revenue values vs binary convert/not-convert 16:41 The restaurant booking app mistake and causation vs correlation 19:33 Experiments are still the only real proof 20:00 Measurement maturity level 1: no MMP, just ASC 22:37 Do you actually need an MMP to start? 23:39 Level 3: why MMP matters (Meta's event-day reporting trap) 25:37 Level 4: cohorted metrics and aligning on day-30 ROAS 26:30 Level 5: incrementality and MMM for the largest spenders 27:35 The 10 conversions per campaign per day threshold 29:30 Why the MMP matters for signal engineering (measurement, not the signal itself) 31:03 MMP vs Conversions API for sending signals 33:04 SDK vs server-side: matching and speed 36:43 Payback periods and when to extend them 40:32 Simple inputs for a basic predictive LTV model 42:52 If you're running Meta to CPT today, what do you change first 44:41 The quantity vs quality of signal tradeoff 46:48 Hot takes: no more hiding behind attribution 48:02 Favorite pricing and packaging tactics seen recently 50:08 Day30's free signal audit offer
14: Fix Activation Before Growth w/ Daphne Tideman
08 avr. 2026
00:50:00
Daphne Tideman, growth advisor and consultant for subscription apps, explains why most retention problems are actually activation problems, how to distinguish vanity activation metrics from ones that predict real retention, and why the aha moment should start in your ads, not just your product.
Daphne walks through her evolution from treating activation as a simple funnel step to seeing it as a layered, behavioral process spanning the first 7 to 30 days. She shares real examples from growth audits where onboarding completion rates looked great but users vanished by day two, and breaks down the "time to first value" vs. "time to core value" framework for thinking about activation in stages. She also makes a case for monthly subscriptions as a faster learning tool for startups, and explains why revenue is a terrible North Star metric.
What you'll learn:
Why onboarding completion is often a vanity metric that hides activation failures
How to identify whether your retention problem is actually an activation problem
Why "any action vs. no action" comparisons overstate the value of weak activation metrics
How to build mini aha moments into onboarding before the paywall
How to use the "time to first value" vs. "time to core value" framework
Why monthly subscriptions can help startups learn faster about activation
How to test whether an activation metric is predictive or just correlated
When user interviews beat quantitative analysis for defining activation
Why extending onboarding can drop completion rates but improve retention
How to diagnose activation vs. retention vs. acquisition problems
Why revenue as a North Star metric leads teams to extract value instead of create it
Key Takeaways:
Onboarding completion is a vanity metric. An app had over 90% onboarding completion on both platforms, but most users were gone by day two. The onboarding was too short and easy to click through. When they extended it and built in value-delivering steps before the paywall, completion dropped but retention improved.
Your retention problem is probably an activation problem. For most apps, losing users in the first 30 days isn't a retention failure. It's an activation failure. Daphne argues we even mislabel it: "day two retention" and "day seven retention" describe periods when you're still activating users, not retaining them. True retention problems show up when users were active early but trickle off later.
Activation should start in the ad. Showing the job to be done and the transformation in your ad creative builds trust before users even open the app. A coding app's best performing ad showed someone coding in a lift, making viewers think "I could find time for that too."
Correlation isn't causation in activation metrics. Any action will always look better than no action. The real work is finding which behaviors, at what volume and timing, predict retention across cohorts and channels.
Mini aha moments beat one big moment. Instead of trying to engineer a single big aha moment (which is often technically difficult), build multiple smaller moments of perceived value. These can be as simple as a personalized plan, a visual showing the outcome, or a first small win before the paywall.
Monthly plans help you learn faster. For startups without much data, monthly subscriptions force users to make a renewal decision every month, which generates faster signal on who is truly activated vs. who is coasting on inertia.
Revenue is a terrible North Star metric. It pushes teams toward extracting value from users rather than creating it. Activation and usage metrics better align the team's incentives with user outcomes.
Daphne Tideman on LinkedIn: https://www.linkedin.com/in/daphnetideman/
00:00 Intro and Daphne's path from e-commerce to app growth consulting 01:20 How activation thinking evolves from 2D to 3D 04:20 Common activation mistakes: oversimplifying and picking the wrong metric 05:50 Why standard metrics weren't predicting retention 07:20 Onboarding completion as a vanity metric: 90% completion, gone by day two 10:20 Activation vs. monetization: which to fix first 13:20 Building mini aha moments into onboarding and ads 17:50 User interviews and the role of emotions in activation 20:20 Your retention problem is actually an activation problem 23:20 Time to first value vs. time to core value framework 27:20 How to test whether an activation metric is real or vanity 29:20 Starting with user interviews vs. data when you lack scale 31:50 Correlation vs. causation: finding the right activation threshold 34:20 Learning from failed experiments 36:50 Diagnosing activation vs. retention vs. acquisition problems 39:20 Why activation problems are more common than retention problems 42:20 Matching subscription models to use cases 44:50 Biggest activation mistake apps make right now 45:50 Lightning round: pricing wins, hot takes, and best activation results
13: The Four Horsemen of Churn w/ Dan Layfield
25 mars 2026
00:59:59
Dan Layfield, author of Subscription Index and former product lead at Codecademy and Uber Eats, explains why churn is the silent ceiling on subscription growth, how to diagnose which type of churn is killing your business, and the pricing trick that can double your LTV overnight.
Dan walks through his four horsemen framework: payment failures, activation issues, pricing and plan mix, and voluntary cancellation. He shares the bottom-up optimization approach he uses with every company, starting with Stripe settings that take 10 minutes to fix.
What you'll learn:
Why your Stripe retry settings are probably wrong and how to fix them in 10 minutes
How to calculate your growth ceiling using churn rate and acquisition numbers
Why payment receipts might be reminding users to cancel every month
How to price annual plans based on your monthly retention data
How to build cancellation flows that save 20% of churning users
Why activation experiments are tricky and often produce duds
Why quality problems are the easiest growth fixes
Key Takeaways:
Churn dictates your ceiling. New users divided by churn rate equals your max subscribers. 1,000 new users with 20% churn = 5,000 subscriber ceiling. Lowering churn raises that ceiling proportionally.
Start at the bottom of the funnel. Stripe settings, dunning emails, card updaters can be fixed in minutes and win back 5% of churn. Do these before tackling bespoke activation problems.
Annual pricing should match monthly LTV plus one or two months. If average retention is five months, price annual at six months. Looks like a steep discount but doubles LTV.
Turn off monthly email receipts. Netflix, Spotify, and Amazon don't send them. That monthly reminder is a monthly prompt to cancel.
Cancellation flows should solve the underlying problem. Pausing works when the need is temporary. Downgrading works when they're paying for unused features.
Dan Layfield on LinkedIn: https://www.linkedin.com/in/layfield/
Timestamps
00:00 Intro and Dan's path from JP Morgan to Codecademy 04:00 Freemium conversion benchmarks: sub-1% vs. good (3%) vs. great (7%) 06:30 The growth ceiling formula 08:00 The four horsemen of churn 12:00 Bottom-up optimization: start with Stripe settings 13:30 Cancellation flow tactics: pause, discount, upgrade/downgrade 19:30 Payment failure quick wins: smart retries, card updater, dunning emails 22:30 The annual pricing trick that doubled LTV at Codecademy 30:00 Activation and the Reforge framework 37:30 Onboarding should show value, not just explain device setup 42:30 Ethical cancellation flows and click-to-cancel legislation 49:30 Screenshot audit: where to start when you're stuck 52:30 Turn off monthly receipts: the easiest churn win 53:30 Lightning round
12: Price Testing for Subscription Apps with Michal Parizek
12 mars 2026
00:42:15
Michal Parizek, pricing and growth lead at Mojo, explains how to predict long-term revenue from short-term price test data, why Apple's automatic regional pricing is wrong for most apps, and how to sequence pricing, packaging, and paywall tests for maximum impact.
Michal walks through the 13-month revenue projection model he built at Mojo, which uses seven-day cancellation rates as a proxy for annual renewal rates. He shares how his team raised yearly prices by 50% in the US and Germany with minimal conversion drop, how they tested free trial lengths and found almost no difference between three-day and seven-day trials, and why the ratio between monthly and yearly plan prices matters more than the absolute price point.
What you'll learn: - How to use seven-day cancellation rates to project 13-month revenue - Why Apple's exchange-rate-only pricing leaves money on the table - How to sequence price tests: price first, then packaging, then paywall design - Why the monthly-to-yearly price ratio drives plan share more than absolute price - How hiding the monthly plan pushed yearly share from 60% to 80% - Why free trials still matter for new users, despite advice to remove them - How three-day trials performed as well as seven-day trials at Mojo - Why your first price test should have big price gaps, not small ones - How traffic source mix can distort price test results - Why a 100% price increase was a short-term winner but long-term loser
Key Takeaways:
- Seven-day cancellation rate is a reliable early signal. 20-30% of cancellations happen in the first seven to ten days. Measure that rate per variant, project renewal rates from it, and you can evaluate a price test without waiting months. Mojo validated this against real data and it held.
- Apple's regional pricing is just exchange rate math. No purchasing power, no local context. Look at your top five markets individually, compare conversion funnels by country, and cross-reference competitor pricing.
- Pricing and packaging beat paywall design in impact. Changing price points, plan structures, and introductory offers had more effect than design or copy. Start with pricing, then plan mix, then layout.
- The monthly-to-yearly price ratio drives plan selection. Changing only the monthly price shifted yearly subscriber share significantly. The perceived deal relative to monthly is a strong behavioral lever.
- Don't remove free trials for new users without testing. Mojo tried it based on popular advice and saw revenue decline. Test it for your app.
- Start price tests with big jumps. Test $40 vs $60 vs $80, not $50 vs $48 vs $52. Find the zone first, refine later.
- Revisit cohorts months after shipping. Mojo's 100% price increase looked great short-term but cancellation rates spiked. The 13-month projection caught it.
Links & Resources - Michal Parizek's Botsi blog post: https://www.botsi.com/blog-posts/pricing-experiments-the-backbone-of-mojos-monetization-success - Michal Parizek on LinkedIn: https://www.linkedin.com/in/michalparizek/
Timestamps 0:00 Intro 1:03 Using seven-day cancellation rates to predict 13-month revenue 3:25 Building the report template and data pipeline 6:13 Validating the renewal rate prediction model 10:03 Benchmarks for new apps without renewal history 12:09 Why Apple's automatic price tiers are wrong 13:33 How to research and set regional prices 17:10 Relationship between pricing, packaging, and paywall design 21:15 Sequencing: price first, then packaging, then design 23:55 Why paywall layout tests that touch plan visibility are most impactful 26:41 Free trial strategy and length testing 31:03 Paid trial options as an emerging trend 33:16 The biggest mistake: not having enough data volume 35:56 Raising prices 50% in the US and Germany 38:46 Start with big price gaps, refine later 40:11 Don't be afraid to test prices
11: Lessons from a Founder: What Sasha Learned Launching a Mental Health App
25 févr. 2026
01:07:31
Sasha, founder of Anticipate (a mental health app), explains why she accepted an overly broad problem statement during validation, how she used Reforge's product-market fit narrative framework to test hypotheses without building, and what she learned after eight rounds of iteration that still didn't land product-market fit.
Sasha came into this with a real edge: years of marketing technology and data consulting for companies like Flo Health gave her the insight to use behavioral data for mental health. But translating deep domain expertise into a focused, sellable product turned out to be a different problem entirely. She walks through the specific moment her PMF interviews led her astray, why the Blue Ocean Strategy canvas revealed she was charging for features users get for free elsewhere, and the five pieces of advice from advisors that finally helped her reframe everything.
What you'll learn:
• Why emotionally compelling answers in user interviews can mislead you into solving problems too large to tackle • How Reforge's PMF narrative framework structures hypothesis validation before a single line of code is written • Why product-market fit interviews need to go past the top-level pain and drill into specific, solvable sub-problems • How the Blue Ocean Strategy canvas revealed Sasha was charging for features available for free • Why willingness to pay and perceived value are not the same thing, and why conflating them kills monetization strategy • How Apple in-app events can give early-stage apps a meaningful boost in rankings and visibility • Why Reddit feedback, brutal as it is, beats feedback from friends and family every time • How to identify your real competitors by talking to people who don't use any product in your category • Why going viral before you understand your retention is more dangerous than growing slowly • How Gamma's "ruthless focus on the first 30 seconds" applies to any early-stage product • Why "hell yes" should be the bar for every slide in your demand validation deck before you build anything • How to layer in analytics tools incrementally rather than setting up a full stack before you need it
Key Takeaways:
Don't take big emotional truths at face value. When Sasha asked users about mental health, they told her they never wanted to experience a crisis again. That's real. But it's so large and ambiguous that no small startup can solve it. She should have pressed further — what specific behaviors or sub-problems sit underneath that fear? One reachable problem beats ten important ones.
Sell before you build. A slide deck that walks users through a problem and proposed solution is a much cheaper way to iterate than building product. If you're not getting "hell yes" reactions slide by slide, the product wouldn't have landed either. Change the deck first.
Willingness to pay is not the same as value. Some use cases are genuinely valuable to users but they'll never pay for them because they see the data as theirs, or because it's available elsewhere for free. Knowing which features fall into which bucket before you write your pricing page saves a lot of pain.
Your real competitors are probably not in your app category. Anticipate doesn't compete with Headspace or Calm. It competes with Apple Health, fitness apps, and the mental math people already do in their heads. Talking to non-customers revealed this, and it completely changed the product strategy.
Be deliberate about your first 100 users. A Reddit launch spike or a Product Hunt bump feels like traction, but the signal is noisy. The first users should be chosen for the quality of feedback they can give, not for their contribution to MRR. Get 10 people who genuinely love the product, understand why, then figure out how to find 100 more of them.
Virality is math, not magic. If viral growth is part of the strategy, it has to be built into the product and marketing engine from the start. A one-off spike from the wrong audience will tank your retention cohorts and give you data that doesn't mean anything.
Build your analytics stack incrementally. Start with your database. Add simple app open events mapped to user IDs. When you know what's missing, layer in Amplitude for product analytics and AppsFlyer for attribution. Don't install tools you don't have a clear use for yet.
Prepare for the long run. One piece of advice Sasha received that stuck: figure out how long you can stay in the game without damaging your quality of life. Early-stage building is a long game. Sustainability matters.
Links & Resources:
Reforge (Product-Market Fit Narrative Course): reforge.com
Blue Ocean Strategy: blueoceanstrategy.com
Rob Snyder / Harvard Innovation Labs (Path to PMF): search "Rob Snyder Harvard Innovation Labs PMF"
Sasha on LinkedIn: https://www.linkedin.com/in/aliaksandralamachenka/
0:00 Beginning 1:21 Intro and Sasha's background in MarTech and mental health 2:20 How the Anticipate idea was born from behavioral data 4:41 Using Reforge's PMF narrative framework before building 8:26 The PMF interview mistake: accepting a big ambiguous problem 14:38 The flight analogy for finding specific, solvable problems 15:22 Should you research less and build faster? 20:47 Why you should start with demand, not a product 21:51 Willingness to pay vs. perceived value in consumer apps 23:37 Being intentional about your first users 27:21 Why Reddit feedback is actually valuable 31:49 Current growth channels and why Sasha paused scaling 34:51 Five pieces of advice from advisors 40:10 Blue Ocean Strategy: mapping competitors and finding gaps 45:21 Why non-consumers are the most important interview group 47:21 Who Anticipate's real competitors actually are 56:18 How to set up analytics step by step as a small team 1:01:15 Gamma's "first 30 seconds" strategy and why it matters 1:02:51 Sasha's next steps and final advice for founders
10: Why the Weird Ad Wins: CEO of Ramdam on Finding UGC Champions | Xavier de Baillenx
11 févr. 2026
00:55:28
Xavier, CEO and co-founder of Ramdam, breaks down how subscription apps can scale creator ads on TikTok and Meta, why volume beats perfection in UGC testing, and where AI-generated video actually makes sense (and where it doesn't).
Xavier spent five years at Match Group working on AI teams after his dating app was acquired. He then launched an app studio and discovered firsthand how painful it was to find winning ad creatives: months of testing 50 different videos just to find one that cut his cost per install by 5x. That frustration became Ramdam, a platform that helps consumer apps produce creator ads at scale. The company now works with Tinder, PhotoRoom, Flo, and other category leaders, delivering over 10,000 creatives per month.
What you'll learn:
Why a 5% success rate on ads is completely normal (and how to structure campaigns around it)
How to start a UGC test: 20-40 creators, 4-5 concepts, $20-50K minimum spend
Why US English ads often perform in non-English speaking markets
How winning apps keep one narrative from ad to paywall
Why TikTok carousel ads are massively underrated for dating apps
How to structure "test" vs "scale" campaigns to measure both CPI and ROAS
When AI-generated video makes sense: hard-to-source personas, scaling winning concepts
Why the ad your team wants to reject might get 350 million views
How Ramdam uses AI to match briefs with creators and QA videos before delivery
Why "happy accidents" from real creators still outperform AI-perfect execution
Key Takeaways:
Volume always wins over perfection. 50 different creators who don't perfectly match your persona will beat 5 who do. You can't predict which ad will work. Even Xavier, after thousands of campaigns, has no idea which ad will succeed when he sees it. The only strategy that works is testing at scale and following the data.
Winning ads have a 2-3 week lifespan. Ad fatigue is real. If you're scaling on TikTok or Meta, you need to refuel with new creatives every month. The biggest spenders are producing 1,000+ creatives per month to stay ahead of fatigue.
Start broad, then replicate winners. Early briefs should leave room for "happy accidents" where creators interpret the concept in their own style. Once you find a winner, run replicate campaigns: same hook, same narrative structure, but new faces and fresh energy.
The ad-to-paywall story must be consistent. Winners keep one promise throughout the entire journey. If the ad says "sleep better in 7 minutes," that same message should appear on the store page, onboarding, and paywall. Breaks in this narrative kill conversion.
AI video is a complement, not a replacement. AI-generated creators work for hard-to-source personas (high-income demographics, pregnant women, complex scenes). But they can't produce the weird, human moments that go viral. Find winning concepts with humans, then scale variations with AI.
TikTok and Meta behave differently. TikTok rewards short (around 10 seconds), trend-driven content with trending sounds. Meta prefers structured narratives, product demos, 15-30 second videos. Carousels perform well on both, especially for storytelling.
Creator diversity expands reach. Meta and TikTok treat ads with the same creator as nearly identical. Using many different faces helps you reach new audiences. This is why Ramdam assigns one creator per video across their 50K creator network.
One ad can change everything. This business follows power law dynamics, similar to the music industry. Most ads do nothing. A small percentage capture all the budget. One viral hit can transform an app's trajectory overnight.
Links & Resources: - Ramdam: ramdam.io - Xavier on LinkedIn: https://www.linkedin.com/in/xavier-de-baillenx/ - Email: xavier@ramdam.io (mention Botsi Podcast for personalized demo) - I also found the TikTok SwipeWipe video: tiktok.com/@vdanielle22/video/7298313654594800942
Timestamps: 00:00 Intro/Teaser 03:00 Xavier's background: Universal Music to Match Group to Ramdam 05:00 UGC formats explained: Classic, Trends, Carousels 09:30 Ad lifespan and creative fatigue 11:30 Why volume and experimentation beat perfection 15:30 Starting a UGC test: creators, concepts, budget 19:00 Creator diversity and platform algorithms 23:00 Balancing authenticity with replication 26:00 TikTok vs Meta: what works on each 30:00 Connecting ad performance to product funnels 36:00 Structuring test vs scale campaigns 38:00 How Ramdam uses AI for creator matching and QA 43:00 AI-generated video: use cases and limitations 49:30 Marketing fundamentals: clarity and authenticity 51:30 Counterintuitive learnings from UGC
9: Frameworks for Meta's AI-driven advertising w/ Marcus Burke
28 janv. 2026
00:50:44
Marcus Burke, Meta Ads consultant, explains why blended CPA is misleading, how creative format determines your audience targeting, and what signal engineering means for subscription apps in an AI-driven ad landscape.
Marcus breaks down his approach to working with Meta's algorithm rather than against it. He advocates for strategic ad set segmentation based on where different creative formats naturally deliver: static ads to Facebook feed, short-form video to Instagram Reels.
The conversation goes deep on the relationship between product design and ad optimization. Marcus explains how your subscription model, trial length, and paywall structure all affect the quality of signal you can send back to Meta. Sometimes optimizing for LTV conflicts with optimizing for ad signal, and growth teams need to navigate that tension intentionally.
What you'll learn:
• Why a $10 cost per trial can lose money while a $100 cost per trial can be profitable • How to use creative format (static vs. video vs. playable) to control placement distribution • Why "broad targeting" often results in narrow reach and high frequency on the same audience • How to structure ad sets by expected delivery rather than demographic targeting • What value rules are and how to use them for country, age, and gender optimization • Why the conversion event you optimize for should determine your account architecture • How to connect onboarding survey data with Meta demographic breakdowns • Why cold social traffic requires a fundamentally different onboarding approach than search traffic • What makes an effective "aha moment" before the paywall • How multi-price point strategies enable broader audience targeting • Why signal engineering is one of the last remaining levers for growth marketers
Key Takeaways:
• Blended CPA hides traffic quality problems. A $10 cost per trial from Instagram Reels represents a completely different audience than $10 from Facebook feed. Break down your metrics by placement to understand what you're actually buying.
• Creative equals targeting. Your media format determines where your ad delivers. Short-form vertical video goes to Reels; statics go to Facebook feed. This isn't a bug but a feature you can use to control your audience mix without hard targeting.
• Guide the algorithm, don't force it. Hard targeting gets expensive fast. Instead, use creative segmentation and value rules to nudge Meta toward your high-value audiences while keeping delivery efficient.
• Your conversion event determines your account structure. If you're optimizing for a shallow event like trial starts, you need more ad sets to compensate for the algorithm's lack of business knowledge. Moving closer to revenue lets you consolidate more.
• Onboarding should match your traffic source. Paid social users were just doom-scrolling and need to be entertained and re-sold on their problem. Search traffic already has intent. Design your onboarding accordingly.
• Create an aha moment before the paywall. Prove value in the first session through something tangible: a sample scan, a personalized analysis, an imported recipe. This converts better than promising value during a 7-day trial.
• Your pricing should match your creative strategy. Young audiences from UGC won't pay $70/year. Older Facebook feed audiences justify higher CPMs. Align your price points with who your ads are actually reaching.
Links & Resources
• Marcus Burke on LinkedIn: https://linkedin.com/in/marcusburke • Growth Festival Presentation: https://www.linkedin.com/posts/marcusburke_postmedia-buying-strategies-scaling-meta-activity-7373668067580563456-iziy
Timestamps
00:00 – Intro clips 01:41 – Introduction and context on Marcus's Growth Festival presentation 02:12 – Why blended CPA is irrelevant and how it differs from blended ROAS 04:36 – How placement affects traffic quality: $100 vs $10 cost per trial 05:11 – Using creative format to control placement distribution 07:32 – Working with the algorithm vs. forcing targeting 10:19 – Why "broad targeting" doesn't mean broad reach 11:06 – Getting placement and demographic data from Meta 14:47 – Layering complexity: placements, demographics, user goals 20:09 – Signal engineering and moving closer to business value 22:30 – Account architecture: stop over-consolidating 26:49 – Should subscription apps test removing trials? 31:52 – Value rules: what they are and how to use them 36:30 – Onboarding for paid social: entertainment over efficiency 39:36 – Creating aha moments before the paywall 45:44 – Multi-price point strategies to capture the full demand curve 48:31 – Wrap-up and where to follow Marcus
8: Shamanth Rao on Subscription Economics, Pricing, and Creative Strategy
13 janv. 2026
00:54:50
Shamanth Rao, founder of Rocketship HQ, explains why subscription economics fundamentally differ from free-to-play, why early ROAS signals are structurally misleading, and why LTV without context means nothing.
Drawing from a decade of hands-on experience across gaming and subscription businesses, Shamanth walks through how cash flow determines viable payback periods, why annual plans are the single most powerful lever in subscription growth, and how pricing strategy reshapes your entire acquisition model. He also dives deep into creative strategy: why ads should sell immediate value, not long-term habits; why relevance matters less than attention; and how winning ad narratives should actively inform your product and onboarding.
What you’ll learn:
• Why subscription apps don’t produce meaningful early monetization signals • Why there is no “correct” payback period • Why LTV without time, channel, platform, and geo context is misleading at best • Why annual plans dramatically reduce uncertainty and unlock scalable acquisition • Why most teams underprice annual plans • How trial length should vary by product type, not defaults • Why ads should sell speed-to-value, not habit formation • How “unrelated” or emotional ads outperform literal product messaging • How high-performing ads should influence product pages, onboarding, and roadmap decisions • Why quizzes and surveys work as both acquisition hooks and monetization levers • Where pay-as-you-go and credit-based pricing models fit — especially for AI apps • Why creative fatigue is a risk management problem, not just a volume problem • How micro-segmentation should directly shape creative production • Why AI-generated ads fail without strong human iteration and judgment
Key Takeaways:
• Subscription ≠ gaming economics. Games have uncapped monetization and instant signals; subscriptions have pricing ceilings and delayed feedback. Applying game-style ROAS logic to subscriptions leads to bad decisions.
• Payback is a cash-flow constraint, not a best practice. The “right” payback window depends on how long your business can afford to wait to get paid back — not what investors or blogs suggest.
• LTV is not a single number. Without time bounds and context (platform, channel, geo), LTV becomes theoretical and misleading. Payback periods make LTV actionable.
• Annual plans change everything. They collapse uncertainty, improve cash flow, and simplify acquisition optimization. For most apps, increasing annual plan adoption and pricing has a bigger impact than almost any other lever.
• Ads are not onboarding. The job of advertising is to interrupt the scroll and sell immediate value, not explain habit formation or long-term effort. That work belongs post-click.
• Attention beats relevance. Ads don’t need to perfectly reflect the product to work; they need to stop the scroll. Winning narratives should then be reflected in onboarding and product experience.
• Creative fatigue is a scaling risk. Over-reliance on a single winning creative can crash performance overnight. Diversification across formats, narratives, and micro-segments is essential.
• AI doesn’t replace taste. It’s easier than ever to generate bad ads at scale. The advantage comes from human judgment, emotional specificity, and iterative refinement — not raw volume.
00:00 – Cold open: Why subscription economics break common growth advice 01:06 – Games vs subscriptions: monetization ceilings and delayed signals 05:12 – Payback periods are cash-flow decisions, not benchmarks 09:26 – Why LTV without context is misleading 12:41 – Pricing as the most powerful lever in subscription growth 15:00 – Why annual plans fundamentally change unit economics 18:13 – Trial length strategy: short vs long trials 19:30 – Why ads should sell immediate value, not habits 25:30 – Why Duolingo is the exception to habit-based advertising 30:30 – When ads should influence product and onboarding decisions 37:41 – One-off purchases, pay-as-you-go, and AI monetization models 40:30 – Creative fatigue and the danger of over-scaling winners 46:00 – Micro-segmentation, AI ads, and human judgment 54:20 – Closing thoughts
7: Ekaterina Gamsriegler: How to engineer growth. Again and again.
17 déc. 2025
00:46:35
- PricePowerPodcast.com - AI Pricing for your app: Botsi.com
Ekaterina Gamsriegler (ex-Mimo, Amplitude Product50’s Top Growth Product Leader) breaks down why most growth teams struggle not because of a lack of ideas — but because they optimize the wrong things, in the wrong order.
Ekaterina walks through real-world examples across onboarding, paywalls, trials, activation, and pricing — showing how user psychology, perceived value, and expectation-setting matter more than dashboards alone.
📖 Episode Chapters:
00:00 Growth Does Not Start with an MMP 01:40 Breaking KPIs into Controllable Inputs 03:56 Why “Breaking Things Down” Gets You 80% There 06:30 Product Analytics vs Attribution 12:00 Onboarding Length vs Paywall Exposure 16:00 Why Averages Are Always Wrong 18:10 The Truth About Personalization 23:30 Why Users Don’t Start Trials 28:30 Understanding Early Trial Cancellations 34:45 Why Longer Sessions Can Be a Bad Sign 38:00 Pricing as a Growth Lever 42:00 Fix the Story Before the Price 44:00 Closing Thoughts
💡 Key Takeaways:
• Growth is a sequencing problem. Teams fail when they jump straight to solutions instead of first building a usable map of user behavior and breaking metrics into their underlying drivers.
• Product analytics beats attribution early. You don’t need a perfect funnel — you need a reliable picture of what users actually do after install. MMPs come later.
• Averages hide the truth. Looking at overall conversion rates masks real issues that only appear when you segment by device, channel, geo, or user intent.
• More exposure ≠ more revenue. Increasing paywall impressions by removing onboarding screens often lowers trial conversion if user intent isn’t built first.
• Personalization rarely delivers big wins. Most onboarding and paywall personalization produces single-digit uplifts while adding major complexity and risk.
• Most early churn is voluntary. Users cancel trials early because they want control, not because they hate the product.
• Time-to-value matters more than time-in-app. Longer sessions often mean confusion, not engagement.
• Lowering prices can work — in specific cases. Misaligned mental price categories, lack of localization, missing feature parity, or mission-driven goals can justify it.
• Pricing issues are often narrative issues. Before changing the price, fix how value is communicated and perceived.
• Sustainable growth comes from focus. The best teams work on 2–3 high-confidence problems at a time — and say no to everything else.
Links & Resources Mentioned:
• Ekaterina on LinkedIn: https://www.linkedin.com/in/ekaterina-shpadareva-gamsriegler/ • Maven course: https://maven.com/mathemarketing/growing-mobile-subscription-apps • Full presentation from Growth Phestival Conference: https://www.canva.com/design/DAGw09v8yIo/lfVoi-Xf4QRm6-ddmtro1A/view • Jacob's Retention.Blog
6: Lucas Moscon: Conversion Values, SKAN, Fingerprinting, MMPs, and Mobile Attribution
04 déc. 2025
00:56:07
Lucas Moscon, one of the most technically knowledgeable people in mobile attribution, breaks down how post-ATT measurement really works, why most marketers are using outdated mental models, and how to build a modern, resilient measurement stack. Lucas clarifies what’s deterministic vs probabilistic today, exposes where MMPs still add value (and where they absolutely don’t), and explains why IP-based fingerprinting quietly powers 90%+ of attribution today. He also walks through SKAN in plain English, conversion-value strategy, web-to-app pipelines, and why looking at blended ROI beats chasing ROAS illusions on iOS.
If you want to understand the actual mechanics behind click → install → revenue pipelines — and why Apple’s privacy tech is failing in practice — this episode is for you.
What you’ll learn:
• Why ATT didn’t “kill” attribution — it forced marketers to juggle deterministic, probabilistic, and blended layers • How Meta/Google matching actually works (spoiler: 90%+ relies on IP, not magic AI) • Why SKAN isn’t enough — and why relying on ROAS on iOS is the least trustworthy metric • How to measure effectively without over-reacting to noisy campaign-level data • When you truly need an MMP today — and why most apps don’t • How to correctly design conversion values for SKAN without over-engineering • Why retention determines how many conversion values you even receive • How to triangulate data across store consoles, subscription platforms, MMPs, and ad networks • Why focusing on payback windows (D60–D180) outperforms optimizing for short-term ROAS • Why probabilistic fingerprinting is still powering the ad ecosystem — and why Apple hasn’t stopped it
Key Takeaways:
• iOS ROAS is the noisiest metric you can use. Without IDFA, everything is extrapolated. High-confidence decision-making must use blended revenue and cohort ROI, not ad-platform ROAS.
• Modern attribution = multiple layers. Post-ATT, performance requires triangulating data from SKAN, ad networks, subscription platforms, and product analytics — not trusting a single source of truth.
• Fingerprinting ≠ complex algorithms — it’s mostly IP. Internal tests showed that greater than 90% of probabilistic matches come from IP alone. All the “advanced modeling” narratives are overstated.
• Most apps don’t need an MMP anymore. Exceptions: running AppLovin/Unity DSPs, React Native/Flutter SDK support gaps, or complex Web-to-App setups where Google requires certified links. Otherwise, MMPs mostly add cost, not clarity.
• Retention determines SKAN visibility. If users don’t reopen the app, conversion values won’t update — meaning SKAN under-reports trials/purchases unless retention is strong.
• Blend deterministic + probabilistic + aggregated signals. The goal isn’t precision — it’s directionally confident decisions across imperfect data. Marketers should work in ranges, not absolutes.
• Longer payback windows unlock scale. Teams willing to accept D60–D180 payback dramatically out-spend competitors optimizing for D7 ROAS — assuming they have strong early-day proxies to detect failing cohorts.
• MMPs don’t magically fix discrepancies. Even with one SDK, marketers still see mismatches across networks, stores, and internal analytics. The “one SDK solves it” narrative is outdated.
Links & Resources
• Appstack: https://www.appstack.tech/ • Appstack library of resources: https://appstack-library.notion.site/ • Lucas Moscon LinkedIn: https://www.linkedin.com/in/lucas-moscon/
00:00 Opening Hot Take: “Are You Really Saturating Meta?” 05:00 Early Indicators & Proxy Metrics (D3–D10) 09:00 Predicting Cohort Success from Day 3–10 11:00 How Click → Install Attribution Actually Works 14:00 Web-to-App Infrastructure (Fingerprinting + SDK Flow) 18:00 Meta/Google Matching: IDFA, AEM, SKAN 24:30 Fingerprinting Reality: Why IP = 90% of Matches 27:00 Apple’s Privacy Messaging vs Actual Enforcement 30:30 How Apple Ads Uses (or Ignores) SKAN 35:00 Should You Use an MMP in 2025? 46:00 SKAN Conversion Value Mapping: The 63/62 Strategy 49:00 Why Retention Determines SKAN Postbacks 54:00 App Stack Overview + Closing Thoughts
5: Barbara Galiza: 5 Golden Rules for Conversion Events
18 nov. 2025
00:44:53
Barbara Galiza (HER, Microsoft, WeTransfer, Mollie) breaks down how subscription apps should structure conversion events, clean up broken tracking, and send the right signals into Meta and Google to improve ROAS. She shares her five golden rules for event design, why most apps send way too many signals, and how speed, value, and PII massively improve match rates. We also cover predictive value (without overbuilding LTV models), why strategy failures masquerade as measurement problems, and how fast event sending boosts attribution quality across platforms.
What you’ll learn
The optimal 3-event conversion structure for Meta/Google (and why tracking more hurts performance)
Why speed of event delivery is one of the strongest levers for match quality & cheaper CPAs
How to incorporate value signals (trial filters, buckets, predicted value) without full LTV modeling
Why using PII (hashed email/phone) dramatically improves attribution & optimization
How to separate measurement vs. optimization data so each system actually does its job
Lightweight ways to identify high-value users early and filter out low-quality trials
Why Meta-reported ROAS doesn’t matter unless your business metrics move too
How to diagnose whether you have a strategy problem or a measurement problem
Why small apps should use holdouts & blended metrics instead of over-complicated attribution setups
How fast event sending helps platforms reconnect the full click → browser → app → purchase chain
Key Takeaways
Keep it to ~3 conversion events. Event tracking is “free,” but every extra event adds maintenance, confusion, and breakage. For ad platforms, you rarely need more than:
a top-funnel/engagement event (e.g. survey completion),
signup/registration (first PII),
trial start (earliest strong revenue proxy).
Design the event ladder from value, not vanity. Early events show intent; signup lets you pass PII; trial start is the closest thing to revenue that usually falls inside platform lookback windows.
Fire events fast. The shorter the delay from click → event, the easier for Meta/others to probabilistically match user journeys. Even within a 24-hour window, “the faster, the better.”
Include value data, but don’t over-engineer LTV. For subscription apps, the actual charge often happens after the lookback window. You don’t need a perfect 2-year LTV model—start by bucketing users (e.g. worth 0 / 5 / 10 / 20) based on early behavior and use that as a value signal.
Predictive value is about ranking users, not forecasting to the penny. The goal is: out of 100 trials, which ~30 are most likely to convert? Use early feature usage (first 24–48 hours), plan views, return sessions, etc. to distinguish high- vs low-value users.
If you don’t send value, platforms optimize for cheap installs. Without a quality or revenue proxy, bid models will chase the lowest-CPI users—often low-intent segments like teens—at the expense of payers.
Deduplicate client + server events on purpose. If you send the same “signup” from multiple sources (SDK, MMP, CAPI), use a deduped “master” event for optimization and keep source-specific events for troubleshooting. Check that SDK_signup + CAPI_signup roughly add up to the unified event.
Pass PII where you legally can. Emails, login IDs, names, location, and device info (when allowed) greatly improve matching and attribution—especially now that IDFA and deterministic links are limited. Always align with privacy law + platform policies.
Separate optimization data from decision data. Events in Meta/Google exist primarily to help their algorithms optimize—not to give you perfect causal measurement. Use them for bidding & creative testing, but use incrementality tests and holistic metrics to decide budget allocation.
Don’t mistake a strategy problem for a measurement problem. If you’re a small app running many channels with tiny budgets and can’t tell what works, the issue is fragmentation—not that you need fancier attribution.
Links & Resources
Fix My Tracking: https://fixmytracking.com/
021 Newsletter: https://www.021newsletter.com/
Barbara Galiza on LinkedIn: https://www.linkedin.com/in/barbara-galiza
4: Jakub Chour: Building your App MarTech Stack
22 oct. 2025
00:48:33
Jakub (HER, Mapy) shares how he rebuilt a subscription app’s MarTech stack from near-zero after joining MAPY (hiking & biking maps): picking an MMP, adding revenue infra, standing up in-app messaging/“HTML onboarding,” and using surveys + activation signals to decide what to monetize. We also cover build vs. buy, cutting tool noise, deep links, web vs. mobile behavior, and clever Figma automation for instant multi-language screenshots.
What you’ll learn
The essential MarTech stack for a subscription app (MMP, revenue infra, analytics/BI, lifecycle—in-app first)
How to choose an MMP (AppsFlyer vs. Branch) and why deep links usually live there
Why in-app messaging (HTML modals) can stand in for onboarding, surveys, and roadmap validation
Methods to discover what users will pay for (surveys, activation metrics, contextual upsells)
When to buy vs. build (and how investor expectations affect that choice)
Managing tool costs in freemium: country-scoped SDKs, MAU-based pricing tradeoffs
Web vs. mobile behavior differences and how that shapes monetization & UX
How to filter vendor hype: pricing page tells, documentation over demos, avoid vague “AI” pitches
A fast path to localized store creatives with Figma + CopyDoc
Key Takeaways
Start with measurement. Without an MMP and clean revenue signals you can’t scale UA or judge payback—set those up first.
In-app > email early. For new/lean teams, prioritize in-app messaging and “HTML onboarding” to collect motivations, segment users (hiker/biker/driver/general), and guide activation.
Show the paywall. Track launch→paywall impression; aim for ~90%+ so you’re reliably creating purchase opportunities, then layer contextual upsells (Strava-style).
Monetize what matters. Use quick surveys + early actions to identify features people value; validate with smoke tests (CTA → deep link) before committing roadmap.
Buy the boring stuff. For attribution, lifecycle, and payments, buy (standards, support, investor-friendly metrics). Build only where you truly differentiate.
Control analytics cost. Scope product analytics SDKs to priority countries (or sample) to align MAU-priced tools with freemium economics.
Deep links live with your MMP. Standalone options are thin, Google Dynamic Links is sunset—lean on AppsFlyer/Branch for reliability.
iOS privacy changed the game. Deferred deep linking and deterministic tracking are less reliable; plan for modeling and guardrails.
Cut through tool noise. If a vendor hides pricing or leads with vague “AI,” proceed with caution; read docs & pricing matrices, not just landing pages.
Automate localization. Use Figma + CopyDoc to export/import copy and auto-generate hundreds of localized screenshots in minutes.
Links & Resources
MAPY (hiking & biking maps): search “MAPY hiking app” in your store
CopyDoc for Figma (bulk copy import/export): https://www.figma.com/community/plugin/900893606648879767/copydoc-text-kit
Connect with Jakub on LinkedIn: https://www.linkedin.com/in/jakubchour/
3: Ashley Black: Google App Campaigns, Value-Based Bidding, and Signal Optimization
15 oct. 2025
00:48:56
Ashley Black, founder of Candid Consulting and former longtime Googler, breaks down how (and when) subscription apps should switch Google App Campaigns from CPA to tROAS, the pitfalls that stall performance, and how to feed better signals (activation/retention events) for durable scale. We also dig into iOS vs. Android realities, exclusions that actually matter, and why “automated” ≠ “set-and-forget.”
What you’ll learn
The most common mistakes when moving from CPI/CPA to tROAS (targets too high, windows too long)
How to set a realistic ROAS target (start ~20% below goal) and ramp it without killing volume
Volume prerequisites for value bidding (why you need revenue events, not just trials)
When tROAS fits (risk tolerance, trial length, budget) and when to stay with CPA
Android vs. iOS with Google: inventory, tracking constraints, and creative needs (YouTube/Shorts)
The right exclusions to apply (existing users, brand, re-installs) and why CPM rising can be good
Using early activation/retention events to improve optimization when trial-start isn’t predictive
Key Takeaways
Don’t over-ask early. Setting day-7 ROAS targets too high and using 30–90 day windows starves delivery. Start with a short window (≈7 days) and a lower target, then stair-step up.
You need real revenue signals. For tROAS to learn, pass purchase/subscription events—trial-start alone won’t cut it. Rule of thumb: aim for ≥10 post-install revenue events/day (often more).
Trial length matters. 30-day trials delay signals; tROAS may burn spend blind. Shorter trials or earlier monetization events make tROAS viable.
Expect a ramp-up. Some accounts stabilize in days; aggressive targets can take weeks to unlock. Be patient and ready to lower targets to gain learning volume.
Scale vs. profit trade-off. CPA often scales easier; tROAS can be more profitable once learned. Consider geo split tests to compare mixes.
Inventory shifts under tROAS. Eligible placements are the same, but you may see more search/Play and higher CPMs—often a sign of higher-quality traffic, not waste.
Exclude smartly. Add exclusions for current users, brand queries, and (optionally) re-installs to protect incrementality.
iOS = different game. Google’s iOS performance lags Android; expect more YouTube/Shorts traffic and lean on strong UGC-style video. Treat iOS Google as a later-stage test.
Optimize for activation. If trial-start users don’t retain, bid to an early in-app action (e.g., completed tutorial, first message) that correlates with D1/D7 retention and occurs fast enough for learning.
Automation needs adults in the room. UAC/PMAX aren’t fire-and-forget—active tuning (targets, assets, exclusions) still moves the needle.
Links & Resources
Ashley Black — Candid Consulting: https://www.candidconsultinggroup.com/
Ashley’s guide to tROAS for subscription apps: https://www.botsi.com/blog-posts/value-based-bidding
Connect with Ashley on LinkedIn: https://www.linkedin.com/in/ashleym-black/
2: Anthony Scarpaci: Designing Referral Programs That Actually Work (The RIGHTT Framework)
09 oct. 2025
01:05:37
Anthony Scarpaci, former Global VP of Growth at Acorns and senior leader at NerdWallet, Betterment, and Blue Apron, joins Jacob Rushfinn (CEO of Botsi) to break down how to build a referral program that performs. He shares his RIGHTT Framework—Relevance, Incentives, Guardrails, Human Centricity, Timing & Tracking—and real examples from fintech, meal kits, and subscription apps.
🧩 The RIGHT Framework
R = Relevance – Incentives should align with your product’s core value. Cash isn’t always king.
Example: GoHunt gives gear credits usable in-app and in its e-commerce store, keeping rewards tied to the customer experience.
I = Incentives – Make them motivating and credible. Urgency (limited-time offers) beats evergreen “set-and-forget” bonuses.
• Consumers are numb to “Give $10 Get $10.” • Guaranteed rewards outperform sweepstakes—people act when they know they’ll get something. • Tie incentives to meaningful product actions that predict retention.
G = Guardrails – Prevent gaming and fraud without killing usability.
The “optimal level of fraud is not zero.” Every layer of anti-fraud friction hurts good users—accept some inefficiency for total-program scale.
• Analyze cohorts for retention / LTV gaps. • Require real product usage (e.g., multiple deliveries in meal kits).
H = Human Centricity – Consistent, authentic, transparent experience across the entire journey.
• Launch after product-market fit and a healthy customer base. • Introduce referral prompts at the right emotional moment: trial start or delight milestone. • Maintain urgency windows for bursts of activity. • Track cohorts, incremental lift, and blended CAC pre- / post-launch.
💡 Key Insights & Takeaways
• Referrals ≠ free users. Model unit economics and compare to your next-best acquisition channel (Meta, Google etc.). • Halo & Cannibalization. Account for organic word-of-mouth you’d get anyway and the extra reach you gain when offers go viral. • Accept some fraud. Zero-fraud programs over-optimize and add friction; “tolerable inefficiency” is a healthy cost of growth. • Design for compounding. Great referrals create chains (friend → friend → friend), not single invites. • Avoid conditioning. Don’t train users to expect giant promos forever—treat large bonuses as events, not defaults. • Influencers as fuel. One creator’s post can 10× signups—plan for the viral halo but don’t depend on it. • Higher-quality leads. Referred users retain better and cost less long-term—social proof raises both acquisition and retention.
🧠 AI Toolbox Anthony Uses
• Lovable / v0.dev / Replit V0 → No-code prototyping & mockups. • Gemini transcription + Claude / ChatGPT → Strategy alignment & theme extraction from founder calls. • OpusClip → Video editing & social creative velocity. • Perplexity → Everyday research & voice-based learning.
1: Gabe Kwakyi: Creative Hits, Influencer Pipelines, and Scaling Meta
07 oct. 2025
00:49:17
Gabe Kwakyi, CEO of Lingvano and mobile growth leader, shares how creative hits powered Lingvano's paid acquisition, how he became CEO, and his testing → scaling → core framework on Meta. We also dig into onboarding/monetization experiments, live-learning bets, community building, and Gabe’s “AI Stack for Startups.”
What you’ll learn
Why a tiny % of creatives drive the majority of paid social results—and how to reliably find them
The playbook to mine influencer content and graduate winners from testing → scaling → core
Budgeting and campaign structure tactics to let new winners break through incumbent hits
When (and for whom) app→web payment flows actually make sense
Parallel growth lanes beyond UA: onboarding, monetization, live sessions, and community
Gabe’s “AI Stack” to go from beginner to intermediate with LLMs
Key Takeaways
Creative hits rule paid social. Treat influencers as your “hit makers”; port high-engagement organic posts into ads and look for fast spend/scale with strong unit economics.
Judge by scale, not vanity. If Meta won’t spend on it, it’s not a hit—pause losers quickly.
Structure matters. Keep an always-on testing campaign; promote winners to a scaling lane (separate ad sets to force initial spend), then into your core.
Expect droughts. Old hits can keep outperforming new tests—reactivate past winners and extend via hook swaps, but keep sourcing creators.
Web payments ≠ free margin. Friction can erase take-rate gains; look for segment fit (e.g., older audiences) and promo-led moments to overcome drop-off. Test before scaling.
Don’t single-thread growth. Run ongoing onboarding/monetization experiments and build community to diversify beyond UA.
Links & Resources
Lingvano (learn ASL, BSL, and more): www.lingvano.com
Gabe’s AI Stack for Startups (go to first featured posts): https://www.linkedin.com/in/gabrielkwakyi/
Advanced App Store Optimization Handbook: https://www.asoebook.com/
Connect with Gabe on LinkedIn: https://www.linkedin.com/in/gabrielkwakyi/