The Price Power Podcast is for all things growth, retention, and monetization for subscription mobile apps. We talk with amazing leaders in the industry to help share their knowledge with you. Hosted by Jacob Rushfinn, CEO of Botsi.
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22: 9x ARR, +47% ARPU, the real tests that won with Michael Bardin | Price Power Podcast Ep. 21
Episode 22
Thursday, August 20, 2026 • Duration 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
Episode 21
Thursday, August 6, 2026 • Duration 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.
20: Opal Killed the Quiz Funnel. What's up next?
Episode 20
Tuesday, July 14, 2026 • Duration 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
19: Lessons From Reviewing 100+ Web Funnels w/ FunnelFox CEO
Episode 19
Thursday, June 25, 2026 • Duration 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.
18: Hybrid Monetization: When and where to start w/ Cristian Rotari
Episode 18
Thursday, June 11, 2026 • Duration 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.
Best of Price Power Podcast from the Last 6 Months | Price Power Podcast Ep. 17
Episode 17
Thursday, May 28, 2026 • Duration 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
Episode 16
Wednesday, May 6, 2026 • Duration 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.
15: How to start with Signal Engineering w/ Shumel Lais
Episode 15
Wednesday, April 22, 2026 • Duration 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.
14: Fix Activation Before Growth w/ Daphne Tideman
Episode 14
Wednesday, April 8, 2026 • Duration 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.
13: The Four Horsemen of Churn w/ Dan Layfield
Episode 13
Wednesday, March 25, 2026 • Duration 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.
Links & Resources
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• 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
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
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
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
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
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
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
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
