19: Lessons From Reviewing 100+ Web Funnels w/ FunnelFox CEO

19: Lessons From Reviewing 100+ Web Funnels w/ FunnelFox CEO

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

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/ * Zing Coach: https://www.zing.coach [https://www.zing.coach] * 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

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

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/ * Subscription Stack framework: https://phiture.com/resources/subscriptionstack/ * 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

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. Links & Resources * Day30: https://day30.ai [https://day30.ai] * Shumel Lais on LinkedIn: https://www.linkedin.com/in/shumellais/ [https://www.linkedin.com/in/shumellais/] 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