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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5: Barbara Galiza: 5 Golden Rules for Conversion Events
Épisode 5
mardi 18 novembre 2025 • Durée 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
Épisode 4
mercredi 22 octobre 2025 • Durée 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 (CTA → deep link) before committing roadmap.
3: Ashley Black: Google App Campaigns, Value-Based Bidding, and Signal Optimization
Épisode 3
mercredi 15 octobre 2025 • Durée 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.
CPA often scales easier; can be more once learned. Consider to compare mixes.
2: Anthony Scarpaci: Designing Referral Programs That Actually Work (The RIGHTT Framework)
Épisode 2
jeudi 9 octobre 2025 • Durée 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.
1: Gabe Kwakyi: Creative Hits, Influencer Pipelines, and Scaling Meta
Épisode 1
mardi 7 octobre 2025 • Durée 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):
9: Frameworks for Meta's AI-driven advertising w/ Marcus Burke
Épisode 9
mercredi 28 janvier 2026 • Durée 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.
8: Shamanth Rao on Subscription Economics, Pricing, and Creative Strategy
Épisode 8
mardi 13 janvier 2026 • Durée 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.
7: Ekaterina Gamsriegler: How to engineer growth. Again and again.
Épisode 7
mercredi 17 décembre 2025 • Durée 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.
6: Lucas Moscon: Conversion Values, SKAN, Fingerprinting, MMPs, and Mobile Attribution
Épisode 6
jeudi 4 décembre 2025 • Durée 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.
10: Why the Weird Ad Wins: CEO of Ramdam on Finding UGC Champions | Xavier de Baillenx
Épisode 10
mercredi 11 février 2026 • Durée 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.
smoke tests
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/
Scale vs. profit trade-off.
tROAS
profitable
geo split tests
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/
💡 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.
Advanced App Store Optimization Handbook: https://www.asoebook.com/
Connect with Gabe on LinkedIn: https://www.linkedin.com/in/gabrielkwakyi/
• 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
• 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
• 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
• 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
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
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