- PricePowerPodcast.com
- AI Pricing for your app: Botsi.com

Ekaterina Gamsriegler (ex-Mimo, Amplitude Product50’s Top Growth Product Leader) breaks down why most growth teams struggle not because of a lack of ideas — but because they optimize the wrong things, in the wrong order.

Ekaterina walks through real-world examples across onboarding, paywalls, trials, activation, and pricing — showing how user psychology, perceived value, and expectation-setting matter more than dashboards alone. 

📖 Episode Chapters:

00:00 Growth Does Not Start with an MMP
01:40 Breaking KPIs into Controllable Inputs
03:56 Why “Breaking Things Down” Gets You 80% There
06:30 Product Analytics vs Attribution
12:00 Onboarding Length vs Paywall Exposure
16:00 Why Averages Are Always Wrong
18:10 The Truth About Personalization
23:30 Why Users Don’t Start Trials
28:30 Understanding Early Trial Cancellations
34:45 Why Longer Sessions Can Be a Bad Sign
38:00 Pricing as a Growth Lever
42:00 Fix the Story Before the Price
44:00 Closing Thoughts

💡 Key Takeaways: 

• Growth is a sequencing problem. Teams fail when they jump straight to solutions instead of first building a usable map of user behavior and breaking metrics into their underlying drivers.

• Product analytics beats attribution early. You don’t need a perfect funnel — you need a reliable picture of what users actually do after install. MMPs come later.

• Averages hide the truth. Looking at overall conversion rates masks real issues that only appear when you segment by device, channel, geo, or user intent.

• More exposure ≠ more revenue. Increasing paywall impressions by removing onboarding screens often lowers trial conversion if user intent isn’t built first.

• Personalization rarely delivers big wins. Most onboarding and paywall personalization produces single-digit uplifts while adding major complexity and risk.

• Most early churn is voluntary. Users cancel trials early because they want control, not because they hate the product.

• Time-to-value matters more than time-in-app. Longer sessions often mean confusion, not engagement.

• Lowering prices can work — in specific cases. Misaligned mental price categories, lack of localization, missing feature parity, or mission-driven goals can justify it.

• Pricing issues are often narrative issues. Before changing the price, fix how value is communicated and perceived.

• Sustainable growth comes from focus. The best teams work on 2–3 high-confidence problems at a time — and say no to everything else.

Links & Resources Mentioned:

• Ekaterina on LinkedIn: https://www.linkedin.com/in/ekaterina-shpadareva-gamsriegler/
• Maven course: https://maven.com/mathemarketing/growing-mobile-subscription-apps
• Full presentation from Growth Phestival Conference: https://www.canva.com/design/DAGw09v8yIo/lfVoi-Xf4QRm6-ddmtro1A/view
• Jacob's Retention.Blog

Lucas Moscon, one of the most technically knowledgeable people in mobile attribution, breaks down how post-ATT measurement really works, why most marketers are using outdated mental models, and how to build a modern, resilient measurement stack. Lucas clarifies what’s deterministic vs probabilistic today, exposes where MMPs still add value (and where they absolutely don’t), and explains why IP-based fingerprinting quietly powers 90%+ of attribution today. He also walks through SKAN in plain English, conversion-value strategy, web-to-app pipelines, and why looking at blended ROI beats chasing ROAS illusions on iOS.

If you want to understand the actual mechanics behind click → install → revenue pipelines — and why Apple’s privacy tech is failing in practice — this episode is for you.

What you’ll learn:

• Why ATT didn’t “kill” attribution — it forced marketers to juggle deterministic, probabilistic, and blended layers
• How Meta/Google matching actually works (spoiler: 90%+ relies on IP, not magic AI)
• Why SKAN isn’t enough — and why relying on ROAS on iOS is the least trustworthy metric
• How to measure effectively without over-reacting to noisy campaign-level data
• When you truly need an MMP today — and why most apps don’t
• How to correctly design conversion values for SKAN without over-engineering
• Why retention determines how many conversion values you even receive
• How to triangulate data across store consoles, subscription platforms, MMPs, and ad networks
• Why focusing on payback windows (D60–D180) outperforms optimizing for short-term ROAS
• Why probabilistic fingerprinting is still powering the ad ecosystem — and why Apple hasn’t stopped it

Key Takeaways:

• iOS ROAS is the noisiest metric you can use. Without IDFA, everything is extrapolated. High-confidence decision-making must use blended revenue and cohort ROI, not ad-platform ROAS.

• Modern attribution = multiple layers. Post-ATT, performance requires triangulating data from SKAN, ad networks, subscription platforms, and product analytics — not trusting a single source of truth.

• Fingerprinting ≠ complex algorithms — it’s mostly IP. Internal tests showed that greater than 90% of probabilistic matches come from IP alone. All the “advanced modeling” narratives are overstated. 

• Most apps don’t need an MMP anymore. Exceptions: running AppLovin/Unity DSPs, React Native/Flutter SDK support gaps, or complex Web-to-App setups where Google requires certified links. Otherwise, MMPs mostly add cost, not clarity.

• Retention determines SKAN visibility. If users don’t reopen the app, conversion values won’t update — meaning SKAN under-reports trials/purchases unless retention is strong.

• Blend deterministic + probabilistic + aggregated signals. The goal isn’t precision — it’s directionally confident decisions across imperfect data. Marketers should work in ranges, not absolutes.

• Longer payback windows unlock scale. Teams willing to accept D60–D180 payback dramatically out-spend competitors optimizing for D7 ROAS — assuming they have strong early-day proxies to detect failing cohorts.

• MMPs don’t magically fix discrepancies. Even with one SDK, marketers still see mismatches across networks, stores, and internal analytics. The “one SDK solves it” narrative is outdated.

Links & Resources

• Appstack: https://www.appstack.tech/
• Appstack library of resources: https://appstack-library.notion.site/
• Lucas Moscon LinkedIn: https://www.linkedin.com/in/lucas-moscon/

00:00 Opening Hot Take: “Are You Really Saturating Meta?”
05:00 Early Indicators & Proxy Metrics (D3–D10)
09:00 Predicting Cohort Success from Day 3–10
11:00 How Click → Install Attribution Actually Works
14:00 Web-to-App Infrastructure (Fingerprinting + SDK Flow)
18:00 Meta/Google Matching: IDFA, AEM, SKAN
24:30 Fingerprinting Reality: Why IP = 90% of Matches
27:00 Apple’s Privacy Messaging vs Actual Enforcement
30:30 How Apple Ads Uses (or Ignores) SKAN
35:00 Should You Use an MMP in 2025?
46:00 SKAN Conversion Value Mapping: The 63/62 Strategy
49:00 Why Retention Determines SKAN Postbacks
54:00 App Stack Overview + Closing Thoughts

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:
  1. a top-funnel/engagement event (e.g. survey completion),
  2. signup/registration (first PII),
  3. 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

Jakub (HER, Mapy) shares how he rebuilt a subscription app’s MarTech stack from near-zero after joining MAPY (hiking & biking maps): picking an MMP, adding revenue infra, standing up in-app messaging/“HTML onboarding,” and using surveys + activation signals to decide what to monetize. We also cover build vs. buy, cutting tool noise, deep links, web vs. mobile behavior, and clever Figma automation for instant multi-language screenshots.

What you’ll learn

• The essential MarTech stack for a subscription app (MMP, revenue infra, analytics/BI, lifecycle—in-app first)
• How to choose an MMP (AppsFlyer vs. Branch) and why deep links usually live there
• Why in-app messaging (HTML modals) can stand in for onboarding, surveys, and roadmap validation
• Methods to discover what users will pay for (surveys, activation metrics, contextual upsells)
• When to buy vs. build (and how investor expectations affect that choice)
• Managing tool costs in freemium: country-scoped SDKs, MAU-based pricing tradeoffs
• Web vs. mobile behavior differences and how that shapes monetization & UX
• How to filter vendor hype: pricing page tells, documentation over demos, avoid vague “AI” pitches
• A fast path to localized store creatives with Figma + CopyDoc

Key Takeaways

• Start with measurement. Without an MMP and clean revenue signals you can’t scale UA or judge payback—set those up first.
• In-app > email early. For new/lean teams, prioritize in-app messaging and “HTML onboarding” to collect motivations, segment users (hiker/biker/driver/general), and guide activation.
• Show the paywall. Track launch→paywall impression; aim for ~90%+ so you’re reliably creating purchase opportunities, then layer contextual upsells (Strava-style).
• Monetize what matters. Use quick surveys + early actions to identify features people value; validate with smoke tests (CTA → deep link) before committing roadmap.
• Buy the boring stuff. For attribution, lifecycle, and payments, buy (standards, support, investor-friendly metrics). Build only where you truly differentiate.
• Control analytics cost. Scope product analytics SDKs to priority countries (or sample) to align MAU-priced tools with freemium economics.
• Deep links live with your MMP. Standalone options are thin, Google Dynamic Links is sunset—lean on AppsFlyer/Branch for reliability.
• iOS privacy changed the game. Deferred deep linking and deterministic tracking are less reliable; plan for modeling and guardrails.
• Cut through tool noise. If a vendor hides pricing or leads with vague “AI,” proceed with caution; read docs & pricing matrices, not just landing pages.
• Automate localization. Use Figma + CopyDoc to export/import copy and auto-generate hundreds of localized screenshots in minutes.

Links & Resources

• MAPY (hiking & biking maps): search “MAPY hiking app” in your store
• CopyDoc for Figma (bulk copy import/export): https://www.figma.com/community/plugin/900893606648879767/copydoc-text-kit
• Connect with Jakub on LinkedIn: https://www.linkedin.com/in/jakubchour/

Ashley Black, founder of Candid Consulting and former longtime Googler, breaks down how (and when) subscription apps should switch Google App Campaigns from CPA to tROAS, the pitfalls that stall performance, and how to feed better signals (activation/retention events) for durable scale. We also dig into iOS vs. Android realities, exclusions that actually matter, and why “automated” ≠ “set-and-forget.”

What you’ll learn

• The most common mistakes when moving from CPI/CPA to tROAS (targets too high, windows too long)
• How to set a realistic ROAS target (start ~20% below goal) and ramp it without killing volume
• Volume prerequisites for value bidding (why you need revenue events, not just trials)
• When tROAS fits (risk tolerance, trial length, budget) and when to stay with CPA
• Android vs. iOS with Google: inventory, tracking constraints, and creative needs (YouTube/Shorts)
• The right exclusions to apply (existing users, brand, re-installs) and why CPM rising can be good
• Using early activation/retention events to improve optimization when trial-start isn’t predictive

Key Takeaways

• Don’t over-ask early. Setting day-7 ROAS targets too high and using 30–90 day windows starves delivery. Start with a short window (≈7 days) and a lower target, then stair-step up.
• You need real revenue signals. For tROAS to learn, pass purchase/subscription events—trial-start alone won’t cut it. Rule of thumb: aim for ≥10 post-install revenue events/day (often more).
• Trial length matters. 30-day trials delay signals; tROAS may burn spend blind. Shorter trials or earlier monetization events make tROAS viable.
• Expect a ramp-up. Some accounts stabilize in days; aggressive targets can take weeks to unlock. Be patient and ready to lower targets to gain learning volume.
• Scale vs. profit trade-off. CPA often scales easier; tROAS can be more profitable once learned. Consider geo split tests to compare mixes.
• Inventory shifts under tROAS. Eligible placements are the same, but you may see more search/Play and higher CPMs—often a sign of higher-quality traffic, not waste.
• Exclude smartly. Add exclusions for current users, brand queries, and (optionally) re-installs to protect incrementality.
• iOS = different game. Google’s iOS performance lags Android; expect more YouTube/Shorts traffic and lean on strong UGC-style video. Treat iOS Google as a later-stage test.
• Optimize for activation. If trial-start users don’t retain, bid to an early in-app action (e.g., completed tutorial, first message) that correlates with D1/D7 retention and occurs fast enough for learning.
• Automation needs adults in the room. UAC/PMAX aren’t fire-and-forget—active tuning (targets, assets, exclusions) still moves the needle.

Links & Resources

• Ashley Black — Candid Consulting: https://www.candidconsultinggroup.com/
• Ashley’s guide to tROAS for subscription apps: https://www.botsi.com/blog-posts/value-based-bidding
• Connect with Ashley on LinkedIn: https://www.linkedin.com/in/ashleym-black/

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.

• Map every touchpoint (ads → onboarding → referral share → reward delivery).
• Reinforce trust (“Your friend invited you”) and celebrate wins (“You earned $10—share again”).

T = Timing & Tracking –

• Launch after product-market fit and a healthy customer base.
• Introduce referral prompts at the right emotional moment: trial start or delight milestone.
• Maintain urgency windows for bursts of activity.
• Track cohorts, incremental lift, and blended CAC pre- / post-launch.

💡 Key Insights & Takeaways

• Referrals ≠ free users. Model unit economics and compare to your next-best acquisition channel (Meta, Google etc.).
• Halo & Cannibalization. Account for organic word-of-mouth you’d get anyway and the extra reach you gain when offers go viral.
• Accept some fraud. Zero-fraud programs over-optimize and add friction; “tolerable inefficiency” is a healthy cost of growth.
• Design for compounding. Great referrals create chains (friend → friend → friend), not single invites.
• Avoid conditioning. Don’t train users to expect giant promos forever—treat large bonuses as events, not defaults.
• Influencers as fuel. One creator’s post can 10× signups—plan for the viral halo but don’t depend on it.
• Higher-quality leads. Referred users retain better and cost less long-term—social proof raises both acquisition and retention.

🧠 AI Toolbox Anthony Uses

•  Lovable / v0.dev / Replit V0 → No-code prototyping & mockups.
•  Gemini transcription + Claude / ChatGPT → Strategy alignment & theme extraction from founder calls.
•  OpusClip → Video editing & social creative velocity.
•  Perplexity → Everyday research & voice-based learning.

🔗 Links & Resources

Anthony Scarpaci → https://www.linkedin.com/in/anthonyscarpaci/
Tunomatic → https://www.tunomatic.com/
Growth Notes Newsletter → https://tunomatic.substack.com/

Gabe Kwakyi, CEO of Lingvano and mobile growth leader, shares how creative hits powered Lingvano's paid acquisition, how he became CEO, and his testing → scaling → core framework on Meta. We also dig into onboarding/monetization experiments, live-learning bets, community building, and Gabe’s “AI Stack for Startups.”

What you’ll learn

• Why a tiny % of creatives drive the majority of paid social results—and how to reliably find them
• The playbook to mine influencer content and graduate winners from testing → scaling → core
• Budgeting and campaign structure tactics to let new winners break through incumbent hits
• When (and for whom) app→web payment flows actually make sense
• Parallel growth lanes beyond UA: onboarding, monetization, live sessions, and community
• Gabe’s “AI Stack” to go from beginner to intermediate with LLMs

Key Takeaways

• Creative hits rule paid social. Treat influencers as your “hit makers”; port high-engagement organic posts into ads and look for fast spend/scale with strong unit economics.
• Judge by scale, not vanity. If Meta won’t spend on it, it’s not a hit—pause losers quickly.
• Structure matters. Keep an always-on testing campaign; promote winners to a scaling lane (separate ad sets to force initial spend), then into your core.
• Expect droughts. Old hits can keep outperforming new tests—reactivate past winners and extend via hook swaps, but keep sourcing creators.
• Web payments ≠ free margin. Friction can erase take-rate gains; look for segment fit (e.g., older audiences) and promo-led moments to overcome drop-off. Test before scaling.
• Don’t single-thread growth. Run ongoing onboarding/monetization experiments and build community to diversify beyond UA.

Links & Resources

• Lingvano (learn ASL, BSL, and more): www.lingvano.com
• Gabe’s AI Stack for Startups (go to first featured posts): https://www.linkedin.com/in/gabrielkwakyi/
• Advanced App Store Optimization Handbook: https://www.asoebook.com/
• Connect with Gabe on LinkedIn: https://www.linkedin.com/in/gabrielkwakyi/