- 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