Yev Marusenko - How I Grow Viral Apps to 1M+ Downloads (just copy me)

Map every step from ad/organic content through install, app use, onboarding and paywall; find the step with the largest drop-off (Sizzle had one over 50%). Every team member attacks that step from their own expertise (onboarding, paywall, product). Yev says these fixes can land within a week, unlike quarter-long incremental feature optimization that gives ~10% improvements.

Make many organic creator videos (multiple creators, multiple hook styles), then put the platform minimum behind each one rather than judging by organic views, since good videos sometimes flop on timing or the algorithm. TikTok: ~20 creatives at the $20 minimum each, can switch off after ~$5; Meta: 30-40 creatives at ~$1 each since it is easier to duplicate. Judge within hours on top-of-funnel clicks only: most get 0-2 clicks, one gets 5-20. Keep spending on that one to see whether it was a fluke, then track it down to purchases and make variations (change the first or last second). Mid-virality videos (top 25%) often convert better than the most viral, clickbaity ones. For a $5K starting budget he'd spread it over as many hypotheses as possible, aiming for 100 variations, and compress '500 videos' of learning into a week. Test the same creative across Google, YouTube, Meta and TikTok; one platform can give 2x results.

Test several creator-sourcing routes in parallel: brand-creator marketplaces (JoinBrands, SideShift, Whop) plus Sizzle's own Discord, alongside in-house designers and contractors. Three goals: (1) raw creative that can be amplified with paid ads, (2) posting volume to trigger the TikTok algorithm, (3) creators posting in their own style on their own accounts, where existing followings and formats (e.g. covering the face and looking shocked) reach the algorithm differently.

Structure of the winning study-app TikTok: (1) hook with no talking, just a shocked facial expression, copied from headlines/hooks working on other viral pages; (2) middle shows a product demo that calls out the target audience so viewers are relevant buyers; (3) a conclusion that differs from the rest of the video but keeps continuity, a pattern interrupt so people stay to the end. No call to action or heavy app naming: the paid ad adds the link, and comments ('What is the app?', 'this is like Quizlet') do the explaining. The goofy ending was the creator's own idea and beat his other endings.

Take a winning (top-10%) creative and split paid traffic by goal. Most goes to 'crazy testing': send tens of thousands of installs in a few days into 3- and 4-way experiments on the paywall and other in-app steps, accepting up to ~$200 cost per purchase (vs $50-$100 profitability targets) as long as it isn't $1,000-$2,000. ~20% of traffic is held to profitability targets using churn (3-7 months) and LTV ($30-$70+ per purchase) by country and device. Keep the two data sets separate so a $300 CPA on test traffic doesn't lead to turning everything off. Also tried new audiences such as teachers and tutors.

Once a video wins, go 'deeper' before 'wider': keep creative and targeting constant and test one factor at a time, e.g. 10 headlines, cutting losers and rechecking weekly that the pattern holds. Then split by country and device (iOS converts much better) rather than student/interest targeting, since the algorithm finds the audience; TikTok works better for US targeting, Meta for global. Each country has its own LTV/MRR goals and may or may not get a free trial. Vary the intro or outro of the same creative to avoid fatigue, ending up with hundreds of ads per ad group. New audiences with different value propositions count as horizontal scaling. He moves through each layer within a week.

Compare in-app analytics and own database with platform-reported numbers. Run lift studies: switch ads off completely to see how much sales fall, then back on. Compare 1-day vs 7-day windows (at least 10% of purchases arrive later), SKAN vs platform-reported (another 10-20%), and App Store Connect analytics by source (another 10-20% spillover from people who saw an ad then searched the store). Use the gap to justify scaling ads that look marginal in-platform.

Set up ads-manager columns in funnel order: install, registration, engagement events, then each paywall step, plus custom metrics expressing step-to-step conversion as percentages so ads and ad sets can be compared directly. When scanning ~20 ads for outliers, look for higher step conversion and lower drop-off, not just cost per purchase. Same view by in-app journey: users who start with a particular product convert ~10x better than those who roam the app.

Run big variants first, then micro-variants within the winner, on a weekly cycle: three registration pages (cleaner, warmer, more social proof) and paywall variants (free vs paid plan comparison, more vs less detail). Track purchases and engagement together, since Sizzle wants students to actually learn and will pick a hybrid that doesn't cut engagement. Track assisted conversions per screen (screens that don't convert themselves but raise later purchases) as a tiebreaker when purchases are neutral.

Over 10 faceless channels on YouTube Shorts/TikTok in easily automated or AI-made niches: fake text messages, SpongeBob and 'president head' comedy, oddly satisfying clips a few seconds long, crushing and breaking things. Everything rides on the first split second of attention and micro-storytelling.

Take formats that go viral in unrelated niches (like his faceless YouTube channels) and apply them to the learning niche. Next strategy he is trying.

Sizzle's founder, a former Facebook VP of AI, announced the launch; connections and PR brought the first users rather than a from-scratch growth hack.