Attribution and signal tactics

The app mapped its trial start to Meta's Purchase event and had CAC near LTV. Optimizing for Purchase pits a $10-15 trial against e-commerce advertisers willing to pay ~$200 per purchase, so you get outbid for high-intent users. Fix: create a duplicate trial-start event in the MMP firing at the same place, map it to a trial (or subscribe) event, let it build 1-2 weeks of history in Events Manager, then split-test Purchase vs the new event. Keep the event funnel logical and minimal; don't send every event you can. Only do this if the current setup isn't working.

Every video ends with 'Comment "app" and I'll send you the link.' A Manychat automation replies to each 'app' comment with a DM ('Hey there, please click the button below to receive the link') containing a direct store link. A one-word comment is easy, and a wall of 'app' comments makes others join in. The main point is engagement: the comment flood signals Instagram to push the video; the direct link also removes the search step.

Comparing Israel (viral, #1) with other markets showed one key difference: network density. They tracked the percentage of new users who got 5 friends on day 1 (~60% in Israel vs ~20% elsewhere), which went with D90 retention of 25-30% vs about half that. The conclusion was to raise density 'whatever it takes' through growth features where users invite friends by using the product, not content marketing (which brings random people without friends). They shipped the common invite and sharing mechanics plus unusual ones (Group Picture, one-minute roll call). Content still seeds the first users, but product-led invites scale it.

Compute RPM (revenue per 1,000 views) from past videos: ~$3-4 for Locked (~10% download-to-sub, $30/yr or $7/wk). Offer CPM deals of $1-2 per 1,000 views, always with a payout cap (e.g. $500) set upfront, so paid CPM stays under RPM.

Optimize for purchases, not installs (costs more but learns more). Attribution via Meta SDK + Aggregated Event Measurement; avoid SKAN; no ATT prompt needed. Targeting is driven by creative, so iterate creatives (AI-generated allowed); ~$500/day to test creatives before scaling. Cross-check against App Store Connect's Meta-sourced numbers (AEM off by ~15-20%).

5 phones with 3-5 TikTok accounts each (15-20 accounts). A shared spreadsheet lists every account, which phone it is on, and its status (warm-up phase 1 or 2, paused, healthy, unhealthy, stopped, blocked). It also records its channels (TikTok only, TikTok + Lemon8, TikTok + Instagram) and its format (reactions, slideshows, other). The team updates views and likes for every video every day and leaves notes on what worked, to decide what to replicate that day.

Before deciding to launch ads in 2016, quantified complaints and negative reviews about ads per million DAU and compared them to complaints about other topics (e.g. a missing language course), alongside neutral retention in A/B tests.

Break down paywall conversion by onboarding answers (age, dream outcome, why quitting, triggers). Found users seeking mental clarity, feeling out of control, or quitting for motivation convert higher; 26-30 year olds convert almost double 18-20 year olds; 'saving money' converts at 23% vs 29% for legal obligations. Used this to aim ads at older audiences and to plan onboarding copy stressing money saved vs the $30/year price.

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.

Content-first: most long-form YouTube videos include a smooth, soft transition to the app in the middle or near the end rather than a hard pitch (hard pitches only on Instagram shorts). Every click from the video and description link is tracked per video, which showed that stories about a user who transformed their body on the app drive more trial starts than broad educational videos (a diet or a workout program) that reach more people. New videos are now planned around stories that tie into the product pitch.

Signal engineering: create a light hurdle for users to clear (here a CAPTCHA added only to test intent) that is a proxy for LTV; users who clear it are treated as high-value, and that event is sent back to the ad platform so it optimizes toward them in real time.

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.