Personalization tactics

Modeled on Clear30's onboarding and Alex Hormozi's value equation (raise dream outcome and perceived likelihood, cut time delay and effort). Flow: 'Let us build your program', enter name, pick goals from many options (dream outcome), how much time you waste daily (pain), which apps are most addictive (later screens show that app), how it makes you feel (show the opposite feeling), 'What have you already tried?' followed by a screen explaining why each method (e.g. screen time limiters) fails, with tappable links to cited research, positioning the app as the best way to quit. Social proof screens sit between questions, and extra trust-building before the screen time and notification permission screens where many drop. A shorter version is being tested because the paywall rate is lower than they want.

In Flo's perimenopause web onboarding funnel (users arriving from ads promising to explain their phase), added a step to determine the user's perimenopause phase. Test 1: almost 10% drop in conversion. Test 2, after design guesses: same drop. A post-mortem segmenting the audience found the questions were hard to answer and almost 75% of users got no phase, seeing an 'Oops, sorry, we were not able to pinpoint your perimenopause phase' screen. Test 3: question rewritten with the medical team, logic slightly changed, and users who still can't be classified just go to the next section with no failure message.

While analysis runs, a 'warming up' screen shows how much data is being processed. Then: minutes of stress from a recent clean day (e.g. 192 minutes), a timeline of their day with stressed/active/recovering periods and an animated zoom on a blue recovery period ('your body was doing a good job' - empowerment after the scare), then an animated 30-day calendar of built-up stress, and the share they relieved on their own (~20-30%). All numbers come from the user's real Apple Watch data.

About 90% of traffic goes to a long personalized quiz on the website. The quiz educates users on the product, overcomes objections, raises perceived value and shows how personalized the plan will be; answers are used to build the user's plan in the app. Tested traffic to the App Store vs the quiz: the quiz converted much higher. An almost identical quiz inside the iOS app converted worse than web, while the same quiz on Android converted about as well as web. They may retest App Store traffic later.

Ad story continues into the funnel: users arriving from an ad get a long question flow (work situation, tools, where they chat, pain points, goals) with answers reflected back and a summary at the end, then 'Get Sam for $1' promo week, then $10/week, straight to Stripe checkout. Answers are actually used to personalize the agent. Direct website visitors skip most questions because they weren't warmed up by an ad. Funnel length and screens are A/B tested.

Ask age in onboarding. Under 25: short flow (key points, disclaimers to accept, straight into the app). 25+: longer flow on nutrition/sleep/water that builds a personal nutrition plan (height, weight -> recommended calories/protein) to convince them tracking matters.

Three tiers (Silver, Gold, Platinum); most users pick Gold and the paywall can't explain everything, so it normally leads with Gold. A machine-learning model using ~900 data points per user (profile, behavior, previous experiment results) predicts propensity to buy Platinum (wider roadside assistance range, identity protection, more services). High-propensity users are shown Platinum instead of Gold. Part of an ML targeting/orchestration layer built in the last year that also targets promotions, feature education, push notifications and in-app messages.

After the gender question, men and women get entirely separate flows with gender-matched screens. Goal screens use images, not text ('What's your ideal body type?' with four body pictures). Later screens reuse answers: 'What feels hardest right now with toning up?' and the final chart shows their goal weight ('180 lb is just the milestone').

After iterating one or two onboarding screens at a time (small wins, hard to make big progress), they made one big jump: from ~5 to ~13 screens ending in the paywall. Added screens include login, notification permissions and mostly personalization (what the user is studying). Login was moved to the last screen, after the paywall.

Test roughly doubled onboarding length (to ~15 screens) with more personalization questions and social proof, then paywall; aim is a trial start in the first session.

Journal layer inside each workout: user logs reps and weights (distance for cardio). Next time that movement comes up, recommended weights are prefilled from the logged data. After each workout a recap shows progress since starting (e.g. '62% increase in dumbbell bench press'), PRs across movements, and journal history lives on the profile, building stored state that makes the app sticky.

Promotional pushes ('come back, you have gifts, check the people') perform far worse than real events (a like, a message). Next best: personalized content such as new people nearby matching the user's search criteria. Bombarding with generic texts loses users.