How He Built an $800K ARR App Using Simple Strategies

Fitbit sync app was a $5.99 paid download; in December 2021 converted it to a free download with subscription, then raised the price with a lifetime option.

Most Bickster apps sell lifetime access. Use lifetime unless the app has real recurring costs (e.g. heavy AI API usage); low-AI-usage apps like the Bible app can still do lifetime. On the Bible app, subscriptions generated support complaints.

After many A/B tests on price and free trials, landed on a simple soft paywall with minimal onboarding (utility app, no emotional build-up): $59.99 lifetime and $39.99/year with a 3-day free trial.

Maintain ~50% organic / 50% paid installs; paid spend (ASA as the main channel, plus Google search and Bing) keeps organic rankings going; only fund a channel at ~2-3x ROAS; don't go too far on paid.

ASA is the Fitbit app's best-performing paid channel; many of Bickster's other apps don't work on ASA because CPI/CPA is too high for their economics.

Use Google Search campaigns (not Google App Install campaigns, which attributed badly post-ATT) plus Bing search. An in-house attribution engine imports conversions into the networks nightly. Bing: low CPCs, rising volume, small but profitable.

Per app, test search-ad traffic to a landing page vs directly to the App Store. Landing page: fits one phone screen, main promise up top, install button right there, no other links.

'Proof of pay': ship a real app with a simple onboarding and a hard paywall showing one offer, submit every price you want to test up front, and run Apple Search Ads at the same daily spend (~$1K/day to learn fast; far less works over a longer period). Change only the lifetime price, walking it up: $4.99, $9.99, $29.99, $49.99. Read results directionally (day-one revenue per user), not for statistical significance. If you can't get past ~$4.99, drop or change the concept.

Treat app concepts like UA creatives: use AI code generation to build a 'minimal believable product' (a bit more than an MVP so users believe it) for several angles (e.g. running app: weight loss / beginners / 5K), sell each angle in UA, and invest in whichever makes the most money. Flow: pick an angle -> build -> ASO -> UA -> paywall -> decision.

Analyzed Chatster (general AI chat) data, found users asking religious queries retained best, and built a dedicated AI Bible app for that segment.

For initial tests: hard paywall (force a decision; soft paywall can come later), no free trial, a single offer. Paywall elements: promise, price, anchor, proof/social proof, one offer. Keep onboarding minimal so onboarding drop-off doesn't contaminate the price test.

Phase 1: ASA exact-match high-intent keywords plus some broad-match discovery for cleaner attribution and less creative noise; phase 2: move to Meta for scale (ASA volume is limited by search). ASA has since been stopped for the Bible app.

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%).

Title with literal, popular high-intent terms; brand name last (if at all); subtitle written to convert, not keyword-stuffed; ignore search volume at launch; simple icon from competitive analysis (test only to avoid a bad one); screenshots 'screenshot, message' with the hero showing what users want, updated with messaging learned from ads; extra custom product pages. Example: 'Bible Companion Chat', 'read scriptures, understand it'.

Icon moves conversion more than screenshots or titles in their experience. For Privacy AI: icon signalling security plus the standard AI sparkle, run ~6 icon tests in 6 weeks. For the book-summary app: test an extreme alternative (cartoon/caricature generated via ChatGPT).

Ran tests with no onboarding at all vs a short one; even a single 'welcome, continue' screen caused big drop-off. Keep onboarding to at most ~2 screens early on; add a richer onboarding only after the concept sells to the paywall.

Use Adapty for paywall A/B tests, analytics and remote onboarding flows so prices, copy and plans change on the fly without new builds or App Review.

Chatster paywall shows one main offer plus a 'view all plans' link for users who want alternatives; which to use depends on stage.

A/B tested vertical vs horizontal plan layout; horizontal also leaves room for small value nuggets like 'cheaper than coffee'. App not specified.

No MMP: use Meta's self-reported attribution (SDK/AEM), in-house attribution for Google and Bing, and Mixpanel with their own dashboards instead of paid analytics tiers.

Host plans to apply the guest's playbook to one of his apps doing okay on ASO: hard paywall, one-time lifetime offer, then test several lifetime price points.

Host's fitness app for 65+ (~$40/year): ran Meta at ~$150/day, $3-5K total, with good hook rates and CTR. Guest's diagnosis: verify attribution, campaign structure, optimize for purchases, iterate creative.