App Store optimization tactics

1) Search the US App Store (on a phone; the web doesn't show long-tail suggestions) for a topic you understand and can build for, e.g. 'calories'. 2) Note autosuggest long-tail terms; ignore new apps' names that appear there briefly. 3) Check whether other apps already own the keyword in title/subtitle, or whether it's a brand term. 4) If US popularity is low (e.g. 5) but Apple suggests it, assume it's searched elsewhere: find the top-ranking app for it and see where it gets its ratings/installs. 5) Deep research with Claude connected to Astro via MCP (his own skill): popularity and difficulty per country, related keywords, competitors per country; ASOMobile shows whether anyone bids on the keyword in Apple Search Ads. Many related keywords with demand is a good sign. Then build, with country-specific titles/subtitles mixing native and English keywords. Example: 'kanji' has popularity 7 in the US but 73 in Japan; 21 of 22 Japanese-learning keywords score better outside the US.

Title: 'Green Noise App'. Subtitle: 'Green Noise for Better Sleep', the App Store autofill suggestion for 'green noise'. Keyword field: 'green,noise,app,free'. Aimed at every autofill variant of the main term (green noise free, free green noise, green noise for better sleep). Done quickly at launch without deeper optimization.

At the start of each year, pre-map the year's product work, holidays and Apple campaigns into a tagged calendar (nomination / in-app event / push). Keep at least 1-2 in-app events live every month; they need no new content: tie them to seasons and moments (Valentine's Day 'plan some love time', Ramadan focus timer, back to school, International Women's Day because of women founders). Submit a featuring nomination every time you adopt an Apple technology (AlarmKit, HealthKit, widgets, Live Activities, Liquid Glass) and before seasonal moments, explaining why you fit. Respect long lead times: for Apple's Global Accessibility Awareness Day in May, submit mid-March to early April. Keep going even when most submissions get no reply; eventually the editorial team gets in touch and requests promotional artwork, which builds the relationship.

First goal after design was getting a two-tab, no-onboarding V1 approved by Apple so the app could be listed as a pre-order while it was still being built and marketed. Pre-orders auto-download at release and Apple emails users that the app is live. Kept the listing up ~2 weeks. Pitfall: pre-orders require a release date; he pushed it back every 2-3 days, forgot once, and the buggy build auto-released to ~1,000 users, so he had to revert to pre-order.

Skip high-popularity head terms (e.g. 'bus times') that VC-backed rivals bid on; instead bid on city-level variants ('Chicago bus times', 'New York bus times'). Drop keywords with no impressions and ones with competitive bids; keep the middle. Exact match only (broad match wasted money). 3-4 related keywords per ad group, each ad group with its own custom product page. Start at $0.50-0.60 cost per tap and raise until ~100% impression share while ads run. Took a few months to find the profitable set.

For each ASA ad group (3-4 related keywords), create a custom product page that copies the standard 10 screenshots and swaps only the first, e.g. 'Live Bus Times' becomes 'Live Chicago Bus Times'. A screenshot template system keeps this fast.

Use Switchr (small web app saved to the home screen) to change the phone's App Store region; browse the Navigation/Travel top lists for that country and note the public-transit keywords popular apps use; type a few characters into App Store search and collect autocomplete suggestions (most people tap a suggestion rather than typing the full query); check candidates in Astro; then load them into Search Ads to test.

Locales like Mexican Spanish and Vietnamese are also indexed in the US store. Keywords are not combined across locales ('bus' in one and 'times' in the other won't index as 'bus times'). When the US English title/subtitle are full, move a medium-popularity keyword from the 100-character keyword field into the title/subtitle of an unused locale (e.g. Vietnamese, where the app isn't translated) to boost its US ranking. Stop doing this once you actually target that country.

Starts from search terms users type when they have a problem. In Astro, create a temporary app, pick a random category or something he wants to build, add a seed keyword (e.g. 'tree identifier'), pull related apps and their keywords, then filter: popularity at least 20 and difficulty at most ~60-70 (e.g. 'wood identification': decent popularity, very low difficulty). Select the target keywords, then check top competitors' monthly revenue in Sensor Tower, the App Store or similar; skip the market if they make less than $100-200/month.

Pick 2-3 top competitors and study only the main feature tied to the keyword. Ask ChatGPT/Gemini (or Cursor/Claude) for a detailed implementation plan listing all screens, features and flows, with specific UI/UX constraints, to see what must be built vs copied. Drag in reusable UI (custom buttons, views, settings, onboarding, paywall); some apps reuse ~90% of code. In Figma, copy screenshot and icon templates from an old project. Have ChatGPT write the description and fill all metadata around the original target keyword. Flutter for the app, Fastlane to ship.

Copied from Cal AI's onboarding: a social-proof slide ('designed for people like you', five stars, member count, real reviews) that triggers the native rating prompt before users see the reviews. Placed after 5 minutes of reflective questions and free feedback, so users are happy when asked.

Immediately shows the native review dialog ('Do you like HabitKit? Please review it on the App Store') right after the user checks off their first habit, their first success moment.