ASO-First Vibe Coding - Stop Building Apps Nobody Wants

1) Pull a competitor's indexed keywords in ASOMobile (e.g. enter Endel; sort by search ads popularity). 2) Paste them into App Radar for difficulty (its difficulty seemed most accurate; check traffic against Apple Ads if unsure). 3) Target keywords with popularity ~30+ and difficulty under ~40. 4) Check the real App Store results: if none of the top apps have the keyword in their title and rating counts are beatable, build; if giants with hundreds of thousands of ratings hold it (e.g. 'calorie tracker': Cal AI 330K, MyFitnessPal 2.3M), skip. 5) Pick 2-6 (up to 10) keywords as pairs, work on 2-3 at a time and move to the next pair whether they rank or not. 6) Build with simple features, add a good-enough paywall and onboarding once downloads come, and only build new features after ~5K. JPEG to PDF Converter: #1 for 'JPEG to PDF Converter' and '... Free' (difficulty 46/49; traffic 48 in MobileAction vs 10 in App Radar), #2 for 'JPEG to PDF'.

Chose the app's distribution (ASO) before building. Launched in March 2023 targeting keywords found in advance; two of them worked well early and drove the first revenue, after which the app began ranking for more generic, higher-traffic terms. Paid ads were added later.

The sellers followed Steve's YouTube keyword-first model: picked keywords Steve knew existed in a hard niche, built the app, and reached #1 for a small number of keywords with traffic. Hard paywall. Steve advises targeting keywords around 30 popularity and up.

After acquiring an app that ranks well organically, scaled its Apple Ads spend way down and watched sales and downloads.

Exact-match campaign where every ad group holds a single keyword (SKAG). Originally done because RevenueCat and MMPs only reported at ad-group level, so one keyword per ad group gave keyword-level revenue data. Kept the setup the seller had built when the app was bought. Spend ~$6-7K last month.

Create a campaign with bidding set to Max Conversions (not Manage Bids), a daily budget and a target CPI ($1-2; Apple suggests ~$30/day per $1 of target). Keep Apple's automatic ad group with Search Match on. Add a second ad group with 4-6 relevant keywords that have traffic, on broad match with Search Match on (Steve's feeling: broad keeps Search Match close to those terms). Now also testing a third ad group with the same keywords on exact match, letting the algorithm choose. Target new users only. For apps making ~$1 per install, this is the paid strategy to try.

Client was profitable on a Basic+ campaign (~$24K/month) plus max conversion. Spend was scaled up, then the team searched for the level ($10K? $15K?) that still gives positive ROAS.

App launched in 2025 relying mainly on Meta (Facebook/Instagram) as its acquisition channel. No setup details given.

Older app whose downloads and sales come almost entirely from organic App Store traffic. No details given.

Developer builds apps around what people already search for, ranks well for those terms, and keeps launching. Apps launched as recently as September and December 2025 reached ~$100K and ~$90K a month (Sensor Tower estimates).

Onboarding asked: do you want to sleep better, feel better, feel calm, stress less, or get anxiety relief? Conversions were compared by answer.

Founder Anya targeted 'panic attack', a lower-volume but lower-difficulty keyword than 'anxiety relief', to rank first.

The Spanish (Mexico) localization is indexed by the US App Store. Put the most valuable keyword in the US title, related keywords in the US subtitle, and more English keywords in the Spanish (Mexico) title and subtitle, even if the app isn't available in Mexico. This fits ~2-10 target keywords across the two title/subtitle pairs.

Use people (a smiling person or couple) in screenshots; Steve does this on his own apps.