Apple Search Ads and paid search tactics

Created an Apple Ads max-conversion campaign targeting all countries except the US, with a few top-of-funnel keywords and a $2 target cost per install. Checked impression share by country and which countries produced paying users (India: impressions, no purchases; Netherlands, Canada, UK: impressions and sales). Cross-checked with Mo's Astro-based Claude skill listing keyword volume by country: Netherlands had the same popularity (~55) as the US but difficulty ~17 vs 60-something. Then stop the worldwide campaign and create a Netherlands-only campaign with more budget.

'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.

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.

Create a Max Conversions campaign. Set the target cost per acquisition (e.g. $2) and a daily budget of 15x the target (~$30/day) instead of Apple's recommended 30x. Add an ad group with the four highest-traffic generic keywords; if there are none (e.g. dating), use the 4-5 biggest competitor brands (Tinder, Bumble, Hinge). Audience: 'Choose specific audiences' -> 'New customers' (only shows to people without the app; does not cut impressions). No age, gender or location targeting. Compared against App Masters' internal 'Basic Plus' campaign.

Step 2: launch app install campaigns first to 'feed the algorithm' and get baseline CPI and CPM for the market. Fill every asset slot: 5 headlines, 5 descriptions, up to 20 images and 20 videos; stock images and videos are fine for a proof of concept. Start at about $10-15/day and ramp up. Ads run across Google's network (Play Store, Search, YouTube, Display, Discover); Play Store ads on searches like 'calorie counter' are the top performer.

Step 4: once assets are strong, launch a tCPA (target cost per acquisition) campaign. Set the daily budget to 10x the target cost, e.g. a $15 free trial needs $150/day, because Google needs about 10 target events per day to optimize. After a month or two of purchases, you know your CAC and can start scaling.

Content that doesn't rank organically gets ads instead, so one piece of content serves both. Bids on the same high-intent commercial keywords targeted for SEO; each ad group has its own landing page tailored to the keyword and intent (e.g. an Instagram page for 'Instagram API'). Uses Google Ads automated bidding with a $120 target CPA (their average CAC threshold). Ad copy sells value, not features, e.g. 'scale 30 days of social media in 1 hour' or 'reach 100K followers fast with our API'. Continuously A/B tests headlines, descriptions and landing pages, tracked in PostHog. Playbook version: 3-5 copy variations per ad group; pause poor keywords and double down on high-ROI ones; raise budget month over month while ROAS holds; track CAC, ROAS, revenue and traffic per channel in a dashboard (Google Sheets, Data Studio).

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.

Instead of an MMP (AppsFlyer/Adjust), a self-built job (written with ChatGPT/Codex) runs daily: pulls Apple Search Ads reports via the API and RevenueCat's daily bulk export (RevenueCat uploads to an S3 bucket; one line of code in the app passes ASA attribution to RevenueCat), joins them on IDs and keyword names, and reports per keyword: impressions, average cost per tap, taps -> trials -> paid. A keyword qualifies for scaling if $1 of spend returns at least $1.20 within 3 months (3 months chosen because of mixed weekly/annual plans).

Google engineers' rule: no more than 20% change in any day to bids or budgets, up or down. Guest is more conservative: at most 20% every 2-3 days, because daily changes keep kicking the campaign back into learning mode. When she pushed beyond it, the system started spending wildly to find new inventory.