How Apple Ads auctions actually work: relevance gates, then bids reshuffle (ConsultMyApp)

ConsultMyApp published a genuinely useful piece that tries to answer the question Apple never spells out: once an app is in the Apple Ads auction, how much does relevance really matter vs bidding and predicted performance?

• Source: ConsultMyApp, “How Does the Apple Ads Auction Actually Work?”
• Read: https://www.consultmyapp.com/blog/how-do-apple-ads-work
• Method (per the article): APPlyzer Apple Search Ads auction stacks (#1 to #5), then a semantic relevance rescoring pass

The one-line lesson

Treat relevance as the price of admission (eligibility), then assume the ordering is mostly commercial. If your “most relevant” app is sitting in slot #4 or #5, it might not be metadata, it might be bid strategy, match type, or expected performance.

What stood out

• Auction order rarely matches “semantic relevance order”. Their analysis suggests relevance matters, but it does not cleanly explain position #1.
• Match type is the hidden lever behind the weird stacks. The piece frames a practical model:
  • Exact match: strong relevance gate
  • Broad match: relaxed gate
  • Search Match: very permissive gate, plus behavioral signals
• The scary failure mode is Search Match leakage. That is where you get the “how is this even here?” auctions that waste spend and confuse competitive analysis.
• There are two useful clusters to look for:
  • ads that appear high despite weak fit (likely aggressive discovery or conquest tactics)
  • ads that are highly relevant but appear low (likely underbid or underperforming)

Why this matters (practically)

If you run Apple Ads like a single blended discovery soup, you can end up:

• paying to show on searches you do not really serve
• misreading performance dips as “creative fatigue”
• missing easy wins where you are relevant but under-positioned

Tiny win (30 minutes)

Pick 10 high spend queries from Search Terms, then:

1. label each as Exact, Broad, or Search Match driven
2. add negatives to shut off the obvious “wrong job” searches
3. isolate 2 to 3 high-fit terms into Exact with their own budget cap

You will usually find at least one term where the fix is not “new screenshots”, it is “stop paying for the wrong intent.”