---
title: Apple Ads match types, search match and negative keywords — check your search terms
slug: apple-ads-match-types-and-search-terms
summary: Why Apple Ads broad match and search match spend on irrelevant queries (Shazam → a TV guide; "diet app" → DIY and digit apps; "fitness" → Apple Fitness+), why new keywords default to broad, the hours-long delay when pausing, and the exact-match + negative-keyword + search-terms-report routine to prevent it.
domain: mobile-apps
tags: [user-acquisition, apple-ads, keywords, match-type, search-match, negative-keywords]
related: [apple-ads-single-keyword-ad-groups, apple-ads-max-conversion-campaign, apple-ads-auction-and-bid-tactics, apple-ads-audience-targeting-pitfalls, aso-ranking-factors]
sources: [yt:EYfeV7fkCeI, yt:W6_bN3AZo_s]
updated: 2026-10-24
---

# Apple Ads match types, search match and negatives

## TL;DR
- **Broad match is "basically open"**: bidding on Shazam served a French TV-guide query, and iTunes served unrelated terms (Thomas Petit).[^1]
- **New keywords default to broad.** Forget to switch to exact and spend "can go wild really fast".[^2] Ariel Michaeli: "always, always" set keywords to **exact**.[^3]
- **Exact still leaks via close variants**: "diet app" also served "DIY app" and "digit app". Add **exact negatives**.[^4]
- **Special characters are treated as spaces**: bidding "fitness" served "Fitness+" (Apple's competing product), and negatives can't exclude it.[^5]
- **Pausing isn't instant.** Petit's broad-match mistake spent ~$700 in a few hours and ~$800 more after he paused, during the hours-long delay. Apple refused a refund.[^6]
- **Read the search terms report, always, whatever you're doing.**[^2][^7]

## Details
**Search match: off for spend, on for insight.**
- Ariel turns search match and "reach all eligible users" off. Apple's automation put the Appfigures app in front of "Hootsuite" and "social media management" searchers, and those terms were pricier because Instagram-related competition is expensive.[^8][^9]
- Petit likes search match outside pure performance. It shows how Apple's algorithm perceives your app. Years after one app moved from "English for kids" to "soft skills training", search match still associated it with language learning, which told him the metadata signal was sticky.[^10]
- After long running, most search-match volume hides in "low volume" terms (fewer than ~5–10 impressions, hidden for privacy). Petit saw only ~10% of terms named.[^11]

**Negative keywords (Ariel's routine).**[^12]
- Add negatives generously. It's easier to remove a negative later than to get money back for a useless tap (you can't).
- Use **broad** negatives for whole unrelated topics (e.g. "Instagram" covers Instagram analytics, followers…).
- Use **exact** negatives for multi-word phrases where broad exclusion might kill wanted traffic (e.g. "social media").

**Expect a Pareto.** The App Store is short-tail by nature. If 5–10 of 10,000 keywords take ~80% of spend, that's normal, "more like 98/2 than 80/20".[^13]

## Disagreements / caveats
- **Steve P. Young's Max Conversion campaign keeps search match on** for discovery ([[apple-ads-max-conversion-campaign]]). Ariel and Petit treat search match/broad as a discovery or insight tool only. Run it in a separate discovery campaign, check search terms often, and harvest winners into exact SKAG groups.
- Petit's examples are from 2023 (French storefront partly); Ariel's from Jan 2025. Apple's matching logic may have changed since.

## See also
- [[apple-ads-single-keyword-ad-groups]] — exact-match structure
- [[aso-ranking-factors]] — the metadata Apple uses to decide relevance
- [[people/thomas-petit]], [[people/ariel-michaeli]]

[^1]: yt:EYfeV7fkCeI @ 09:02 — Thomas Petit
[^2]: yt:EYfeV7fkCeI @ 09:49 — Thomas Petit
[^3]: yt:W6_bN3AZo_s @ 05:41 — Ariel Michaeli
[^4]: yt:EYfeV7fkCeI @ 11:30 — Thomas Petit
[^5]: yt:EYfeV7fkCeI @ 12:17 — Thomas Petit
[^6]: yt:EYfeV7fkCeI @ 13:03 — Thomas Petit
[^7]: yt:EYfeV7fkCeI @ 12:17 — Thomas Petit
[^8]: yt:W6_bN3AZo_s @ 04:01 — Ariel Michaeli
[^9]: yt:W6_bN3AZo_s @ 04:48 — Ariel Michaeli
[^10]: yt:EYfeV7fkCeI @ 10:38 — Thomas Petit
[^11]: yt:EYfeV7fkCeI @ 10:38 — Thomas Petit
[^12]: yt:W6_bN3AZo_s @ 07:18 — Ariel Michaeli
[^13]: yt:EYfeV7fkCeI @ 03:16 — Thomas Petit