Clothing

Clothing Growth Playbook (Playbook)

How operators harvest converting terms, isolate match types, and prevent self-competition in fast-changing apparel demand.

Reality note
This is a category-aligned playbook. Benchmark ranges are grounded in public metric definitions and public category aggregates. If you want to publish this as a customer case study, replace ranges with verified Amazon Ads report data (and keep date range + definitions).
Clothing cover
0.45% - 0.90%
CTR benchmark
10% - 12%
CVR benchmark
20% - 25%
ACoS benchmark

How Satlis makes this executable

SP 8-Level Campaign Structure: build a clean, layered structure and get copy-ready level lists for faster iteration.

Search Term Harvester + Negative Keyword Miner: weekly “harvest winners / negate waste” loop.

Cannibalization Check: dedup before expansion so CPC and attribution stay stable.

SP 8-Level Campaign Structure → Search Term Harvester → Negative Keywords → Cannibalization Check → Sign in to run this →

Why clothing is different

Apparel demand shifts by season, style, and color. “Winning” terms can decay quickly without weekly harvesting.

Match-type separation matters: Exact is for control, Phrase for coverage, and Broad for exploration.

What you should ship each week

A “winners” sheet: terms to promote into Exact/Phrase, plus recommended starting bids.

A “waste” sheet: terms to negate, with a reason tag (irrelevant intent, low-CVR pattern, etc.).

References (public)