Amazon PPC · Case study
Managing $1.3M in Amazon Ad Spend Into $9M+ Attributed Revenue
$1.3M+ in Amazon ad spend managed into $9M+ attributed revenue at 6.95x ROAS through margin-led campaign segmentation and search-term discipline.
Marketplace, $1.3M+ managed · run personally by Hasnain Jameel
The situation
What was actually going wrong
The account was running significant spend across a broad catalogue with campaigns organised by convenience rather than by margin or intent. Profitable products subsidised unprofitable ones invisibly, and search term reports had not been acted on in months.
The work
What I changed
Catalogue segmented by margin and lifecycle
Products were grouped by contribution rather than by category, so launch products, profit drivers and clearance lines could each have their own targets.
Campaign architecture rebuilt around match types
Exact, phrase and broad were separated deliberately, with harvesting flows moving proven terms into exact-match campaigns and negatives applied upstream.
Search terms worked weekly
Wasted spend was removed continuously rather than reviewed occasionally, which in a catalogue of this size was a substantial recurring saving.
Bids tied to real profitability
Targets were set from actual contribution margin per product rather than a single account-wide ACoS goal, which is how most large accounts quietly lose money on their best sellers.
The outcome
What it produced
At scale, Amazon PPC is an accounting exercise as much as a marketing one. The account that knows its per-product margin wins.
Straight answers
Questions about this work
Only for a very narrow catalogue. Across mixed margins, one target guarantees overspending on thin products and underspending on profitable ones.
They are the core maintenance task. Unworked search terms are the largest source of waste in almost every large Amazon account.
The principles do: margin-led targets, deliberate match type architecture and continuous term management apply anywhere with an auction and a catalogue.
Measurement & limitations
Results are as reported by the ad platforms, analytics or the client at the time and have not been independently audited. They are attributed results, not controlled incrementality tests, unless stated otherwise. Outcomes depend on the business, market and budget, and past results do not guarantee future performance.
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