A worked Amazon PPC audit example

Fictional teaching example. These are invented US-dollar figures, not a customer account, an engine-run receipt or measured AutoPPC results. The purpose is to show what a useful audit needs to explain before anyone changes an ad.

Assume one US Sponsored Products account, a settled 30-day reporting window and a seller-selected target ACOS of 25%. No recent bid changes occurred in the first example. These assumptions matter: different history, stock or settings can change the correct decision.

Decision 1: a converting keyword above target

The keyword has 100 clicks, $120 spend and $300 in ad-attributed sales. Its current base bid is $1.50. The seller has chosen a maximum single-review bid reduction of 20% for this illustration.

Observed CPC = $120 / 100 = $1.20
Observed ACOS = $120 / $300 = 40%
Revenue per click = $300 / 100 = $3.00
Reference target CPC = $3.00 × 25% = $0.75
20% reduction boundary from current bid = $1.50 × 80% = $1.20

The performance reference is $0.75 per click, but the current bid and the allowed step are separate facts. Applying the illustrative reduction limit gives a candidate base bid of $1.20, subject to the rest of the account's checks. It does not guarantee a $0.75 CPC or immediate 25% ACOS.

The review should show all five numbers and the source window. It should also check whether a placement change, stock issue or newer account action makes this candidate inappropriate. A sentence saying only “lower the bid because ACOS is high” omits the most useful evidence.

Decision 2: a zero-order query

A second query has 20 clicks, $18 spend and zero orders. Its realized CPC is $0.90. Calling the $18 “guaranteed savings” would be wrong: it is observed past spend, not a recoverable balance or a forecast of future demand.

The next question is whether the term has enough evidence under the seller's rules and whether blocking it would harm useful traffic. Suppose the proposed negative phrase would also match a related query that converted. That conflict is a reason to withhold the broader negative and inspect the scope.

The useful audit output is: $18 zero-order spend observed; negation needs further review because converting traffic may be affected. A held finding is not a missed saving. The literal query and proposed negative match type should remain visible.

Decision 3: an efficient keyword with missing stock evidence

A third keyword has 50 clicks, $30 spend and $200 ad-attributed sales. Its CPC is $0.60 and ACOS is 15%, below the 25% target. There may be room for more traffic, but the audit has no reliable current stock-cover evidence.

Observed CPC = $30 / 50 = $0.60
Observed ACOS = $30 / $200 = 15%
Reference target CPC = ($200 / 50) × 25% = $1.00

The $1.00 reference does not justify jumping the bid to $1.00. It is derived from a historical sample. A raise needs the applicable evidence, stock and size-of-change checks. In this example, the decision is hold the raise until inventory evidence is available.

The audit should name the missing input and the action needed to resolve it. “AI recommends scaling” is not an adequate explanation of the tradeoff.

What leaves the review

If the first candidate clears all applicable checks and the seller approves it, the change packet identifies the exact target, marketplace, old $1.50 bid, proposed $1.20 bid and evidence window. The other two examples remain held. Adding all three findings together as a savings total would mix an action with unresolved questions.

In AutoPPC's paid workflow, approved advertising changes become marketplace-specific bulk files, with a matching reverse file recording supported inverse operations. The seller uploads the forward file in Amazon and checks the processing result. AutoPPC then verifies expected state on a later read-only pull.

An undo file can help restore supported prior settings. It cannot refund spend, recover missed orders or make a partial upload disappear. Check intervening account changes before applying an older inverse.

How to use this example

Ask any tool or analyst to show the same chain for one recommendation in your account: data, arithmetic, current state, safeguards, human decision, delivery result and later measurement. Keep projected impact separate from observed results.

For the underlying method, read how to set a bid. For a complete review, use the audit checklist. For a product tour using fictional demo data, open AutoPPC's demo.