Amazon PPC audit checklist
Audit the decisions behind the metrics—not only the metrics.
A useful Amazon PPC audit connects advertising data to margin, inventory, campaign purpose, and decision authority. This checklist helps established brands find where control is weak before bids or structures are changed.
Based on nearly nine years of hands-on Amazon PPC work and PPCScale’s documented Detect → Review → Approve → Execute → Record → Verify control loop.
Reviewed 10 October 2026 · Educational framework, not a performance guarantee.Direct answer
What should an Amazon PPC audit include?
An Amazon PPC audit should review commercial constraints, campaign architecture, search-term ownership, targeting, negatives, bids, placements, budgets, retail readiness, measurement, and change controls.
The output should separate verified findings from hypotheses, rank actions by expected impact and risk, and state what must be measured after a change. A list of high-ACoS campaigns is not a complete audit.
Record the business constraints first.
Advertising data cannot tell you the correct decision without the commercial limits around it.
- Confirm the marketplace, category, product lifecycle, current objectives, and decision period.
- Record contribution-margin assumptions separately from ACoS or ROAS. Advertising-attributed sales are not profit.
- Identify inventory limits, stockout risk, promotions, seasonality, launches, and variation relationships.
- Define which products are growth priorities, maintenance products, tests, or constrained inventory.
- Document who can approve material changes and which changes require advance notice.
Decision question: What business outcome is the account trying to support, and what must not be damaged while pursuing it?
Map campaign purpose and target ownership.
Count is less important than whether each campaign has a clear job and controllable inputs.
- Label the intended purpose of automatic, broad, phrase, exact, product-targeting, brand-defense, and research activity.
- Find duplicated keyword and ASIN targets, then determine whether overlap is intentional or unmanaged.
- Check whether search terms can be isolated, negated, budgeted, and measured at the required level.
- Review portfolios, naming, state, ad groups, advertised products, and legacy campaigns for reporting ambiguity.
- Do not rebuild solely to create a tidier naming convention; compare the control benefit with migration risk and lost continuity.
Decision question: Can the team explain which campaign owns a target, why it owns it, and how another campaign is prevented from competing unintentionally?
Trace search terms from discovery to control.
Search-term work should distinguish evidence strength and intent rather than apply one harvesting rule everywhere.
- Identify converting queries still buried in discovery activity and check whether isolation would improve control.
- Review non-converting spend against click volume, conversion delay, price, relevance, and product maturity before negating.
- Check whether harvested terms are negated from discovery only when that matches the account’s ownership model.
- Separate branded, competitor, category, product, and ambiguous intent where their economics or strategy differ.
- Inspect product targets and close/loose/substitute/complement behavior alongside keyword search terms.
Decision question: Which queries are proven, exploratory, wasteful, or still inconclusive—and what evidence supports that label?
Review bids and placements as one system.
A base bid cannot be evaluated independently from placement adjustments, bidding strategy, conversion, and campaign purpose.
- Calculate the effective exposure created by base bids and placement multipliers.
- Compare top-of-search, rest-of-search, and product-page behavior where sufficient data exists.
- Check whether bid changes account for conversion delay and enough clicks to support the decision.
- Identify rules or software making changes without a visible rationale, authority limit, or rollback path.
- Separate a bid problem from a relevance, price, listing, review, offer, or inventory problem.
Decision question: Is the account paying more because a placement is valuable, or because combined controls are not being reviewed together?
Examine budget pressure and allocation.
A campaign running out of budget is a prioritization signal, not automatic proof that its budget should increase.
- Find campaigns that exhaust budgets early and identify what traffic is lost afterward.
- Compare budget allocation with product priority, inventory, margin, demand, launch stage, and campaign purpose.
- Look for lower-priority campaigns spending while higher-priority campaigns are constrained.
- Review whether shared portfolio budgets or automated rules hide product-level tradeoffs.
- Document the expected opportunity cost before moving budget.
Decision question: If another dollar becomes available, which campaign should receive it, and what evidence makes that the best use?
Test whether reporting supports decisions.
ACoS describes ad spend relative to attributed ad sales. TACoS describes ad spend relative to total sales. Neither alone proves profitability or causation.
- Review total sales, ad sales, spend, ACoS, TACoS, conversion, CPC, sessions, units, and available organic context together.
- State attribution windows, reporting latency, comparison periods, promotions, stock conditions, and other changes.
- Separate observed correlation from a claim that PPC caused every movement.
- Check whether dashboards reconcile with the advertising console and whether definitions stay consistent.
- Record a baseline before material changes so the next review is meaningful.
Decision question: Would another reviewer understand exactly what improved, over which period, and what the data cannot establish?
Convert findings into a controlled action plan.
An audit becomes useful when it explains sequence, authority, risk, and verification.
- For every finding, record the evidence, affected scope, expected benefit, confidence, effort, and downside risk.
- Separate urgent control failures from experiments and low-impact cleanup.
- Choose targeted repair, staged restructuring, or no change based on the evidence.
- Define who approves and executes each action, plus the rollback trigger.
- Set the next review point and the signals that would confirm, reject, or complicate the hypothesis.
Decision question: What should change first, why is it first, and what result would make the team stop or revise the plan?