Automation runs on whatever is already in your account. If the account has structural problems before the rules go in, the rules will run those problems at scale. An audit before automation is not a nice-to-have. It is the difference between automation that compounds good decisions and automation that compounds bad ones.
This checklist covers the ten things worth checking before you activate any automated rules.
| # | Check | Look for |
|---|---|---|
| 1 | Campaign structure | Match types mixed in one campaign or ad group |
| 2 | Negative keyword gaps | Top-spending search terms that are not relevant |
| 3 | Duplicate keywords | The same keyword in several campaigns, bidding against itself |
| 4 | Budget pacing | Campaigns hitting their cap before 2pm |
| 5 | ACoS trajectory | The 30-day trend, not one reading |
| 6 | Day of week | Weekends behaving differently from weekdays |
| 7 | Hourly heatmap | Hours that spend without converting |
| 8 | Ad group size | Bloated ad groups that average away signal |
| 9 | Existing automation | Native rules that would conflict |
| 10 | Placements | Current multipliers by campaign |
1. Campaign structure
Are your campaigns organized by match type, or is everything mixed together? The standard structure separates auto campaigns (for discovery), broad match (for term research), phrase match (for traffic shaping), and exact match (for scaling proven terms). Mixed campaigns produce mixed data, which makes performance signals harder to read and automation decisions less accurate.
If campaigns are misnamed or if match types are combined in a single ad group, fix this before you add rules. Rules react to campaign-level data. Campaigns with unclear structure give rules unclear inputs.
2. Negative keyword gaps
Pull the search term report for the last 30 days and sort by spend descending. How many of the top spending terms are genuinely relevant to your product? The ones that are not belong on your negative keyword list.
Automation does not fix negative keyword gaps. It keeps spending on irrelevant terms faster. A clean negative keyword list is the highest-leverage pre-automation action because it removes the obvious waste before rules start running.
3. Duplicate keywords across campaigns
If the same keyword appears in multiple campaigns (especially at the same match type), Amazon's auction system pits your campaigns against each other. You bid against yourself, which inflates the cost of every placement you win.
Check for cross-campaign keyword overlap before automation. When automation fires performance rules on two campaigns containing the same keyword, it may react to signals that are partly caused by internal competition rather than external market conditions.
4. Budget pacing patterns
Pull your daily spend data for the last 30 days. Are any campaigns consistently hitting their daily budget cap before 2pm? That pattern means traffic is being cut off during peak hours because the budget ran out earlier in the day.
Budget rules applied to a campaign that is already hitting its cap daily will not have the intended effect. The first fix is the cap level, not the automation. After you adjust the cap to a level that allows the campaign to run fully through peak hours, then layer in the budget rules.
5. ACoS trajectory (not point-in-time)
A single ACoS reading tells you almost nothing useful. Thirty days of ACoS readings tells you whether the trend is improving, stable, or worsening. Performance rules react to ACoS thresholds, so activating them on an account where ACoS is already trending in the wrong direction requires understanding why.
If ACoS is rising, identify the cause before setting performance rules. A keyword that has suddenly become more competitive, a product listing change that reduced conversion rates, a competitor promotion that shifted category dynamics. Rules applied to a symptom without understanding the cause will mask the problem but not fix it.
The discussion of how long Amazon ads take to show results is relevant here because ACoS trajectory is only meaningful with enough data behind it.
6. Day-of-week performance patterns
Some accounts have significantly different performance on weekends versus weekdays. If your account runs the same rules seven days a week without accounting for that pattern, you are applying uniform logic to non-uniform behavior.
Pull the last 30 days of performance broken out by day of week. If Friday through Sunday consistently shows different ACoS or conversion patterns than Monday through Thursday, that pattern should inform how you configure dayparting schedules.
7. Hourly performance (the heatmap check)
Before setting any dayparting rules, look at your hourly performance data. Your Amazon dayparting heatmap shows which hours generate orders and which hours spend budget without converting.
The heatmap check is the most direct input for dayparting automation. Without it, a dayparting schedule is a guess. With it, the schedule reflects what your account actually does.
8. Ad group size
Ad groups with too many keywords are harder to analyze and harder to automate effectively. A single ad group with 50 keywords produces campaign-level metrics that average across all of them, making it difficult to identify which specific terms are performing and which are not.
This is a structural fix, not a rule fix. If ad groups are bloated, split them before automation runs on them.
9. What is already running automatically
Check whether Amazon's native automated rules are active on any campaigns. Native rules run once per day on performance thresholds you set inside the console. If you layer third-party automation on top of existing native rules, you may be applying two sets of logic to the same campaigns simultaneously.
Document what is already running before you add anything new. Turn off any native rules that will conflict with what you are about to set up.
10. Placement performance by campaign
Pull placement data (top of search, rest of search, product pages) for each campaign. Placement multipliers interact with your bid adjustments and dayparting schedules. A placement multiplier of 0% at top of search means your campaign is not competing for the highest-intent placements. A multiplier of 200% means top-of-search placements cost triple your base bid.
Know what your current multipliers are before automation starts. Rules that change budgets or bids without accounting for placement multipliers can produce surprising results in accounts where multipliers are aggressive.
What to do with the audit results
Most audits surface two or three things that need attention before any rules go in. Fix those first. The most common: a negative keyword gap, at least one campaign hitting its daily budget cap before peak hours, and at least one match type mixing that needs separation.
Once those are fixed, start with one rule type. Dayparting is usually the right first rule because it has clear, observable inputs (hourly heatmap data) and obvious outputs (campaigns pause and resume on schedule). It is the easiest rule to verify is working correctly before you layer in budget or performance rules.
Frequently asked questions
How long does an Amazon PPC account audit take? A basic audit of the ten items in this checklist takes one to two hours for a typical account. Accounts with many campaigns, complex match type structures, or a large negative keyword backlog take longer. The audit does not need to be exhaustive before you start automation. Fix the blocking issues first and run the rest of the audit in parallel with your first rules.
Should I pause campaigns during a PPC audit? No. Run the audit against live data. Pausing campaigns to audit them removes the current performance signal and restarts the learning process when you reactivate them. The audit is an analytical exercise, not a shutdown.
How often should I audit my Amazon PPC account? A structural audit makes sense every 60 to 90 days. The monthly review checklist covers the same ground at a higher level each month, but a full structural audit (matching this checklist) is deeper and should happen on a quarterly cadence, or any time you are making a significant change like adding automation for the first time, switching software tools, or taking over an account you did not build.
Off Hours runs five rule types including dayparting, budget rules, and performance rules on a 15-minute cadence. Once the audit is done, the rules are ready to run. Start a free 14-day trial.