Reading PPC data the right way — Part 1.

Amazon's advertising console is generous with metrics and miserly with context. Two-thirds of the bad PPC decisions we see come not from missing data but from over-reading small samples or comparing across attribution windows that do not line up. This episode is the first of a two-parter on reading the reports honestly.

Attribution windows — the silent variable

Amazon attributes a PPC click to a sale within a 7-day window by default. That window is invisible in most report views and gets quietly conflated with shorter windows in other tools. Comparing your 7-day attribution PPC ACOS to your same-day shopify ACOS is comparing two unrelated numbers.

The discipline: pick one window, document it, and never compare across windows. For Amazon's own reports, 7-day is the default and the one we use for every analysis in this course.

Sample size before significance

Most keyword-level decisions get made on samples too small to support them. A keyword with 38 clicks and 2 orders has a "5.3 % CVR" in the report and a 95 % confidence interval that runs roughly from 1 % to 16 %. Acting on that 5.3 % as if it were the truth is how accounts oscillate.

Rules of thumb for "enough data to act":

• ≥ 100 clicks before judging a keyword's CVR.
• ≥ 14 days before judging a campaign's trend.
• ≥ 4 weeks before judging a strategy change in absence of seasonality.

Cohort thinking, not snapshot thinking

Amazon's console shows snapshots. The reality is cohorts: the keyword you launched in week 1 has different economics than the same keyword launched in week 12, because the ASIN has more history, the listing has more reviews, and the auction has different competitors.

When auditing an account, group keywords by "first impression date" and read CVR by cohort. Newer cohorts usually convert worse not because the keyword changed but because the account is bidding into a colder ASIN/keyword pair.

Separating noise from drift

• A single bad day is noise. Don't act.
• A bad week is data. Investigate.
• A bad month is drift. Act.

Part 2 (Episode 22) picks up where this episode stops: moving from reading data to calculating from it.