Amazon Attribution Windows: How To Align Strategy With Complete Data

On Amazon, moving too quickly without the right data can be counterproductive. This is because attribution windows, reporting delays, and consumer behaviour mean that if changes are implemented prematurely, there is a risk of prematurely ending campaigns that could otherwise have been profitable.

Understanding Attribution Windows

Amazon Advertising uses a last-touch attribution model, with a 14-day conversion window for Sponsored Products, Sponsored Brands, and Sponsored Display.

Essentially, the final advertisement a customer clicks before making a purchase receives the credit, provided that the conversion occurs within a 14-day period. This is important because a customer might click an ad today and complete their purchase nearly two weeks later.

Consequently, assessing performance prematurely will underreport revenue, inflate the Advertising Cost of Sales (ACoS), and carry the risk of pausing traffic that is actually profitable. For instance, eliminating a keyword after just two days because of a lack of immediate sales might remove potential conversions that would have materialised on days 10 or 12.

Why Data Sufficiency Matters

Amazon is not a short-cycle channel. Reporting delays add another layer:

• Search Query Performance (SQP): Lags by several days and should be reviewed over weeks to reveal trends.

• Brand Analytics: Updates weekly, not daily.

• Advertising Console Reports: Conversions continue to backfill during the full 14-day window.

Optimising these datasets before stabilising them leads to decisions based on incomplete information rather than statistically valid insights.

Why Consistency Beats Constant Tweaks

Effective optimisation requires stability. Frequent adjustments, such as changing bids, pausing keywords, or restructuring campaigns too quickly, can actually reset Amazon’s learning process. Moreover, this distorts performance data.

Sometimes, deliberately slow performance is the correct approach. For example, campaigns are often run with very low bids during testing or when using out-of-aisle targeting. In these In these In these scenarios, the likelihood of an immediate conversion is lower. These specific campaigns may, therefore, take longer to result in a sale. However, when a sale does occur, it is typically at a highly profitable cost. Cutting them prematurely, based on limited initial data, would mean missing those profitable conversions entirely.

Ultimately, consistency allows key patterns to emerge. This includes identifying which search queries truly drive sales, which placements sustain margins, and how customers behave across the full attribution cycle.

From Insight To Action

In inherited accounts, campaigns are often adjusted daily, strategies shift constantly, and data never reaches statistical significance. Consequently, this results in wasted spending and missed insights.

Instead, the approach here is disciplined. Firstly, strategies are set. Then, attribution windows are allowed to complete. Finally, refinements are made using Search Query Performance (SQP), Brand Analytics, and Advertising Console data once it is statistically valid. Therefore, every optimisation is guaranteed to drive long-term profitability rather than rely on incomplete numbers.

Want to see your Amazon performance skyrocket?  Book a call with our team or email us at amazon@marketrocket.co.uk to take your brand to the next level.