AMAZON / ANALYTICS & INTELLIGENCE

Amazon data made readable enough to drive weekly decisions.

Amazon produces an enormous volume of data — Brand Analytics, search query performance, market basket, repeat purchase, conversion funnels, advertising metrics — and most operators never read more than a fraction of it. The articles in this category cover what to track, what to ignore, how to consolidate the read into a weekly view, and how to turn data into the operating decisions the account actually depends on.

Amazon analytics surface

WHAT THIS CATEGORY COVERS

Reporting that drives decisions, not reporting that fills dashboards.

The articles in this category cover the operational read of Amazon analytics — Brand Analytics for search and category intelligence, search query performance for listing and PPC alignment, market basket for cross-sell strategy, repeat purchase for product economics, and the consolidated view that combines them into a weekly decision surface. The goal is the read the team actually uses on Monday, not the dashboard that exists for governance theatre.

  • Brand Analytics treated as actionable intelligence with a defined output
  • Search query performance integrated into PPC and listing decisions
  • Reporting consolidated to one weekly view the team can act on
Amazon decision intelligence

FREQUENTLY ASKED

Common analytics questions.

What is Amazon Brand Analytics?

Brand Analytics is the suite of reporting available to Brand Registry sellers — search query performance, market basket analysis, item comparison, demographics, and repeat purchase behavior. Each report has a specific operational use; the combined read across them is what makes Brand Analytics worth checking weekly.

What is Search Query Performance?

Search Query Performance shows the actual queries buyers used to reach a brand's listings, the impression and click share for each query, and the funnel from search to purchase. It is the closest signal to organic ranking position; it informs both listing optimization and PPC structure.

How do you measure repeat purchase on Amazon?

Brand Analytics' Repeat Purchase Behavior report shows what share of buyers repurchase within defined windows. The number matters most for consumables, subscription-fit products, and brand-loyalty categories. For one-time purchase products, the metric is less informative — the analytical lift comes from category fit rather than from the metric itself.

What metrics actually drive Amazon decisions?

ACoS and TACoS for advertising, conversion rate by listing, click-through rate at thumbnail level, search query share, repeat purchase for consumables, IPI for FBA capacity, account health score, and margin per unit factoring fees. The list is shorter than most dashboards suggest; the discipline is consolidating that read into one weekly view.

Amazon weekly decision view

Analytics is operational when the report drives the next decision; otherwise the dashboard is documentation, not intelligence.

ARTICLES IN THIS CATEGORY

Analytics & intelligence — operating reads.

Frameworks for Brand Analytics, Search Query Performance, market basket, repeat purchase, and the weekly reporting cadence that turns data into decisions.

Articles are being prepared

Articles in this category are being added. The first batch covers Search Query Performance interpretation, weekly reporting frameworks, and Brand Analytics decision integration.

RELATED CATEGORIES

Sibling categories under the Amazon hub.

PPC & advertising

Where the analytical read directly informs bid, search-term, and budget decisions.

Listings & content

Where Search Query Performance and conversion data drive listing iteration.

Fees & profitability

Margin per unit, FBA fee structure, and the economics layer underneath the metrics.

NEXT

When reporting needs to be wired into the weekly cadence.

Continuous management engagements include consolidated reporting, escalation rules, and the cadence the team uses to act on Amazon data.

Amazon management

Working integration, not slides.

Tell us what is breaking. We will quickly tell you whether the problem is architectural, operational, or executional.