Claude for Amazon

Diagnostics

Find the layer behind a sales drop

Diagnose an ASIN sales decline by comparing matching periods across traffic, order activity, price, inventory, offer status, and listing changes. The workflow returns ranked hypotheses and specific checks, not a single unsupported verdict.

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Works with Amazon

Find the layer behind a sales drop illustration

Describe the task

Start with a bounded request

Diagnose an ASIN sales decline by comparing matching periods across traffic, order activity, price, inventory, offer status, and listing changes. The workflow returns ranked hypotheses and specific checks, not a single unsupported verdict.

Prompt 
Diagnose the sales decline for [ASIN] across [CURRENT PERIOD] versus [COMPARISON PERIOD]. Use the supplied traffic, units, sales, price, inventory, Buy Box, campaign, promotion, listing-change, variation, and review timeline. Test traffic loss, weaker order activity, offer changes, stock constraints, and Buy Box changes separately. Rank hypotheses by evidence and propose the smallest next check for each.

Do not edit the listing, price, inventory, or campaigns.
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Provide the working context

Bring the source material Claude should trust

  • Matching-period traffic, units ordered, and sales data for the affected ASIN
  • Price, inventory, Buy Box, campaign, and promotion history for the same dates
  • A timeline of listing edits, variation changes, reviews, or operational incidents

Review what Claude does

Inspect the reasoning and the proposed output

  • Checks whether the decline begins with traffic, order activity, or both
  • Aligns price, stock, Buy Box, campaign, and content changes to the timeline
  • Ranks explanations by supporting and conflicting evidence
  • Proposes the smallest next check that can distinguish competing causes

Ready to run it?

Open the prepared prompt, then keep the final call with the operator.

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FAQ

Review the working boundaries

Why separate traffic from conversion when an ASIN loses sales?

Because the next useful check depends on where the decline begins. Falling visits call for a different investigation from stable traffic with weaker order activity, while stock, price, offer, or Buy Box changes can affect both. A good diagnosis aligns each signal by date and keeps competing explanations visible until evidence distinguishes them.

What context should I provide first?

Start with matching-period traffic, units ordered, and sales data for the affected ASIN. Then provide price, inventory, Buy Box, campaign, and promotion history for the same dates. Keep dates, marketplaces, ASINs, and source definitions attached to the material so Claude can distinguish current evidence from background context.

What should be reviewed before acting?

A diagnostic brief with a dated signal map, ranked hypotheses, and a focused verification plan. Before approval, check the source records, stated assumptions, and every proposed change against the current account context.