Skip to content
<- All docs

Guides

Amazon Returns Monitor

Turn Amazon FBA and MFN returns into a report showing which ASINs come back, why, and which ones you can fix — from the AI client you already use.

Updated Jun 25, 2026 9 min read

Amazon Returns Monitor turns your returns data into a report that tells you which products are coming back, why, and which ones you can actually do something about. You ask for it in plain language from the AI client you already use — name a window, say "run my returns report," and it pulls the data, breaks it down, and hands you the result.

It reads your FBA (Fulfillment by Amazon) and MFN returns, ranks the ASINs driving the volume, sorts every return reason into what you can fix versus what you can't, and flags the products that need attention now — with the recurring complaint pulled straight from customer comments.

It runs on Kuudo's hosted infrastructure as a Skill on the Amazon Selling Partner MCP. There is nothing to deploy or operate, and it never changes your account — it analyzes returns and produces a deliverable you control.

This page covers what it does, what to say, what it surfaces, how it keeps the numbers honest, and how it pairs with Amazon Listing Optimizer and Amazon Agent Iris to fix the returns you can fix.

What it does

  • Break it down — returns by ASIN (Amazon Standard Identification Number), SKU, reason, and disposition: which products drive the volume, and the dominant reason for each.
  • Separate what you can fix from what you can't — every return reason sorted into controllable, remorse, ops, and other, so you spend effort where a fix would actually move the number.
  • Flag the ones that need action now — red-flag ASINs with a high return rate and a fixable cause, each with two or three representative customer comments and a grounded hypothesis for what's wrong.
  • Trend it and hand it off — this period versus the one before, delivered as a chat summary, a Word document, an Excel workbook, or an interactive dashboard.

Example prompts

You drive it in plain language from your AI client — no report types, columns, or APIs to remember. Replace bracketed values like [ASIN], [SKU], or [brand] with your own.

Run the report

  • "Run my returns report for the last 30 days."
  • "Pull FBA returns for last month and compare them to the month before."
  • "Give me a returns report for my US account I can send to the team."

See what's coming back

  • "Which of my ASINs have the most returns?"
  • "Break my returns down by product and reason."
  • "What's my most-returned SKU, and what's the top reason for it?"

Understand the reasons

  • "Group my return reasons into what I can fix versus buyer's remorse."
  • "How much of my return volume is 'not as described' versus 'changed their mind'?"
  • "Which returns are defects or damage, and which are just preference?"

Find the problem ASINs

  • "Flag any ASIN with a return rate high enough to worry about."
  • "Show me the products where the returns are something I could actually fix."
  • "Which ASINs come back as 'customer damaged' more than half the time?"

Get the return rate right

  • "What's my return rate for [ASIN] — and tell me how you measured it."
  • "I only have the returns file, no sales data. What can you still tell me?"
  • "Compare [ASIN]'s return rate to my portfolio median."

Read the customer comments

  • "What are customers actually saying when they return [ASIN]?"
  • "Pull the recurring complaint from the comments on my worst-returning products."
  • "Are buyers complaining about sizing, the photos, or quality?"

Disposition and reimbursement

  • "How many of my returns came back unsellable?"
  • "Where's the gap between units returned and units I can resell — am I owed reimbursements?"
  • "Break down returns by disposition for [ASIN]."

Trend it over time

  • "Is my return rate going up or down versus last period?"
  • "Which reasons grew the most this month?"
  • "Did my red-flag ASIN list change from last month?"

Pick the format

  • "Give me the workbook so my BI team can slice it."
  • "Make it a one-page exec summary."
  • "Build me a dashboard I can explore and filter by reason."

Turn the findings into fixes

  • "Which of these returns trace back to a bad listing or a misleading photo?"
  • "Take my top 'not as described' ASINs and tell me what to fix."
  • "List the controllable-return ASINs so I can hand them to the listing optimizer."

Most real sessions chain these together. A typical one runs: "Run my returns report for last month" → "Which ASINs are red-flagged?" → "Why is [ASIN] coming back?" → "What are customers saying?" → "Which of these are listing or image problems?" Treat it as a conversation, not a set of one-off commands.

What it surfaces

When it runs a full report, it leads with the urgent and works down, so you read the things that need action first:

  • Red-flag ASINs, up top — the products to deal with now: a high return rate, enough shipped volume to be real, and a top reason you can fix. These lead the report; they're never buried in an appendix.
  • Reason breakdown — every return reason, grouped into controllable, remorse, ops, and other, so you can see at a glance what a fix would move and what it wouldn't.
  • Return concentration — which ASINs drive the volume, with the dominant reason and per-ASIN rate for each.
  • Customer-comment themes — the recurring complaint behind your worst returns, mined from real buyer comments, not guessed.
  • Disposition and reimbursement — what came back sellable versus unsellable, with a callout when the gap suggests you're owed a reimbursement.
  • Fulfillment-center concentration — when returns cluster at one FC, a packaging or handling signal worth a look.
  • Time trend — daily and weekly movement, with partial weeks flagged so a refresh lag doesn't read as a real dip.
  • Prior-period deltas — this window against the one before, on volume, rate, reason mix, and which ASINs joined or left the red-flag list.

A product gets red-flagged when more than 40% of its shipped units come back, it shipped enough to clear the noise, and the top reason is something a listing, catalog, or quality fix would address. It also flags any ASIN coming back as "customer damaged" more than half the time, regardless of rate — that pattern usually means a packaging failure or return abuse.

Straight talk on the return rate

A return rate is one of the easiest numbers on Amazon to quote wrong, because returns and sales are dated and counted differently. The monitor won't hand you a bare percentage and let you misread it:

Every rate carries a label for how it was measured. A share of returns — what slice of your returns a reason or ASIN accounts for — is never dressed up as a return rate against units sold. When the data lets it tie returns back to the orders that caused them, it says so and states the follow window.

And it keeps facts separate from policy. What the data shows — counts, reasons, concentration — it states plainly. Account-health thresholds, which Amazon changes by program and region, it flags as verify in Seller Central rather than quoting a number as if it were the rule. When a recommendation leans on an actual Amazon rule, it grounds that in Amazon Agent Atlas, Kuudo's indexed Amazon knowledge base, instead of inventing one.

From diagnosis to fix

The monitor is a diagnosis. It tells you which products are bleeding returns and the reason behind each — but it doesn't change anything. The controllable bucket it surfaces is exactly what two companion tools repair.

  • "Not as described" and image-driven returns usually mean the photos oversell or under-show the product. Amazon Agent Iris regenerates compliant, accurate images from your real product and hosts them where Amazon can fetch them, so the picture matches what shows up at the door. See the Iris docs.
  • Sizing, fit, contradictions, and thin or wrong copy live in the listing text. Amazon Listing Optimizer rewrites titles, bullets, and size guidance grounded in the product's real attributes and Amazon's rules, then publishes the fix once you approve it.

The full loop runs in one place: run the returns report → read the controllable ASINs and their modal reason → hand the copy and sizing problems to the Optimizer and the image problems to Iris → re-list → re-run the report next period and watch the controllable rate fall. Diagnosis, fix, proof — without leaving your AI client.

Limits and honest framing

  • It reads; it doesn't change anything. No writes to your account. It analyzes returns and produces a report; fixing the listings behind them is a separate, opt-in step.
  • A real return rate needs shipment data. Without a units-sold denominator it gives you rankings and reason shares, not a validated rate — and labels them as exactly that.
  • Account-health thresholds aren't hard-coded. Policies shift by program and region; it flags risk and points you to your own Seller Central numbers rather than stating a threshold as fact.
  • Returns data lags and uses its own dates. Amazon's daily refresh trails real time, and returns and sales are indexed by different events; it states the exact UTC window it pulled so a time-zone or refresh gap doesn't fool you.
  • It only sees the account you connect. Every pull is scoped to the selected seller identity and marketplace.
<- Previous Amazon Listing Optimizer Next -> Amazon Search Query Analyzer