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Amazon Search Query Analyzer

Diagnose where each ASIN loses in search — visibility, clicks, or conversion — from Search Query Performance data, with a named fix for every gap.

Updated Jun 25, 2026 9 min read

Amazon Search Query Analyzer reads your Search Query Performance (SQP) data and tells you, for each ASIN (Amazon Standard Identification Number) and each search term, exactly where you're losing — showing up, getting clicked, or closing the sale — and what to change to win it back. You ask for it in plain language from the AI client you already use.

It walks the funnel one search term at a time. For a query like "large dog bed," it compares your share of impressions, clicks, and purchases against the whole market and your peers, finds the stage that's leaking, and names the fix — a title, a hero image, a price test, a faster delivery promise, or a variant cleanup.

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 diagnoses, ranks, and recommends; making the changes is your call.

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

What it does

  • Diagnose the funnel — for every search-term-and-ASIN pair, where you stand on impression share, click share, and purchase share against the market and your peers, and which stage is the bottleneck.
  • Name the fix for each gap — invisible for a term, weak click-through, conversion friction, slow delivery, price, or variant cannibalization, each mapped to a concrete change.
  • Rank by upside, not volume — opportunities scored by how much recoverable sales sit behind the gap, so the top of the list is the highest-leverage work, not just the biggest keyword.
  • Tag who owns each fix — every recommendation marked as a listing change, an ops change, or an ad play, so the right person picks it up.

Example prompts

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

Run the analysis

  • "Analyze my Search Query Performance for [ASIN]."
  • "Run an SQP diagnostic on my top sellers for the last month."
  • "Pull search query performance for [parent ASIN] and all its children."

Find where you're losing

  • "For [ASIN], am I losing on visibility, clicks, or conversion?"
  • "Where in the funnel is [ASIN] leaking — show me the biggest gap."
  • "Which search terms drive impressions for [ASIN] but no sales?"

Visibility and SEO

  • "Which high-volume searches is [ASIN] barely showing up for?"
  • "What terms should [ASIN] rank for but doesn't?"
  • "My impression share is low on '[search term]' — what would help?"

Click-through

  • "People see [ASIN] but don't click — is it the image or the title?"
  • "Which queries have strong impressions but a weak click-through rate?"
  • "Why is my CTR below the benchmark on '[search term]'?"

Conversion

  • "People click [ASIN] but don't buy — what's the friction?"
  • "Where is my click share beating my purchase share?"
  • "Is it price or shipping that's killing conversion on '[search term]'?"

Price and shipping

  • "Am I priced above the market on the terms where I'm losing sales?"
  • "Which ASINs lose purchases to slow delivery?"
  • "Show me queries where a faster shipping promise would move the needle."

Variants and cannibalization

  • "Are my own variants competing for the same search?"
  • "Which child ASIN should own '[search term]'?"
  • "Find where my products eat into each other in search."

Rank the opportunities

  • "Rank my search-term opportunities by upside, not just volume."
  • "What are the ten fixes that would recover the most sales?"
  • "Give me a prioritized to-do list from this report."

Sort by who owns the fix

  • "Split the recommendations into listing fixes, ops fixes, and ad plays."
  • "Show me only the changes I can make on the listing itself."
  • "Which of these are shipping or inventory problems, not listing ones?"

Turn the findings into fixes

  • "Take my low-CTR ASINs and tell me which images to redo."
  • "Hand the SEO and copy fixes to the listing optimizer."
  • "Which of these need a new hero image versus a title rewrite?"

Most real sessions chain these together: "Run SQP for [ASIN]" → "Where am I losing?" → "It's clicks — is it the image?" → "Which terms is this worst on?" → "Send those to Iris." Treat it as a conversation, not a set of one-off commands.

What it surfaces

The analyzer reads each search term as a funnel and tells you where it breaks:

  • The funnel, stage by stage — your impression share, click share, and purchase share for the term, against the market total and your peer set, so the leaking stage is obvious.
  • A named cause for every gap, each pointing at a specific fix:
    • High-volume term, low impression share → an SEO and PDP update toward the intent behind the query. You're invisible for something people search.
    • Clicks beat impressions but purchases don't keep up → a conversion fix on price versus the market median, ratings, or the delivery promise.
    • CTR below the query benchmark while you hold impression share → a hero image and title fix. You show up; you just don't win the click.
    • Purchases trail clicks with a high slow-shipping share → a delivery-speed fix and a clearer delivery promise.
    • Priced above the market median with weak purchase share → a bounded price test.
    • Underperforming your own brand peers on a term → a variant fix or a re-balance of on-page emphasis, so your products stop competing with each other.
  • Lift-aware ranking — every opportunity scored by the size of the gap times the volume behind it, so the list leads with recoverable sales rather than raw search volume.
  • Owner tags — each recommendation marked listing, ops, or ads, so a copy change, a fulfillment change, and a campaign change never get confused for one another.
  • Confidence badges — each finding carries whether it had enough data, which peer benchmarks it used, and its attribution scope, so you can tell a real signal from noise at a glance.

Straight talk on the numbers

Search Query Performance is powerful and easy to over-read. The analyzer holds a few lines so it doesn't send you chasing noise:

It won't make a strong call on thin data. Below a floor of impressions and clicks for a term, it flags the row as thin and softens the recommendation instead of telling you to rebuild a listing over a handful of events.

  • The scope is the search results page, over a recent window — not lifetime, and not every path to a sale. Every finding says so, so you weigh it accordingly.
  • It keeps organic and paid apart. SQP is organic search; it won't compute ACoS or ROAS (return on ad spend) by bolting ad spend onto these totals. Sponsored tactics are tagged separately and routed to your ads workflow.
  • It paces changes. After it recommends a test on a term, it waits out a cooldown before recommending another on the same term, so you measure the result instead of stacking edits you can't tell apart.

From diagnosis to fix

The analyzer tells you where each product loses in search and what to change. It doesn't change anything itself — and the fixes it names are exactly what its companion tools execute.

  • "You show up but nobody clicks" is almost always the main image or the title. Amazon Agent Iris regenerates a compliant, click-winning hero image from your real product and hosts it where Amazon can fetch it. See the Iris docs.
  • "You're invisible for a term," or "people click but the copy doesn't close" lives in the listing text. Amazon Listing Optimizer rewrites titles, bullets, and search terms toward the intent the analyzer found, grounded in the product's real attributes and Amazon's rules, and publishes once you approve.
  • The ad-tagged plays — bids, budget, and placement on the terms where you're strong and want more — belong on the Amazon Ads MCP.

The loop runs in one place: run the SQP diagnostic → read the ranked gaps → send image gaps to Iris, copy and SEO gaps to the Optimizer, bid gaps to Ads → re-pull next window and watch the share gaps close. From "where am I losing" to "fixed," without leaving your AI client.

Limits and honest framing

  • It reads; it doesn't change anything. It diagnoses and ranks; making the changes is a separate, opt-in step.
  • Thin data gets soft calls. Low-traffic terms are flagged, not force-ranked. Give it a window with real volume for confident recommendations.
  • Short window, search-page scope. SQP captures recent search-results behavior, not lifetime performance or every route to purchase.
  • Organic only. It won't blend ad spend into these numbers; ad tactics route to your ads workflow with their own tags.
  • It only sees the account you connect. Every pull is scoped to the selected seller identity and marketplace.
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