ListingGoodAI Recommendation Engine
Appeal & Review Analysis

Turn Amazon suspensions and negative reviews into a fixable plan

When a listing gets taken down or reviews turn sour, the clock is running. ListingGood turns Amazon's vague rejections and angry 1-star reviews into a structured, submission-ready action plan — in minutes, not days.

Open the Appeal & Review tool →

Two tools that cover the post-sale rescue layer

Most "AI for Amazon" products stop at the buy box — they score discoverability and ranking. ListingGood also covers what happens after Amazon or customers push back.

Plan of Action

POA generator for suspensions & takedowns

For account suspensions, listing takedowns, trademark / IP complaints, authenticity claims, and EU GPSR or product-safety notices.

Input: the notification text (or just the violation type), your marketplace, and category.

Output: a submission-ready POA in Amazon's expected three-part structure — root cause → corrective action → preventive action — plus a plain-language summary you can paste straight into Seller Central.

Cost: 10 stars (from monthly allowance or permanent wallet)
Review Analysis

Root-cause analysis of negative reviews

Paste a batch of negative reviews (or one representative review) and the AI classifies the real problem.

Four root-cause buckets: product defect / quality, expectation mismatch (listing over-promised), shipping & packaging, or description / policy accuracy.

Output: the dominant theme, a severity read, and concrete fixes for your listing and product.

Cost: 3 stars per analysis

Why this is a blind spot for discovery-only tools

Tools like ZonGuru's Helix optimize for discovery and ranking — COSMO / Rufus readability, keyword coverage, listing scores. They are excellent at helping you get found.

But the moment Amazon issues a takedown, or a product ships with a defect and reviews crater, those tools go quiet. That is the exact moment sellers lose money fastest — and have the fewest AI tools to help.

ListingGood covers the rescue layer: when a listing is at risk or already down, you need a plan to fix it and get reinstated, not another ranking score. Appeal & Review Analysis is purpose-built for that part of the lifecycle.

How it works

  1. Paste the trigger. The suspension notice, takedown email, or a set of negative reviews.
  2. Pick marketplace & category. So the POA matches local policy — for example, EU GPSR safety requirements versus US authenticity disputes.
  3. Get the plan. A structured POA (or review root-cause report) plus the specific fixes to make to your listing or product.

Related guides

Go deeper on the lifecycle this page covers — and the tools that feed an appeal-ready listing.

Frequently asked questions

What is a POA and when do I need one?
A Plan of Action (POA) is the document Amazon requires when you appeal a suspension, takedown, or policy violation. You need one whenever Seller Central asks you to "provide a plan of action" to reinstate an account or listing.
Does ListingGood write the POA in Amazon's required format?
Yes. The generator outputs the three-part structure Amazon's reviewers expect — root cause, corrective action, and preventive action — so your appeal reads the way their teams are trained to skim.
Can it handle EU GPSR and product-safety notices?
Yes. Select the EU marketplace and the relevant category, and the POA is framed against GPSR / product-safety obligations rather than a generic US template.
How is review analysis different from just reading reviews?
Reading reviews is slow and subjective. The analysis classifies every review into a root-cause bucket, surfaces the dominant theme across the batch, and tells you whether the fix belongs in the product, the listing copy, or fulfillment.
How much does it cost?
A POA costs 10 stars and a review analysis costs 3 stars. Stars come from your monthly subscription allowance or your permanent wallet — new accounts receive a starter balance.
Is my data kept private?
The text you paste is used only to generate your plan and is not published or used to train shared models. Treat any notification as sensitive and avoid pasting unrelated personal data.