Amazon FBAAdvanced

Amazon PPC Search Term Review Prompt

Review search term reports and surface negatives, winners, and copy ideas from PPC data.

PPC reports are useful, but they are slow to interpret when the query set gets large. This prompt helps you summarize search term performance so you can spot wasted spend, promising terms, and listing angles to test next.

Copy-And-Paste Prompt

Use This Prompt

Works well in ChatGPT, Claude, Gemini. Replace any bracketed variables before you run it.

Variables to customize

[Search Term Report Data][Campaign Goal][Target ACoS or Margin Threshold][Listing or Product Context]
Act as an Amazon PPC analyst.

Your task is to review my search term report and summarize what to negative, what to scale, and what insights belong in the listing or keyword strategy.

Use these inputs when available:
- [Search Term Report Data]
- [Campaign Goal]
- [Target ACoS or Margin Threshold]
- [Listing or Product Context]

Requirements:
- Separate inefficient spend from promising queries.
- Identify patterns, not just one-off terms.
- Recommend negatives, bid focus, and listing insights.
- Explain decisions in plain English.

Return the answer in this format:
1. Queries to scale
2. Queries to negative or de-prioritize
3. Listing and keyword takeaways
4. Next test ideas

Tone and style: data-led and actionable

Ask me concise follow-up questions only if a missing detail would materially change the quality of the final answer.

Example Inputs

Goal

Lower waste while preserving keyword discovery

Target ACoS

Below 28%

Context

New product with moderate review count

Sample Output

Scale queries that convert around 'travel organizer waterproof' and 'cable case for backpack' because they are showing stronger purchase intent. Negative broad lifestyle searches that spend heavily without converting, especially generic 'tech organizer' variants with weak click-to-order behavior.

This is a mock example only. Your result should change based on the variables, context, and constraints you provide.

What this prompt is designed to do

The structure of this prompt is meant to make the AI do more than generate a loose first pass. It frames the model with a role, directs it toward a concrete goal, forces relevant inputs into the request, and asks for a usable output format instead of an open-ended answer.

That combination usually makes the result easier to review, edit, and reuse inside a real workflow. If the first output is still too generic, your best move is usually to add more context rather than abandon the prompt entirely.

Category-specific guidance

  • Use real keyword, review, and margin inputs whenever possible.
  • Tell the model your category, compliance constraints, and positioning strategy.
  • Separate what the product actually does from what the marketing angle should emphasize.
  • Review any final listing copy for Amazon policy and claim compliance before publishing.

Pair This Prompt With Amazon FBA Resources

These related calculators and guides add more depth when you want to connect this amazon fba prompt to real numbers, strategy, or supporting tools.

Amazon FBA Calculators

Pair prompt-led listing and launch work with calculator-backed profit, fee, and break-even analysis.

Open resource

Amazon Seller Tools

Use the listing cleaners and formatting utilities alongside the prompt templates when polishing catalog copy.

Open resource

Amazon FBA Guides

Jump into in-depth guides on fees, margins, and launch strategy when you want more strategic context behind the prompts.

Open resource

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Amazon PPC Search Term Review Prompt FAQ

Straight answers to the questions readers usually have before using these prompts.

How should I customize the amazon ppc search term review prompt prompt?

Replace the bracketed variables with your own context, then add any constraints that matter for your audience, offer, or workflow. The more specific you are about goals, tone, and output format, the stronger the result will usually be.

Can I use this amazon fba prompt with ChatGPT, Claude, or Gemini?

Yes. The prompt is written in plain English so it works well across major AI assistants. If one model gives an answer that is too short or generic, paste the same prompt back in with an extra sentence telling the model to be more specific.