Connect Amazon Ads to ChatGPT

Cross-Client Amazon Ads Health Scan

Using the Catchr MCP, pull Amazon Ads performance for [List of Client Accounts] over [Period] and compare it with [Comparison Period]. For each account, summarize Cost, Sales, Purchases, Units Sold, Impressions, Clicks, Click Through Rate, and ROAS, using only field combinations supported by the relevant Amazon Ads report. Score each account against [Client ROAS Target], [Maximum Cost Growth], and [Minimum Sales Target], label it Healthy, Watch, or Critical, explain the data-backed reason for every Watch or Critical label, and return a ranked list of the accounts the consultant should review first today.

Client Growth and Efficiency Review

Using the Catchr MCP, analyze Amazon Ads results for [Client Account Name/ID] over [Period] versus [Comparison Period]. Break down Campaign Name, Advertised ASIN or Sponsored Product Ad ASIN, Cost, Sales, Purchases, Units Sold, ROAS, and Sponsored Product ACOS Clicks 14d; where Sponsored Brands data is available, also report Sales New To Brand 14d and Orders New To Brand 14d. Keep incompatible ad-product fields in separate sections, identify the campaigns and products driving growth or efficiency decline, and write a client-ready review with three wins, three risks, and one specific next action for each risk.

Multi-Account Budget Pacing Watchlist

Using the Catchr MCP, review Amazon Ads budget pacing for [List of Client Accounts] from [Period Start] through [As-of Date]. Use Campaign Name, Campaign Status, Campaign Budget Amount or Sponsored Product Campaign Daily Budget, Date, and Cost to compare actual spend with a straight-line pacing plan based on [Approved Budget or Daily Budget]. Flag campaigns projected to overspend or underspend by more than [Pacing Threshold %], quantify the projected variance by account, and produce a prioritized watchlist with the budget action, owner, and client-facing explanation recommended for each issue.

Morning Client Priority Queue

Using the Catchr MCP, pull Amazon Ads data for [List of Client Accounts] for [Today or Recent Period] and compare it with [Baseline Period]. For each client, review Cost, Sales, Purchases, Units Sold, ROAS, Click Through Rate, Sponsored Product Cost Per Click, and Campaign Status against [Client KPI Targets]. Flag sudden efficiency deterioration, stalled delivery, or unusual spend movement, rank the clients by urgency and estimated business impact, and give me one concise explanation plus the first account-level action to investigate for each priority.

Monthly Amazon Ads Client Report

Using the Catchr MCP, pull Amazon Ads performance for [Client Account Name/ID] for [Reporting Period] and [Previous Period]. Summarize Cost, Impressions, Clicks, Click Through Rate, Purchases, Sales, Units Sold, ROAS, and Sponsored Product ACOS Clicks 14d, then break the material changes down by Campaign Name and Advertised ASIN or SKU where supported. Write a concise client-ready report in plain English covering what improved, what declined, which campaigns or products caused the change, the limitations of Amazon Ads attribution, and the three priorities recommended for [Next Period].

Keyword and Target Bid Backlog

Using the Catchr MCP, analyze Amazon Ads keyword and targeting performance for [Client Account Name/ID] over [Period]. Use Keyword or Sponsored Product Keyword Text, Match Type, Keyword Bid or Sponsored Product Keyword Bid, Targeting Text, Sponsored Product Target Bid, Clicks, Cost, Purchases, Sales, ROAS, and Sponsored Product ACOS Clicks 14d where compatible. Compare each item with [Minimum Clicks], [Target ROAS], and [Maximum ACOS], diagnose whether the issue is weak conversion, inefficient cost, low volume, or insufficient evidence, and create a freelancer-friendly backlog ranked by expected impact and effort with the proposed bid or targeting test, success metric, and review date.

ASIN Advertising Profitability Map

Using the Catchr MCP, pull Amazon Ads performance for [Account Name/ID] over [Period] by Advertised ASIN and Advertised SKU, or the corresponding Sponsored Product Ad ASIN and SKU fields supported by the report. Summarize Cost or Sponsored Product Spend, Sales, Purchases, Units Sold, ROAS, and Sponsored Product ACOS Clicks 14d, then compare each product with [Target ROAS], [Maximum ACOS], and [Minimum Purchase Volume]. Classify every ASIN as Scale, Maintain, Fix, or Pause, show the evidence behind the classification, and recommend a concrete budget or targeting action without treating advertising sales as total store revenue.

Search Term Harvesting Plan

Using the Catchr MCP, analyze Amazon Ads search-term performance for [Account Name/ID] over [Period] using Search Term, Keyword, Match Type, Campaign Name, Ad Group Name, Clicks, Cost, Purchases, Sales, ROAS, and Sponsored Product ACOS Clicks 14d where those fields are compatible. Apply [Minimum Clicks], [Minimum Purchases], [Target ROAS], and [Maximum ACOS] to identify search terms to promote into dedicated exact-match keywords, terms to keep testing, and likely negative-keyword candidates. Return a prioritized action table with supporting metrics, the recommended match type or exclusion, and the campaign or ad group where the change should be considered.

Placement Efficiency Optimizer

Using the Catchr MCP, compare Amazon Ads performance for [Account Name/ID] over [Period] by Placement Classification and Campaign Name. Report Top Of Search Impression Share, Impressions, Clicks, Click Through Rate, Sponsored Product Cost Per Click, Cost, Purchases, Sales, and ROAS where supported, and benchmark each placement against [Target ROAS] and [Target Purchase Volume]. Identify placements that deserve more investment, those wasting spend, and those lacking enough data; then recommend specific placement or bid-allocation tests with a success metric and [Review Date].

Amazon Ads Data Integrity Audit

Using the Catchr MCP, audit Amazon Ads data for [Account Name/ID or All Connected Accounts] over [Period]. Check completeness and uniqueness for Date, Campaign ID, Campaign Name, Ad Group ID, Keyword ID, Targeting ID, Advertised ASIN, and Advertised SKU where present; test that Impressions, Clicks, Cost, Purchases, Sales, and Units Sold are non-negative; and validate that Clicks do not exceed Impressions and that calculated Click Through Rate, cost per click, and ROAS reconcile with their component fields within [Tolerance]. Return an exception table with account, grain, failed rule, observed values, severity, and recommended validation step, while keeping incompatible report grains separate.

Attribution Window Consistency Check

Using the Catchr MCP, pull Amazon Ads conversion metrics for [Account Name/ID] over [Period] at [Campaign or ASIN Grain]. Where the same report supports them, compare Sponsored Product Purchases 1d, 7d, 14d, and 30d; Sales 1d, 7d, 14d, and 30d; and Units Sold Clicks 1d, 7d, 14d, and 30d. Flag missing windows, unexpected decreases as the attribution window expands, abrupt changes in the 14-day-to-7-day uplift versus [Historical Baseline], and inconsistent Same SKU versus Other SKU components. Produce a diagnostic table distinguishing likely freshness lag, schema or extraction issue, and plausible business mix change, with the next validation query for each finding.

Daily Performance and Mix Outlier Monitor

Using the Catchr MCP, pull at least [Lookback Period, e.g., 90 days] of Amazon Ads data for [Account Name/ID or All Connected Accounts], grouped by Date and Campaign Name and segmented by [Advertised ASIN, Placement Classification, or Match Type]. Monitor Impressions, Clicks, Click Through Rate, Cost, Purchases, Sales, Units Sold, and ROAS, compare each daily value with its trailing [Baseline Window, e.g., 28-day] mean and standard deviation, and flag deviations beyond [Z-score Threshold]. For every outlier, separate level change from campaign or product mix shift, classify it as likely data-quality or genuine performance movement, cite the supporting fields, and recommend the next query or business action.

Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Connect Amazon Ads to ChatGPT

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How to connect Amazon Ads to ChatGPT ?

Authorize the Amazon Ads account in Catchr

Add the Catchr MCP from official ChatGPT store

Choose, adapt, and run a prompt.

A simple client question should not trigger an hour of data prep.

Pull another CSV

Rebuild the context

Summarize it by hand

Ask the account. Get to the explanation.

Prepare data before every question.

Connect once. Keep asking.

Twelve Amazon Ads prompts for analyzing real account data.

Amazon Ads to ChatGPT FAQs

What Amazon Ads data can ChatGPT analyze through Catchr?

Which Amazon Ads metrics and breakdowns can I use in ChatGPT?

Can ChatGPT compare Amazon Ads campaign types and placements?

Can I analyze Amazon Ads performance by ASIN, keyword, or search term?

What types of prompts work best for Amazon Ads analysis?

Can I combine Amazon Ads with Amazon Seller, Shopify, or other ad platforms?

Can I analyze multiple Amazon Ads accounts or marketplaces together?

What do I need to connect Amazon Ads to ChatGPT with Catchr?

How recent is the Amazon Ads data, and how much history can I analyze?

Can ChatGPT change my Amazon Ads campaigns through Catchr?

How much does it cost to connect marketing data to ChatGPT with Catchr?

‍Can I try the ChatGPT integration before choosing a plan?

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