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Connect Amazon Seller to ChatGPT

Connect Amazon Seller to ChatGPT and ask about any metrics or dimensions using current integrations data. No CSV exports or manual campaign summaries.

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How to connect Amazon Seller to ChatGPT ?

Three steps to your first source-aware prompt.

Step one

Authorize the Amazon Seller account in Catchr

Select Amazon Seller, sign in, and choose the client or business accounts you want to connect.

Client A · Connected sources
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HubSpot 1 CRM portal Connected
Step two

Add the Catchr MCP from official ChatGPT store

Configure the Catchr remote MCP connection in ChatGPT using the connector details from Catchr.

Step three

Choose, adapt, and run a prompt.

Replace account, period, KPI target, and threshold variables before you ask ChatGPT.

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

“Why did CPA rise?” sounds simple. The answer is usually buried across exports, formulas, filters, and tabs before the analysis can even start.

Pull another CSV

Choose the right account, date range, breakdowns, and fields. Then wait for the file and do it again when the question changes.

Rebuild the context

Rename columns, fix formats, join tabs, and check formulas before you can trust the comparison.

Summarize it by hand

Turn the numbers into a client-ready answer, then repeat the whole process for the inevitable follow-up.

Ask the account. Get to the explanation.

Catchr MCP gives ChatGPT access to the marketing accounts you select, so each follow-up starts with the same marketing context instead of a new manual export.

The hard way

Prepare data before every question.

  • Export files from the marketing platform
  • Reformat and reconcile columns
  • Rebuild the analysis for each follow-up
The Catchr way

Connect once. Keep asking.

  • Select the right accounts and data
  • Ask in the language your team already uses
  • Investigate with follow-up questions

Twelve Amazon Seller prompts for analyzing real account data.

Choose your role, open a workflow, then replace the bracketed variables before pasting the prompt into ChatGPT. Every prompt asks for evidence, limits, and a concrete output.

Agency promptCopy prompt
Cross-Client Seller Health Command Center
Using the Catchr MCP, pull Amazon Seller data for [List of Client Account IDs or Marketplaces] over [Period] and compare it with [Comparison Period]. For each client, summarize Ordered Product Sales Amount, Units Ordered, Total Order Items, Sessions, Unit Session Percentage, Buy Box Percentage, Refund Rate, and Received Negative Feedback Rate. Assess results against [Client Targets], label each account Healthy, Watch, or Critical, and rank the accounts requiring the consultant's attention. For every Watch or Critical account, identify the ASIN, SKU, traffic, conversion, Buy Box, refund, or feedback signal driving the status and recommend one evidence-based next action.
Client Marketplace Performance Brief
Using the Catchr MCP, analyze Amazon Seller performance for [Client Account ID] in [Marketplace ID] over [Reporting Period] versus [Previous Period]. Break down Ordered Product Sales Amount, Units Ordered, Total Order Items, Sessions, Page Views, Unit Session Percentage, Buy Box Percentage, Units Refunded, and Refund Rate by Parent ASIN, Child ASIN, SKU, and Title where supported. Explain which products drove the change, separate traffic, conversion, Buy Box, and refund effects, and produce a client-ready brief with key wins, risks, and three prioritized recommendations for [Next Period].
Multi-Client Order Operations Watchlist
Using the Catchr MCP, review Amazon Seller order records for [List of Client Account IDs] over [Recent Period]. Use Amazon Order ID, Order Purchase Date, Order Last Update Date, Order Status, Item Status, Fulfillment Channel, Ship Service Level, Sales Channel, Product ASIN, Product SKU, Item Quantity, Order Total Amount, and shipping geography to identify stalled, cancelled, or otherwise exceptional orders according to [Operational Rules]. Rank clients by exception volume and value exposure, show the affected orders and products, distinguish fulfillment patterns from isolated cases, and provide a consultant-ready watchlist with the owner and next action for each issue.
Morning Seller Account Priority Queue
Using the Catchr MCP, pull Amazon Seller performance for [List of Client Account IDs] for [Recent Period, e.g., yesterday or month to date] and compare it with [Baseline Period]. For each client, review Ordered Product Sales Amount, Units Ordered, Sessions, Unit Session Percentage, Buy Box Percentage, Refund Rate, Received Negative Feedback Rate, and Orders Shipped against [Client KPI Targets]. Rank the clients I should check today, explain the precise account, ASIN, or SKU signal behind each priority, and give me the first investigation or client-communication action to take.
Monthly Amazon Seller Client Report
Using the Catchr MCP, pull Amazon Seller data for [Client Account ID] in [Marketplace ID] for [Reporting Period] and [Previous Period]. Summarize Ordered Product Sales Amount, Shipped Product Sales Amount, Units Ordered, Units Shipped, Total Order Items, Sessions, Page Views, Unit Session Percentage, Buy Box Percentage, Units Refunded, and Refund Rate, then break down the material changes by Parent ASIN, Child ASIN, SKU, and Title. Write a concise client-ready report in plain English covering what improved, what declined, why it changed according to the available seller data, and the three priorities recommended for [Next Period].
SKU Recovery Action Backlog
Using the Catchr MCP, analyze Amazon Seller performance for [Client Account ID] over [Period] versus [Comparison Period] by Parent ASIN, Child ASIN, SKU, and Title. Use Sessions, Buy Box Percentage, Unit Session Percentage, Units Ordered, Ordered Product Sales Amount, Average Selling Price Amount, Units Refunded, and Refund Rate to identify products losing sales momentum. Diagnose whether each issue is most consistent with traffic loss, Buy Box loss, weaker conversion, price movement, or refunds, require [Minimum Sessions or Sales Threshold] before prioritizing an item, and create a freelancer-friendly backlog ranked by expected impact and effort with a specific test, success metric, and review date.
ASIN Growth Opportunity Matrix
Using the Catchr MCP, pull Amazon Seller data for [Account ID] in [Marketplace ID] over [Period] and [Comparison Period], grouped by Parent ASIN, Child ASIN, SKU, and Title. For each product, report Sessions, Page Views, Buy Box Percentage, Unit Session Percentage, Units Ordered, Total Order Items, Ordered Product Sales Amount, Average Selling Price Amount, and Refund Rate. Classify products as Scale, Improve Conversion, Recover Buy Box, Protect, or Review based on [Business Thresholds], explain the evidence for each classification, and recommend the highest-impact merchandising, pricing, availability, or listing action without inventing inventory or advertising data.
Traffic-to-Sale Conversion Diagnostic
Using the Catchr MCP, analyze Amazon Seller traffic and sales for [Account ID] over [Period] versus [Comparison Period]. By Parent ASIN, Child ASIN, SKU, and Title, compare Sessions, Page Views, Browser Sessions, Mobile App Sessions, Buy Box Percentage, Order Item Session Percentage, Unit Session Percentage, Units Ordered, and Ordered Product Sales Amount. Identify whether each material sales increase or decrease is primarily associated with traffic volume, device mix, Buy Box visibility, conversion efficiency, or average selling price; quantify the contribution where the available fields allow it and return a prioritized optimization plan with [Target Metric] and [Review Date].
Refund and Customer Experience Early Warning
Using the Catchr MCP, monitor Amazon Seller customer-experience risk for [Account ID] over [Recent Period] and compare it with [Baseline Period]. Analyze Units Refunded, Refund Rate, Feedback Received, Negative Feedback Received, Received Negative Feedback Rate, Claims Granted, Claims Amount, Units Shipped, and Orders Shipped by Parent ASIN, Child ASIN, SKU, and Title where available. Flag products exceeding [Refund Threshold], [Negative Feedback Threshold], or [Claims Threshold], distinguish sudden spikes from persistent issues, estimate the affected sales exposure using available seller sales fields, and create an action list for product quality, listing accuracy, packaging, or fulfillment investigation.
Seller Order and Sales Integrity Audit
Using the Catchr MCP, audit Amazon Seller data for [Account ID or All Connected Accounts] over [Period]. At order grain, check Amazon Order ID and Product SKU uniqueness at the expected item grain, required dates, valid Order Status and Item Status combinations, non-negative Item Quantity and Order Total Amount, and Purchase Date not later than Order Last Update Date. Separately, at sales grain, validate non-negative Ordered Product Sales Amount, Shipped Product Sales Amount, Units Ordered, Units Shipped, Units Refunded, Total Order Items, and Orders Shipped. Reconcile order- and sales-level trends only at compatible date, marketplace, ASIN, and SKU grains within [Tolerance], and return an exception table with failed rule, observed values, severity, and next validation step.
Extraction Freshness and Coverage Monitor
Using the Catchr MCP, assess Amazon Seller data freshness and coverage for [Account ID or All Connected Accounts] over [Lookback Period]. Use Extracted Date, Date, Marketplace ID, Amazon Order ID, Parent ASIN, Child ASIN, SKU, Ordered Product Sales Amount, Sessions, and Units Ordered to detect stale extractions, missing dates, duplicate records at [Expected Grain], unexpected null dimensions, and flatlined metrics. Quantify each issue by marketplace and affected period, distinguish likely pipeline failures from plausible no-activity days using cross-field evidence, and output a monitoring summary plus a remediation-ready exception table.
Marketplace and B2B Mix Observatory
Using the Catchr MCP, analyze Amazon Seller business mix for [Account ID or All Connected Accounts] over [Period] and [Comparison Period]. By Marketplace ID, Parent ASIN, Child ASIN, SKU, and [Week or Month], compare Ordered Product Sales Amount, Units Ordered, Sessions, Unit Session Percentage, Buy Box Percentage, and their B2B counterparts: Ordered Product Sales B2B Amount, Units Ordered B2B, Sessions B2B, Unit Session Percentage B2B, and Buy Box Percentage B2B. Calculate B2B shares only from compatible fields, flag statistically or commercially material shifts beyond [Change Threshold], identify the products and marketplaces driving them, and produce a data-team brief separating genuine mix changes, sparse B2B coverage, and possible extraction anomalies.
Replace [variables] Facts ≠ hypotheses Action + follow-up metric

Amazon Seller to ChatGPT FAQs

Clear answers for marketers comparing connectors, data sources, reporting workflows, and dashboard setup.

What Amazon Seller data can ChatGPT analyze through Catchr?

ChatGPT can query connected Amazon Seller data for sales, orders, products, customers, discounts, refunds, inventory, and fulfillment reporting.

Representative measures include Ordered Product Sales Amount, Units Ordered, Sessions, Page Views, and Buy Box Percentage. Useful breakdowns include Date, Product SKU, Product ASIN, Product Name, and Sales Channel. The exact field set depends on the selected dataset and compatible reporting reporting level.

How granular can Amazon Seller analysis be in ChatGPT?

The available detail depends on the fields returned for the selected dataset. Useful breakdowns include Date, Product SKU, Product ASIN, Product Name, and Sales Channel.

Ask ChatGPT to state the reporting level it used and keep incompatible levels separate before calculating totals, rates, or comparisons.

Can ChatGPT identify product and operational risks in Amazon Seller?

Yes. ChatGPT can surface product, stock, margin, refund, cancellation, or fulfillment patterns that deserve review. Relevant fields include Ordered Product Sales Amount, Units Ordered, Sessions, Page Views, and Buy Box Percentage.

Set the business target, comparison period, and minimum volume before requesting priorities or recommendations.

Can ChatGPT compare acquisition, customer, and revenue mix in Amazon Seller?

Yes. ChatGPT can compare how products, channels, customer groups, or markets contribute to sales and repeat business. Useful breakdowns include Date, Product SKU, Product ASIN, Product Name, and Sales Channel.

Ask it to separate observed data, possible explanations, and the next validation step so an unusual value is not presented as a proven cause.

What types of prompts work best for Amazon Seller analysis?

Strong prompts name the action, account or entity, date range, comparison, and KPI or threshold.

  • Monitor: “Cross-Client Seller Health Command Center”
  • Diagnose: “Seller Order and Sales Integrity Audit”
  • Find opportunities: “SKU Recovery Action Backlog”
  • Report: “Monthly Amazon Seller Client Report”

Can I combine Amazon Seller with other data sources in ChatGPT?

Yes, when the other sources are also connected to Catchr. Useful combinations include advertising, analytics, CRM, email, or finance sources.

Align dates, currencies, identifiers, and definitions first. If the sources cannot be joined reliably, ask for a side-by-side comparison rather than a single attributed result.

Can I analyze multiple Amazon seller accounts or marketplaces together?

Yes, provided each one is connected and available through Catchr MCP. Ask for separate results first, then create the combined view.

Specify the store, marketplace, currency, timezone, catalog, period, and target. This keeps one large entity or a definition mismatch from distorting the comparison.

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

You need access that can authorize and view the relevant Amazon Seller data, a Catchr workspace with the source connected, and Catchr MCP enabled in ChatGPT.

After selecting the required entities, you can ask questions in natural language without preparing a new CSV export for each analysis.

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

ChatGPT analyzes the records returned through the connected Catchr source; it should not assume the data is real time. Freshness and historical coverage can vary by dataset, field, selected period, and source API limits.

Ask it to state the latest order, inventory, fulfillment, or extraction date, together with the timezone, requested period, and any missing intervals before interpreting a trend.

Can ChatGPT change anything in Amazon Seller through Catchr?

No. Catchr MCP provides Amazon Seller data for analysis; it does not give ChatGPT permission to change orders, products, prices, inventory, fulfillment, or store settings.

Use the result to prepare an action plan, then make operational changes in Amazon Seller. Prefer aggregated outputs whenever names, contact details, or record-level information are not required.

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

Catchr MCP is included in every standard plan—there is no separate fee for the ChatGPT integration. The Starter plan begins at $20 per month when billed annually, or $24 when billed monthly.

It includes 3 platforms, 10 accounts, unlimited users, and unlimited requests. Larger plans increase the number of platforms and accounts available. See Catchr pricing

‍Can I try the ChatGPT integration before choosing a plan?

Yes. Catchr offers a 14-day free trial with no credit card required.

You can use the trial to connect your marketing sources, authorize Catchr MCP in ChatGPT, and test questions using your own data before subscribing.

Still have a question ? 

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