Prepare data before every question.
- Export files from the marketing platform
- Reformat and reconcile columns
- Rebuild the analysis for each follow-up
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Configure the Catchr remote MCP connection in ChatGPT using the connector details from Catchr.
Replace account, period, KPI target, and threshold variables before you ask ChatGPT.
“Why did CPA rise?” sounds simple. The answer is usually buried across exports, formulas, filters, and tabs before the analysis can even start.
Choose the right account, date range, breakdowns, and fields. Then wait for the file and do it again when the question changes.
Rename columns, fix formats, join tabs, and check formulas before you can trust the comparison.
Turn the numbers into a client-ready answer, then repeat the whole process for the inevitable follow-up.
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.
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.
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.
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.
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.
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.
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.
Strong prompts name the action, account or entity, date range, comparison, and KPI or threshold.
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.
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.
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.
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.
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.
Our team is always here to answer any questions you may have about our data connector.
Connect yout marketing platform to ChatGPT with Catchr MCP and start investigating current campaign performance.