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.
ChatGPT can analyze records and fields from an Airtable table available through Catchr. Catchr discovers each Base / Table pair and returns the table’s own fields, so the available data depends on the selected Airtable table rather than a fixed list of marketing metrics.
ChatGPT can use supported date dimensions such as Date, Year, Year month, Week of the year, Quarter of the year, Day of the week, and Hour of the day. Specify the date field, period, grouping, and comparison you want in the prompt.
Catchr maps discovered fields with Airtable field IDs, which keeps the underlying mapping stable when a field’s display name changes. Use the current field names in your ChatGPT prompts so requests remain clear to reviewers and clients.
Catchr preserves linked records, attachments, multi-select values, and other complex Airtable values as JSON strings, while scalar numbers, booleans, and date-like values keep their appropriate native types. Ask ChatGPT to explain how it interpreted complex values before using them in summaries or comparisons.
Strong prompts name the Base / Table pair, relevant fields, filters, date range, grouping, and desired output. For example, ask ChatGPT to summarize records by month, identify missing values, compare categories, or prepare a client-ready table from specified Airtable fields.
Yes, when Airtable and the other sources are connected and available through Catchr. Specify the matching identifiers, dates, and definitions; if a reliable join is not possible, ask ChatGPT for a side-by-side comparison instead of a merged result.
Yes, provided the relevant Base / Table pairs are available through Catchr. Ask for separate results first, then request a combined comparison using clearly identified common fields so differences in table structure do not distort the analysis.
You need an Airtable personal access token that can access the relevant data, a Catchr workspace with Airtable connected, and Catchr MCP enabled in ChatGPT. Catchr then discovers the available bases and tables for selection.
ChatGPT analyzes the Airtable records returned through the connected Catchr source and should not assume they are real time. Freshness and historical coverage depend on the selected table, its records, the requested period, and Airtable API limits, so ask ChatGPT to state the latest available date and flag missing intervals.
No. Catchr MCP provides Airtable data to ChatGPT for analysis and does not grant permission to create, edit, or delete Airtable records. Use the output to prepare recommendations or an action list, then review and apply any changes in Airtable.
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.