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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Meta Ads
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Google Ads
2 advertising accounts
Connected
Google Analytics 4
1 web property
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HubSpot
1 CRM portal
Connected
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 Google Play Store data for [List of Client Accounts or Apps] over [Period] and compare it with [Comparison Period]. For each app, summarize Store listing visitors, Store listing acquisitions, Store listing conversion rate, Daily User Installs, Daily User Uninstalls, Daily Crashes, Daily ANRs, Daily Average Rating, New Subscriptions, Active Subscriptions, and Canceled Subscriptions wherever available. Label each app Healthy, Watch, or Critical against [Client Targets or Historical Baseline], explain the evidence behind every Watch or Critical label, and rank the accounts the consultant should review first with one concrete next action per account.
ChatGPT can query connected Google Play Store data for store visibility, acquisition, downloads, installs, ratings, subscriptions, sales, and territory reporting.
Representative measures include Store listing conversion rate, Store listing acquisitions, Store listing visitors, Daily User Installs, and Daily User Uninstalls. Useful breakdowns include Date, Traffic Source, Search Term, UTM campaign, and UTM Source. 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, Traffic Source, Search Term, UTM campaign, and UTM Source.
Ask ChatGPT to state the reporting level it used and keep incompatible levels separate before calculating totals, rates, or comparisons.
Yes. ChatGPT can compare listing traffic, downloads, installs, conversion, ratings, or supported revenue signals. Relevant fields include Store listing conversion rate, Store listing acquisitions, Store listing visitors, Daily User Installs, and Daily User Uninstalls.
Set the business target, comparison period, and minimum volume before requesting priorities or recommendations.
Yes. ChatGPT can surface territory, product, device, acquisition-source, release, or subscription patterns. Useful breakdowns include Date, Traffic Source, Search Term, UTM campaign, and UTM Source.
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 mobile attribution, advertising, review, analytics, 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 app, product, territory, currency, release period, and KPI target. This keeps one large entity or a definition mismatch from distorting the comparison.
You need access that can authorize and view the relevant Google Play Store 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 app, store-performance, sales, or subscription reporting date, together with the timezone, requested period, and any missing intervals before interpreting a trend.
No. Catchr MCP provides Google Play Store data for analysis; it does not give ChatGPT permission to change app listings, products, subscriptions, releases, or store settings.
Use the result to prepare an action plan, then make operational changes in Google Play Store. 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.