Connect App Store Connect to ChatGPT

Multi-App Portfolio Health Scan

Using the Catchr MCP, pull App Store Connect data for [List of Client App IDs] over [Period, e.g., the last 7 days] and compare it with [Comparison Period]. For each App Name, report Impressions, Product Page View, Tap, Downloaded Count, Install Count, Uninstall Count, Subscribers, and total developer proceeds calculated from Summary Sales units multiplied by Summary Sales Developer Proceeds (per unit). Calculate product-page-view-to-download and install-to-uninstall ratios where the inputs are available, assess each app against [Client KPI Targets or Historical Baseline], label it Healthy, Watch, or Critical, and rank the accounts the consultant should review first with the measurable reason and one next action for every warning.

Client App Growth and Revenue Brief

Using the Catchr MCP, analyze App Store Connect performance for [Client App ID] over [Reporting Period] versus [Previous Period]. Show Impressions, Product Page View, Tap, Downloaded Count, Install Count, Summary Sales units, and total developer proceeds calculated row by row from Summary Sales units and Summary Sales Developer Proceeds (per unit). Break the largest changes down by Engagement Source Type, Downloaded Source Type, Downloaded Territory, Summary Sales Country Code, and Summary Sales Product Identifier. Produce a client-ready briefing with an executive summary, three wins, three risks, evidence for each finding, and prioritized actions for acquisition, product-page optimization, or monetization; do not claim causation when the data only shows correlation.

Cross-Client Subscription Risk Watchlist

Using the Catchr MCP, review subscription health for [List of Client App IDs] over [Period] and compare it with [Comparison Period]. For each app, summarize Subscribers, Active Standard Price Subscriptions, Subscription Billing Retry, Subscription Grace Period, and Subscription Event Quantity grouped by Subscription Event, Subscription Event Cancellation Reason, Subscription Event Name, and Subscription Event Client Country. Apply [Risk Thresholds], identify apps with rising cancellations, billing-retry exposure, or grace-period exposure, rank the risks by affected subscriber volume, and return a client-ready watchlist with the supporting values, likely area to investigate, and a concrete retention or billing follow-up for each app.

Morning Client App Priority Queue

Using the Catchr MCP, pull month-to-date App Store Connect data for [List of Client App IDs] and compare each app with [Previous-Month-to-Date Period] and [Client Targets]. For every App Name, summarize Downloaded Count, Install Count, Uninstall Count, Summary Sales units, Subscribers, Subscription Billing Retry, and Subscription Grace Period. Quantify pacing against the supplied targets, flag material deterioration using [Thresholds], and rank the clients I should work on today. For every flagged client, name the largest measurable driver by Downloaded Source Type, Downloaded Territory, Install / Uninstall App Version, or Subscription Event and give one specific first action.

Monthly App Store Client Report

Using the Catchr MCP, create a client-ready App Store Connect report for [Client App ID] covering [Current Month] versus [Previous Month]. Summarize Impressions, Product Page View, Downloaded Count, Install Count, Uninstall Count, Summary Sales units, total developer proceeds calculated from units and Summary Sales Developer Proceeds (per unit), Subscribers, and Review Rating. Explain the most important changes by Downloaded Source Type, Downloaded Territory, Summary Sales Product Identifier, Subscription Name, and Review Territory. Structure the output as executive summary, what worked, what declined, data-backed explanations, and the three priorities for [Next Month], using plain English and clearly marking unavailable values.

Post-Release Adoption and Quality Check

Using the Catchr MCP, evaluate the release of [App Version] for [Client App ID] during [Post-Release Period] against [Pre-Release Period or Previous Version]. Use Downloaded App Version and Install / Uninstall App Version to compare Downloaded Count, Install Count, and Uninstall Count by Date, Downloaded Device, and Downloaded Territory; also summarize Review Rating, Review Title, and Review Content since [Release Date]. Flag territories or devices with weak adoption, an elevated uninstall-to-install ratio, or deteriorating ratings against [Thresholds]. Produce a concise client update and a prioritized action list separating store-listing, compatibility, and product-quality investigations.

App Store Acquisition Funnel Optimizer

Using the Catchr MCP, pull App Store Connect discovery and download data for [E-commerce App ID] over [Period] and compare it with [Previous Period]. Report Impressions, Product Page View, Tap, Downloaded Count, and Install Count, then calculate impression-to-page-view, page-view-to-tap, tap-to-download, and download-to-install rates wherever the required fields are available. Break performance down by Engagement Source Type, Downloaded Source Type, Downloaded Page Title, Downloaded Campaign, Downloaded Device, and Downloaded Territory. Identify the three largest funnel leaks and recommend a specific product-page, campaign, or market test for each, including [Success Threshold] and [Review Date].

Market and Product Sales Opportunity Map

Using the Catchr MCP, analyze App Store Connect sales for [E-commerce App ID] over [Period] versus [Comparison Period]. Group Summary Sales units, Summary Sales Customer Price, and Summary Sales Developer Proceeds (per unit) by Summary Sales Product Identifier, Summary Sales Product Type Identifier, Summary Sales Country Code, Summary Sales Device, and Summary Sales Order Type. Calculate total developer proceeds row by row as units multiplied by developer proceeds per unit, rank products and territories by units, proceeds, and period-over-period growth, apply [Minimum Unit Threshold], and produce a scale, monitor, or fix recommendation for each material segment with the exact evidence and next commercial action.

Subscription Offer and Retention Scorecard

Using the Catchr MCP, evaluate subscription monetization for [E-commerce App ID] over [Period]. Compare Subscribers, Active Standard Price Subscriptions, Active Free Trial Introductory Offer Subscriptions, Free Trial Promotional Offer Subscriptions, Subscription Billing Retry, and Subscription Grace Period; then analyze Subscription Event Quantity by Subscription Event, Subscription Event Offer Type, Subscription Event Promotional Offer Name, Subscription Event Name, and Subscription Event Cancellation Reason. Compare with [Previous Period or Offer Baseline], identify which offers are associated with stronger paid progression or higher cancellation and billing risk, and recommend three retention or offer tests with a measurable success metric, minimum sample requirement, and review window.

App Analytics Data Integrity Audit

Using the Catchr MCP, audit App Store Connect data for [App ID or All Connected Apps] over [Period]. At Date and App Id level, check completeness and uniqueness for App Name and the relevant source, territory, device, campaign, page, and version dimensions. Validate that Impressions, Product Page View, Tap, Downloaded Count, Install Count, and Uninstall Count are non-negative; flag duplicate dimension combinations, missing dates or identifiers, impossible unique counts above corresponding totals, and funnel values that require investigation. Return a reproducible exception table with app, date, failed rule, affected fields, observed values, severity, and the exact validation query or pipeline check to run next.

Daily App Performance Outlier Monitor

Using the Catchr MCP, retrieve daily App Store Connect data for [App ID or All Connected Apps] over [Lookback Period, e.g., 90 days]. Monitor Impressions, Product Page View, Downloaded Count, Install Count, Uninstall Count, Summary Sales units, Subscribers, Subscription Billing Retry, and Subscription Grace Period against a trailing [Baseline Window, e.g., 28 days]. Flag deviations beyond [Threshold, e.g., 2 standard deviations and 25%], localize each anomaly by App Name, source, territory, device, product, or subscription where available, and classify it as likely data-quality, mix shift, release effect, or genuine business change. Show the evidence, confidence level, and next query needed to verify every classification.

Sales and Subscription Reconciliation Audit

Using the Catchr MCP, reconcile App Store Connect sales and subscription datasets for [App ID or All Connected Apps] over [Period]. Compare Summary Sales units by Summary Sales Product Identifier and Summary Sales Subscription with Subscriber Units by Subscriber Subscription Apple ID and with Subscription Event Quantity by Subscription Event Apple ID, aligned by date and country where available. Separately compare calculated sales proceeds from Summary Sales units multiplied by Summary Sales Developer Proceeds (per unit) with Subscriber Developer Proceeds aggregated in the same Subscription Proceeds Currency, without combining currencies. Apply [Materiality Threshold], flag missing mappings, duplicate records, currency mismatches, unexplained volume gaps, and refunds via Subscriber Refund, then return an issue table with magnitude, likely layer, and exact remediation or source check.

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

Connect App Store Connect to ChatGPT

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How to connect App Store Connect to ChatGPT ?

Authorize the App Store Connect 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 App Store Connect prompts for analyzing real account data.

App Store Connect to ChatGPT FAQs

What App Store Connect data can ChatGPT analyze through Catchr?

How granular can App Store Connect analysis be in ChatGPT?

Can ChatGPT analyze app acquisition and store performance in App Store Connect?

Can ChatGPT compare markets, products, releases, or subscription signals in App Store Connect?

What types of prompts work best for App Store Connect analysis?

Can I combine App Store Connect with other data sources in ChatGPT?

Can I analyze multiple apps or territories together?

What do I need to connect App Store Connect to ChatGPT with Catchr?

How recent is the App Store Connect data, and how much history can I analyze?

Can ChatGPT change anything in App Store Connect 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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