App Store Connect Analytics Help
Analytics Reports API
Overview
The Analytics Reports API lets you export App Store Connect Analytics data in bulk, enabling you to perform offline analysis and integrate App Store performance into your own data systems.
Analytics reports provide access to the most granular data available through App Store Connect, while still preserving privacy. You can use these reports for custom analysis, reporting, and automation.
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Data format: Reports are delivered as compressed, tab-delimited text files (
.txt.gz), a standard format that’s easy to ingest with most data processing tools. -
Access and roles: Accessing reports requires an App Store Connect API key with the appropriate role. An Admin role is required to request a new Analytics Report type for the first time. Once a report type has been requested, API keys with Sales and Reports or Finance roles can download generated reports.
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Initial setup: You can make two types of requests. An
ONGOINGrequest generates reports on a recurring basis (daily, weekly, and monthly), while aONE_TIME_SNAPSHOTprovides a comprehensive collection of all available historical data. Once you make anONGOINGreport request, you will receive your first report approximately 24-48 hours later. -
Data completeness: Data for a given day is considered complete two days after the reporting date. Learn more about data completeness.
Available report categories
Analytics reports are grouped into several categories, each focused on a different part of the app lifecycle.
App Store engagement
These reports show how people discover and interact with your app on the App Store.
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App Store Discovery and Engagement Report
Detailed data on impressions, page views, and taps across product pages, store sheets, and In-App Events. Includes dimensions such as event type, page type, source type, and aggregated counts.
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Insights into how users engage with your app's product pages and In-App Events on the web.
App Store commerce
These reports focus on downloads, pre-orders, and purchases.
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Measures how often your app is downloaded from the App Store.
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Details pre-orders placed and canceled for your app.
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Revenue and purchase data, including purchase type, content name, sales, proceeds, and paying users, with attribution to download sources.
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Details about subscription lifecycle changes, including the number of offer starts, offer renewals, conversions to paid, paid renewals, voluntary and involuntary churn, and more. Includes the ability to attribute subscription events back to the download source.
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Details about the number of active and churned subscriptions on the App Store, including the total number of paid subscriptions, free trials, paid offers, subscriptions in billing issues, and more. Includes the ability to attribute subscription states back to the download source.
Note: These reports replace legacy Sales and Trends subscription reports, making it easier to connect downloads to subscription outcomes in a privacy-preserving way.
App usage
These reports help you understand how users interact with your app after download. Usage data is collected only from users who have opted in to share diagnostics and usage information.
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Engagement and usage metrics for App Clips.
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Crash data broken down by app version and device type.
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App Store Installations and Deletions Report
Install and delete activity over time.
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The percentage of users who opt in to share diagnostics and usage data.
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Session counts and average session duration.
Privacy and data availability
Analytics Reports are built with user privacy in mind. Data is aggregated and processed using privacy-enhancing techniques to prevent individual identification. Standard reports omit fields that could easily identify individual users. Detailed reports that include additional fields apply extra safeguards. Thresholding, for example, omits rows with data from fewer than five users or five unique devices. Some usage reports are not generated if minimum thresholds are not met. This means data may not appear for low-volume activities or new features. Additionally, a small amount of statistical noise is added to metrics, preventing the reverse-engineering of individual activity while preserving accuracy at scale.