App Store Connect Analytics Receives Largest Update Since Launch — Over 100 New Metrics and Cohort Analysis Tools

What Changed

On March 24, 2026, Apple released a major expansion of the Analytics platform in wiki:app-store-connect. The update introduces over 100 new metrics and several entirely new reporting capabilities that shift how developers can track post-acquisition performance.

The core additions:

• Monetization and subscription data: Developers now see In-App Purchase performance, offer effectiveness, subscription retention curves, and churn analysis directly inside Analytics. Previously, pulling a complete monetization picture required stitching together data from multiple places in App Store Connect, Sales and Trends, and third-party attribution tools. That fragmentation is now resolved for most use cases.

• Cohort analysis tools: Users can be segmented by download date, traffic source, region, or offer start date — then tracked longitudinally to compare how different groups perform over time. For example, if you launched in a new region in Q1 2026, you can now compare how long it takes users from that market to convert versus users from established markets, how retention differs, and whether monetization velocity varies by source.

• Peer group benchmarks: Two new monetization-specific benchmarks — download-to-paid conversion and proceeds per download — allow developers to compare their app's business metrics against anonymized industry data in the same category. These benchmarks use differential privacy to protect individual app performance within peer groups.

• Subscription data export via API: Two new subscription reports can now be exported programmatically via the Analytics Reports API, enabling offline analysis and integration into internal BI systems without manual CSV downloads.

• Enhanced filtering: Developers can now apply up to seven filters simultaneously to selected metrics, which makes it possible to drill into very specific segments — for example, users who downloaded from a specific campaign, in a specific country, during a specific week, who started a trial offer.

Apple also published a comprehensive App Store Analytics Guide in the Help section, which walks through how to structure data-driven ASO strategies using the new metrics.

What This Means for Practitioners

Before this update, wiki:app-store-connect Analytics served mostly as a top-line performance view — impressions, downloads, conversion rate, crash data. If you wanted to understand monetization, you had to open Sales and Trends. If you wanted cohort retention, you had to use a third-party analytics tool or build your own reporting stack. If you wanted to compare your app's revenue metrics to peers, you had no baseline.

Now, a significant portion of that measurement stack lives in one place. This matters most for teams that are:

• Running paid acquisition campaigns and need to validate whether different traffic sources produce different lifetime value profiles
• Managing subscription apps and need to track retention, churn, and offer performance without exporting data to external tools
• Operating in multiple markets and need to compare how monetization develops across regions over time
• Testing pricing strategies and need peer benchmarks to validate whether their conversion rates are competitive

The cohort capabilities are particularly useful for evaluating the lagging effects of ASO changes. If you update your wiki:metadata in March and want to see whether users acquired in April monetize differently than users acquired in February, you can now segment by download date and compare revenue per user, trial start rate, and subscription conversion across those groups.

Important Limits and Privacy Thresholds

These are powerful additions, but they come with boundaries that affect how you interpret the data.

Engagement metrics reflect opt-in users only. Active device counts and session data only include users who agreed to share diagnostics and usage information with you. This means your engagement figures represent a subset of your actual user base, not the full installed base. The percentage who opt in varies by market and app category, which means you cannot assume the sample is representative.

Privacy thresholds apply throughout Analytics. Certain acquisition sources, app referrers, web referrers, and campaign links require a minimum volume of data before they appear in reporting. If you are not seeing a particular source, it may be below the threshold rather than absent. These thresholds extend to the new subscription and cohort views.

Conversion rate calculations differ from install-based metrics. Total Downloads combines First-Time Downloads and Redownloads. Conversion rate is calculated as total downloads divided by unique impressions. Both are meaningful, but they measure different things. If you are comparing conversion performance across sources or testing creative variations, make sure you are using the metric that aligns with what you are actually trying to optimize.

Peer benchmarks use differential privacy. The peer group comparison values are generated using differential privacy techniques to protect individual app performance within the group. This means the benchmarks are directionally useful but not precise to the decimal. If your app's proceeds per download is 5% above the peer median, that is meaningful. If it is 0.5% above, you are likely within noise.

Before making a significant decision based on a new metric — especially cohort or benchmarking data — confirm what that metric includes, what it excludes, and whether privacy thresholds could be affecting the view.

Where This Fits in the Larger Measurement Picture

Apple has also been expanding measurement capabilities on the paid acquisition side. Separately from the Analytics update, Apple Ads introduced a redesigned Insights workspace with flexible reporting across campaign groups, campaigns, ad placements, and keywords. Insights now includes predefined Performance reports and Advanced Impression Share reports, plus the ability to edit metrics, dimensions, and filters for custom views.

The division of labor is now clearer:

• Apple Ads Insights covers the paid side of your account: what is driving changes in spend efficiency, which placements are contributing the most, which search terms are converting, how performance compares across countries and regions.

• App Store Connect Analytics covers what happens on the App Store itself: how people find your app, whether they download it, what they do after, how different user groups perform over time, and how your business outcomes develop post-acquisition.

If you are running Apple Search Ads campaigns, you now have visibility into both sides of the funnel in native Apple tooling. That does not eliminate the need for attribution platforms or product analytics tools — especially if you run cross-channel campaigns or need event-level data — but it does mean you can answer a much broader set of questions without exporting data or building custom dashboards.

For teams that rely on downstream outcomes beyond installs, the combination of cohort analysis in Analytics and flexible reporting in Insights makes it possible to connect acquisition decisions to revenue outcomes with much less friction than before.

What to Do Next

If you are not already using App Store Connect Analytics regularly, the April 2026 update makes it worth revisiting. Here is where to start:

• Open the new Analytics Guide in the App Store Connect Help section and walk through the updated metrics and report types. Apple structured the guide around common use cases — understanding acquisition sources, evaluating wiki:conversion-rate trends, tracking retention, analyzing monetization — so you can map the new capabilities to your actual workflow.

• Set up a cohort view for your most recent product change or market expansion. If you launched a new feature, changed your paywall, or entered a new region in the last 90 days, segment users by download date and compare how the new cohort performs relative to earlier cohorts. This is the fastest way to validate whether a change is working.

• Check peer benchmarks for your category. If your download-to-paid conversion or proceeds per download are significantly below the peer median, that is a signal to audit your pricing strategy, paywall design, or trial offer structure. If you are above the median, you have validation that your monetization mechanics are competitive.

• Export subscription data via the API if you maintain your own BI stack. The programmatic export eliminates the need for manual CSV downloads and makes it easier to integrate App Store subscription performance into your company's internal dashboards.

• Apply multiple filters to drill into specific segments that matter for your business. If you run paid campaigns in specific regions, filter by source, country, and date range to isolate performance for those campaigns and compare them to organic users in the same markets.

The update does not replace third-party analytics, attribution, or product tools — especially for cross-platform apps or teams that need event-level granularity — but it does collapse a meaningful portion of the post-acquisition measurement stack into one system. For iOS-first teams, subscription apps, and developers who want to reduce dependency on external tools, this is the most significant expansion of first-party app performance visibility Apple has shipped in years.