ASC Metrics You analyze the user's official App Store Connect data synced into Appeeky — exact downloads, revenue, IAP, subscriptions, and trials. This is first-party data, not estimates. Prerequisites Appeeky account with ASC connected (Settings → Integrations → App Store Connect) Indie plan or higher (2 credits per request) Data syncs nightly; up to 90 days of history available If ASC is not connected, prompt the user to connect it at appeeky.com/settings and return. Initial Assessment Check for app-marketing-context.md — read it for app context Ask: What do you want to analyze? (downloads, revenue, subscriptions, country breakdown, trend comparison) Ask: Which time period? (default: last 30 days) Ask: Specific app or all apps? Fetching Data Step 1 — List available apps GET /v1/connect/metrics/apps Match the user's app to an app_apple_id if not already known. Step 2 — Get overview (portfolio) GET /v1/connect/metrics?from = YYYY-MM-DD & to = YYYY-MM-DD Step 3 — Get app detail (single app) GET /v1/connect/metrics/apps/:appId?from = YYYY-MM-DD & to = YYYY-MM-DD Response includes: daily[] , countries[] , totals . See full API reference: appeeky-connect.md Analysis Frameworks Period-over-Period Comparison Fetch two equal-length windows and compare: Metric Prior Period Current Period Change Downloads [N] [N] [+/-X%] Revenue $[N] $[N] [+/-X%] Subscriptions [N] [N] [+/-X%] Trials [N] [N] [+/-X%] Trial → Sub Rate [X]% [X]% [+/-X pp] What to look for: Downloads rising but revenue flat → pricing or paywall issue Trials rising but conversions flat → paywall or onboarding issue Revenue rising but downloads flat → good monetization improvement Daily Trend Analysis From daily[] , identify: Spikes — Did a feature, update, or press trigger them? Drops — Correlate with app updates, seasonality, or algorithm changes Trend direction — 7-day moving average vs prior 7 days Country Breakdown Sort countries[] by downloads and revenue: Top 5 by downloads — Are you investing in ASO for these markets? Top 5 by revenue — Higher ARPD (avg revenue per download) = prioritize ASO High downloads, low revenue — Markets with weak monetization Low downloads, high revenue — Under-tapped premium markets (localize) Revenue Quality Check Compute from the data: Metric Formula Benchmark ARPD Revenue / Downloads
$0.05 good; > $0.20 excellent Trial rate Trials / Downloads 20% means strong paywall reach Sub conversion Subscriptions / Trials 25% is strong Revenue per sub Revenue / Subscriptions Depends on pricing Output Format Performance Snapshot 📊 [App Name] — [Period] Downloads: [N] ([+/-X%] vs prior period) Revenue: $[N] ([+/-X%]) Subscriptions: [N] ([+/-X%]) Trials: [N] ([+/-X%]) IAP Count: [N] ([+/-X%]) Trial→Sub: [X]% Top Markets (downloads): 1. [Country] — [N] downloads, $[N] 2. [Country] — [N] downloads, $[N] 3. [Country] — [N] downloads, $[N] Key Observations: - [What the trend means] - [Any anomaly and likely cause] - [Opportunity identified] Recommended Actions: 1. [Specific action based on data] 2. [Specific action based on data] Trend Alert When a significant change (>20%) is detected, flag it: ⚠️ Downloads dropped [X]% this week Possible causes: [list 2-3 hypotheses] Next steps: [specific diagnostic actions] Common Questions "Why did my downloads drop?" Pull daily trend — when did it start? Check if an update shipped on that date Check keyword rankings (use keyword-research skill) Check competitor activity (use competitor-analysis skill) "Which countries should I localize for?" Pull country breakdown → sort by downloads → flag high-download, non-English markets → use localization skill "Is my monetization improving?" Compare trial rate and trial→sub rate period over period → use monetization-strategy skill for paywall improvements