Imported from appeeky/aso-skills (skills/asc-metrics/SKILL.md) via skills.sh. Install upstream with npx skills add appeeky/aso-skills --skill asc-metrics. Copyright stays with the author.

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

1. Check for app-marketing-context.md — read it for app context
2. Ask: What do you want to analyze? (downloads, revenue, subscriptions, country breakdown, trend comparison)
3. Ask: Which time period? (default: last 30 days)
4. 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:

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:

1. Top 5 by downloads — Are you investing in ASO for these markets?
2. Top 5 by revenue — Higher ARPD (avg revenue per download) = prioritize ASO
3. High downloads, low revenue — Markets with weak monetization
4. Low downloads, high revenue — Under-tapped premium markets (localize)

Revenue Quality Check

Compute from the data:

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?"

1. Pull daily trend — when did it start?
2. Check if an update shipped on that date
3. Check keyword rankings (use keyword-research skill)
4. 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

Related Skills

• app-analytics — Full analytics stack setup and KPI framework
• monetization-strategy — Improve subscription conversion and paywall
• retention-optimization — Reduce churn using the metrics as input
• localization — Expand top-performing markets seen in country data
• ua-campaign — Validate whether paid installs show in downloads spike