asc-metrics

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• SKILL.md

SKILL.md (first lines)

# 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](https://appeeky.com) 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

```bash
GET /v1/connect/metrics/apps
```

Match the user's app to an `app_apple_id` if not already known.

### Step 2 — Get overview (portfolio)

```bash
GET /v1/connect/metrics?from=YYYY-MM-DD&to=YYYY-MM-DD
```

### Step 3 — Get app detail (single app)

```bash
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](../../tools/integrations/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:
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:

| 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]
```

From appeeky/aso-skills, MIT.

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