asc-metrics

$28

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

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