/analytics-interpretation

name: analytics-interpretation
description: Interpret app metrics and make data-driven decisions. Covers DAU/MAU, retention, LTV, ARPU, App Store Connect analytics, AARRR funnel analysis, cohort analysis, and diagnostic decision trees. Use when user wants to understand their metrics, diagnose problems, or build a data-driven growth plan.
allowed-tools: [Read, Glob, Grep, AskUserQuestion]
last_verified: 2026-07-16
review_by: 2027-06-22

Analytics Interpretation

Interpret your app's metrics, diagnose problems, and make data-driven decisions. Works with App Store Connect data, third-party analytics, or raw numbers the user provides.

When This Skill Activates

Use this skill when the user:

• Wants to understand their app metrics or analytics
• Asks about retention, LTV, ARPU, or churn
• Wants to know if their metrics are good or bad
• Needs help interpreting App Store Connect analytics
• Wants a data-driven growth plan
• Asks "what should I focus on to grow?"
• Has metrics data and wants to know what it means

Process

Step 1: Gather Context

Ask the user via AskUserQuestion:

1. **App type and monetization model**

• Free with ads, freemium, subscription, paid upfront, or hybrid?

2. **Current metrics they have access to**

• App Store Connect? Third-party analytics (Mixpanel, Firebase, Amplitude)?

3. **Specific numbers they can share**

• Downloads, DAU/MAU, retention, revenue, conversion rates?

4. **What they want to know**

• "Are my metrics good?" / "What should I fix?" / "Should I keep going?"

Also pull App Store Connect **peer group benchmarks** (App Analytics → Benchmarks) before interpreting any trend — they establish whether a metric is "bad for you" or "bad for the category."

How Peer Group Benchmarks Work

• **Peer group** = App Store category + business model (free / freemium / paid / paidmium / subscription) + download-volume band
• **Benchmarked metrics**: conversion rate, D1/D7/D28 retention, crash rate, average proceeds per paying user
• You see the peer group's **25th / 50th / 75th percentile** bands (example: day-1 retention 13.4% / 21.3% / 27.4%)
• Differential privacy adds noise and groups have minimum sizes — judge by **which quartile you're in**, not exact deltas
• Improving ≠ done: an app that lifted conversion +5.5% over 90 days can still sit in the bottom half of its peer group

| Below peers on... | Reach for... | |-------------------|--------------| | Conversion rate | Product Page Optimization + Custom Product Pages | | Retention | In-app events + App Clips | | Proceeds per paying user | Pricing tier review + promoted in-app purchases |

Step 2: Identify Key Metrics by App Type

Different monetization models have different north star metrics.

Free with Ads

| Metric | Why It Matters | |--------|---------------| | DAU/MAU | More daily users = more ad impressions | | Session length | Longer sessions = more ad views | | Sessions per day | More sessions = more revenue opportunities | | Ad impressions/revenue | Direct revenue driver | | D1/D7/D30 retention | Users must come back for ads to work |

Freemium (One-Time Unlock)

| Metric | Why It Matters | |--------|---------------| | Conversion rate (free → paid) | Primary revenue driver | | Time to conversion | How long before users see enough value | | Feature adoption | Which features drive upgrades | | Revenue per download | Overall monetization efficiency | | D7 retention (free users) | Must retain long enough to convert |

Subscription

| Metric | Why It Matters | |--------|---------------| | Trial start rate | Top of subscription funnel | | Trial → paid conversion | Critical conversion point | | Monthly churn rate | Determines LTV | | LTV (lifetime value) | Revenue per subscriber over their lifetime | | Payback period | Months to recoup acquisition cost | | MRR / ARR | Business health snapshot | | Subscriber retention (Month 1-12) | Long-term revenue curve |

Paid Upfront

| Metric | Why It Matters | |--------|---------------| | Downloads per day/week | Direct revenue driver | | Revenue per download | Should equal price minus Apple's cut | | Refund rate | Product quality signal (keep < 5%) | | Ratings and reviews | Social proof drives more downloads | | Organic vs. paid ratio | Sustainability indicator |

Step 3: App Store Connect Analytics Interpretation

The App Store Funnel

Impressions (your app appeared in search/browse)
    ↓ Tap-through rate = Product Page Views / Impressions
Product Page Views (user tapped to see your page)
    ↓ Conversion rate = Downloads / Product Page Views
Downloads (user installed your app)
    ↓ D1 retention
Day 1 Active Users
    ↓ D7 retention
Day 7 Active Users
    ↓ D30 retention
Day 30 Active Users
    ↓ Monetization
Paying Users

App Store Connect Definitions (get these right)

• **Conversion rate** = total downloads ÷ **unique** impressions (not raw impressions)
• **Total downloads** = first-time downloads + redownloads; auto-downloads (device syncing) are excluded
• Segment every funnel metric by the **4 source types** — App Store browse, App Store search, app referrer, web referrer — and by **page type**: product page vs. store sheet vs. no page. A strong product-page CVR can hide a weak store-sheet CVR
• Up to **7 filters** stack per metric (WWDC25) — e.g. search traffic + one territory + store sheet
• **Payer metrics** (WWDC25): Download-to-Paid Conversion and Average Proceeds per Download connect acquisition quality to revenue

Interpreting Each Funnel Step

**Impressions → Product Page Views (Tap-Through Rate)**

| Rating | TTR | Interpretation | |--------|-----|---------------| | Good | > 8% | Icon and title are compelling | | Average | 4-8% | Room to improve first impression | | Poor | < 4% | Icon, title, or subtitle need work |

What to fix if low:

• App icon not standing out (test bolder colors, simpler design)
• Title not communicating value (add keyword after brand name)
• Subtitle