Analytics Interpretation Skill for Claude

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:

• App type and monetization model
• Free with ads, freemium, subscription, paid upfront, or hybrid?
• Current metrics they have access to
• App Store Connect? Third-party analytics (Mixpanel, Firebase, Amplitude)?
• Specific numbers they can share
• Downloads, DAU/MAU, retention, revenue, conversion rates?
• 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 too vague (make it specific: "Budget Tracker" not "Finance App")
• Poor search ranking (see keyword-optimizer skill)

Product Page Views → Downloads (Conversion Rate)

| Rating | CVR | Interpretation |

|--------|-----|---------------|

| Good | > 40% | Screenshots and description are effective |

| Average | 25-40% | Some friction on the product page |

| Poor | < 25% | Major product page issues |

What to fix if low:

• First 3 screenshots not showing core value
• No app preview video (adds 15-25% lift)
• Description too long before showing key benefits
• Bad ratings visible (address review issues first)
• Price too high relative to perceived value

Downloads → Day 1 Retention

| Rating | D1 | Interpretation |

|--------|-----|---------------|

| Good | > 35% | Onboarding delivers on promise |

| Average | 20-35% | Some users confused or disappointed |

| Poor | < 20% | App not delivering expected value |

What to fix if low:

• Onboarding too long or confusing
• App Store screenshots overpromised
• Core value not visible in first session
• Permissions requested too early (camera, notifications)
• Performance issues (slow launch, crashes)

Day 1 → Day 7 Retention

| Rating | D7 | Interpretation |

|--------|-----|---------------|

| Good | > 20% | Users forming habit |

| Average | 10-20% | Some users finding value |

| Poor | < 10% | Most users abandoning after trying |

What to fix if low:

• No reason to come back (add notifications, reminders, streaks)
• Core loop not engaging enough
• Too complex — users haven't learned enough features
• Missing "aha moment" in first week

Day 7 → Day 30 Retention

| Rating | D30 | Interpretation |

|--------|-----|---------------|

| Good | > 10% | Strong product-market fit signal |

| Average | 5-10% | Decent but room to grow |

| Poor | < 5% | Retention cliff — users churning |

What to fix if low:

• Feature depth too shallow (users exhaust value)
• No progression or new content
• Competitor doing it better
• Consider: is this a "use once" tool, not a habit app?

Step 4: AARRR Funnel Analysis

The pirate metrics framework — diagnose where your funnel leaks.

Acquisition: How do users find you?

| Metric | Benchmark | Diagnostic |

|--------|-----------|-----------|

| Organic search impressions | Growing month-over-month | Are your keywords working? |

| Browse impressions | Category-dependent | Are you getting featured/editorial? |

| Referral traffic | > 10% of total | Do users share your app? |

| Paid acquisition CPA | < 1/3 of LTV | Is paid acquisition sustainable? |

Questions to ask:

• What are your top 3 acquisition sources?
• Is organic growing or shrinking?
• What's your cost per install (if running ads)?

Activation: Do users experience the core value?

| Metric | Benchmark | Diagnostic |

|--------|-----------|-----------|

| Onboarding completion | > 70% | Is onboarding too long? |

| "Aha moment" reached | > 50% in first session | Do users discover core value? |

| First key action taken | > 40% of installs | Are users doing the main thing? |

Questions to ask:

• What is the one action that defines "this user gets it"?
• How many steps to reach that action?
• What percentage of new users complete it?

Retention: Do users come back?

| Metric | Benchmark | Diagnostic |

|--------|-----------|-----------|

| D1 retention | 25-40% | First impression quality |

| D7 retention | 15-25% | Habit formation |

| D30 retention | 8-15% | Product-market fit |

| DAU/MAU ratio | 15-30% | Daily engagement strength |

Questions to ask:

• Where is the biggest retention drop-off?
• What do retained users do differently from churned users?
• Is there a retention cliff at a specific day?

Revenue: Are users paying?

| Metric | Benchmark | Diagnostic |

|--------|-----------|-----------|

| Free → trial rate | 10-30% | Is the paywall compelling? |

| Trial → paid rate | 40-60% | Does the trial demonstrate value? |

| ARPU (all users) | Category-dependent | Overall monetization efficiency |

| ARPPU (paying users) | 5-20x ARPU | Are payers happy with value? |

Questions to ask:

• At what point do users encounter the paywall?
• What's the conversion rate at each paywall touchpoint?
• Do longer-retained users convert at higher rates?

Referral: Do users tell others?

| Metric | Benchmark | Diagnostic |

|--------|-----------|-----------|

| Organic multiplier | > 1.0 | Each user brings > 1 new user |

| Share rate | > 5% of MAU | Users actively sharing |

| Rating/review rate | > 1% of MAU | Users willing to vouch publicly |

| Average rating | > 4.5 | High satisfaction |

Questions to ask:

• Is there a share feature in the app?
• Do you ask for ratings at the right moment?
• What triggers a user to recommend your app?

Step 5: Cohort Analysis (Subscription Apps)

How to Read a Cohort Retention Table

              Month 0  Month 1  Month 2  Month 3  Month 4  Month 5
Jan cohort    100%     62%      55%      50%      48%      46%
Feb cohort    100%     58%      51%      46%      44%      —
Mar cohort    100%     65%      59%      54%      —        —
Apr cohort    100%     70%      63%      —        —        —
May cohort    100%     68%      —        —        —        —

What to look for:

• Month 0 → Month 1 drop: The biggest drop. Industry average is 30-50% churn. If yours is > 50%, trial experience needs work.

• Flattening curve: Retention should flatten over time. If Month 3 → Month 4 → Month 5 are similar, you've found your "natural retention floor."

• Improving cohorts: Compare Jan vs. Apr cohorts at the same month. If Apr Month 1 (70%) > Jan Month 1 (62%), your product improvements are working.

• Retention cliff: A sudden drop at a specific month often indicates:
• Month 1: Annual subscribers who don't renew
• Month 3: Users who gave it a fair try and decided no
• Month 12: Annual subscribers hitting renewal

Comparing Cohorts to Measure Impact

When you ship a change, compare cohorts before and after:

Before change (Jan-Mar avg):  Month 1 retention = 58%
After change (Apr-May avg):   Month 1 retention = 69%

Improvement: +11 percentage points → significant positive impact

Rules of thumb:

• < 3 percentage point change: likely noise
• 3-10 percentage point change: meaningful, keep the change
• > 10 percentage point change: major win, double down on this direction

App Store Connect Subscription Metrics (WWDC25)

App Analytics carries 50+ subscription metrics, organized as states (subscribers in an offer, paying full price, with billing issues, churned) and events (movement between states):

• Net Paid Plans — new paid starts vs. voluntary + involuntary churn — is the single best subscription health headline
• Track Subscription Retention two ways: by months-since-subscribing and by offer start (example shape: 67% trial→paid, then 78% retained at 3 months, 73% at 6)
• Offers do three jobs — acquire (introductory), retain (promotional), win back — measure offer→full-price conversion for each job separately

Segment Cohorts by Acquisition Source

Cohort tables get sharper when split by source or custom product page. Example: a "runner" CPP segment converting at 1.3% vs. 3% overall means three different levers from one segmented number — fix that page's creative, redirect its ad spend, and re-engage its cohort via in-app events.

Step 6: Diagnostic Decision Trees

Use these when the user says "my [metric] is bad, what do I do?"

Low Impressions (< 1,000/day for established app)

Low impressions
├── Are you ranking for any keywords?
│   ├── NO → ASO problem: optimize title, subtitle, keywords
│   │        See keyword-optimizer skill
│   └── YES → Are those keywords high-volume?
│       ├── NO → Target higher-volume keywords
│       └── YES → Are you ranking in top 10?
│           ├── NO → Improve rankings (more ratings, better conversion)
│           └── YES → Expand to more keywords or new markets

High Impressions, Low Product Page Views (TTR < 4%)

Low tap-through rate
├── Is your icon professional and distinctive?
│   ├── NO → Redesign icon (test 3 variants)
│   └── YES → Is your title clear and keyword-rich?
│       ├── NO → Rewrite title: [Brand] - [Value Keyword]
│       └── YES → Is your subtitle compelling?
│           ├── NO → Rewrite subtitle with specific benefit
│           └── YES → Check competitor positioning — are you differentiated?

Good Downloads, Bad Retention (D1 < 25%)

Poor day-1 retention
├── Is onboarding complete rate > 70%?
│   ├── NO → Simplify onboarding (fewer steps, skip option)
│   └── YES → Do users reach "aha moment" in first session?
│       ├── NO → Restructure first-run experience to show core value immediately
│       └── YES → Are there performance issues (crashes, slow load)?
│           ├── YES → Fix stability first (check crash reports)
│           └── NO → Does the app match what screenshots promised?
│               ├── NO → Align marketing with actual product
│               └── YES → Core value may not be strong enough → user research needed

Good Retention, Low Revenue (conversion < 3%)

Low monetization
├── Do users see the paywall?
│   ├── NO → Add natural paywall touchpoints (feature gates, usage limits)
│   └── YES → Is the paywall compelling?
│       ├── NO → Redesign paywall (show value, social proof, feature comparison)
│       └── YES → Is the price right?
│           ├── TOO HIGH → Test lower price point or add cheaper tier
│           ├── TOO LOW → Users may not perceive enough value — test higher price
│           └── SEEMS RIGHT → Is trial experience showcasing premium features?
│               ├── NO → Onboard users to premium features during trial
│               └── YES → Test different trial lengths or offer types

Step 7: Invest, Iterate, Pivot, or Sunset?

Based on the overall picture, recommend one of four paths:

Invest (Double Down)

Signals:

• D7 retention > 40%
• Growing organically (installs increasing without paid acquisition)
• Users actively requesting features
• Conversion rate improving over time
• Strong ratings (> 4.5 stars)

Action: Increase development speed, consider marketing spend, expand to new platforms.

Iterate (Keep Improving)

Signals:

• D7 retention 20-40%
• Some organic growth but not accelerating
• Mixed user feedback (some love it, some confused)
• Conversion rate stable but not great

Action: Focus on the retention cliff. Find what retained users do differently and make all users do that. A/B test paywall and onboarding.

Pivot (Change Direction)

Signals:

• D7 retention < 20% after 3+ iterations
• Engagement concentrated in unexpected feature
• Users using app differently than intended
• Specific segment retains well, others don't

Action: Double down on the unexpected use case. Rebuild around what users actually do, not what you planned.

Sunset (Move On)

Signals:

• Declining metrics across the board
• No organic growth despite multiple iterations
• Users not engaging even after onboarding improvements
• Opportunity cost too high (other ideas with more potential)

Action: Put app in maintenance mode. Stop active development. Consider open-sourcing or selling. Redirect energy to next project.

Important caveat: Sunsetting is not failure. Most successful indie developers shipped several apps before finding the one that worked.

Reference Files

See metrics-reference.md for:

• Detailed metric definitions and formulas
• Benchmark ranges by app category (social, productivity, games, utilities)
• App Store Connect specific metric definitions
• Red/yellow/green thresholds for all key metrics

Output Format

Present analysis as an Analytics Health Report:

# Analytics Health Report: [App Name]

## Overview
**App type:** [Free/Freemium/Subscription/Paid]
**Stage:** [Pre-launch/Early/Growing/Established]
**Data period:** [Date range analyzed]

## Funnel Health

| Stage | Metric | Value | Rating | Action |
|-------|--------|-------|--------|--------|
| Acquisition | Impressions/day | X,XXX | 🟢/🟡/🔴 | ... |
| Acquisition | Tap-through rate | X.X% | 🟢/🟡/🔴 | ... |
| Activation | Conversion rate | X.X% | 🟢/🟡/🔴 | ... |
| Retention | D1 retention | XX% | 🟢/🟡/🔴 | ... |
| Retention | D7 retention | XX% | 🟢/🟡/🔴 | ... |
| Retention | D30 retention | XX% | 🟢/🟡/🔴 | ... |
| Revenue | Conversion rate | X.X% | 🟢/🟡/🔴 | ... |
| Revenue | LTV | $XX.XX | 🟢/🟡/🔴 | ... |

## Primary Bottleneck
**[Stage name]** — [One sentence explanation of the biggest problem]

## Recommended Actions (Priority Order)
1. 🔴 [Critical fix] — Expected impact: [X]
2. 🟠 [High priority] — Expected impact: [X]
3. 🟡 [Medium priority] — Expected impact: [X]

## Overall Assessment
**Recommendation:** [Invest / Iterate / Pivot / Sunset]
**Rationale:** [2-3 sentences]

References

• metrics-reference.md — Metric definitions, formulas, and benchmarks
• app-store/keyword-optimizer/ — For ASO-related fixes
• monetization/ — For pricing and paywall optimization
• testing/ — For A/B test methodology