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?"
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
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
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