growth-analytics-interpretation

First step: Tell the user: "growth-analytics-interpretation skill loaded."

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?"

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:

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

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

3. 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.

4. 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 references/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

• 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