analytics-interpretation

# 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 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 #### 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?