UitbreidenOS Analytics Tracking Skill
When to activate
- Setting up event tracking for a web app or marketing site
- Designing a measurement plan before implementing analytics
- Debugging broken or missing analytics data
- Building funnels to find conversion drop-offs
- Analysing retention cohorts to understand churn
- Choosing between analytics tools (GA4, Mixpanel, Amplitude, PostHog)
When NOT to use
- Business intelligence or SQL-level data warehouse queries — that's a data-ml task
- A/B testing framework setup — use the experiment-designer skill
- Privacy/GDPR compliance audits for tracking — use the privacy-pia skill
Instructions
Measurement plan
Build a measurement plan for [product/site].
Product type: [SaaS / ecommerce / content site / mobile app]
Business goals: [what outcomes matter — signups, purchases, retention, engagement]
Current analytics setup: [GA4 / Mixpanel / Amplitude / PostHog / none]
Team: [developer + analyst / solo / marketing team]
Measurement plan structure:
1. North Star Metric:
[The one number that best captures product health]
e.g. Weekly Active Users / MRR / Activation Rate
2. Supporting metrics (level 2):
[3-5 metrics that explain the North Star]
3. Key user events to track:
For each event:
- Event name: [snake_case, consistent naming]
- Trigger: [what user action fires this]
- Properties: [key attributes to capture — plan: string, amount: number, etc.]
- Why: [what business question does this answer?]
4. Funnels to measure:
- [Acquisition funnel: source → signup → activation]
- [Core product funnel: login → key action → value moment]
- [Monetisation funnel: trial → upgrade → retention]
5. Dashboards needed:
- [Executive: MRR, churn, NPS]
- [Product: activation rate, feature adoption, retention]
- [Marketing: traffic, conversion, CAC by channel]
Produce the event tracking plan as a table:
Event | Trigger | Properties | Priority | Dashboard
GA4 implementation
Set up GA4 event tracking for [website/app].
Site type: [marketing site / web app / ecommerce]
Framework: [Next.js / React / vanilla JS / WordPress]
Goals: [track these conversions — list]
Implementation plan:
1. Base setup:
- Install GA4 via gtag.js or GTM (use GTM if marketers need to add tags later)
- Configure data stream and measurement ID
- Enable Enhanced Measurement for: scrolls, outbound clicks, file downloads, site search
2. Custom events to implement:
Event: [name]
Code:
gtag('event', '[event_name]', {
event_category: '[category]',
event_label: '[label]',
value: [optional numeric value],
[custom_parameter]: '[value]'
});
Where to fire: [component / page / action]
3. Conversion events:
Mark these as conversions in GA4 admin:
- [signup_complete]
- [purchase]
- [demo_requested]
Mark in: Admin → Events → Mark as conversion
4. Audiences for remarketing:
- Trial users who didn't convert (visited /pricing 2+ times)
- High-intent visitors (3+ pages, 2+ minutes)
5. Debug and verify:
- GA4 DebugView: enable debug mode in GTM or add ?debug_mode=1
- Realtime report: confirm events firing live
- Check for duplicate events (fire once, not on every re-render)
Generate the implementation code for my framework.
Funnel analysis
Analyse my conversion funnel and identify drop-offs.
Funnel steps: [list each step in order]
Example: Homepage → Signup page → Email confirmed → Dashboard → Feature used → Upgrade
Current conversion rates per step (if known): [X%]
Analytics tool: [GA4 / Mixpanel / Amplitude / PostHog]
Timeframe: [last 30 / 60 / 90 days]
Segments to compare: [mobile vs desktop / channel / plan type]
Analysis structure:
1. Overall funnel conversion (first step → last step): [X%]
2. Step-by-step drop-off:
Step 1 → 2: [X% drop — high/medium/low compared to benchmarks]
Step 2 → 3: [X% drop]
[continue for each step]
3. Worst drop-off step: [which step loses the most people]
Hypotheses for why:
- [friction in the UI?]
- [missing information?]
- [technical bug?]
- [expectation mismatch?]
4. Experiments to run:
- [one change per hypothesis, measurable in analytics]
5. Segmentation insight:
- Do mobile users drop at a different step than desktop?
- Do paid ad visitors convert differently than organic?
Query to run in [tool]: [write the funnel query or steps to set it up]
Retention cohort analysis
Run a retention cohort analysis for [product].
Analytics tool: [Mixpanel / Amplitude / PostHog / GA4 / raw SQL]
Retention definition: [user returned and did X within Y days]
Time window: [weekly / monthly cohorts]
Product age: [X months of data available]
Cohort analysis setup:
1. Define retention event: [the action that counts as "retained"]
- Not just "logged in" — define meaningful engagement
- e.g. "Used core feature", "created item", "sent message"
2. Build cohort table:
- Rows: signup cohorts (week or month of first use)
- Columns: Day 1, Day 7, Day 14, Day 30, Day 60, Day 90
- Cell: % of users who returned on that day
3. Interpret the shape:
- Flat curve after Day 14: product has found its retention floor (good)
- Continuous decline with no floor: product-market fit problem
- Steep Day 1 drop: onboarding problem, not retention
- Recent cohorts better than older: improvement trend (good)
4. Identify which cohorts retain best:
- By acquisition channel (organic vs paid)
- By signup feature used in first session
- By plan or segment
5. Intervention experiment:
Based on drop-off at Day [X], test: [email / in-app nudge / feature highlight]
Write the query for [tool] and interpret the results.
Tool selection guide
Help me choose the right analytics tool.
Product stage: [pre-launch / early growth / scaling]
Team: [solo / 2-5 / 5+]
Technical resources: [developer available / no-code only]
Budget: $[X/month]
Primary need: [product analytics / marketing attribution / ecommerce / BI]
Data privacy requirements: [GDPR / HIPAA / no special requirements]
Compare:
GA4 — Best for: marketing sites, ecommerce, Google Ads integration. Free. Limited product analytics depth.
Mixpanel — Best for: SaaS product analytics, funnel and retention analysis. Starts free (limited events).
Amplitude — Best for: larger product teams, cohort analysis, predictive analytics. Free tier available.
PostHog — Best for: self-hosted option, feature flags + analytics in one, open source. Free up to 1M events/month.
Plausible / Fathom — Best for: privacy-first, no cookie consent needed, simple traffic metrics. $9-14/month.
Heap — Best for: retroactive event capture (no pre-planning needed), enterprise.
Recommendation for my situation: [tool + why + cost at my scale]
Example
User: Our SaaS has a 23% trial-to-paid conversion, but we don't know where people drop. We use PostHog. Help me set up funnel tracking.
Claude's plan:
Funnel to build in PostHog:
- Trial signup complete → 2. Dashboard first load → 3. Core feature used (define: "project created") → 4. Invited a team member → 5. Upgrade to paid
PostHog funnel setup: In PostHog: Insights → Funnels → add these 5 steps in order. Set conversion window: 14 days (length of your trial).
Prediction for where you'll see the drop:
- Step 1→2 (signup → dashboard): usually 85-95%. If lower, email confirmation is broken or slow.
- Step 2→3 (dashboard → first action): this is typically the biggest drop (40-60%). Empty state friction.
- Step 3→4 (solo use → team invite): 15-30% invite rate is normal for team tools.
- Step 4→5 (active → paid): if activation (steps 1-4) is complete, 50-70% should convert.
First experiment: Add a "quick start" checklist in the empty dashboard state with 3 tasks. Each task completion event = tracked. Empty state is the #1 lever at step 2→3.