| name | product-analytics |
| description | Product analytics: define metrics frameworks, build dashboards, analyse feature adoption, measure activation and retention, interpret data to make product decisions |
Product Analytics Skill
When to activate
- Deciding which metrics to track for a product or feature
- Analysing why a feature has low adoption after launch
- Building a product metrics dashboard from scratch
- Interpreting retention or activation data to find problems
- Preparing a data-informed product review or roadmap decision
- Designing a metrics framework (North Star, L1/L2 hierarchy)
When NOT to use
- Setting up the analytics infrastructure — use the analytics-tracking skill
- A/B test design and statistics — use the experiment-designer skill
- Marketing attribution analysis — that's paid-ads or analytics-tracking
Instructions
Metrics framework design
Design a metrics framework for [product].
Product: [describe]
Stage: [pre-PMF / growing / scaling]
Business model: [subscription / usage-based / freemium / marketplace]
Team size: [1-5 / 6-20 / 20+]
Metrics hierarchy:
Level 0 — North Star Metric (1 metric):
[The single metric that best represents value delivered to users]
Must be: leading indicator of revenue, measurable, actionable by the team
Examples: DAU, weekly active projects, messages sent, reports generated
Level 1 — Pillar metrics (3-5 metrics):
[The components that explain the North Star]
Framework: Acquisition, Activation, Retention, Referral, Revenue (AARRR)
Level 2 — Diagnostic metrics (for each pillar):
[Metrics that help diagnose why a L1 metric is moving]
Example framework for a B2B SaaS tool:
NSM: Weekly Active Teams (teams with ≥ 3 members who used core feature this week)
L1:
- New teams signed up (Acquisition)
- % who invited 3+ members in week 1 (Activation)
- % retained at week 4 (Retention)
- Net Revenue Retention (Revenue)
L2 (for Activation):
- Time to first core action
- % who completed onboarding checklist
- Invite sent rate in session 1
Design the framework for my product. Include: what to NOT track (vanity metrics).
Feature adoption analysis
Analyse adoption for [feature].
Feature: [describe what it does]
Launch date: [X weeks/months ago]
Current adoption rate: [X% of eligible users have used it]
Target adoption rate: [X%]
Analytics tool: [Mixpanel / Amplitude / PostHog / GA4]
Adoption analysis framework:
1. Define "adopted":
□ First use? (awareness) — too loose
□ Used X times? (engagement) — better
□ Used in X% of sessions? (habit) — best
[Set clear adoption threshold before analysing]
2. Funnel from feature discovery to adoption:
- Saw feature entry point: [X%]
- Clicked / initiated: [X%]
- Completed first use: [X%]
- Returned and used again: [X%]
- "Adopted" (by your definition): [X%]
3. Segmentation (which users adopt vs. don't):
- By user role / plan / company size
- By activation cohort (newer vs. older users)
- By primary use case or workflow
4. Barriers to adoption (qualitative):
- Is the feature discoverable? (check: do users even know it exists?)
- Is the value immediately clear? (first use experience)
- Does it require setup or prerequisite state?
- Is there a competing workflow already in use?
5. Recommendations by drop-off point:
- Low awareness → in-app announcement, tooltip, email
- Low first-use completion → simplify the UI or add guided setup
- Low repeat use → check if core value was delivered in first use
Query to run in [analytics tool] + interpretation of results.
Retention analysis
Analyse retention data and identify improvement opportunities.
Product: [describe]
Retention definition: [user did X within Y days]
Current D1/D7/D14/D30 retention: [X% / X% / X% / X%]
Benchmark for your category: [look up your vertical — varies widely]
Analytics tool: [tool]
Retention analysis steps:
1. Shape analysis:
- Flattening curve: retention reaches a floor → product has core retention (good)
- Continuously declining: no retention floor → PMF problem, not an optimization problem
- Step-function drop at specific day: something happens at that moment (trial expires? email stops? feature limit hit?)
2. Cohort comparison:
- Compare weekly cohorts — are recent cohorts retaining better than older ones?
- Improving: your changes are working
- Declining: something regressed (feature degraded, competition improved)
- Flat: no improvement, no regression
3. Segment retention:
Which users retain best?
- Channel (organic vs paid — organic typically retains 2-3x better)
- Feature usage (users who used feature X retain at Y% vs Z% for non-users)
- Onboarding path (completed checklist vs didn't)
- Company size or plan
4. Identify the "activation feature":
Find the event/feature that correlates highest with day-30 retention.
Run: event correlation → retention analysis in Amplitude or Mixpanel
Make this feature part of the onboarding flow.
5. Intervention design:
D1 drop (< 40% return day 1): onboarding problem
D7 drop: habit formation problem (push notifications, email, in-app nudge)
D30 drop: value deepening problem (new features, integrations, team expansion)
Analyse my retention data and recommend the highest-leverage intervention.
Product review dashboard
Design a weekly product review dashboard for [product/team].
Team: [product / engineering / full company]
Frequency: [weekly / bi-weekly]
Goals: [make roadmap decisions / identify regressions / track OKR progress]
Dashboard sections:
1. North Star Metric (week over week):
[Metric name]: [current value] vs [last week] vs [same week last month]
Trend: ↑/↓ [X%] — [is this within expected range?]
2. Acquisition:
New signups: [X] (week) / [X] (month) / [X target]
CAC by channel: [organic / paid / referral]
3. Activation:
Activation rate (defined as [X]): [X%] vs target [X%]
Time to activation: p50 [Xh] / p90 [Xh]
4. Retention:
D7 retention: [X%] / D30 retention: [X%]
Weekly active users trend: [sparkline data]
5. Revenue:
MRR: $[X] — [+/-X% WoW]
New MRR: $[X] / Expansion: $[X] / Churn: $[X]
Net MRR change: $[X]
6. Product health:
Top feature by usage this week: [X]
Feature adoption on recent launches: [names + %)
Support volume: [X tickets] — [trend]
7. Experiments:
Running: [test name] — [days remaining] — [current result]
Concluded this week: [test name] — [result] — [decision]
8. Key questions for this week:
- [What changed and why?]
- [What needs a decision?]
- [What do we not know yet?]
Build this dashboard in [Mixpanel / Amplitude / PostHog / Metabase / Notion].
Example
User: Our SaaS (B2B, team collaboration tool) has 35% D30 retention. We think it's an activation problem. Analytics in Mixpanel. What should we investigate?
Claude's analysis plan:
35% D30 for a B2B collaboration tool is below typical (45-60% for the category). But "activation problem" is a hypothesis — let's validate it before fixing.
Step 1 — Confirm the drop-off location in Mixpanel:
Build this funnel: Signup → First login → Team invite sent → Core feature used × 3 → D30 retained.
If retention rate for users who complete all funnel steps is > 60%, the problem IS activation (not product value). You have a good product that most users never experience.
Step 2 — Find the activation predictor:
In Mixpanel: Retention → Compare by "Did event X in first 7 days." Test these events:
- Invited a team member → retained at D30?
- Used [core feature] 3+ times → retained at D30?
- Connected an integration → retained at D30?
The event with the highest D30 retention correlation = your aha moment. Make this the activation goal.
Step 3 — Find the barrier:
If the activation event is "team invite sent" and only 22% of signups send one in week 1, the question is: why don't they invite their team?
Segment by: solo signups vs. team signups. If solo signups retain at 15% and team signups at 58%, the problem is you're acquiring too many solo users, not that your activation flow is broken.
Decision tree output:
- Low activation rate + high post-activation retention → fix the activation flow
- High activation rate + low retention → fix the product value or target segment
- Low activation + low post-activation retention → deeper discovery needed
