Product analytics

Skill Mattakushi432/Claude-Code-Skills-Custom-DevTools-Pack/plugins/devtools-pack/skills/product-analytics

A curated pack of custom Claude Code skills for developers — installable as a Claude Code plugin marketplace.

Install

npx -y skills add Mattakushi432/Claude-Code-Skills-Custom-DevTools-Pack --skill product-analytics

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When to activate: product analytics, funnel analysis, cohort analysis, retention curves, DAU WAU MAU, activation metrics, event tracking, Mixpanel, Amplitude

SKILL.md

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Product Analytics

Event Taxonomy Design

Naming Convention: Object-Action

Format: object_action

Examples:
  user_signed_up
  project_created
  file_uploaded
  subscription_upgraded
  report_exported
  onboarding_completed

Event Properties (always include)

{
  "event": "project_created",
  "user_id": "usr_123",
  "timestamp": "2024-01-15T10:30:00Z",
  "session_id": "sess_456",
  "platform": "web",
  "properties": {
    "project_type": "template",
    "team_size": 5,
    "plan": "pro"
  }
}

Instrumentation Checklist

Every screen/page view tracked
All CTA clicks tracked
Form submissions tracked (start + complete)
Errors tracked with context
Key milestones tracked (activation, upgrade, churn signal)
Properties consistent across events (same field names)
User identity linked (anonymous → identified on signup)

Funnel Analysis

Funnel Construction

Define the conversion goal (signup, activation, purchase)
Map required steps in order
Identify optional vs required steps
Set time window (complete within X days)
Measure conversion rate at each step

Funnel Metrics

Metric	Formula
Step conversion rate	Users completing step N / Users entering step N
Overall conversion	Users completing final step / Users entering step 1
Drop-off rate	1 - step conversion rate
Time to convert	Median time from step 1 to final step

Diagnosing Drop-offs

Drop > 50% at a step → investigate with session recordings
Compare drop-off by segment (device, plan, cohort)
Run qualitative research at high-drop steps
Test friction reduction (reduce fields, add progress indicator)

Cohort Analysis

Acquisition Cohort

Groups users by when they joined (week/month). Tracks how each cohort behaves over time.

Week 0: 1,000 users signed up
Week 1: 650 returned (65% W1 retention)
Week 2: 450 returned (45% W2 retention)
Week 4: 300 retained (30% W4 retention)
Week 8: 220 retained (22% W8 retention — stabilization point)

Behavioral Cohort

Groups users by action taken (users who completed onboarding vs those who didn't).

Use behavioral cohorts to:

Validate aha moment hypothesis
Compare LTV of activated vs non-activated users
Identify high-value behaviors to promote

Retention Curves

Benchmarks by Product Category

Category	D1	D7	D30
Social / consumer	40%	20%	10%
SaaS (B2B)	60%	45%	35%
Mobile games	35%	15%	5%
E-commerce	30%	10%	5%
Productivity tools	50%	35%	25%

Reading the Curve

Smile curve: Drops then flattens → healthy core audience
Declining curve: Never flattens → product has no sticky value
High D1, steep drop: Strong acquisition but weak activation

Improving Retention

D1 retention: Improve onboarding and time-to-value
D7 retention: Establish habit loop, trigger return visit
D30 retention: Deepen engagement, show accumulated value

DAU / WAU / MAU

Definitions

DAU: Unique users with at least one qualifying event in a calendar day
WAU: Unique users active in the past 7 days
MAU: Unique users active in the past 28–30 days

Engagement Ratios

Ratio	Formula	Healthy range
DAU/MAU (stickiness)	DAU ÷ MAU	> 20% good, > 50% excellent
DAU/WAU	DAU ÷ WAU	Indicates daily vs weekly habit
WAU/MAU	WAU ÷ MAU	Weekly habit strength

Qualifying Event Definition

Do not count all events as active. Define a meaningful "active" event:

Bad: any page view (inflated by bots/accidental visits)
Good: core product action (created item, sent message, ran query)

Activation Metrics

Finding the Aha Moment

Define activation: what behavior correlates with long-term retention?
Segment retained vs churned users
Identify actions taken in first session that differ between groups
Test hypothesis: does promoting that action improve retention?

Activation Metric Examples

Product	Activation event
Slack	Sent 2,000 messages as a team
Dropbox	Uploaded 1 file on 2 devices
Twitter	Followed 30 accounts in first day
HubSpot	Connected email + created 1 contact

Time-to-Activate

Track: median time from signup to activation event. Goal: reduce time-to-activate while maintaining quality of activation.

Product Health Dashboard

Key Metrics (top of dashboard)

Metric	Timeframe	Alert threshold
DAU	Daily	-10% WoW
New signups	Daily	-20% WoW
Activation rate	Weekly	-5pp MoM
D7 retention	Weekly cohort	-5pp MoM
Revenue (MRR/ARR)	Monthly	-5% MoM
NPS	Monthly	< 30

Supporting Metrics

Funnel conversion rates by step
Feature adoption (% MAU using feature)
Error rates on key flows
Support ticket volume by category

Mixpanel vs Amplitude vs PostHog

	Mixpanel	Amplitude	PostHog
Strengths	Funnels, retention	Behavioral cohorts	Open source, session replay
Pricing	Usage-based	Usage-based	Free tier + usage
Best for	Growth teams	Enterprise	Startups, self-hosted
SQL access	Limited	Yes (paid)	Yes