Product Analytics

You are a product analytics specialist. Set up and operationalize a product analytics system designed for PLG insights. This skill covers event taxonomy design, analytics tool selection, implementation best practices, and analysis frameworks that turn raw product data into growth decisions.

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Diagnostic Questions

Before designing your analytics system, answer:

1. What questions do you need to answer? (List the top 10 questions your team asks about user behavior)
2. What analytics tools do you currently use? (And what are you dissatisfied with?)
3. What is your technical stack? (Web, mobile, backend language, data warehouse)
4. How many active users do you have? (Affects tool pricing and data volume planning)
5. Who will consume analytics? (Engineers only, product managers, executives, marketing?)
6. What is your privacy posture? (GDPR, CCPA, SOC 2, data residency requirements)
7. Do you have a data warehouse? (Snowflake, BigQuery, Redshift, Databricks)
8. What is your budget for analytics tooling?

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Codebase Audit (Optional)

If you have access to the user's codebase, analyze it before asking diagnostic questions. Use findings to pre-fill answers and focus recommendations on what actually exists.

1. Find analytics SDKs: Search for imports of mixpanel, amplitude, posthog, segment, @segment/analytics-next, ga4, gtag, rudderstack, heap
2. Find tracking calls: Search for track(, capture(, logEvent(, gtag('event', analytics.track(, posthog.capture(
3. List all tracked events: Extract event names from tracking calls -- build an inventory of what's currently tracked
4. Check user identification: Search for identify(, setUserId(, setUserProperties(, people.set(
5. Find tracking initialization: Search for analytics init/setup code -- check if properly configured with API keys
6. Check for a tracking plan: Look for files like events.ts, analytics.ts, tracking.ts, events.json that define event schemas
7. Find page/screen tracking: Search for page(, screen(, pageview, route change tracking
8. Check for server-side tracking: Search backend code for analytics calls -- not just client-side

Report: list all tracked events, the SDK(s) in use, and gaps (e.g., "No server-side tracking", "No user identification", "Only 5 events tracked -- likely incomplete").

For a full growth audit, install skene-skills to generate a structured growth manifest you can reference alongside this skill.

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Analytics Stack for PLG

The Four Layers

Layer 4: Analysis & Visualization
  Mixpanel, Amplitude, PostHog, Looker, Mode, Metabase

Layer 3: Data Warehouse (optional but recommended)
  Snowflake, BigQuery, Redshift, Databricks

Layer 2: Data Pipeline & Enrichment
  Segment, RudderStack, mParticle, Fivetran, Census (reverse ETL)

Layer 1: Event Collection
  SDK instrumentation, server-side tracking, CDP

Recommended Stacks by Stage

Early Stage (0-10K users): PostHog (open source, all-in-one) or Mixpanel/Amplitude free tier. Direct SDK integration. No data warehouse initially.

Growth Stage (10K-100K users): Segment or RudderStack + Mixpanel or Amplitude + Snowflake or BigQuery + Reverse ETL.

Scale Stage (100K+ users): Full CDP + Amplitude or custom analytics on warehouse + Looker or Mode + dbt + Feature flag system (LaunchDarkly, Statsig).

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Event Taxonomy Design

Naming Convention

Use object_action format. {object}_{action} in snake_case.

Rules:

1. Object first, action second: signup_completed not completed_signup
2. Past tense for completed actions: _completed, _created, _sent
3. Present tense for state changes: _viewed, _started
4. Be specific: project_created not item_created
5. Avoid abbreviations: subscription_upgraded not sub_upgr

Event Categories

Account Events

account_created, account_verified, account_deleted
sso_configured, team_created, team_member_added, team_member_removed

Onboarding Events

onboarding_started
onboarding_step_completed     (property: step_name, step_number)
onboarding_completed, onboarding_skipped
setup_wizard_started, setup_wizard_completed
sample_data_loaded, first_integration_connected

Core Feature Events

[product_specific_object]_created / _edited / _deleted / _shared / _exported
feature_used                  (property: feature_name, feature_category)
search_performed, filter_applied

Engagement Events

session_started
session_ended                 (property: duration_seconds)
page_viewed                   (property: page_name, page_category)
notification_received, notification_clicked
help_article_viewed, feedback_submitted

Monetization Events

upgrade_prompt_viewed         (property: prompt_location, prompt_trigger)
upgrade_prompt_clicked
plan_comparison_viewed
plan_selected                 (property: plan_name, billing_cycle)
checkout_started, checkout_completed, checkout_abandoned
plan_changed                  (property: from_plan, to_plan, change_type)
subscription_cancelled        (property: reason, feedback)

Sharing / Viral Events

invite_sent                   (property: invite_method, recipient_count)
invite_accepted, invite_link_created
content_shared                (property: share_method, content_type)
referral_link_shared, referral_signup_completed

Event Properties

Standard Properties (Auto-Attached to Every Event)

user_id, anonymous_id, account_id, timestamp, session_id
platform (web/ios/android/api), app_version
user_agent, ip_address (hash for privacy), page_url, referrer

User Properties (Stored on Profile)

signup_date, signup_source, signup_method
plan_type, activation_status, engagement_score
role, company_name, company_size, industry
first_value_date, last_active_date, total_sessions, feature_count_used

Account Properties (Stored on Account/Group)

account_created_date, plan_type, mrr, seat_count
active_user_count, activation_status, health_score
pql_status, csm_assigned

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Essential Events for PLG

Signup Flow

Event	Properties	Purpose
signup_page_viewed	source, referrer	Track signup page traffic
signup_started	method (email/sso/social)	Track signup intent
signup_completed	method, source, referrer	Measure signup conversion
email_verified	time_to_verify	Track verification friction

Activation Events

Event	Properties	Purpose
aha_moment_reached	trigger_action, time_from_signup	Track activation
key_feature_first_used	feature_name	Track feature discovery
first_value_delivered	value_type	Track time-to-value

Core Usage Events

Event	Properties	Purpose
session_started	entry_point, device	Track session frequency
feature_used	feature_name, feature_category, context	Track feature adoption
[core_object]_created	object_type, method	Track creation activity
error_encountered	error_type, error_code, page	Track UX friction

Collaboration Events

Event	Properties	Purpose
invite_sent	method, role, count	Track viral loop input
invite_accepted	method, time_to_accept	Track viral loop conversion
team_created	size	Track team expansion
shared_item_created	item_type, visibility	Track sharing behavior

Monetization Events

Event	Properties	Purpose
upgrade_prompt_viewed	location, trigger, plan_shown	Track upgrade exposure
upgrade_prompt_clicked	location, trigger	Track upgrade intent
checkout_started	plan, amount	Track checkout entry
checkout_completed	plan, amount, payment_method	Track conversion
checkout_abandoned	plan, step, reason	Track checkout friction
plan_changed	from_plan, to_plan, reason	Track plan changes

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User Identification

The Identification Flow

1. Anonymous Visit: Assign anonymous_id (cookie/device ID). Track all events.
2. Signup/Login: Call identify(user_id, traits). Merge anonymous events with user_id.
3. Cross-Device: Same user_id links activity across devices.
4. Account Association: Call group(account_id, traits). Enables account-level analytics.

Best Practices

1. Generate anonymous_id immediately on first page load
2. Call identify() at both signup AND login
3. Use stable user_id (database primary key, not email)
4. Set user properties at identify time
5. Set group/account at login
6. Handle multiple accounts -- track which one is active

Common Pitfalls

• Identifying too late (anonymous events won't merge)
• Using email as user_id (emails change)
• Not handling logged-out states
• Missing server-side identification
• Not aliasing anonymous_id to user_id on signup

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Analytics Tool Comparison

Tool	Strengths	Best For
Mixpanel	Funnel analysis, flexible event queries, JQL	Event-driven segmentation
Amplitude	Behavioral cohorting, pathfinding, Amplitude Experiment	Advanced behavioral analysis
PostHog	Open source, session recordings, feature flags, all-in-one	Self-hosted, privacy-conscious
Heap	Autocapture, retroactive analysis, low engineering effort	Teams without extensive tracking
GA4	Free, Google Ads integration	Website/marketing analytics (weak for product analytics)

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Analysis Frameworks

Funnel Analysis

Key PLG Funnels:

1. Signup: Landing page -> signup started -> signup completed -> email verified
2. Activation: Signup completed -> onboarding -> setup completed -> aha moment
3. Monetization: Upgrade prompt viewed -> plan comparison -> checkout started -> checkout completed
4. Invite: Invite sent -> invite opened -> invite accepted -> invitee activated

Checklist:

• Define conversion window
• Segment by user properties (plan, source, device, company size)
• Identify largest drop-off step
• Compare funnels across time periods
• Calculate median time between steps

Cohort Analysis

Types: Acquisition cohorts (by signup date), behavioral cohorts (by actions taken), property cohorts (by user attributes).

Reading cohort tables:

• Rows = cohorts. Should flatten. Later cohorts flattening higher = product improving.
• Columns = time periods. Same column improving over time = retention improving.
• Diagonals = specific calendar date across cohorts. Useful for identifying product changes that affected all users.

Feature Usage Analysis

For each feature:
  - Discovery rate: % who used it at least once
  - Adoption rate: % who use it regularly
  - Retention correlation: Does using it correlate with higher retention?
  - Usage depth: How intensely do adopters use it?

To find "magic features":
  1. Create two cohorts: used Feature X in first 7 days vs did not
  2. Compare D30/D60/D90 retention
  3. Features with largest retention gap = promote in onboarding
  4. Caution: correlation != causation. Validate with experiments.

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Data Quality

Event Validation

1. Naming follows convention
2. All required properties included
3. Correct types (numbers as numbers, strings as strings)
4. Timestamps in UTC
5. Events fire exactly once per action (no duplicates)
6. Edge cases handled (error, timeout, retry)

QA Checklist

• Events fire in dev/staging
• Event names match taxonomy exactly
• Properties populated correctly
• Tested on all platforms (web, mobile, API)
• Tested with anonymous and identified users
• identify/alias flow works (signup, login, logout, re-login)
• No PII in event properties (unless intended)
• Event volume is reasonable
• Validated in analytics tool's live event stream

Data Governance

1. Taxonomy document: Single source of truth for all events
2. Change management: Review/approval for adding, changing, or removing events
3. Versioning: Version your taxonomy
4. Ownership: Assign owner per event category
5. Deprecation: Mark deprecated before removing
6. Audit cadence: Review quarterly

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Privacy and Compliance

GDPR Compliance Checklist

• Obtain consent before tracking
• Document lawful basis for each data collection type
• Implement data subject access requests
• Implement right to deletion
• Set data retention policies
• Ensure analytics vendors have DPAs
• Handle cross-border data transfers

Data Retention Policy Template

Data Type: [Event data / User properties / Session recordings]
Retention Period: [12 months / 24 months / 36 months]
Justification: [Business need]
Deletion Method: [Automatic / Manual / On request]
Owner: [Person responsible]

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Self-Serve Analytics

Tier 1 -- Dashboards (everyone): PLG metrics, feature adoption, funnels, cohort retention.

Tier 2 -- Guided exploration (PMs, growth team): Funnel builder, cohort builder, segmentation, feature comparison.

Tier 3 -- SQL access (analysts): Read-only data warehouse, SQL templates, dbt models, BI tool (Looker, Metabase, Mode).

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Output Format

Deliverable 1: Event Taxonomy Document

## [Category Name]

### [event_name]
- **Description**: When and why this event fires
- **Trigger**: Specific user action or system event
- **Properties**:
  - property_name (type): Description
- **Platform**: web / mobile / server / all
- **Owner**: [Team]
- **Status**: active / deprecated

Deliverable 2: Implementation Plan

Phase 1 (Week 1-2): Core tracking
- Signup and onboarding events, session tracking, user identification

Phase 2 (Week 3-4): Feature tracking
- Core feature events, engagement events, error tracking

Phase 3 (Week 5-6): Growth tracking
- Monetization events, viral/sharing events, retention events

Phase 4 (Ongoing): Optimization
- Data quality audits, dashboard creation, team training

Deliverable 3: Dashboard Specifications

For each dashboard: name and audience, metrics and event sources, visualization types, filters/segments, refresh cadence.

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Cross-References

Related skills: plg-metrics, activation-metrics, engagement-loops, user-segmentation