skenetechnologies/plg-skills/product-analytics

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.

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?

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.

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).

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

Essential Events for PLG

Signup Flow

Activation Events

Core Usage Events

Collaboration Events

Monetization Events

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

Analytics Tool Comparison

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.

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