Analytics

Product & Data Analytics

Scope: product/business events & funnels. System health/SLOs → .claude/skills/observability/SKILL.md; relevance → .claude/skills/search/SKILL.md.

When to use

• Designing an event taxonomy and tracking plan for a new product or feature.
• Integrating a client-side or server-side analytics SDK (Segment, Amplitude, Mixpanel, PostHog, RudderStack, GA4, Heap).
• Building or auditing a data warehouse pipeline: raw events → warehouse (BigQuery, Snowflake, Redshift, ClickHouse) → dbt models → BI dashboards.
• Implementing consent management and privacy-safe tracking (GDPR, CCPA, COPPA).
• Designing A/B or multivariate experimentation infrastructure (feature flags, assignment, analysis).
• Building product dashboards, funnels, retention curves, or cohort analyses.
• Auditing tracking coverage: finding gaps where events are missing or definitions are inconsistent.
• Migrating from one analytics provider to another.

Applies to web apps, mobile apps (iOS/Android/React Native/Flutter), backend APIs, AI/agent products, games, and data platforms.

Workflow

Phase 1 — Define Before You Instrument

1. Start with business questions, not events. Analytics serve decisions. Before defining a single event, list the top 10 business questions the team needs to answer in the next quarter. Examples: "What percentage of sign-ups complete onboarding?", "Which feature correlates with 90-day retention?", "What is the conversion rate from free to paid by acquisition channel?". The event taxonomy exists to answer these questions — not to log everything and figure it out later.

2. Design the event taxonomy. Use a consistent naming convention across the entire product. The most widely adopted pattern:

<noun>_<verb>   (snake_case)
  object        → what was acted on
  action        → what happened (past tense)

Examples:
  user_signed_up
  onboarding_step_completed
  feature_enabled
  checkout_started
  payment_succeeded
  subscription_cancelled
  search_performed
  experiment_assigned

Document every event in a tracking plan (see Output format). Each event entry specifies: event name, trigger description (when it fires), properties (name, type, required/optional, example value), platforms (web/iOS/Android/server), owner (team/squad).

3. Define a standard property schema. Every event carries a base set of properties automatically (via the SDK or middleware):

# Identity
anonymous_id      string   pre-auth session identifier
user_id           string   post-auth stable identifier (internal ID, not email)

# Session / context
session_id        string
timestamp         ISO-8601 UTC
timezone          string (IANA)
locale            string (BCP-47)

# Platform
platform          web | ios | android | server
app_version       string (semver)
os_version        string
browser           string (web only)

# Acquisition
utm_source        string
utm_medium        string
utm_campaign      string
referrer          string

# Experiment
active_experiments  array<{experiment_id, variant}>

Custom events then add their own properties on top of this base. Never put PII (email, name, phone) in event properties unless it is a deliberate, consent-gated action (e.g., identify call for CRM sync). Use internal IDs everywhere.

4. Identify PII risks and design the consent layer. Map every event property to a PII classification: non-PII, pseudonymous (user_id), or PII (email, name, IP). IP address is PII under GDPR. For EU/UK users, you need consent before firing analytics events to third-party tools. Design this before instrumentation starts:

• Implement a Consent Management Platform (CMP) or use the SDK's built-in consent controls (Segment Consent Manager, PostHog's cookieless mode, GA4 consent mode).
• Server-side tracking reduces browser-level PII exposure and is more resilient to ad blockers.
• If targeting users under 13 (COPPA) or under 16 (GDPR child provisions), analytics must be strictly necessary only.

Phase 2 — Instrument

5. Choose the instrumentation layer: client-side, server-side, or hybrid.

Use Segment or RudderStack as the Customer Data Platform (CDP) to route one stream of events to multiple destinations (warehouse, Amplitude, Intercom, CRM) rather than adding multiple SDKs.

6. Implement tracking plan enforcement. Schema drift kills data quality. Enforce it:

• Use TypeScript types or a JSON Schema for each event, generated from the tracking plan. Use Segment Protocols, RudderStack Data Catalog, or Amplitude Data to enforce schemas at ingestion.
• In CI: add a lint step that validates any new analytics.track() call against the tracking plan definition. Reject events with unknown names or missing required properties.
• In production: log schema violations to a dead-letter queue; alert on spike in violations.

7. Instrument using the identify → group → track → page/screen pattern.

// On auth:
analytics.identify(userId, { plan: 'pro', created_at: '...' })

// On org-level context (B2B):
analytics.group(orgId, { name: 'Acme Corp', employee_count: 500 })

// On every meaningful action:
analytics.track('checkout_started', {
  cart_value_cents: 4999,
  item_count: 3,
  currency: 'USD'
})

// On page/screen views (web/mobile):
analytics.page('Checkout', { step: 'address' })

Use server-side identify and track for business events to prevent client-side tampering. The server is the source of truth for conversion and revenue events.

Examples shown in JS/Node; the same patterns apply across platforms — equivalents (mobile, Go, Python, etc.) follow the Standards table.

Phase 3 — Warehouse Pipeline

8. Design the raw → modeled → serving layer architecture.

Raw events (immutable, append-only)
  └── Segment/RudderStack → warehouse raw schema
        e.g. bigquery.segment_raw.tracks, .pages, .identifies

Staging models (dbt, clean & typed)
  └── stg_users.sql, stg_events.sql, stg_sessions.sql

Mart models (business-oriented)
  └── fct_events.sql          → one row per event
  └── fct_sessions.sql        → sessionized events
  └── dim_users.sql            → user attributes at snapshot
  └── fct_funnel_steps.sql    → funnel analysis
  └── fct_retention_cohorts.sql

BI layer (Looker, Metabase, Superset, Mode)
  └── reads from mart models only

Preserve raw events immutably. Never transform in place. dbt transforms are deterministic and re-runnable — if a business logic rule changes, re-run the model rather than patching raw data.

9. Define sessions and identity stitching. Sessions: group events by anonymous_id with a 30-minute inactivity timeout (configurable). This requires a sessionization SQL window function in dbt. Sessions that cross auth boundaries (anonymous → identified) must be merged so the full journey is visible.

Identity stitching: when a user signs up, alias their anonymous_id to user_id. Record this alias event. Your warehouse model must propagate the user_id back to pre-signup events for that anonymous_id. Without this, sign-up conversion is systematically undercounted.

10. Implement dbt models with tests. Every dbt model must have:

# schema.yml
models:
  - name: fct_events
    columns:
      - name: event_id
        tests: [unique, not_null]
      - name: user_id
        tests: [not_null]
      - name: event_name
        tests: [not_null, accepted_values: {values: [...]}]
      - name: event_timestamp
        tests: [not_null]

Run dbt test in CI. A failing uniqueness test on event_id indicates duplicate event delivery — fix the deduplication logic, not the test.

Phase 4 — Experimentation

11. Design the experiment assignment layer. Experiments require: assignment (which variant?), exposure logging (when was the user first assigned?), and analysis (did the variant move the metric?).

• Use a feature flag + experimentation platform: PostHog, LaunchDarkly, Statsig, Optimizely, GrowthBook (open source), or a homegrown feature flag service.
• Assign variants server-side for consistency across platforms and to prevent variant leakage.
• Log an experiment_assigned event at first exposure with {experiment_id, variant, assignment_method}.
• Enforce one experiment assignment per user per experiment (sticky assignment). If the user's bucket changes mid-experiment, the data is polluted.
• Define the metric (primary + guardrail) and minimum detectable effect before launching the experiment. Use a sample size calculator (statsig.com/calculator, or scipy.stats.norm).

12. Run analysis correctly.

• Use the exposure event (not the page view or sign-up) as the experiment entry point. Analyzing users who were never exposed is a common bias.
• Use two-sample t-test (continuous metrics) or z-test for proportions (conversion metrics). Apply Bonferroni correction if testing multiple metrics.
• Do not peek at results and stop early when p < 0.05 — this inflates false positives. Use sequential testing (CUPED, mSPRT) if you need early stopping guarantees.
• Run experiments for at least one full business cycle (typically 2 weeks) to avoid day-of-week effects.

Standards

Do:

• Define the tracking plan in a versioned document before writing any instrumentation code. Treat it like a schema migration.
• Use server-side events for all business-critical conversions (payment, sign-up, subscription). Client-side events are supplementary.
• Enforce the event schema in CI and at ingestion time. Reject or quarantine events that fail validation.
• Store raw events immutably. Model in dbt; never transform raw tables.
• Use internal user IDs in events, never email addresses. Resolve to PII only in controlled, consented contexts.
• Version your dbt models and run dbt test in CI.
• Document every metric definition: numerator, denominator, filter, time window. "DAU" is ambiguous without this.
• Set data retention policies: raw events 12–24 months hot, archive thereafter. PII-bearing tables have shorter retention + right-to-erasure procedures.

Do not:

• Put PII (email, phone, full name, IP) in general event properties without explicit consent and a documented retention policy.
• Instrument without a tracking plan — "log everything and figure it out later" produces unmaintainable, contradictory data.
• Create events named button_clicked without a noun — checkout_button_clicked vs nav_menu_button_clicked are different events with different semantics.
• Add high-cardinality identifiers (order IDs, session UUIDs) as properties on high-volume events if the warehouse bill is a concern — use them on low-volume business events only. (For metric-label cardinality in time-series backends, observability owns the guidance: see .claude/skills/observability/SKILL.md.)
• Mutate or delete raw event records. Corrections belong in a correction table or a dbt is_valid flag.
• Share A/B test results before the experiment has reached statistical significance and the pre-defined minimum sample size.
• Run multiple experiments on the same user population concurrently without interaction analysis.

Common mistakes to avoid

• Identity fragmentation: desktop anonymous_id, mobile anonymous_id, and user_id all treated as separate users. Fix: alias calls at sign-up; warehouse-level identity stitching.
• Conversion undercounting: counting sign-ups only for users who triggered the server-side event, missing users whose request failed after payment. Fix: log payment_succeeded after confirmation, not after client redirect.
• Schema drift accumulation: property names change across releases (user_plan → subscription_tier → plan_name). Fix: breaking changes in the tracking plan require a migration ticket and a dbt model update on the same day.
• Experiment novelty effect: testing a new UI element sees an inflated conversion in week 1 due to novelty. Fix: run experiments for at least 2 full business cycles; check for novelty decay in daily breakdown.
• Self-selection bias in cohort analysis: comparing users who adopted feature X to those who didn't — adopters are inherently more engaged. Fix: use experiment assignment as the causal mechanism, not feature adoption.
• Warehouse cost explosion: loading raw events into BigQuery with scan-based pricing and running queries on the full history. Fix: partition on event_timestamp; cluster on event_name; require partition filters in queries.
• Missing funnel denominators: reporting "20% of users completed step 3" when the denominator was anyone who ever visited the page, not users who entered the funnel at step 1. Fix: anchor every funnel to a clearly defined entry event.

Output format

Primary deliverables:

• Tracking plan: docs/specs/tracking-plan.md — event taxonomy table (event name, trigger, properties, platform, owner), identity strategy, consent layer design, SDK/CDP choice.
• Data model spec: docs/specs/analytics-data-model.md — warehouse schema layers (raw, staging, mart), dbt model graph, retention policy, identity stitching logic.
• Experimentation runbook: docs/runbooks/experimentation.md (use .claude/templates/runbook.md) — how to create, launch, analyze, and close an experiment; statistical requirements.

Supporting artifacts (in project source):

• Tracking plan enforcement schema (JSON Schema or TypeScript types per event).
• Analytics middleware / wrapper module with base properties injected.
• dbt schema.yml with tests for each mart model.
• dbt staging and mart model SQL.
• CI lint step for tracking plan compliance.
• Consent gate implementation (CMP integration or SDK consent controls).

Related checklists

• .claude/checklists/privacy-compliance.md — consent layer, PII in events, retention policies, GDPR right-to-erasure, CCPA opt-out.
• .claude/checklists/production.md — analytics pipeline health monitoring; alert on event volume drop (missing tracking).
• .claude/checklists/data-model.md — warehouse schema design, partitioning, access control.

Related agents

• .claude/agents/engineering/data-engineer.md — warehouse pipeline, dbt models, event ingestion.
• .claude/agents/engineering/backend-engineer.md — server-side event instrumentation.
• .claude/agents/quality/privacy-compliance-auditor.md — consent design, PII classification, GDPR/CCPA/COPPA review.
• .claude/agents/core/product-manager.md — tracking plan ownership, metric definitions, experiment prioritization.
• .claude/agents/domain/saas-domain-expert.md — SaaS-specific metrics (MRR, churn, NRR, product-qualified leads).
• .claude/agents/domain/ecommerce-domain-expert.md — e-commerce funnels, cart abandonment, GMV tracking.