analytics-workflow

name	analytics-workflow
description	Data & product analytics workflow: event taxonomy design, instrumentation (Segment, PostHog, Mixpanel, Amplitude, GA4), analytics pipelines, funnel and retention analysis, and dashboard design. Complements observability (infrastructure metrics) with product/user behavior tracking.

Analytics Workflow

Scope: Product and user behavior analytics — what users do in your product and why. For infrastructure metrics (latency, error rate, CPU), see the observability skill.

When to Activate

• Adding event tracking to a new feature
• Designing an analytics event taxonomy from scratch
• Integrating Segment, PostHog, Mixpanel, Amplitude, or GA4
• Building a funnel, retention, or cohort analysis
• Creating a product dashboard (DAU, activation, conversion)
• Setting up an analytics data pipeline (warehouse → BI tool)
• Answering "are users actually using X?"

---

Event Taxonomy Design

The golden rule: define your taxonomy before writing any tracking code.

Event naming convention

<object>_<action>          # noun_verb, past tense
user_signed_up
project_created
payment_failed
feature_viewed
onboarding_step_completed

Avoid:

• click (too generic — what was clicked?)
• pageView (camelCase inconsistency)
• btn_clicked (abbreviation)
• event1 (meaningless)

Event schema

Every event should carry:

Property	Type	Description
event	string	Event name (e.g. project_created)
user_id	string	Authenticated user ID (nullable for anon)
anonymous_id	string	Pre-auth session ID
timestamp	ISO8601	Client event time
properties	object	Event-specific payload
context.app	object	App name + version
context.page	object	URL, title, referrer

Taxonomy tiers

Tier	Events	Purpose
Core lifecycle	user_signed_up, user_signed_in, subscription_started	North star metrics
Activation	onboarding_step_completed, first_project_created	Aha moment tracking
Feature usage	feature_viewed, feature_used, feature_abandoned	Engagement depth
Revenue	checkout_started, payment_succeeded, plan_upgraded	Monetization
Errors	error_displayed, action_failed	Friction points

---

Instrumentation

Segment (universal router)

// Track event
analytics.track('project_created', {
  project_id: project.id,
  template_used: project.template,
  team_size: team.members.length,
});

// Identify user (call on login + profile changes)
analytics.identify(user.id, {
  email: user.email,
  plan: user.subscription.plan,
  created_at: user.createdAt,
  company: user.company?.name,
});

// Group (associate user with account/org)
analytics.group(org.id, {
  name: org.name,
  plan: org.plan,
  employee_count: org.size,
});

Segment destinations: route one event stream → Mixpanel, Amplitude, BigQuery, Redshift, Braze simultaneously. No multi-SDK installs.

PostHog (self-hosted / OSS)

import posthog from 'posthog-js';

posthog.init('phc_xxx', { api_host: 'https://eu.posthog.com' });

// Feature flags (integrated with analytics)
if (posthog.isFeatureEnabled('new-checkout')) {
  // show new checkout
}

// Group analytics
posthog.group('company', orgId, { name: org.name, plan: org.plan });

// Session recording (enable selectively)
posthog.startSessionRecording();

PostHog advantages: feature flags + A/B tests + session recording in one tool. Good for GDPR (EU cloud or self-host).

Server-side tracking (Node.js)

import Analytics from '@segment/analytics-node';

const analytics = new Analytics({ writeKey: process.env.SEGMENT_WRITE_KEY });

// Server events (payments, backend actions)
analytics.track({
  userId: user.id,
  event: 'payment_succeeded',
  properties: {
    amount: charge.amount,
    currency: charge.currency,
    plan: subscription.plan,
  },
  context: { ip: request.ip },
});

// Flush before process exit
await analytics.closeAndFlush();

---

Analytics Pipeline Architecture

[App events] → [Segment / PostHog]
                     │
              ┌──────┴──────────┐
              │                 │
         [Real-time]      [Warehouse]
         (Mixpanel,        (BigQuery /
          Amplitude)        Snowflake /
                            Redshift)
                                │
                          [dbt models]
                                │
                         [BI Dashboard]
                         (Metabase /
                          Looker /
                          Tableau)

dbt event model pattern

-- models/events/fct_events.sql
with raw as (
  select * from {{ source('segment', 'tracks') }}
),
cleaned as (
  select
    id,
    anonymous_id,
    user_id,
    event                                    as event_name,
    timestamp                                as occurred_at,
    json_extract_path_text(properties, 'project_id') as project_id,
    received_at
  from raw
  where timestamp >= '2024-01-01'
)
select * from cleaned

---

Key Product Metrics

Acquisition

• CAC = Total marketing spend / New customers
• Traffic by channel = sessions per source/medium

Activation

• Activation rate = Users who hit "aha moment" / Signups
• Time to first value = median time from signup → first key action

Retention

-- Weekly retention cohort
select
  date_trunc('week', first_seen)  as cohort_week,
  date_trunc('week', occurred_at) as activity_week,
  count(distinct user_id)         as active_users
from user_activity
group by 1, 2
order by 1, 2

Revenue

• MRR = sum of monthly recurring revenue
• Churn rate = Customers lost this month / Customers at start of month
• LTV = Average MRR per customer / Churn rate

Funnel analysis

select
  count(distinct case when step = 'signup'          then user_id end) as s1_signup,
  count(distinct case when step = 'email_verified'  then user_id end) as s2_email,
  count(distinct case when step = 'project_created' then user_id end) as s3_project,
  count(distinct case when step = 'invite_sent'     then user_id end) as s4_invite
from funnel_events
where cohort_date >= current_date - 30

---

Dashboard Design Principles

1. One metric per card — not "users and sessions" on one chart
2. Show trend, not snapshot — 30-day sparkline next to the number
3. Segment by default — always allow drill-down by plan/channel/cohort
4. Alert on anomalies — set thresholds, not just displays
5. Executive vs. operator views — different granularity for different audiences

Recommended dashboard stack

Use case	Tool
Self-hosted BI	Metabase (free), Redash
Enterprise BI	Looker, Tableau, Power BI
Product analytics	Mixpanel, Amplitude, PostHog
Real-time	Grafana (Prometheus backend)
Ad-hoc SQL	Mode, Count, Querybook

---

Privacy & Compliance

• Anonymize before analysis: hash PII before storing in warehouse
• Respect opt-out: honor Do Not Track, GDPR consent signals
• Data retention: define TTL for raw events (90 days recommended)
• PII in properties: never log password, ssn, credit_card_number

// Sanitize before tracking
analytics.track('form_submitted', {
  form_id: form.id,
  // email: user.email,  ← DO NOT include PII in event properties
  field_count: form.fields.length,
});

---

Related

• observability — infrastructure metrics, traces, alerts
• load-testing — performance under load
• /instrument command — add event tracking to existing code