Analytics Plan

Create comprehensive analytics tracking plans to measure feature success.

When to Use

• Before implementing a new feature (define what to track)
• When launching an experiment
• When setting up product analytics
• When defining success metrics

Used By

• Data Analyst (primary owner)
• Growth Marketer (growth metrics)
• Product Manager (success metrics)
• Full-Stack Engineer (implementation)

Analytics Plan Template

# Analytics Plan: [Feature/Initiative Name]

**Author**: [Name]
**Date**: [Date]
**Status**: Draft | Approved | Implemented

---

## Overview

### Feature Description
[Brief description of the feature]

### Business Questions
What decisions will this data inform?

1. [Question 1]
2. [Question 2]
3. [Question 3]

### Success Criteria
How will we know if this feature is successful?

- **Primary Metric**: [Metric] - Target: [X]
- **Secondary Metric**: [Metric] - Target: [X]
- **Guardrail Metric**: [Metric] - Should not decrease by [X%]

---

## Event Tracking

### Core Events

| Event Name | Trigger | Properties | Priority |
|------------|---------|------------|----------|
| `[event_name]` | [When fired] | [Key properties] | P1 |
| `[event_name]` | [When fired] | [Key properties] | P1 |
| `[event_name]` | [When fired] | [Key properties] | P2 |

### Event Specifications

#### `feature_viewed`
**Trigger**: When user views the feature for the first time in session

**Properties**:
| Property | Type | Required | Description |
|----------|------|----------|-------------|
| `source` | string | Yes | Where user came from |
| `variant` | string | No | A/B test variant |
| `user_tier` | string | Yes | Free/Pro/Enterprise |

**Example**:
```json
{
  "event": "feature_viewed",
  "properties": {
    "source": "navigation",
    "variant": "control",
    "user_tier": "pro"
  }
}

feature_action_completed

Trigger: When user completes the primary action

Properties:

action_type

time_to_complete

success

Funnel Definition

Primary Funnel: [Feature Adoption]

Step 1: feature_viewed
    ↓ [Target: 80%]
Step 2: feature_started
    ↓ [Target: 60%]
Step 3: feature_completed
    ↓ [Target: 40%]
Step 4: feature_repeated (within 7 days)

Funnel Analysis Questions

• Where is the biggest drop-off?
• How does drop-off vary by user segment?
• What's the time between steps?

User Properties

has_used_feature

feature_usage_count

first_feature_use

last_feature_use

Segments

Key Segments to Analyze

Dashboard Requirements

Overview Dashboard

Purpose: Daily monitoring of feature health

Metrics to Include:

• Daily Active Users (DAU)
• Feature adoption rate
• Primary action completion rate
• Error rate

Filters:

• Date range
• User segment
• Platform

Deep Dive Dashboard

Purpose: Understanding patterns and opportunities

Charts to Include:

• Funnel visualization
• Cohort retention
• Time-based trends
• Segment comparison

Experiment Plan (if applicable)

Hypothesis

[Change] will lead to [X% improvement] in [metric] because [reason].

Test Setup

• Control: [Current experience]
• Variant: [New experience]
• Allocation: [50/50 or other]
• Duration: [X weeks]
• Sample Size Needed: [X users per variant]

Success Metrics

Analysis Plan

• Primary analysis at [X] days
• Segment analysis by [dimensions]
• Document learnings regardless of outcome

Implementation Checklist

Before Development

• Analytics plan reviewed by data/product
• Event names follow naming convention
• Success metrics approved

During Development

• Events implemented with correct properties
• Events fire at correct times
• Properties populated correctly

Before Launch

• Events tested in staging
• Dashboard created
• Baseline metrics captured
• Alert thresholds set

After Launch

• Verify data flowing correctly
• Check for data quality issues
• Monitor metrics daily for first week

Data Quality Checks

---

## Event Naming Convention

### Format

[object]_[action]

### Objects (nouns)
- `page` - Page views
- `button` - Button interactions
- `form` - Form interactions
- `feature` - Feature usage
- `subscription` - Subscription events
- `user` - User lifecycle

### Actions (past tense verbs)
- `viewed` - Something was seen
- `clicked` - Something was clicked
- `submitted` - Form was submitted
- `started` - Process began
- `completed` - Process finished
- `failed` - Something went wrong

### Examples

page_viewed button_clicked form_submitted feature_started feature_completed subscription_upgraded user_signed_up

---

## Property Guidelines

### Always Include
- `timestamp` - When event occurred
- `user_id` - Logged-in user identifier
- `session_id` - Session identifier
- `platform` - web/ios/android
- `page` - Current page/screen

### Contextual Properties
- `source` - What triggered the action
- `variant` - A/B test variant
- `value` - Numeric value if applicable
- `error_type` - For error events

### Naming Rules
- Use `snake_case`
- Be descriptive but concise
- Use consistent naming across events
- Document allowed values for enums

---

## Metrics Definitions

### Common Metrics

**Daily Active Users (DAU)**

Count of unique users with any event in past 24 hours

**Activation Rate**

(Users who completed key action) / (Users who signed up) × 100

**Retention Rate (Day N)**

(Users active on day N) / (Users who signed up N days ago) × 100

**Feature Adoption**

(Users who used feature) / (Total users) × 100

**Conversion Rate**

(Users who completed goal) / (Users who started flow) × 100

---

## Quick Reference

### Before Feature Launch
1. Define success metrics
2. Create event tracking plan
3. Implement events
4. Test in staging
5. Set up dashboard
6. Capture baseline

### After Feature Launch
1. Verify data quality
2. Monitor daily
3. Analyze after 1 week
4. Deep dive after 1 month
5. Document learnings