What This Template Is For
Behavioral analytics moves your product team beyond vanity metrics (page views, sessions) into the actions users actually take. Instead of asking "how many people visited the dashboard?", you ask "how many people created their first report within 48 hours of signup?" The first question tells you about traffic. The second tells you about product-market fit.
Most product teams track too many events or too few. Too many, and your data warehouse fills with noise that nobody queries. Too few, and you cannot answer basic questions about user behavior. This template provides a structured approach to defining what to track, how to organize events, and how to extract patterns from behavioral data.
The Product Analytics Handbook covers the strategic foundations of behavioral measurement. For retention-specific behavioral analysis, the cohort analysis template pairs well with this one. The session duration metric and feature adoption rate metric guides explain how to measure specific behavioral outcomes from the events you define here. If you are building your analytics stack from scratch, the analytics audit template helps assess your current instrumentation gaps.
How to Use This Template
- Map your product's core user journey from signup through activation to habitual use.
- Define the event taxonomy using the naming conventions in section one. Consistency here saves hundreds of hours of data cleaning later.
- Identify 3-5 key behavioral segments based on the actions users take (not who they are).
- Set up the tracking plan with properties for each event.
- Run the initial analysis to identify behavioral patterns.
- Document findings and update the tracking plan as the product evolves.
The Template
Section 1: Event Taxonomy Design
A clean event taxonomy is the foundation of behavioral analytics. Use a consistent naming convention so every team member can find and interpret events without a decoder ring.
Naming Convention
| Element | Format | Example |
|---|---|---|
| Object | Noun, singular | report, task, invite |
| Action | Past-tense verb | created, completed, sent |
| Full event name | object_action | report_created, task_completed, invite_sent |
| Property prefix | Context qualifier | report_type, task_priority, invite_method |
Event Categories
| Category | Purpose | Examples |
|---|---|---|
| Activation | Track first-time key actions | workspace_created, first_report_created, team_member_invited |
| Engagement | Track core loop repetition | report_created, dashboard_viewed, comment_added |
| Monetization | Track revenue-driving actions | plan_upgraded, addon_purchased, billing_updated |
| Retention signals | Track return behavior | session_started, notification_clicked, saved_view_opened |
| Churn signals | Track disengagement | export_triggered, downgrade_initiated, support_ticket_created |
Section 2: Core Event Tracking Plan
For each event, document the name, trigger condition, and properties. This becomes the source of truth for engineering implementation.
| Event Name | Trigger | Required Properties | Optional Properties |
|---|---|---|---|
[e.g., report_created] | [User clicks "Create Report" and saves] | report_id, report_type, user_id, timestamp | template_used, data_source, collaborators_count |
[e.g., task_completed] | [User marks task as done] | task_id, project_id, user_id, timestamp | time_to_complete, task_priority, assignee_changed |
[e.g., invite_sent] | [User sends team invite] | invite_id, invite_method, user_id, timestamp | role_assigned, custom_message, resend |
| [Event 4] | [Trigger] | [Properties] | [Properties] |
| [Event 5] | [Trigger] | [Properties] | [Properties] |
| [Event 6] | [Trigger] | [Properties] | [Properties] |
| [Event 7] | [Trigger] | [Properties] | [Properties] |
| [Event 8] | [Trigger] | [Properties] | [Properties] |
Tracking Plan Rules
- ☐ Every event has a single, unambiguous trigger condition
- ☐ All properties use snake_case naming
- ☐ Timestamps are UTC ISO 8601
- ☐ User IDs are consistent across events (same identifier everywhere)
- ☐ No PII (email, full name) in event properties unless required and GDPR-compliant
- ☐ Each event fires exactly once per trigger (no duplicates from retries or re-renders)
Section 3: Behavioral Segments
Define segments based on what users do, not who they are. Behavioral segments are more predictive of retention and revenue than demographic segments.
| Segment Name | Definition (actions) | Expected % of Users | Retention Hypothesis |
|---|---|---|---|
| Power Users | [e.g., "Created 5+ reports AND logged in 4+ days in the past 7 days"] | [e.g., 8-12%] | [Highest retention, most likely to upgrade] |
| Activated Users | [e.g., "Completed onboarding AND created first report within 48 hours"] | [e.g., 25-35%] | [Strong retention if activation happens fast] |
| Passive Users | [e.g., "Logged in 2+ times but completed 0 core actions in 7 days"] | [e.g., 20-30%] | [High churn risk, need engagement nudges] |
| At-Risk Users | [e.g., "No login for 7+ days after being active for 2+ weeks"] | [e.g., 15-25%] | [Likely to churn within 30 days without intervention] |
| New Users | [e.g., "Signed up within last 7 days, onboarding incomplete"] | [e.g., 10-20%] | [Retention depends on time-to-first-value] |
Segment Validation Checklist
- ☐ Each segment is mutually exclusive (a user can belong to only one at a time)
- ☐ Segments cover 90%+ of active users
- ☐ Segment definitions use measurable, queryable criteria
- ☐ Segments refresh on a defined cadence (daily, weekly)
- ☐ At least one segment identifies churn risk before the user is already gone
Section 4: Behavioral Pattern Analysis
Once you have 4+ weeks of tracked events, analyze patterns using these frameworks.
Frequency Distribution
| Action | Daily Active Users | Median Actions/Week | P90 Actions/Week | Trend (4-week) |
|---|---|---|---|---|
| [Core action 1] | [N] | [N] | [N] | [Up/Down/Flat] |
| [Core action 2] | [N] | [N] | [N] | [Up/Down/Flat] |
| [Core action 3] | [N] | [N] | [N] | [Up/Down/Flat] |
Sequence Analysis
Document the most common action sequences for users who convert vs. those who churn. This reveals the "happy path" and the "danger path."
| Sequence | Users Who Retained (30d) | Users Who Churned (30d) |
|---|---|---|
| [Action A then Action B then Action C] | [e.g., 65% followed this path] | [e.g., 12% followed this path] |
| [Action A then Action D (skip B)] | [e.g., 15%] | [e.g., 45%] |
| [Action A only (no follow-up)] | [e.g., 5%] | [e.g., 38%] |
Time-to-Action Analysis
| Action | Median Time from Signup | Users Who Complete Within 24h | Correlation with 30d Retention |
|---|---|---|---|
| [First core action] | [e.g., 3.2 hours] | [e.g., 42%] | [e.g., r = 0.73] |
| [Second core action] | [e.g., 2.1 days] | [e.g., 18%] | [e.g., r = 0.61] |
| [Third core action] | [e.g., 5.4 days] | [e.g., 9%] | [e.g., r = 0.44] |
Section 5: Insights and Actions
Translate patterns into product decisions. Each finding should map to a specific action.
| Finding | Evidence | Segment Affected | Recommended Action | Priority |
|---|---|---|---|---|
| [e.g., "Users who create a report within 24h retain 2.3x better"] | [Retention rate: 68% vs. 29%] | New Users | [Redesign onboarding to push report creation earlier] | [P0 / P1 / P2] |
| [e.g., "Invite flow drops off at role selection step"] | [38% abandonment at step 3] | Activated Users | [Simplify role selection or default to 'member'] | [P0 / P1 / P2] |
| [Finding 3] | [Data] | [Segment] | [Action] | [Priority] |
| [Finding 4] | [Data] | [Segment] | [Action] | [Priority] |
Use the RICE framework to prioritize these actions, or score them with the RICE calculator for a quantitative ranking.
Filled Example: SaaS Project Management Tool Onboarding
Event Taxonomy (subset)
| Event Name | Trigger | Key Properties |
|---|---|---|
workspace_created | User creates first workspace | workspace_id, template_used, user_id |
project_created | User creates a project | project_id, workspace_id, project_template |
task_created | User creates a task | task_id, project_id, assignee_id |
member_invited | User sends team invite | invite_id, invite_method, role |
task_completed | User marks task done | task_id, time_to_complete_hours |
integration_connected | User connects an external tool | integration_type, workspace_id |
Behavioral Segments
| Segment | Definition | % of Users | 30d Retention |
|---|---|---|---|
| Power Users | 10+ tasks completed/week AND 3+ collaborators | 9% | 94% |
| Team Builders | 2+ invites sent AND 1+ project created within 7 days | 22% | 78% |
| Solo Explorers | 1+ project created, 0 invites sent, active 3+ days | 31% | 52% |
| Passive Browsers | Login 2+ times, 0 projects created | 24% | 18% |
| One-and-Done | Single session only | 14% | 3% |
Key Findings
| Finding | Evidence | Action Taken |
|---|---|---|
| Team Builders retain 4.3x better than Solo Explorers | 78% vs. 18% 30d retention | Added "Invite your team" prompt to onboarding step 2 |
| Users who connect an integration within 48h retain 2.1x better | 71% vs. 34% 30d retention | Surfaced integration setup in the first-run experience |
| Task creation within 1 hour of signup is the strongest activation signal | r = 0.81 correlation with 30d retention | Redesigned empty state to guide users toward first task |