User Activity Cohort lets you analyse how users engage with your website or app over time by grouping them on a key action and tracking whether they come back. It’s one of the clearest signals of digital experience and brand health — helping teams understand retention, churn, and long-term engagement across channels and segments. Think of it as your answer to a simple but critical question — are users coming back, or are they quietly disappearing?Documentation Index
Fetch the complete documentation index at: https://docs.ocular.dev/llms.txt
Use this file to discover all available pages before exploring further.
What is a User Activity Cohort
A User Activity Cohort groups users who perform a specific Start Event within the same time period, then tracks whether those same users return to perform a Return Event in subsequent periods. It reveals:- Retention rates over time
- Churn patterns at specific intervals
- Engagement trends by acquisition channel
- Event adoption and continued usage patterns
Why this matters
For business stakeholders
Answers the trajectory questions:
- Are we building an engaging experience, or a leaky bucket?
- Which acquisition channels bring users who actually stay?
- Is our retention improving month over month?
For analysts & growth teams
Answers the diagnostic questions:
- What % of users who signed up last week are still active?
- Which features do power users return to repeatedly?
- Which user segments retain best, and why?
How to use it — a worked example
Scenario — a D2C skincare brand
A direct-to-consumer skincare brand acquires users through Instagram ads, influencer partnerships, and Google Shopping. The marketing team wants to know which channels drive loyal, repeat buyers — not just first-time purchasers.Defining the retention logic
Every User Activity Cohort is anchored on two events:- Start Event — determines who enters the cohort
- Return Event — determines whether those users are retained
Configuration
| Setting | Value |
|---|---|
| Start Event | add_to_cart — signals initial product interest |
| Return Event | session_start — indicates the user returned to browse |
| Split By | Entry UTM Source (Breakout mode) |
| Measure | Users (unique) |
| Time Granularity | Week |
| Date Range | Last 90 days |
| Value Representation | Percentage |
What the cohort reveals
Sample retention output:| Acquisition Source | Week 0 | Week 1 | Week 2 | Week 4 | Week 8 | Week 12 |
|---|---|---|---|---|---|---|
| Instagram Ads | 100% | 28% | 15% | 8% | 5% | 4% |
| Influencer Posts | 100% | 42% | 31% | 22% | 18% | 16% |
| Google Shopping | 100% | 38% | 27% | 19% | 15% | 13% |
| Meta Ads | 100% | 25% | 12% | 6% | 4% | 3% |
| YouTube Ads | 100% | 35% | 24% | 16% | 12% | 10% |
Actionable insights by team
- 📦 E-commerce teams
- 🚀 Growth teams
- 📊 Analytics & leadership
Insights
- Sharp drop-off between Week 1 (~34%) and Week 2 (~22%) across channels
- Users who don’t return by Week 2 rarely become repeat visitors
- Trigger an automated WhatsApp/email journey at Day 10:
- Product usage education
- Reviews and before/after results
- Limited-time repeat-purchase offer
How the User Activity Cohort works
Feature layout
The User Activity Cohort screen has two parts:- Retention Curve — visual trend at the top
- Cohort Table — detailed breakdown below
Visualisation options
- Chart type: Line, Area, Bar
- Number format: Absolute values or Percentages
Cohort table — what each column means
| Column | What it shows |
|---|---|
| A | Cohort period (e.g. Week of Nov 20–26) |
| B | Cohort aggregations (unique users, total revenue) |
| Subsequent columns | Retention in later periods |
Defining your analysis — the query bar
The top of the screen lets you define the behaviour and parameters for your analysis.Every selection here changes who is considered retained.
1. Measure & events
Measure determines what fills each cohort cell.
| Measure | Description |
|---|---|
| Users | Distinct users (by pseudo_user_id) who performed the selected events during the selected time period |
| Ecommerce Revenue | Total purchase revenue from ecommerce events attributed to users in the cohort |
| Orders | Total purchase events recorded for users in the cohort |
| Sessions | Sessions initiated by users in the cohort |
| Activities | Total count of GA4 events performed by users in the cohort |
2. Time and granularity
- Date Range: 7d, 30d, This Month, FYTD, Custom, etc.
- Granularity: Day, Week, Month, Quarter, Year
3. Start Event
Defines who enters the cohort.| Start Event | Use case |
|---|---|
session_start | General visits |
user_signed_up | New users |
added_to_cart | Purchase intent |
product_viewed | Content or item interest |
app_opened | App usage |
4. Return Event
Defines what counts as retention.| Start Event | Return Event | Insight |
|---|---|---|
added_to_cart | session_start | Browse retention |
session_start | session_start | Visit frequency |
product_viewed | added_to_cart | Intent → action |
user_signed_up | session_start | New user engagement |
app_opened | app_opened | App retention |
5. Split By (segmentation)
- Entry UTM Source / Medium / Campaign
- Entry City, Country
- Entry Device
- Entry Page
Breakdown mode
Cohort period stays the primary row. Each cohort week is broken down into nested sub-rows, one per split-by value.Example: Split by Entry City → expand Week Jan 1–7 to see Mumbai, Delhi, Bangalore as nested rows inside it, each with its own Week 0/1/2/4… retention.Use when you want to see the segment composition within each acquisition period.
Breakout mode
Split-by value becomes the primary row. Each segment is broken out into its own complete cohort row across cohort weeks.Example: Split by Entry City → one row for the Mumbai cohort, one for Delhi, one for Bangalore, each with its own Week 0/1/2/4 retention timeline.Use when you want to compare retention curves side-by-side across segments.
6. Advanced filtering & logic
- Entry UTM Source = Instagram
- Entry City = Mumbai
- Entry Device = Mobile
- Return UTM Source = Email
- Return Device = Desktop
- Return Page = Product Detail Page
| Question | Entry filter | Return filter |
|---|---|---|
| Do mobile browsers convert better when they return on desktop? | Entry Device: Mobile | Return Device: Desktop |
| How many Instagram-acquired users returned via our email campaigns? | Entry UTM Source: Instagram | Return UTM Source: Email |
| Do users who travel retain engagement across locations? | Entry City: Mumbai | Return City: Delhi, Bangalore |
| Combination | Logic |
|---|---|
| Multiple values within the same filter | OR |
| Different filter types combined | AND |
Tips for analysing retention
Key interpretation rules
- A user belongs to only one cohort — based on their first Start Event. A user in the Jan 1–6 cohort won’t also appear in Jan 7–14.
- A user can appear in multiple retention periods — they could be counted in Week 1, Week 5, or all weeks. This is the desired behaviour and indicates strong retention.
Save a cohort for reuse
You can save a User Activity Cohort configuration to reuse without rebuilding it.
- Start Event and Return Event definitions
- Measure selection
- Date range and granularity
- Split-by configuration and mode
- Entry and Return filters