# Daily Retention Metrics for Cohort Analysis

**URL:** <https://community.omni.co/t/daily-retention-metrics-for-cohort-analysis/352>\
**Category:** Modeling\
**Tags:** modeling\
**Created:** [July 7, 2025, 9:41pm UTC](https://community.omni.co/t/daily-retention-metrics-for-cohort-analysis/352 "2025-07-07T21:41:01Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![conor](https://yyz2.discourse-cdn.com/flex004/user_avatar/community.omni.co/conor/32/45_2.png) [@conor](https://community.omni.co/u/conor)\
**Post date:** [July 7, 2025, 9:41pm UTC](https://community.omni.co/t/daily-retention-metrics-for-cohort-analysis/352/1 "2025-07-07T21:41:01Z")

</div>

## Overview

Daily retention metrics are crucial for understanding user engagement and product stickiness. These are often used in Saas products, gaming and eLearning. This guide demonstrates how to build a comprehensive retention analysis system in Omni by connecting your daily active users table with your user dimension table. Key Metrics to Track

1. **D0** : Same-day engagement (multiple sessions on signup day)
2. **D1** : Next-day return (critical early engagement indicator)
3. **D7** : Weekly retention (product-market fit signal)
4. **D30** : Monthly retention (long-term habit formation)

### Core Tables Required

- **Daily Active Users Table** (`daily_active_users`): Contains user activity by date
- **User Dimension Table** (`dim_users`): Contains user attributes and first seen dates

### Key Relationship

The relationship between these tables is typically established through `user_id` as the primary key, enabling cohort analysis and retention calculations.

## Building the Retention Framework

### 1. Retention Day Dimension

First, create a dimension to calculate days since first activity:

```auto
retention_day:
  sql: DATE_DIFF(${daily_active_users.event_date}, ${dim_users.user_first_seen_date}, DAY)
  description: "Days Since First Activity""

```

### 2. Days Since User Signup

Create a helper dimension for eligibility calculations. I’m using curren\_date() (assuming Snowflake but swap this out for your database’s corresponding date function).

```auto
days_since_user_signup:
  sql: DATE_DIFF(CURRENT_DATE(), ${dim_users.user_first_seen_date}, DAY)
  description: "Days since first activity (from today)"

```

## Core Retention Metrics Implementation

### D1 Retention (Day 1)

**D1 Retained Users:**

```auto
d1_retained_users:
  sql: ${daily_active_users.user_id}
  label: D1 Retained Users
  format: ID
  aggregate_type: count_distinct
  filters: 
    retention_day:
      is: 1

```

**D1 Eligible Users:**

```auto
d1_eligible_users:
  sql: ${daily_active_users.user_id}
  label: D1 Eligible Users
  format: ID
  aggregate_type: count_distinct
  filters: 
    days_since_user_signup:
      greater_than: 0

```

**D1 Retention Rate:**

```auto
d1_retention_rate:
  sql: 1.0 * ${d1_retained_users}/ NULLIF(${d1_eligible_users},0)
  label: D1 Retention Rate
  description: "Percentage of users who returned on day 1"

```

## Best Practices

### 1. Data Quality Considerations

- Always use `NULLIF(${eligible_users},0)` to prevent division by zero errors
- Ensure your `user_first_seen_date` is properly populated in dim\_users
- Use `current_date()::date` for consistent date calculations

### 2. Activity Definition Considerations

If you have a specific definition for what constitutes “active” usage (e.g., must perform certain actions, spend minimum time, etc.), consider adding a `daily_active_flag` to your daily users table and including that in calculations.