Getting Started with Cohorts

The Cohort tool allows you to understand how often your customers return and engage with your website or product.

• Analyze drivers of user retention as well as the factors related to churn
• Create user cohorts based on repeated behaviors and attributes
• Identify customers with high lifetime value (LTV)

Build your Cohort Analysis

Define your Cohort

To begin a cohort query, determine an initiating event (called the Cohort event). The first event of a cohort is required; a user must complete the initiating event or Cohort event and then return to perform a second event which is explained in the section “Target Behavior”. Custom Events and Merged Events can be used. As with other tools, you may apply a Filter Where.

You may select a different time zone from your project time zone on a per query basis by locating the globe icon on the top right of the query screen.

You can chain multiple events in a sequence using an “and then performed” clause to define your cohort of users.

Generations and Breakouts

In addition to the first event, cohorts can be defined by a shared generation or a shared property. A generation is a unit of time, such as a month. A monthly cohort would include all users who entered the cohort during that month. A property is a characteristic or attribute, such as device type. Cohorts defined by device type would include all users with an iPhone, all users with an Android, etc. A user will only appear once in the results of a cohort analysis. For generation cohorts, users will be put into the property breakout in which they first appear during the time interval.

Target Behavior

After selecting an initiating event, you must select a Target Behavior event. This second event of a cohort is also required; Custom Events and Merged Events can be used. As with the initiating event, you are also able to apply a Filter Where.

• Event: Often this is an event that is repeated multiple times, such as a purchase. This is the event that represents the subsequent user behavior that you wish to analyze.
• Revenue: Target behavior can also be represented as revenue. Using revenue as the target behavior will analyze the revenue generated over time by each cohort.

Customize Your Cohort Settings

Once you have defined your cohort, you can continue to add precision to your analysis with the following settings:

Date and Time Range Settings

Every query requires you to select a date range. In Cohort analysis, the date range refers to the time period during which a user completes all steps of the cohort query, defined in Row A. All new queries default to Last 30 Days. To open the date range selector dropdown, click on Last 30 Days. The start date is the first day to be included in the search. The end date is the last day. As mentioned in Cohort Basics, you can set the time zone for your Cohort query.

Fixed start and end dates Use the date range selector on the left side of the dropdown to select a start date and end date from the calendar. You can also enter a specific date by selecting on the date at the top of the selector and entering a value. Use the left and right arrows to navigate the calendar. Tick the Today checkbox to create a dynamic end date.

Dynamic date ranges The right side of the dropdown lists all of the dynamic date ranges that are available. You may choose a dynamic date range, for example Last 7 Days or Last Full Month. This will automatically update the date range of your query each time you view it, counting backwards from today. If you select Last Full Week, then Analytics will analyze the most recent complete week, defined as Monday to Sunday. If you select Last Full Month, then Analytics will analyze the most recent complete month. You can quickly navigate the calendar to select full months using the links in the lower left corner of the dropdown.

Custom ranges To save a custom date range, for example Last 45 Days, simply click Add Custom Date Range in the lower right corner of the dropdown. Your previously used custom date ranges will be saved for future use and are viewable alongside the default dynamic date ranges.

Generation

The generation setting in cohort analyses determines how users are grouped based on when they first performed a specified event. It defines the cohort’s starting point and can be set to an hourly, daily, weekly, or monthly interval. For example, if you create monthly cohorts based on newsletter signups, users will be grouped by their signup month (e.g., January signups, February signups, etc.).

Hourly

• Groups users based on the hour they first performed the event.
• Example: Users who made their first purchase at 10 AM are in a different cohort from those who purchased at 11 AM.

Daily

• Groups users based on the day they first performed the event.
• Example: Users who signed up for a trial on March 1 are in a different cohort from those who signed up on March 2.

Weekly

• Groups users based on the week they first performed the event.
• Example: Users who downloaded an app in the first week of March belong to a different cohort than those who downloaded it in the second week.

Monthly

• Groups users based on the month they first performed the event.
• Example: Users who subscribed to a newsletter in January are in a different cohort from those who subscribed in February.

Recurring vs. First-Time

Cohort queries can track either recurring target behavior or the first occurrence of a behavior after the initial event.

• Recurring: A user appears multiple times if they repeat the target behavior.
• First-Time: A user appears only once, indicating when they first completed the target behavior.

Example:
A box subscription company tracks purchases after a “Newsletter Sign Up” event.

• Recurring Query: If User A signed up in January and purchased in March, April, and May, they appear in the January cohort and in the Month 3, 4, and 5 columns.
• First-Time Query: User A appears only in the Month 3 column for their first purchase.

First-time queries enforce exclusivity per interval, while recurring queries allow users to appear in multiple intervals.

Cumulative vs. Non-Cumulative

By default, cohort queries use the Non-Cumulative setting.

• Non-Cumulative: Shows the count or percentage of users who completed the target behavior within each interval.
• Cumulative: Displays a running total of users who completed the target behavior over time (only available for first-time behavior).

For more details, see Cumulative vs. Non-Cumulative Analysis in Cohort.

Available Metrics:

• Non-Cumulative Percent: Percentage of users who completed the target behavior in each time interval or breakout.
• Non-Cumulative Count: Number of users who completed the target behavior at each interval.
• Cumulative Percent: Running percentage of users completing the target behavior for the first time.
• Cumulative Count: Running total of users completing the target behavior for the first time.

Visualization Options

Cohort analyses have four different visualization options: Circle Heatmap, Heatmap, Line Chart and Area Chart. You can toggle between these options in the visualization dropdowns. You can also download a cohort analysis as a CSV file.

Annotations

Cohort annotations act as general notes about the cohort analysis over the entire designated date range. To add an annotation to Cohort, click on the Annotation flag icon in the query builder window and click Add an Annotation. To access existing annotations, click the Annotation icon in the Data Panel. For more information about annotations, visit this article.