Retention Analysis


Using Data Analytics requires coding in order to collect data on events. Please refer to the SDK documentation required for your website/app on the left menu for each language.

Use Hackle's Data Analytics > Retention to gain a deep understanding of the retention rate of customers on your product.

The Retention tab can be found within the Data Analytics menu.

What is retention?

Retention rate is the return percentage of users using your product within a set period of time.
For example, among users who have visited your homepage in the last month, the number of users who visit the homepage again (counted in the unit of days) can be calculated for the N-day retention. You can view the users in a cohort grouped by their first visit date, or you can view the results with all users grouped together within a specific time period.

While the common user behavior used to calculate retention is through the user behavior is through the number of visits, you can also calculate the retention based on different types of user behavior such as signups/subscriptions/purchases. By examining retention alongside the major funnel analysis data of your product, you will be able to obtain new insights.

Why you should care about retention
People who make products want users to use their products repetitively, rather than a single, one-time usage. In general, a high retention rate means that users use the app or product on a regular basis. Subsidiary metrics such as user engagement, loyalty, and interests can stem from the main metric of retention rates. Such metrics can be helpful in determining the overall direction for your product.

Retention Curve


Figure 1-1


Figure 1-2

In [Figure 1-1], if 10 users started using the service for the first time on the same day (Day0), and 7 users carried out a specified behavior or event on Day7, 7/10=70% would be the retention rate of Day7. Similarly, if you want to see the retention rate within the span of 28 days, the N-day retention on Day28 will be 20%.

Usually as shown in [Figure 1-2] the frequency of the user's actions gradually decreases after initial entry, and hence the shape of the graph ins [Figure 1-1] is a downward-sloping curve. Therefore, in order to understand the trend of these users, it is recommended to look at a graph form of retention rates rather than a chart form to understand how the user's behavioral cycle changes.

Retention Smile Curve
Are you aware of Evernote's Smile Graph? As explained above, the retention curve is usually downward-sloping. However, for products that accumulate more users over time, a smile curve occurs where the percentage of returning users increases over time. This means that the value of the service has increased for users. Figuring out how long it takes for the smile curve to appear may be one way to view your retention graph and understand your product.


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Create Retention Chart using Hackle

Step 1: Select Event


Figure 2-1

You need to select the events that go into both the Start and Return categories.
The Start event is based on the first point of contact or usage while the Return event is based on the return or recurring usage after N-days (number of days). Hence if you select "Main_Login" as your Start event and "Product_view_purchase" as your Return event, you are setting the condition as users who came back to "purchase" your product after they "visited your homepage" during a set time period.

Please check how to set the desired retention through the following examples.

  • Example 1: Want to analyze the daily retention of users who have recently revisited the service in the past 7 days.
  • Start event: Any Event (Any Event is the total of all events sent to the hackle, suitable for measuring 'visits')
  • Return event: Any Event
  • Period: Daily, recent 7 days
  • Example 2: Want to analyze the weekly re-purchase retention of users who have visited the service in the past 30 days.
  • Start event: Any Event
  • Target event: Purchase
  • Period: Weekly, recent 30 days

Supported Retention Types
Currently, Hackle provides N-day and Unbounded retention types. You can select the appropriate type depending on the service type to get the desired results.

  1. N-day Retention:
    Measures the ratio of users who have reused within a specific day or period. This is the most basic type that comes to mind when talking about retention analysis. It is suitable for products with short and repetitive reuse cycles, such as food delivery or messengers, OTT, etc. If the measurement period is daily, it is expressed as N-day, if it is weekly, it is expressed as N-week, and if it is monthly, it is expressed as N-month.

  2. Unbounded Retention:
    Measures the ratio of users who have reused even after a specific day or period. It is more complex than N-day, but it is suitable for determining retention for commerce services with long repurchase cycles, or where users do not reuse regularly in a short period of time. Since the ratio of users who have reused even after a specific period is measured, the 100% - Unbounded Retention rate is the same as the service dropout rate.

Meanwhile, if a user has performed several start events over a period of time, the user will be counted as below
Check 'Unique user among segments': Users are included only during the period during which the FIRST start event occurred. Since one user is included in only one user group, you can expect more rigorous analysis results. This option is recommended when you have enough users per cohort.
Uncheck 'Unique user among segments: The user is included for each period during which the EVERY start event occurred. Even if the total number of users is small, you can assess the retention trend over time.
*If a user has caused start event several times within a day or week, it is counted as one person by default, regardless of the options above

Step 2: Visualize Results

If you selected the events accordingly as shown in Step 1, you can visualize the retention (revisit) rate by each cohort.
In Hackle's retention analysis, when both the Start event and the Return event are selected, both the graph and table chart are immediately displayed at the bottom as shown in [Figure 3-1] and [Figure 3-2].
After selecting your events, you can also customize the period. You can also hover over the mouse to check the actual retention percentage and the number of users for a specific time/day.


Figure 3-1 Retention Graph


Figure 3-2 Retention Table Chart

Configuration of Hackle's Retention Table Chart
As shown in [Figure 3-2], the structure of Hackle's retention table chart consists of 4 elements: cohort, cohort size, period, and retention rate.

  1. Cohort: A group of customers that share the same characteristics. In Hackle's Retention Analysis, this characteristic is the event initiated date or the group of people who initiated the event you specified on a particular date. This dimension will be placed on the vertical axis.

  2. Cohort size (volume): The number of people in each cohort and the pool of users who initiated the "start event". For example, if you set your start event as users who logged in and return event as visiting the product detail page, the number of logged-in users becomes the cohort size or total volume of each cohort, and the unique number of users who initiate the return event becomes the numerator for calculating the retention rate starting from Day0 onwards.

  3. Period: The time period for your cohort analysis. For example, if you select 7 days, you can see the retention data from Day0 to Day7.

  4. Retention: You can check the aggregated retention by period by grouping a row based on the start event date. You can check the unique count and retention rate (%) based on volume. The higher the retention rate, the darker the shadows in each table.

Step #3: Saving your Retention Analysis


Figure 4-1

When you save a retention file, you can check the saved report in the Data Report menu on the left in order to monitor it continuously.