User classification move metrics
Overview
Based on users' in-app activity and purchase-related data, this feature automatically distinguishes activity strength and purchase strength with the K-Means clustering technique, and based on them, defines and provides user classification types.
Among users who accessed during the selected period, it provides the move status by type based on the classification types on the first and last access dates, so you can check users' usage flow.
- These metrics are updated every day at 7:00 AM Korea Standard Time (KST).
- You can check them just by integrating the SDK, without sending separate logs.
What can you do?
Business/marketing users
- You can quantify user churn flows within the selected period, such as whale→dolphin and light→non-access, and decide when to run reactivation campaigns.
- With the most frequent classification move status, you can immediately identify the user move patterns that occur most often.
Data analysts
- With the dot move map of the user distribution before and after classification moves, you can visually explore move patterns at the individual user level.
- By changing the period, you can compare and analyze changes in user types before and after specific events or updates.
Quick start
- In the left menu, go to the User > User Classification page and click the User Classification Move tab.
- Set the Project and Period to analyze.
- In the User Classification Type Movement table, check the number and ratio of users who moved from the initial classification type to the final classification type.
- Visually explore the move paths of individual users in the dot move map.
Full features
Key concepts
| Concept | Description |
|---|---|
| Activity Strength | The user's engagement with the app, measured based on access and play data for the 3 days up to and including the reference date. Divided into four levels: high / medium / low / new |
| Purchase Strength | The user's payment tendency, measured based on cumulative purchase data from the first access date to the reference date. Divided into four levels: high / medium / low / non-paying |
| Initial Classification Type | The user type classified based on the first access date within the selected period |
| Final Classification Type | The user type classified based on the last access date within the selected period |
| Non-Access | Users whose last access was 3 or more days before the end date of the selected period (dormant for 3 days or more) |
Metric terms
Activity strength
A user's activity strength is measured based on data for the 3 days up to and including the reference date, using the user's access date as the reference date.
Example
Example: A user who accessed on January 10 → measured using data from January 8 to January 10 (3 days)
Activity strength consists of four levels (high, medium, low, and new), and the measured items are as follows. However, if activity strength is new, the user is a new user on the reference date itself, regardless of the measured items below.
- Number of logins in the 3 days up to and including the reference date
- Number of login days in the 3 days up to and including the reference date
- Daily average number of logins in the 3 days up to and including the reference date (number of logins / number of login days)
- Total app usage time in the 3 days up to and including the reference date (seconds)
- Average app usage time for each hour from 00:00 to 23:00 in the 3 days up to and including the reference date (seconds)
- Number of rewarded ad views in the 3 days up to and including the reference date
- Whether a push was opened in the 3 days up to and including the reference date
Purchase strength
A user's purchase strength is measured based on cumulative data for the entire period from the user's first access date to the reference date, using the user's access date as the reference date.
Example
Example: If the first access date of a user who accessed on January 10 is January 1 → measured using data from January 1 to January 10
Purchase strength consists of four levels (high, medium, low, and non-paying), and the measured items are as follows. However, if purchase strength is non-paying, the user has no purchase history from the first access date through the reference date, regardless of the measured items below.
- Time from the user's first login to the first purchase (unit: days)
- Total number of payments by the user from the first login to the reference date
- Average payment amount per payment from the user's first login to the reference date (total payment amount of the user / total number of payments of the user)
User classification type
User classification types are defined based on activity strength and purchase strength, and if the criteria overlap, the user is classified into the higher type.
| Type | Classification criteria |
|---|---|
| Whale User | Both activity strength and purchase strength are "high" |
| Dolphin User | Either activity strength or purchase strength is "high" |
| Middle User | Either activity strength or purchase strength is "medium" |
| Light User | Both activity strength and purchase strength are "low" |
| Non-Paying User | Purchase strength is "non-paying" regardless of activity strength |
| New User | Activity strength is "new" regardless of purchase strength |
Metric details
User classification type movement
This covers users whose classification types on the first and last access dates differ.
Example
Example: If you select January 1 to January 10 as the period, a user who accessed on January 1, 3, 5, and 10 is included in the display if the user was classified as a whale on January 1 and as a dolphin on January 10, so that the type changed. The user is excluded from the display if the user was also classified as a whale on January 10, so that the type did not change.
You can check the number and ratio of users who moved from the initial classification type (based on the first access date) to the final classification type (based on the last access date) during the selected period.
- User Count: The number of users who were classified as type A at the initial classification and moved to type B at the final classification.
- User Ratio (%): The ratio of the number of users who moved to type B at the final classification to the number of type A users at the initial classification.
Example
Example: In the period from January 1 to January 10, if 100 of the 1,000 whale users at the initial classification moved to dolphin at the final classification, it is displayed as 100 users (10%).
The Non-Access type of the final classification type refers to users who have been dormant for 3 days or more, that is, users whose last access was 3 or more days before the end date of the selected period.
Example
Example: If you select January 1 to January 10 as the period, users whose last access was before January 8 are classified as the non-access type.
Most frequent classification type moves
In the user classification type movement table, you can check the classification move type with the largest number of users among the moves, excluding moves whose final classification type is non-paying or non-access.
User distribution before classification move & user distribution after classification move
With the color-coded dot move map, you can check how each user moved from the initial classification result to the last classification result during the selected period. 
- Users whose classification move result is non-paying or Non-Access (dormant for 3 days or more) are displayed in the user distribution before classification move graph but not in the user distribution after classification move graph.
- Each dot in the graph represents one user whose classification moved during the selected period, and the color of each dot is the color of the user's classification type at the initial classification.
- When you hover the mouse over each dot in the graph, you can check the user's initial classification type and last classification type.
Notes & tips
- Classification move metrics aggregate only users whose types differ between the first and last access dates. Users whose type did not change within the period are not displayed.
- The non-access type is determined based on the end date of the selected period. The longer the period you set, the higher the ratio of non-access users can be.
- The metrics are updated every day at 7:00 AM (KST), so real-time data for the current day is not reflected.
- If you find a type where move patterns are concentrated, combine it with User classification metrics to check the characteristics of that user group.
Related menus
- User classification metrics — Check the definitions of user classification types and the activity strength and purchase strength criteria
- Segment — Create segments based on user classification and use them for targeting campaigns