User Classification Metrics¶
Overview¶
Based on game activity and purchase data, this feature automatically distinguishes activity strength and purchase strength through K-Means clustering, then defines user classification types accordingly. The values are provided based on the last access date of users who accessed during the selected period.
- This metric is updated every day at 7 AM Korea Standard Time (KST).
- It can be checked through SDK integration without separate log transmission.
What Can You Do?¶
Business/Marketing Users¶
- You can quickly define target groups by understanding the size and ratio of user classification types such as whale, dolphin, and non-paying users.
- Clicking a cell of the desired type lets you immediately create a segment and connect it to a targeting campaign.
- You can review user distribution by country or OS to build strategies suited to regional and platform characteristics.
Data Analysts¶
- Through the activity strength/purchase strength classification table and the distribution of attributes by type, you can analyze user group behavior patterns from multiple angles.
- By changing the period, you can compare trends in user count and ratio by user classification type and identify the causes of changes in game metrics.
Quick Start¶
- In the left menu, click User > User Classification.
- Set the Project and Period for which you want to view the metrics.
- Check the user count by activity strength/purchase strength and the ratio by type in the User Classification Type table.
Full Features¶
Key Concepts¶
| Concept | Description |
|---|---|
| Reference Date | The last access date of users who accessed during the selected period |
| Activity Strength | A user's game engagement measured based on access and play data for the 3 days including and before the reference date. Divided into 4 levels: high / medium / low / new |
| Purchase Strength | A user's spending tendency measured based on cumulative purchase data from the first access date to the reference date. Divided into 4 levels: high / medium / low / non-paying |
| User Classification Type | Six user types defined by combining activity strength and purchase strength |
Metric Terms¶
Activity Strength¶
A user's activity strength is measured based on data for the 3 days including and before the reference date, using the user's access date as the reference date.
Example
Example: For a user who accessed on January 10 -> measurement uses data from January 8 to January 10 (3 days)
Activity strength consists of 4 levels (high, medium, low, new), and the measured items are as follows. However, if activity strength is new, the user is a new user on the reference date regardless of the items below.
- Number of logins in the 3 days including and before the reference date
- Number of access days in the 3 days including and before the reference date
- Daily average number of logins in the 3 days including and before the reference date (login count / login days)
- Total game playtime in the 3 days including and before the reference date (seconds)
- Average game playtime by hour from 00:00 to 23:00 in the 3 days including and before the reference date (seconds)
- Number of rewarded ad views in the 3 days including and before the reference date
- Whether a push was opened in the 3 days including and before the reference date
Purchase Strength¶
A user's purchase strength is measured based on cumulative data for the full 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 a user who accessed on January 10 first accessed on January 1 -> measurement uses data from January 1 to January 10
Purchase strength consists of 4 levels (high, medium, low, 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 to the reference date regardless of the items below.
- Time from the user's first login to first purchase (days)
- Total number of payments made by the user from first login to the reference date
- Average payment amount per payment from first login to the reference date (user total payment amount / user total payment count)
User Classification Type¶
User classification types are defined based on activity strength and purchase strength, and when criteria overlap, users are classified into the higher-priority type.
| Type | Classification Rule |
|---|---|
| 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¶
Metric values are provided based on the last access date of users who accessed during the selected period.
Example
Example: If the period is selected as January 1 to January 10, a user who accessed on January 1, 3, 5, and 10 is based on January 10, while a user who accessed only on January 1 is based on January 1.
User Classification Type¶
In the left table within User Classification Type, you can view the user count and ratio by activity strength/purchase strength, and in the right table, the user count and ratio by user classification type.
User count and ratio by activity strength/purchase strength 
- You can view the user count and composition ratio by activity strength/purchase strength classification.
- Clicking a cell in this table lets you create a segment with users matching the clicked activity strength/purchase strength.
User count and ratio by user classification type 
- You can view the user count and ratio by user classification type.
- Clicking a type name or cell in this table lets you create a segment for the classification type you want to target.
- Since the sum of user classification type ratios in the table is 100%, clicking all classification types allows targeting all users within the selected period.
Attribute Distribution by User Classification Type¶
You can check the distribution of attribute values by user classification type. 
| Attribute | Description |
|---|---|
| Playtime (min/avg) | The average playtime during the selected period by user classification type, converted to minutes for the last access day |
| Login Days (avg) | The average number of login days during the 3 days including the last access in the selected period by user classification type |
| Login Count (avg) | The average login count during the 3 days including the last access in the selected period by user classification type |
| Daily Average Login Count | The average value of login days / login count by user classification type |
| Push Response Rate (avg) | The percentage of pushes opened during the 3 days including the last access in the selected period by user classification type |
| Rewarded Ad Views (avg) | The number of rewarded ads viewed during the 3 days including the last access in the selected period by user classification type |
| Days to First Purchase (avg) | The average number of days from the user's first access to their first purchase by user classification type |
| Cumulative Payment Amount | The total payment amount from the user's first access to the last access within the selected period by user classification type |
| Payment Amount per User | The payment amount per user (cumulative payment amount / paying user count) for users who made at least one purchase from the user's first access to the last access within the selected period by user classification type |
OS Distribution by User Classification Type¶
You can check the proportion of a specific OS within each user classification type. 
| Display | OS |
|---|---|
| I | iOS |
| A | Android |
| W | Windows |
| M | Mac |
| P | PC |
| B | Alibaba Yun OS |
| T | Tizen |
Note
If the OS is unknown, it means the OS value was not included in the Hive login log or there are no users belonging to that user classification type.
Top 10 Countries by User Classification Type¶
When you hover over each graph, you can see the country codes and ratios for the top 10 countries by user classification type, and ratios for values outside the top 10 are aggregated as etc. 
- Country codes follow ISO 3166-1 alpha-2 (2-byte country code) and are two uppercase letters.
- If the country code is unknown, it means the country value was not included in the Hive login log or there are no users belonging to that user classification type.
Create Segment¶
You can create a segment by clicking a cell in the user classification type legend or table and clicking the Create Segment button. 
The created segment can be used as follows:
- Snapshot Download: You can download the selected users' attribute data as a CSV file through Segment > Segment Snapshot > Download.
- Targeting Campaign: You can configure a targeting campaign for the selected users through Segment > Targeting Campaign.
How to Create
- Click one or more cells you want to target in the user classification type legend or table.
- When the popup appears after clicking the Create Segment button, click Confirm.
- Click the Go to Segment Page button to move to the segment page.
Notes & Tips¶
- Metric values are calculated based on each user's last access date within the selected period. The same user may be classified differently depending on the period.
- If classification criteria overlap, the user is classified into the higher-priority type. Example: high activity + medium purchase -> dolphin user
- Since the metric is updated every day at 7 AM (KST), real-time data for the current day is not reflected.
- If you create a segment and use it together with the User Classification Move Metrics, you can analyze user type movement trends in depth.
Related Menu¶
- User Classification Move Metrics - Check user classification movement status within the selected period
- Segment - Create segments based on user classification and use snapshots and targeting campaigns