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User Activity Tracking

Overview

User Activity Tracking is a feature that lets you view event data at the individual user level.

You can compare and analyze activity patterns of users who negatively affect the game, such as suspected abuse, bans, or refund abuse, and users who positively affect the game, such as whale users and high-spending users.

What Can You Do?

Business/Marketing Users

  • Reviewing the activity flow of whale users can provide insights you can use to design VIP benefits.
  • By checking the last activity event of a churned user, you can identify the reason for churn.
  • You can quickly respond by immediately viewing the activity history of users making refund requests or CS inquiries.

Data Analysts

  • By directly checking the representative behavior patterns of a specific user group, you can refine segment conditions more precisely.
  • You can inspect received event attribute values one by one, allowing you to validate data quality directly.
  • Activity flow analysis lets you examine user behavior in the exact stages where users drop out of funnels.

Developers

  • Looking directly at a user's activity flow lets you instantly verify with real data whether attribute values are being sent correctly.
  • Checking the activity flow of internal test accounts helps you quickly debug event transmission logic.
  • If there are missing or incorrect attributes, you can confirm them immediately in the actual received data.

Quick Start

  1. Click User > User Activity Tracking in the left sidebar.
  2. Select the Project to analyze.
  3. Enter a User ID in the top search bar and search, or select a group from the User Group List at the bottom and click a user to move to the user detail page.
  4. Set the Date Range.
  5. Check the user's event flow in Activity Flow in chronological order.

Full Features

Key Concepts

Concept Description
User Group A predefined user group by analysis purpose (whale users, new users, etc.)
Activity Flow A time-series graph listing events triggered by the user in chronological order
Event Attribute Additional information sent together with each event (items, amount, level, etc.)
User Info Basic information such as the user's first access date, last access date, and LTV

Explore User Groups

Check target users through the provided user groups. user_activity_tracking_01.png

  • Group List: User groups by analysis purpose are shown in a list on the left panel.
  • Select User: Selecting a specific user from the user list within a group moves you to that user's detail information screen.

Search by User ID

Search directly using the user's unique identifier (userId). user_activity_tracking_02.png

  1. Enter the user ID in the top search bar to search.
  2. Click the user ID to move to that user's activity detail screen.

Activity Flow

Check the events triggered by the selected user in chronological order.

user_activity_tracking_03.png

Item Description
Event Occurrence Time The exact date and time the event was received (to the second)
Event Name The name of the event that occurred
Event Attribute List of attributes sent together with the event

View User Info by Date

Click the graph icon shown for each date in the activity flow, and the User Last Info section will change to the user's information for that date.

Use this to compare or track changes in a user's status at a specific point in time, such as level, payment amount, or country, by date.

Daily Activity Count

You can see trends in how often the activities shown in the activity flow occurred by day. This is useful for identifying spikes or drops in activity at a specific point in time.

user_activity_tracking_04.png

Click Go to to view the graph on a larger screen.

User Last Info

Displays user attribute information based on the last date in the selected period. user_activity_tracking_05.png

Item Description
First Access Date The user's first access date.
Dormant Days Number of consecutive days the user has not logged in.
User Classification Type The user's user classification type (for example, whale).
Account Level The user's account-based level.
Lifetime The period from the user's first access date to their last access date.
Total Access Count Cumulative access count since the user's first access.
Daily Average Session Count The average number of sessions per day based on the user's access days.
Period Playtime(Seconds) The user's playtime during a specific period (unit: seconds).
Total Playtime(Seconds) Cumulative playtime since the user's first access (unit: seconds).
First Purchase Date The user's first purchase date.
Total Payment Amount(KRW) Cumulative payment amount since the user's first access (unit: KRW).
Total Payment Amount(USD) Cumulative payment amount since the user's first access (unit: USD).
Total Payment Count Cumulative number of payments since the user's first access.
LTV(KRW) The user's lifetime value in KRW (total payment amount / total access days).
LTV(USD) The user's lifetime value in USD (total payment amount / total access days).
Country The user's country (for example, Republic of Korea).
Language The user's language (for example, Korean).
Market The market where the user installed the game (for example, Google Play).
Server ID The user's server ID (for example, global).
Authentication Method The authentication method used when the user accessed the game (for example, Facebook).
Hive Ban Status The user's current Hive ban status. (Ban active / Ban lifted / No ban history)
OS Version The user's operating system version (for example, 13.0.1).
App Version The user's game app version (for example, 1.6.5).
Custom User Attribute Event Custom user attribute values defined and sent directly by the game.

Example Use Cases

Analyzing Whale User Behavior Patterns

  1. Select a user from the Users whose last user classification type is whale group.
  2. Analyze the activity flow centered on payment-related events (hive_product_purchase, etc.).
  3. Identify which events occurred before payment, such as level up or content completion.
  4. Build a guided content strategy based on the analysis.

Investigating Refund Abuse Users

  1. Search for the ID of the user requesting a refund.
  2. Check the temporal relationship between purchase events and game-play events.
  3. Review whether there is a pattern of requesting a refund immediately after receiving an item.

Analyzing Suspected Abuse Users

  1. Obtain the user ID suspected of abuse during service monitoring.
  2. Search for that user and review the activity flow and information.
  3. Check whether there are abnormal patterns in the activity flow, such as many events in a short period or abnormal attribute values.

Notes & Tips

  • The longer the query period, the longer it may take for data to appear. Query only the period necessary for your analysis.
  • Check target users through the Segment feature or the User Classification feature.
  • Segment - Define user groups with segments and use user lists from snapshots
  • User Classification - Check user classification status based on K-means clustering
  • Event - Define events displayed in the activity flow