Multi-Dimensional Data Analytics
Identify key players' behaviors
Interpret metrics from different dimensions to help the operation team efficiently dismantle operational problems
Date Comparison
Understand the trend changes of specific metrics by comparing data on cyclical events in different periods
Cross-Dimensional Analysis
Support multi-dimensional grouping of any analysis index to mine data results under different dimension combinations
User Property Analysis
Support cross-analysis under different attributes of users to grasp more detailed population distribution
SQL Visualization
SQL Custom Analytics supports Multi-dimensional tables to meet the pivotal requirements of personalized scenarios
Pre-built models for no-code data analysis
Events
Events
Monitor the core metrics of the game in real-time and observe the payment trend of player behavior; drill down according to different dimensions to observe the user situation by channel and platform.
Retention
Retention
Grasp the retention/loss of players in a specified period of time; Flexibly select initial behaviors and return visit behaviors, so that retention analysis is not limited to conventional retention scenarios, and the impact of core functions on user retention/payment is fully understood.
Funnel
Funnel
The main nodes in the product can be flexibly defined as funnel steps, the transformation/loss of each key step can be compared and analyzed, and the main loss points of the product can be located for targeted optimization.
Distribution
Distribution
Distribution analysis divides the metric values based on the different dimensions or metrics. It obtains the distribution of users in the numerical intervals of each dimension to analyze the user’s participation and stickiness for certain gameplays.
Flows
Flows
Through intelligent algorithms, the actual usage path of users in the product is calculated, the user behavior preferences are insightful, and the real behavior trajectory of the user is restored; Break the subjective judgment of operations and products on user behavior, so that decisions can be based on evidence.
SQL IDE
SQL IDE
Efficient and flexible SQL editing tools, using the powerful underlying query performance of the TE platform, can quickly query custom statements for very few scenarios that the analysis model cannot cover, and support the export and visualization of results.
User Property Analysis
User Property Analysis
User property analysis is an exclusive analysis model based on custom user attributes, which can aggregate and compare user attributes in various dimensions, comprehensively grasp user characteristics, help analysts grasp the current situation of all users, and help build user portraits.
Segmentation
Segmentation
Set up players’ characteristic groups based on their differentiated behaviors, and support the targeted analysis of designated players in each of the major analysis models to provide data support for subsequent refined operations.
Labels
Tags
Label analysis helps to build a label and portrait system, which can be combined with various analysis models for detailed analysis, enrich the dimension of data analysis, and drill deeply into the user group to achieve the user’s refined operation and precision marketing.
Interval
Interval
Interval analysis analyzes the time interval between two specified events generated by a player to help understand how often a player produces a certain core behavior and optimize the user experience in a targeted manner.
Drill down player data deeply, full link tracking
For the essential or general metrics in the analysis scenario, the deep drilling of a particular data result can be targeted to obtain more detailed data, restore the user’s data link, realize more fine-grained data analysis, and achieve the purpose of optimizing decision-making.
