Data Engine
Provides a variety of data analysis models that comprehensively cover daily analytical needs.
Request a demo

Ten analysis models to meet various application scenarios
Event analysis
Retention analysis
Funnel analysis
Distribution analysis
Path analysis
SQL query
Attribute analysis
Interval analysis
User cohort
User tagging
Monitor core in-game metrics in real-time to understand user behavior trends. Perform drill-down analysis by different dimensions and support arithmetic operations to flexibly generate complex metrics.

User journey data correlation and multidimensional cross-analysis
Multidimensional analysis
Support multi-dimensional grouping of any analysis metric to quickly identify changes in metrics across different dimensions.


Cross analysis
Identify different user groups based on a specific analysis metric, and then conduct cross-analysis using these groups as new dimensions.
Date trend analysis
Compare the performance of specific metrics over different time periods to understand their trend variations.

Full-chain user behavior tracking and drill-down insights

In analysis scenarios, it is important to not only focus on overall metrics but also examine data from multiple dimensions.
This includes drilling down into the behavior sequences of individual key users, providing deeper insights into user feedback at a more detailed level. This approach offers strong support for data-driven optimization decision-making.
This includes drilling down into the behavior sequences of individual key users, providing deeper insights into user feedback at a more detailed level. This approach offers strong support for data-driven optimization decision-making.