DataTower.ai

User behavior analysis

A variety of data analysis models comprehensively cover the daily user analysis needs.

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Offering a variety of general-purpose analysis models to meet the needs of various application scenarios.

Event analysis
Retention analysis
Funnel analysis
Distribution analysis

Path analysis
SQL query
Attribute analysis
Interval analysis

Monitor core in-app 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.

Offering a variety of general-purpose analysis models to meet the needs of various application scenarios.

Event analysis

Retention analysis

Master the retention and churn rates of users over specific time periods, and expand retention analysis scenarios by flexibly defining initial and revisit behaviors.

Funnel analysis

Flexibly define key app milestones as funnel steps, and analyze their original conversion or churn rates at each step, enabling quick issue identification and iterative optimization.

Distribution analysis

Based on dimensional metrics, categorize metric values into ranges and analyze user distribution within each range. This allows for a deeper analysis of user engagement and stickiness with specific features.

Path analysis

Track user paths to gain insights into behavior preferences and trends by selecting key events. Provide data support for operations and product development , facilitating product iteration and optimization.

SQL query

For scenarios not covered by predefined analysis models, efficient SQL compilation tools can be used to quickly query custom SQL statements and visualize the results.

Attribute analysis

Analyze user attributes with custom models, supporting filtering and comparison across dimensions. Gain deep insights into user characteristics to understand their status and build user profiles.

Interval analysis

By analyzing the time interval between two user events, you can gain insights into core behavior frequency and leverage data to optimize user experience.

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.

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