Overview

SolarEngine provides the User Analysis feature to help you conduct various types of analysis on user behavior and user properties, covering the majority of analytical scenarios in the industry. You can save the results obtained through analysis models as richly formatted reports and share them with team members through visual dashboards, achieving real-time metric monitoring and data-driven business operations.

The following overview section explains the basic concepts of the analysis models in SolarEngine, and introduces different analytical scenarios of them.

1. Event Analysis

This is the most basic model, which calculates aggregate metrics for specific user actions over a period of time, such as how much revenue was generated today, or tracks metric trends, such as whether the daily active users (DAU) are stable.

2. Funnel Analysis

This model is used to analyze the number and proportion of users who complete specified steps in a sequential order. It allows you to quickly understand conversion and drop-off rates at specific stages, such as identifying drop-off points during user onboarding or analyzing game level completion rate to identify potential issues.

3. Retention Analysis

This model focuses on the retention rate, a core KPI in mobile app business. By selecting initial events and returning events, you can quickly obtain metrics like second-day retention, third-day retention, seventh-day retention, or configure one more returning event for deeper insights, such as customer LTV.

4. Distribution Analysis

Based on a specific event, this model divides users into different groups. You can segment users based on factors such as purchase frequency or total payment amount, and view the user counts and proportions within each segment.

5. User Analysis

This model supports cross-analysis of two dimensions and allows you to compare between multiple user groups. You can quickly understand the characteristics of specific user segments and implement targeted operation strategies.

6. User Tag

This model is used to assign specific tags to users who share a common characteristic, such as "7-day active", and group them into a user cohort. By tracking the behavior of different user cohorts via the five analysis models above, you can further understand users, observe trends, compare performance, and tailor marketing and product optimization strategies.