Analytics

The Analytics Module in Thinking Engine (TE) can help you conduct various analyses on data about user behaviors and properties, enabling better data-based decision making. The analysis models extracted by us from our industry experience are best-in-class no-code analytics applicable to most analysis scenarios in the industry. You can save the data conclusions obtained from our analysis models as reports of various forms and share them with others via dashboard.

In the Basic Concepts section, we provide a description of the basic concepts involved in the analysis module and also an introduction to the basic ideas behind the analysis models. Different models are designed for different analysis scenarios. The basic concepts can help you see how to choose the right analysis model according to your analysis needs.

Events is the most basic model, which can calculate the aggregated metrics of user’s specific behaviors over a period of time, or the trend in metrics, such as whether the number of daily active uses (DAU) remains steady, how much today’s revenue is, etc.

Retention analyzes the retention rate, one of the KPIs. By selecting the trigger and return actions, you can get the new user’s retention rate at the second, third or seventh day easily. You can also calculate the LTV and ROI by “Calculate another metric”.

Funnel is used to analyze the number and percentage of users who complete specified steps in order. This enables you to quickly know the conversion or churn rate at specific steps, such as points to leave newbie guide or level stay data, and find possible problems.

Interval can analyze the conversion duration between two events in causal relationship, such as the median conversion time spent from registration to first payment, or the distribution of time spent in building upgrading. It can also be used as a supplement to funnel analysis.

Distribution divides intervals based on user participation. You can divide by the frequency of participation, number of days, etc., or by the sum of specific property of each user, such as the cumulative payment amount, and view the number and percentage of users in each interval.

Flows is generally used for exploratory analysis. You can visualize the inflow and outflow of users before and after key points through Sankey diagrams, and analyze user’s behavioral preferences, such as which activities users engage in first after logging in each day, or the key play pattern used by users before their loss.

Composition can support cross analysis of two dimensions, and also compare the data/performance of different cohorts. This feature enables you to quickly grasp the user profile of specific user cohort, make refined operation plans and create tasks for pushing in Engage.

SQL IDE supports custom query for data of all projects under the current cluster, including events, users, tagged cohorts, etc. If no existing models fit your analysis scenario, or your analysis metric involves data from multiple projects, you can calculate your metrics directly using SQL statements, and display the data results using Visualization Module.

Based on our service experience from thousands of projects, we have distilled

Best Practices on how to apply analytic models for your reference and use taking into account existing cases and your own analytic needs. In addition, our Template Gallery feature can enable you to create basic analysis metrics quickly.