# Behavior Analysis

TIP

This section will provide a brief introduction to Behavioral Analysis section to help you better understand how we use models to build reports. If you are interested in a certain section, you can click to enter the corresponding section. Before viewing this chapter, it is recommended that you read the TA Quick Use Guide

Event (Event) represents a certain or a series of meaningful behaviors of the user, such as registering an account, upgrading cards, purchasing gift bags, etc. Through behavior analysis, the user's real use process can be restored, which is an important content in game data analytics. For different analysis scenarios, TA provides a variety of analysis models, you can choose according to the actual situation.

Event analysisis the most basic model in behavior analysis. It can calculate the aggregation index of specific behaviors generated by users within a period of time, understand the customer engagement of each behavior and the development trend of the indicators, such as the number of registered users or the amount of payment per day.
Retention analysisis a model based on two events-initial event and return visit event. It counts the number and proportion of users who have done initial event and have done return visit event in future dates, such as the next day retention of newly registered users, seven-day retention, LTV, ROI, etc.
Funnel analysisis an analysis model that analyzes the transformation of users in the specified steps in the behavior flow. It can quickly grasp the transformation of products in each step link within a period of time, such as using the AARRR model, or checking a certain in-game operation event. In which link the loss of users is serious, etc.
Distribution analysisis an analysis model that divides users into different intervals based on their participation in an event. It can check the number and proportion of users in different intervals. At the same time, other indicators can be further analyzed based on the users in this area, such as dividing by the number of login days in the past 30 days, and then looking at the average payment amount of users in different areas.
Interval analysisis an analysis model that analyzes the time interval between two specified events generated by users. It can understand the occurrence frequency of a certain core behavior of users, or obtain the conversion time of two events with causal relationship before and after, such as the transition time from registration to first payment or the interval time between two payments.
Flow analysisis an exploratory model for analyzing behavior order, behavior preference, key nodes and transformation efficiency. The behavior flowing in and out before and after critical nodes can be visually viewed by Sanky diagram, such as what activities the user first participates in after logging in each day, or whether there is commonality in the last behavior before the user pays for it.
SQL queryis an advanced function of TA. If the existing analysis model of TA is difficult to meet the needs, you can calculate according to your own logic through SQL query; TA also supports generating reports based on calculation results combined with SQL based visualizationand displaying them on dashboard. In addition, if you have cross-project data analytics requirements, it can also be implemented through SQL queries.

The above is a brief introduction to the contents involved in behavior analysis. You can click to enter each section for details. If you are interested in user analysis, please refer to user analysis.