In the User Analysis section, you can perform multi-dimensional tag analysis, RFM analysis, AIPL analysis, and AIPL flow analysis. During the analysis process, you can customize the user-generated population that meets a certain rule condition for subsequent marketing reach.
In addition, you can use the Audience Analysis feature to perform perspective analysis and RFM analysis to identify the core characteristics of the selected population and support subsequent marketing strategy planning.
User analysis types include:
Multi-dimensional Analysis: You can import data from tables based on data sources and perform drill-down analysis based on conditions such as grouping and filtering. Help enterprises understand the user's use of products, and further explore the main factors affecting the change of indicators.
Perspective Analysis: provides insight into the distribution of consumer tag characteristics based on user tags, including user attributes and custom tags.
RFM Analysis: The RFM model is an important means to measure user value and user profitability. It describes the value status of a user by using three indicators: recent transaction behavior, overall transaction frequency, and transaction amount. According to these three indicators, eight categories of users are divided: high-value users, key users, key development users, key retention users, general value users, general maintenance users, general development users, and potential users. Through insight into the distribution of different types of users and consumer behavior characteristics, grasp their value characteristics, to provide support for marketing decisions.
AIPL User Analysis: The AIPL model divides users into four stages: cognition, interest, purchase, and loyalty. It provides insight into the number of users of various types and the corresponding change trends, and realizes the quantification of brand user assets and linked operations.
AIPL Flow Analysis: analyzes the conversion and loss of various users in the AIPL model within a specified period of time, and accurately grasps the distribution of users at each stage to achieve efficient link conversion.
The following chapters will be introduced.