Brief introduction of CDA license holder

Liu Wei, a master of computer information technology from NAU University in the United States, is a third-level CDA data analyst. He is currently working in the data governance position of Jiangsu Baoying Agricultural and Commercial Bank.

I. Core interpretation of AARRR model

The AARRR model is widely used in the fields of user growth and market operation. It contains five key indicators:

Acquisition: refers to the process of acquiring new users. Different platforms have different definitions of recruitment. For example, APP may take registration as the standard, while brands of e-commerce platforms may regard commodity purchase transactions as recruitment.

Activation: After users join the platform, they need to be active to generate value. Each platform defines the activation behavior according to its own core business. For example, Xiaohongshu takes the user's first browsing, liking and favorite as the activation behavior, while the fitness APP takes the user's completion of the first exercise as the activation standard. The activation threshold and time cycle vary from platform to platform.

Retention: Retention refers to the continuous use of products or services by users within a certain cycle. The retention period of different products has different definitions, and the brand will determine the repurchase cycle according to the nature of the product. For example, cosmetic brands may measure the retention by the repurchase rate within 12 months. In order to improve the retention rate, enterprises will adopt a variety of operational activities, such as issuing repurchase coupons, pushing reminder information, etc.

Revenue: The economic benefits generated by users' retention. For the platform, the sources of income include advertising, product sales, membership fees, etc. Enterprises guide users to consume through accurate push, so as to achieve revenue growth.

Referral (fission): Users bring new users to the platform through word-of-mouth communication and other means to expand the scale of users.

II. Funnel Model Application Case Sharing - NAU University Project in the United States

Recruitment: Attract potential customers who are interested in data analysis, machine learning and deep learning by posting course posters in WeChat groups.

Activation: Guide users to click on the original text of the poster for details, and register an account to complete the user activation.

Retention: The project provides insight into the needs of students and provides customized services according to the professional background and job needs of students. At the same time, the characteristics such as the self-matching speed learning mode and going to the United States to attend the graduation ceremony have attracted and retained students.

Benefits: Students apply for the project and complete their studies, which brings benefits to the project. In the process of learning, students have improved their skills and laid the foundation for their subsequent career development.

Fission: After students are satisfied with the project experience, they will independently share it on WeChat Moments and other channels and recommend it to more people, realizing user fission.

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III. Practical battle of credit card operation board construction

(I) Data collection: key and complex basic work

Data collection is the foundation for building Kanban and occupies an important position in the whole process. Take the credit card information table as an example, there is a problem of missing values in the card issuance channel data. At this time, it is necessary to communicate with the business personnel to understand that the card issuance channel can be judged by the card number; with the help of IT technology, SQL statements can be written for data filling to ensure the integrity and accuracy of the data.

(II) Component design: funnel-oriented visualization

The component design is based on the idea of the funnel model, which is divided into four sections: acquiring users, activating users, retaining users and obtaining income.

Acquisition of users: Analyze from the two dimensions of card issuance channels and card issuance types, show the number of customers acquired by different channels through the bar chart, and present the card issuance type information with a group table, and clearly present the customer acquisition differences between channels and types.

Activate users: Design two components: the comparison of the number of credit card transactions and the trend of the monthly transaction rate, and display them with the combination chart and area chart of the bar chart and the line chart respectively.

It is clear that the definition of credit card activation is to open a card and complete a consumption, and visually reflect the user's activation through the chart.

Retained users: conduct an in-depth analysis of the retention rate and the user's life status, and use the group table, ring chart and detailed table to display the data. The retention rate is calculated based on different time periods, such as calculating the retention rate in January, February and March, and evaluating the retention of users at different stages.

The roogram divides users into categories such as one-time users, loyal users, new users, inactive users and lost users, which provides a basis for precise marketing.

Acquire income: Through the installment income table, use a scatter plot to analyze the relationship between the number of installments and business types, and use a tree chart to show the impact of age distribution on revenue. It also draws an installment income trend chart to analyze the relationship between installment amount, income and interest rate, and provide data support for business decision-making.

If you are familiar with the above content and want to know your real level, you can find simulation questions in the CDA certification applet for testing.

(III) Dial assembly: follow the principles and improve the value of decision-making

Discover good data and have good insight: select data and fields that can accurately describe the business status to provide strong support for analysis.

Noise reduction, simplicity comes first: The chart design should be concise and clear, and avoid overly complicated designs to distract decision-makers, such as using simple pie charts instead of 3D pie charts.

Accurate expression improves the accuracy of data expression: When it comes to numerical calculation, it is necessary to ensure that the function analysis is accurate, and the data expression is generalized and can change accurately with the change of dimensions.

To draw the finishing touch, there should be a distinct mark: set special marks, such as the warning line in the transaction rate trend chart, to help decision-makers quickly locate problems, find abnormal nodes in the business, and take timely measures.

IV. AI helps credit card operation analysis

Advertising and customized marketing: according to the characteristics of user groups of different card types, formulate targeted advertising strategies and customized marketing plans to improve marketing effectiveness.

Optimize user experience and activate users: It is recommended to continue to optimize user experience and launch more preferential activities, promotions and points reward programs in the first month of card opening to attract users to use credit cards and improve the activation rate.

Retention of users and targeted marketing: Targeted marketing for user groups with high risk of loss, take personalized retention measures to improve the user retention rate.

Enrich installment products and cooperation: launch diversified installment products, strengthen cooperation with merchants, expand revenue channels, and improve the overall revenue capacity of credit card business.

AI plays an important role in the analysis of credit card operations. Many AI tools, such as the office raccoon, are very useful. After entering the data of the credit card operation board, AI can put forward analysis conclusions and improvement measures from many aspects.

Sum up

This sharing provides valuable practical experience and ideas for data analysts and credit card operation practitioners. Through an in-depth understanding of the AR model, mastering the construction method of Kanban, and using AI technology, we can analyze credit card operation data more accurately, provide strong support for business decision-making, and promote the sustainable growth of credit card business.