For the data analysis of credit card consumption, if you can get everyone's credit card consumption data (a person may have multiple credit cards), then how to analyze the credit card consumption data.
For the analysis of user consumption behavior, there are many ideas that still need to first clarify the target of the analysis, and then collect and process the required data information according to the analysis of the target. That is, the data analysis itself is KPI-driven, so if we start with the most primitive data details, how should we expand and expand the data dimension?
For people with credit cards, the credit card bills we receive often have the simplest consumption details, as follows:
Consumption list (card holder card number, name, consumer merchant, consumption time, consumption amount)
It can be seen that the consumption detail data itself is relatively simple. If it is not combined with other data dimensions, simply doing statistical analysis will not produce much meaning. Any data analysis needs to be combined with the dimensional expansion of the original data. After the dimensional expansion, the entire data model will be richer, and multi-dimensional analysis and data aggregation can be generated.
From the above consumption detailed list data, the following extensions can be made simply.
Personnel information (person's name, ID number, age, name, occupation type, residence address, family information)
Business information (business name, business address, business type)
With personnel information, there is the first level of expansion, that is, the aggregation of data can be based on the attribute dimension of personnel, that is, the consumption detail data we get can be aggregated according to consumer gender, age group, occupation type, etc. The unique identification code for a person is not the name, but the person's ID number, that is, through the ID number, we can aggregate the consumption data of one person and multiple credit cards.
With business information, we can aggregate different types of consumption data according to the business type of the business . At the same time, it can be seen that the detailed address information of the business itself cannot be aggregated. Then we must consider the hierarchical expansion of a single attribute itself in the attributes of the main object, that is, we can expand the address information, that is, city - "district - "area - "consumption area - "business district - "large shopping mall - "specific address .
If the address has this extension, we can see that the final consumption data can be aggregated by consumption area. We can analyze the consumption summary data of a certain business district or shopping mall, and this data itself is obtained from the original consumption detail data. The model is extended.
To do this, you can see that any dynamic consumption detail data must be matched with a large amount of basic master data. These basic master data may have a table structure or a dimensional structure. These data must be sorted out and mapped in detail. consumption details. In this way, the final consumption data is easy to perform multi-dimensional analysis and aggregation based on dimensions.
Consumption time itself is also an important dimension. Through time, we can summarize data according to time period. At the same time, time itself can be expanded layer by layer by year, quarter, and month, which is also a structure that can be expanded hierarchically. At the same time, it can be noticed that the time itself can also analyze the consumption frequency, that is, the data of the number of card swipes in a certain time period can be reversed to the heat information of a certain area itself in certain time periods according to the consumption frequency.
If it is only the credit card consumption list data, it is difficult for us to locate the specific product SKU information. If it is a large supermarket, the detailed user consumption and purchase data can also be detailed to specific products. Dimension attribute expansion is another content that can be expanded, analyzed and aggregated.
The data itself may be related, and the data of credit card consumption can often be directly related to other data, such as major events in a region itself, marketing activities held in a region, and traffic flow data in a certain region obtained from the transportation department. . These may be related to the final consumption data in some sense.
If it is only from the credit card data itself, as mentioned earlier, the business scope of the merchant can be located according to the merchant, whether it is catering or selling clothes. Then, according to different business types, the consumption data of credit card can be counted separately, and then we can analyze whether the consumption of clothing will increase when the consumption of catering increases, that is, whether the catering merchants have any sales of other items in a shopping mall. Motivation, etc.?
For the same reason, can we analyze whether there is a certain correlation between the consumption data of people of different age groups? What types of merchandise sales do these correlations exist for? These analyses will facilitate us to formulate more effective targeted marketing strategies.
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