Customer shopping habit analysis can be understood as analyzing where the customer’s daily shopping peak is, and analyzing whether the shopping peak periods of customers in different regions are different. This article combines the two dimensions of time and region to explain the reasons for the fluctuation of customer unit price. In this section, the author will combine these two dimensions and obtain the shopping habits of customers in different regions through a series of data collation and analysis. The data source is still the order report in the background. The parameters required for analysis are purchase-date (purchase time) and ship-state (shipping status. There are two screening methods: 24-hour total order volume change pattern and 24-hour order volume change pattern in different regions.
After completing the data screening, you can build a visualization chart of customer shopping habits. The previous section introduced how to use order reports to analyze order volume and average customer unit price fluctuations.
Customer shopping habit analysis can be understood as a deeper level of order volume fluctuation analysis, that is, data screening and visualization of order volume fluctuations in different regions.
This section uses CA, FL, and TX as cases for explanation. After filtering the order report, you can get a comparison of order volumes in different time periods in the three major regions. After screening out orders in different regions and time periods, operators need to calculate the order ratios in different time periods to ensure the accuracy of customer portraits.
CA, FL, TX The order ratio of the three major regions in different time periods = the orders generated in a single time period in the region = the total orders generated in all time periods in the region.
After completing the above data screening steps, you can combine different data for visual analysis. The operator first draws a “overall trend of order volume” bar chart for the order ratio data in different time periods.
If the operator needs to observe the overall order volume fluctuation trend of the store in all regions, a new visual bar chart can be drawn.
In addition to the daily change trend of order volume, the operator can also draw a line chart of customer shopping habits based on the changes in the daily order ratio in different regions.