User shopping habit analysis can be understood as where is the user’s daily shopping peak? Are there different shopping peaks for users in different regions? Combine the two dimensions of time and region, combine these two dimensions together, and obtain the shopping habits of users in different regions through a series of data collation and analysis. The data source is still the order report in the background data. The analysis parameters include purchase-date and ship-state. The screening methods are divided into the following two categories.
24-hour total order volume change pattern;
24-hour order volume pattern in different regions. After completing the data screening, you can build a visualization chart of user shopping habits.
User shopping habit analysis can be understood as a deeper level of single-day order volume fluctuation analysis, that is, data screening and visualization of single-day order volume fluctuations in different regions. The operation method is similar to the method explained above, so I will not repeat it here. You can directly download the table “User Shopping Habit Analysis” for viewing.
After filtering the order report, the order volume comparison in different time periods in the three major states is obtained.
After filtering out orders in different regions and time periods, it is necessary to calculate the order ratio in different time periods to ensure the accuracy of user portrait data.
The proportion of orders in different time periods in the three major states of CA, FL, and TX = 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 data screening steps, you can combine different data for visual analysis. The operator can first draw a bar chart of the “overall trend of order volume” based on the daily change data of order volume.
If you need to observe the overall order volume fluctuation trend of the store in all regions, draw a new visual bar chart.
In addition to the daily change trend of order volume, you can also draw a line chart of “user shopping habits” based on the changes in the daily order ratio in different regions.