Cross-border e-commerce store diagnosis: applying DuPont analysis to explore the root causes of sales decline
Theoretical basis
By analyzing sales data from multiple angles and realizing data-based operations, we can accurately judge consumer behavior, competitor operations and market conditions, and find operational problems. After optimization, precise operations and maximum benefits are achieved. Store diagnosis is carried out by studying sales data. The analyzed data mainly includes the number of visitors, product conversion rate, customer unit price, etc. By comparing with the industry average and industry benchmarks, the store operation status can be judged and improved.
Analysis ideas
A certain store experienced a significant decline in sales during the period from March to June. Here is a copy of the store’s important indicator data for May and June. The following is a store diagnosis of the store through DuPont analysis. First, calculate the month-on-month growth rate of each indicator based on the two time periods of May and June, and then disassemble and create a DuPont analysis table based on the core e-commerce formula “Sales = Number of Visitors × Payment Conversion Rate × Customer Unit Price”, and finally observe data.
Analytical methods
Use the DuPont analysis method to analyze and diagnose changes in various indicators to find the root cause of store problems.
Detailed explanation of indicators
The DuPont analysis method uses the relationship between several major indicator ratios to comprehensively analyze the operating status of the store. The month-on-month growth rate refers to comparing the volume with the previous period to calculate the growth rate.
Practical mission
We collected the relevant indicator data of the store in May and June, calculated the month-on-month growth rate of each indicator, and built a model using the DuPont analysis method to discover the store’s problems.
(1) Open data source
Open the data source “Project Three Tasks One Store Diagnosis”.
(2) Calculate month-on-month growth
Calculate the month-on-month growth rate of each indicator. The calculation formula is: month-on-month growth rate = (June data – May data) / May data. Enter “=(C2-B2)/B2” in cell D2 and fill it downwards. Select the data in the “month growth” column, right-click, select the “Format Cells” option, change the data type to “Percent”, and retain two decimal places.
(3) Setting up the model
Create a new worksheet Sheet2 at the bottom of the workbook, reference the data results above, and use the DuPont analysis method to build a tree model. How to reference tabular data? Here, take the indicator name “Payment amount” as an example. Enter “one” in any cell of the newly created Sheet2 worksheet, then switch to the Sheet1 worksheet, and select the data that needs to be referenced. Pressing the Enter key will automatically return to the Sheet2 worksheet. Below the indicator name, you need to quote the sales data of the last month, and also quote the relevant growth data on the right, and quote the results of the overall relevant data of “Payment amount”. The DuPont analysis method is mainly based on split thinking. According to the golden formula of e-commerce, the “Payment amount” can be split down into the analysis of three indicators: “Number of visitors”, “per ticket sales” and “Conversion rate” . Cite relevant data. It can be seen that the increase of “Number of visitors” is -80.48%. “Number of visitors” can be split to the next level to form a small diagnostic model.
(4) Data analysis
Observing the model, we learned that the decline in payment amount was mainly due to problems in the new visitor (number of new visitors) link. After knowing the existing problem, you need to observe the number of visitors from different perspectives to find out the cause of the problem.
Diagnosis process and steps
(1) Obtain relevant data for online store diagnosis;
(2) Compare online store data and analyze internal reasons;
(3) Compare industry and competitor data and analyze external reasons;
(4) Discuss countermeasures and put forward feasible suggestions.
Diagnosis content
It mainly conducts year-on-year, month-on-month and peer analysis from four aspects: sales, number of visitors, conversion rate and customer unit price.