Learn how to do e-commerce data analysis (with operation analysis index framework)

Before entering the text , First of all, we should clarify the steps of data analysis , No organized order , It's easy to get into a mess in massive data .

Generally speaking , The steps of data analysis are as follows ：

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01 What data should e-commerce operators know ？

The basic index of e-commerce data analysis is a huge system , It is mainly divided into 8 Category indicators ,120 Sub indicators , As shown in the figure below ：（ Pure hand beating , It's a little long ）

• Overall operational indicators

Slave flow 、 Order 、 Overall sales performance 、 Control the overall indicators , At least have a general understanding of the operation of e-commerce platform , How is the operation , To lose is to gain .

• Website traffic indicators

That is to analyze the visitors who visit your website , Based on these data, we can Improve the webpage , And analyze the behavior of visitors .

• Sales conversion indicators

Analyze the data from order to payment , Help improve commodity conversion . It can also analyze the data with frequent exceptions .

• Customer value indicators

The premise of accurate operation is customer relationship management , The core of customer relationship management is customer classification ：

RFM Model is a classic classification model , The model uses the three core dimensions in the general trading process ：

• Recent consumption (Recency)
• Consumption frequency (Frequency)
• Consumption amount (Monetary)

After classifying customers, the core link is customer management , You can use the sales funnel model , combination CRM The principle of the system manages the whole process of customer sales .

• Commodity indicators

Mainly analyze the types of goods , Which goods sell well , Inventory , As well as the ability to model relationships , Analyze which products are more likely to be sold at the same time , And bundling .

• Marketing campaign indicators

Mainly monitor the effect of an activity on e-commerce websites , As well as monitoring advertising indicators .

• Risk control indicators

Analyze seller comments , And complaints , Find the problem , Correct the problem .

• Market competition index

Mainly analyze market share and website ranking , Make further adjustments .

02 except Excel What tools are available ？

There are many data tools commonly used in e-commerce operations , such as ：

• Industry commodity index analysis tool ： Ali index
• Inquiry of goods 、 purchase 、 Processing platform ： Alibaba
• E-commerce platform data analysis tools ： Tmall business consultant 、 JD business intelligence etc.

The above are some vertical and professional tools in the field of e-commerce , But if you just want to find a more convenient and fast , Can replace Excel Visual data analysis tool , It's enough to find a ready-made dashboard template cover , Like this ：

There are also other tool websites , Just follow your own needs , Don't choose too many tools , Enough is enough. .

03 E-commerce data analysis needs Excel What are the important functions ？

When doing e-commerce data analysis , image 【 PivotTable 】、Vlookup() Functions are very common functions , I found an example , Share it and you can have a look ：

• Data sources

The data comes from an e-commerce platform 1 Monthly sales data , This includes data retained by users 、 Commodity sales data 、 Commodity price data 、 Product browsing data .

• Business needs

Now the business department needs you to analyze ：

1. 1 month 5 Japanese DAU How much is the ？
2. From the perspective of retention , When did the highest quality new users come from ？
3. stay 1 month 15 day ,SKU What is the sales activation rate ？
4. goods “ category T582” On which day is the purchase conversion rate the highest ？
5. 1 month 10 On the day of ARPU What is the value ？

（1）1 month 5 Japanese DAU How much is the ？

DAU： Daily active user , It means new users of the day + Retained users from the previous few days to now

Calculation 1 month 5 Japanese DAU
date	Added on the same day	1 Daily retention	2 Daily retention	3 Daily retention	4 Daily retention
1 month 1 Japan	8598	2503	3314	2985	2966
1 month 2 Japan	5936	2860	2751	2628
1 month 3 Japan	9709	2709	2775
1 month 4 Japan	6349	3432
1 month 5 Japan	6680

• 1 month 5 Japanese DAU=6680+3432+2775+2628+2966
• 1 month 5 The remaining number of days =18481

（2） From the perspective of retention , When did the highest quality new users come from ？

• User retention rate = Survey the remaining users / New users of the day

With 7 Day as column ：

according to 7 Daily retention rate the highest retention quality of users is 1 month 9 Japan 、1 month 17 Japan , Respectively reached 52.35%、44.41, The lowest is 1 month 3 Japan 、1 month 12 Japan , The user retention rate is only 16.24%、16.36%

（3） stay 1 month 15 day ,SKU What is the sales activation rate ？

• SKU(stock keeping unit, A unit of stock )
• SKU Sales activation rate = The number of categories with sales records on that day /SKU total

Trade name （ Company ： Pieces of ）	1 month 15 Japan
category T827	18
category T441	25
category T636	22
category T462	51
category T747	18
category T420	24
category T424	48
category T706	0
category T621	46

The above table shows some data ,SKU The total number indicates how many rows there are in the commodity name column , The sales data is 1 month 15 The daily sales volume is greater than 0

• SKU total ：108、 stay 1 month 15 Products sold every day ：90
• SKU Sales activation rate =90/108=83.33%

（4） goods “ category T582” On which day is the purchase conversion rate the highest ？

The data are as follows ：

• Conversion rate = Total purchases of the day / Total number of user views

Measured from the conversion rate category T582 Sales , Among them in 1 month 29 Japan The conversion rate of is the highest , Reached 71.11%, The second is 1 month 16 Japan Reached 68.29%.

（5）1 month 10 On the day of ARPU What is the value ？

• ARPU Average revenue per user or average revenue per user
• ARPPU Average revenue per paying user
• same day ARPU= Total sales of the day / On the day DAU

Of the day DAU According to the first requirement , It is easy to know for 27405

• Total sales of the day = Unit price of each commodity x Quantity of each commodity , And then add up
• Total sales of the day =235317
• same day ARPU=235317 / 27405 = 8.57, It's equivalent to everyone contributing to the platform 8.57 element

Last , Data analysis is a kind of Objective analysis There are no subjective factors to lead to the deviation of the conclusion , We must understand the business indicators 、 Familiarity with business processes .

above .