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Analysis on user behavior loss of data exploration e-commerce platform

2022-07-01 01:41:00 【Extension Research Office】

Link to the original text ：http://tecdat.cn/?p=27482

The source of the original text is ： The official account of the tribal public

With the development of Internet and e-commerce , People are used to shopping online . In China, , E-commerce platform is very popular . In every year's 618、 double 11、 double 12 In the activity , A large number of users browse commodities on e-commerce platforms such as Taobao , Or collection 、 Add to the shopping cart or buy directly . By using SQL Analysis of user behavior , We can mine the purchase rules of users , Know the heat of the product , Combined with the marketing strategy of the store , Make it more refined 、 More accurate operation , Let the business get better growth .

The dataset contains user behavior , By the user ID、 product ID、 Product category ID、 Behavior type and time stamp consist of . About... Data were imported in this analysis 383 Ten thousand . During the import process , Associated with the primary key 5 A field , Duplicate values are eliminated during import .

Transformation between user behaviors

User behavior transformation vulnerability analysis

From the funnel diagram above, we can see , Enter from the user APP Browse the page to start , The final conversion rate of the purchase link is 2%. After the user clicks on the page , The loss of users is huge . What is the conversion rate from browse to purchase ？

User purchase path analysis

Click on - Collection - Analysis of the transformation path of purchase ： After the user browses the product , There are about 1/5 Of users , Then collect about 13.26% Of users .

Transformation path analysis ： As can be seen from the above figure , After the user browses the product , There are about 41.13% Of users will join the shopping cart , Far above Users who make collections , But after joining the shopping cart , Only 17% About half of the users finally made the purchase , exceed 80% Of users did not purchase . We need to analyze this link . The reason for speculation may be ：

1、 Adding a shopping cart is to compare the prices of the same product in different stores ;

2、 In order to collect the bill , All minus ;

3、 Put it aside , Buy it in a few days ;

4、 etc. Other event discounts

The relationship between the product clicked by the user and the order placed

Here we focus on the relationship between the products clicked by users and the products ordered by users , Whether it supports our hypothesis ： The products pushed by the platform do not meet the needs of users .

Top nine product categories by product hits ：

After sorting the clicks , Analyze the best-selling products , Find out the relationship between click and purchase . As can be seen from the figure above , The purchase rate of the products with the highest click through rate is only 0.56%, And the click through rate is 6 The purchase rate of our products has reached 5.8%.

Conclusion ： Suppose it is true

From the above analysis, we can draw a conclusion , The push mechanism of e-commerce platform is unreasonable , The products pushed cannot match the needs of users , As a result, the user cannot find the desired product during browsing , therefore Conversion rate ： The proportion of users actually buying is very low , That is, the loss of users is serious .

All the information in this article （ Including but not limited to analysis 、 forecast 、 Suggest 、 data 、 Charts, etc ） For reference only , Extension data （tecdat） We shall not be liable for any loss arising out of or in part of this article .