E-commerce operator Xiaobai, how to get started quickly and learn data analysis?

Xiaobai, who engaged in e-commerce a few days ago, found me , He said that recently he found that his shop had a high browsing volume , But the transaction rate is very low , I don't know why , Want me to help analyze . Although Lao Li is very familiar with data analysis , But I have never engaged in e-commerce . therefore , After chatting with friends who have been engaged in e-commerce for more than ten years , The following e-commerce analysis process is obtained , It is suggested that friends who are engaged in e-commerce operation should collect them quickly ！

First step ： First clarify the purpose of data analysis

It's because of the decline in performance , I want to find out the problem by analyzing the data ？ Or to cooperate with other departments , It needs to be supported by data analysis , Give the corresponding marketing strategy . Purpose is different , The selected data indicators are also very different , So before data analysis, you need to think clearly about the purpose of this data analysis ？

The second step ： Understand the common data indicators of e-commerce , Familiar with business process

Here is a set of indicators , Include 8 Parts of

1、 Overall operational indicators
2、 Website traffic indicators
3、 Sales conversion indicators
4、 Customer value indicators
5、 Commodity indicators
6、 Marketing campaign indicators
7、 Risk control indicators
8、 Market competition index

The third step ： Learn the analysis ideas in mature data analysis cases of e-commerce industry , For your own use

Take JD e-commerce as an example , Set up shop and personnel indicators , Check the status of the store , At the same time, we can do store analysis and user portrait according to the existing data , Find the pain points in the store , Give the marketing strategy .

1、 Clear analysis ideas

2、 Process and clean existing data

Data processing varies from person to person , Omit here , The data analysis tool used is FineBI

3、 Analyze the data （ a key ）

① Overall analysis
The first part is the layout of the overall analysis , Construction indicators include ： Browse volume , Order quantity , The number of users , The proportion of men and women ,VIP Level proportion , Customer age distribution , sales TOP 10 City . The style is mainly commercial .

The conclusion drawn from the analysis

The male and female consumer groups in JD stores account for half respectively , Little difference
The main customers are from 348、204、119 City
Main customers of the store VIP The higher proportion is 1 level , Occupy 30.19%, The second is 6 Rank occupation 27.42%, Need guidance 1 Level 1 user is upgraded to 2 level ,6 Level 1 user is upgraded to 7 level
The main age distribution of customers is mainly in the fifth grade , Next is the sixth grade , We can develop products suitable for users according to their age
② User portrait
The second part is user portrait , The indicators and models constructed are

User life state model ： Calculate the proportion and quantity of each current period according to the life cycle of users from new users to lost users .

Repeat purchase rate ： Calculate the repurchase rate according to the multiple purchases of users , Analyze user stickiness .

Behavior funnel ： According to the funnel conversion rate of each user's behavior, which behavior conversion rate is abnormal , In addition, although buying and paying attention are not the last acts , In order to maintain the rationality of the data , Put them last . The transformation effect can also be analyzed .

RFM Model ： According to the last purchase quantity , Frequency of purchase , The purchase amount will divide users into 8 Types of users , Different marketing strategies can be implemented .

The conclusion drawn from the analysis

1、 Our loyal customers account for 80%, User stickiness is very high
2、4 The repurchase rate fell in August , Declined 3.12%, You need to use coupons and other means to call old users , Increase the repurchase rate of users
3、 The conversion rate from views to orders is 6%, In line with the general state of e-commerce industry , The evaluation transformation after placing an order is 37%, The number of comments can be increased in the form of positive feedback , In addition, the number of additional purchases and attention is far less than that under , You can use coupons , Small gifts and other means to guide users to pay attention , Preferential pre-sale activities guide users to purchase
4、 The majority of our customers are general value customers and general retention customers , The sum of the two accounts for 60%, It shows that most consumers have low consumption power and , The main business of the company should be pushed to the customer , Let the product information reach customers through publicity and promotion
5、 The second is to retain users 、 Important development users , Important to maintain users and important value users , Proportion 40%, For important retention users , The consumption frequency is not high, which is far from the recent consumption , You can send messages , Retain users by issuing activity coupons , For important development users , This part of consumption frequency is not high , Low loyalty , Potential users , We must focus on the development of .
6、 The main customers of this store come from 741 Countries and 2136 Country , The second is 2316,187 Country .
③ Scoring analysis
The indicators are highly praised TOP 10 product , Bad review TOP 10 product , The proportion of good and bad reviews , Weekly review trends

The conclusion drawn from the analysis

1、 The top three products of high praise are 217024,219334,25448, It can become the main product of the store , The top three bad products are 217024,171917,25338. Poor products should be optimized
2、 The praise rate of the store is 98.95%, The product quality and service are good on the whole
3、 The overall trend of positive and negative comments is relatively stable
④ Store and brand analysis
Charts and indicators have ： Hot items in the store ,7 level VIP Brand preferences ,0 Analyze store data , The number of fans in millions of fan stores ,VIP Count , Score comparison .

The conclusion drawn from the analysis

1、0 There are altogether 656 home , One of the shops 5566, Number of fans 0, Store reviews －1,vip Count 201,VIP Good number , But the evaluation is negative , Check whether the customer service of your store is not in place .0 Fans zombie shops should be reasonably cleared , So as not to occupy resources .
2、 Millions of fan stores ,1912,3790 Shop's vip The quantity is extremely low , You need to convert the number of fans into VIP, Publicity can be strengthened , Or upgrade VIP Send small gifts to promote the store VIP promote .
3、 Hot products in stores can be upgraded to key products , Dali propaganda .
4、 according to 7 Brand preferences of first-class members , Recommend products of relevant brands , Achieve precision marketing , Promote transaction rate .
Complete data analysis report