What is e-commerce data analysis?

E-commerce data analysis includes data status of large industries and large platforms, and can also be detailed data analysis of certain dimensions as small as stores, single products, and SKUs.

In addition to conventional product models, product prices, promotion information, store names, etc., other dimensions can also be customized, which can be said to be able to fully display channel violations and meet diverse inspection scenarios. need.

Control from the perspective of traffic, orders, overall sales performance, and overall indicators. At least have a general understanding of the e-commerce platform you operate, how it is operating, and whether you are losing or making money.

The e-commerce analysis data method is as follows:

1. Insight into needs based on user portraits

User portraits are user information labelling, by collecting users’ social attributes , consumption habits, preference characteristics and other dimensions of data, and then describe the characteristics and attributes of users or products, analyze and count these characteristics, and mine potential value information, thereby abstracting the full picture of the user's information.

2. Analyze user sources based on channel data

For e-commerce sellers, the most important thing to analyze the "number of visitors" is to analyze the "traffic source". Analyze the "quantity" and "payment conversion rate" of different traffic sources, find out the traffic sources with higher "payment conversion rate" and find ways to improve them. This can not only increase the "number of visitors" but also improve the overall "payment conversion rate".

At this time, using data analysis tools can provide an overview of the performance of different channels and give a target conversion rate. When it comes to organic search, analyzing metrics like search volume and keyword rankings can help you gain insights into where to spend your ad budget, how to make it easier for users to search for you, and more.

3. Data analysis of in-store conversion rate

When users come to the store, we have to find ways to convert them into customers, but as we all know, not everyone who comes to the store Users will click the add to cart button. Even after adding to your shopping cart, you may change your mind and leave the site. So in this step, we can use the following e-commerce conversion indicators to track and optimize the online shopping experience:

1. Sales conversion rate - the ratio of users who have purchased to all users who came to the store.

2. Average order value - the average amount of orders placed by users.

3. Shopping cart abandonment rate - the proportion of uncompleted orders among all orders generated.

4. Improve the ROI of marketing promotion

For stores, traffic has now entered the era of stock, and marketing channels are scattered and complex. Sellers need to increase the RIO of promotion based on digital marketing. Through data analysis, we will strengthen the accuracy of online marketing, expand new offline marketing scenarios, and use data intelligence to complete the layout of all scenarios and links to achieve a combination of efficient conversion and product effectiveness.

5. Product data analysis

1. Product data analysis

①Overall analysis: divided into two parts: sales performance and shopping behavior. Sales performance includes the revenue generated by each product, the number of users who have purchased at least once, average order price, quantity, number of refunds, etc. For shopping behavior, you can see the number of people who added to the shopping cart among the users who browsed the product details page; or the number of people who finally placed an order after browsing the product details page.

② Shopping behavior analysis - We can base on more data related to the product, such as the number of visits to the product browsing page, the number of visits to the product details page, the products added to/removed from the shopping cart, and the products entering the settlement stage. , and the number of purchases to analyze user shopping behavior.

2. Sales data analysis

We can find out the income, taxes, freight, refund amount, and number of goods sold from the background data analysis. Among them, total sales are presented in the form of amount, which is one of the best "overall main indicators" (OMM) to measure the operating status of our online store. It can be used to measure the overall growth and development trend of the business.

6. User retention data analysis

Smart merchants know the value of loyal customers. Being able to retain users will bring you long-term revenue.

Always remember that acquiring new users is much more expensive than retaining old users. Research shows that increasing user retention rate by 5% can bring 25% to 95% profit.

7. User recommendation data analysis

For sellers, we need to identify which users are your true love. Not only do they love your product, they are willing to recommend it to family and friends, they are literally your brand ambassadors. Successful e-commerce companies will pay close attention to the indicators at this stage and respond in a timely manner.