The concept and importance of data analysis

The concept of data analysis

The cross-border e-commerce retail industry we are engaged in is more fragmented than the traditional B2B foreign trade industry and has a longer service chain than domestic e-commerce retail. Therefore, in order to stand out in the increasingly competitive cross-border retail industry, the control of each business chain needs to be more refined and more organized.

Therefore, relying on the traditional management model to manage the entire process of the increasingly sophisticated cross-border industry has obviously failed to meet the needs of the company’s development. For operating companies or any platform, data mining, sorting, classification analysis, and execution capabilities are playing an increasingly important role in the development of the company.

Data analysis is a relatively general concept, which refers to the process of using appropriate statistical analysis methods to analyze a large amount of collected data, and to conduct detailed research and summary of the data in order to extract useful information and form conclusions. This process is also a support process for Wish’s operational decision-making. In terms of practicality, data analysis can help business owners or account operators make judgments so that they can take appropriate actions.

What key points do we need to understand in Wish operations through data?

First, sales side: Through Wish backend data, we can mine the traffic, conversion rate, checkout ratio of each product, as well as product reviews, the efficiency of sales staff operating the store, daily maintenance, etc. Through some auxiliary software, we can see some data on the traffic, products and various subcategories of the entire Wish platform, including the performance of our own store and the performance of the main merchants on the platform.

Second, operation side: Through Wish backend data, we can see our performance in shipping, order processing, and the performance of the logistics providers we choose. At the same time, the refund situation, counterfeit identification, and user service performance during the operation process can be intuitively reflected through the backend data. However, relying solely on the data of the Wish backend, we can only observe the results, and it is difficult to find out which specific details have room for optimization, so we should set up certain data monitoring models in the financial, logistics, procurement ports and other processes to further discover and optimize problems in the process.

Third, overall planning: We need to regularly integrate a series of financial data based on Wish’s backend and its own data statistics to help business owners or operators understand the investment, income, and input-output ratio of the Wish platform in different stages, so that everyone can better decide how to invest resources and manpower in the Wish platform at each stage, and whether the product direction needs to be adjusted, whether the Wish model needs to be adjusted, and other relatively macro but very important decisions.