Since Alibaba International Station is a B2B platform, the buyer group is not the end consumer like B2C websites, and the order data cannot be used directly to evaluate the product selection effect. In particular, buyers with their own customized needs need to go through inquiry, sample order, order, shipment, etc., and the entire transaction cycle is relatively long. Therefore, the evaluation of product selection and testing effect is mainly divided into the following two dimensions. The first dimension is the operational data effect of the product, which is used to evaluate whether the product selection is successful, and mainly analyzes the selection effect of customized products. The core operational data dimensions of Alibaba International Station are mainly divided into search exposure, search clicks, search click-through rate, number of inquiries, number of TM consultations (number of Wangwang consultations), and feedback rate. Search exposure refers to the number of times the seller’s product information or company information is seen by buyers on pages such as the search results list page or the category browsing list. Search clicks are the real-time clicks on the seller’s products in the search, and the number of clicks on the seller’s product information or company information on pages such as the search results list or the category browsing list. Search click rate refers to clicks/exposure. The number of inquiries refers to the number of real-time inquiries received by the seller, which are valid inquiries sent by buyers for the seller’s product information and company information (excluding system spam inquiries, TM inquiries, etc.). The feedback rate refers to the number of inquiries/number of clicks. The general judgment criteria are whether the click rate is greater than 1% (the higher the data, the better) and whether the feedback rate is greater than 10% (the higher the data, the better).

The second dimension is the transaction data effect of the product, which mainly assesses the selection effect of the RTS (Requst To Send) product, which is divided into the number of paying buyers of the product, the conversion rate from visitors to paying buyers, the number of repeat purchases, and the on-time delivery rate. The number of paying buyers of the product refers to the number of buyers after deduplication. If the same buyer generates multiple orders, the number of buyers is recorded as 1. The conversion rate from visitors to paying buyers refers to the number of paying buyers for orders/the number of visitors for products. The more paying buyers there are, the lower the conversion rate requirement. The number of repeat purchases refers to the number of buyers who generate 2 or more orders.

After a new product is put on the shelves, it is necessary to record the product’s operating data and transaction data every day or every week. Through tabular management, the market feedback of the product can be analyzed, and targeted optimization can be carried out according to the data changes of the product. If the data of each indicator is 0 after the product is put on the shelves, and there is still no improvement after optimization, it means that there is a problem with the product and it needs to be abandoned or optimized before being put on the shelves again.

Product selection is an iterative process. You may have selected hundreds of products at the beginning. After continuous screening and comparison, it is finally determined that only five or six products are put on the shelves for sale. In the end, there may be only one or two products that can become hot-selling products, or even none. At the same time, the market is constantly changing, and the minds and preferences of buyers are constantly evolving. Therefore, product selection and testing is a long-term job. As long as the store is still in operation, the work of product selection will not stop. Practice makes perfect, and sellers will also “upgrade and fight monsters” in the continuous product selection process to accumulate more experience.