The data in the Wish merchant backend can help merchants understand the operation of their own stores in a comprehensive manner. However, as the saying goes, “Knowing yourself and the enemy will ensure victory in a hundred battles.” If we only understand the situation of our own stores and do not compare it with the data of the entire industry, it is difficult for us to make accurate plans in operations.
There are many tools on the market that can capture Wish industry data, such as the early Votobo, Miku, Haiying Data, as well as Super Store Manager and Merchant Network. The functions of these software are similar, but each has its own characteristics. Merchants can choose the right software according to their needs.
Introduction to common market research and analysis tools
We will take a commonly used software as an example to introduce the usage of such software.
The homepage of such software will display the overall traffic and sales trends of the Wish platform.
The number of merchants on the Wish platform, the sales of merchants, and the overall sales of products are also displayed in the software.
The above information can only be used as a reference in normal operations. Merchants should make good use of the statistical analysis of product categories. Merchants can check the product category data to determine the current hot-selling categories on the Wish platform.
By selecting a sub-category, you can see the number of products, average price, average daily sales and dynamic sales of a certain category. These data are worth paying attention to. The number of products needs to be compared to see the competitiveness, for example, the average daily sales can be divided by the number of products. For the lip gloss category, it is 16022/4756, and the result is 3.37. The dynamic sales ratio of this single product is also quite high, and it is rising month-on-month. It can be compared with the children’s clothing category. Although the average daily sales of children’s clothing are much higher than lip gloss, the result of dividing sales by products is only 0.25. At the same time, the dynamic sales ratio of the children’s clothing category is far less than that of the lip gloss category, so new stores can give priority to the lip gloss category if there are no products.
But at the same time, we also found that the average customer price of lip gloss products is too low, and the evaluation scores of hot-selling products are not high. Therefore, for merchants who have average unit price requirements or store rating requirements, selling lip gloss products requires more product selection.
In addition to the analysis of industry data, this type of software has the function of statistical store hot-selling rankings and product hot-selling rankings.
Take the store statistics function as an example, we can see which stores’ products are always hot-selling and which stores are growing rapidly. This does not mean that we can find these stores and “copy” their products back to our own stores, because this practice is usually not obviously useful. We can further analyze the data of such star stores through these software, compare them with the data of our own stores, and find out the gaps.
We selected a store with a high ranking displayed in the software and made the following simple comparison with a certain merchant’s store.
It can be found that the top-ranked stores are far ahead of the merchant’s store in terms of sales ratio, number of certified products, and number of online products. For the latter, it is necessary to expand the product line, increase the sales ratio of existing stores, and at the same time maintain the performance of existing hot products and strive for more products to be certified.
Similarly, for the hot product data provided by the software, you can filter from multiple dimensions such as listing time, unit price, number of positive reviews, and compare with the situation of your own store.
Before the product is listed, merchants can also use the software to query, view the orders of similar products, the average unit price range, the use of tags and sales methods, to help themselves complete higher-quality product advertising.