1. Targeted product selection
In the field of Amazon product selection, most operators select products from the perspective of “competitors”. For example, when the sales of a certain product suddenly increase, a large number of operators will follow up to produce and sell similar products; when a new store has several “explosive products” in a certain category, many operators will also enter the category to “get a piece of the pie”. Whether it is selecting products from other independent stations or from the domestic 1688 wholesale website, these are all product selection methods from the perspective of “competitors”. After analyzing the regional distribution of customers through order reports, operators can try to select products from the perspective of “consumers”, that is, analyze which regions the consumers of the store come from? What are the characteristics of consumers in these regions? What are the differences between consumers in other regions and consumers in these regions? Through this series of analysis and dissection, operators can have a clearer understanding of the positioning of their own products.
2. Comparison of market differences and analysis of operational capabilities of multiple stores
In most cross-border e-commerce companies, operators or their operational teams often do not use the single-store operation model, but rather the multi-store operation model, that is, operating multiple stores at the same time. At this time, how to distinguish the operational level of each store becomes a problem. For example, if a team operates three stores A, B, and C at the same time, the average daily performance of store A is US$4,000, the average daily performance of store B is US$2,000, and the average daily performance of store C is US$1,000. At this time, most people would think that store A has the highest operational level and store C has the lowest operational level. However, this hasty conclusion is not necessarily correct for the following reasons.
(1) Store performance is a comprehensive indicator, which is related to factors such as store health, store operator level, and branding level.
We cannot directly correspond store performance to operational level.
(2) Different stores have different store weights because of the length of time they have been open. Stores with a shorter opening time have lower weights. Even if the new store operator has a higher level of operation, the new store’s performance cannot surpass that of the old store in the short term. (3) Selling 1,000 pieces of a product a day is different from selling 10 pieces of 100 products a day. The former is a “hot item” model, and the latter is a “spread item” model. Although the two may be equal in performance, the operating methods and technical content are completely different. Through the analysis of the above content, operators can no longer judge heroes purely by “performance”, but can find all target markets from the different audience areas of multiple stores, so as to “find gaps and catch them all in one fell swoop.”