The so-called digital product selection is to structure the sales data of countless listings on the Amazon platform through big data and provide certain presentation and calculation logic to help sellers choose the competitor products they want to refer to. Jumping out of the matter of product selection, all data analysis has two basic points: one is the data source, and the other is the analysis logic. The software for various data-based product selections is actually the same. It uses crawlers on the data source or obtains it through some gray channels, and after obscuring the data to a certain extent, various analysis functions are made. For example, put the sales valuation and the BSR curve in the same chart, judge the low and peak seasons, correlate the keywords, the products related to the keywords, and the corresponding sales, and do market trend analysis. The most homogenous function should be to provide data filters for data in a certain period of time, allowing sellers to conduct data from various data conditions such as price, category, rating, BSR range, delivery method, and sales range. Select to get the product reference list of the seller’s custom logic. The logical analysis method of data-based product selection can be implemented by anyone who knows a little bit of technology. Even people who don’t understand technology can do it through the fool operation of Excel. Due to the data openness of the platform, it has become an artifact of copying money. At present, the reason for the prevalence of data-based product selection in the Amazon e-commerce industry is true because there are too many sellers, and everyone is in urgent need of copying artifacts. Why copy money? Frankly speaking, it is indeed the safest choice to take the path that others have taken, sell products that others have already sold, and use lower prices, better copywriting, and more dazzling pictures to grab the market. It’s just that in the long run, this road should be narrower and narrower because the industry shuffle is already obvious. When you have fewer and fewer sellers for benchmarking, the more you rely on your independent innovation decision-making. Data-based product selection is a malformed product selection method. Most people cannot understand data objectively. There are N reasons for the popularity of a product on Amazon, which can be roughly divided into two aspects: product selection and operation. The sales result of each company is the sum of the efficiency results of operation and product selection. We see the result, want the same result, but cannot replicate all the reasons. For product selection personnel, at most, they can only replicate a small part of the “selection” reasons, but it is difficult to be confident that they will sell well. This is the reason why operations and product development are torn apart. If you rely too much on data-based product selection, you may lack the patience to understand customer needs and lose opportunities for product and business optimization. The current mainstream product innovation and differentiation methods are based on the pain points mentioned in the review of the best-selling products to iterate and improve the products. It has to be said that this “differentiation” method may have begun to homogenize. So for people who really want to make a good product, how to truly understand customer needs is the most important thing. If it is not the product development of a shop, the most important meaning of the product selection software should be in the two things of “judge the trend” and “find a good product entry point”. After you have read the competing product information on Amazon, you should calm down and think about why this product is selling well, what problems it solves for customers, and whether there is a better product. The digitization you see is the result of actual behavior. Research needs are more important than data. The biggest morbid state of current product development work is that you are too obsessed with data-based product selection and neglect the research on demand. When selecting products through data, I was still entangled in the accuracy of the data of certain software. In fact, when others sell well, it means the right time and place. It is impossible to replicate the same time, place, and people for the same product. What’s more, many sellers sometimes don’t even know why their products explode, let alone look at their opponents in data research. From the perspective of Internet traffic, Amazon is also competing with other websites for traffic from Google. In the past, the product sales at a certain node were not good, or it may just be a good product that has not yet matched the customer’s needs, but the Amazon algorithm has found that there are good products. , It will increase the flow of traffic to the outside of the site and get more orders to be traded on Amazon. (This is also the meaning of the selection of products outside the station)