A hot-selling product can increase sales and ranking at an astonishing speed in a short period of time. Therefore, sellers can use this logic to crawl Amazon’s ranking information to find out which products have the potential to be hot-selling products.
People who are engaged in Amazon operations know that although product ranking is a comprehensive ranking, it is mainly related to the size of the order volume. If the order volume of a certain style of a seller’s product increases rapidly, its ranking will also rise rapidly.
So, how can sellers find these changing data?
We know that the product traffic and order volume of a certain style of other sellers on the Amazon platform are invisible, and only the changes in product rankings can be obtained on the front end of the platform. Of course, we can crawl data through crawlers to obtain reference data for product selection.
The data crawling and product selection process is as follows:
●Select the major categories of products involved on the Amazon platform.
●Use crawlers to crawl the ranking information of the current 400 pages of products in this category.
●Set the data crawling update cycle, which is recommended to be 4~12 hours.
●Import the ranking change data into the data analysis software, build a model, and evaluate whether the product is a hit.
●Use product optimization methods to systematically optimize the product to be put on the shelves, and strive to defeat competitors before other sellers gain a large market share.
After using the crawler program to obtain the ranking of each Amazon product, the seller needs to conduct data analysis based on the ranking of each product. Generally speaking, it is divided into the following steps:
●Confirm the product selection exit mechanism.
●Eliminate hot-selling models with stable rankings.
●Find potential models.
●Real-time tracking of the ranking of potential models and ultimately determine the models to be put on the shelves.