Cross-border e-commerce product selection skills: detailed explanation of data capture method
The characteristic of popular products is that they can quickly increase sales and rankings in a short period of time, which makes grabbing ranking information on the Amazon platform an effective product selection method. Although product ranking is affected by many factors, the size of the order is one of the most important factors. Therefore, when the order volume of a certain product increases rapidly, its ranking will also rise rapidly.
To use data scraping to select products, you first need to understand how to obtain these key data. Since the product traffic and order volume information of other sellers on the Amazon platform is not public, sellers can only speculate on their performance by observing changes in product rankings. At this time, the crawler program becomes a powerful tool that can help sellers obtain the required data as a reference for product selection.
The specific operation process includes selecting the target product category on the Amazon platform and using crawler technology to crawl the ranking information of the top 400 pages of products in the category. It is recommended to set a frequency of updating data every 4 to 12 hours to keep abreast of the latest developments. Next, import the collected ranking change data into professional data analysis software and build a model to evaluate which products are likely to become hits.
After obtaining the preliminary analysis results, it is necessary to further screen out the styles with real potential. This process usually includes confirming the product selection exit mechanism, eliminating hot-selling products with stable rankings but slow growth, focusing on discovering new products that are rapidly rising in rankings, and continuing to track them until finally identifying products worthy of being put on the shelves.
For sellers with weak supply chains, they can also grab hot-selling product information from wholesale websites such as 1688, and then introduce it to the Amazon platform for sale. However, it is worth noting that the data capture and analysis work should last at least one week, and to reduce uncertainty, an additional week of observation is required to verify the style potential. Considering the time gap between data analysis and actual sales, the entire cycle is best controlled within 21 days.
To overcome the above challenges, two strategies can be adopted. One is to first find out some potentially popular styles through data collection, and then find out whether these styles have corresponding links on domestic wholesale websites. If there are and support small batch ordering, you can place an order directly for testing; the other is based on Preliminary data analysis begins immediately with sample production, and once style potential is confirmed, mass production is scheduled and products are ensured to be delivered to Amazon warehouses within a month.
In addition, cross-border e-commerce sellers can also use wholesale platforms such as 1688 as the source of product selection, analyze their sales data and resell outstanding products to Amazon users. Taking skirts as an example, you can search for relevant keywords on 1688, check the sales situation, and obtain the sales volume through crawler tools to determine which items are hot sellers or potential hits in the near future.
Although this approach is useful for monitoring competitor dynamics in real time and responding quickly to market trends, it also has some limitations. For example, there may be a lag in ranking data, and this method places high demands on sellers’ technical capabilities and data processing efficiency.