The key application of regional distribution data in Amazon product selection and multi-store management
In the current field of Amazon product selection and multi-store management, operators often select products and formulate strategies from the perspective of competitors. For example, when sales of a certain product surge, many sellers will quickly follow up and produce similar products; or when a new store has multiple hot-selling products in a certain category, many operators will try to enter this market one after another. This competitor-based product selection method is common in various channels ranging from independent websites to domestic wholesale websites (such as 1688).
However, by analyzing order reports and gaining insight into the regional distribution of users, operators can make more precise product selections from the perspective of consumers. The key is to understand which areas the main consumers of the store come from, what characteristics the consumers in these areas have, and what are the differences from consumers in other areas. After such in-depth analysis, operators can more clearly determine the market positioning of their products.
For example, the red framed area as shown in the figure is the “head market” of a certain store. Combined with the chart information, it can be seen that the main audience of this product is concentrated in the East and West Coast of the United States, rather than inland areas. This provides operators with an opportunity to further analyze the core competitiveness of their products. For example, do users in coastal areas prefer a specific design? Or are inland areas more price-sensitive and therefore unwilling to buy high-priced products? After solving the above problems, operators can adjust product selection strategies in a targeted manner to avoid consuming too many resources in the “low order market”, thereby achieving higher operational efficiency.
In the case of multi-store management, operators need to reasonably evaluate the operation level of each store. Many cross-border e-commerce merchants adopt a model of operating multiple stores at the same time, which makes it relatively complicated to judge the operational performance of each store. For example, if there are stores A, B, and C in the team, their average daily performance is US$4,000, US$2,000, and US$1,000 respectively. The seemingly simple performance data cannot directly determine the operating level of the store.
The reasons include: performance is a comprehensive indicator, closely related to health, branding and other factors. Stores have different weights due to different store opening times; in addition, differences in sales models, such as high sales volume of a product and Each of the products has a small sales volume, and the technical requirements and strategies for operation are also significantly different.
In order to form a more comprehensive understanding of the operational capabilities of multi-store operators, the regional distribution of users can be analyzed for each store. By identifying different “head markets” and “long tail markets” areas on the map, team managers can intuitively identify the market coverage of different stores. For example, if the market areas of store A and store B overlap, and their main audience areas are significantly different, managers can make effective judgments to identify potential problems and development opportunities for each store.
To sum up, regional distribution data provides a valuable perspective for Amazon product selection and multi-store management, allowing operators to formulate more effective strategies through precise analysis in a complex market environment to achieve profits. Maximize and improve operational efficiency.