Integrated application of data analysis and user portraits in Amazon operations

In Amazon operations, sellers need to pay attention to multi-dimensional data. This approach can not only increase traffic, but also increase repurchase rates and sales. The concept of user portrait is becoming more and more important in the field of e-commerce. It can abstract each specific information of the buyer into a label, thereby providing targeted products and services. By analyzing these tags, sellers can more accurately capture the needs and behavioral characteristics of target customers, especially during the selection process of non-standard clothing products.

Data analysis methods and tools

Excel is a commonly used tool in product data analysis. Using Excel to visualize sales data can help sellers understand the market performance of various products. For example, by performing logarithmic processing on the product ranking data of the “Clothing, Shoes & Jewelry” category, sellers can clearly identify the number of listings in different ranking ranges, thereby understanding sales performance and market competition. Through visual forms such as histograms, sellers can more intuitively analyze the number of “not shipped” products and the corresponding ranking criteria.

Construction and application of user portraits

In the process of building user portraits, sellers can organize information through product sales data and customer portrait tools. Although Amazon provides certain user data statistics functions for brand sellers with high sales volume, for non-brand sellers, this function limits their ability to build user portraits. In this context, sellers need to gradually improve user portraits through their own sales data and market observations to provide strong support for subsequent product selection, marketing and operations.

For example, in the product selection process, it is particularly important to analyze the specific needs of target customers. Whether to include pocket functions in the design of a clothing product should be judged based on user portraits. Young consumers tend to pay more attention to the overall lines of clothing, while older people may prefer practical designs. If sellers can effectively select products for these segmented groups, the market competitiveness of their products will be improved.

The impact of logistics speed

In the Amazon sales process, in addition to the characteristics of the product itself, logistics speed is also an important factor in determining customer satisfaction. The self-shipping model (FBM) provides sellers with a variety of logistics options, including postal parcels, international express delivery, and international dedicated lines. Sellers need to find a balance between ensuring logistics efficiency and product delivery costs to meet the needs of different customer groups.

To sum up, through the in-depth integration of data analysis and user portraits, Amazon sellers can formulate operational strategies more intelligently, optimize product selection, and improve customer experience, thereby occupying a place in the fiercely competitive market.