Analysis of user shopping habits and price discrimination: looking at hidden needs from survey data in different regions
Analysis of user shopping habits is an important step in understanding market trends. Through in-depth research on the peak shopping periods of users in different regions, potential patterns of shopping behavior can be revealed, thereby guiding operational strategies. This article will comprehensively analyze how to build a visual chart of user shopping habits through order data, and how to apply these data to the optimization of price discrimination strategies.
Overview of shopping habits of users in different regions
The analysis of user shopping habits can be carried out by combining the two dimensions of time and region, which includes research on daily shopping peaks and shopping peak periods in different regions. Key analysis parameters include purchase date (purchase-date) and shipping status (ship-state). Operators can filter data based on the changing patterns of 24-hour total order volume and the changing patterns of order volumes in different regions. For example, data filtered in CA, FL and TX regions can be used to compare order volumes in different time periods, and the results can help build a visual chart of users’ shopping habits.
After completing the data screening, the operator can proportionally calculate the order volume in different time periods in the three major states and regions to ensure the accuracy of the user profile data. The proportion of orders in each region can be calculated by the order volume generated in a single time period in the region and the total order volume generated in all time periods in the region. Through visualization methods such as bar charts and line charts, operators can more intuitively grasp the overall trend of order volume and changes in user shopping habits.
Optimal application of price discrimination strategy
User shopping habit data can not only be used for trend analysis, but also support the implementation of price discrimination strategies. Price discrimination refers to the implementation of different price strategies for different users, usually based on factors such as the user’s occupation, region, gender or shopping habits. For example, operators can set different sales prices for users in different professions or regions on the same platform. This strategy can significantly increase profits in practice.
There are two specific operation methods:
- Set multiple Listing sub-variations: Each sub-variation sets different high and low prices according to the user’s region and time period, and displays the corresponding Listing during peak periods.
- Set different prices in different time periods: Based on the price sensitivity of user shopping peak areas, low-price listings are displayed in high-sensitivity areas, and high-price listings are displayed in low-sensitivity areas. .
In order to avoid the negative impact that frequent modifications to a listing may have on its weight, it is recommended that operators adopt the first strategy, displaying only one listing that meets the requirements during a suitable time period, and the remaining listings can be hidden through appropriate methods, such as Adjust the main image and more. In actual operations, comprehensive analysis of user shopping habits data in all areas of the store is the key to formulating accurate price discrimination strategies.
The impact of Chinese overseas shopping user behavior
According to market research, the shopping habits formed by overseas shopping users are also closely related to changes in the domestic market. The consumption demand of overseas shopping users continues to grow, especially categories such as beauty and personal care, maternal and infant products, and food, which have become major consumption targets. This also prompts merchants to pay more attention to marketing methods such as limited-time discounts when formulating price strategies to further attract target users.
In short, a comprehensive analysis of user shopping habits in different regions and corresponding price discrimination strategies can not only improve operational efficiency, but also bring considerable economic benefits. This data-based operation strategy is undoubtedly a major advantage in modern e-commerce competition.