In listing optimization, user shopping habits data can be applied to a relatively high-level operation: price discrimination.
Price discrimination is essentially a price difference, which usually refers to the fact that when a provider of goods or services provides goods or services of the same level and quality to different recipients, they implement different sales prices or charging standards among recipients. For example, if the operator sells a product to user A for $5 and user B for $6 on Amazon, this behavior has constituted price discrimination, and the seller has obtained excess profits through such behavior.
1 On the Amazon platform, the operation skills of “price discrimination” can generally be divided into the following 6 categories: 1. User occupation-oriented price discrimination, that is, sellers will set different prices for users of different occupations, such as low prices for student users and high prices for working users.
2. User region-oriented price discrimination, that is, setting different sales prices for users in different regions.
3. User race-oriented price discrimination, that is, setting different sales prices for users of different races.
4 User language-driven price discrimination, that is, setting different sales prices for users with different language habits.
5 User gender-driven price discrimination, that is, setting different sales prices for users of different genders.
6 User shopping habits-driven price discrimination, that is, setting different sales prices for users with different shopping habits.
Operators can obtain a visual chart of user shopping habits in different regions through order reports. One of the charts is a line chart of user shopping habits in CA and FL.
In the process of specific operation, operators can complete the “price discrimination” operation in the following two forms.
1 Set multiple listing sub-variants, and set different high and low prices for each sub-variant. Then, different listings are displayed during the peak shopping period of users in different regions. The displayed listing price depends on the price sensitivity of the area that belongs to the user shopping peak period during the time period. Low-priced listings are displayed in areas with high price sensitivity, and high-priced listings are displayed in areas with low price sensitivity.
2 Set different prices for the same listing in different time periods. The price depends on the price sensitivity of the area that is the user’s peak shopping period during the time period. Low prices are displayed in areas with high price sensitivity, and high prices are displayed in areas with low price sensitivity.
Considering that frequent changes to listing prices may affect the listing weight, it is recommended to use the first form of operation. While displaying a suitable listing on the front desk, other listings can be prohibited from display. There are many ways to prohibit the display of a listing, such as deleting the main image, or setting the main image to an image that does not meet Amazon’s specifications.
The audience of a listing or store is not just users in CA and FL, so in the actual operation process, the operator needs to analyze the shopping habits of users in all regions of the store before coming up with the most accurate “price discrimination” operation strategy.