1. Advertising application of customer shopping habit data
The advertising application of customer shopping habit data can be divided into two aspects: the first aspect is the optimization of advertising exposure time, and the second aspect is the optimization of single click bidding of advertising. This section mainly describes the application of the first aspect. Cross-border operators can use bar charts to show the daily changes in the total order volume of the store.
Cross-border operators can mark the overall shopping peak period of the store.
6:00~19:00 US time is the shopping peak period, so this period is also the traffic peak period of Amazon US Station, so operators can use this period to maximize advertising efficiency. It should be noted that the best time here is not the time with the lowest advertising cost of sales (ACOS), but the time with the highest advertising efficiency in the same time, which is suitable for products in the growth stage rather than the stable stage.
2. Listing optimization application of customer shopping habit data
In listing optimization, customer shopping habit data can be applied to more advanced operations-price discrimination. Price discrimination is essentially a price difference, which usually refers to the situation where the provider of goods or services provides different goods or services of the same grade and quality to different recipients, and implements different sales prices or charging standards for different recipients. For example, when selling the same product on Amazon, the operator sells it to customer A for $5 and to customer B for $6. This behavior constitutes price discrimination. The operator obtains excess profits through such behavior. On the Amazon platform, “price discrimination” can generally be divided into the following six categories.
(1) Price discrimination based on customer occupation, that is, setting different prices for customers of different occupations, such as setting lower prices for student customers and higher prices for working customers.
(2) Price discrimination based on customer region, that is, setting different sales prices for customers in different regions.
(3) Price discrimination based on customer race, that is, setting different sales prices for customers of different races.
(4) Price discrimination based on customer language, that is, setting different sales prices for customers with different language habits.
(5) Price discrimination based on customer gender, i.e. setting different sales prices for customers of different genders.
(6) Price discrimination based on customer shopping habits, i.e. setting different sales prices for customers with different shopping habits.
This article mainly explains the use of “price discrimination” in combination with “price discrimination based on customer shopping habits”.
After determining the time period for implementing the “price discrimination” strategy, it is necessary to determine the possibility of implementing the “price discrimination” strategy. This requires determining whether there is a difference in price sensitivity between customers in the CA region and the FL region. This article introduces a data visualization method for comparing the order volume and average customer unit price in different regions, that is, by comparing the bar chart obtained by “comparison number values”, the operator can mark the CA region and the FL region on the chart. Then, the operator can refer to the price sensitivity of different regions to divide the visualization table. Combined, it can be known that: CA region belongs to the medium price sensitivity range, and FL region belongs to the high price sensitivity range. Then, customers in CA region tend to buy products with higher prices, while customers in FL region tend to buy products with lower prices. The conditions for implementing the “price discrimination” strategy exist. Its specific operation logic is: when the shopping peak period of customers in CA region arrives, the operator can increase the sales price to obtain excess profits. At the same time, there is no need to worry that the price increase will lead to a decline in sales in FL region, because the shopping peak period of customers in FL region has not yet arrived. Later, when the shopping peak period of customers in FL region arrives, the operator can adjust the sales price back to the original position, thereby prompting more FL Customers in the region place orders to purchase.
In the specific operational practice, operators can use the following two methods to implement the “price discrimination” strategy.
(1) Set up multiple sub-variants, and set different high and low prices for each sub-variant. Then, when different regions reach the peak shopping period, different sub-variants are displayed. The displayed sales price depends on the price sensitivity of the region during the peak shopping period. Regions with high price sensitivity display low-priced listings, and regions with low price sensitivity display high-priced listings.
(2) Set different prices for the same listing in different time periods. The specific sales price depends on the price sensitivity of the region during the peak shopping period. Regions with high price sensitivity display low prices, and regions with low price sensitivity display high prices. Considering that frequent price changes may affect product weight, the first method is recommended. While displaying a suitable sub-variant on the front end, other sub-variants can be prohibited from displaying.