亞馬遜平台競價廣告投放對企業業績的影響

Abstract

Cross-border e-commerce is gradually becoming an essential part of national trade and even globalization. In the cross-border e-commerce industry, Amazon is the largest cross-border e-commerce platform in the world, with more than 300 million active users per day. Bidding search advertising on Amazon's platform is the unique advertising pattern, and the only way for sellers to boost product sales is to invest in advertising, so that advertising is an indispensable tool for cross-border e-commerce companies to survive in the market. This paper focuses on the relationship between advertising investment and performance (sales and profits).

This paper first introduces the important role of cross-border e-commerce in the Chinese and global economy and some characteristics of bidding search advertising in the Amazon platform, so as to clarify the irreplaceable role of advertising investment in cross-border e-co
merce companies. Then, a double fixed effects (individual fixed and time fixed) model is constructed using the advertising investment and performance data of Gudao E-Commerce Company from June to July in 2022. The regression results demonstrate that each additional RMB 1 of advertising investment can bring about RMB 7.68 of total revenue and about RMB 1.29 of total profit. Based on the baseline model, the paper also finds significant mediating and moderating effects for advertising revenue (profit) and product cost respectively. Advertising investment first affects advertising revenue (profit), and then performance, with the mediating effect accounting for about 70% of the total effect, while different product costs also moderate the relationship between advertising investment and performance. Since advertising in the Amazon platform is divided into automatic and manual advertising, this paper also conducts regression analysis by automatic and manual advertising input, separately. The results find that automatic advertising is more suitable for product pre-investment, while manual advertising is a guarantee for product profitability in the later.

There are four different features of products of Gudao e-commerce company, so this paper makes four types of heterogeneity analysis, that is, building regression models based on four different features of products respectively . Firstly, the products are divided into eight product categories, and regress separately. we finds that the products in the hardware category are the most sensitive to advertising investment, while the tools category was usually the least, Suggests that there is the strongest relationship between performance and advertising investment in the hardware category and the loosest in the tools category; secondly, the products are divided by business teams, i.e., products that are developed and sales under the same business team are classified as one category, and from the regression results, we can intuitively find out the business capabilities of the development and sales groups included in the business teams, as well as the level of tacit understanding between different development. The paper also classifies products by the geographical area, and finds that the US market has the most potential, but also the most competitive advertising, while the UK market is the opposite by US. Finally, the paper categories products by their star rating on Amazon and finds that for products with 1 to 4 stars, the higher the star rating the better the product performance, while 5 star products need to be further classified into starter and mature products, with starter products typically performing weaker and mature products performing the best, which is true to the actual business situation.

Based on the above research, it can help cross-border e-commerce companies to have a deeper understanding of the relationship between advertising investment and performance on the Amazon platform, and to target their advertising investment with different strategies for different categories, different geographies, different business groups and different star ratings.

Date of Award	8 Jan 2024
Original language	Chinese (Traditional)
Awarding Institution	City University of Hong Kong
Supervisor	Qiying Hu (External Supervisor) & Houmin YAN (Supervisor)