A Profit-Maximizing Strategy for Advertising on the e-Commerce Platforms
This work addresses the challenge of configuring advertising strategies for e-commerce vendors, particularly those with limited resources, to reduce unproductive clicks and improve sales, representing an incremental improvement in domain-specific optimization.
The paper tackles the problem of ineffective online advertising strategies that lead to high costs and low sales conversions for e-commerce vendors, presenting a profit-maximizing strategy that optimizes feature selection to increase buyer conversion probability, with empirical validation on real-world Tmall data showing effective optimization under budgetary constraints.
The online advertising management platform has become increasingly popular among e-commerce vendors/advertisers, offering a streamlined approach to reach target customers. Despite its advantages, configuring advertising strategies correctly remains a challenge for online vendors, particularly those with limited resources. Ineffective strategies often result in a surge of unproductive ``just looking'' clicks, leading to disproportionately high advertising expenses comparing to the growth of sales. In this paper, we present a novel profit-maximing strategy for targeting options of online advertising. The proposed model aims to find the optimal set of features to maximize the probability of converting targeted audiences into actual buyers. We address the optimization challenge by reformulating it as a multiple-choice knapsack problem (MCKP). We conduct an empirical study featuring real-world data from Tmall to show that our proposed method can effectively optimize the advertising strategy with budgetary constraints.