A Profit-Maximizing Strategy for Advertising on the e-Commerce Platforms

TL;DR

This paper introduces a profit-maximizing strategy for online advertising on e-commerce platforms, optimizing targeting options to improve conversion rates and reduce unproductive clicks, based on real-world data from Tmall.

Contribution

It presents a novel model reformulating advertising strategy optimization as a multiple-choice knapsack problem for better profit maximization.

Findings

Effective optimization of advertising strategies with budget constraints

Reduction in unproductive 'just looking' clicks

Improved conversion rates on real-world e-commerce data

Abstract

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.