Towards Revenue Maximization with Popular and Profitable Products

TL;DR

This paper introduces a profit-oriented framework for identifying on-shelf popular and profitable products to maximize revenue, leveraging consumer behavior insights and an algorithmic approach validated on real-world data.

Contribution

It proposes a novel framework and algorithms for finding on-shelf popular and profitable products, addressing seasonal sales and inventory trends for revenue maximization.

Findings

Effective identification of profitable products using the proposed algorithm.

Demonstrated efficiency and effectiveness through experiments on real datasets.

Enhanced marketing strategies by predicting hot product trends.

Abstract

Economic-wise, a common goal for companies conducting marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Consumer behavior is crucially important in economy and targeted marketing, in which behavioral economics can provide valuable insights to identify the biases and profit from customers. Finding credible and reliable information on products' profitability is, however, quite difficult since most products tends to peak at certain times w.r.t. seasonal sales cycle in a year. On-Shelf Availability (OSA) plays a key factor for performance evaluation. Besides, staying ahead of hot product trends means we can increase marketing efforts without selling out the inventory. To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing. To tackle the revenue maximization problem, we model the k-satisfiable product concept and propose an algorithmic framework for searching OPPP and its variants. Extensive experiments are conducted on several real-world datasets to evaluate the effectiveness and efficiency of the proposed algorithm.