Product selection for promotion planning

Abstract

This paper addresses a very important question-how to select the right products to promote in order to maximize promotional benefit. We set up a framework to incorporate promotion decisions into the data-mining process, formulate the profit maximization problem as an optimization problem, and propose a heuristic search solution to discover the right products to promote. Moreover, we are able to get access to real supermarket data and apply our solution to help achieve higher profits. Our experimental results on both synthetic data and real supermarket data demonstrate that our framework and method are highly effective and can potentially bring huge profit gains to a marketing campaign.

Customer‐centric category selection for mobile and print promotions in loyalty reward programs

“…To the best of our knowledge, only two scholars have considered the stand‐alone category selection problem: Yang and Hao (2010) and Reutterer et al (2017). Each of these studies belongs to the data analytics research stream, and their contributions build on knowledge gained from studying the related product assortment problem (Brijs, Swinnen, Vanhoof, & Wets, 2004; Wong & Wang, 2005) and the data‐mining methodology of “association rule mining.” The product assortment problem investigates what products to offer in the context of restricted retail space.…”

“…Yang and Hao (2010) employ a different model, one that is applicable to both the product and category selection problem. These authors ground their model on the product's promotional effect, which has been studied extensively.…”

“…To quantify these effects for a single category, Yang and Hao (2010) calculate all promotional effects of the frequent categories (or items) and then determine their values as the aggregate of those individual category or item effects. Thus the promotional effect of an item A on some other item B is defined as the added value (Tan, Kumar, & Srivastava, 2004) of this association—that is, the strength of the “ A → B ” rule minus the frequency with which A is purchased.…”

“…However, both the Yang and Hao (2010) and the Reutterer et al (2017) category selection models have two downsides that inherently lead them to select suboptimal categories. First, they focus on the promotional effects of a category and ignore essential metrics (eg, the redemption rate, fixed costs of promotion).…”

“…Although researchers have examined customer responses to category‐specific offers (Swaminathan & Bawa, 2005), investigated customer choice processes, and provided generalized recommendations for the related product selection problem (Ailawadi, Harlam, César, & Trounce, 2006; Dhar & Raju, 1998; Osuna, González, & Capizzani, 2016; Venkatesan & Farris, 2012), we are not aware of any published work on profit optimization models for the design of retailers' category‐specific promotions. The data analytics research stream does provide the models proposed by Yang and Hao (2010) and Reutterer, Hornik, March, and Gruber (2017). Yet as we shall discuss, their models neglect cherry‐picking effects and ignore such essential metrics as the redemption rate.…”