The Value of Personalized Pricing

Abstract

Increased availability of high-quality customer information has fueled interest in personalized pricing strategies, that is, strategies that predict an individual customer’s valuation for a product and then offer a price tailored to that customer. Although the appeal of personalized pricing is clear, it may also incur large costs in the forms of market research, investment in information technology and analytics expertise, and branding risks. In light of these trade-offs, our work studies the value of personalized pricing strategies over a simple single-price strategy. We first provide closed-form lower and upper bounds on the ratio between the profits of an idealized personalized pricing strategy (first-degree price discrimination) and a single-price strategy. Our bounds depend on simple statistics of the valuation distribution and shed light on the types of markets for which personalized pricing has little or significant potential value. Second, we consider a feature-based pricing model where customer valuations can be estimated from observed features. We show how to transform our aforementioned bounds into lower and upper bounds on the value of feature-based pricing over single pricing depending on the degree to which the features are informative for the valuation. Finally, we demonstrate how to obtain sharper bounds by incorporating additional information about the valuation distribution (moments or shape constraints) by solving tractable linear optimization problems. This paper was accepted by David Simchi-Levi, revenue management and market analytics.

Evaluating the ending‐9 pricing strategy along the online shopping funnel

“…In that regard, our proposed strategy could be perceived as a variation of personalized pricing strategies. Past literature has demonstrated the value of such a pricing strategy (e.g., Elmachtoub et al., 2021) and has designed these strategies from the perspectives of consumer demand and/or inventory (e.g., Aydin & Ziya, 2009; Chen et al., 2010; Cohen et al., 2018; Miao et al., 2022; Wang et al., 2021); customer demand, trust, and valuation (e.g., Besbes & Lobel, 2015; Fang & Whinston, 2007; Gilbert et al., 2014; Su, 2007; Zhang et al., 2014); product and service bundling (e.g., Song & Li, 2018); product characteristics and quality (e.g., Ban & Keskin, 2021; Levin et al., 2010). This paper hopes to add to the past literature by proposing a pricing strategy based on the cost (as defined in the minimum spending criteria) and expected utility and revenue of purchasing the shopping cart (as defined in revenue‐based selection).…”

Influence of showrooming and price matching strategy to combat showrooming under different costs

“…Scholars also use game theoretic models to compare different pricing strategies, such as personalized versus single pricing (Elmachtoub et al, 2021), random pricing versus flat price strategy (Wu et al, 2014), fixed versus variable pricing (Tang and Yin, 2007), consistent versus inconsistent pricing (Kong et al, 2020) and everyday low price strategy versus highlow strategy (Park et al, 2020).…”

Market Segmentation Trees

“…Beyond its application to bid landscape forecasting, IRTs also offer a powerful new tool for personalized pricing (Elmachtoub et al 2021). In these settings, the contextual variables x are features encoding the visiting customer, the decision p is the price of the offered product, and the response y is a binary indicator of whether the customer purchased the product at that price.…”