A mathematical reformulation of the reference price

Abstract

Reference prices have long been studied in applied economics and business research.One of the classic formulations of the reference price is in terms of an iterative function of past prices. There are a number of limitations of such a formulation, however. Such limitations include burdensome computational time to estimate parameters, an inability to truly account for customer heterogeneity, and an estimation procedure that implies a misspecified model. Managerial recommendations based on inferences from such a model can be quite misleading. We mathematically reformulate the reference price by developing a closed-form expansion that addresses the aforementioned issues, enabling one to elicit truly meaningful managerial advice from the model. We estimate our model on a real world data set to illustrate the efficacy of our approach. Our work is not only useful from a modeling perspective, but also has important behavioral and managerial implications, which modelers and non-modelers alike would find useful.

Interaction effects of different price claims and contextual factors on consumers' reference price adaptation after exposure to a price promotion

“…Such models contain different explanatory variables as well as parameters that mirror the weight assigned to the respective explanatory variables. Consumers' reference price in these models is usually a weighted combination of previous beliefs and external price claims (Dayaratna and Kannan, 2012;Erdem et al, 2010;Moon et al, 2006;Moon and Voss, 2009). Table 1 gives an overview of models that explain price beliefs in the context of pricerelated advertising.…”

Behavioral response to online pricing: empirical and managerial insights

“…Research has found evidence of asymmetric effect across many industries, markets and situations. Bronnenberg and Wathieu (1996), Briesch et al (1997), Mazumdar and Papatla (2000), Erdem et al (2001), Han et al (2001), Kivetz et al (2004), Moon et al (2006), Terui and Dahana (2006), Pauwels et al (2007), Newman and Newman (2007), Habib and Miller (2009), Masiero and Hensher (2010), Ataman and Rooderkerk (2010), Rose and Masiero (2010), Delle Site and Filippi (2011), Nicolau (2011), Neumann et al (2012), Koppalle et al (2012), Dayaratna and Kannan (2012) and Hu et al (2012) have found robust empirical support for the asymmetric effect of price increases and decreases, and for loss aversion. Jayakumar (2016) documented behavioral lessons from Flipkart’s Big-Billion Day sale in India and recommended that e-tailers should use the principles of behavioral economics, including framing effects, reference price and principles of loss aversion to influence customer decision-making in their favor.…”

“…, Rose and Masiero (2010), DelleSite and Filippi (2011), Nicolau (2011), Neumann et al (2012,Koppalle et al (2012),Dayaratna and Kannan (2012) andHu et al (2012) have found robust empirical support for the asymmetric effect of price increases and decreases, and for loss aversion Jayakumar (2016). documented behavioral lessons from Flipkart's Big-Billion Day sale in India and recommended that e-tailers should use the principles of behavioral…”

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“…Subsequent to the findings by Kalyanaram and Little (1994), many researchers in marketing have found that the loss-aversion effect in pricing is significant. Briesch et al (1997), Bronnenberg and Wathieu (1996), Dayaratna and Kannan (2012), Delle Site and Filippi (2011), Erdem et al (2001), Habib and Miller (2009), Han et al (2001), Hess and Rose (2009), Hess et al (2012), Hu et al (2012), Johnson and Meyer (1995), Kalyanaram and Little (1989), Kalyanaram and Winer (1995), Kivetz et al (2004), Kopalle et al (2012aKopalle et al ( , 2012b, Kwak (2007), Masiero and Hensher (2010), Mazumdar and Papatla (2000), Moon et al (2006), Newman and Newman (2007), Neumann et al (2012), Nicolau (2011), Pauwels et al (2007), Rose and Masiero (2010), and Dahana (2006a, 2006b) have established loss aversion across a variety of data sources, product and service categories, and methods of analyses. Researchers have established these findings across individual levels and/or aggregate data from Australia, Europe, United States, and other markets, and across consumer-packaged goods data (such as bacon, beverages, chocolate, coffee, cola, crackers, detergent, drinks, ketchup, orange juice, peanut butter, and tuna), consumer durables data (such as personal computers, laptops, and digital cameras), and service data (car travel, flight travel, holiday destination choice, and hospital services).…”