Data science has enabled “black box” algorithmic decision-making (Pasquale 2013), replete with technical complexity and secrecy, representing an evolution from classical pricing approaches and making a compelling case for price conditioning in certain industries (Acquisti and Varian 2005). Such processes could incorporate a combination of data-mining techniques to harness inferential analytics (Furnas 2012) and information asymmetries to exploit behavioral discrimination (Ezrachi and Stucke 2016a).Ī paucity of descriptive research obscures the extent to which executives implement pricing strategies consistent with theoretical models and determinants (Noble and Gruca 1999 Rao and Kartono 2009).

Liozu and Hinterhuber ( 2014) suggest a causal model of specific capabilities that includes “robust internal processes related to price setting, pricing tools, and training” (p. Evidence supports the assertion that developing significant pricing capabilities positively impacts overall firm performance (Dutta et al. Decision-making within the pricing function has evolved from an individual clerical task driven by instinct to a cross-functional team activity that drives strategic value (Mitchell 2012). Pricing is a tremendously important component of the organizational marketing mix (Borden 1964).

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