Paper Number
1668
Paper Type
Short Paper
Abstract
The price is the most important determinant for product sales and is highly influential for a company's success. Nevertheless, price determination often follows individuals’ rules of thumb augmented with product and economic performance indicators. With the increasing dissemination of artificial intelligence in organizations and society, the accuracy of price determination in retail might be enhanced by sophisticated pricing algorithms. Technological developments further increase the number of pricing algorithms and pricing tools available. Against this backdrop, we applied Nickerson et al.’s (2013) approach, proposing a taxonomy for describing pricing algorithms in retail. The taxonomy consists of 19 dimensions and 59 characteristics. Analyzing 70 pricing tools revealed a high specialization for selected retail domains, a focus on competitor monitoring and dynamic pricing, and a minor use of current machine learning techniques. This is a first attempt at structuring pricing algorithms and developing a price management toolbox that constructs artificial intelligence-enabled pricing algorithms.
Recommended Citation
Brackmann, Clemens; Wulfert, Tobias; Busch, Jan; and Schütte, Reinhard, "The Art of Retail Pricing: Developing a Taxonomy for Describing Pricing Algorithms" (2024). ECIS 2024 Proceedings. 3.
https://aisel.aisnet.org/ecis2024/track04_impactai/track04_impactai/3
The Art of Retail Pricing: Developing a Taxonomy for Describing Pricing Algorithms
The price is the most important determinant for product sales and is highly influential for a company's success. Nevertheless, price determination often follows individuals’ rules of thumb augmented with product and economic performance indicators. With the increasing dissemination of artificial intelligence in organizations and society, the accuracy of price determination in retail might be enhanced by sophisticated pricing algorithms. Technological developments further increase the number of pricing algorithms and pricing tools available. Against this backdrop, we applied Nickerson et al.’s (2013) approach, proposing a taxonomy for describing pricing algorithms in retail. The taxonomy consists of 19 dimensions and 59 characteristics. Analyzing 70 pricing tools revealed a high specialization for selected retail domains, a focus on competitor monitoring and dynamic pricing, and a minor use of current machine learning techniques. This is a first attempt at structuring pricing algorithms and developing a price management toolbox that constructs artificial intelligence-enabled pricing algorithms.
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