Authors:
Christian Daase
1
;
Seles Selvan
1
;
Dominic Strube
2
;
Daniel Staegemann
1
;
Jennifer Schietzel-Kalkbrenner
3
and
Klaus Turowski
1
Affiliations:
1
Institute of Technical and Business Information Systems, Otto-von-Guericke University, Magdeburg, Germany
;
2
Hochschule Wismar, University of Applied Sciences, Technology, Business and Design, Wismar, Germany
;
3
Berufliche Hochschule Hamburg, Hamburg, Germany
Keyword(s):
Retail Pricing Model, Dynamic Pricing, Retail Revenue, Artificial Intelligence, Systematic Literature Review.
Abstract:
Setting product prices poses both challenges and chances for retailers, as higher prices per stock keeping unit might lead to lower customer volume, while lower prices might result in insufficient turnover in relation to costs. In the age of digitalization and artificial intelligence, understanding price determinants becomes even more important as customer preferences shift and alternatives for purchasing products, such as online, are within easy reach. Based on a systematic literature review, this study aims to build a comprehensive model of traditional factors influencing customers’ price perception as fair, with an extension towards AI-driven data integration and use case design to ultimately realize dynamic pricing models such as real-time demand pricing, personalized pricing and further machine learning-based approaches. The final visualization is intended as guidance for practitioners to evaluate their pricing strategies to determine if factors are currently being overlooked an
d to consider how they could be incorporated into future decisions. Researchers can also use the insights gained to build upon and expand the potential of AI integration into pricing automation.
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