From code to coin: Unravelling the practical business impact of algorithmic pricing

Peter Rathnow, Benjamin Zeller and Matthias Lederer
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Peter Rathnow: Professor, Technische Universität and the International School of Management Munich, Germany
Benjamin Zeller: Director Corporate Strategy, Ivoclar Vivadent AG, Liechtenstein
Matthias Lederer: Professor, OTH Technical University of Applied Sciences Amberg-Weiden, Germany

Journal of AI, Robotics & Workplace Automation, 2024, vol. 3, issue 3, 234-246

Abstract: This paper examines the impact of algorithmic pricing on company value and presents the results of 31 expert interviews in various industries. The focus is on nine hypotheses, including the efficiency of automation, segmentation and value-based pricing, efficient resource allocation, perception of fairness, price transparency and uncertainty, the use of personal data and price competition. The surveys show that algorithmic pricing has considerable potential for efficiency gains through automation, particularly in terms of revenue growth. The potential for cost savings, however, varies from sector to sector. Segmentation and value-based pricing are proving to be feasible, but their implementation requires clear justification and economic rationale. Efficient resource allocation through dynamic pricing shows potential, especially in industry-specific scenarios. The perception of unfairness can have a negative impact, however, and personalised pricing risks alienating customers and reducing business value. Price transparency and uncertainty have a mixed effect, as customers often place more value on the transparency of alternative offers than on the company’s pricing process. The use of personal data poses a low risk to company value if it is transparent and mutually beneficial. The potential intensification of competition through algorithmic pricing depends on the market structure and the company’s objectives; however, algorithmic pricing can be used as an instrument to reduce price competition and increase company value. Overall, the results make it clear that the integration of algorithmic pricing strategies must be carefully and ethically designed in order to sustainably increase company value.

Keywords: algorithmic pricing; dynamic pricing; shareholder value; artificial intelligence; profit maximisation; field report; practical experience (search for similar items in EconPapers)
JEL-codes: G2 M15 (search for similar items in EconPapers)
Date: 2024
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