Pricing and data science: The tale of two accidentally parallel transitions

Accidentally parallel at the beginning, the transition to val ue-based pricing and transiotin to pricing data science have blended harmoniously, changing the pricing landscape. Using the marketing capability approach, I show that the introduction of pricing data science is costly and requires higher management support. Despite its cost, algorithmic price optimisation allows one to react swiftly to changes in demand. The optimisation process is applied to inherent ly non-linear, multimodal, and right-skewed pricing data. Presenting the interactions between new computational techniques and value-data pricing, I concentrate on altered percepotins of price elasctiity, value-driver esmtiaotins, and contract opportunity analysis. I would like to thank to Małgorzata Majewska-Wallusch and Syed Ali Abbas for comments on the earlier drafts of this paper. I would also like to thank Irina Nezbudiy, Gabor Kapus, Tomasz Wyszyński, Maximilien Jougounoux, Boris Moroseac, Michał Taraszewski, Izabela Mikuła, Grzegorz Bańda, Radosław Stropa, Paul Wilkins, Claudia Espiati Bonilla, Arkhip Tikh, Natalie Sologuren, Nick Holnaider, and Patrick Moreau for sharing with me their invaluable pricing and data science experience.