Pricing and data science: The tale of two accidentally parallel transitions
DOI:
https://doi.org/10.18559/ebr.2023.2.739Keywords:
data science, machine learning, value-based pricing, pricingAbstract
Accidentally parallel at the beginning, the transition to value-based pricing and transition 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 inherently non-linear, multimodal, and right-skewed pricing data. Presenting the interactions between new computational techniques and value-data pricing, I concentrate on altered perceptions of price elasticity, value-driver estimations, and contract opportunity analysis.
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