Titlebook: Data-driven Retailing; A Non-technical Prac Louis-Philippe Kerkhove Book 2022 The Editor(s) (if applicable) and The Author(s), under exclus

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Anticipate When Customers Will Do Somethinga medical context to model patient survival. These models can be perfectly repurposed to optimize the timing of a retailers messaging, to coincide with moment when this messaging has the greatest impact.

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Louis-Philippe KerkhoveOffers tools to manage data-centric projects and ask the right questions.Provides user-friendly practical knowledge on data management.Covers theory and practice of data analysis and management

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O. Shakuntala,G. Raghavendra,S. K. AcharyaThis chapter discusses the philosophy behind algorithmic marketing. This is an umbrella term for techniques that aspire to make marketing efforts more personalized and effective. The meaning of customer centricity and its implications for algorithmic marketing are discussed at length.

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Andreas Daberkow,Marcus Ehlert,Dominik KaiseDespite being one of the first data use cases quoted in a retail context, customer segmentation is often done badly. This chapter discusses an approach that creates customer segments that are behaviorally different, and show different customer value potential. The resulting segments are easier to translate into specific value-increasing actions.

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Understanding Demand and ElasticityDemand curves and elasticity models are the bread and butter of basic economics courses. In spite of this, the simplified models that are thought translate poorly to the day-to-day situation of a retailer. This chapter provides a brief recapitulation of the basics, as well as some approaches that are manageable in practice.

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