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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Improving the List Priceng to change. Prices change over time, depending on circumstances and perhaps also depending on who is making the purchase. This chapter provides an introduction on how data-driven decision-making can be used to improve prices and consequently retailer profits.

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Optimizing Markdowns and Promotionse improved using data-driven decision-making. Both permanent price reductions (markdowns) and temporary promotional discounts are discussed. The main focus is the former, as the markdown decision is often influenced less by the marketing objectives and can more easily be delegated to a data-driven a

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Anticipate What Customers Will Dots outs the broad lines, these models are the precision instruments that allow for the identification of specific individuals. The focus of the chapter is on what a retailer can do with these models, rather than on the technical workings of such models.

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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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The Retailer’s Pricing Challengemain where most data-driven solutions operate is discussed. Next, the major pricing challenges are introduced by discussing the goals that are pursued by a retailer under specific conditions. Later chapters go into more depth as to how these goals can be achieved in practice.

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Anticipate What Customers Will Dots outs the broad lines, these models are the precision instruments that allow for the identification of specific individuals. The focus of the chapter is on what a retailer can do with these models, rather than on the technical workings of such models.