Abstract
The price of a product used to be something fixed. A product had a price just like it had a weight, a color, and a brand. More and more this is starting 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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Notes
- 1.
Another term for this might be dynamic pricing, but this concept is often defined more narrowly in literature. Dynamic pricing itself is discussed in the chapter and is seen as a possible part of variable list pricing—but not synonymous.
- 2.
I am not a lawyer; please do not take legal advice from me. When in doubt, consult with a legal professional.
- 3.
This relates to FIFO-/LIFO-style calculations: is the value of the inventory the value to replace it? Or should it be valued at the original cost of purchasing?
- 4.
This is of course relative, and a small price change may be highly relevant if large volumes of a product are sold.
- 5.
Assuming that the retailer is not in the habit of consistently overstocking, meaning that there is always superfluous inventory.
- 6.
This type of balancing between exploration and exploitation is typical for many algorithms. At some point in time, it is more beneficial to learn about the environment; at other points, it is more appropriate to use existing knowledge. Example of this can be found in optimization algorithms that often have elements that diversify the solution, such as the mutation operator in an evolutionary algorithm. At the same time, many algorithms also employ local search techniques to improve incrementally on existing good solutions.
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Kerkhove, LP. (2022). Improving the List Price. In: Data-driven Retailing. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-031-12962-9_3
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