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Data-driven retailing = a non-techni...

Kerkhove, Louis-Philippe.

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Data-driven retailing = a non-technical practitioners' guide /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven retailing/ by Louis-Philippe Kerkhove.
Reminder of title:	a non-technical practitioners' guide /
Author:	Kerkhove, Louis-Philippe.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xv, 257 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Part I. Pricing -- Chapter 1. The Retailer's Pricing Challenge -- Chapter 2. Understanding Demand and Elasticity -- Chapter 3. Improving the List Price -- Chapter 4. Optimizing Markdowns and Promotions -- Part II. Inventory Management -- Chapter 5. Product (Re-)distribution and Replenishment -- Chapter 6. Managing Product Returns -- Part III. Marketing -- Chapter 7. The Case for Algorithmic Marketing -- Chapter 8. Better Customer Segmentation -- Chapter 9. Anticipate What Customers Will Do -- Chapter 10. Anticipate When Customers Will Do Something -- Part IV. Conclusion -- Chapter 11. Where Retail Is Headed Next.
Contained By:	Springer Nature eBook
Subject:	Retail trade - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-12962-9
ISBN:	9783031129629

Data-driven retailing = a non-technical practitioners' guide /
Kerkhove, Louis-Philippe.

Data-driven retailinga non-technical practitioners' guide /[electronic resource] :by Louis-Philippe Kerkhove. - Cham :Springer International Publishing :2022. - xv, 257 p. :ill. (some col.), digital ;24 cm. - Management for professionals,2192-810X. - Management for professionals..

Part I. Pricing -- Chapter 1. The Retailer's Pricing Challenge -- Chapter 2. Understanding Demand and Elasticity -- Chapter 3. Improving the List Price -- Chapter 4. Optimizing Markdowns and Promotions -- Part II. Inventory Management -- Chapter 5. Product (Re-)distribution and Replenishment -- Chapter 6. Managing Product Returns -- Part III. Marketing -- Chapter 7. The Case for Algorithmic Marketing -- Chapter 8. Better Customer Segmentation -- Chapter 9. Anticipate What Customers Will Do -- Chapter 10. Anticipate When Customers Will Do Something -- Part IV. Conclusion -- Chapter 11. Where Retail Is Headed Next.

This book provides retail managers with a practical guide to using data. It covers three topics that are key areas of innovation for retailers: Algorithmic Marketing, Logistics, and Pricing. Use cases from these areas are presented and discussed in a conceptual and comprehensive manner. Retail managers will learn how data analysis can be used to optimize pricing, customer loyalty and logistics without complex algorithms. The goal of the book is to help managers ask the right questions during a project, which will put them on the path to making the right decisions. It is thus aimed at practitioners who want to use advanced techniques to optimize their retail organization.

ISBN: 9783031129629

Standard No.: 10.1007/978-3-031-12962-9doiSubjects--Topical Terms:

880324
Retail trade
--Data processing.

LC Class. No.: HF5429.15 / .K47 2022

Dewey Class. No.: 658.87002856312

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505 0 $a Part I. Pricing -- Chapter 1. The Retailer's Pricing Challenge -- Chapter 2. Understanding Demand and Elasticity -- Chapter 3. Improving the List Price -- Chapter 4. Optimizing Markdowns and Promotions -- Part II. Inventory Management -- Chapter 5. Product (Re-)distribution and Replenishment -- Chapter 6. Managing Product Returns -- Part III. Marketing -- Chapter 7. The Case for Algorithmic Marketing -- Chapter 8. Better Customer Segmentation -- Chapter 9. Anticipate What Customers Will Do -- Chapter 10. Anticipate When Customers Will Do Something -- Part IV. Conclusion -- Chapter 11. Where Retail Is Headed Next.
520 $a This book provides retail managers with a practical guide to using data. It covers three topics that are key areas of innovation for retailers: Algorithmic Marketing, Logistics, and Pricing. Use cases from these areas are presented and discussed in a conceptual and comprehensive manner. Retail managers will learn how data analysis can be used to optimize pricing, customer loyalty and logistics without complex algorithms. The goal of the book is to help managers ask the right questions during a project, which will put them on the path to making the right decisions. It is thus aimed at practitioners who want to use advanced techniques to optimize their retail organization.
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