Demand prediction in retail: a practical guide to leverage data and predictive analytics

By:

Cohen, Maxime C

Material type: Text

TextPublication details: Springer Switzerland 2022Description: xvii, 155 pISBN:

9783030858575

Subject(s):

DDC classification:

658.500727 COH

Summary: From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.

List(s) this item appears in: Public Policy & General Management

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Marketing	658.500727 COH (Browse shelf(Opens below))	1	Available		004705

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Previous	658.5 LEM Product management in practice:	658.5 SUB Innovation analytics: tools for competitive advantage	658.500285 HOF Digital product management: frameworks - tools - cases	658.500727 COH Demand prediction in retail:	658.5342 SOL The new chameleons: how to connect with consumers who defy categorization	658.575 BAN Product leadership:	658.575 CAG Inspired:	Next

From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture.

This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.

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