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Retail space analytics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Retail space analyticsedited by Ahmed Ghoniem, Bacel Maddah.
其他作者:	Ghoniem, Ahmed.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xii, 181 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Stores, RetailData processing.
電子資源:	https://doi.org/10.1007/978-3-031-27058-1
ISBN:	9783031270581$q(electronic bk.)

Retail space analytics
Retail space analytics[electronic resource] /edited by Ahmed Ghoniem, Bacel Maddah. - Cham :Springer International Publishing :2023. - xii, 181 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3392214-7934 ;. - International series in operations research & management science ;v. 140..

Effect of Customer Travel Behavior on Grid Layout and Shelf -- A Solver-Free Heuristic for Store-wide Shelf Space Allocation -- In-Store Tra c Density Estimation -- A Simulation Based Tool to Guide Periodic Changes in a Supermarket Layout -- Data-Driven Analytical Grocery Store Design -- Optimizing Stock-Keeping Unit Selection for Promotional Display Space at Grocery Retailers -- Merchandise Placement Optimization -- Problems and Opportunities of Applied Optimization Models in Retail Space Planning.

This edited volume presents state-of-the-art research that can leverage large-scale sensory data collected in grocery/retail stores where a single customer visit may generate nearly 10,000 data points. For decades, retail shelf space optimization has been confined to the analysis of product allocation decisions over a limited number of shelves, often taken in isolation. Such models incorporated interesting concepts relating to space and cross-space elasticity in the design of planograms. Although useful, these models have not addressed the bigger picture of planning store shelf space in a more holistic manner. It is important to note that the space planning analytics in the book are particularly important in an era where e-commerce is on the rise and brick-and-mortar retailing is declining and experiencing severe crises (the retail apocalypse) This is the first research-oriented book that examines novel problems in store space analytics, triggered by modern-day sensory technologies, customer trackers, and transactional tools (point-of-sales, etc.) In fact, such transformative technologies have prompted the development of new and exciting business practices, accompanied by the need for powerful data-driven models and analyses in retail shelf space and layout planning. The book will facilitate developing algorithms and decision tools that allow a better leverage of the data collected from these mediums.

ISBN: 9783031270581$q(electronic bk.)

Standard No.: 10.1007/978-3-031-27058-1doiSubjects--Topical Terms:

944013
Stores, Retail
--Data processing.

LC Class. No.: HF5429.15

Dewey Class. No.: 381.10285

Retail space analytics
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520 $a This edited volume presents state-of-the-art research that can leverage large-scale sensory data collected in grocery/retail stores where a single customer visit may generate nearly 10,000 data points. For decades, retail shelf space optimization has been confined to the analysis of product allocation decisions over a limited number of shelves, often taken in isolation. Such models incorporated interesting concepts relating to space and cross-space elasticity in the design of planograms. Although useful, these models have not addressed the bigger picture of planning store shelf space in a more holistic manner. It is important to note that the space planning analytics in the book are particularly important in an era where e-commerce is on the rise and brick-and-mortar retailing is declining and experiencing severe crises (the retail apocalypse) This is the first research-oriented book that examines novel problems in store space analytics, triggered by modern-day sensory technologies, customer trackers, and transactional tools (point-of-sales, etc.) In fact, such transformative technologies have prompted the development of new and exciting business practices, accompanied by the need for powerful data-driven models and analyses in retail shelf space and layout planning. The book will facilitate developing algorithms and decision tools that allow a better leverage of the data collected from these mediums.
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