@article{1469586,
      author = {Ghoniem, Ahmed, and Maddah, Bacel,},
      url = {http://library.usi.edu/record/1469586},
      title = {Retail space analytics /},
      abstract = {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.},
      doi = {https://doi.org/10.1007/978-3-031-27058-1},
      recid = {1469586},
      pages = {1 online resource (xii, 181 pages) :},
}