Case Studies in Data driven Merchandising

• Verfasst von: Shellman, Erin [VerfasserIn]
• Titel: Case Studies in Data driven Merchandising
• Institutionen: Safari, an O'Reilly Media Company. [MitwirkendeR]
• Verf.angabe: Shellman, Erin
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Data Science Salon
• Jahr: 2020
• Umfang: 1 online resource (1 video file, approximately 29 min.)
• Fussnoten: Online resource; Title from title screen (viewed March 24, 2020)
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic videos ; local
• K10plus-PPN: 172663311X
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3736268319
• Lokale URL Inst.: Zum Volltext

Abstract

For retail businesses, inventory is simultaneously the company's greatest asset and its largest risk. Competitor pricing, markdowns, and returns all threaten the margins that drive success, and in practice, inventory doesn't always move according to plan. To win in this highly competitive and rapidly evolving industry, it's essential to have a flexible toolkit that accurately produces forecasts and intelligently adapts to unplanned inventory dynamics. In this talk, I'll outline how Nordstrom applies data science and machine learning to build a wholistic view of inventory management from assortment, through stocking with intelligent size runs, and ending with a customer experience that gets the right product to the right customer at the right time.