Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power |
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Institution: | 1. Department of Management, Università Politecnica delle Marche, Piazzale Martelli Raffaele, 8, 60121, Ancona, Italy;2. Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy;1. United Arab Emirates University, United Arab Emirates;2. United Arab Emirates University, P.O. Box 15551, Al-Ain, United Arab Emirates;3. Nottingham Business School, Nottingham Trent University, Nottingham, United Kingdom;4. University of Sadat City, Sadat City, Menofia, Egypt;5. Faculty of Tourism and Hotels, University of Sadat City, Egypt;6. College of Business Administration, Jazan University, Saudi Arabia;1. School of Management, Jinan University, Guangzhou, Guangdong Province, 510632, China;2. Faculty of Business Administration, University of Macau, Macau, 999078, China;3. School of Management and Economics, Beijing Institute of Technology Beijing, 100084, China |
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Abstract: | Retailers need to manage a series of complex decisions relating to numerous products. To reduce this complexity, they have introduced category management practices, which consider groups of similar products (categories) that can be managed separately as single business units (SBUs). Although the concept that the store offer should be organised as a category mix and that this strategy allows for better overall store management is already consolidated, retailers still struggle to adopt an approach to the store performance measurement starting from a category level perspective. Nowadays, the available methods for measuring categories’ performance are quite limited. The current trend sees the measurement of category performance mainly based on sell-out data that are ill-equipped to fully address category management issues. Retailers should broaden their field of analysis not only by focusing on the product/sales perspective but also by including other methodologies such as shopper behaviour analysis. In this regard, the use of technology offers the retail sector new perspectives for those analysis. Therefore, we intend to contribute to the ongoing debate on the retail analytics topic by presenting a shopper behaviour analytics system for category management performance monitoring. More in detail, we could derive a new key performance indicator, category conversion power (CCP), aimed at analysing and comparing the single categories organised within the store. The research is based on a unique dataset obtained from a real-time locating system (RTLS), which allowed us to collect behavioural data togheter with sell-out data (from POS scanner). We argue that retailers could exploit this new analytical method to gain more understanding at the category level and therefore make data-driven decisions aimed at improving performance at the store level. |
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Keywords: | Category management Performance measurement Shopper behaviour Retail marketing RTLS technology Big data |
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