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A data-driven approach to measure restaurant performance by combining online reviews with historical sales data
Institution:1. Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal;2. ALGORITMI Research Centre, University of Minho, Guimarães, Portugal;3. Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal;4. INESC-ID, Lisboa, Portugal;1. Conrad N. Hilton College of Hotel & Restaurant Management, University of Houston, 4450 University Dr. #227, Houston, TX, 77204, United States;2. Department of Hotel Management, Cheju Halla University, 28 Halladae-gil, Heungeop-myeon, Wonju-si, Gangwon-do, South Korea;3. Chaplin School of Hospitality & Tourism Management, Florida International University, Biscayne Bay Campus, 3000 Northeast 151 Street, North Miami, LF, 33181, United States;1. Department of Marketing & Tourism Management, College of Business Administration, Capital University of Economics and Business, Beijing, 100070, China;2. Department of Apparel, Events, and Hospitality Management, College of Human Sciences, Iowa State University, Ames, IA, 50010, USA;3. Howard Feiertag Department of Hospitality & Tourism Management, Pamplin College of Business, Virginia Tech, Blacksburg, VA, 24061, USA;1. College of Management, Shenzhen University, Shenzhen, China;2. School of Management, Xiamen University, Xiamen, China;3. Organizational Behavior and Human Resource Management Department, China Europe International Business School (CEIBS), Shanghai, China;4. Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China;5. School of Management, CISME, Zhijiang College, Zhejiang University of Technology, Hangzhou, China;1. Department of Marketing, Events and Tourism, University of Greenwich, London SE10 9LS, United Kingdom;2. School of Hospitality and Tourism, Auckland University of Technology, WH Building, 49 Wellesley Street East, Auckland 1010, New Zealand;1. School of Business Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan Province, 611130, China;2. School of Hospitality Business Management, Carson College of Business, Washington State University, Pullman, WA, 99163, USA;3. Senior Research Fellow, School of Tourism & Hospitality, University of Johannesburg, South Africa
Abstract:Restaurant management requires customer responsiveness to deal with increasingly higher expectations and market competitiveness. This study proposes an approach to simplify the decision-making process of restaurant managers by combining both live social media customer feedback and historical sales data in a sales forecast model (based on TripAdvisor data and the Bass model).Our approach was validated with internal and external (i.e., online reviews) data gathered from six restaurants. The collected data was processed using data analytics for developing a dashboard that provides value for restauranteurs by taking advantage of online reviews and sales forecast. Such dashboard was evaluated by restaurant management experts, which provided positive feedback, highlighting in particular the time saved in the decision-making process.
Keywords:Restaurant management  Business performance  Customer relationship management  Online review  Text mining  Data analytics
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