Validating Bluetooth logging as metric for shopper behaviour studies |
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| Affiliation: | 1. Daniels College of Business, 337 Joy Burns Center, 2044 E. Evans Avenue, University of Denver, Denver, CO 80208, USA;2. David Nazarian College of Business and Economics, California State University, Northridge, CA 91330-8245, USA;3. School of Business, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA;4. School of Hotel, Restaurant & Tourism Management, University of South Carolina, Columbia, SC 29208, USA;1. Department of Economics, University of Alberta Edmonton, Alberta, Canada T6G 2H4;2. Department of Economics, University of Waterloo, Ontario, Canada N2L 3G1;1. Department of Advertising, Public Relations, and Retailing, College of Communication, Arts and Sciences, Michigan State University, USA;2. College of Business Administration Ajou University, San 5 Wonchun-Dong Youngton-Gu, Suwon 443‐749, Republic of Korea;1. Center for Decision Neuroscience, Department of Marketing, Copenhagen Business School, Solbjerg Plads 3, 2000 Frederiksberg, Denmark;2. Department of Operations Management, Copenhagen Business School, Solbjerg Plads 3, 2000 Frederiksberg, Denmark;3. Department of Marketing, Copenhagen Business School, Solbjerg Plads 3, 2000 Frederiksberg, Denmark;1. School of Hotel & Tourism Management, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;2. School of Hotel and Restaurant Administration, Oklahoma State University, Stillwater, OK 74078, USA;3. School of Business, Sun Yat-Sen University, Guangzhou, China |
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| Abstract: | The ability to track shoppers as they move through retail environments using signals emitted by their communication devices kindles the interest of practitioners and researchers. This data collection method is cheap and has the ability to supply big data for shopper insights. However, this non-probabilistic sampling method can possibly under- or over-represent certain groups of the shopper population. This study assesses the validity of the data describing the length of shopping trips and representativeness of the sample of shoppers carrying Bluetooth-enabled devices. The authors track unique Bluetooth logs in-store and compare to simultaneously collected data from a manual, systematic sample of 324 shoppers observed and interviewed in the same supermarket. A comparison of the results obtained from the two samples (auto-logging and manual systematic) drawn from the same population indicates automated Bluetooth tracking produces very similar (r=.92, p<.001) trip lengths to that observed manually. Basket size, spend and occupation of Bluetooth trackable shoppers are similar to those with no Bluetooth-enabled devices. These findings present compelling evidence that the Bluetooth auto-logging method holds great potential for retail practice and research. An expected under-representation of the oldest demographic (66 y.o. and over) in the Bluetooth discoverable sample calls for complementary methods of data collection to minimise representation bias in real-time tracking technologies for shopper research. The benefits of using auto-logging data describing shopping trip length for retail practitioners and researchers are discussed. |
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| Keywords: | Bluetooth tracking Shopper behaviour Sampling Retailing |
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