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The provenance of loyalty card data for urban and retail analytics

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  • Rains, Tim
  • Longley, Paul

Abstract

The deployment of loyalty card and other consumer data in geographic research brings opportunities to explore and understand patterns of purchasing behaviour in unprecedented detail. However, valid generalisation requires thorough evaluation of their potential bias. We argue that, in competitive markets where consumers can choose to shop across competing retailers, loyalty card data from just one of these may not represent a ‘complete’ view of all purchases, and that this ‘completeness’ must be controlled for when assessing bias. To this end, we undertake a UK wide analysis of loyalty card data assembled by a major UK grocery retailer and provide guidelines for their effective deployment in the domains of urban and retail analysis. We assess, for the first time, the ‘completeness’ of circa 500 million customer transactions recorded by a major customer loyalty programme in representing the overall purchasing patterns of circa 16 million consumers across the entire UK, and develop a method by which to do this. Moreover, no operator has complete national store coverage, and so we suggest ways of accommodating this when conducting analysis using loyalty card data. We illustrate the importance of these issues before providing recommendations for the wider use of consumer loyalty card data.

Suggested Citation

  • Rains, Tim & Longley, Paul, 2021. "The provenance of loyalty card data for urban and retail analytics," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
  • Handle: RePEc:eee:joreco:v:63:y:2021:i:c:s0969698921002162
    DOI: 10.1016/j.jretconser.2021.102650
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    References listed on IDEAS

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    1. Cortiñas, Mónica & Elorz, Margarita & Múgica, José Miguel, 2008. "The use of loyalty-cards databases: Differences in regular price and discount sensitivity in the brand choice decision between card and non-card holders," Journal of Retailing and Consumer Services, Elsevier, vol. 15(1), pages 52-62.
    2. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    3. Ho, Hillbun & Tien, Keng-Ming (Terence) & Wu, Anne & Singh, Sonika, 2021. "A sequence analysis approach to segmenting credit card customers," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    4. Birkin, Mark, 2019. "Spatial data analytics of mobility with consumer data," Journal of Transport Geography, Elsevier, vol. 76(C), pages 245-253.
    5. Frasquet, Marta & Ieva, Marco & Ziliani, Cristina, 2021. "Online channel adoption in supermarket retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    6. Treadgold, Alan & Reynolds, Jonathan, 2016. "Navigating the New Retail Landscape: A Guide for Business Leaders," OUP Catalogue, Oxford University Press, number 9780198745754.
    7. Felgate, Melanie & Fearne, Andrew, 2012. "Using Supermarket Loyalty Card Data to Inform Better Promotional Strategies," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144966, International European Forum on System Dynamics and Innovation in Food Networks.
    8. Neil Wrigley & Steve Wood & Dionysia Lambiri & Michelle Lowe, 2019. "Corporate convenience store development effects in small towns: Convenience culture during economic and digital storms," Environment and Planning A, , vol. 51(1), pages 112-132, February.
    9. David Martin & Samantha Cockings & Samuel Leung, 2015. "Developing a Flexible Framework for Spatiotemporal Population Modeling," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(4), pages 754-772, July.
    10. Melis, Kristina & Campo, Katia & Lamey, Lien & Breugelmans, Els, 2016. "A Bigger Slice of the Multichannel Grocery Pie: When Does Consumers’ Online Channel Use Expand Retailers’ Share of Wallet?," Journal of Retailing, Elsevier, vol. 92(3), pages 268-286.
    11. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    12. Gijsbrechts, Els & Campo, Katia & Nisol, Patricia, 2008. "Beyond promotion-based store switching: Antecedents and patterns of systematic multiple-store shopping," International Journal of Research in Marketing, Elsevier, vol. 25(1), pages 5-21.
    13. Sarantopoulos, Panagiotis & Theotokis, Aristeidis & Pramatari, Katerina & Doukidis, Georgios, 2016. "Shopping missions: An analytical method for the identification of shopper need states," Journal of Business Research, Elsevier, vol. 69(3), pages 1043-1052.
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