Abstract
The abundance of customer behavioral data alters the design and application of customer analytics systems and approaches. Segmentation is a common customer analytics practice, but researchers highlight that traditional segmentation approaches are not enough. We coin the term “visit segmentation” and devise a visit segmentation approach. When designing or applying a new information system or approach, it is important to consider factors related to the input data, the application context, the users, and all the relevant requirements. Considering the literature, this paper identifies such factors that affect customer analytics approaches and systems. We explore how these factors affect segmentation through applying our segmentation approach to three heterogeneous retailers, e.g., the products’ variety a shopper purchases in each visit seems to be crucial to the segmentation. The more attention data analysts and designers pay to these factors, the more reliable segmentation results they will get and, thus, improved retail decisions are expected.
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References
Aeron, H., Kumar, A., & Moorthy, J. (2012). Data mining framework for customer lifetime value-based segmentation. Journal of Database Marketing and Customer Strategy Management, 19(1), 17–30. https://doi.org/10.1057/dbm.2012.1.
Albadvi, A., & Shahbazi, M. (2009). A hybrid recommendation technique based on product category attributes. Expert Systems with Applications, 36(9), 11480–11488. https://doi.org/10.1016/j.eswa.2009.03.046.
Andriotis, K., Agiomirgianakis, G., & Mihiotis, A. (2008). Measuring tourist satisfaction: A factor-cluster segmentation approach. Journal of Vacation Marketing, 14(3), 221–235. https://doi.org/10.1177/1356766708090584.
Bailey, C., Baines, P. R., Wilson, H., & Clark, M. (2009). Segmentation and customer insight in contemporary services marketing practice: Why grouping customers is no longer enough. Journal of Marketing Management, 25(3–4), 227–252. https://doi.org/10.1362/026725709X429737.
Balan, U. M., & Mathew, S. K. (2020). Personalize, summarize or let them read? A Study on Online Word of Mouth Strategies and Consumer Decision Process. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-09980-9.
Baltas, G., Argouslidis, P. C., & Skarmeas, D. (2010). The role of customer factors in multiple store patronage: A cost-benefit approach. Journal of Retailing, 86(1), 37–50. https://doi.org/10.1016/j.jretai.2010.01.005.
Bell, D. R., & Lattin, J. M. (1998). Shopping behavior and consumer preference for store Price format: Why “large basket” shoppers prefer EDLP. Marketing Science, 17(1), 66–88. https://doi.org/10.1287/mksc.17.1.66.
Bell, D. R., Corsten, D., & Knox, G. (2011). From point of purchase to path to purchase: How Preshopping factors drive unplanned buying. Journal of Marketing, 75(1), 31–45. https://doi.org/10.1509/jmkg.75.1.31.
Bi, Z., Faloutsos, C., & Korn, F. (2001). The “DGX” distribution for mining massive, skewed data. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ‘01, 17–26. https://doi.org/10.1145/502512.502521.
Boone, D. S., & Roehm, M. (2002). Retail segmentation using artificial neural networks. International Journal of Research in Marketing, 19(3), 287–301. https://doi.org/10.1016/S0167-8116(02)00080-0.
Boratto, L., Carta, S., Kaltenbrunner, A., & Manca, M. (2018). Guest editorial: Behavioral-data Mining in Information Systems and the big data era. Information Systems Frontiers, 20(6), 1153–1156. https://doi.org/10.1007/s10796-018-9884-1.
Boztuǧ, Y., & Reutterer, T. (2008). A combined approach for segment-specific market basket analysis. European Journal of Operational Research, 187(1), 294–312. https://doi.org/10.1016/j.ejor.2007.03.001.
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79–95. https://doi.org/10.1016/j.jretai.2016.12.004.
Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63(2014), 67–80. https://doi.org/10.1016/j.dss.2013.08.008.
Chen, M. C., Chiu, A. L., & Chang, H. H. (2005). Mining changes in customer behavior in retail marketing. Expert Systems with Applications, 28(4), 773–781. https://doi.org/10.1016/j.eswa.2004.12.033.
Chen, Y. L., Kuo, M. H., Wu, S. Y., & Tang, K. (2009). Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electronic Commerce Research and Applications, 8(5), 241–251. https://doi.org/10.1016/j.elerap.2009.03.002.
Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management, 19(3), 197–208. https://doi.org/10.1057/dbm.2012.17.
Cheng, C. H., & Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36(2009), 4176–4184. https://doi.org/10.1016/j.eswa.2008.04.003.
Cho, Y. H., & Kim, J. K. (2004). Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26(2), 233–246. https://doi.org/10.1016/S0957-4174(03)00138-6.
Cho, Y. H., Kim, S. H., & Kimb, J. K. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23(2002), 329–342. https://doi.org/10.1016/S0957-4174(02)00052-0.
Cil, I. (2012). Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10), 8611–8625. https://doi.org/10.1016/j.eswa.2012.01.192.
Cliffs, E. (2005). Unilever unveils breakthrough shopper study that shows how Americans shop today and challenges traditional retail beliefs. Business Wire. https://www.businesswire.com/news/home/20050413005523/en/Unilever-Unveils-Breakthrough-Shopper-Study-That-Shows-How-Americans-Shop-Today-And-Challenges-Traditional-Retail-Beliefs.
Delen, D., & Zolbanin, H. M. (2018). The analytics paradigm in business research. Journal of Business Research, 90(2018), 186–195. https://doi.org/10.1016/j.jbusres.2018.05.013.
Demoulin, N. T. M., & Zidda, P. (2009). Drivers of customers’ adoption and adoption timing of a new loyalty card in the grocery retail market. Journal of Retailing, 85(3), 391–405. https://doi.org/10.1016/j.jretai.2009.05.007.
Denning, P. J. (1997). A new social contract for research. Communications of the ACM, 40(2), 132–134. https://doi.org/10.1145/253671.253755.
Dong, Y., & Lian, S. (2012). Automatic and fast temporal segmentation for personalized news consuming. Information Systems Frontiers, 14(3), 517–526. https://doi.org/10.1007/s10796-010-9256-y.
Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117(June), 284–289. https://doi.org/10.1016/j.jbusres.2020.06.008.
Dwivedi, Y. K., Rana, N. P., Slade, E. L., Singh, N., & Kizgin, H. (2020). Editorial introduction: Advances in theory and practice of digital marketing. Journal of Retailing and Consumer Services, 53, 101909. https://doi.org/10.1016/j.jretconser.2019.101909.
ECR Europe. (2011). The Consumer and Shopper Journey Framework. https://www.ecr.digital/wp_contents/uploads/2016/09/The_Consumer_and_Shopper_Journey_Framework.pdf.
Germann, F., Lilien, G. L., Fiedler, L., & Kraus, M. (2014). Do retailers benefit from deploying customer analytics? Journal of Retailing, 90(4), 587–593. https://doi.org/10.1016/j.jretai.2014.08.002.
Gijsbrechts, E., Campo, K., & Nisol, P. (2008). Beyond promotion-based store switching: Antecedents and patterns of systematic multiple-store shopping. International Journal of Research in Marketing, 25(1), 5–21. https://doi.org/10.1016/j.ijresmar.2007.07.001.
Goes, P. B. (2014). Big data and IS research. MIS Quarterly, 38(3), 3–8.
Griva, A. (2019). Data-driven innovation in shopper marketing: A business analytics approach for visit segmentation in the retail industry. Department of Management Science & Technology, Athens University of Economics and Business. Retrieved from https://www.didaktorika.gr/eadd/handle/10442/46349
Griva, A., Bardaki, C., Sarantopoulos, P., & Papakiriakopoulos, D. (2014). A data mining-based framework to identify shopping missions. In: L. Mola, A. Carugati, A. Kokkinaki, & N. Pouloudi (Eds.), MCIS 2014 proceedings. Verona, Italy. http://aisel.aisnet.org/mcis2014/20
Griva, A., Bardaki, C., Pramatari, K., & Papakiriakopoulos, D. (2018). Retail business analytics: Customer visit segmentation using market basket data. Expert Systems with Applications, 100(2018), 1–16. https://doi.org/10.1016/j.eswa.2018.01.029.
Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139–155. https://doi.org/10.1177/1094670506293810.
Han, S., Ye, Y., Fu, X., & Chen, Z. (2014). Category role aided market segmentation approach to convenience store chain category management. Decision Support Systems, 57(1), 296–308. https://doi.org/10.1016/j.dss.2013.09.017.
Hevner, A. R., & Chatterjee, S. (2015). Design research in information systems: Theory and practice. In J. Vom Brocke (Ed.), Association for Information Systems (AIS), Reference Syllabi Association (pp. 1–9). https://doi.org/10.1145/2992154.2996777.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quartely, 28(1), 75–105.
Homburg, C., Steiner, V. V., & Totzek, D. (2009). Managing dynamics in a customer portfolio. Journal of Marketing, 73(5), 70–89. https://doi.org/10.1509/jmkg.73.5.70.
Hong, T., & Kim, E. (2012). Segmenting customers in online stores based on factors that affect the customer’s intention to purchase. Expert Systems with Applications, 39(2), 2127–2131. https://doi.org/10.1016/j.eswa.2011.07.114.
Hung, L. P. (2005). A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Systems with Applications, 29(2), 383–392. https://doi.org/10.1016/j.eswa.2005.04.016.
Ju, J., Liu, L., & Feng, Y. (2019). Design of an O2O citizen participation ecosystem for sustainable governance. Information Systems Frontiers, 21, 605–620. https://doi.org/10.1007/s10796-019-09910-4.
Kahn, B. E., & Schmittlein, D. C. (1992). The relationship between purchases made on promotion and shopping trip behavior. Journal of Retailing, 68(3), 294–315.
Kar, A. K. (2020). What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-10045-0.
Karafili, E., Spanaki, K., & Lupu, E. C. (2018). An argumentation reasoning approach for data processing. Computers in Industry, 94, 52–61. https://doi.org/10.1016/j.compind.2017.09.002.
Karimi, S. (2019). Cross-visiting behaviour of online consumers across retailers’ and comparison sites, a macro-study. Information Systems Frontiers. https://doi.org/10.1007/s10796-019-09967-1.
Karumur, R. P., Nguyen, T. T., & Konstan, J. A. (2018). Personality, user preferences and behavior in recommender systems. Information Systems Frontiers, 20(6), 1241–1265. https://doi.org/10.1007/s10796-017-9800-0.
Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3(2011), 57–63. https://doi.org/10.1016/j.procs.2010.12.011.
Kim, J. K., Cho, Y. H., Kim, W. J., Kim, J. R., & Suh, J. H. (2002). A personalized recommendation procedure for internet shopping support. Electronic Commerce Research and Applications, 1(2002), 301–313. https://doi.org/10.1016/S1567-4223(02)00022-4.
Klein, A., & Schmitz, G. (2016). Main-format dominance in consumers’ FMCG cross-format shopping patterns. Journal of Retailing and Consumer Services, 30(2016), 105–115. https://doi.org/10.1016/J.JRETCONSER.2015.11.011.
Kohavi, R., Mason, L., Parekh, R., & Zheng, Z. (2004). Lessons and challenges from mining retail e-commerce data. Machine Learning, 57, 83–113. https://doi.org/10.1023/B:MACH.0000035473.11134.83.
Konuş, U., Verhoef, P. C., & Neslin, S. A. (2008). Multichannel shopper segments and their covariates. Journal of Retailing, 84(4), 398–413. https://doi.org/10.1016/j.jretai.2008.09.002.
Liao, S. H., Chen, Y. J., & Hsieh, H. H. (2011). Mining customer knowledge for direct selling and marketing. Expert Systems with Applications, 38(5), 6059–6069. https://doi.org/10.1016/j.eswa.2010.11.007.
Likas, A., Vlassis, N., Verbeek, J., & J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461. https://doi.org/10.1016/S0031-3203(02)00060-2.
Lin, X., Featherman, M., Brooks, S. L., & Hajli, N. (2019). Exploring gender differences in online consumer purchase decision making: An online product presentation perspective. Information Systems Frontiers, 21(5), 1187–1201. https://doi.org/10.1007/s10796-018-9831-1.
Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J., & Wu, S. (2013). Clustering validation measures. IEEE Transactions on Cybernetics, 43(3), 982–994.
Liu, J., Liao, X., Huang, W., & Liao, X. (2019). Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision. Omega (United Kingdom), 83, 1–13. https://doi.org/10.1016/j.omega.2018.01.008.
Lockshin, L. S., Spawton, A. L., & Macintosh, G. (1997). Using product, brand and purchasing involvement for retail segmentation. Journal of Retailing and Consumer Services, 4(3), 171–183. https://doi.org/10.1016/S0969-6989(96)00048-3.
Mansingh, G., Rao, L., Osei-Bryson, K. M., & Mills, A. (2013). Profiling internet banking users: A knowledge discovery in data mining process model based approach. Information Systems Frontiers, 17(1), 193–215. https://doi.org/10.1007/s10796-012-9397-2.
March, S. T., & Storey, V. C. (2008). Design science in the information systems discipline: An introduction to the special issue on design science research. MIS Quarterly, 32(4), 725–730.
Markus, M. L., Majchrzak, A., & Gasser, L. (2002). A design theory for systems that support emergent knowledge processes. MIS Quartely, 26(3), 179–212. https://doi.org/10.2307/4132330.
McKinsey Global Institute. (2011). Big data : The next frontier for innovation, competition, and productivity.
Miguéis, V. L., Camanho, A. S., & Falcão e Cunha, J. (2012). Customer data mining for lifestyle segmentation. Expert Systems with Applications, 39(10), 9359–9366. https://doi.org/10.1016/j.eswa.2012.02.133.
Mikalef, P., Pappas, I. O., Krogstie, J., & Pavlou, P. A. (2020). Big data and business analytics: A research agenda for realizing business value. Information and Management, 57(1), 103237. https://doi.org/10.1016/j.im.2019.103237.
Motiwalla, L., Deokar, A. V., Sarnikar, S., & Dimoka, A. (2019). Leveraging data analytics for behavioral research. Information Systems Frontiers, 21(4), 735–742. https://doi.org/10.1007/s10796-019-09928-8.
Muhammad, S. S., Dey, B. L., & Weerakkody, V. (2018). Analysis of factors that influence customers’ willingness to leave big data digital footprints on social media: A systematic review of literature. Information Systems Frontiers, 20(3), 559–576. https://doi.org/10.1007/s10796-017-9802-y.
Nakano, S., & Kondo, F. N. (2018). Customer segmentation with purchase channels and media touchpoints using single source panel data. Journal of Retailing and Consumer Services, 41(2018), 142–152. https://doi.org/10.1016/j.jretconser.2017.11.012.
Noble, S. M., Lee, K. B., Zaretzki, R., & Autry, C. (2017). Coupon clipping by impoverished consumers: Linking demographics, basket size, and coupon redemption rates. International Journal of Research in Marketing, 34(2), 553–571. https://doi.org/10.1016/j.ijresmar.2016.08.010.
Ozarslan, S., & Eren, P. E. (2018). MobileCDP: A mobile framework for the consumer decision process. Information Systems Frontiers, 20(4), 803–824. https://doi.org/10.1007/s10796-015-9601-2.
Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16(3), 479–491. https://doi.org/10.1007/s10257-018-0377-z.
Park, C. H., Park, Y.-H., & Schweidel, D. A. (2014). A multi-category customer base analysis. International Journal of Research in Marketing, 31(3), 266–279. https://doi.org/10.1016/j.ijresmar.2013.12.003.
Reutterer, T., Mild, A., Natter, M., & Taudes, A. (2006). A dynamic segmentation approach for targeting and customizing direct marketing campaigns. Journal of Interactive Marketing, 20(3–4), 43–57. https://doi.org/10.1002/dir.
Rust, R. T., & Huang, M.-H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206–221. https://doi.org/10.1287/mksc.2013.0836.
Sarantopoulos, P., Theotokis, A., Pramatari, K., & Doukidis, G. (2016). Shopping missions: An analytical method for the identification of shopper need states. Journal of Business Research, 69(3), 1043–1052. https://doi.org/10.1016/j.jbusres.2015.08.017.
Schuff, D., Corral, K., St. Louis, R. D., & Schymik, G. (2018). Enabling self-service BI: A methodology and a case study for a model management warehouse. Information Systems Frontiers, 20(2), 275–288. https://doi.org/10.1007/s10796-016-9722-2.
Simon, H. A. (1996). The sciences of the artificial (3rd ed.). MIT Press. https://doi.org/10.1016/S0898-1221(97)82941-0.
Skallerud, K., Korneliussen, T., & Olsen, S. O. (2009). An examination of consumers’ cross-shopping behaviour. Journal of Retailing and Consumer Services, 16(3), 181–189. https://doi.org/10.1016/j.jretconser.2008.11.012.
Sorensen, H., Bogomolova, S., Anderson, K., Trinh, G., Sharp, A., Kennedy, R., Page, B., & Wright, M. (2017). Fundamental patterns of in-store shopper behavior. Journal of Retailing and Consumer Services, 37(2017), 182–194. https://doi.org/10.1016/j.jretconser.2017.02.003.
Srikant, R., & Agrawal, R. (1995). Mining generalized association rules. In VLDB ‘95 Proceedings of the 21th International Conference on Very Large Data Bases (pp. 407–419).
Thach, L., & Olsen, J. (2015). Profiling the high frequency wine consumer by price segmentation in the US market. Wine Economics and Policy, 4(1), 53–59. https://doi.org/10.1016/j.wep.2015.04.001.
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society, Series B: Statistical Methodology, 63(2), 411–423. https://doi.org/10.1111/1467-9868.00293.
Trivedi, N., Asamoah, D. A., & Doran, D. (2018). Keep the conversations going: Engagement-based customer segmentation on online social service platforms. Information Systems Frontiers, 20(2), 239–257. https://doi.org/10.1007/s10796-016-9719-x.
Videla-Cavieres, I. F., & Ríos, S. a. (2014). Extending market basket analysis with graph mining techniques: A real case. Expert Systems with Applications, 41(2014), 1928–1936. https://doi.org/10.1016/j.eswa.2013.08.088.
Walls, J. G., Widmeyer, G. R., & El Sawy, O. A. (1992). Building an information system design theory for vigilant EIS. Information Systems Research, 3, 36–59. https://doi.org/10.1287/isre.3.1.36.
Walters, R. G., & Jamil, M. (2003). Exploring the relationships between shopping trip type, purchases of products on promotion, and shopping basket profit. Journal of Business Research, 56(1), 17–29. https://doi.org/10.1016/S0148-2963(01)00201-6.
Wang, M., Li, X., & Chau, P. Y. K. (2020). Leveraging image-processing techniques for empirical Research: Feasibility and Reliability in Online Shopping Context. Information Systems Frontiers. https://doi.org/10.1007/s10796-020-09981-8.
Widdowson, N. (2005). Shopper missions. Unilever Partners for Growth. https://partnersforgrowth.unilever.com/business-advice/shopper-missions/
Wilkie, W. L. (1978). Issues and advances in segmentation research. Journal of Marketing Research, 15(3), 317–337. https://doi.org/10.2307/3150973.
Wolf, M., & McQuitty, S. (2011). Understanding the do-it-yourself consumer: DIY motivations and outcomes. AMS Review, 1, 154–170. https://doi.org/10.1007/s13162-011-0021-2.
Wood, S. (2018). The evolution of shopper behaviour. Ipsos. https://www.ipsos.com/sites/default/files/ct/publication/documents/2018-05/the_evolution_of_shopper_behaviour.pdf
Xiao, L., Mi, C., Zhang, Y., & Ma, J. (2019). Examining consumers’ behavioral intention in O2O commerce from a relational perspective: An exploratory study. Information Systems Frontiers, 21(5), 1045–1068. https://doi.org/10.1007/s10796-017-9815-6.
Yao, Z., Sarlin, P., Eklund, T., & Back, B. (2012). Temporal customer segmentation using the self-organizing time map. In Proceedings of the 16th International Conference on Information Visualisation (pp. 234–240). https://doi.org/10.1109/IV.2012.47.
Zeleny, J., Burget, R., & Zendulka, J. (2017). Box clustering segmentation: A new method for vision-based web page preprocessing. Information Processing and Management, 53(3), 735–750. https://doi.org/10.1016/j.ipm.2017.02.002.
Zhou, J., Zhai, L., & Pantelous, A. A. (2020). Market segmentation using high-dimensional sparse consumers data. Expert Systems with Applications, 145(2020), 1–17. https://doi.org/10.1016/j.eswa.2019.113136.
Acknowledgements
We would like to thank Wharton Customer Analytics Initiative (https://wcai.wharton.upenn.edu), for providing the dataset regarding the Fortune 500 Specialty retailer. This research has been supported, in part, by the European Research Council under the H2020 project Transforming Transport https://transformingtransport.eu/ (Under Grand agreement no: 731932), and by the Science Foundation Ireland grant 13/RC/2094.
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Griva, A., Bardaki, C., Pramatari, K. et al. Factors Affecting Customer Analytics: Evidence from Three Retail Cases. Inf Syst Front 24, 493–516 (2022). https://doi.org/10.1007/s10796-020-10098-1
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DOI: https://doi.org/10.1007/s10796-020-10098-1