nach oben

Information Systems Frontiers

Erschienen in:

04.01.2021

Factors Affecting Customer Analytics: Evidence from Three Retail Cases

verfasst von: Anastasia Griva, Cleopatra Bardaki, Katerina Pramatari, Georgios Doukidis

Erschienen in: Information Systems Frontiers | Ausgabe 2/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel How Do Individuals Justify and Rationalize their Criminal Behaviors in Online Romance Fraud?

Nächster Artikel How the Structures Provided by Social Media Enable Collaborative Outcomes: A Study of Service Co-creation in Nonprofits

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J., & Wu, S. (2013). Clustering validation measures. IEEE Transactions on Cybernetics, 43(3), 982–994.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

Titel: Factors Affecting Customer Analytics: Evidence from Three Retail Cases
verfasst von: Anastasia Griva
Cleopatra Bardaki
Katerina Pramatari
Georgios Doukidis
Publikationsdatum: 04.01.2021
Verlag: Springer US
Erschienen in: Information Systems Frontiers / Ausgabe 2/2022
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-020-10098-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2022

Blockchain-Based Agile Supply Chain Framework with IoT

Personality Antecedents and Consequences of Internet Addiction Vis-à-vis Internet Habit: a Theory and an Empirical Investigation

Editorial: Special Issue on “Bright ICT: Security, Privacy and Risk Issues”

The Role of ICT Laws and National Culture in Determining ICT Diffusion and Well-Being: A Cross-Country Examination

Systematic Literature Review of E-Learning Capabilities to Enhance Organizational Learning

Building Consumers’ Trust in Electronic Retail Platforms in the Sub-Saharan Context: an exploratory study on Drivers and Impact on Continuance Intention

Premium Partner