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Clustering Consumers and Cluster-Specific Behavioural Models

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Abstract

Social media has almost become ubiquitous in everyday communications and interactions between customers and brands. A novel clustering algorithm, that has shown high scalability in previous applications, is applied to analyse and segment an online consumer behaviour dataset. It is based on the computation of a Minimum-Spanning-Tree and a k-Nearest Neighbour graph ( MST-kNN). Cluster-specific consumer behaviours relating to customer engagement are predicted using symbolic regression analysis which, in a commercial setting, would provide the basis for personalized marketing strategies. Five major clusters were found in the dataset of 371 respondents who answered questions from theoretical marketing constructs related to online consumer behaviours. They are labelled as follows: ‘Brand Rationalists’, ‘Passive Socializers’, ‘Immersers’, ‘Hedonic Sharers’ and ‘ Active Participators’. For each of these clusters, a linear model of customer engagement was predicted using symbolic regression analysis. These models inform possible personalized marketing strategies after proper segmentation of the customers based on their online consumer behaviour, rather than simple demographic characteristics.

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References

  1. M Aljukhadar and S Senecal. Segmenting the online consumer market. Marketing Intelligence & Planning, 29(4):421–435, 2011.

    Article  Google Scholar 

  2. AhmedShamsul Arefin, Mario Inostroza-Ponta, Luke Mathieson, Regina Berretta, and Pablo Moscato. Clustering Nodes in Large-Scale Biological Networks Using External Memory Algorithms, volume 7017 of Lecture Notes in Computer Science, book section 36, pages 375–386. Springer Berlin Heidelberg, 2011.

    Google Scholar 

  3. B Aviad and G Roy. A decision support method, based on bounded rationality concepts, to reveal feature saliency in clustering problems. Decision Support Systems, 54(1):292–303, 2012.

    Article  Google Scholar 

  4. Amit Bhatnagar and Sanjoy Ghose. A latent class segmentation analysis of e-shoppers. Journal of Business Research, 57(7):758–767, 2004.

    Article  Google Scholar 

  5. C Blattberg, Robert, Byung-Do Kim, and A Neslin, Scott. Database management: Analyzing and managing customers, 2008.

    Google Scholar 

  6. Petter Bae Brandtzæg, Jan Heim, and Amela Karahasanović. Understanding the new digital divide—a typology of internet users in Europe. International Journal of Human-Computer Studies, 69(3):123–138, 2011.

    Article  Google Scholar 

  7. Colin Campbell, Carla Ferraro, and Sean Sands. Segmenting consumer reactions to social network marketing. European Journal of Marketing, 48(3/4):432–452, 2014.

    Article  Google Scholar 

  8. Kit Yan Chan, C. K. Kwong, and B. Q. Hu. Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods. Applied Soft Computing, 12(4):1371–1378, 2012.

    Article  Google Scholar 

  9. Anil Chaturvedi, J. Douglas Carroll, Paul E. Green, and John A. Rotondo. A feature-based approach to market segmentation via overlapping k-centroids clustering. Journal of Marketing Research (JMR), 34(3):370–377, 1997.

    Article  Google Scholar 

  10. S. C Chu and Y Kim. Determinants of consumer engagement in electronic word of mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1):47–75, 2011.

    Article  Google Scholar 

  11. Natalie Jane de Vries and Jamie Carlson. Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. J Brand Manag, 21(6):495–515, 2014.

    Article  Google Scholar 

  12. Natalie Jane de Vries, Jamie Carlson, and Pablo Moscato. A data-driven approach to reverse engineering customer engagement models: Towards functional constructs. PLoS ONE, 9(7):e102768, 2014.

    Article  Google Scholar 

  13. Wayne S. DeSarbo, Kamel Jedidi, and Indrajit Sinha. Customer value analysis in a heterogeneous market. Strategic Management Journal, 22(9):845–857, 2001.

    Article  Google Scholar 

  14. Arne Floh, Alexander Zauner, Monika Koller, and Thomas Rusch. Customer segmentation using unobserved heterogeneity in the perceived-value–loyalty–intentions link. Journal of Business Research, 67(5):974–982, 2014.

    Article  Google Scholar 

  15. Mary Foster, Bettina West, and Anthony Francescucci. Exploring social media user segmentation and online brand profiles. Journal of Brand Management, 19(1):4–17, 2011.

    Article  Google Scholar 

  16. O Giustolisi and D. A Savic. A symbolic data-driven technique based on evolutionary polynomial regression. Journal of Hydroinformatics, 8(3):207–222, 2006.

    Article  Google Scholar 

  17. Paul E. Green, Frank J. Carmone, and David P. Wachspress. Consumer segmentation via latent class analysis. Journal of Consumer Research, 3(3):170–174, 1976.

    Article  Google Scholar 

  18. J. L Gross and J Yellen. Handbook of Graph Theory. Discrete Mathematics and its Applications. CRC Press LLC, Boca Raton, Florida, 2004.

    Google Scholar 

  19. Chris Hand, Francesca Dall’Olmo Riley, Patricia Harris, Jaywant Singh, and Ruth Rettie. Online grocery shopping: the influence of situational factors. European Journal of Marketing, 43(9/10):1205–1219, 2009.

    Article  Google Scholar 

  20. Thorsten Hennig-Thurau, Edward C. Malthouse, Christian Friege, Sonja Gensler, Lara Lobschat, Arvind Rangaswamy, and Bernd Skiera. The impact of new media on customer relationships. Journal of Service Research, 13(3):311–330, 2010.

    Article  Google Scholar 

  21. J. L Herlocker, J. A Konstan, A Borchers, and J Riedly. An algorithmic framework for performing collaborative filtering. In Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230–237. ACM New York, 1999.

    Google Scholar 

  22. Mario Inostroza-Ponta, Regina Berretta, Alexandre Mendes, and Pablo Moscato. An automatic graph layout procedure to visualize correlated data, pages 179–188. Springer, 2006.

    Google Scholar 

  23. Mario Inostroza-Ponta, Regina Berretta, and Pablo Moscato. QAPgrid: A two level QAP-based approach for large-scale data analysis and visualization. PLOS One, 6(1):e14468, 2011.

    Article  Google Scholar 

  24. Mario Inostroza-Ponta, Alexandre Mendes, Regina Berretta, and Pablo Moscato. An integrated QAP-based approach to visualize patterns of gene expression similarity, pages 156–167. Springer, 2007.

    Google Scholar 

  25. Benedikt Jahn and Werner Kunz. How to transform consumers into fans of your brand. Journal of Service Management, 23(2):344–361, 2012.

    Article  Google Scholar 

  26. Anil K. Jain. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8):651–666, 2010.

    Article  Google Scholar 

  27. T. D Klastorin. Assessing cluster analysis results. Journal of Marketing Research (JMR), 20(1):92–98, 1983.

    Article  Google Scholar 

  28. Paul Legris, John Ingham, and Pierre Collerette. Why do people use information technology? a critical review of the technology acceptance model. Information & Management, 40(3):191–204, 2003.

    Article  Google Scholar 

  29. E. C. Malthouse, M. Haenlein, B. Skiera, E. Wege, and M. Zhang. Managing customer relationships in the social media era: Introducing the social CRM house. Journal of Interactive Marketing, 27(4):270–280, 2013.

    Article  Google Scholar 

  30. R Mancha, M. T Leung, J Clark, and M Sun. Finite mixture partial least squares for segmentation and behavioral characterization of auction bidders. Decision Support Systems, 57(1):200–211, 2014.

    Article  Google Scholar 

  31. J Marsden, D Budden, H Craig, and P Moscato. Language individuation and marker words: Shakespeare and his Maxwell’s demon. PLOS One, 8(6):1–12, 2013.

    Article  Google Scholar 

  32. Heloisa Helena Milioli, Renato Vimieiro, Carlos Riveros, Inna Tishchenko, Regina Berretta, and Pablo Moscato. The discovery of novel biomarkers improves breast cancer intrinsic subtype prediction and reconciles the labels in the METABRIC data set. PLOS ONE, 10:e0129711, 2015.

    Article  Google Scholar 

  33. Leila M. Naeni, Natalie Jane de Vries, Rodrigo Reis, Ahmed Shamsul Arefin, Regina Berretta, and Pablo Moscato. Identifying communities of trust and confidence in the charity and not-for-profit sector: a memetic algorithm approach. In The 7 th IEEE International Conference on Social Computing and Networking (Socialcom). IEEE, 2014.

    Google Scholar 

  34. A O’Cass and J Carlson. Examining the effects of website-induced flow in professional sporting team websites. Internet Research, 20(2):115–134, 2010.

    Article  Google Scholar 

  35. H Ouwersloot and G Odekerken-Schröder. Who’s who in brand communities – and why? European Journal of Marketing, 42(5/6):571–585, 2008.

    Article  Google Scholar 

  36. K. Y Peters, A. M Chen, B. O Kaplan, and K Pauwels. Social media metrics—a framework and guidelines for managing social media. Journal of Interactive Marketing, 27(4):281–298, 2013.

    Article  Google Scholar 

  37. M Schmidt and H Lipson. Eureqa, 2013.

    Google Scholar 

  38. G. F Smits and M Kotanchek. Pareto-front exploitation in symbolic regression, book section 17, pages 283–299. Springer, US, 2005.

    Google Scholar 

  39. Jenny van Doorn, Katherine N. Lemon, Vikas Mittal, Stephan Nass, Doreén Pick, Peter Pirner, and Peter C. Verhoef. Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3):253–266, 2010.

    Article  Google Scholar 

  40. Viswanath Venkatesh and Fred D. Davis. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2):186–204, 2000.

    Article  Google Scholar 

  41. Elaine Wallace, Isabel Buil, Leslie de Chernatony, and Michael Hogan. Who “likes” you … and why? a typology of Facebook fans. Journal of Advertising Research, 54(1):92–109, 2014.

    Article  Google Scholar 

  42. Michel Wedel and Wagner A. Kamakura. Market Segmentation: Conceptual and Methodological Foundations. International Series in Quantitative Marketing. Kluwer Academic Publishers, Norwell, Massachusetts, USA, 2 edition, 2000.

    Google Scholar 

  43. Jierui Xie, Stephen Kelley, and Boleslaw K. Szymanski. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Comput. Surv., 45(4):1–35, 2013.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Dr. Ahmed Shamsul Arefin for his help in providing the clustering result used in this study. Pablo Moscato acknowledges previous support from the Australian Research Council Future Fellowship FT120100060 and Australian Research Council Discovery Projects DP120102576 and DP140104183.

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Authors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries & Pablo Moscato

  2. Newcastle University Business School, The University of Newcastle, Callaghan, NSW, Australia

    Jamie Carlson

Authors
  1. Natalie Jane de Vries
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  2. Jamie Carlson
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  3. Pablo Moscato
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Correspondence to Natalie Jane de Vries .

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Editors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Pablo Moscato

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries

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de Vries, N.J., Carlson, J., Moscato, P. (2019). Clustering Consumers and Cluster-Specific Behavioural Models. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-06222-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06221-7

  • Online ISBN: 978-3-030-06222-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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