International Journal on Customer Relations

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Customer Satisfaction and Product Clustering for Gen Next Customers- A Multivariate and Simulation Study


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1 Department of Commerce, Sri Sathya Sai Institute of Higher Learning, Andhra Pradesh, India
     

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Developing marketing strategies for serving gen next customers has become vital and complex in the current times. This paper studies 'product clustering' as a marketing strategy to improve customer satisfaction of gen next customers. The relationship between product clustering and customer satisfaction is studied using multivariate statistical tools. Further, using cluster analysis techniques, customer satisfaction has been used as a criterion to develop product clusters. The complexity of product clustering has also been highlighted by conducting a Monte Carlo simulation. The paper shows that appropriate product clustering is indeed crucial in serving gen next customers better.

Keywords

Product Clustering, Gen Next Customers, Multivariate Analysis, Simulation.
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  • Bhatnagar, A. (2004). Product assortment choice for independent Web-based call service centers. Journal of Services Marketing, 18(3), 224-234.

  • Breur, T. (2007). How to evaluate campaign response-The relative contribution of data mining models and marketing execution. Journal of Targeting, Measurement and Analysis for marketing, 15(2), 103-112.

  • Chang, S. E., Changchien, S., & Huang, R. H. (2006). Assessing users' product-specific knowledge for personalization in electronic commerce. Expert systems with applications, 30(4), 682-693.

  • Duffy, J., & Block, E. (2007). Designing for the Times. Design Management Review, 18(1), 37-43.

  • Flavian, C., & Gurrea, R. (2006).The role of readers' motivations in the choice of digital versus traditional newspapers. Journal of Targeting, Measurement and Analysis for Marketing, 14(4), 325-335.

  • Guo, S., Wang, M., & Leskovec, J. (2011, June). The role of social networks in online shopping: information passing, price of trust, and consumer choice. In Proceedings of the 12th ACM conference on Electronic commerce (pp. 157- 166).ACM.

  • Heim, G. R., & Sinha, K. K. (2002). Service process configurations in electronic retailing: A taxonomic analysis of electronic food retailers. Production and Operations Management, 11(1), 54-74.

  • Hulten, B. (2007). Customer segmentation: the concepts of trust, commitment and relationships. Journal of Targeting, Measurement and Analysis for Marketing, 15(4), 256-269.

  • Kaciak, E., & Cullen, C. W. (2006). Analysis of means-end chain data in marketing research. Journal of Targeting, Measurement and Analysis for Marketing, 15(1), 12-20.

  • Kotler, P., Keller, K., Koshy, A., & J ha, M. (2009). Marketing Management a South Asian perspective, Dorling Kindersley (India) Pvt. Ltd., New Delhi, 13.

  • Lin, C. T., Hong, W. C., Chen, Y. F., & Dong, Y. (2010). Application of salesman-like recommendation system in 3G mobile phone online shopping decision support. Expert Systems with Applications, 37(12), 8065-8078.

  • Lonial, S., Menezes, D., & Zaim, S. (2000). Identifying purchase driving attributes and market segments for PCs using conjoint and cluster analysis. Journal of economic and social research, 2(2), 19-37.

  • Maciag, T., Hepting, D. H., Hilderman, R. J., & Slezak, D. (2007).Evaluation of a dominance-based rough set approach to interface design.In Frontiers in the Convergence of Bioscience and Information Technologies, 2007. FBIT 2007 (pp. 409-416). IEEE.

  • Magazine, M. J., & Polak, G. G. (1998). Optimal machine setup and product clustering for printed circuit board manufacturing. Raj So in College of Business Working Paper, 02-006.

  • Martin-Ruiz, D., & Rondan-Cataluna, F. J. (2008). The nature and consequences of price unfairness in services: a comparison to tangible goods. International Journal of Service Industry Management, 19(3), 325-352.

  • Muk, A. (2007). Cultural influences on adoption of SMS advertising: A study of American and Taiwanese consumers. Journal of Targeting, Measurement and Analysis for Marketing, 16(1), 39-47.

  • Pepall, L. (1990). Market demand and product clustering. The Economic Journal, 100(399), 195-205.

  • Rust, R. T., & Verhoef, P. C. (2005). Optimizing the marketing interventions mix in intermediate-term CRM. Marketing Science, 24(3), 477-489.

  • Sharif, K. (2012). Impact of category management practices on customer satisfaction-Findings from Kuwaiti grocery retail sector. Journal of Targeting, Measurement and Analysis for Marketing, 20(1), 17-33.

  • Watanapa, B., & Watanapa, S. (2008, March). A fuzzy approach for personalized product clustering with flexible discriminating power.In Advanced Information Networking and Applications-Workshops, 2008.AINAW 2008. 22nd International Conference on IEEE, 567-572.

  • Yan-Hong, Q., & Guang-Xing, W. (2009, April).On mapping approach of CN to FR in product family improvement. In Measuring Technology and Mechatronics Automation, 2009.ICMTMA'09. International Conference on IEEE, 3, 794-797).

  • Zhaofeng, C. (2003). On hi-tech product clustering mode. Nankai Business Review, 5, 014.

  • Ziliani, C. (2006). Target promotions: how to measure and improve promotional effectiveness through individual customer information. Journal of Targeting, Measurement and Analysis for Marketing, 14(3), 249-259.


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  • Customer Satisfaction and Product Clustering for Gen Next Customers- A Multivariate and Simulation Study

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Authors

N. Sivakumar
Department of Commerce, Sri Sathya Sai Institute of Higher Learning, Andhra Pradesh, India

Abstract


Developing marketing strategies for serving gen next customers has become vital and complex in the current times. This paper studies 'product clustering' as a marketing strategy to improve customer satisfaction of gen next customers. The relationship between product clustering and customer satisfaction is studied using multivariate statistical tools. Further, using cluster analysis techniques, customer satisfaction has been used as a criterion to develop product clusters. The complexity of product clustering has also been highlighted by conducting a Monte Carlo simulation. The paper shows that appropriate product clustering is indeed crucial in serving gen next customers better.

Keywords


Product Clustering, Gen Next Customers, Multivariate Analysis, Simulation.

References