Customer segmentation model based on value generation for marketing strategies formulation

  • Alvaro Julio Cuadros Full Time Professor, Escuela de Ingeniería Industrial, Universidad del Valle, Cali
  • Victoria Eugenia Domínguez Project Manager
Palabras clave: Segmentation, Customer value, Artificial neural network, Self-organized maps

Resumen

Whendeciding in which segment to invest orhowto distribute the marketing budget, managers generally
take risks in making decisions without considering the real impact every client or segment has over organizational
profits. In this paper, a segmentation framework is proposed that considers, firstly, the calculation
of customer lifetime value, the current value, and client loyalty, and then the building of client segments
by self-organized maps. The effectiveness of the proposed method is demonstrated with an empirical
study in a cane sugar mill where a total of 9 segments of interest were identified for decision making.

Descargas

La descarga de datos todavía no está disponible.

Biografía del autor/a

Alvaro Julio Cuadros, Full Time Professor, Escuela de Ingeniería Industrial, Universidad del Valle, Cali

Citas

Bayón, T., Gutsche, J., & Bauer, H. (2002). Customer equity marketing: Touching the intangible. European Management Journal, 20, 213–222.

Black, A., Wright, P., & Bachman, J. (1998). In search of shareholder value. London: Price Waterhouse.

Caicedo, B., & López, J. (2009). Una aproximación práctica a las redes neuronales artificiales. Cali: Universidad del Valle.

Chan, S. L., & Ip, W. H. (2011). A dynamic decision support system to predict the value of customer for new product development. Decision Support Systems, 52, 178–188.

Chattopadhyay, M., Dan, P. K., & Mazumdar, S. (2012). Application of visual clustering properties of self organizing map in machine-part cell formation. Applied Soft Computing, 12, 600–610.

Chien-Wen, H. (2012). Using the Taguchi method for effective market segmentation. Expert Systems with Applications, 39, 5451–5459.

Criado, M., Arroyo, J., & López, J. (2005). Organizaciones virtuales y redes neuronales. Algunas similitudes. Estudios Gerenciales, 97, 117–128.

Ghaseminezhad, M. H., & Karami, A. (2011). A novel self-organizing map (SOM) neural network for discrete groups of data clustering. Applied Soft Computing, 11, 3771–3778.

Glazer, R., & Dhar, R. (2003). Como manejar el riesgo de su cartera de clientes. Harvard Business Review, 81, 68–75.

Gupta, S., & Lehmann, D. (2006). Managing customers as investments. Upper Saddle River, NJ: Wharton School Publishing.

Han, S. H., Lu, S. X., & Leung, S. C. H. (2012). Segmentation of telecom customers based on customer value by decision tree model. Expert Systems with Applications, 39, 3964–3973.

Hanafizadeh, P., & Mirzazadeh, M. (2011). Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method – ADSL market of a telecommunication company. Expert Systems with Applications, 38, 198–205.

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, 2127–2131.

Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications, 26, 181–188.

Jain, D., & Singh, S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16, 34–46.

Jonathan, Z. B. (2005). Market segmentation: A neural network application. Annals of Tourism Research, 32, 93–111.

Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behaviour: Case study. Procedia Computer Science, 3, 57–63.

Keh, H. T., & Lee, Y. H. (2006). Do reward programs build loyalty for services? The moderating effect of satisfaction on type and timing of rewards. Journal of Retailing, 82, 127–136.

Kiang, M. Y., Hu, M. Y., & Fisher, D. M. (2006). An extended self-organizing map network for market segmentation–A telecommunication example. Decision Support Systems, 42, 36–47.

Kim, S.-Y., Jung, T.-S., Suh, E.-H., & Hwang, H.-S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31, 101–107.

Kumar, V., & Reinartz, W. (2006). Customer relationship management: A database approach. New York, NY: John Wiley.

Kuo, R. J., An, Y. L., Wang, H. S., & Chung, W. J. (2006). Integration of self-organizing feature maps neural network and genetic K-means algorithm for market segmentation. Expert Systems with Applications, 30, 313–324.

Kuo, R. J., Wang, H. S., Hu, T.-L., & Chou, S. H. (2005). Application of ant Kmeans on clustering analysis. Computers and Mathematics with Applications, 50, 1709–1724.

Lars, M.-W. (2007). The effects of loyalty programs on customer lifetime duration and share of wallet. Journal of Retailing, 83, 223–236.

Lee, S. C., Suh, Y. H., Kim, J. K., & Lee, K. J. (2004). A cross-national market segmentation of online game industry using SOM. Expert Systems with Applications, 27, 559–570.

Mulhern, F. (1999). Customer profitability analysis: Measurement, concentration, and research directions. Journal of Interactive Marketing, 13, 25–40.

Ordóñez, D., Dafonte, C., Arcay, B., & Manteiga, M. (2012). HSC: A multi-resolution clustering strategy in Self-Organizing Maps applied to astronomical observations. Applied Soft Computing, 12, 204–215.

Payne, A., & Holt, S. (2001). Diagnosing customer value: Integrating the value process and relationship marketing. British Journal of Marketing, 12, 159–182.

Rust, R., Zeithaml, V., & Lemon, K. (2000). Driving customer equity: How customer lifetime value is reshaping corporate strategy. New York, NY: The Free Press.

Sahoo, A. K., Zuo, M. J., & Tiwari, M. K. (2012). A data clustering algorithm for stratified data partitioning in artificial neural network. Expert Systems with Applications, 39, 7004–7014.

Seret, A., Verbraken, T., Versailles, S., & Baesens, B. (2012). A new SOM-based method for profile generation: Theory and an application in direct marketing. European Journal of Operational Research, 220, 199–209.

Stahl, H. K., Matzler, K., & Hinterhuber, H. H. (2003). Linking customer lifetime value with shareholder value. Industrial Marketing Management, 32, 267–279.

Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing, 68, 106–125.

Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218, 211–229.

Verhoef, P. C., & Donkers, B. (2001). Predicting customer potential value an application in the insurance industry. Decision Support Systems, 32, 189–199.

Wei, J.-T., Lin, S.-Y., Weng, C.-C., & Wu, H.-H. (2012). A case study of applying LRFM model in market segmentation of a children's dental clinic. Expert Systems with Applications, 39, 5529–5533.

Publicado
2014-03-25
Cómo citar
Cuadros, A. J., & Domínguez, V. E. (2014). Customer segmentation model based on value generation for marketing strategies formulation. Estudios Gerenciales, 30(130), 25-30. https://doi.org/10.1016/j.estger.2014.02.005
Sección
Artículo de investigación