Nowadays, organic products have become part of green consumerism movement that “focus on renewable resources and protecting soil and water to improve the quality of life for generations to come” (USDA, 2002). Despite of consumers’ growing support to preserve the environment, the consumption and market share of organic food still constitutes a low percentage compare to total food (Aryal 2008; Bonnini and Oppenheim, 2008). The gap between consumers’ attitude and action toward organic products implies that the business is still lack of knowledge about the true potential of organic customers, i.e. those who accept organic products. It is conceivable that there are some influencing factors as such consumers’ attitude cannot be translated into action.
Therefore, it is suggested that the business should learn the features of customers those taking action, and in turn, use that knowledge to direct organic products’ marketing activities in order to be more effective. Customer relationship management (CRM) is a process designed to collect data related to customers, to grasp features of customers, and to apply those qualities in specific marketing activities (Swift, 2001). In the analytical CRM, these data are stored and analyzed through a range of analytical tools in order to generate customer profiles, identify behavior patterns, determine satisfaction level, and support customer segmentation; thus, customers are more effectively segmented and offered products and services are fitted to customer buying profiles (Xu and Walton, 2005). Nevertheless, even though a wide range of CRM systems are being developed and implemented in practice, application of analytical CRM has been low, due to lack of focus on gaining customer knowledge for strategic decision making from CRM systems, and lack of analytical CRM solutions (Xu and Walton, 2005).
Organizations can strategically use analytical CRM to support customer knowledge acquisition (Xu and Walton, 2005). However, effective use of customer knowledge, particularly in the context of marketing decisions, is still inchoate in many organizations (Bose and Sugumaran, 2003). Knowledge Management (KM), in particular, has been defined as the process of capturing the collective expertise and intelligence in an organization and using them to foster innovation through continued organizational learning (Nonaka, 1991). Then, a framework that link KM to CRM is suggested to effectively use customer knowledge. A significant amount of research has applied KM-CRM framework (Xu and Walton, 2005; Bueren et al., 2005). Nevertheless, this framework still needs to be developed and applied in other industry segments or other potential fields (Bueren et al., 2005).
This study applies a KM-analytical CRM framework to create knowledge about organic customer profile and to identify relevant knowledge that is needed to target the potential customers, i.e. the customers who accept organic products. Case study research was conducted to investigate background of companies that will provide detailed Keywords Customer relationship management Knowledge management Organic customer profile Organic products contextual data so as to better interpret the result of research data (Creswell, 2003). The purpose of this study is to develop a KM-CRM model for organic products acceptance based on: first, investigation toward customer data mining activities within CRM framework; second, restructuring existing customer data in the company to create knowledge about organic customer profile; third, identification of relevant customer knowledge that support company’s need to target organic customers.
As the conclusion, KM-CRM model for organic products acceptance is developed based on the three steps of actions: customer data mining and analysis, creating knowledge about customer, and identification of relevant customer knowledge for targeting organic customers. Individual factors resulted from quantitative study are used as the basis in developing the model. And then, KM is embedded into CRM to determine degree of KM support in each step of action. This model is line with the current concept of knowledge-based system which includes knowledge of consumers’ behaviors from statistical analysis in CRM (Bensoussan et al., 2009); and includes the three antecedents success factors of knowledgebased system, i.e. knowledge codifiability, absorptive capacity, and inter-organizational relationship quality (Argote et al., 2003). Knowledge codifiability relate to how the consumer knowledge can be codified as thoroughly and as accurately as possible. Absorptive capacity is the ability of a firm to recognize the value of new external information, assimilate it, and apply it to commercial ends. Inter-organizational relationship relate to the effective knowledge sharing between knowledge sources and its recipients, which requires their direct and intimate interaction (Hippel, 1998). As managerial implication, first, this study provides basis to the company in restructuring knowledge about organic customer profile within KM – analytical CRM framework, which is useful in targeting organic customers to be more effective; second, the research proposed different contextual applicability of KM-CRM integration in environmentally friendly products domain. As its limitation, CRM implementation in the companies is still in the early stage; and majority of them only focusing at the operational level, e.g. customer transaction. Thus, the implementation of KM-CRM in the organizations is beyond the scope of this study.
References
Aryal, K.P. 2008. General perceptions of producer, traders and consumers about organic products in Kathmandu valley. In P. Chaudhary; K. Aryal and D. Tharu (ed.). Proceedings of International Workshop on Opportunities and Challenges of Organic productsion and Marketing in South Asia, NPG, p.120-124. Kathmandu, Nepal.
Aryal, K.P., Chaudhary, P., Pandit, S. and Sharma, G. 2009. Consumers’ willingness to pay for organic products: a case from Kathmandu Valley. The Journal of Agriculture and Environment 10: 12-22.
Bensoussan, A., Mookerjee, R., Mookerjee, V. and Yue, W. T. 2009. Maintaining diagnostic knowledge-based systems: a control-theoretic approach, Management Science 55 (2): 294-310.
Bonini, S. and Oppenheim, J. 2008. Cultivating the Green Consumer. Stanford Social Innovation Review: 56- 61.
Bose, R. and Sugumaran, V. 2003. Application of knowledge management technology in customer relationship management. Knowledge and Process Management 10 (1): 3-17.
Bueren, A., Schierholz, R., Kolbe, L.M. and Brenner, W. 2005. Improving performance of customer-processes with knowledge management. Business Process Management Journal 11(5): 573-588
Buttle, F. 2009. Customer relationship management: concept and technologies. Oxford: Elsevier.
Cicia, G., Giudice, T.D. and Scarpa, R. 2002. Consumers’ perception of quality in organic food: A random utility model under preference heterogeneity and choice correlation from rank-orderings. British Food Journal 104 (3/4/5): 200-213.
Chin, W.W. 1998. The partial least squares approach for structural equation modeling, in Marcoulides, G.A. (Ed.), Modern Methods for Business Research, pp. 295-336. New York: Laurence Erlbaum Associates.
Creswell, J.W. 2003. Research design: qualitative, quantitative, and mixed methods approaches, 2nd edition. Thousand Oaks, California: Sage Publication.
Crie, D. and Micheaux, A. 2006. From customer data to value: What is lacking in the information chain? Database Marketing and Customer Strategy Management 13(4): 282-299.
Davenport, T.H., Harris, J.G. and Kohli, A.K. 2001. How do they know their customers so well? MIT Sloan Management Review 42 (2): 63-73. Eisenhardt, K.M. 1989. Building theories from case study research. Academy of Management Review 14 (4): 532-550.
Gebert, H., Geib, M., Kolbe, L. and Brenner, W. 2003. Knowledge-enabled customer relationship management: integrating customer relationship management and knowledge management concepts. Journal of Knowledge Management 7 (5): 107-123.
Goodhue, D. L. 2007. Comment on Benbasat and Barki’s ‘Quo Vadis TAM’ article. Journal of The Association for Information Systems 8 (4): 219-222.
Hippel, V. E. 1998. The Source of innovation. New York and Oxford: Oxford University Press.
Holmbom, A.H., Sarlin, P., Yao, Z., Eklund, T. and Back, B. 2013. Visual data-driven profiling of green consumers. Proceedings of 2013 17th International Conference on Information Visualisation. p. 291-298. Turku-Finland: TUCS.
Kotorov, R. 2002. Ubiquitous organisation: organisational design for e-CRM. Business Process Management Journal 8 (3): 218-32.
Lambe, P. 2001. Knowledge-based CRM: a map. Downloaded from http://www.straitknowledge.com
Lehmann, D.R., Zahay, D. and Peltier, J.W. 2012. Survey analyze customer relationship management using balanced scorecard. Journal of Interactive Marketing 27(2013): 1-16.
Nonaka, I. 1991. The knowledge-creating company. Harvard Business Review: 96-104.
Ryan, G.W. and Bernard, H.R. 2000. Data management and analysis method. Published in: Handbook of Qualitative Research, 2nd Ed. Norman Densin and Yvonna Lincoln, Eds. p. 769-802. Thousand Oaks, CA: Sage Publications
Swift, R.S. 2001. Accelerating Customer Relationship Using CRM and Relationship Technologies. Englewood Cliffs, NJ: Prentice-Hall.
Xu, M. and Walton, J. 2005. Gaining customer knowledge through analytical CRM. Industrial Management and Data Systems 105 (7): 955-971.
Yin, R. K. 2003. Case study research: Design and methods 3rd ed. Vol. 5. Thousand Oaks: Sage