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Most marketing executives, along with advertising practitioners, understand the intrinsic value of collecting customer-related information, but also comprehend the challenges involved in leveraging this knowledge to generate intelligent, proactive conduits that could be used to add value to the customer as well as the organization. This is where the interplay between customer relationship management and data mining comes in to facilitate a process that assists organizations to sift through stratums of ostensibly unrelated data for significant relationships, where they can proactively anticipate, rather than merely react to, customer needs and expectations (Linoff & Berry, 2008). Within the broad scope of data mining, this paper purposes of evaluating some underlying issues in customer relationship management.
In data mining, the term ‘lift’ denotes a measure of the performance of a particular model at forecasting or grading cases and events through the application of statistical modeling, random choice model, or segmentation. Consequently, the term is mostly used as a simple correlation measure of the improvement in response between two or more causal agents, not mentioning that it also assists businesses and other ventures to filter out misleading ‘strong’ associations of the form item A is dependent on item B (Han & Kamber, 2006).
Parvatiyar & Sheth (2001) defines Customer Relationship Management (CRM) as “…a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers to create superior value for the company and the customer” (p. 5). As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization to know and comprehensively understand its markets and customers in order to select the most profitable customers as well as identify those no longer worth targeting (Rygielski & Wang, 2002).
The above explanation demonstrates the convergence between CRM and data mining – the extraction of concealed extrapolative information from large databases with a view to, among other things, identify the most valuable customers and predict future behaviors in order to initiate proactive, knowledge-driven decisions (Parvatiyar & Sheth, 2001). These are precisely the most important benefits of CRM – ability to identify the most valuable customers, ability to predict future behaviors, and; ability to make proactive, knowledge-driven decisions that enhance customer value as well as an organizational value. Added to these benefits, CRM is not only effective in managing relationships between businesses and consumers (B2C) but also in business-to-business (B2B) environments (Rygielski & Wang, 2002).
In B2B environments, for example, it is a well-known fact that modern business environments are characterized by numerous transactions, diverse custom contracts, and ever more complicated pricing schemes. In such a scenario, therefore, CRM can be used to facilitate the processes when various agents of seller and buyer organizations communicate and collaborate. In equal measure, some CRM initiatives such as customized catalogs, e-mail alerts, personalized business portals, new product information, and targeted product offers can abridge the procurement process and improve effectiveness and efficiencies for both organizations, or assist in enhancing the effectiveness of the sales pitch (Rygielski & Wang, 2002).
The scalability of a CRM system simply means that the system is capable of handling additional volume and growth in the event of either planned or spontaneous economic expansion (Shelly et al., 2010). Predicting the future needs and expectations of customers, along with future shifts in behaviors, according to these authors, is not an exact science and, as such, organizations need to invest in scalable CRM systems that are always informed by careful research and planning informed by various methodologies, including data mining.
Reference List
Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. San Francisco, CA: Morgan Kaufmann Publishers. Web.
Linoff, G.S., & Berry, M.J. (2011). Data mining techniques: For marketing, sales, and customer relationship management. Indianapolis, IN: Wiley Publishing, Inc. Web.
Parvatiyar, A., & Sheth, J.N. (2001). Customer relationship management: Emerging practice, process, and discipline. Journal of Economic & Social Research, 3(2), 1-34. Web.
Rygielski, C., Wang, J.C., & Yen, D.C. (2002). Data mining techniques for customer relationship management. Technology in Society, 24(1), 483-502. Web.
Shelly, G.B., Cashman, T.J., & Rosenblatt, H.J. (2010). System analysis and design, 8th Ed. Boston MA: Cengage Learning. Web.
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