Recommendations for Organizations
The many factors of data mining and their use for profiling customers and their needs also create opportunities for organizations to build greater levels of trust with their customers as well. And trust is the greatest asset any marketer can have today. The following are a series of recommendations for how organizations can address demographic influences that impact their marketing strategies in light of concerns surrounding the ethics of data mining.
First, it is imperative, across all demographic segments that marketers make a deliberate a very clear effort to explain their opt-in and opt-out policies and also provides evidence that they do what they claim to in this area. The greatest challenge for the consumer is controlling their personal information online and ensuring it is well managed to their preferences (Pratt. Conger, 2009). Marketers who give consumer control over their data in this way will be significantly ahead compared to competitors, who often will use personal data unethically and often to the determine of the personal privacy of the customers (Pratt. Conger, 2009). This first recommendation is based on the need of marketers to always strive for higher levels of authenticity, transparency and trust with their customers, as these are the most powerful differentiators they have.
The second recommendation is to provide real-time access to how a given customers' information is being used or not, and show fidelity to the policies of opt in or opt out. This approach is done well by many online marketers including Amazon.com. While their recommendation system is one of the best in the word at predicting what customers want, the company is very careful to ensure that only the data that a given customer opens in for is provided across the CRM systems within the company (Wen-Yu, 2012).
The third recommendation is to create and continually invest in customer advisory councils that are invited to see how customer data...
The objective of these councils is to create a high level of trust with the customer and show first-hand how their data is being used. A primary objective of these programs is to also show very clearly how the company is being forthright and focused on how to give customers control of their data (Payne, Trumbach, 2009).
Fourth, companies must public and rate themselves on a series of ethics standards that show how they are performing relative to plan. This must be published on their website and also shared across social media. The intent of this is to be very clear about the intentions and actions of their efforts to sell more while also showing they have oversight in place to manage the ethics of information as well.
References
Adams, N.M. (2010). Perspectives on data mining. International Journal of Market Research, 52(1), 11.
Bose, I., & Chen, X. (2009). Hybrid models using unsupervised clustering for prediction of customer churn. Journal of Organizational Computing and Electronic Commerce, 19(2), 133.
Kaiser, C., & Bodendorf, F. (2012). Mining consumer dialog in online forums. Internet Research, 22(3), 275-297.
Kiron, D. (2012). Why detailed data is as important as big data. MIT Sloan Management Review, 53(4), 1-3.
Mitrano, T. (2008). Facebook 2.0. EDUCAUSE Review, 43(2), 72.
Payne, D., & Trumbach, C.C. (2009). Data mining: Proprietary rights, people and proposals. Business Ethics, 18(3), 241-252.
Pratt, J.H., & Conger, S. (2009). Without Permission: Privacy on the Line. International Journal of Information Security and Privacy (IJISP), 3(1), 30-44.
Samuelson, D.A. (2012). Text analytics. OR-MS Today, 39(3), 16.
Shaw, M.J., Subramaniam, C., Gek, W.T., & Welge, M.E. (2001). Knowledge management and data mining for marketing. Decision Support Systems, 31(1),…
