Data Mining Techniques for Customer Lifecycle Management

A. V. Murali

Abstract: Businesses in every industry strive to increase customer value and achieve a high level of customer satisfaction. Sales teams focus on opportunities for cross-sell and up-sell while customer care focusses on certain key metrics such as first call resolution, quick resolution to customers issues, high service levels and quality scores. Huge amounts of data are generated by various teams in an organization as a result of interactions with their customers and prospects. Data analysts perform different types of analysis on this data to meet various objectives such as finding out the root cause of issues, predicting some trend or suggesting plans and schedule. Employing data mining techniques not only gives a better insight into the problem areas but also reveals unknown associations between variables. This paper elaborates on the data mining techniques such as association rule mining, anomaly detection, classification, clustering and regression and how businesses can take advantage of these techniques to gain better insight into customer lifecycle and build better customer relationships.

Keywords: Data mining, Association rule mining, Anomaly detection, Classification, Clustering, Regression, Customer lifecycle

How to Cite?: A. V. Murali, "Data Mining Techniques for Customer Lifecycle Management", Volume 5 Issue 6, June 2016, International Journal of Science and Research (IJSR), Pages: 362-367, https://www.ijsr.net/getabstract.php?paperid=NOV164293, DOI: https://dx.doi.org/10.21275/NOV164293