Decision support system for customer churn reduction approach

For every business, where the product escalation of an organization depends on proper distribution of product and services in a region, analyzing customer behavior over time in that region can produce useful results. In this article, we strive to develop a system, which when provided past customer records will predict the forthcoming customer behavior. The proposed system is specially designed to find patterns and predict changes in customer behavior for mobile service providers. The aggregation of comparison and referencing of behavioral patterns, both on off-line and on-line, represents a tremendous opportunity for understanding modeling past behaviors and predicting future behaviors. Several aspects of business and real life trends, both on and off the Web, modified over time from such observable changes, as the augmentation and evolution of contents, to more quasi yet important dynamics. The research effort in this article aimed at exploring the temporal dynamics of consumer behavior, investigating how the behavior can deploy, and predict changes of raised concern about a particular service or brand. Specifically, the informational goals behind the queries, and the search results, will finally affect the business while reducing rate of attrition of customer from particular legacy of product or services.