Development of Policy Designing Technique by Analyzing Customer Behavior Through Big Data Analytics

Technological developments and market trends are two leading affairs of the current era, posing customer as most important entity to be caught. Use of big data analytics to retain customers by offering them customer-oriented policies and making them feel important and precious for the service-providing company is the core thought behind this research paper. A framework to obtain process and analyze service usage data, with a new algorithm known as Altered Genetic K-Means clustering algorithm based on mapReduce is presented here. This paper implements mapReduce-based Altered Genetic K-Means Clustering (AGKM) algorithm on data acquired from BSS/OSS of telecom CRM and cleaned by R, to categorize customers having similar call activities. Results show that specific group of customers such as students, senior citizens, housewives, business people, and employees can be identified and according to their call timings, durations, call types, net usage, etc., policies (tariff plans in this case) can be designed. The novelty of this work is in its thought of capturing customers by knowing them well in place of first predicting churn and then taking action.