Recognition of Telecom Customer’s Behavior as Data Product in CRM Big Data Environment

This paper approaches toward the standardization of telecom customer’s behavior by specifying the call activities like frequency, duration, time of calls with the type of calls like local, national, and international. In the same way specifying the SMS/MMS activities as behavior of customers plus the rate of data pack and talk-time recharge. It is an attempt to identify meaningful attributes to describe behavior of customer plus study the available call detail records in big data environment and recognize the procedure that uses customer behavior for the designing of data product which is tariff plan.

[1]  Shiow-Yang Wu,et al.  Sequence-Growth: A Scalable and Effective Frequent Itemset Mining Algorithm for Big Data Based on MapReduce Framework , 2015, 2015 IEEE International Congress on Big Data.

[2]  Edward Y. Chang,et al.  Pfp: parallel fp-growth for query recommendation , 2008, RecSys '08.

[3]  Yeli Li,et al.  Behavior-Based Telecom Tariff Service Design with Neural Network Approach , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.