Call Detail Record Analysis: K-Means Clustering With R
Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics. Most telecom companies use CDR information for fraud detection by clustering the user profiles, reducing customer churn by usage activity, and targeting the profitable customers by using RFM analysis.In this blog, we will discuss clustering of the customer activities for 24 hours by using unsupervised K-means clustering algorithm. It is used to understand a segment of customers with respect to their usage by hours.For example, customer segment with high activity may generate more revenue. Customer segment with high activity in the night hours might be fraud ones.
A daily activity file from Dandelion API is used as a data source, where the file contains CDR records generated by the Telecom Italia cellular network over the city of Milano. The daily CDR activity file contains information for 10, 000 grids about SMS in and out, Call in and out, and Internet activity. The structure of the dataset is as follows: As it has five million records, a subset of the file containing activity information for 500 square IDs is used as a use case.
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May 15, 2017 at 10:27AM