This document provides an overview of data mining in the telecommunications industry. It discusses how telecom companies generate tremendous amounts of data and can use data mining tools to extract hidden knowledge and insights from large datasets. Specifically, data mining allows telecom companies to better understand customers through segmentation and profiling, detect fraud, analyze network performance, and identify factors that influence customer call patterns to improve profitability. The document also covers types of telecom data, data preparation techniques like clustering, and applications of data mining such as marketing, fraud detection, and network fault isolation.

1. Prepared by –
Mohsin Nadaf, TE IT
Trinity College of Engineering & Research, Pune

2. Contents
 Introduction
 What is Data Mining?
 Need of Data mining in Telecommunication
 Customer Segmentation and Profiling
 Types of Telecommunication Data
 Data Preparation and Clustering
 Applications
 Conclusion

3. Introduction
 Fast growing Industry
 Data, the base of Telecommunication
 Generation of tremendous amount of Data
 Knowledge based Expert-System
 Use of Data Mining and its tools
 Uncovering hidden information
 Future Decisions

4. What is Data Mining?
 Extracting Knowledge hidden in large volumes of data
 Identifying potentially useful and understandable data


5.  Technical approaches like
 Clustering,
 Data summarization
 Classification
 Analyzing Changes
 Detecting anomalies

6. Data Mining in
Telecommunications
 To detect frauds
 To know customers
 Retain Customers
 What products and services yield highest amount of
profit?
 What are the factors that influence customers to call
more at certain times?

7. Customer Segmentation and
Profiling
 Customer Segmentation
-To describe the process of dividing customers into
homogeneous groups on the basis of shared or
common attributes (habits, tastes, etc).
 Difficulties :
-Relevance and quality of data
-Intuition
-Continuous process
-Over-segmentation

8.  Customer Profiling
-Describing customers by their attributes, such as
age, gender, income and lifestyles
 Parameters-
-Geographic
-Cultural and ethnic
-Economic conditions
-Age and Gender
-Attitudes and beliefs
-Lifestyle
-Knowledge and Awareness

9. Types of Telecommunication Data
 Call-Detail Data
 Network Data
 Customer Data
 Call-Detail Data
-average call duration
-average call originated/generated
-call period
-call to/from different area code

11.  Network Data
-Complex configuration of equipments-
-Error Generation
-To support Network Management functions

12.  Customer Data
-Database of information of Customers
-Name
-Age
-Address
-Telephone type
-Subscription Type
-Payment History

13. Data Preparation and Clustering
 Data preparation
-To be prepared in the required format
 Tasks:
Discovering and Repairing inconsistent data
format
Deleting unwanted data fields
Combining data
Mapping of values
Normalization of the variables

14.  Clustering
-Grouping of Similar things
 Cluster Analysis
-Organization of objects into groups, according to
similarities among them.

15. Applications
 Marketing/Customer Profiling
 Fraud Detection
 Network Fault Isolation

16. CONCLUSION
 Early adopter of Data mining technology
 To detect frauds
 Helps to know the Customer
 Serve them Better
 Yield more profit
 Reduced much of Human based analysis
 Essential for Telecommunication companies

17. Future Trends
 Additional themes on data mining
 New Methods for Complex types of Data
 Invisible Data mining(mining as a built in function)
 Reduction in Human work
 Advanced methods in Data mining

18. REFERENCES
 Data mining in Telecommunication by Gray M. Weiss,
Fordham University
 Customer Segmentation and Customer Profiling for a Mobile
Telecommunications Company Based on Usage
Behaviour, S.M.H Jansen, July 17, 2007
 IJSETT -Applications of Data Mining by Simmi Bagga and Dr.
G.N.Singh
 A new approach to classify and describe telecommunication
services, A.Lehmann1,2, W.Fuhrmann3, U.Trick1, B.Ghita²
 Sasisekharan, R., Seshadri, V., Weiss, S. Data mining and
forecasting in large-scale telecommunication networks.
IEEE Expert 1996; 11(1):37-43.

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