Data Mining in Telecommunications

Prepared by –Mohsin Nadaf, BE ITUniversity of Pune

ContentsIntroductionWhat is Data Mining?Need of Data mining in TelecommunicationCustomer Segmentation and ProfilingTypes of Telecommunication DataData Preparation and ClusteringApplicationsConclusion

IntroductionFast growing IndustryData, the base of TelecommunicationGeneration of tremendous amount of DataKnowledge based Expert-SystemUse of Data Mining and its toolsUncovering hidden informationFuture Decisions

What is Data Mining?Extracting Knowledge hidden in large

volumes of dataIdentifying potentially useful and

understandable data

Technical approaches like Clustering, Data summarization ClassificationAnalyzing ChangesDetecting anomalies

Data Mining in TelecommunicationsTo detect frauds To know customersRetain CustomersWhat products and services yield highest

amount of profit?What are the factors that influence customers

to call more at certain times?

Customer Segmentation and ProfilingCustomer 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

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

Types of Telecommunication DataCall-Detail DataNetwork DataCustomer Data

Call-Detail Data-average call duration-average call originated/generated-call period-call to/from different area code

Network Data

-Complex configuration of equipments--Error Generation-To support Network Management functions

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

Data Preparation and ClusteringData preparation

-To be prepared in the required formatTasks:

Discovering and Repairing inconsistent data format

Deleting unwanted data fieldsCombining dataMapping of valuesNormalization of the variables

Clustering-Grouping of Similar things

Cluster Analysis-Organization of objects into groups,

according to similarities among them.

Applications

Marketing/Customer ProfilingFraud DetectionNetwork Fault Isolation

Future TrendsAdditional themes on data miningNew Methods for Complex types of DataInvisible Data mining(mining as a built in

function)Reduction in Human workAdvanced methods in Data mining

CONCLUSIONEarly adopter of Data mining technologyTo detect fraudsHelps to know the CustomerServe them BetterYield more profitReduced much of Human based analysisEssential for Telecommunication companies

REFERENCESData mining in Telecommunication by Gray M. Weiss, Fordham

UniversityCustomer 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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