Telecom Data Mining

When it comes to data mining tools that means professional tools which can answer business questions that have been traditionally too time-consuming to solve and these techniques also allow the transformation of raw data into business knowledge.

Techniques include:

1-    Genetic Algorithm

2-    Rule induction

3-    Clustering

4-    Decision Tree

5-    Associations

6-     Neural Network

Data Mining can determine characteristic customer clusters on the basis of collected historic data points, such as the frequency and timely distribution of customers’ usage of services.

  • Calls
  • Text Messages
  • Multimedia Messages “MMS”
  • Navigation
  • Mail exchange

For each of these customer patterns the company can offer tailored customer-life-cycle messages and offers. The telecommunications industry was an early adopter of data mining technology and therefore many data mining applications exist. Several typical applications are described in this section. These applications are divided into three application areas: fraud detection, marketing/customer profiling and network fault isolation. Fraud is a serious problem for telecommunication companies, leading to billions of dollars in lost revenue each year. Fraud can be divided into two categories: subscription fraud and superimposition fraud. Subscription fraud occurs when a customer opens an account with the intention of never paying for the account charges.  Superimposition fraud involves a legitimate account with some legitimate activity, but also includes some “superimposed” illegitimate activity by a person other than the account holder.

About Mind Commerce

Analysis of telecom and ICT infrastructure, technologies, and applications.
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