subscriber data mining in telecommunication

Supervisor

Babu Ram Dawadi

Co-Supervisor

Manoj Ghimire

Subscriber Data Mining for Business Reporting and Decision Making in Telecommunication

Project Member

Bishal Timilsina

Bishnu Bhattarai

Narayan Kandel

Niroj Karki

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PRESENTATION OUTLINE1. Introduction

2. Block Diagram

3. Data Collection

4. Data Preprocessing & Loading

5. Data Mart Design

6. OLAP Design

7. Data Mining Algorithm

8. Visualization

9. Result & Conclusion

10. Reference2

INTRODUCTION

Problem Statements

Product based strategies rather than customer based strategies

Problem on CRM

Ignorant about customer behaviours

Objectives

Customer Segmentation

New Campaign

Customer Relationship

Management

Reporting

To know more about customer, their call detail

Churn Prediction3

BLOCK DIAGRAM

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DATA COLLECTION

1. csv format

2. Txt format

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ETL PROCESS

Extract Extract tab separated data from txt file using bash shell & regular expression

Transform Male to m

Female to f

Business_call to 1

Non_business_call to 0

Load Load data to mysql database using python script

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DATA MART DESIGN

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OLAP DESIGN

Purpose:

Slice, Dice, Roll up, Drill Down operation

Design Basis:

4 dimension representation

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DATA MINING

1. RFM Methods

R Recency (x axis)

F Frequency (y axis)

M Monetary (z axis)

Step

Attribute Selection & K-means Clustering

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DATA MINING …

2. Two Phase Clustering

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Objective

Customer Segmentation

How?

1st clustering -> Diamond, Gold,

Silver …

2nd cluster -> Demographic

cluster

Now compare cluster based on

attribute value

DATA MINING …

2. Two Phase Clustering

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Objective

Customer Segmentation

How?

1st clustering -> Diamond,

Gold, Silver …

2nd cluster -> Demographic

cluster

Now compare cluster based on

attribute value

DATA MINING …

2. Two Phase Clustering

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DATA MINING …

3. Gaussian Distribution

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Objective

Churn Prediction through call

diameters

How?

Predict customer with value

outside 90% confident range

Accuracy?

With increase in data size->

accuracy increase

VISUALIZATION

1. Demographic Visualization

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VISUALIZATION

2. CDR Visualization

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VISUALIZATION

3. Time Series Visualization

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VISUALIZATION

4. OLAP Visualization

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VISUALIZATION

5. OLAP Visualization …

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VISUALIZATION

6. OLAP Visualization…

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RESULT & CONCLUSION

With the implementation of this software, telecommunication will be able to

know more about customer & their call behavior

Customer Segmentation help them

1.To maintain effective customer relationship management

2.To launch specific offers focusing on specific groups

Alert them about customer churn behavior

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LIMITATION & FURTHER ENHANCEMENT

Limitation

Data load time is high

Our System Isn’t customizable for all query

Further Enhancement

Customer Segmentation accuracy could be improve by including customer life time value & apriori algorithm

Reporting tool could be made more general & flexible

Competitor Analysis

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THANK YOU

Any Queries?

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