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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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