Application of Data Mining Techniques to Customer Profile Analysis in the Ethiopian Electric Power Corporation
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Date
2011-06
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Addis Ababa University
Abstract
Data mining is progressively used in information systems as a technology to support decision
making activities within business processes. Electric power industries are being pushed to
understand and quickly respond to the individual needs and wants of their customers due to
the dynamic and highly competitive nature of the industry and customers. Customer
Relationship Management (CRM) is the overall process of exploiting customer data and
information, and using it to increase the revenue generated from an existing customer and
attract new customers by creating good relationship with them accordingly. To implement
CRM, electric power industries can use their customer databases to get a better understanding
of their customers. And thus, to extract this important customer information from available
databases, data mining techniques play a great role.
In this research the applicability of clustering and classification data mining techniques to
implement CRM in the Ethiopian Electric Power Corporation (EEPCo) have been explored
within the approach of CRISP-DM process model. After understanding business objective of
the corporation, customer profiles are collected, cleansed, transformed, integrated and finally
prepared for experimenting with the clustering and classification algorithms to develop a
model. The final dataset prepared for experimentation consists of 50000 customer records.
The K-means clustering algorithm was used to segment customer records into clusters with
similar behaviors. In the classification sub-phase, J48 decision tree and Naive Bayes
algorithms were employed. Using the final dataset different clustering models at K values of
4, 5, and 6 with different seed values have been experimented and evaluated against their
performances. Consequently, the cluster model at K value of 4 with seed size 1000 has
shown a better performance. Finally, its output is used as an input for decision tree and Naive
Bayes classification models. First the different classification models with decision tree and
Naive Bayes algorithms are experimented with different parameters. Among these, a J48
decision tree model that showed a classification accuracy of 99.894% was selected. The
results of this study were encouraging and confirmed the belief that applying data mining
techniques could indeed support CRM activities at EEPCo. In the future, more segmentation
and classification studies by using a possible large amount of customer records and
employing other clustering and classification algorithms could yield better results.
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Keywords
Data Mining Techniques to Customer Profile Analysis