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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/8680
Title: Application of Data Mining Techniques to Customer Profile Analysis in the Ethiopian Electric Power Corporation
???metadata.dc.contributor.*???: Getachew Jemaneh
Abebe, Hailemariam
Keywords: Ethiopia;Ethiopian Electric Power Corporation;Data Mining Techniques;Customer Profile Analysis
Issue Date: Jun-2011
Publisher: 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.
URI: http://hdl.handle.net/123456789/8680
Appears in Collections:Thesis - Information Science

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