Application of Data Mining Techniques for Customers Segmentation and Prediction: the Case of Buusaa Gonofa Microfinance Institution

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Addis Ababa University


Identifying customers which are more likely potential to a product and service offering is an important issue. In customers identification data mining has been used extensively to predict potential customers for a product and service. The final goal of this thesis is to build a model that helps to classify customers for Buusaa Gonofa microfinance institution product and service. Since there are no predefined classes, that describe the customers of the institution, the researcher uses clustering techniques that resulted in the appropriate number of clusters. Then, a predictive model was developed to predict potential customers. This predictive model achieved an accuracy of 99.95%. For modeling purpose, data was gathered from the institution head office. Since irrelevant features result in bad model performance, data preprocessing was performed in order to determine the inputs to the model. Thus, various data mining techniques and algorithms were used to implement each step of the modeling process and alleviate related difficulties. K-means was used as a clustering algorithm to segment customers‟ record into clusters with similar characters. Different parameters were used to run the clustering algorithm before reaching at segment that made business sense. J48 decision tree algorithm was used for classification purpose. In addition to those attributes that are believed by the experts to have high impact on customer segmentation, attributes value of loan amount have a big influence. Generally, the result of the study was encouraging, which reinforces the possible application of data mining solution to the microfinance industry, particularly, in customer segmentation and prediction in Buusaa Gonofa microfinance institution



Data Mining