Applying Data Mining Techniques for Customers Segmentation and Prediction: the Case of Dashen Bank
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Date
2023-06
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Addis Ababa University
Abstract
The banking sector has changed significantly in how it does business, putting a greater emphasis on contemporary technology to stay competitive. The banking sector has begun to understand how critical it is to build a knowledge base and use it to the bank's advantage in the field of strategic planning. Finding clients who are more likely to be interested in a product or service is a crucial task. Data mining has been widely used for customer segmentation and identification in order to predict potential customers for a given product or service.
This study uses a six-step hybrid Knowledge Discovery Process model with the goal of applying data mining for the purpose of customer segmentation and prediction. The necessary data was gathered from the Bank's CBS database, and then pre-processing operations like data transformation and cleansing were performed in order to produce high-quality data for use in data mining with WEKA software. The goal of this thesis is to create a model that can be used to categorize Dashen Bank customers based on their transactional data and forecast which customers will be profitable for the bank. Since there are no predefined classes that describe the customers of the bank, the researcher uses clustering techniques (such as Kmeans, Filtered cluster and Farthest First) that resulted in the appropriate number of clusters for customer segmentation. K-means clustering, which divides potential customers based on their monthly credit turnover, produces the best descriptive model. By labeling the unlabeled data set as a result of clustering, classification algorithms like J48 Decision Trees, K Nearest
Neighbor (KNN), and Naive Bayes can be used to build a model that allows for customer prediction. Researchers divide the data into three distinct clusters based on the transactional amount range by using a clustering algorithm. The labels "SMALL," "MEDIUM," and "CORPORATE" are applied to these clusters. "The range of transaction is the main distinction between these clusters. Experimental result shows that, out of the three algorithms, J48 decision tree with 70/30 test mode have the highest performance accuracy of 92.08%, which is selected for customer prediction.
After consulting with experts, the data mining analysis revealed intriguing and unexpected attributes and patterns. The findings indicate that customers who exhibit basic deposit behavior with an overdraft facility are classified as corporate transactional customers. On the other hand, customers who hold a USD account are also categorized as corporate customers, which results in higher profitability for the bank. The study is based on only transactional data and customers are segmented on their monthly activities. To get 360 customers view, further research needs to be done towards coming up with customer profiling and customer relationship management (CRM) system
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Keywords
Dashen Bank, Data Mining, Customer Segmentation, Customer Prediction