Penerapan Data Mining untuk Mendukung Pemasaran Produk Pinjaman Bank

Abstract

Competition in the banking sector requires every bank to compete to make types of credit products that are able to attract customers to want to transact with banks. Marketing with a product centric strategy needs to be combined with a customer centric to keep Customer Relationship Management in good order with customers. Customer identity can be used to realize the concept of a customer centric marketing strategy. This study describes the role of Data Mining on customer data accompanied by the application of Decision Tree techniques with the C4.5 algorithm to identify the most influential fields in predicting loan products.
 The customer data fields used in this study include total balance, date of birth, occupation, and status. Selection of fields based on the terms and conditions that apply to each type of loan. The results of applying the C4.5 algorithm to customer data in the formation of loan product prediction decision trees show that the work attribute is the dominant attribute (highest Gain value) among the other attributes. Prediction results are validated with Confusion Matrix with an accuracy value for loan product prediction of 82%.
 The results of this study concluded that the customer database is a source of data in the prediction process of loan products to be offered to customers, which can be used to support the marketing of bank loan products with a customer centric orientation.