Prediksi Keberhasilan Pemasaran Layanan Jasa Perbankan Mengunnakan Algoritma Logistic Regreesion

Abstract

Determining public interest in marketing banking services using data mining techniques. Prospect segmentation is one of the processes used in the marketing strategy of the banking industry. Data mining support plays an important role in classifying potential bank customers and evaluating the success of marketing their services. This is important to support the conclusion about the success rate of telemarketers in carrying out bank marketing tasks. a product whose way of working requires information about potential customers. This is a classification technique that is often used to classify prospects using logistic regression according to research maps supporting prospect data mining. Defining an accurate data mining classification algorithm to predict telemarketing success based on a 2010 experiment. In marketing banking service products, the results of the evaluation process of this algorithm are determined by cross-validation, Confusion Matrix, ROC curve and T-test. The logistic regression algorithm is more accurate with an accuracy of 92.32% and an AUC value of 0.962, so the algorithm used is included in the good classification group.