A Novel Scheme for Marketing by Study Customer Behavior Using Data Mining Technique

A Novel Scheme for Marketing by Study Customer Behavior Using Data Mining Technique

Authors- Research Scholar Mrs. Anusha Mardia, Assistant Professor Dr. Dilip Kumar Choudhary

Abstract-In the rapidly evolving landscape of marketing, companies increasingly rely on sophisticated techniques to target their campaigns effectively. This paper presents a novel framework for customer segmentation and targeted marketing using advanced data mining and machine learning techniques. With the proliferation of customer data, traditional methods of segmentation have proven insufficient for accurately identifying and targeting potential customers. This study explores various data mining techniques, including Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, and Gradient Boosting Classifiers, to develop a robust model for predicting customer behavior based on historical data. Various studies on consumer purchasing behaviors have been presented and used in real problems. Data mining techniques are expected to be a more effective tool for analyzing consumer behaviors. However, the data mining method has disadvantages as well as advantages. Therefore, it is important to select appropriate techniques to mine databases. The objective of this paper is to know consumer behavior, his psychological condition at the time of purchase and how suitable data mining method apply to improve conventional method. Moreover, in an experiment, association rule is employed to mine rules for trusted customers using sales data in a super market industry

DOI: /10.61463/ijset.vol.12.issue4.238