Customer Purchase Prediction and Potential Customer Identification for Digital Marketing Using Machine Learning

Abstract

In recent years, digital marketing has surpassed traditional marketing as the preferred technique of reaching customers. Researchers and academics may utilize it for social media marketing and for predicting client buy intent, among other applications. It can boost customer happiness and sales by facilitating a more personalized shopping session, resulting in higher conversion rates and a competitive advantage for the retailer. Advanced analytics technologies are utilized in conjunction with a dynamic and data-driven framework to expect whether or not a customer will make a purchase from the organization within a certain time frame. To increase income and stay ahead of the competition, one must understand customer buying habits. Several sectors offered rules to explore a consumer's potential based on statistics results. A machine learning algorithm for detecting potential customers for a retail superstore is proposed using an engineering approach.