An Intelligent Personalized Digital Coupon System for Shopping Mall Retail

Abstract

In recent years, there has been a proliferation of innovative applications of deep learning and large data. For instance, marketing and corporate administration are two examples. Customer attrition management is a critical component of marketing that can significantly impact the efficiency of a business. This research demonstrates that real-time big data analysis can be employed to send personalized discount offers to consumers who are at risk of leaving. As a result, the rate of customer loss decreased, while the rate of purchase increased. At the outset, we will employ cluster analysis to examine consumer groups that are limited to two dimensions. The subsequent step is to analyze the clickstream data from each cohort and develop a model for predicting attrition in real time. This data can be employed to generate sales that are tailored to the preferences of each consumer. The effectiveness of the plan was evaluated by examining both increased sales and higher conversion rates. The results indicate that a hybrid model that integrates attrition estimation with recommendation systems is more effective in predicting and engaging consumer behavior than traditional individual models. Support Vector Machines enable online businesses to determine the most effective allocation of their marketing budgets in order to increase customer retention, increase sales, and reduce the likelihood of customer turnover.