PERSONALIZED DIGITAL COUPON ISSUANCE FOR ENHANCING RETAIL REVENUE IN SHOPPING MALLS

Abstract

Big data and deep learning have recently gained prominence in a variety of industries, such as business administration and marketing, and have found new applications. Customer attrition management is a critical component of marketing that directly influences the efficiency of a company. This research demonstrated that real-time big data analysis may result in the provision of personalised discount coupons to clients with a high turnover rate. Consequently, consumer attrition decreased, while buy conversion rates increased. Initially, cluster analysis is employed to investigate two-dimensional consumer categories. Afterwards, we evaluate the clickstream data for each cohort in order to construct a real-time attrition prediction model. Sales that are customised to the preferences of each customer are generated and distributed through the use of insights such as these. The strategy's effectiveness was evaluated by factoring in revenue growth and conversion rates. The findings indicate that a hybrid model that integrates attrition estimation and recommendation systems outperforms conventional individual models in terms of consumer behaviour forecasting and engagement. By employing Support Vector Machine to objectively assess churn probability and purchase trends, online businesses can optimise their marketing expenditures, retain consumers, and boost sales.