The use of churn prediction to improve customer retention in grocery e-retailing

Abstract

As retailers embrace the online shopping experience and technology advances, it is now also vital for retailers to pay attention to customer churn since it has a detrimental impact on the company's corporate development and reputation. To mitigate the negative effects of customer churn on grocery retail businesses, this study will look at how machine learning and deep learning churn prediction models are applied, as well as data analytical findings on customer retention. The implications of customer churn and how it impacts grocery businesses will be the subject of thorough research. Furthermore, an analysis of previously gathered data sets will reveal significant discoveries, customer preferences, and behaviours related to Churn. The study will examine how churn prediction affects a company's profitability, reputation, and operational efficiency. Following the study of the dataset, a thorough framework will be suggested with the main goal of proactive churn control, thereby limiting its effects on the overall growth of the company. This thesis aims to contribute to current efforts to improve corporate company growth by studying customer behavioural patterns most associated with churn and then suggesting solutions to the challenges