Machine LearningBased Prediction of Customer Lifetime Value Using Purchase Behaviour and Customer Ratings

For companies looking to better understand consumer behaviour and enhance their decision-making processes, customer analytics has become a crucial component. Predicting Customer Lifetime Value (CLTV), which assists businesses in estimating the long-term value of consumers based on their purchasing patterns and engagement levels, is one of the most crucial components of customer analytics. This study suggests a machine learning-based method for evaluating consumer data and forecasting CLTV based on past purchasing patterns, customer reviews, and demographic characteristics. buy frequency, average order value, total spending, days since last buy, satisfaction level, and membership type are just a few of the parameters included in the information. To find correlations between these variables and customer lifetime value, a linear regression model is used. To assess the predictive performance of the model, the dataset is split into training and testing sets. The effectiveness of machine learning in customer value prediction is demonstrated by the experimental findings, which reveal that the suggested model achieves good predictive capabilities with an R2 score of 0.83. Businesses can enter client data into the system, which is built as a web-based application, to receive real-time CLTV predictions. This method can help businesses make data-driven business decisions, optimise marketing initiatives, and enhance client retention methods.