Customer Lifetime Value Prediction and Retention Strategies

Abstract

Customer Lifetime Value (CLV) plays a pivotal role in understanding the true worth of a customer to a business and developing effective customer retention strategies. This study aims to analyse and predict CLV using a retail dataset, employing formula-based calculations, regression modeling, and advanced analytical techniques. The research methodology encompasses the calculation of key metrics such as Average Order Value (AOV), Purchase Frequency, Churn Rate, and Profit Margin, which serve as inputs for the CLV computation. A predictive model is developed using Linear Regression, incorporating customer attributes and transaction data as independent variables and CLV as the dependent variable. The study reveals several key insights, including a high R-squared value of 0.96 for the Linear Regression model, indicating a strong correlation between the independent variables and CLV. Additionally, the model exhibits low values of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), attesting to its predictive accuracy. The results align with existing literature and highlight the significance of leveraging transactional data and customer behaviour patterns for CLV estimation. Based on the CLV insights, the study proposes retention strategies such as personalized marketing campaigns, tiered loyalty programs, customer service excellence initiatives, re-engagement campaigns, and product/service enhancements. These strategies aim to maximize customer value and foster long-lasting customer relationships, contributing to the field of customer relationship management and predictive analytics. The research acknowledges potential limitations and assumptions while identifying avenues for future work, including the incorporation of additional data sources, exploration of advanced machine learning techniques, longitudinal studies, and the integration of CLV insights with other business intelligence tool