Predictive Analytics for Customer Lifetime Value (CLV) Optimization: Estimating CLV to Inform Strategic Marketing Decisions for Maximizing Profitability

Customer lifetime value (CLV) estimates can be predicted with the help of the predictive analytics tools while the customer acquisition costs can be calculated simultaneously. This research explores various predictive models such as regression analyses, machine learning algorithms like neural networks or decision trees among others as well as cohort analysis for forecasting CLV. We will also look at how these models affect customer segmentation, retention and acquisition strategies improvement. A case example will be provided where we analyze an e-commerce transaction dataset so as to demonstrate how predictive analytics assist in making optimal decisions that result in increased profits while maintaining existing customers over time.