How to Model The Expected Value of Marketing Campaigns

Expected value modeling provides an advanced framework for optimizing marketing campaign targeting. The process begins by building a machine learning model to predict a customer's purchase likelihood. A confusion matrix is then used to assign specific business costs and benefits to each potential outcome, such as the profit from a successful sale versus the cost of an unsuccessful ad click. This allows for the calculation of an expected profit equation, which reveals the minimum purchase probability a customer must have to be profitably targeted. This approach can be extended by creating profit curves to visualize expected returns at various targeting thresholds, aiding in budget-constrained decision-making.