Predictive models are primarily used for identifying likely defectors but also have applications in?

Predictive models leverage historical data and statistical algorithms to forecast future outcomes, making them invaluable in various applications beyond just identifying likely defectors. One significant application is in fraud detection and risk assessment.

In fraud detection, predictive models analyze patterns and behaviors that have been associated with fraudulent activities, allowing organizations to proactively identify and mitigate potential risks. This application is crucial for financial institutions and e-commerce businesses where the stakes are high. Similarly, in risk assessment, predictive models help organizations evaluate the likelihood of adverse events occurring, such as loan defaults or insurance claims, by analyzing data trends and customer behaviors.

Other options, while relevant to business operations, do not align as closely with the predictive capabilities of these models. For example, cost reduction is often a secondary benefit rather than a primary use case, and while brand awareness and customer service improvements are important business goals, they do not typically involve the type of data-driven forecasting that predictive models are designed to provide. Therefore, the application of predictive models in fraud detection and risk assessment highlights their versatility and necessity in informed decision-making processes across diverse fields.