As product managers working on AI/ML projects, we often find ourselves caught between model metrics like accuracy or F1 scores and actual business impact. While data scientists focus on improving model performance, our job is to ensure these improvements translate into tangible business results.

The key difference is simple: model metrics tell you how well your model performs technically, while business metrics show the actual value it creates. For example, a model with 99% accuracy might still fail to deliver business value if it’s solving the wrong problem.

1. Start with Clear Business Goals

Define what you’re trying to achieve. Are you reducing fraud? Increasing sales? Improving user experience? Be specific about the impact you want to have on the business. For example, in an e-commerce platform, the business goal can be “increase average order value.” In a financial service, your business goal might be “reduce fraudulent transactions.”

2. Define Your Intervention

Determine the kind of action you want to take to achieve your goal. For example, to reduce fraud, your target action is blocking suspicious transactions. To increase the order value, you might want to recommend five items that are relevant to the user’s shopping cart.

3. Map Model Impact to Business Outcomes

Understand how your model directly influences business goals. An ML model can help achieve the fraud detection goal by predicting which transactions are fraudulent. A recommendation system can increase average order value by predicting which items the user is most likely to purchase.

4. Choose Measurable Business Metrics

Select metrics that directly tie to your goals. For fraud detection, track “dollars saved from prevented fraud.” For recommendations, measure “Incremental revenue from recommended items.” Note that your business metric might affect the metric used to track the model’s accuracy.

5. Consider Constraints

Don’t optimize one metric at the expense of others. A fraud detection system that catches all fraud but blocks too many legitimate transactions isn’t successful. These constraints become your secondary metrics to monitor.

Remember: The best business metrics are specific, measurable, and directly tied to business value. They should tell a clear story about how your ML model impacts the bottom line.

Example: Customer Churn Prediction

Say you want to create an AI-powered customer churn prediction system for your company. The business goal you want to achieve is to reduce the number of customers who unsubscribe from your service after being with you for at least one year.

The target intervention is to give a discount to customers who are likely to churn and ask them to fill out a survey about their problems with the product. You need to reach out at least two months before the customer churns to have enough time to turn them around.

Machine learning can help achieve this goal by predicting which customers will churn. Your target business metric is “percent of customers churned after one year.” Your ML model’s metric will be “probability of churn within the next two months.” Your model will only be trained and applied to customers who have been on the service for at least a year (note how the business goal affects the model metric and training).

Churn prediction is coming at a cost, which is giving discounted access to the product and the costs of having customer service staff process the survey results. These costs are your constraints. You want to make sure the costs of preventing customer churn do not exceed the revenue obtained from retaining customers.

To enhance your skills in working on AI/ML products, you can benefit from: