The Difference Between Model Accuracy and Business Accuracy

A model can be 95% accurate…
yet deliver 0% business value.

Why❔
Because data science metrics ≠ business metrics.

📌 Examples:
- A fraud model catches tiny fraud but misses large ones
- A churn model predicts already obvious churners
- A recommendation model boosts clicks but reduces revenue

Always align ML metrics with business KPIs.
Otherwise, your “great model” is just a great illusion.