Measuring return on investment
Evaluating ROI for an AI trading product combines financial performance metrics with operational and customer success indicators. Focus on both absolute returns and risk-adjusted measures to get a full picture.
Core performance metrics:
- Net returns: Profit after fees, commissions, and costs over a defined period.
- Risk-adjusted returns: Sharpe ratio, Sortino ratio, and maximum drawdown to contextualize returns relative to risk taken.
- Win rate and average trade size: Operational indicators of strategy behavior.
Operational and business metrics:
- Customer acquisition cost (CAC) and lifetime value (LTV): Measure the economics of monetizing the product.
- Churn and retention rates: Reflect ongoing customer satisfaction and product stickiness.
- Uptime and latency: Operational reliability impacts user trust and perceived value.
Calculating ROI:
- Aggregate net profit generated (for managed products) or revenue from subscriptions/licensing.
- Subtract operating costs: data, hosting, development, compliance, and support.
- Divide net profit by total investment or operating costs to compute ROI.
Example elements to include:
- Direct model alpha: Excess returns attributed to model signals beyond benchmarks.
- Cost of capital: Opportunity cost of funds deployed.
- Non-financial value: Time savings, automation benefits, or improved decision-making.
A comprehensive ROI assessment blends quantitative performance with business health. For commercial products, show both user-facing returns (performance metrics) and unit economics (CAC:LTV) to demonstrate sustainable monetization.