The metrics that actually matter
Response time (before vs. after), the percentage of leads that get a qualifying question answered, and the percentage that make it to a booked call or a written proposal — see Cost of Slow Lead Response for the reasoning behind the first of these.
What NOT to use as your primary metric
Raw conversation volume or "number of leads touched" sounds impressive but doesn't tell you if outcomes improved — a system that talks to more people without converting more of them isn't a win.
A baseline before you can measure improvement
You need your pre-automation numbers (response time, conversion rate) to know if anything actually changed — a Revenue Audit captures this baseline before any build starts, specifically so this comparison is possible later.
Being honest about attribution
Not every improvement is caused by the automation — seasonality, marketing changes and other factors move these numbers too. A fair ROI read compares like-for-like periods, not just "before the project" vs. "after," ideally.
Questions
What's a reasonable timeframe to measure ROI over?+
Usually a few months of real lead volume — a single week is too small a sample to draw a fair conclusion from.
Does LATYNEX provide ongoing reporting on this?+
Yes — monthly reporting is included as part of AI Sales Manager, giving the ongoing numbers needed for this comparison.
What if the numbers don't improve?+
That's useful, honest information too — it points to a different gap (see When Does a Company Not Need an AI Agent) rather than being hidden.