How to Know If an Automation Is Working

We ask every client to define success metrics before we build anything. Not vague goals, specific, measurable checkpoints. Here's the framework we use.

For voice agents: we track call volume before and after, by call type. We measure containment rate, what percentage of calls the voice agent handles without transferring to a human. We track customer satisfaction on the calls the voice agent handles (via a short post-call survey if possible). And we track the team's time saved: how many calls per week are no longer touching a human.

For data automations: we track error rates before and after. How many times data was entered incorrectly in a manual system? After the automation, that should be zero. We track the time cost, how many hours per week does the team spend moving this data manually? After the automation, that should be zero. And we track latency: how long between a trigger event and the data arriving in the destination system?

These aren't complicated metrics. Most businesses have the data already, in call logs, in system reports, in the team's memory. The hard part is writing them down before you start, not after.

The checkpoint that matters most: error rate. If your manual process had a 5% error rate and your automation has a 0% error rate, the automation is working even if everything else looks the same. Errors are expensive, they cost time to fix, relationships to repair, and sometimes money. Eliminating them is the clearest ROI an automation can deliver.

Define your metrics before you build. Measure at 30 days and 90 days. If the numbers aren't moving, the automation needs fixing. If they are, you have proof of concept for the next one.