How to Prove Outbound Call Quality With Clear Metrics (Full Transcript)

[00:00:00] Speaker 1: It's really important to set the baseline of like, what do you guys define as success? What is the adherence of what you consider poor? Like for us, we try to tell them, like if you think a poor call is because it didn't sound the way that you wanted it to sound, it shouldn't be considered a poor call. A poor call is, is this a reputational risk for your business or not? Like having those definitions matters so that you can set the expectations with them, especially if they've never done these before. I'm not even talking about AI calls, just outbound calls in general. That's one part of it, right? A lot of the qualitative stuff that we started to do now is, or I'm sorry, the quantitative stuff that we've actually started to do now has been measuring out how many technical issues are we dealing with on-call. Now, my dev team, including Julian, who's in the back, wants to kill me because of that, but like we want to measure it so that we can see of all of our connected calls, how many of these are things that we can actually address versus not address so that we can go in and say, for every single dev cycle that we do, here's how we're gonna attack this because quality matters now, right? More than ever. It's still sexy. There's a lot of space where people still don't know enough about this, but focusing in on how many of these are actually quality calls, the other big measure that we have internally, and we share these with clients and they love this, is how many of these calls are ending in a natural goodbye. So regardless of if there was a double thought, if there was a long delay from a perspective, what you can go out and show them is, hey, this call ended in a natural goodbye, like a human being had that same conversation. So really from a quality perspective, and so we've been measuring that and that's been a big, big sticking point to prove that these calls are actually going well.