How Voice A/B Tests Can Lift In-Store AI Sales Results (Full Transcript)

[00:00:00] Speaker 1: Hi, I'm Yue Liu. We're building an in-store salesperson. So, one of our hypotheses is that we want to use different voices to drive, we want to test different voices to test conversion results. We're close to getting into the stores, we're launching our pilot sometime next month. But I'm just curious if you guys have experience or have done ABCD testings of different voices on driving your business results. And also personalize the voice, right? Basically, different voice for different customers, male, female, and also the vocabulary you use in your conversation.

[00:00:36] Speaker 2: I do think it's a big piece because we have clients that are specifically in the South, right? And there's a Southern draw that they may have. Or in the Midwest, I'm a fast talker, right? Versus somebody who's here in New York, right? Like, there's different ways that we kind of communicate. So, one of the things that I want to accomplish is exactly what you said, which is, A, using some baseline demographic data to determine, based on age even, the voice that's actually going out there. We haven't tried it yet, but our customers have said that this is something that they would certainly want to adopt and want to do. Just from a baseline perspective, we've tested out male versus female, just that very, very baseline. Female voices have been performing way better for us than male voices have been. Quality-wise, response-wise, for all of our clients, yeah. Just the quality of voice, but then just even the length of the conversation tends to be better with the female voices that we have. Again, no science really behind it. We've done a few different A-B testing of voice types, but beyond that, we haven't really gotten too far yet with it.