The Exchanges

Q Interesting. How do people beef up? I mean, I imagine the downs. I mean, I would love using your platform, but let's say I'm H&M and I sign up and you tell me you need these 10 profiles of salespeople to match the traffic data. They're going to think, crap, I've got to go hire four extra personalities I didn't have before. How do you manage that?

A Yeah. So, you know, the way it works is that, um, So first of all, we look at a metric called shopper yield. So the top salespeople will have a shopper yield metric of almost 10 X what the average salesperson will do. So let's say for instance, H&M, maybe the top person does a hundred dollars per shopper. The average may do 10 dollars per shopper. But what happens is the, the kind of traffic varies throughout the day. So there are times in the day when you need really, really great sellers. You need a hundred dollars per shopper type of shopper yield kind of people. And there are times when the You know, traffic is very transactional. So it really doesn't matter who you have. So, um, so really what it is, is what is the right people to have at the right time. Now we don't hypothesize in terms of, well, you need this kind of people. What we do is we look at the current staff and we figure out, okay, given the staff you have available and given the traffic coming in, what is the right mix of people? So we don't try to say, well, what if you had this kind of Uh, characteristics of, of salespeople. We just use existing salesforce that they have. We just shuffle them around so that they could actually sell more.

AI assessment note: “We just use existing salesforce that they have. We just shuffle them around”

Q Yeah. Well, I'm, I'm, listen, I'm rooting for you. How are you finding new customers?

A You know, it's, it's really weird. You know, like before we were trying to sell sensor data, it was, it was like pulling teeth, like, like trying to find customers. It was really, really hard. Now it's almost the exact opposite. We're not even trying and people are like calling us. We were getting all these leads. We get referrals. And I think the reason why is because, um, the value proposition is pretty strong for the retailer. So essentially for every dollar that they pay us, they get about 20 to 30 dollars back, sometimes even 40 dollars back in terms of return on investment. So they get massive return in terms of every dollar they pay us, they get about 20 to 40 dollars in terms of extra revenue.

AI assessment note: “We're not even trying and people are like calling us. We were getting all these leads. We get referrals.”

Q Interesting. So I love the fact that pricing is actually tied to the utility metric. Most that most directly correlates to value that your software provides, but it also makes it challenging. It probably takes some time to scale to that point from a first touch of a potential customer. How do you manage the onboarding?

A Yeah. So that's actually one of the toughest challenges we have because, um, in order for our system to work, we need many integrations. So we need the punch data. So the time and attendance. So when are people working, we need HR data. We need to know how much people will make, who's available to work, um, you know, what kind of restrictions they have. We need to also have, uh, the point of sales data. So there's several different types of systems that we have to integrate to. So we're very much an enterprise class of solution. Um, we need to integrate it all to all these different systems, and sometimes to even people's workforce management system. Um, and then all this data feeds into our, our machine learning models that allows us to forecast what is the right mix of people to have. So the onboarding is, um, not quite a, you know, free trial, then you run and roll. It's, it's more of a You know, it's more of a, um, we have to accept these integrations. Then once we have the integration set up, then we can actually optimize the schedules for our retailers.

AI assessment note: “in order for our system to work, we need many integrations”

Q Okay. And how did you said you finally got product market fit in 2016? What metrics were you looking at where you said, yep, we hit it. Today's a good day. We crossed the, we crossed the river.

A Yeah. People ask me this all the time. Like, how do you know you have product market fit? Um, so before we had product market fit, our sales cycle was like, Quarters to close deals, literally quarters. And, you know, I'll put on my knee pads begging to close deals. It's really, really difficult. And so when people ask me, how do you know if you have product market fit? I tell them, look, if it's easy to sell, you probably have product market fit. If it's really hard to sell, as if it's like pushing a boulder uphill, it's probably not a great fit. So now we're able to close deals in about one or two meetings, maybe three meetings. So it's like, but we, we, we had like three different business models. We, you know, started one thing. Yes. No exaggeration. Yeah. So we, um, so I felt kind of like Moses in the desert, you know, like trying to find a promised land where like, you're, you're trying to get this thing to, to work and nobody's buying. Right. It was really hard to sell. And, um, yeah, so it just, it's, it's, it's very frustrating. And so this, um, you know, so Uh, trying to find market. I have great admiration for people that find product market fit because it's really hard to do.

AI assessment note: “now we're able to close deals in about one or two meetings”