The Exchanges

Q Yep. So if someone's paying you 500 grand per year, or actually let's do your low rent, 250 grand per year, or about 20 grand a month, what do you need to look at when you're signing them up as a customer and how much gross merchandising value do they need to be doing for you to know that there's room to shave off 250 worth of cost savings?

A Well, we, we've been able to get a minimum of three percent top line sales, you know, so if you're a, If you're a hundred million dollar company, that means you're, we're, we're helping you generate three million in, in, in a new sales of which two thirds is cost of goods sold. So we would drive a million to the bottom line in that case. So a million there. We're getting paid two 50. That would be the kind of low end of ROI. So typically we're working in companies at the small end that are a hundred million. And most of our clients are kind of in the 1,000,000,002 billion dollar range. And then we have some really large clients who are, who are in the ten billion dollar plus range.

AI assessment note: “typically we're working in companies at the small end that are a hundred million”

Q Ah, I see. And is that, you know, typically unsafe? So we'll see like a 20% discount. Is that similar to what you got?

A Yeah. Yeah. I'll be about a 20% discount to the next round. And we expect, you know, like we expect to do a B round within 12 to 15 months. So the goal is, you know, to cross the ten million ARR threshold this year or next year. You know, I think that that's the plan we build. We feel the market coming back a lot more sales activity in the last month. I think companies are realizing that the new normal is going to be, you know, the old normal is never coming back and everyone's come to grips with, they got to get back to business. So feeling the momentum picking up again, actually significantly in the last 30 days.

AI assessment note: “Yeah. Yeah. I'll be about a 20% discount to the next round.”

Q So obvious question when we talk about anything retail is, is COVID good or bad for you?

A I think in the short term, it was bad. We had some, you know, uh, you know, big pause on the sales button, you know, so enterprise sales, big ACV, you know, our ACV is like half a million and up. And so, you know, sales slowed, a few retailers had trouble and paused services. So our kind of revenue this year was flat. Slightly down compared to last year, but I think overall the market, uh, the, our value proposition will be stronger post COVID as, uh, you know, companies will look for more automation. It's going to be more competitive than ever. So having kind of, you know, pricing, better pricing, better promotions, uh, and being able to automate more of the work that human beings do retail merchants are overloaded with work. So I think given that e-commerce has gone up dramatically. So I think for us, I, Well, I wouldn't wish it on anybody. I think in the long run, it's a good thing for us.

AI assessment note: “I think in the short term, it was bad... in the long run, it's a good thing”

Q So how many quota carrying reps do you still have today then?

A So today we have. I'd say we have like, uh, you know, three quota carrying reps, but we, but the process now, but we've added with the leadership team being more involved in sales. I'd say our, before our sales team was about, uh, eight in total. Now it's like 11 in total with a different mix of skills, right? So I think we sell enterprise software. The realization was that We're selling a partnership wrapped around a product, not just a product. And so getting the people who deliver to our customers more involved in selling, that's been the big change. And so that mix of quota carrying rep versus subject matter expert, that's where we really. Brought in more subject matter experts and gave them, you know, a role to participate in sales more.

AI assessment note: “I'd say we have like, uh, you know, three quota carrying reps”

Q once you swipe your consumer swipes a credit card in a daisy intelligence retailer outlet, that's sort of after the fact data. You then have to sort of use after the fact data to make, to enable the retailer to make decisions before they have any real data to base that on, right? So, so how does analyzing data after the fact help you make the retailers make smarter decisions?

A Well, because we have a mathematical theory that's independent of the data. It's like the laws of physics. You know, if you had like, I think of an autonomous race car, you know, you wouldn't, There's no historical data how to drive a lap around a racetrack. You're not building a predictive model, so you have the laws of physics, and you say, okay, I can simulate the laws of physics, and so, and then the historical data just kind of configures the simulation, like the gravity, wind resistance, friction of rubber on the road, so the same way we use the historical data to calculate cannibalization, halo sails, elasticity, and then we plug it into our laws of physics, which is this kind of theory of retail, and Similarly, we have a theory of risk, so we're simulating the future, and you don't necessarily have to have ever done it in the past. It doesn't need a labeled example, so the historical data just lets us calculate seasonality and, ah, you know, elasticity, cannibalization, halo, pull forward, and we plug those into our laws of physics, and then simulate the future to say, What's the optimal decision set I can execute, even though you've never done it before.

AI assessment note: “the historical data just kind of configures the simulation”

Q Well, so walking through, you just told me pre-show that you went out and raised another round. How were you able to raise another round where you weren't getting crazy diluted considering the company had shrunk a bit?

A Um, because, uh, you know, our existing investors believe in the company. In Canada, there's government matching programs to help, uh, companies. You know, we're in a hot space, AI. We're one of the, we're the fat, one of the fastest growing companies in Canada. Even though this year was a spin, a slight decline, we're still in the top hundred fastest growing companies. So between the space, the belief in our value proposition, um, our existing investors put some money in it and the Canadian, uh, kind of government backed VC funds put a couple. So we, you know, we raised like five million, uh, A couple of weeks ago. So that's our runway, you know, for another. 18 months.

AI assessment note: “our existing investors believe in the company. In Canada, there's government matching programs”

Q Did you change anything Gary about your burn? I mean, again, back in April, I think you told me you're burning about 500,000 dollars a month in net burn. Uh, 65 people on the team. Did you have to trim anywhere to get more runway?

A Yeah, we trimmed. I mean, we lowered burn. We let some people go. I think it was, you know, an opportunity to, you know, make sure we have the right people on the bus, you know, throw us a bit of that, and then just, you know, cut back burn to extend the runway, and, uh, you know, even though we raised money, we're still, you know, making sure we have 18 months of runway, that's, that's the That's the worst case scenario. And so that's, that's, that's the plan we have in place. And so we're really being very, very diligent on making sure whatever we spend generates ROI. So we're focused on spending on things that are sales, marketing, customer success. So new customers and keeping existing customers. That's the, that's the focus of the investment capital.

AI assessment note: “Yeah, we trimmed. I mean, we lowered burn. We let some people go.”

Q That's great. Talk to me about, about churn. So obviously churn's critical in this kind of business. What's your revenue churn been over the past year?

A Our revenue churn is negative. So, um, you know, we've been growing, we've lost a few customers. The big challenge for us is change management. You know, we're flying right into the face of human machine interaction. So some merchants are Have a, you know, don't want to have us stepping on their toes and telling them what to promote and what prices to charge. That's their job today. So in the companies where we haven't achieved that change, we've lost a few early clients. But, and overall our clients have been paying more. We typically give them a discount in the first year, uh, to account for the ramp up time and the second year the fees go up. So from, from the revenue perspective, it's negative churn, although we've.

AI assessment note: “Our revenue churn is negative.”

Q Gary, you're considering a raise. What general kind of range are you at today in terms of monthly recurring revenue?

A Yeah, we're kind of at four million in ARR, and, and so we're kind of right at the threshold of where a lot of the kind of VCs, large VCs, series A, you know, so, you know, we, you know, we're looking kind of at a ten million plus raise. The more, the better. I think ten million goes quickly. It allows us maybe to scale to one global market and, uh, to invest more in our kind of fintech offering, which we've kind of been very opportunistic about to date. Um, and, uh, we envision then to do a series B to grow the scale of the company beyond, uh, you know, outside to other markets. So we're thinking kind of either Europe, Asia, Latin America, and if we find a partner who's willing to put in more, then we'd go to more markets at once.

AI assessment note: “we're kind of at four million in ARR”

Q Got it. And quota carrying reps, is that where you trimmed?

A Um, we trimmed a little bit on, I mean, there's some, yeah, some reps. We trimmed a few sales reps. We trimmed, because we changed, we also took this time to retrench, like a six month pause. We kind of, you know, updated our sales process and our sales team and changing the way we go to market a little bit. So, so there's some changes in that. So there's a couple of direct sales positions that we eliminated, getting different kind of people involved in sales. So more of the senior leadership team being involved in sales and trimmed a few client Client people and a few kind of, uh, general and administrative staff really, you know, kind of, you know, driven the access really, but didn't, didn't, uh, take away any staff that's directly contributing to customer sat or bringing new sales. I think that was the.

AI assessment note: “We trimmed a few sales reps.”

Q Isn't that always the fight though? Every vendor to that retailer is, is trying to have direct attribution so that you can charge more. That's the whole battle.

A Yeah, and we, and no retailer will ever admit to us that you did this, right? Like, because if they do that, it's negotiating leverage. So, I mean, we, we have a very solid mathematical calculation that's pretty, pretty strong, and, and, uh, you know, and then we're asking, say, hey, we'd like to measure all the other things you did, too, so we can do attribution. We're, you know, we're happy, you know, we're not saying that they're not doing nothing, you know, and so that's, we're figuring out how to tell the value attribution story, and I think once we, then we're on the cusp of that, I think that will, you Drive our, our growth more. You know, we've been more of a challenger sales process in the past, and so we're stopping the challenger process and being more, hey, what you do is great. You know, the world has changed, you know, there's risk of change, you know, our software works in the new world, and, you know, you can keep doing what you're doing, and so, you know, retailers gotta say, is the risk of staying the same more or less than the risk of changing? So now, as opposed to saying, we're really great, and you know, what you did was crappy, you know, that was totally it.

AI assessment note: “Yeah, and we, and no retailer will ever admit to us that you did this”

Q Gravity is a law. You can't arbitrage gravity, no matter what it's going, things are coming back to the ground. So if what you're saying really is a mathematical rule, like a law, and you make it something that everyone starts using you, well, it can't work for everybody because at some point consumers don't spend money across all the retailers using Daisy intelligence. How do you count for this?

A You know, we're optimizing the market given a given entire market. We're looking at all the competitors in the market as well. So it's optimizing a total company and we've modeled the retail dynamics and insurance dynamics to saying, if you do these things and obviously, you know, there's some assumptions like. You know, the market is fixed. You know, there's a seven hundred billion dollar grocery market and, you know, for the grocery industry and, you know, given that the money is fixed, yes, it will shift between companies. You know, you know, every dollar you grow is taken from somewhere. You know, it's either customers not spending that at another competitor or a restaurant or something else. So, so that, that's the dynamics we figured out. And, you know, it's not like, I wouldn't say it's like Einstein's theory of relativity. That's a very absolute provable laws. Ours are, You know, they work to drive massive sales growth. I mean, we've grown our client total company revenue by three to five percent, which, you know, on a, on a base of, uh, you know, our largest customers, it's more than a billion dollars in annual revenue. So it, it works good enough to generate that value. I would say it's kind of, it's, uh, it's, it's not an absolute law, but it's, it's pretty close because it works. We use the exact same math at every single customer.

AI assessment note: “given that the money is fixed, yes, it will shift between companies”