Overproof CEO Marc De Kuyper on Using AI to Predict Beverage Consumption Trends

Hello, and welcome to the latest edition of the Techstrong AI Leadership Insight series today, where with Mark Kuer, who's the CEO for over Proof. And we're talking about how AI is being applied in the beverage industry because, well, there's a lot of things that are related to each other that are not intuitively obvious. Mark, welcome to show.

Thank you. Glad to be here. So what exactly are your customers doing with ai?

Because a lot of folks would assume that after, I don't know, several centuries of understanding how to sell beverages, there would be nothing new under the sun, but what do we discover? Yeah, it's interesting. So the, the beverage alcohol space globally, uh, and particularly here in the us, um, they traditionally have like under-leveraged, uh, data.

Um, it's a very fractured market. So, um, the, the industry as a whole tends to run behind on trends like this, uh, in general. Um, and what we are doing with over proof is not only helping our customers understand the market better with the use of ai, but really present ourselves as a data partner and AI partner and a solutions architect.

Um, help them navigate how to apply AI beyond just the data that we deliver, but also like how to improve their, um, other verticals like risk management and supply chain sales management and support. Well, gimme an example of how somebody in the beverage industry is doing that. I'm sure you don't need to name names, but just describe the use case In the US specifically.

There is a three tier distribution system, and what that means is a supplier, our customers have to sell to distributors who are the middlemen and then sell onto the retail. So that's on-premise, which is hospitality or off-premise liquor stores or any, any store where you buy your product directly from the store. And the complexity there is that there's always information coming from has to channel through a distributor.

Um, and some of that information is not shared strategically, um, or just very delayed or fractional. So what we do is we help our customers understand where they are present on menus. Uh, that's a big part of what we do, at least, um, by analyzing, um, 80% of menus out there, uh, that we collect, looking for our customer's brands, and then tell them in relation to their own sales performance, how a specific cocktail is, is performing for their sales volume.

Uh, a perfect example, uh, we have a customer that has a national, actually a global focus on the espresso martini. And this is an an up trending cocktail? Absolutely, but not in every market.

So we analyze their, their local sales performance for vodkas. They have a few in their portfolio, and then look at, okay, which in which cocktail is that particular drink, uh, performing the best? And in some of the markets, they strategically decided to move away from focus on their brand in espresso martini.

And in this case they moved back to the Moscow Mule, uh, and they saw an immediate sales list sometimes, uh, in, in the range of seven 8%, which is for those bigger companies, easily millions of dollars. Does that resonate? Yeah, I get you.

I've been in a couple of bars in my day, but, um, I guess my question to you is, is it on the menu or are there other data points to collect? 'cause sometimes when you go into a restaurant or a bar, it's something that's sitting on the bar itself or behind the bar that kind of tips you one way or the other. So what kind of data points are we collecting?

Lack? We, we, we are focused on, um, on menu data. Um, we have some software applications where we can track, you know, back bar placement or in the stores when, when, uh, samplings are done.

Um, so we track multiple data points, but where, where the volume is, is really generated is a placement on the menu and not just in the drinks list, like specifically in a cocktail that drives vol. Uh, the, the majority of volume for our customers. Um, once you're an established brand, um, it's, you know, the consumer starts calling for a drink.

So that's when most of more mature brands focus on back bar placements, what's what we call it. Um, and that is becomes important, but the majority of brands are still being built, are still trying to reach the consumer. And it won't be like, like you would, you know, like mention a brand like Tito's.

Tito's on the rocks is something you hear a lot. But a, a new vodka brand you won't hear a consumer call that. So you wanna be in the menu and we're helping our customers understand which menus to target, which venues to target, and what specific cocktail, uh, is highly, is best recommended for, uh, each brand.

Is it my imagination or are there more different types of cocktails than ever? It seems like every time I go into someplace somebody's invented something new and different. And is that a deliberate strategy?

'cause they're experimenting with which things might resonate with the end customer? No, absolutely. So, um, trends are changing all the time.

They're seasonal. Um, uh, some are related to literally the temperature outside, but, um, sometimes it's, it's just, uh, a movement of, of trends, um, that's not necessarily related to the weather. But, um, what, so where I think, um, AI can, can really play a role is, is predict when a trend is upcoming.

So the aggregate of menu data and actually sell into a bar with the, with the notion or with the, with the da, with the data back report saying that we are seeing that the specific spreads not to call out brands all the time, um, is, is popping up on menus all around you at a specific price. We suggest to take this like our brand in this cocktail and, and help the, their customers, the, the, the bars and restaurants to, uh, to, you know, be be b ahead of a trend also. Is the AI you're using largely predictive or is it generative, or what type of AI are you applying to this Predictive when it comes to, like, we can predict when a bar, uh, is due to change the menu, the menu cycles.

Well, it's called some bars change on the season. Some bars change, uh, three, four times a year or specific dates. Uh, we have historic data of about three years.

Um, so we can, we can actually see or predict when a bar is about to change a menu. The other part type of AI that we use is to classify these strengths. So to your earlier question, like which cocktails, how do you classify a cocktail?

Like a martinis a martini, but they're leechy martinis. There's their, uh, you know, some people would say like the, the classic martini is, is vodka based, orgin based, there's espresso martini, there's the, uh, uh, passion fruit martini and so forth. So our models automatically classify, and once there's enough data or enough of the same, uh, data points that we find, let's say the passion fruit martini, then we start to classify specifically as a passion fruit martini because the data is, is large enough to start doing that.

So the classification and the, uh, segmentation of a, of specific cocktail is where, um, where we, we have a lot of, um, benefit of, of ai. Um, and then the other part is the sales performance. Sales performance is more looking at correlations between sales or, um, consumer data or social media data where we bring, um, where we do basically proactive, um, analysis or automated generated reports for sales forces to act on.

So this creation of actual insights, um, that traditionally happened through an a an army of, uh, analysts at the larger companies, smaller companies don't have that luxury of having so many analysts looking at data. So that's where, um, the, the AI comes into play, um, to automate that and provide Salesforce with, uh, actual insights. Are you also applying this to things like beer and wine, or is that more closely or tracked already?

No, so definitely, um, we analyze every aspect of the menu, including food where beer brands can use the, the data is, is understanding like the composition of, of, um, um, of a menu, like how many are, um, draft on draft versus on the bottle, what kind of drinks, uh, what kind of beers are, um, on draft or, you know, sold by the bottle. Um, and even certain flavor profiles. So we, we use online data to understand like what kind of IPA, is it more fruity or more hoppy?

And then we, we index, um, that kind of information as well. Another, uh, application of using more than just, um, the spirits data is the food menu. Um, so we, we help the Salesforce of our customers understand what, what cocktail fits best with a couple of drinks, uh, uh, food items on the menu and automatically generate a, a, a sales template where they can say, train the, the waiters in, in a specific bar, which is a very, uh, common thing to do.

Uh, you wanna train the wait staff on, on your product. So when you do this, we can, you can recommend, like, these are three, three items on your menu that pair very well with my drink, specifically in that cocktail that's listed. And you give them kind of a sales argument to upsell from one drink to, um, um, to the drink.

You're, you're selling, we're applying that to wine and we're applying that to, um, to beer as well. What about the retail outlets themselves and all the liquor stores? Is that another place where you're collecting data from or is that already in my point of sale system anyway?

So, um, in the off-premise, which is liquor stores for the mo most part, um, we have a, a, a software application that, uh, tracks the execution of sampling events. So tasting. Um, so we track who, who, which consumer comes in, uh, male, female, approximate age group, did the person try, did the person, like did the person buy?

So it's really conversion data. Um, we correlate that to the weather, uh, to location in the store, um, obviously the timing day of the week, time of the day, and, um, bring that in context of, of, um, of the may trend as well and help our customers, um, understand when and where to best do their samplings. Um, and, um, and then optimize even to the level of like, which talent should we use again to sell our product in, um, during those events.

So also there, it's all about data collection and, and bringing back the insights, uh, to optimize the execution. So how do you know when I go to the bar and I order drinks that it's actually for me because, well, I live in a multi-generational house and I might be out with my father-in-law or my son and some of the stuff that they drink I wouldn't touch with a 10 foot bull. So in a bar, it's, it's usually, it's the flavor that, um, that, that, um, that informs a decision.

Um, so what we are seeing a lot is, um, customers using, um, regional data or local data to help sell, like this is a specific flavor profile that works for, for this area, and that typically is aligned with what consumers mostly like to drink at the personal level. Like it's, it's, it's still, um, I think a challenge to, to help in a consumer make a decision other than the example I gave earlier where, uh, you could recommend a specific flavor that goes well with, um, with a food item. So if someone orders a bronzino, um, our data shows, for instance, that a, a yuzu infused product works pretty well with the branda and, and it's ordered often.

So we're using the data there more as a recommendation engine, but not really to understand what the consumer wants. It's more like a recommendation. So yeah.

'cause in that scenario you might be accused of actually stalking people to a level where that might be too granular. Right? Exactly.

Exactly. What what we are working on is a, is a new application where, um, after tasting in the store and someone likes the product, um, that, or even purchase the product, we help, um, convert that a consumer like to go to a bar and actually order that, that drink or that, that that particular product. Um, and that's a very hard, um, not to crack.

Uh, and the way we're doing that is basically a QR code, uh, coupon sponsored by the brand owner so you can get a discount, um, at the bar level. Mm-hmm. But that's something that's pretty tough to do, Is one of the things that you're kind of taken advantage of is that people have a lot of affinity for whatever it is that they decide to drink over time.

And, um, they're creatures of habit and they tend to order the same thing over and over again. Um, uh, on the one level, I imagine that's a good thing. On the other end, it might be a bad thing.

So how do you nudge somebody over to something in a way that doesn't annoy them? Uh, that's a golden question. Um, uh, if I had the answer, I would probably also, uh, have a, uh, more focus on, on that, the actual sales part of, of this.

But, uh, I don't know if I can answer that like a data where AI can assist in that. Um, it, it, it, it, that's more an interaction, especially in bars in hospitality. It's really about the recommendation that the server or the bartender provides you with.

Um, so it's, it comes back to training, um, and that's what a lot of brands focus on to train the wait staff and, and, and to, to win over the hearts of bartenders because they ultimately are the one introducing you to a change of your habit. Um, and, um, the data can help them to better understand like when to do like the recommendations or the, the, um, fruit flavor fit, that kind of stuff. Um, but in the, in the hospitality, it's, it really is about winning as many bartenders and wait staff people for your brand.

That's why it's not easy to, to win in this market. Well, s you heard it here. A lot of people out there are pretty keenly interested in what your favorite alcohol beverages at any given moment.

So next time you have one, just think of all the people who wanna know what you're drinking. Hey, mark, next being on the show. Thank you.

Appreciate it. Thank you all for watching the latest episode of the text on that AI leadership series. You can find this episode and others on our website.

We invite you to check them all out Until Lynn, we'll see you next time.