Speaker 1: Welcome to Chopping It Up. I'm your host, Mike Hanlon,
Speaker 1: the senior restaurant and food service analysts at Bloomberg Intelligence.
Speaker 1: Our research and that of bi's five hundred analysts around
Speaker 1: the globe can be found exclusively on the Bloomberg terminal.
Speaker 1: If you enjoy the pod, I'd love it if you
Speaker 1: could leave us a review on Apple or Spotify. Today
Speaker 1: we're joined by Mike Luciano, founder and CEO of signal
Speaker 1: Flare dot ai. Signal Flare is a decision intelligence platform
Speaker 1: for restaurants and other industries that translates data into decision
Speaker 1: frameworks using AI and machine learning models. The company was
Speaker 1: the twenty twenty four Snowflake Startup Challenge winner. Welcome back
Speaker 1: to the podcast.
Speaker 2: Mike, Oh, it's my pleasure of Michael. Good to see you,
Speaker 2: good to hear you.
Speaker 1: Yeah, I was going through my notes yesterday. I can't
Speaker 1: believe it was three years ago?
Speaker 2: Is it really?
Speaker 1: Yea three years ago? You were you were episode number four?
Speaker 2: All right, well I was. I was honored then honored
Speaker 2: to be back, so thanks for thanks for having me again.
Speaker 1: Well, I always love talking about their industry with you,
Speaker 1: so it's great to have you back on episode seventy six,
Speaker 1: UH signal flair. Like I said, three years now, time flies?
Speaker 1: What inspired you to start the company?
Speaker 2: So, you know, i'd been as as you know, you
Speaker 2: know this, I'm on middle of my my third decade
Speaker 2: doing UH data and analytics for restaurants. And when I
Speaker 2: started in it, there weren't that many people who cared
Speaker 2: that much about data for restaurants, especially pulling it out
Speaker 2: of pulling out of the point of sale and you know,
Speaker 2: figuring out what it all means and building building models, UH,
Speaker 2: you know, can predict things. UH. And the world's changed
Speaker 2: a lot since then. So and this is you know,
Speaker 2: that was a few a few companies ago, and you
Speaker 2: know the last one I uh, I sold and exited.
Speaker 2: And then you know, I took some time off, you know,
Speaker 2: around twenty twenty, and turned out that some other people
Speaker 2: took some time off around them too, And as I
Speaker 2: started to analyze data, because people were still coming to
Speaker 2: me to say, you know, can you analyze this? And
Speaker 2: so forth, and a lot of the old methods, you know,
Speaker 2: when demand patterns just completely change like they did during
Speaker 2: that time, and even trade areas completely changed as people
Speaker 2: changed working from home and you know, and so you know,
Speaker 2: I had to come at a lot of these problems
Speaker 2: from a completely different in a completely different way. You know.
Speaker 2: So instead of using a lot of these linear models
Speaker 2: that look at trends over time, say, okay, well, how
Speaker 2: do I change both the statistical approaches to more of
Speaker 2: a machine learning approach but also the data sets. Right,
Speaker 2: how can I instead of just starting with the point
Speaker 2: of sale data, how do I pull data from mobile
Speaker 2: you know, mobile digital platforms, right, Uh, you know, cell
Speaker 2: phone devices to understand you know where people are shopping
Speaker 2: when they're shopping there, and you know who they are
Speaker 2: right going into every restaurant and every retailer, and you know,
Speaker 2: and then you know, bringing in local economic data and uh,
Speaker 2: credit card panels, just masses of external data you that
Speaker 2: tells us more things about restaurant consumers before I start
Speaker 2: layering and the point of sale data. Uh. And what
Speaker 2: I found was two things, right, One is that it
Speaker 2: builds an infinitely more robust model. And in two, instead
Speaker 2: of always looking in the rear view mirror, you can
Speaker 2: start to layer in things that are forward looking. So
Speaker 2: for instance, when you know when gas prices started to spike,
Speaker 2: right before you know the inflation like really went nuts.
Speaker 2: We were able to take that information because we know,
Speaker 2: you know, which restaurants are more dependent on people who
Speaker 2: drive more. We can get all of that from from
Speaker 2: the data, and we know and we can calculate what
Speaker 2: people's disposable income is, not just uh you know, not
Speaker 2: just you know what the areas, uh you know income
Speaker 2: levels are. Uh. So when we can factor that in
Speaker 2: and we know that some of these things are leading indicators, uh,
Speaker 2: then we can use that data to say, you know
Speaker 2: what these restaurants are the ones they are going to
Speaker 2: be having trouble in you know, three to five months.
Speaker 2: So what kind of strategies to you employee? Right? Do
Speaker 2: you do you do discounting? Right? Or do you send
Speaker 2: different messaging? But when you start to use it in
Speaker 2: that framework, then you know, then it becomes uh, you know,
Speaker 2: planning tools, not just you know, backward looking analysis. So
Speaker 2: so so long, long story short. I did that for
Speaker 2: a while and really moved it out with with some
Speaker 2: chains more on a consulting basis, and about two years
Speaker 2: ago I said, you know what this is. This this
Speaker 2: is a platform, right, this is this is a much
Speaker 2: different thing than what I've done in the past in
Speaker 2: terms of being able to make it, you know, scalable
Speaker 2: and accessible to like really any size restaurant. Yeah, it's
Speaker 2: really cool.
Speaker 1: What's how many units does your smallest customer have?
Speaker 2: I think our smallest is a unit. You know, we're
Speaker 2: in the process now of taking all of that data
Speaker 2: and putting it into a platform that's going to be
Speaker 2: a lot easier to access, you know, even I think
Speaker 2: to independent restaurants because the way that the way that
Speaker 2: you had to operate the data in the past, you know,
Speaker 2: to do the kind of work that we're doing, and
Speaker 2: we had to pull in all of their clean point
Speaker 2: of sale data and so forth. But now we know
Speaker 2: what's actually going on, you know, in the surrounding community,
Speaker 2: right we know, you know how people are spending and
Speaker 2: what the trends are, and you know what segments they're
Speaker 2: spending and how much they're spending and all of that.
Speaker 2: So uh and we're putting uh, you know, an AI
Speaker 2: interface over it that also leverages all of the major
Speaker 2: major models. So you know, by doing that, we're able
Speaker 2: to cross section our machine learning models, you know, with
Speaker 2: the broader market understanding. And you know, you know, before
Speaker 2: I was a in restaurant data and tech. I was.
Speaker 2: I was restaurant operator. So the idea that we can
Speaker 2: you know, go from you know, early days working only
Speaker 2: with the very largest mega chains to now being able
Speaker 2: to provide similar things all the way down to small
Speaker 2: chains and independence and that that excites me, right, not
Speaker 2: just for you know, for the technology, but for what
Speaker 2: we were able to do for the industry.
Speaker 1: Yeah, it's really cool. So you know, as I mentioned,
Speaker 1: it's a decision intelligence platform. Did you always envision it
Speaker 1: to be that or was it? Did it start you
Speaker 1: know with menu pricing? You know, was there one problem
Speaker 1: that you were most about solving back when you started it?
Speaker 2: Yeah, So you know, I think you know, as you know,
Speaker 2: and you know a lot of folks who I've worked
Speaker 2: with over the over the years, you know, I've built
Speaker 2: a lot of pricing models, right, so you know a
Speaker 2: lot of you know, my my last company you know,
Speaker 2: still is the pricing engine for for a lot of
Speaker 2: national chains out there. So you know the methodology of
Speaker 2: how you apply sophisticated pricing algorithms to restaurants. It was
Speaker 2: the starting point. But the way that I was building it,
Speaker 2: you know, in order to build a really robust pricing model.
Speaker 2: You need to not just understand, you know, what people
Speaker 2: are paying for the price, and you know, it's not
Speaker 2: just about competitive benchmarking. That's that's a part of it,
Speaker 2: but you really need to understand what the fundamental demand
Speaker 2: is around each restaurant so that you're not just saying,
Speaker 2: you know, okay, how is this trending or how do
Speaker 2: I compare against them? But how do you layer everything
Speaker 2: from you know, sentiment to macroeconomic, microeconomic, you know, competitor
Speaker 2: performance data. How do you take all of those things?
Speaker 2: And once you bring all those things in, then the
Speaker 2: kinds of questions that you can answer go way beyond pricing, right,
Speaker 2: it goes to everything about you know, how do I
Speaker 2: optimize my market? Right? How do I look into new markets?
Speaker 2: You know, how do I start planning budget planning? And
Speaker 2: how do I do scenario analysis, you know, to to
Speaker 2: be able to react or to prepare for contingencies you know,
Speaker 2: in the competitive market or in the macroeconomic landscape. I think,
Speaker 2: you know, what I've been doing my whole career really
Speaker 2: has been what I call that last mile of turning
Speaker 2: the data into the decision. But in the past, you
Speaker 2: know that had to really happen by bringing insights and
Speaker 2: visuals and summary data to a really small our analysts,
Speaker 2: and all of that is is changing now because you know,
Speaker 2: when you can bring that kind of summary data in
Speaker 2: and have it read by an l M or or
Speaker 2: or an analyst agent, it need to be trained, right.
Speaker 2: It's still like having you know, an intern, you need
Speaker 2: to you need to bring along and teach. But it's
Speaker 2: moving so quickly that I'm just every day I get
Speaker 2: more surprised and impressed and excited about what those you know,
Speaker 2: what those possibilities are. Yeah, that's cool.
Speaker 1: And you were kind enough to invite me to your
Speaker 1: AI Restaurants summit recently. It was a great event and
Speaker 1: I learned a lot, but a couple of things stood out. One,
Speaker 1: can you talk a little bit about why data quality
Speaker 1: is so important for AI and machine learning projects? I
Speaker 1: was kind of floored about the number of product projects
Speaker 1: that fail due to data quality issues.
Speaker 2: Yes, yes, no, really really important. And it's funny because
Speaker 2: you know, maybe a year ago I was at another
Speaker 2: conference and you know, I heard somebody talking about you
Speaker 2: know how oh well, now in the age of AI,
Speaker 2: you know, you don't need to worry about the data
Speaker 2: anymore because a I will just clean it up and
Speaker 2: it's like nothing could be farther from the truth, right
Speaker 2: there are you know, there are ways that you can
Speaker 2: use AI to help you, you know, clean it up
Speaker 2: and so forth. But you really the standard in the
Speaker 2: bar for data, you know, cleanliness and context and and
Speaker 2: usability is higher, not lower, right, because what an ll
Speaker 2: M will do, right, what these you know, you know,
Speaker 2: the chat GPTs and the clauds and all these they
Speaker 2: will give you an answer that sounds wonderful, right, even
Speaker 2: if there's no data to support it. They they you know,
Speaker 2: what's amazing about them, right, is that they can get creative, right,
Speaker 2: as creative as a person. So you know, the data
Speaker 2: might not be there right, or it might interpret it
Speaker 2: completely wrong, but the pros sounds wonderful, right. And if
Speaker 2: you've got you know, crappy data that's not structured, and
Speaker 2: you know, in fact, really what they're best at is
Speaker 2: reading text documents. You know, if all of your data
Speaker 2: is in a data warehouse, you need another layer over
Speaker 2: that to say, okay, well how do I summarize this
Speaker 2: and how do I chunk it out? And how do
Speaker 2: I make sure that it's in usable nuggets, so that
Speaker 2: then the machine can can can interpret it. You know,
Speaker 2: people are using you know, these for calculations, but they're
Speaker 2: not good at calculating. You know. Maybe it's a unpopular
Speaker 2: you know fact, right, I don't know popular opinion. It's
Speaker 2: a fact. They can invoke tools, right that can help
Speaker 2: to do a calculation. But when it comes down to it, right,
Speaker 2: I think you know the stats that I put out there.
Speaker 2: You know, look, you know eighty percent of of AI
Speaker 2: projects are still failing, and seventy five percent of those
Speaker 2: are failing because they didn't get the data, right.
Speaker 1: Yeah, staggering numbers. Yeah, and you mentioned the fact that
Speaker 1: ll ms are terrible at math. We also discuss some
Speaker 1: other concerning issues, including hallucinations and excessive praise.
Speaker 2: Yeah, it is interesting how you know they can be
Speaker 2: tuned right, and you can tell it, you know, don't
Speaker 2: don't praise me, or you can tell it, you know,
Speaker 2: you know, you can try and give it a personality,
Speaker 2: which is fascinating. But yeah, I mean hallucinations is just
Speaker 2: you know, a fancy way of you know, of saying
Speaker 2: you know, it just it's just b s's right, their
Speaker 2: language models, right, they read you know everything that's ever
Speaker 2: been written, and then they figure out how to you know,
Speaker 2: you ask a question and then it's you know, searching
Speaker 2: this repository to try and say it in the way
Speaker 2: or the style that you specified. So you know, you know,
Speaker 2: and you know an example of like you know, bad
Speaker 2: mathroom an l M. Right, it's like you know, we
Speaker 2: we we're constantly doing you know, tests and validations, but
Speaker 2: you you don't know what the answer is that you're
Speaker 2: going to get because it's all probabilistic, right. What you know,
Speaker 2: what we do in analytics is deterministic. Right. You need
Speaker 2: a number, right that you've got some level of certainty
Speaker 2: and there's this and there's a clear calculation to it.
Speaker 2: You know. L l M s are all you know, Hey,
Speaker 2: if you ask this question, then the probability of this, this,
Speaker 2: this and that right ur X and y and that's
Speaker 2: what all these sort of computational nodes are doing as
Speaker 2: it's doing the calculation. So, uh, you know, so if
Speaker 2: you telling it, you know, an l l M without
Speaker 2: any more instructions, you know, hey, tell me what the
Speaker 2: year of a year sales are for you know, X,
Speaker 2: Y and Z company. You know twenty you know this
Speaker 2: year versus last year. The most likely answer you're going
Speaker 2: to get out of that is going to be something
Speaker 2: that says, oh, well, you're down about you know, fifty percent,
Speaker 2: you know versus last year. Why because we're about halfway
Speaker 2: through the year, right, So it's comparing this year, you know,
Speaker 2: the last year without any more you know, clarity than that.
Speaker 2: So as you're either figuring out how to prompt it
Speaker 2: and ask the questions correctly, or you're trying to figure
Speaker 2: out how to do the engineering in the background, you know,
Speaker 2: the things that the ways that we talk about things
Speaker 2: and the common vernacular that we use, you know in
Speaker 2: this industry, as you know, it's like, okay, well, what's
Speaker 2: a check average? You know, as fifteen different operators, what
Speaker 2: you know what that means to them? Right? How are
Speaker 2: you calculating that check average? It's all right, Well, you
Speaker 2: kind of need a single standard, right, or a very
Speaker 2: clear definition of all of these things in order for
Speaker 2: them to work and to learn.
Speaker 1: Yeah, I learned at the event that it's important on
Speaker 1: us to ask the right question. That's you get better
Speaker 1: at using these lll ms. One of the subjects that
Speaker 1: I brought up was just concern about you know, security
Speaker 1: of your data and what the lll ms are doing
Speaker 1: with your data. Actually saw a great tweet today. It
Speaker 1: was Stealing from one person is called theft. Stealing from
Speaker 1: everyone all at once is called chat GBT.
Speaker 2: Yes, no, I think. I think that this is a
Speaker 2: reality of where we are. It's really important because you know, people, organizations, right,
Speaker 2: first of all, it's moving so quickly, and you know,
Speaker 2: once any you know, analyst, employee, marketing, you know, administrator,
Speaker 2: whoever it is in the organization, once they start using it,
Speaker 2: they start to find all of these ways that, oh
Speaker 2: this can actually save me. You know this amount of
Speaker 2: time you know doing these three, four or five tasks.
Speaker 2: So it's addictive very quickly, right because people or like
Speaker 2: I can do a better job with it. You know,
Speaker 2: it's one of the best productivity tools you know, invented
Speaker 2: probably since you know, since Lotus one, two three, right
Speaker 2: precursor to to Excel for those of who don't know. So,
Speaker 2: if you don't have a policy for enterprise level, which
Speaker 2: typically right the the g p T, the l M
Speaker 2: will say that in their enterprise versions, you know, none
Speaker 2: of your data is used to train their models. There's
Speaker 2: another way of using it, which is, if you use
Speaker 2: the A p I S in your own environment, then
Speaker 2: you know, there's no backward propagation, right because it's it's
Speaker 2: being calculated in yours and you're using you know, you're
Speaker 2: getting the benefit of it, but there's no way that
Speaker 2: it can actually backward propagate, which is, you know, which
Speaker 2: is the better the better process. But if you have
Speaker 2: no policy, right and you're not, you know, and then
Speaker 2: if people are taking your company data and just using
Speaker 2: it on a free version or a personal version, all
Speaker 2: of that information is being you know, you're you're you're
Speaker 2: donating to the you know, to the broader knowledge base
Speaker 2: of the world right with whatever you put in there.
Speaker 2: So so yes, you need to be looking at enterprise
Speaker 2: grade tools to be able to use this, but don't
Speaker 2: fool yourself that, you know, if you're not using it,
Speaker 2: then uh, you know that then you're safe. You know,
Speaker 2: if you don't have a policy in place, then you're
Speaker 2: probably in more security danger than than if you have
Speaker 2: one and you're actually teaching people the right way to
Speaker 2: use it.
Speaker 1: Great, let's switch gears. With all the inflation we've seen
Speaker 1: since twenty twenty, pricing menus down to the unit level
Speaker 1: is more important than ever. And you know, I guess
Speaker 1: what I'm wondering is what chains are doing it right?
Speaker 1: Right now?
Speaker 2: Yeah, I think you know, without naming name specifically, but
Speaker 2: I think that the things that you see are common
Speaker 2: with success are you know, those that I think are
Speaker 2: are actually well. One thing is that, you know, what
Speaker 2: we keep on seeing that qs RUH is lagging, you know,
Speaker 2: even entry level casual dining and and fast casual and
Speaker 2: when economic times are uncertain, usually we expect that people
Speaker 2: are going to trade all the way down to QSR
Speaker 2: because it's the least expensive option. But that is not
Speaker 2: what we're seeing, right. We're seeing that it's you know,
Speaker 2: it's you know, it's a lot of the fast casuals
Speaker 2: and so forth that are doing a lot better, especially
Speaker 2: if the cuisine that they're serving up is not yet
Speaker 2: you know, at a saturation point and in a lot
Speaker 2: of their markets. Right, I think what the consumer is
Speaker 2: telling us is that, you know, they are making choices,
Speaker 2: but their choices are being guided not by what's the
Speaker 2: least expensive thing that I can get, but what actually
Speaker 2: represents the value, Right, what's going to what's going to
Speaker 2: give me better service, what's going to be the taste
Speaker 2: profile that I want, because if I'm going to dine out,
Speaker 2: you know, maybe less often, I want to make sure
Speaker 2: that those occasions that I am dining out, you know,
Speaker 2: I'm actually getting what I want.
Speaker 1: Yeah, that one has caught us by surprise over the
Speaker 1: last five months, the fact that casual dining and it's
Speaker 1: broad based. It's not just Chili's, it's broad based. Most
Speaker 1: of the casual dining chains we cover, you know, Cheesecake Factory,
Speaker 1: Texas Roadhouse still crushing it, Dartin Cracker Barrel, all these
Speaker 1: chains are now performing qs R. Part of it is
Speaker 1: they're they're lapping easier comp but they're being them by
Speaker 1: a wide margin. Man. We haven't seen that, you know,
Speaker 1: in a market, in a slower restaurant spending market. I mean,
Speaker 1: I don't think I've never seen it, you know, since
Speaker 1: I've been in the business.
Speaker 2: Well, you know, the other thing that we really need
Speaker 2: to think about in context, right, is how how the
Speaker 2: industry shrank and then grew, you know, from twenty nineteen
Speaker 2: to now and where it's going. You know, we all
Speaker 2: remember back in mid COVID eight times, right, you know,
Speaker 2: staggering numbers of closings. Right, It's like, okay, like twenty
Speaker 2: percent of the industry was closing. And but you know
Speaker 2: what if you had a drive through, you know you
Speaker 2: were living you know, you were living the good life.
Speaker 2: You know, you had you know, there was you know,
Speaker 2: there were a few businesses that were as good as
Speaker 2: you knows as having a drive through right in an
Speaker 2: underserved market, right. And what ended up happening is that
Speaker 2: unit account started to grow, but it was all limited service.
Speaker 2: So the ratio of limited service to full service restaurants
Speaker 2: is completely different than it was back in twenty eighteen,
Speaker 2: twenty nineteen, big overbuilding of you know, of limited service restaurants,
Speaker 2: not not as much, you know, catching up with the
Speaker 2: full service. So you know, and yeah, and the way
Speaker 2: that the trade areas you know change, right. You know,
Speaker 2: people sort of you know move to more you know,
Speaker 2: more rural where they could because their workplaces would do it.
Speaker 2: Maybe they had to move a little bit closer, but
Speaker 2: you know, not so much into city centers. So all
Speaker 2: of those factors together, right, are showing these traditional metrics
Speaker 2: of same store sales as opposed to total industry growth right
Speaker 2: or total industry usage. They are two very different looks
Speaker 2: at the industry, right, because the the saturation of markets,
Speaker 2: especially when you look at different cuisine types and uh
Speaker 2: service styles. Because population growth is also, you know, is
Speaker 2: not near what it was five years ago either, So
Speaker 2: rest the restaurant build build out is outpacing population growth
Speaker 2: by about you know, about five x.
Speaker 1: Wow, five x is a lot. What I found interesting
Speaker 1: about twenty twenty as well, is like QSR had that
Speaker 1: year where they were able to raise prices pretty aggressively
Speaker 1: because they were the only game in town, and so
Speaker 1: I feel like their pricing got ahead of itself faster
Speaker 1: than it did for some of their full service competitors.
Speaker 2: Yeah, I think, I think you're right. And it wasn't
Speaker 2: just the menu prices. It was also that they eliminated
Speaker 2: every discount, right, So all the dollar menus disappeared, right,
Speaker 2: all of those you know, all of those you know,
Speaker 2: special things that you get, you know, if they gave
Speaker 2: you some price certainty inside those restaurants for some period
Speaker 2: of time, all of those just just vanished. Right. That's
Speaker 2: starting to come back, But they sort of retrained customers, right,
Speaker 2: They started, you know, started to find things that were
Speaker 2: you know, oh well, I can get better food you
Speaker 2: know all the time you know, at these different types
Speaker 2: of uh, you know, restaurants, you know, also limited service
Speaker 2: you know. But I but I like it better, right,
Speaker 2: So it really I think it created something that you know,
Speaker 2: I mean, we'll see if people revert. But qs R
Speaker 2: every day is getting more and more like off premise
Speaker 2: grocery you know, or you know, and even the convenience
Speaker 2: stores are getting you know, getting better, you know at
Speaker 2: you know, really upping their game. So those options, right,
Speaker 2: you know, competing there where it used to be the
Speaker 2: safe the safe bet you know, you know, recession resistant,
Speaker 2: it's not really that that's not really the case anymore
Speaker 2: now that they seem to be more vulnerable, you know.
Speaker 1: And and I want to kind of hit on one
Speaker 1: of the points you made about the industry and how
Speaker 1: much it's growing faster and how much faster it's growing
Speaker 1: than population growth. So as everybody knows there's a lot
Speaker 1: of bankruptcies last year, you know, are we are we
Speaker 1: still overbuilt even after these bankruptcies or are we approaching
Speaker 1: over built? Where where does it stand today?
Speaker 2: So you know, we got updated data that compared you know,
Speaker 2: end of twenty twenty four to twenty three. And so
Speaker 2: what we what we knew from then is that you
Speaker 2: population and also we got the migration report, right, so
Speaker 2: we start so we know how people are moving domestically
Speaker 2: and internationally, right, So which which you know, which communities
Speaker 2: are growing fast, which ones are shrinking, and which of
Speaker 2: those are happening from uh international migration. Right. What we
Speaker 2: haven't seen yet in this year, which I'm i hear
Speaker 2: a lot anecdotally, is how much has the you know,
Speaker 2: tightened immigration policies affected border towns and so forth right.
Speaker 2: It is how much you know what's going on with
Speaker 2: the population base, and is that population based less likely
Speaker 2: to you know, go out and use restaurants and so
Speaker 2: forth right. So I think that even before that we
Speaker 2: saw that, yes, you know, the restaurant growth, even after
Speaker 2: taking into account closures and bankruptcies, is still growing a
Speaker 2: lot faster than population. I think we're going to see,
Speaker 2: you know, in retrospect that community that you're in makes
Speaker 2: a big difference in terms of, you know, how saturated
Speaker 2: you are and how hard you've got to work for that,
Speaker 2: you know, that share of stomach.
Speaker 1: Yeah, it's interesting. I know Wingstop had mentioned that that
Speaker 1: spending from their Hispanic customers was kind of weak around
Speaker 1: them around the tariff, you know, all the teriff excitement
Speaker 1: back in April.
Speaker 2: Yeah, and I've I've heard that from you know, more
Speaker 2: than a more than a couple of brands you know
Speaker 2: that that have you know, higher Hispanic populations. And you know,
Speaker 2: you know it shouldn't shouldn't be shouldn't be too surprising, right,
Speaker 2: you know, you make, you make certain choices, and not
Speaker 2: all choices are good for everyone for sure.
Speaker 1: Uh So cp I was released yesterday. Did anything about
Speaker 1: that report stand out to you?
Speaker 2: Yeah, Look, I think the restaurants are still you know,
Speaker 2: as you know, you know headline CPI is is is
Speaker 2: moderating closer to target, still above restaurants actually you know,
Speaker 2: ticked up a little bit, you know, particularly uh, full
Speaker 2: service restaurants, which you know, as you mentioned, you know,
Speaker 2: limited service was you know, was increasing prices you know,
Speaker 2: by a lot more for for quite a few years now,
Speaker 2: for the past several months, you know, actually going into
Speaker 2: end of last year, we've seen that full service price
Speaker 2: increases are are are now ahead of you know cp
Speaker 2: I for for limited service. So it'll be interesting to see,
Speaker 2: you know, how that happens. And I think. Look, it's
Speaker 2: it's consumer driven really because you know, when we compare,
Speaker 2: when we compare cp I going back you know, even
Speaker 2: three years, uh two, check averages you know from you know,
Speaker 2: credit card sampling data and so forth. You know, we're
Speaker 2: seeing that, you know, check averages in the industry are
Speaker 2: not growing at nearly the rate of of of cp I.
Speaker 2: So what's that telling us, Right, that's telling us that
Speaker 2: restaurants are increasing their prices. But h but consumers are
Speaker 2: managing their spend, you know, to not go too far
Speaker 2: outside of what they're you know, what they were, what
Speaker 2: they planned, right. Uh So that that's that's that I
Speaker 2: think is important because it means that restaurants that think
Speaker 2: that they can just you know, do a one time
Speaker 2: you know, three percent, three three and a half four
Speaker 2: percent increase, they're probably only you know, after your customer
Speaker 2: you know, manages their checked, they're probably only getting you know,
Speaker 2: maybe two percent of that, right. I mean, the real
Speaker 2: ratios of just you know, across the board types of increases,
Speaker 2: you know, are about fifty percent flow through, right, Whereas
Speaker 2: if you instead said, you know what if we went
Speaker 2: lower you know, in a more measured way, you know,
Speaker 2: and hit the one and a half two percent. You know,
Speaker 2: when you're doing it that way, you're staying more within
Speaker 2: what the consumers willing to take. You can measure it,
Speaker 2: you can maybe even do more, but you're more likely
Speaker 2: to get in the nineties, you know, than you know,
Speaker 2: and that ends up being much healthier for for the brand. Right.
Speaker 2: I think a lot of the larger companies understand that,
Speaker 2: you know, and you know, they may a lot of
Speaker 2: them even like may say, oh, we're not changing our prices,
Speaker 2: but you know what they're franchise systems, So yes, they
Speaker 2: are increasing the prices. But the better guidance they can
Speaker 2: have in terms of you know, being moderate, right and
Speaker 2: making sure that you're measuring what the consumer is willing
Speaker 2: to spend along with it, that's how you make sure
Speaker 2: that you're growing and growing in a healthy way.
Speaker 1: Yeah, we're seeing a lot of our companies are showing
Speaker 1: a decline in their mix, and you know, part of
Speaker 1: it is due to alcohol, but you know, part of
Speaker 1: it is probably customers managing checks and to your point, yeah,
Speaker 1: it's a part of its supply and demand. For some
Speaker 1: of these chains that are doing a better job, they're
Speaker 1: drawing more people in they're able to raise prices. Some
Speaker 1: chains that are doing much better. I already mentioned Chili's
Speaker 1: Cracker Barrel. They're also starting to lean into strategic pricing
Speaker 1: right now.
Speaker 2: Yeah, I mean you have to write and I think
Speaker 2: it's actually a lesson that restaurants need to you know,
Speaker 2: look to other ends streets about, you know, because in
Speaker 2: the past and during certain times, you know, especially during inflation.
Speaker 2: Right when it's inflationary, it's like, oh, I'm going to
Speaker 2: increase my prices by you know, three and a half
Speaker 2: four percent in some cases you know, eight percent, and
Speaker 2: it's like, oh yeah, you know, full flow through terrific, right,
Speaker 2: look at our check average. It's like, okay, well that
Speaker 2: that's that's not normal times in the world. Right. So
Speaker 2: it's actually on the back end of those types of
Speaker 2: inflation bubbles that it gets it becomes much harder. But costs,
Speaker 2: haven't you know, come back down to earth, especially you
Speaker 2: know in our industry where it's the cost of labor, right,
Speaker 2: this is becoming even more scarce. How do you how
Speaker 2: do you make sure that you are threading that needle
Speaker 2: right because it is you know, if you go too high,
Speaker 2: you're going to get punished. By the consumer. But if
Speaker 2: you go too low, we're a thin margin business, right,
Speaker 2: You've got to you know, you are going to have
Speaker 2: to pass on some thing to the consumer just to
Speaker 2: make the economics work.
Speaker 1: For sure. Week restaurants spending by low income consumers has
Speaker 1: been publicized for a while now, at least eighteen months
Speaker 1: or so. A few of the companies that cover said
Speaker 1: the cohort was weak in March. Has their spending recovered
Speaker 1: with industry seam source sales in the second quarter.
Speaker 2: Yeah, So what we're seeing is, you know, I guess
Speaker 2: I would call it a flattening, right. So, you know,
Speaker 2: particularly last year, we saw you know, pretty major fallout
Speaker 2: of you know, of the of the lowest income consumer.
Speaker 2: But I think they got you know, beaten down, you know,
Speaker 2: to sort of a minimum minimum level, or at least
Speaker 2: compared to other other cohorts, right, we had the upper
Speaker 2: and middle income customers that were still pretty strong. So
Speaker 2: but what we've seen really over the over the course
Speaker 2: of this year is that, you know, while the low
Speaker 2: income customers don't necessarily come back and drove, That's not
Speaker 2: what I'm suggesting, but the mix of customer you know,
Speaker 2: is has sort of stabilized at the lowest and the
Speaker 2: highest income. But what we're really seeing is the middle
Speaker 2: falling out, right, So you know, it's you know, sort
Speaker 2: of this trickle across and you know, so I think
Speaker 2: that that's you know, it may be contrary to what
Speaker 2: we've been thinking about. You know, you know, strapped customers
Speaker 2: still still an important consideration, but even in like you know,
Speaker 2: fast casual, what I was surprised to see is that,
Speaker 2: you know, the the mix of lower income customers and
Speaker 2: fast casual is actually growing because the middle has fallen
Speaker 2: out so much. That has implications, right, which means that, okay, well,
Speaker 2: if the low income customers are now making up a
Speaker 2: bigger mix of your of your customer base, then that
Speaker 2: means that you're also likely to experience more uh, you know,
Speaker 2: more more price sensitivity.
Speaker 1: Yeah, for sure, it's concerning obviously if the middle income weakends.
Speaker 1: You know, we've had a couple of our companies mentioned
Speaker 1: a little deterioration there, but hasn't really been broad based
Speaker 1: against across the national chains that we cover. You know,
Speaker 1: hopefully the talk about tax cuts, which should help the
Speaker 1: middle middle class, will support their spending in the second half.
Speaker 2: We'll see yeah, I think you know the whole you know,
Speaker 2: it's been a year that you know, the the word
Speaker 2: of the year I think is is turning into uncertainty, right,
Speaker 2: So you know, and and that's when you see the
Speaker 2: you know, consumer survey based data and so forth. Right,
Speaker 2: you know you're seeing look, and a lot depends on
Speaker 2: what party you are, right, But but even there, right,
Speaker 2: there's a there's a universal agreement that you know, things
Speaker 2: are things are very uncertain, right, And you might say, oh,
Speaker 2: well that's good, right, or maybe that's the big differentiator
Speaker 2: by by party, Right, is it good or is it bad?
Speaker 2: That is uncertain Well, it depends what the outcome is
Speaker 2: either way. It means that you know, there are customers
Speaker 2: and companies and so forth that are you know, don't
Speaker 2: don't love the uncertainty, right, So so they're going to
Speaker 2: delay some decisions, you know. So to your point on
Speaker 2: this in the later half of the year, if there's
Speaker 2: tax cuts and so on and so forth, you know,
Speaker 2: could that help We'll eat, yes, But it depends on
Speaker 2: who believes that, you know, who believes that that's coming, right,
Speaker 2: because it's that it's for a lot of people, it's
Speaker 2: you know, how do I feel, you know, that dictates
Speaker 2: how much I'm going to spend, right.
Speaker 1: Yeah, for sure, fine dining same source sales that were
Speaker 1: very weak in twenty twenty three and twenty four. What
Speaker 1: are you seeing in that segment? And do you think
Speaker 1: higher asset prices can boost results later this year?
Speaker 2: So I think that's also very much a mixed bag, right.
Speaker 2: So going across markets, you know, d m as, even
Speaker 2: micro markets. You know, if you are if you're in
Speaker 2: one of those areas where there are very few fine
Speaker 2: dining options, then you know you're and you've got a good,
Speaker 2: you know, good consumer base for it, then you're probably
Speaker 2: doing very well. But then there's also you know, some
Speaker 2: that are that are not doing so well right because
Speaker 2: of you know, where where they are and how they
Speaker 2: compare to say, you know, the more affordable full service options.
Speaker 2: So you know, again I think like the we've rarely
Speaker 2: seen things so call it trade area dependent, right, it's
Speaker 2: you know that can really dictate you know, who's looking
Speaker 2: good and who's and who's.
Speaker 1: Not right now, okay, and CDR chains have increased their
Speaker 1: marketing spend a good bit over the last six months
Speaker 1: to a year, particularly on TV ads. Why hasn't the
Speaker 1: industry made greater inroads with one to one marketing?
Speaker 2: I think that there's a there's an in between level, right.
Speaker 2: I think that what the industry you know, gets wrong
Speaker 2: oftentimes in implementing you know, what's called one to one
Speaker 2: marketing is how different say their psychographic customer study you know,
Speaker 2: translates into well, how do I actually operationalize that through
Speaker 2: a database campaign that can actually be geared toward individual customers.
Speaker 2: And that's a huge leap, right, especially for marketing departments
Speaker 2: that you know, oftentimes you know came from you know
Speaker 2: consumer uh you know, uh consumer package goods background or
Speaker 2: something where you know, these psychographic studies that are have
Speaker 2: really been effective in you know, broad based advertising, but
Speaker 2: you cannot convert you know, those psychographics into you know,
Speaker 2: behavioral data, right that you collect within you know, within
Speaker 2: those technologies that can do the one to one. The
Speaker 2: other thing is that you know, most of the ones
Speaker 2: that are being designed that are capable of one to
Speaker 2: one marketing in restaurant industry, it's all about the It's
Speaker 2: all about the CD right, the consumer or the customer
Speaker 2: database our data platform. So and that means that in
Speaker 2: order to communicate certain things to people at that level,
Speaker 2: they already have to be in your database, right, an
Speaker 2: identified customer who you can talk to, which is great, right,
Speaker 2: it's an effective thing has been to wave through the industry.
Speaker 2: But the reality of that is that you can't you
Speaker 2: can't grow your customer reach, you know, through the database
Speaker 2: of people who are already your customers. Right, So how
Speaker 2: do you take that same concept and apply it to
Speaker 2: people outside of your outside of your database, you know,
Speaker 2: and say, okay, well, I know that the people who
Speaker 2: like my brand, who are heavy consumers of this and
Speaker 2: so on and so forth, they live here, right, and here's
Speaker 2: how I can get to them, because it's close enough
Speaker 2: to my stores to be able to do that. That
Speaker 2: is not that is not a common, uh usage of
Speaker 2: the technology, So they sort of revert back to the
Speaker 2: you know, to the mass market. Even in you know,
Speaker 2: even in d m A level you know, uh, digital spending,
Speaker 2: usually it's you know, you've got to buy the whole
Speaker 2: d m A right, if you want to get very
Speaker 2: specific about your your your demo. But really the technology
Speaker 2: exists so that you know, you can say, look, I
Speaker 2: just want people in this neighborhood, you know, to be
Speaker 2: able to see this kind of ad because I know
Speaker 2: that you know, all of them are you know, within
Speaker 2: a distance, right, and that becomes much less expensive, uh
Speaker 2: to execute. But it's it's not commonly understood. I think
Speaker 2: across you know, across the marketing spectrum, and.
Speaker 1: What's your view on QSR discounting, you know, more or
Speaker 1: less the same Over the next six to twelve months, I.
Speaker 2: Think it's going to become more and I think that
Speaker 2: there's there's plenty of discounting going on. I think that
Speaker 2: a lot of them, you know, we're trying to keep it,
Speaker 2: you know, to our database, you know, conversation, you know,
Speaker 2: a little bit more low key, low profile, you know,
Speaker 2: give people very specific discounts and that works within you know,
Speaker 2: within your existing customer base. Right if you've identified them
Speaker 2: as as as low income, it doesn't help you to
Speaker 2: expand your reach, you know. And I think that you know,
Speaker 2: one of the things and we're starting to see it, right,
Speaker 2: is you know, what can I get for five bucks
Speaker 2: or what can I get for ten bucks? Right? Because really,
Speaker 2: if we look historically through the through the industry, people
Speaker 2: think about the discount as being so important, but really,
Speaker 2: if you look at the successful campaigns over the past
Speaker 2: twenty five years, it's really been about, you know, price certainty, right,
Speaker 2: how much am I going to spend when I go out? Right?
Speaker 2: Is it three dollars? Is it five dollars? You know,
Speaker 2: ten dollars even you know Ruth Chris had the Rus
Speaker 2: Chris classic, Right, It's like, okay, you know eighty five dollars, right,
Speaker 2: but you knew that you could go there and that's
Speaker 2: what you're going to spend done a per person basis, right,
Speaker 2: And I think that that helps people to make these
Speaker 2: these decisions, right to say this is how much money
Speaker 2: you know I'm going to spend today, right, or this
Speaker 2: how much money it is in per per head in
Speaker 2: my family. Right. So so I think that you know,
Speaker 2: those I think we're going to see a lot more
Speaker 2: of that, you know, hammering certain price points that consumers
Speaker 2: see appealing.
Speaker 1: It's like one of the first things you think about
Speaker 1: is how much you want to spend for occasions like
Speaker 1: a lunch. And you know, I remember you were one
Speaker 1: of the first to pour cold water on dynamic pricing,
Speaker 1: and that was a big part of it. Right. It's like,
Speaker 1: if I don't know how much I'm going to spend
Speaker 1: for my cheeseburger. You know, I kind of eliminates it
Speaker 1: from my consideration set.
Speaker 2: That's right. And look, I think you know, there are
Speaker 2: cases for you know, dynamic pricing, and there are ways
Speaker 2: that it works in times that it works. But but
Speaker 2: you need to be very careful about it, right, because
Speaker 2: what does it do you know psychologically into your relationship
Speaker 2: with the customer? You know, a better use case to me, right,
Speaker 2: rather than changing you know, your price of a cheeseburger
Speaker 2: by you know, a dime twenty five cents, you know,
Speaker 2: by the minute or whatever is, you know, how do
Speaker 2: I dynamically choose the right bundle, right and try? And
Speaker 2: you know, think of think about price for my customers,
Speaker 2: not as how much they're spending on each subcomponent of
Speaker 2: their order, but how do I know, how do I
Speaker 2: get them to spend more with me in that visit?
Speaker 2: Not because I've forced them to you know, swallow a
Speaker 2: higher price on everything, but because I did a better
Speaker 2: job of using technology to you know, to upsell the
Speaker 2: things that they actually want to add to their order, right,
Speaker 2: so that you know, instead of spending five bucks with me,
Speaker 2: they're spending seven. And that's not because I just increased,
Speaker 2: you know, the prices by by two bucks. Right, it's
Speaker 2: because you know they got an add on, right, or
Speaker 2: they got something that actually makes them enjoy the experience more.
Speaker 1: Yeah, then for help selling is a much better use
Speaker 1: case in my opinion. Look, Man, I could do this
Speaker 1: with you all day. Thanks for doing this, man.
Speaker 2: Yeah, no, thanks for having me, Michael. It's always it's
Speaker 2: always a it's always a pleasure, and you know I
Speaker 2: love talking about this stuff anyway, So you know what,
Speaker 2: we'll have to meet up over a beverage sometime soon
Speaker 2: for sure.
Speaker 1: Man. I want to also thank the audience for tuning in.
Speaker 1: If you'd like to learn more about signal Flarer dot ai,
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