June 24, 2025 • 42 mins
SignalFlare.ai’s Lukianoff on Data Analytics, AI: Choppin’ It Up
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Speaker 1 (00:13):
Welcome to Chopping It Up. I'm your host, Mike Hanlon,
the senior restaurant and food service analysts at Bloomberg Intelligence.
Our research and that of bi's five hundred analysts around
the globe can be found exclusively on the Bloomberg terminal.
If you enjoy the pod, I'd love it if you
could leave us a review on Apple or Spotify. Today
we're joined by Mike Luciano, founder and CEO of signal
Flare dot ai. Signal Flare is a decision intelligence platform
(00:37):
for restaurants and other industries that translates data into decision
frameworks using AI and machine learning models. The company was
the twenty twenty four Snowflake Startup Challenge winner. Welcome back
to the podcast.
Speaker 2 (00:51):
Mike, Oh, it's my pleasure of Michael. Good to see you,
good to hear you.
Speaker 1 (00:55):
Yeah, I was going through my notes yesterday. I can't
believe it was three years ago?
Speaker 2 (00:58):
Is it really?
Speaker 1 (00:59):
Yea three years ago? You were you were episode number four?
Speaker 2 (01:03):
All right, well I was. I was honored then honored
to be back, so thanks for thanks for having me again.
Speaker 1 (01:08):
Well, I always love talking about their industry with you,
so it's great to have you back on episode seventy six,
UH signal flair. Like I said, three years now, time flies?
What inspired you to start the company?
Speaker 2 (01:21):
So, you know, i'd been as as you know, you
know this, I'm on middle of my my third decade
doing UH data and analytics for restaurants. And when I
started in it, there weren't that many people who cared
that much about data for restaurants, especially pulling it out
of pulling out of the point of sale and you know,
(01:43):
figuring out what it all means and building building models, UH,
you know, can predict things. UH. And the world's changed
a lot since then. So and this is you know,
that was a few a few companies ago, and you
know the last one I uh, I sold and exited.
And then you know, I took some time off, you know,
(02:05):
around twenty twenty, and turned out that some other people
took some time off around them too, And as I
started to analyze data, because people were still coming to
me to say, you know, can you analyze this? And
so forth, and a lot of the old methods, you know,
when demand patterns just completely change like they did during
(02:27):
that time, and even trade areas completely changed as people
changed working from home and you know, and so you know,
I had to come at a lot of these problems
from a completely different in a completely different way. You know.
So instead of using a lot of these linear models
that look at trends over time, say, okay, well, how
(02:49):
do I change both the statistical approaches to more of
a machine learning approach but also the data sets. Right,
how can I instead of just starting with the point
of sale data, how do I pull data from mobile
you know, mobile digital platforms, right, Uh, you know, cell
(03:09):
phone devices to understand you know where people are shopping
when they're shopping there, and you know who they are
right going into every restaurant and every retailer, and you know,
and then you know, bringing in local economic data and uh,
credit card panels, just masses of external data you that
(03:30):
tells us more things about restaurant consumers before I start
layering and the point of sale data. Uh. And what
I found was two things, right, One is that it
builds an infinitely more robust model. And in two, instead
of always looking in the rear view mirror, you can
(03:51):
start to layer in things that are forward looking. So
for instance, when you know when gas prices started to spike,
right before you know the inflation like really went nuts.
We were able to take that information because we know,
you know, which restaurants are more dependent on people who
(04:11):
drive more. We can get all of that from from
the data, and we know and we can calculate what
people's disposable income is, not just uh you know, not
just you know what the areas, uh you know income
levels are. Uh. So when we can factor that in
and we know that some of these things are leading indicators, uh,
(04:32):
then we can use that data to say, you know
what these restaurants are the ones they are going to
be having trouble in you know, three to five months.
So what kind of strategies to you employee? Right? Do
you do you do discounting? Right? Or do you send
different messaging? But when you start to use it in
that framework, then you know, then it becomes uh, you know,
planning tools, not just you know, backward looking analysis. So
(04:56):
so so long, long story short. I did that for
a while and really moved it out with with some
chains more on a consulting basis, and about two years
ago I said, you know what this is. This this
is a platform, right, this is this is a much
different thing than what I've done in the past in
terms of being able to make it, you know, scalable
and accessible to like really any size restaurant. Yeah, it's
(05:19):
really cool.
Speaker 1 (05:19):
What's how many units does your smallest customer have?
Speaker 2 (05:23):
I think our smallest is a unit. You know, we're
in the process now of taking all of that data
and putting it into a platform that's going to be
a lot easier to access, you know, even I think
to independent restaurants because the way that the way that
you had to operate the data in the past, you know,
(05:45):
to do the kind of work that we're doing, and
we had to pull in all of their clean point
of sale data and so forth. But now we know
what's actually going on, you know, in the surrounding community,
right we know, you know how people are spending and
what the trends are, and you know what segments they're
spending and how much they're spending and all of that.
So uh and we're putting uh, you know, an AI
(06:06):
interface over it that also leverages all of the major
major models. So you know, by doing that, we're able
to cross section our machine learning models, you know, with
the broader market understanding. And you know, you know, before
I was a in restaurant data and tech. I was.
I was restaurant operator. So the idea that we can
(06:28):
you know, go from you know, early days working only
with the very largest mega chains to now being able
to provide similar things all the way down to small
chains and independence and that that excites me, right, not
just for you know, for the technology, but for what
we were able to do for the industry.
Speaker 1 (06:45):
Yeah, it's really cool. So you know, as I mentioned,
it's a decision intelligence platform. Did you always envision it
to be that or was it? Did it start you
know with menu pricing? You know, was there one problem
that you were most about solving back when you started it?
Speaker 2 (07:02):
Yeah, So you know, I think you know, as you know,
and you know a lot of folks who I've worked
with over the over the years, you know, I've built
a lot of pricing models, right, so you know a
lot of you know, my my last company you know,
still is the pricing engine for for a lot of
national chains out there. So you know the methodology of
(07:25):
how you apply sophisticated pricing algorithms to restaurants. It was
the starting point. But the way that I was building it,
you know, in order to build a really robust pricing model.
You need to not just understand, you know, what people
are paying for the price, and you know, it's not
(07:46):
just about competitive benchmarking. That's that's a part of it,
but you really need to understand what the fundamental demand
is around each restaurant so that you're not just saying,
you know, okay, how is this trending or how do
I compare against them? But how do you layer everything
from you know, sentiment to macroeconomic, microeconomic, you know, competitor
(08:07):
performance data. How do you take all of those things?
And once you bring all those things in, then the
kinds of questions that you can answer go way beyond pricing, right,
it goes to everything about you know, how do I
optimize my market? Right? How do I look into new markets?
You know, how do I start planning budget planning? And
(08:28):
how do I do scenario analysis, you know, to to
be able to react or to prepare for contingencies you know,
in the competitive market or in the macroeconomic landscape. I think,
you know, what I've been doing my whole career really
has been what I call that last mile of turning
the data into the decision. But in the past, you
(08:51):
know that had to really happen by bringing insights and
visuals and summary data to a really small our analysts,
and all of that is is changing now because you know,
when you can bring that kind of summary data in
and have it read by an l M or or
or an analyst agent, it need to be trained, right.
(09:14):
It's still like having you know, an intern, you need
to you need to bring along and teach. But it's
moving so quickly that I'm just every day I get
more surprised and impressed and excited about what those you know,
what those possibilities are. Yeah, that's cool.
Speaker 1 (09:31):
And you were kind enough to invite me to your
AI Restaurants summit recently. It was a great event and
I learned a lot, but a couple of things stood out. One,
can you talk a little bit about why data quality
is so important for AI and machine learning projects? I
was kind of floored about the number of product projects
(09:52):
that fail due to data quality issues.
Speaker 2 (09:55):
Yes, yes, no, really really important. And it's funny because
you know, maybe a year ago I was at another
conference and you know, I heard somebody talking about you
know how oh well, now in the age of AI,
you know, you don't need to worry about the data
anymore because a I will just clean it up and
it's like nothing could be farther from the truth, right
(10:16):
there are you know, there are ways that you can
use AI to help you, you know, clean it up
and so forth. But you really the standard in the
bar for data, you know, cleanliness and context and and
usability is higher, not lower, right, because what an ll
(10:37):
M will do, right, what these you know, you know,
the chat GPTs and the clauds and all these they
will give you an answer that sounds wonderful, right, even
if there's no data to support it. They they you know,
what's amazing about them, right, is that they can get creative, right,
as creative as a person. So you know, the data
(10:58):
might not be there right, or it might interpret it
completely wrong, but the pros sounds wonderful, right. And if
you've got you know, crappy data that's not structured, and
you know, in fact, really what they're best at is
reading text documents. You know, if all of your data
is in a data warehouse, you need another layer over
that to say, okay, well how do I summarize this
(11:20):
and how do I chunk it out? And how do
I make sure that it's in usable nuggets, so that
then the machine can can can interpret it. You know,
people are using you know, these for calculations, but they're
not good at calculating. You know. Maybe it's a unpopular
you know fact, right, I don't know popular opinion. It's
a fact. They can invoke tools, right that can help
(11:44):
to do a calculation. But when it comes down to it, right,
I think you know the stats that I put out there.
You know, look, you know eighty percent of of AI
projects are still failing, and seventy five percent of those
are failing because they didn't get the data, right.
Speaker 1 (12:01):
Yeah, staggering numbers. Yeah, and you mentioned the fact that
ll ms are terrible at math. We also discuss some
other concerning issues, including hallucinations and excessive praise.
Speaker 2 (12:15):
Yeah, it is interesting how you know they can be
tuned right, and you can tell it, you know, don't
don't praise me, or you can tell it, you know,
you know, you can try and give it a personality,
which is fascinating. But yeah, I mean hallucinations is just
you know, a fancy way of you know, of saying
(12:37):
you know, it just it's just b s's right, their
language models, right, they read you know everything that's ever
been written, and then they figure out how to you know,
you ask a question and then it's you know, searching
this repository to try and say it in the way
or the style that you specified. So you know, you know,
(13:00):
and you know an example of like you know, bad
mathroom an l M. Right, it's like you know, we
we we're constantly doing you know, tests and validations, but
you you don't know what the answer is that you're
going to get because it's all probabilistic, right. What you know,
what we do in analytics is deterministic. Right. You need
(13:23):
a number, right that you've got some level of certainty
and there's this and there's a clear calculation to it.
You know. L l M s are all you know, Hey,
if you ask this question, then the probability of this, this,
this and that right ur X and y and that's
what all these sort of computational nodes are doing as
it's doing the calculation. So, uh, you know, so if
(13:46):
you telling it, you know, an l l M without
any more instructions, you know, hey, tell me what the
year of a year sales are for you know, X,
Y and Z company. You know twenty you know this
year versus last year. The most likely answer you're going
to get out of that is going to be something
that says, oh, well, you're down about you know, fifty percent,
(14:10):
you know versus last year. Why because we're about halfway
through the year, right, So it's comparing this year, you know,
the last year without any more you know, clarity than that.
So as you're either figuring out how to prompt it
and ask the questions correctly, or you're trying to figure
out how to do the engineering in the background, you know,
the things that the ways that we talk about things
(14:32):
and the common vernacular that we use, you know in
this industry, as you know, it's like, okay, well, what's
a check average? You know, as fifteen different operators, what
you know what that means to them? Right? How are
you calculating that check average? It's all right, Well, you
kind of need a single standard, right, or a very
clear definition of all of these things in order for
(14:53):
them to work and to learn.
Speaker 1 (14:55):
Yeah, I learned at the event that it's important on
us to ask the right question. That's you get better
at using these lll ms. One of the subjects that
I brought up was just concern about you know, security
of your data and what the lll ms are doing
with your data. Actually saw a great tweet today. It
(15:16):
was Stealing from one person is called theft. Stealing from
everyone all at once is called chat GBT.
Speaker 2 (15:22):
Yes, no, I think. I think that this is a
reality of where we are. It's really important because you know, people, organizations, right,
first of all, it's moving so quickly, and you know,
once any you know, analyst, employee, marketing, you know, administrator,
(15:43):
whoever it is in the organization, once they start using it,
they start to find all of these ways that, oh
this can actually save me. You know this amount of
time you know doing these three, four or five tasks.
So it's addictive very quickly, right because people or like
I can do a better job with it. You know,
it's one of the best productivity tools you know, invented
(16:05):
probably since you know, since Lotus one, two three, right
precursor to to Excel for those of who don't know. So,
if you don't have a policy for enterprise level, which
typically right the the g p T, the l M
(16:26):
will say that in their enterprise versions, you know, none
of your data is used to train their models. There's
another way of using it, which is, if you use
the A p I S in your own environment, then
you know, there's no backward propagation, right because it's it's
being calculated in yours and you're using you know, you're
getting the benefit of it, but there's no way that
(16:47):
it can actually backward propagate, which is, you know, which
is the better the better process. But if you have
no policy, right and you're not, you know, and then
if people are taking your company data and just using
it on a free version or a personal version, all
of that information is being you know, you're you're you're
(17:10):
donating to the you know, to the broader knowledge base
of the world right with whatever you put in there.
So so yes, you need to be looking at enterprise
grade tools to be able to use this, but don't
fool yourself that, you know, if you're not using it,
then uh, you know that then you're safe. You know,
(17:32):
if you don't have a policy in place, then you're
probably in more security danger than than if you have
one and you're actually teaching people the right way to
use it.
Speaker 1 (17:42):
Great, let's switch gears. With all the inflation we've seen
since twenty twenty, pricing menus down to the unit level
is more important than ever. And you know, I guess
what I'm wondering is what chains are doing it right?
Right now?
Speaker 2 (17:57):
Yeah, I think you know, without naming name specifically, but
I think that the things that you see are common
with success are you know, those that I think are
are actually well. One thing is that, you know, what
we keep on seeing that qs RUH is lagging, you know,
(18:18):
even entry level casual dining and and fast casual and
when economic times are uncertain, usually we expect that people
are going to trade all the way down to QSR
because it's the least expensive option. But that is not
what we're seeing, right. We're seeing that it's you know,
it's you know, it's a lot of the fast casuals
and so forth that are doing a lot better, especially
(18:41):
if the cuisine that they're serving up is not yet
you know, at a saturation point and in a lot
of their markets. Right, I think what the consumer is
telling us is that, you know, they are making choices,
but their choices are being guided not by what's the
least expensive thing that I can get, but what actually
(19:02):
represents the value, Right, what's going to what's going to
give me better service, what's going to be the taste
profile that I want, because if I'm going to dine out,
you know, maybe less often, I want to make sure
that those occasions that I am dining out, you know,
I'm actually getting what I want.
Speaker 1 (19:15):
Yeah, that one has caught us by surprise over the
last five months, the fact that casual dining and it's
broad based. It's not just Chili's, it's broad based. Most
of the casual dining chains we cover, you know, Cheesecake Factory,
Texas Roadhouse still crushing it, Dartin Cracker Barrel, all these
chains are now performing qs R. Part of it is
(19:36):
they're they're lapping easier comp but they're being them by
a wide margin. Man. We haven't seen that, you know,
in a market, in a slower restaurant spending market. I mean,
I don't think I've never seen it, you know, since
I've been in the business.
Speaker 2 (19:51):
Well, you know, the other thing that we really need
to think about in context, right, is how how the
industry shrank and then grew, you know, from twenty nineteen
to now and where it's going. You know, we all
remember back in mid COVID eight times, right, you know,
(20:14):
staggering numbers of closings. Right, It's like, okay, like twenty
percent of the industry was closing. And but you know
what if you had a drive through, you know you
were living you know, you were living the good life.
You know, you had you know, there was you know,
there were a few businesses that were as good as
you knows as having a drive through right in an
(20:34):
underserved market, right. And what ended up happening is that
unit account started to grow, but it was all limited service.
So the ratio of limited service to full service restaurants
is completely different than it was back in twenty eighteen,
twenty nineteen, big overbuilding of you know, of limited service restaurants,
(20:58):
not not as much, you know, catching up with the
full service. So you know, and yeah, and the way
that the trade areas you know change, right. You know,
people sort of you know move to more you know,
more rural where they could because their workplaces would do it.
Maybe they had to move a little bit closer, but
you know, not so much into city centers. So all
(21:22):
of those factors together, right, are showing these traditional metrics
of same store sales as opposed to total industry growth right
or total industry usage. They are two very different looks
at the industry, right, because the the saturation of markets,
(21:43):
especially when you look at different cuisine types and uh
service styles. Because population growth is also, you know, is
not near what it was five years ago either, So
rest the restaurant build build out is outpacing population growth
by about you know, about five x.
Speaker 1 (22:04):
Wow, five x is a lot. What I found interesting
about twenty twenty as well, is like QSR had that
year where they were able to raise prices pretty aggressively
because they were the only game in town, and so
I feel like their pricing got ahead of itself faster
than it did for some of their full service competitors.
Speaker 2 (22:24):
Yeah, I think, I think you're right. And it wasn't
just the menu prices. It was also that they eliminated
every discount, right, So all the dollar menus disappeared, right,
all of those you know, all of those you know,
special things that you get, you know, if they gave
you some price certainty inside those restaurants for some period
(22:47):
of time, all of those just just vanished. Right. That's
starting to come back, But they sort of retrained customers, right,
They started, you know, started to find things that were
you know, oh well, I can get better food you
know all the time you know, at these different types
of uh, you know, restaurants, you know, also limited service
(23:12):
you know. But I but I like it better, right,
So it really I think it created something that you know,
I mean, we'll see if people revert. But qs R
every day is getting more and more like off premise
grocery you know, or you know, and even the convenience
stores are getting you know, getting better, you know at
(23:33):
you know, really upping their game. So those options, right,
you know, competing there where it used to be the
safe the safe bet you know, you know, recession resistant,
it's not really that that's not really the case anymore
now that they seem to be more vulnerable, you know.
Speaker 1 (23:48):
And and I want to kind of hit on one
of the points you made about the industry and how
much it's growing faster and how much faster it's growing
than population growth. So as everybody knows there's a lot
of bankruptcies last year, you know, are we are we
still overbuilt even after these bankruptcies or are we approaching
(24:11):
over built? Where where does it stand today?
Speaker 2 (24:15):
So you know, we got updated data that compared you know,
end of twenty twenty four to twenty three. And so
what we what we knew from then is that you
population and also we got the migration report, right, so
we start so we know how people are moving domestically
(24:37):
and internationally, right, So which which you know, which communities
are growing fast, which ones are shrinking, and which of
those are happening from uh international migration. Right. What we
haven't seen yet in this year, which I'm i hear
(24:57):
a lot anecdotally, is how much has the you know,
tightened immigration policies affected border towns and so forth right.
It is how much you know what's going on with
the population base, and is that population based less likely
to you know, go out and use restaurants and so
forth right. So I think that even before that we
(25:19):
saw that, yes, you know, the restaurant growth, even after
taking into account closures and bankruptcies, is still growing a
lot faster than population. I think we're going to see,
you know, in retrospect that community that you're in makes
a big difference in terms of, you know, how saturated
(25:41):
you are and how hard you've got to work for that,
you know, that share of stomach.
Speaker 1 (25:46):
Yeah, it's interesting. I know Wingstop had mentioned that that
spending from their Hispanic customers was kind of weak around
them around the tariff, you know, all the teriff excitement
back in April.
Speaker 2 (25:59):
Yeah, and I've I've heard that from you know, more
than a more than a couple of brands you know
that that have you know, higher Hispanic populations. And you know,
you know it shouldn't shouldn't be shouldn't be too surprising, right,
you know, you make, you make certain choices, and not
all choices are good for everyone for sure.
Speaker 1 (26:20):
Uh So cp I was released yesterday. Did anything about
that report stand out to you?
Speaker 2 (26:26):
Yeah, Look, I think the restaurants are still you know,
as you know, you know headline CPI is is is
moderating closer to target, still above restaurants actually you know,
ticked up a little bit, you know, particularly uh, full
service restaurants, which you know, as you mentioned, you know,
(26:48):
limited service was you know, was increasing prices you know,
by a lot more for for quite a few years now,
for the past several months, you know, actually going into
end of last year, we've seen that full service price
increases are are are now ahead of you know cp
I for for limited service. So it'll be interesting to see,
(27:12):
you know, how that happens. And I think. Look, it's
it's consumer driven really because you know, when we compare,
when we compare cp I going back you know, even
three years, uh two, check averages you know from you know,
credit card sampling data and so forth. You know, we're
(27:33):
seeing that, you know, check averages in the industry are
not growing at nearly the rate of of of cp I.
So what's that telling us, Right, that's telling us that
restaurants are increasing their prices. But h but consumers are
managing their spend, you know, to not go too far
outside of what they're you know, what they were, what
(27:56):
they planned, right. Uh So that that's that's that I
think is important because it means that restaurants that think
that they can just you know, do a one time
you know, three percent, three three and a half four
percent increase, they're probably only you know, after your customer
you know, manages their checked, they're probably only getting you know,
(28:19):
maybe two percent of that, right. I mean, the real
ratios of just you know, across the board types of increases,
you know, are about fifty percent flow through, right, Whereas
if you instead said, you know what if we went
lower you know, in a more measured way, you know,
and hit the one and a half two percent. You know,
(28:40):
when you're doing it that way, you're staying more within
what the consumers willing to take. You can measure it,
you can maybe even do more, but you're more likely
to get in the nineties, you know, than you know,
and that ends up being much healthier for for the brand. Right.
I think a lot of the larger companies understand that,
you know, and you know, they may a lot of
(29:02):
them even like may say, oh, we're not changing our prices,
but you know what they're franchise systems, So yes, they
are increasing the prices. But the better guidance they can
have in terms of you know, being moderate, right and
making sure that you're measuring what the consumer is willing
to spend along with it, that's how you make sure
that you're growing and growing in a healthy way.
Speaker 1 (29:24):
Yeah, we're seeing a lot of our companies are showing
a decline in their mix, and you know, part of
it is due to alcohol, but you know, part of
it is probably customers managing checks and to your point, yeah,
it's a part of its supply and demand. For some
of these chains that are doing a better job, they're
drawing more people in they're able to raise prices. Some
chains that are doing much better. I already mentioned Chili's
(29:47):
Cracker Barrel. They're also starting to lean into strategic pricing
right now.
Speaker 2 (29:52):
Yeah, I mean you have to write and I think
it's actually a lesson that restaurants need to you know,
look to other ends streets about, you know, because in
the past and during certain times, you know, especially during inflation.
Right when it's inflationary, it's like, oh, I'm going to
increase my prices by you know, three and a half
four percent in some cases you know, eight percent, and
(30:12):
it's like, oh yeah, you know, full flow through terrific, right,
look at our check average. It's like, okay, well that
that's that's not normal times in the world. Right. So
it's actually on the back end of those types of
inflation bubbles that it gets it becomes much harder. But costs,
haven't you know, come back down to earth, especially you
(30:34):
know in our industry where it's the cost of labor, right,
this is becoming even more scarce. How do you how
do you make sure that you are threading that needle
right because it is you know, if you go too high,
you're going to get punished. By the consumer. But if
you go too low, we're a thin margin business, right,
(30:56):
You've got to you know, you are going to have
to pass on some thing to the consumer just to
make the economics work.
Speaker 1 (31:03):
For sure. Week restaurants spending by low income consumers has
been publicized for a while now, at least eighteen months
or so. A few of the companies that cover said
the cohort was weak in March. Has their spending recovered
with industry seam source sales in the second quarter.
Speaker 2 (31:21):
Yeah, So what we're seeing is, you know, I guess
I would call it a flattening, right. So, you know,
particularly last year, we saw you know, pretty major fallout
of you know, of the of the lowest income consumer.
But I think they got you know, beaten down, you know,
to sort of a minimum minimum level, or at least
(31:44):
compared to other other cohorts, right, we had the upper
and middle income customers that were still pretty strong. So
but what we've seen really over the over the course
of this year is that, you know, while the low
income customers don't necessarily come back and drove, That's not
what I'm suggesting, but the mix of customer you know,
(32:04):
is has sort of stabilized at the lowest and the
highest income. But what we're really seeing is the middle
falling out, right, So you know, it's you know, sort
of this trickle across and you know, so I think
that that's you know, it may be contrary to what
we've been thinking about. You know, you know, strapped customers
(32:24):
still still an important consideration, but even in like you know,
fast casual, what I was surprised to see is that,
you know, the the mix of lower income customers and
fast casual is actually growing because the middle has fallen
out so much. That has implications, right, which means that, okay, well,
if the low income customers are now making up a
bigger mix of your of your customer base, then that
(32:47):
means that you're also likely to experience more uh, you know,
more more price sensitivity.
Speaker 1 (32:53):
Yeah, for sure, it's concerning obviously if the middle income weakends.
You know, we've had a couple of our companies mentioned
a little deterioration there, but hasn't really been broad based
against across the national chains that we cover. You know,
hopefully the talk about tax cuts, which should help the
middle middle class, will support their spending in the second half.
Speaker 2 (33:18):
We'll see yeah, I think you know the whole you know,
it's been a year that you know, the the word
of the year I think is is turning into uncertainty, right,
So you know, and and that's when you see the
you know, consumer survey based data and so forth. Right,
you know you're seeing look, and a lot depends on
what party you are, right, But but even there, right,
(33:44):
there's a there's a universal agreement that you know, things
are things are very uncertain, right, And you might say, oh,
well that's good, right, or maybe that's the big differentiator
by by party, Right, is it good or is it bad?
That is uncertain Well, it depends what the outcome is
either way. It means that you know, there are customers
and companies and so forth that are you know, don't
(34:06):
don't love the uncertainty, right, So so they're going to
delay some decisions, you know. So to your point on
this in the later half of the year, if there's
tax cuts and so on and so forth, you know,
could that help We'll eat, yes, But it depends on
who believes that, you know, who believes that that's coming, right,
because it's that it's for a lot of people, it's
(34:28):
you know, how do I feel, you know, that dictates
how much I'm going to spend, right.
Speaker 1 (34:32):
Yeah, for sure, fine dining same source sales that were
very weak in twenty twenty three and twenty four. What
are you seeing in that segment? And do you think
higher asset prices can boost results later this year?
Speaker 2 (34:46):
So I think that's also very much a mixed bag, right.
So going across markets, you know, d m as, even
micro markets. You know, if you are if you're in
one of those areas where there are very few fine
dining options, then you know you're and you've got a good,
you know, good consumer base for it, then you're probably
doing very well. But then there's also you know, some
(35:08):
that are that are not doing so well right because
of you know, where where they are and how they
compare to say, you know, the more affordable full service options.
So you know, again I think like the we've rarely
seen things so call it trade area dependent, right, it's
you know that can really dictate you know, who's looking
(35:30):
good and who's and who's.
Speaker 1 (35:32):
Not right now, okay, and CDR chains have increased their
marketing spend a good bit over the last six months
to a year, particularly on TV ads. Why hasn't the
industry made greater inroads with one to one marketing?
Speaker 2 (35:46):
I think that there's a there's an in between level, right.
I think that what the industry you know, gets wrong
oftentimes in implementing you know, what's called one to one
marketing is how different say their psychographic customer study you know,
translates into well, how do I actually operationalize that through
(36:11):
a database campaign that can actually be geared toward individual customers.
And that's a huge leap, right, especially for marketing departments
that you know, oftentimes you know came from you know
consumer uh you know, uh consumer package goods background or
something where you know, these psychographic studies that are have
really been effective in you know, broad based advertising, but
(36:36):
you cannot convert you know, those psychographics into you know,
behavioral data, right that you collect within you know, within
those technologies that can do the one to one. The
other thing is that you know, most of the ones
that are being designed that are capable of one to
one marketing in restaurant industry, it's all about the It's
(36:59):
all about the CD right, the consumer or the customer
database our data platform. So and that means that in
order to communicate certain things to people at that level,
they already have to be in your database, right, an
identified customer who you can talk to, which is great, right,
(37:22):
it's an effective thing has been to wave through the industry.
But the reality of that is that you can't you
can't grow your customer reach, you know, through the database
of people who are already your customers. Right, So how
do you take that same concept and apply it to
people outside of your outside of your database, you know,
(37:45):
and say, okay, well, I know that the people who
like my brand, who are heavy consumers of this and
so on and so forth, they live here, right, and here's
how I can get to them, because it's close enough
to my stores to be able to do that. That
is not that is not a common, uh usage of
the technology, So they sort of revert back to the
(38:07):
you know, to the mass market. Even in you know,
even in d m A level you know, uh, digital spending,
usually it's you know, you've got to buy the whole
d m A right, if you want to get very
specific about your your your demo. But really the technology
exists so that you know, you can say, look, I
just want people in this neighborhood, you know, to be
(38:28):
able to see this kind of ad because I know
that you know, all of them are you know, within
a distance, right, and that becomes much less expensive, uh
to execute. But it's it's not commonly understood. I think
across you know, across the marketing spectrum, and.
Speaker 1 (38:44):
What's your view on QSR discounting, you know, more or
less the same Over the next six to twelve months, I.
Speaker 2 (38:50):
Think it's going to become more and I think that
there's there's plenty of discounting going on. I think that
a lot of them, you know, we're trying to keep it,
you know, to our database, you know, conversation, you know,
a little bit more low key, low profile, you know,
give people very specific discounts and that works within you know,
within your existing customer base. Right if you've identified them
(39:13):
as as as low income, it doesn't help you to
expand your reach, you know. And I think that you know,
one of the things and we're starting to see it, right,
is you know, what can I get for five bucks
or what can I get for ten bucks? Right? Because really,
if we look historically through the through the industry, people
think about the discount as being so important, but really,
(39:37):
if you look at the successful campaigns over the past
twenty five years, it's really been about, you know, price certainty, right,
how much am I going to spend when I go out? Right?
Is it three dollars? Is it five dollars? You know,
ten dollars even you know Ruth Chris had the Rus
Chris classic, Right, It's like, okay, you know eighty five dollars, right,
but you knew that you could go there and that's
(39:59):
what you're going to spend done a per person basis, right,
And I think that that helps people to make these
these decisions, right to say this is how much money
you know I'm going to spend today, right, or this
how much money it is in per per head in
my family. Right. So so I think that you know,
those I think we're going to see a lot more
of that, you know, hammering certain price points that consumers
(40:23):
see appealing.
Speaker 1 (40:24):
It's like one of the first things you think about
is how much you want to spend for occasions like
a lunch. And you know, I remember you were one
of the first to pour cold water on dynamic pricing,
and that was a big part of it. Right. It's like,
if I don't know how much I'm going to spend
for my cheeseburger. You know, I kind of eliminates it
from my consideration set.
Speaker 2 (40:45):
That's right. And look, I think you know, there are
cases for you know, dynamic pricing, and there are ways
that it works in times that it works. But but
you need to be very careful about it, right, because
what does it do you know psychologically into your relationship
with the customer? You know, a better use case to me, right,
(41:06):
rather than changing you know, your price of a cheeseburger
by you know, a dime twenty five cents, you know,
by the minute or whatever is, you know, how do
I dynamically choose the right bundle, right and try? And
you know, think of think about price for my customers,
not as how much they're spending on each subcomponent of
(41:26):
their order, but how do I know, how do I
get them to spend more with me in that visit?
Not because I've forced them to you know, swallow a
higher price on everything, but because I did a better
job of using technology to you know, to upsell the
things that they actually want to add to their order, right,
(41:48):
so that you know, instead of spending five bucks with me,
they're spending seven. And that's not because I just increased,
you know, the prices by by two bucks. Right, it's
because you know they got an add on, right, or
they got something that actually makes them enjoy the experience more.
Speaker 1 (42:03):
Yeah, then for help selling is a much better use
case in my opinion. Look, Man, I could do this
with you all day. Thanks for doing this, man.
Speaker 2 (42:11):
Yeah, no, thanks for having me, Michael. It's always it's
always a it's always a pleasure, and you know I
love talking about this stuff anyway, So you know what,
we'll have to meet up over a beverage sometime soon
for sure.
Speaker 1 (42:22):
Man. I want to also thank the audience for tuning in.
If you'd like to learn more about signal Flarer dot ai,
you can go to signal Flare dot ai. It doesn't
get any simpler than that. And if you liked our discussion,
please share it with your friends and colleagues. Check back
soon for another interview on chopping it Up.
Welcome to Chopping It Up. I'm your host, Mike Hanlon,
the senior restaurant and food service analysts at Bloomberg Intelligence.
Our research and that of bi's five hundred analysts around
the globe can be found exclusively on the Bloomberg terminal.
If you enjoy the pod, I'd love it if you
could leave us a review on Apple or Spotify. Today
we're joined by Mike Luciano, founder and CEO of signal
Flare dot ai. Signal Flare is a decision intelligence platform
(00:37):
for restaurants and other industries that translates data into decision
frameworks using AI and machine learning models. The company was
the twenty twenty four Snowflake Startup Challenge winner. Welcome back
to the podcast.
Speaker 2 (00:51):
Mike, Oh, it's my pleasure of Michael. Good to see you,
good to hear you.
Speaker 1 (00:55):
Yeah, I was going through my notes yesterday. I can't
believe it was three years ago?
Speaker 2 (00:58):
Is it really?
Speaker 1 (00:59):
Yea three years ago? You were you were episode number four?
Speaker 2 (01:03):
All right, well I was. I was honored then honored
to be back, so thanks for thanks for having me again.
Speaker 1 (01:08):
Well, I always love talking about their industry with you,
so it's great to have you back on episode seventy six,
UH signal flair. Like I said, three years now, time flies?
What inspired you to start the company?
Speaker 2 (01:21):
So, you know, i'd been as as you know, you
know this, I'm on middle of my my third decade
doing UH data and analytics for restaurants. And when I
started in it, there weren't that many people who cared
that much about data for restaurants, especially pulling it out
of pulling out of the point of sale and you know,
(01:43):
figuring out what it all means and building building models, UH,
you know, can predict things. UH. And the world's changed
a lot since then. So and this is you know,
that was a few a few companies ago, and you
know the last one I uh, I sold and exited.
And then you know, I took some time off, you know,
(02:05):
around twenty twenty, and turned out that some other people
took some time off around them too, And as I
started to analyze data, because people were still coming to
me to say, you know, can you analyze this? And
so forth, and a lot of the old methods, you know,
when demand patterns just completely change like they did during
(02:27):
that time, and even trade areas completely changed as people
changed working from home and you know, and so you know,
I had to come at a lot of these problems
from a completely different in a completely different way. You know.
So instead of using a lot of these linear models
that look at trends over time, say, okay, well, how
(02:49):
do I change both the statistical approaches to more of
a machine learning approach but also the data sets. Right,
how can I instead of just starting with the point
of sale data, how do I pull data from mobile
you know, mobile digital platforms, right, Uh, you know, cell
(03:09):
phone devices to understand you know where people are shopping
when they're shopping there, and you know who they are
right going into every restaurant and every retailer, and you know,
and then you know, bringing in local economic data and uh,
credit card panels, just masses of external data you that
(03:30):
tells us more things about restaurant consumers before I start
layering and the point of sale data. Uh. And what
I found was two things, right, One is that it
builds an infinitely more robust model. And in two, instead
of always looking in the rear view mirror, you can
(03:51):
start to layer in things that are forward looking. So
for instance, when you know when gas prices started to spike,
right before you know the inflation like really went nuts.
We were able to take that information because we know,
you know, which restaurants are more dependent on people who
(04:11):
drive more. We can get all of that from from
the data, and we know and we can calculate what
people's disposable income is, not just uh you know, not
just you know what the areas, uh you know income
levels are. Uh. So when we can factor that in
and we know that some of these things are leading indicators, uh,
(04:32):
then we can use that data to say, you know
what these restaurants are the ones they are going to
be having trouble in you know, three to five months.
So what kind of strategies to you employee? Right? Do
you do you do discounting? Right? Or do you send
different messaging? But when you start to use it in
that framework, then you know, then it becomes uh, you know,
planning tools, not just you know, backward looking analysis. So
(04:56):
so so long, long story short. I did that for
a while and really moved it out with with some
chains more on a consulting basis, and about two years
ago I said, you know what this is. This this
is a platform, right, this is this is a much
different thing than what I've done in the past in
terms of being able to make it, you know, scalable
and accessible to like really any size restaurant. Yeah, it's
(05:19):
really cool.
Speaker 1 (05:19):
What's how many units does your smallest customer have?
Speaker 2 (05:23):
I think our smallest is a unit. You know, we're
in the process now of taking all of that data
and putting it into a platform that's going to be
a lot easier to access, you know, even I think
to independent restaurants because the way that the way that
you had to operate the data in the past, you know,
(05:45):
to do the kind of work that we're doing, and
we had to pull in all of their clean point
of sale data and so forth. But now we know
what's actually going on, you know, in the surrounding community,
right we know, you know how people are spending and
what the trends are, and you know what segments they're
spending and how much they're spending and all of that.
So uh and we're putting uh, you know, an AI
(06:06):
interface over it that also leverages all of the major
major models. So you know, by doing that, we're able
to cross section our machine learning models, you know, with
the broader market understanding. And you know, you know, before
I was a in restaurant data and tech. I was.
I was restaurant operator. So the idea that we can
(06:28):
you know, go from you know, early days working only
with the very largest mega chains to now being able
to provide similar things all the way down to small
chains and independence and that that excites me, right, not
just for you know, for the technology, but for what
we were able to do for the industry.
Speaker 1 (06:45):
Yeah, it's really cool. So you know, as I mentioned,
it's a decision intelligence platform. Did you always envision it
to be that or was it? Did it start you
know with menu pricing? You know, was there one problem
that you were most about solving back when you started it?
Speaker 2 (07:02):
Yeah, So you know, I think you know, as you know,
and you know a lot of folks who I've worked
with over the over the years, you know, I've built
a lot of pricing models, right, so you know a
lot of you know, my my last company you know,
still is the pricing engine for for a lot of
national chains out there. So you know the methodology of
(07:25):
how you apply sophisticated pricing algorithms to restaurants. It was
the starting point. But the way that I was building it,
you know, in order to build a really robust pricing model.
You need to not just understand, you know, what people
are paying for the price, and you know, it's not
(07:46):
just about competitive benchmarking. That's that's a part of it,
but you really need to understand what the fundamental demand
is around each restaurant so that you're not just saying,
you know, okay, how is this trending or how do
I compare against them? But how do you layer everything
from you know, sentiment to macroeconomic, microeconomic, you know, competitor
(08:07):
performance data. How do you take all of those things?
And once you bring all those things in, then the
kinds of questions that you can answer go way beyond pricing, right,
it goes to everything about you know, how do I
optimize my market? Right? How do I look into new markets?
You know, how do I start planning budget planning? And
(08:28):
how do I do scenario analysis, you know, to to
be able to react or to prepare for contingencies you know,
in the competitive market or in the macroeconomic landscape. I think,
you know, what I've been doing my whole career really
has been what I call that last mile of turning
the data into the decision. But in the past, you
(08:51):
know that had to really happen by bringing insights and
visuals and summary data to a really small our analysts,
and all of that is is changing now because you know,
when you can bring that kind of summary data in
and have it read by an l M or or
or an analyst agent, it need to be trained, right.
(09:14):
It's still like having you know, an intern, you need
to you need to bring along and teach. But it's
moving so quickly that I'm just every day I get
more surprised and impressed and excited about what those you know,
what those possibilities are. Yeah, that's cool.
Speaker 1 (09:31):
And you were kind enough to invite me to your
AI Restaurants summit recently. It was a great event and
I learned a lot, but a couple of things stood out. One,
can you talk a little bit about why data quality
is so important for AI and machine learning projects? I
was kind of floored about the number of product projects
(09:52):
that fail due to data quality issues.
Speaker 2 (09:55):
Yes, yes, no, really really important. And it's funny because
you know, maybe a year ago I was at another
conference and you know, I heard somebody talking about you
know how oh well, now in the age of AI,
you know, you don't need to worry about the data
anymore because a I will just clean it up and
it's like nothing could be farther from the truth, right
(10:16):
there are you know, there are ways that you can
use AI to help you, you know, clean it up
and so forth. But you really the standard in the
bar for data, you know, cleanliness and context and and
usability is higher, not lower, right, because what an ll
(10:37):
M will do, right, what these you know, you know,
the chat GPTs and the clauds and all these they
will give you an answer that sounds wonderful, right, even
if there's no data to support it. They they you know,
what's amazing about them, right, is that they can get creative, right,
as creative as a person. So you know, the data
(10:58):
might not be there right, or it might interpret it
completely wrong, but the pros sounds wonderful, right. And if
you've got you know, crappy data that's not structured, and
you know, in fact, really what they're best at is
reading text documents. You know, if all of your data
is in a data warehouse, you need another layer over
that to say, okay, well how do I summarize this
(11:20):
and how do I chunk it out? And how do
I make sure that it's in usable nuggets, so that
then the machine can can can interpret it. You know,
people are using you know, these for calculations, but they're
not good at calculating. You know. Maybe it's a unpopular
you know fact, right, I don't know popular opinion. It's
a fact. They can invoke tools, right that can help
(11:44):
to do a calculation. But when it comes down to it, right,
I think you know the stats that I put out there.
You know, look, you know eighty percent of of AI
projects are still failing, and seventy five percent of those
are failing because they didn't get the data, right.
Speaker 1 (12:01):
Yeah, staggering numbers. Yeah, and you mentioned the fact that
ll ms are terrible at math. We also discuss some
other concerning issues, including hallucinations and excessive praise.
Speaker 2 (12:15):
Yeah, it is interesting how you know they can be
tuned right, and you can tell it, you know, don't
don't praise me, or you can tell it, you know,
you know, you can try and give it a personality,
which is fascinating. But yeah, I mean hallucinations is just
you know, a fancy way of you know, of saying
(12:37):
you know, it just it's just b s's right, their
language models, right, they read you know everything that's ever
been written, and then they figure out how to you know,
you ask a question and then it's you know, searching
this repository to try and say it in the way
or the style that you specified. So you know, you know,
(13:00):
and you know an example of like you know, bad
mathroom an l M. Right, it's like you know, we
we we're constantly doing you know, tests and validations, but
you you don't know what the answer is that you're
going to get because it's all probabilistic, right. What you know,
what we do in analytics is deterministic. Right. You need
(13:23):
a number, right that you've got some level of certainty
and there's this and there's a clear calculation to it.
You know. L l M s are all you know, Hey,
if you ask this question, then the probability of this, this,
this and that right ur X and y and that's
what all these sort of computational nodes are doing as
it's doing the calculation. So, uh, you know, so if
(13:46):
you telling it, you know, an l l M without
any more instructions, you know, hey, tell me what the
year of a year sales are for you know, X,
Y and Z company. You know twenty you know this
year versus last year. The most likely answer you're going
to get out of that is going to be something
that says, oh, well, you're down about you know, fifty percent,
(14:10):
you know versus last year. Why because we're about halfway
through the year, right, So it's comparing this year, you know,
the last year without any more you know, clarity than that.
So as you're either figuring out how to prompt it
and ask the questions correctly, or you're trying to figure
out how to do the engineering in the background, you know,
the things that the ways that we talk about things
(14:32):
and the common vernacular that we use, you know in
this industry, as you know, it's like, okay, well, what's
a check average? You know, as fifteen different operators, what
you know what that means to them? Right? How are
you calculating that check average? It's all right, Well, you
kind of need a single standard, right, or a very
clear definition of all of these things in order for
(14:53):
them to work and to learn.
Speaker 1 (14:55):
Yeah, I learned at the event that it's important on
us to ask the right question. That's you get better
at using these lll ms. One of the subjects that
I brought up was just concern about you know, security
of your data and what the lll ms are doing
with your data. Actually saw a great tweet today. It
(15:16):
was Stealing from one person is called theft. Stealing from
everyone all at once is called chat GBT.
Speaker 2 (15:22):
Yes, no, I think. I think that this is a
reality of where we are. It's really important because you know, people, organizations, right,
first of all, it's moving so quickly, and you know,
once any you know, analyst, employee, marketing, you know, administrator,
(15:43):
whoever it is in the organization, once they start using it,
they start to find all of these ways that, oh
this can actually save me. You know this amount of
time you know doing these three, four or five tasks.
So it's addictive very quickly, right because people or like
I can do a better job with it. You know,
it's one of the best productivity tools you know, invented
(16:05):
probably since you know, since Lotus one, two three, right
precursor to to Excel for those of who don't know. So,
if you don't have a policy for enterprise level, which
typically right the the g p T, the l M
(16:26):
will say that in their enterprise versions, you know, none
of your data is used to train their models. There's
another way of using it, which is, if you use
the A p I S in your own environment, then
you know, there's no backward propagation, right because it's it's
being calculated in yours and you're using you know, you're
getting the benefit of it, but there's no way that
(16:47):
it can actually backward propagate, which is, you know, which
is the better the better process. But if you have
no policy, right and you're not, you know, and then
if people are taking your company data and just using
it on a free version or a personal version, all
of that information is being you know, you're you're you're
(17:10):
donating to the you know, to the broader knowledge base
of the world right with whatever you put in there.
So so yes, you need to be looking at enterprise
grade tools to be able to use this, but don't
fool yourself that, you know, if you're not using it,
then uh, you know that then you're safe. You know,
(17:32):
if you don't have a policy in place, then you're
probably in more security danger than than if you have
one and you're actually teaching people the right way to
use it.
Speaker 1 (17:42):
Great, let's switch gears. With all the inflation we've seen
since twenty twenty, pricing menus down to the unit level
is more important than ever. And you know, I guess
what I'm wondering is what chains are doing it right?
Right now?
Speaker 2 (17:57):
Yeah, I think you know, without naming name specifically, but
I think that the things that you see are common
with success are you know, those that I think are
are actually well. One thing is that, you know, what
we keep on seeing that qs RUH is lagging, you know,
(18:18):
even entry level casual dining and and fast casual and
when economic times are uncertain, usually we expect that people
are going to trade all the way down to QSR
because it's the least expensive option. But that is not
what we're seeing, right. We're seeing that it's you know,
it's you know, it's a lot of the fast casuals
and so forth that are doing a lot better, especially
(18:41):
if the cuisine that they're serving up is not yet
you know, at a saturation point and in a lot
of their markets. Right, I think what the consumer is
telling us is that, you know, they are making choices,
but their choices are being guided not by what's the
least expensive thing that I can get, but what actually
(19:02):
represents the value, Right, what's going to what's going to
give me better service, what's going to be the taste
profile that I want, because if I'm going to dine out,
you know, maybe less often, I want to make sure
that those occasions that I am dining out, you know,
I'm actually getting what I want.
Speaker 1 (19:15):
Yeah, that one has caught us by surprise over the
last five months, the fact that casual dining and it's
broad based. It's not just Chili's, it's broad based. Most
of the casual dining chains we cover, you know, Cheesecake Factory,
Texas Roadhouse still crushing it, Dartin Cracker Barrel, all these
chains are now performing qs R. Part of it is
(19:36):
they're they're lapping easier comp but they're being them by
a wide margin. Man. We haven't seen that, you know,
in a market, in a slower restaurant spending market. I mean,
I don't think I've never seen it, you know, since
I've been in the business.
Speaker 2 (19:51):
Well, you know, the other thing that we really need
to think about in context, right, is how how the
industry shrank and then grew, you know, from twenty nineteen
to now and where it's going. You know, we all
remember back in mid COVID eight times, right, you know,
(20:14):
staggering numbers of closings. Right, It's like, okay, like twenty
percent of the industry was closing. And but you know
what if you had a drive through, you know you
were living you know, you were living the good life.
You know, you had you know, there was you know,
there were a few businesses that were as good as
you knows as having a drive through right in an
(20:34):
underserved market, right. And what ended up happening is that
unit account started to grow, but it was all limited service.
So the ratio of limited service to full service restaurants
is completely different than it was back in twenty eighteen,
twenty nineteen, big overbuilding of you know, of limited service restaurants,
(20:58):
not not as much, you know, catching up with the
full service. So you know, and yeah, and the way
that the trade areas you know change, right. You know,
people sort of you know move to more you know,
more rural where they could because their workplaces would do it.
Maybe they had to move a little bit closer, but
you know, not so much into city centers. So all
(21:22):
of those factors together, right, are showing these traditional metrics
of same store sales as opposed to total industry growth right
or total industry usage. They are two very different looks
at the industry, right, because the the saturation of markets,
(21:43):
especially when you look at different cuisine types and uh
service styles. Because population growth is also, you know, is
not near what it was five years ago either, So
rest the restaurant build build out is outpacing population growth
by about you know, about five x.
Speaker 1 (22:04):
Wow, five x is a lot. What I found interesting
about twenty twenty as well, is like QSR had that
year where they were able to raise prices pretty aggressively
because they were the only game in town, and so
I feel like their pricing got ahead of itself faster
than it did for some of their full service competitors.
Speaker 2 (22:24):
Yeah, I think, I think you're right. And it wasn't
just the menu prices. It was also that they eliminated
every discount, right, So all the dollar menus disappeared, right,
all of those you know, all of those you know,
special things that you get, you know, if they gave
you some price certainty inside those restaurants for some period
(22:47):
of time, all of those just just vanished. Right. That's
starting to come back, But they sort of retrained customers, right,
They started, you know, started to find things that were
you know, oh well, I can get better food you
know all the time you know, at these different types
of uh, you know, restaurants, you know, also limited service
(23:12):
you know. But I but I like it better, right,
So it really I think it created something that you know,
I mean, we'll see if people revert. But qs R
every day is getting more and more like off premise
grocery you know, or you know, and even the convenience
stores are getting you know, getting better, you know at
(23:33):
you know, really upping their game. So those options, right,
you know, competing there where it used to be the
safe the safe bet you know, you know, recession resistant,
it's not really that that's not really the case anymore
now that they seem to be more vulnerable, you know.
Speaker 1 (23:48):
And and I want to kind of hit on one
of the points you made about the industry and how
much it's growing faster and how much faster it's growing
than population growth. So as everybody knows there's a lot
of bankruptcies last year, you know, are we are we
still overbuilt even after these bankruptcies or are we approaching
(24:11):
over built? Where where does it stand today?
Speaker 2 (24:15):
So you know, we got updated data that compared you know,
end of twenty twenty four to twenty three. And so
what we what we knew from then is that you
population and also we got the migration report, right, so
we start so we know how people are moving domestically
(24:37):
and internationally, right, So which which you know, which communities
are growing fast, which ones are shrinking, and which of
those are happening from uh international migration. Right. What we
haven't seen yet in this year, which I'm i hear
(24:57):
a lot anecdotally, is how much has the you know,
tightened immigration policies affected border towns and so forth right.
It is how much you know what's going on with
the population base, and is that population based less likely
to you know, go out and use restaurants and so
forth right. So I think that even before that we
(25:19):
saw that, yes, you know, the restaurant growth, even after
taking into account closures and bankruptcies, is still growing a
lot faster than population. I think we're going to see,
you know, in retrospect that community that you're in makes
a big difference in terms of, you know, how saturated
(25:41):
you are and how hard you've got to work for that,
you know, that share of stomach.
Speaker 1 (25:46):
Yeah, it's interesting. I know Wingstop had mentioned that that
spending from their Hispanic customers was kind of weak around
them around the tariff, you know, all the teriff excitement
back in April.
Speaker 2 (25:59):
Yeah, and I've I've heard that from you know, more
than a more than a couple of brands you know
that that have you know, higher Hispanic populations. And you know,
you know it shouldn't shouldn't be shouldn't be too surprising, right,
you know, you make, you make certain choices, and not
all choices are good for everyone for sure.
Speaker 1 (26:20):
Uh So cp I was released yesterday. Did anything about
that report stand out to you?
Speaker 2 (26:26):
Yeah, Look, I think the restaurants are still you know,
as you know, you know headline CPI is is is
moderating closer to target, still above restaurants actually you know,
ticked up a little bit, you know, particularly uh, full
service restaurants, which you know, as you mentioned, you know,
(26:48):
limited service was you know, was increasing prices you know,
by a lot more for for quite a few years now,
for the past several months, you know, actually going into
end of last year, we've seen that full service price
increases are are are now ahead of you know cp
I for for limited service. So it'll be interesting to see,
(27:12):
you know, how that happens. And I think. Look, it's
it's consumer driven really because you know, when we compare,
when we compare cp I going back you know, even
three years, uh two, check averages you know from you know,
credit card sampling data and so forth. You know, we're
(27:33):
seeing that, you know, check averages in the industry are
not growing at nearly the rate of of of cp I.
So what's that telling us, Right, that's telling us that
restaurants are increasing their prices. But h but consumers are
managing their spend, you know, to not go too far
outside of what they're you know, what they were, what
(27:56):
they planned, right. Uh So that that's that's that I
think is important because it means that restaurants that think
that they can just you know, do a one time
you know, three percent, three three and a half four
percent increase, they're probably only you know, after your customer
you know, manages their checked, they're probably only getting you know,
(28:19):
maybe two percent of that, right. I mean, the real
ratios of just you know, across the board types of increases,
you know, are about fifty percent flow through, right, Whereas
if you instead said, you know what if we went
lower you know, in a more measured way, you know,
and hit the one and a half two percent. You know,
(28:40):
when you're doing it that way, you're staying more within
what the consumers willing to take. You can measure it,
you can maybe even do more, but you're more likely
to get in the nineties, you know, than you know,
and that ends up being much healthier for for the brand. Right.
I think a lot of the larger companies understand that,
you know, and you know, they may a lot of
(29:02):
them even like may say, oh, we're not changing our prices,
but you know what they're franchise systems, So yes, they
are increasing the prices. But the better guidance they can
have in terms of you know, being moderate, right and
making sure that you're measuring what the consumer is willing
to spend along with it, that's how you make sure
that you're growing and growing in a healthy way.
Speaker 1 (29:24):
Yeah, we're seeing a lot of our companies are showing
a decline in their mix, and you know, part of
it is due to alcohol, but you know, part of
it is probably customers managing checks and to your point, yeah,
it's a part of its supply and demand. For some
of these chains that are doing a better job, they're
drawing more people in they're able to raise prices. Some
chains that are doing much better. I already mentioned Chili's
(29:47):
Cracker Barrel. They're also starting to lean into strategic pricing
right now.
Speaker 2 (29:52):
Yeah, I mean you have to write and I think
it's actually a lesson that restaurants need to you know,
look to other ends streets about, you know, because in
the past and during certain times, you know, especially during inflation.
Right when it's inflationary, it's like, oh, I'm going to
increase my prices by you know, three and a half
four percent in some cases you know, eight percent, and
(30:12):
it's like, oh yeah, you know, full flow through terrific, right,
look at our check average. It's like, okay, well that
that's that's not normal times in the world. Right. So
it's actually on the back end of those types of
inflation bubbles that it gets it becomes much harder. But costs,
haven't you know, come back down to earth, especially you
(30:34):
know in our industry where it's the cost of labor, right,
this is becoming even more scarce. How do you how
do you make sure that you are threading that needle
right because it is you know, if you go too high,
you're going to get punished. By the consumer. But if
you go too low, we're a thin margin business, right,
(30:56):
You've got to you know, you are going to have
to pass on some thing to the consumer just to
make the economics work.
Speaker 1 (31:03):
For sure. Week restaurants spending by low income consumers has
been publicized for a while now, at least eighteen months
or so. A few of the companies that cover said
the cohort was weak in March. Has their spending recovered
with industry seam source sales in the second quarter.
Speaker 2 (31:21):
Yeah, So what we're seeing is, you know, I guess
I would call it a flattening, right. So, you know,
particularly last year, we saw you know, pretty major fallout
of you know, of the of the lowest income consumer.
But I think they got you know, beaten down, you know,
to sort of a minimum minimum level, or at least
(31:44):
compared to other other cohorts, right, we had the upper
and middle income customers that were still pretty strong. So
but what we've seen really over the over the course
of this year is that, you know, while the low
income customers don't necessarily come back and drove, That's not
what I'm suggesting, but the mix of customer you know,
(32:04):
is has sort of stabilized at the lowest and the
highest income. But what we're really seeing is the middle
falling out, right, So you know, it's you know, sort
of this trickle across and you know, so I think
that that's you know, it may be contrary to what
we've been thinking about. You know, you know, strapped customers
(32:24):
still still an important consideration, but even in like you know,
fast casual, what I was surprised to see is that,
you know, the the mix of lower income customers and
fast casual is actually growing because the middle has fallen
out so much. That has implications, right, which means that, okay, well,
if the low income customers are now making up a
bigger mix of your of your customer base, then that
(32:47):
means that you're also likely to experience more uh, you know,
more more price sensitivity.
Speaker 1 (32:53):
Yeah, for sure, it's concerning obviously if the middle income weakends.
You know, we've had a couple of our companies mentioned
a little deterioration there, but hasn't really been broad based
against across the national chains that we cover. You know,
hopefully the talk about tax cuts, which should help the
middle middle class, will support their spending in the second half.
Speaker 2 (33:18):
We'll see yeah, I think you know the whole you know,
it's been a year that you know, the the word
of the year I think is is turning into uncertainty, right,
So you know, and and that's when you see the
you know, consumer survey based data and so forth. Right,
you know you're seeing look, and a lot depends on
what party you are, right, But but even there, right,
(33:44):
there's a there's a universal agreement that you know, things
are things are very uncertain, right, And you might say, oh,
well that's good, right, or maybe that's the big differentiator
by by party, Right, is it good or is it bad?
That is uncertain Well, it depends what the outcome is
either way. It means that you know, there are customers
and companies and so forth that are you know, don't
(34:06):
don't love the uncertainty, right, So so they're going to
delay some decisions, you know. So to your point on
this in the later half of the year, if there's
tax cuts and so on and so forth, you know,
could that help We'll eat, yes, But it depends on
who believes that, you know, who believes that that's coming, right,
because it's that it's for a lot of people, it's
(34:28):
you know, how do I feel, you know, that dictates
how much I'm going to spend, right.
Speaker 1 (34:32):
Yeah, for sure, fine dining same source sales that were
very weak in twenty twenty three and twenty four. What
are you seeing in that segment? And do you think
higher asset prices can boost results later this year?
Speaker 2 (34:46):
So I think that's also very much a mixed bag, right.
So going across markets, you know, d m as, even
micro markets. You know, if you are if you're in
one of those areas where there are very few fine
dining options, then you know you're and you've got a good,
you know, good consumer base for it, then you're probably
doing very well. But then there's also you know, some
(35:08):
that are that are not doing so well right because
of you know, where where they are and how they
compare to say, you know, the more affordable full service options.
So you know, again I think like the we've rarely
seen things so call it trade area dependent, right, it's
you know that can really dictate you know, who's looking
(35:30):
good and who's and who's.
Speaker 1 (35:32):
Not right now, okay, and CDR chains have increased their
marketing spend a good bit over the last six months
to a year, particularly on TV ads. Why hasn't the
industry made greater inroads with one to one marketing?
Speaker 2 (35:46):
I think that there's a there's an in between level, right.
I think that what the industry you know, gets wrong
oftentimes in implementing you know, what's called one to one
marketing is how different say their psychographic customer study you know,
translates into well, how do I actually operationalize that through
(36:11):
a database campaign that can actually be geared toward individual customers.
And that's a huge leap, right, especially for marketing departments
that you know, oftentimes you know came from you know
consumer uh you know, uh consumer package goods background or
something where you know, these psychographic studies that are have
really been effective in you know, broad based advertising, but
(36:36):
you cannot convert you know, those psychographics into you know,
behavioral data, right that you collect within you know, within
those technologies that can do the one to one. The
other thing is that you know, most of the ones
that are being designed that are capable of one to
one marketing in restaurant industry, it's all about the It's
(36:59):
all about the CD right, the consumer or the customer
database our data platform. So and that means that in
order to communicate certain things to people at that level,
they already have to be in your database, right, an
identified customer who you can talk to, which is great, right,
(37:22):
it's an effective thing has been to wave through the industry.
But the reality of that is that you can't you
can't grow your customer reach, you know, through the database
of people who are already your customers. Right, So how
do you take that same concept and apply it to
people outside of your outside of your database, you know,
(37:45):
and say, okay, well, I know that the people who
like my brand, who are heavy consumers of this and
so on and so forth, they live here, right, and here's
how I can get to them, because it's close enough
to my stores to be able to do that. That
is not that is not a common, uh usage of
the technology, So they sort of revert back to the
(38:07):
you know, to the mass market. Even in you know,
even in d m A level you know, uh, digital spending,
usually it's you know, you've got to buy the whole
d m A right, if you want to get very
specific about your your your demo. But really the technology
exists so that you know, you can say, look, I
just want people in this neighborhood, you know, to be
(38:28):
able to see this kind of ad because I know
that you know, all of them are you know, within
a distance, right, and that becomes much less expensive, uh
to execute. But it's it's not commonly understood. I think
across you know, across the marketing spectrum, and.
Speaker 1 (38:44):
What's your view on QSR discounting, you know, more or
less the same Over the next six to twelve months, I.
Speaker 2 (38:50):
Think it's going to become more and I think that
there's there's plenty of discounting going on. I think that
a lot of them, you know, we're trying to keep it,
you know, to our database, you know, conversation, you know,
a little bit more low key, low profile, you know,
give people very specific discounts and that works within you know,
within your existing customer base. Right if you've identified them
(39:13):
as as as low income, it doesn't help you to
expand your reach, you know. And I think that you know,
one of the things and we're starting to see it, right,
is you know, what can I get for five bucks
or what can I get for ten bucks? Right? Because really,
if we look historically through the through the industry, people
think about the discount as being so important, but really,
(39:37):
if you look at the successful campaigns over the past
twenty five years, it's really been about, you know, price certainty, right,
how much am I going to spend when I go out? Right?
Is it three dollars? Is it five dollars? You know,
ten dollars even you know Ruth Chris had the Rus
Chris classic, Right, It's like, okay, you know eighty five dollars, right,
but you knew that you could go there and that's
(39:59):
what you're going to spend done a per person basis, right,
And I think that that helps people to make these
these decisions, right to say this is how much money
you know I'm going to spend today, right, or this
how much money it is in per per head in
my family. Right. So so I think that you know,
those I think we're going to see a lot more
of that, you know, hammering certain price points that consumers
(40:23):
see appealing.
Speaker 1 (40:24):
It's like one of the first things you think about
is how much you want to spend for occasions like
a lunch. And you know, I remember you were one
of the first to pour cold water on dynamic pricing,
and that was a big part of it. Right. It's like,
if I don't know how much I'm going to spend
for my cheeseburger. You know, I kind of eliminates it
from my consideration set.
Speaker 2 (40:45):
That's right. And look, I think you know, there are
cases for you know, dynamic pricing, and there are ways
that it works in times that it works. But but
you need to be very careful about it, right, because
what does it do you know psychologically into your relationship
with the customer? You know, a better use case to me, right,
(41:06):
rather than changing you know, your price of a cheeseburger
by you know, a dime twenty five cents, you know,
by the minute or whatever is, you know, how do
I dynamically choose the right bundle, right and try? And
you know, think of think about price for my customers,
not as how much they're spending on each subcomponent of
(41:26):
their order, but how do I know, how do I
get them to spend more with me in that visit?
Not because I've forced them to you know, swallow a
higher price on everything, but because I did a better
job of using technology to you know, to upsell the
things that they actually want to add to their order, right,
(41:48):
so that you know, instead of spending five bucks with me,
they're spending seven. And that's not because I just increased,
you know, the prices by by two bucks. Right, it's
because you know they got an add on, right, or
they got something that actually makes them enjoy the experience more.
Speaker 1 (42:03):
Yeah, then for help selling is a much better use
case in my opinion. Look, Man, I could do this
with you all day. Thanks for doing this, man.
Speaker 2 (42:11):
Yeah, no, thanks for having me, Michael. It's always it's
always a it's always a pleasure, and you know I
love talking about this stuff anyway, So you know what,
we'll have to meet up over a beverage sometime soon
for sure.
Speaker 1 (42:22):
Man. I want to also thank the audience for tuning in.
If you'd like to learn more about signal Flarer dot ai,
you can go to signal Flare dot ai. It doesn't
get any simpler than that. And if you liked our discussion,
please share it with your friends and colleagues. Check back
soon for another interview on chopping it Up.
Host
Michael Halen
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