Unveiling Dynamic Pricing and Analytics in Restaurant Tech with Mike

In this episode of the Restaurant Technology Guys Podcast, the hosts dive into a compelling discussion with Mike, a veteran in the field of restaurant technology with over 25 years of experience. Introduced by Tammy Billings, Mike shares his journey from restaurant operations to mastering the quantitative aspects of pricing, analytics, and data during a time when the concept was still novel. He delves into his early work with McDonald's and his ventures into data-driven businesses during the financial collapse, focusing on leveraging AWS cloud technologies. Mike introduces his latest venture, SignalFlare, and the advanced methodologies they use to analyze consumer behavior and pricing dynamics. They explore the impact of COVID-19 on demand patterns, the importance of understanding the emotional connection consumers have with restaurants, and the future of dynamic pricing. Mike explains how SignalFlare helps both large chains and smaller operations make informed pricing decisions, driven by extensive data sets and machine learning. He emphasizes the incremental ROI and the evolving intelligence of these systems over time. The discussion concludes with insights on decision intelligence for the restaurant industry, urging operators to adopt advanced pricing methodologies to stay competitive.

00:00 Welcome to the Restaurant Technology Guys Podcast
00:19 Introducing Mike and His Journey in Restaurant Tech
00:32 The Evolution of Restaurant Data Analytics
01:59 Launching a New Venture Amidst Financial Collapse
03:59 Rethinking Data Analysis Post-COVID
06:15 The Power of Modern Data Sets in Restaurant Pricing
08:55 Making Data-Driven Decisions Accessible to All Restaurants
10:43 The Importance of Context in Data Analysis
12:18 Understanding Customer Behavior Beyond Price Elasticity
26:15 Introducing SignalFlare: A Decision Intelligence Tool for Operators
31:22 The Future of Pricing and Menu Management in Restaurants
38:45 Why Embracing Data-Driven Decisions is Crucial for Restaurants

Transcript

Speaker: 0:02

This is the Restaurant Technology Guys podcast. Helping you run your restaurant better.

Jeremy: 0:11

Welcome back to the restaurant technology guys podcast. I think everyone out there for joining us. It's a privilege to come on the air with you guys each and every week to share some cool restaurant tech news today is no different. And, I want to introduce the world to somebody that I got introduced to by a good friend of mine. Who's been on the show as well. Tammy Billings, a lot of you guys will know Tammy, but, Mike, why don't you go into a little bit about who Mike is? And then we can talk a little bit about what you get to do, because I'm excited for, our audience to hear more about, about your latest venture.

Mike Lukianoff: 0:41

Yeah, first, thanks for having me. I'm excited to be here. yeah, so I've been in restaurant technology for boy, 25 years now. before that I was in, in restaurant operations before I went back to get my, my graduate degree. And I actually went back to grad school thinking I was going to go back into restaurant operations, maybe at a, on a larger scale. but I got very interested in the quantitative side of things. And, when I got out, I got very focused on, pricing and analytics and data. And, this is going back years before really, anybody was particularly interested in restaurant data. In fact, it was an odd concept at that point. Nobody was really pulling, data out of point of sale. And, I started working for really the first company that started, analyzing, data and building, price elasticity, right? To understand, how, when prices change, consumers change their behavior. And, at that point there were, we were just, Very small group. And I was an early partner in that, in that endeavor. And, we got, I don't think it's necessarily luck, right? There's a little bit of luck and skill and success. and, we had a good break and, became the global pricing provider for McDonald's. So we went from, small outfit to a multinational in a hurry. and I ran the domestic business for about, like six or seven years there. And that, taught me a lot about not just, econometric modeling, but also how to build software and so on and so forth. And, and then I started my own company. Some of it with pricing, but also promotional analytics and social media analytics, but really the whole idea of how much data can you pack into different kinds of algorithms? that business, I grew actually, I started that one, during the depths of the, financial, financial collapse, which was, In some ways it was, I thought at the time, what a terrible time to start a business. but in retrospect, it was probably the best time I could imagine because there was nobody was going to invest in you, right? There was, so you had to bootstrap it and figure out. Exactly. Exactly. So I became a very early adopter of the AWS cloud before anybody was really even, everybody thought I was absolutely nuts, but, at that time, I could rent, it was before they even had persistent storage, so I was just renting, a few hours in the afternoon, to load data up and run, crazy algorithms and then take it down. So I could, run really elaborate. algorithms, for maybe a couple of thousand dollars in an afternoon, but, but instead of. at that time, back in 2008 to run a data business, you'd probably have to, you were buying a server farm,

Jeremy: 3:29

Yeah. You were building it all yourself and yeah, there wasn't the elasticity, I guess I know Mike, you talk about your operations days. Talk to me about your latest venture. Cause I think, back from being in operations, going to school, you got into tech, which is, those are always my favorite people to talk to because they're, they understand what it looks like to be in a restaurant and deal with the guests and deal with all of the fun that goes along with that. And they're not just building models that are. that are in a cloud somewhere that nobody's ever actually going to understand. and that, that carried you through to, to the tech side. But, I guess you've launched your latest endeavor. I'd love to hear what, what is that? and then we'll talk about what it does, cause I think it's going to take a lot of that, more than 20 years worth of experience that you've had over the last, last period and, really helping brands out.

Mike Lukianoff: 4:11

Yeah, no, this is by far, I think my most exciting, I think largely because, after all this time, you just start from a completely different place. and some of it was, some deep thinking and reinvention during COVID, because right before COVID I had actually, so I had sold my last business and then I had exited. I didn't realize the whole world was also exiting, during COVID and I started, As the world started to come out of that period, all of the methods that you had to do that I've been using for decades require a certain stable period of demand.

Jeremy: 4:50

Yes.

Mike Lukianoff: 4:52

and that didn't exist anymore, but

Jeremy: 4:54

Yes.

Mike Lukianoff: 4:55

people were still coming to me and saying, can you still do something to help us figure this

Jeremy: 5:00

Day parts. I remember talking to operators and they're like, I don't understand. My Tuesday afternoons are my busiest time. Those were my slowest times for the last 20 years. And now Tuesday afternoons I get hit and Fridays are dead. during the COVID period, so you're, to your point and, so it's interesting.

Mike Lukianoff: 5:14

And even afterward, right? Because then all of a sudden, even like people in their trade areas, right? So now, people, are they working from home? Are they, are they going into work? Did they move from, from, packed cities into rural? so the way that people were navigating their trade areas, where they lived, everything was completely different. Even, What units shuttered, what was opening, where people, you know, the digitization. So I had to really rethink, everything about how we analyze things. And everything that I had built before was about how do you go to the point is directly the point of sale, analyze the behavior. Of how people are purchasing things at the point of sale. And then from there, you back into who the consumer is and where they're coming from and some trade area information and some economic data. and it worked, but now it doesn't right. Because you don't have that trending data. So I said, what if you change the methodologies, the statistical methodologies themselves. And you start with, completely different datasets. I started pulling in mobile datasets, device data where you actually know who's going into each restaurant, where they came in before. a lot of restaurants are using that kind of data, but they're using it as an endpoint, right? I'm using it as an input, right? To these models using credit card data. So I know, okay, they're spending this much money at my restaurant. How much are they spending when they're at? At other restaurants or when they're going to other retailers, right? who are these people, how what sort of what sort of economic situation are they in? what's what are the local economics like, 20 years ago? the data that you could get of the local economy, whether it's local gas prices or things that are really affecting people's disposable income at a very local level You It just wasn't knowable. And now you can get all of this kind of information. So really you're, you start to really understand, who's coming into each restaurant, and what their, what their spending power is. So I started building these models at first on a consulting basis. And then I started to see, look, as. As gas prices start to change, and home heating oil starts to change, and as inflation starts to go up, before I even get point of sale data from people, I can start to predict how different restaurants are going to, not just how price sensitive they're going to be, but start to really understand what the whole, demand patterns are going to be for these restaurants. I was actually, what started off as a hypothesis. I was just shocked at how, yeah, how, how robust these models started to become. So

Jeremy: 8:10

and I think that led you to your latest venture, So talk to me a little bit about, is that really the forming of the company? You were doing this work as a consultant. You said, this is really a product that I can turn around and put in the hands of, a restaurant, brands to be able to make these decisions that you were doing in a, I say one off basis, but really to some specific, end users that had engaged with you.

Mike Lukianoff: 8:30

that's exactly right. Because in the past, the types of methodologies and the way that things were built, when you relied just on the point of sale data, you had to wait You had to depend on the brands themselves to have really clean point of sale data. You had to rely on them to give you all of the data first before you could tell them anything about their, about their brand. So it really meant that you had to be a certain size chain. With a certain level of sophistication, before you could have any of these methods applied to you. So really what it did was it changed my ability to be able to take some of these methods that were really only available to the mega change for 20 years and start applying them to smaller and smaller chains at really better prices. With this, with the same kind of rigor. and scalably, right? Because what I was doing before didn't really have the kind of. scalability because you had to, every single restaurant is different and everybody's data is different. so yeah, so it was this moment of revelation that, all of a sudden there's a new, there's a new business in this.

Jeremy: 9:42

and I think it's, I think I'm going to, I'm going to make a couple of statements here, Mike, because I've been in the space for almost as long as you have, I've watched different brands, guess at these pricing models, guess at the reasons why sales are up, guess at the reasons why sales are down. We've had quite a few companies on in the last year that have been doing market research data really to understand their consumers and, or, but very few have gotten to the level of understanding how much price, can change a behavior, how much price can change the behavior up, down, sales up, sales down. And the other piece I would say to you is oftentimes when I think about these things, it's a lot of people take it from a very mathematical, mathematical perspective. They look at it and go, if I change the price by this and I sell the same amount, I'm going to make X amount more in profit, which is purely a math equation and you being from a restaurant business, you realize that. Just because I changed price up or down, depending upon the nature of what's going on, it may change. So it's not always just pure math. Sometimes there's science, sometimes there's math. Sometimes there's even emotion in it. Talk to me about how you think about that as it, as you are very deep into the data and understanding how those things go.

Mike Lukianoff: 10:54

you're I think you really hit it, right? Because it's you have to understand the data in context and you have to understand the math and you have to understand it a really deep level what these different equations are actually doing, right? it's the day of the, citizen, data scientist, right? So anybody can download a price elasticity equation. Wonderful, right? So I hear, I hear on, on the news, people saying, Oh, I have no price elasticity in my brand. And I'm going, I'm like, I'm like in tears, right? Cause I'm going, Oh. Boy, how did they even run this? Because one of the problems right now is that, and look, I've been preaching right to the industry for decades that,

Jeremy: 11:44

my go for it now. Keep going.

Mike Lukianoff: 11:48

so I've been preaching to the industry for decades that you really, that they have to, They should be paying attention to, to price elasticity and you should be measuring it. But the trouble is if you're not measuring these things the right way. Then you could be getting a lot of false positives.

Jeremy: 12:08

You talk about how elastic and people are, can go download these elastic models, but ultimately it's, they're not a linear equation. And I think oftentimes people see these things and just think it's straight linear and it doesn't work that way. ultimately the data has proven out that it doesn't work that way. So talk to me a little bit about how you guys consider that.

Mike Lukianoff: 12:28

Yeah. The restaurant industry is not like a lot of other industries, right? it's not a commodity, right? It's not the same thing as buying a stapler, right? when you go in to, an office depot and you buy a stapler, and the price of the stapler changes, If it's too expensive, you put down the stapler and you don't buy it, right? If it's, out, out of your reach in a restaurant, it doesn't work that way, right? You go into a restaurant and if the price of a hamburger is in. reached a price where you're going to change your behavior. You don't just get out, leave the restaurant typically, right? Usually you're going to change something about your behavior in that moment, and it might be not buying the beverage, right? Or you might not buy the side, right? There's something about that in, in that moment that you might change, but you're not losing that. The traffic count, right? That comes later, right? So when you're measuring something in restaurants. You need to understand what's happening, different purchase cycles, right? And you need to understand that, people are managing their basket and earlier purchase cycles. And then later on, you might be losing frequency and then you may be losing the traffic altogether, right? They also need to understand that because it's not a commodity, it's an emotional experience, right? restaurants are one of the few, One of the few, call it, products, that actually appeals to all five senses, right? think about

Jeremy: 14:02

Okay. Didn't think about that, but yeah, you're right.

Mike Lukianoff: 14:06

and if you are creating, an experience, and look, I think the first time I saw one of these, these value equations, where it says, okay, the value, of an experience, is a function of, the total experience, right? the quality of the food and, and, and the service. as a function of price, that sort of resonated with me. And I thought, if that's true, then you should be able to, make a mathematical equation out of it over time and test it. And I got obsessed with this. over, 20 some years, every chance that I got to be able to put in, whether it's customer satisfaction or any, anything that could indicate, whether the customers are happy or reacting to something in a later pipe purchase cycle, I'm putting that into the equation, not just, did they change their behavior right? And in that, in, in one moment,

Jeremy: 15:02

Yeah. And did sales go up or sales go down based on that? and I think that's one of the things that I think makes some of the stuff that, talking to you pre show. Talking to Tammy a little bit about the product is you guys take a lot larger, data sets a lot more different areas that, that impact this cycle. And again, I attribute some of it back to the fact that it sounds like from a data science perspective, you're really good and understand data sets and then take on top tech on top of that. The fact that you came out of the restaurant space, talk to me about the different, because historically. Again, if Steve's running a bar, Steve knows who's coming in. Steve knows who's leaving. Steve can change his price. But as you get to scale, it's impossible to do that. Once you get back beyond one or two locations, because Steve's bar, he knows who's coming in, who's not coming in. If he changes the price of Miller Lite, he knows, Hey, Joe didn't come in anymore. You can't do that. You are capturing, you talked about earlier credit card spend. You talked about mobile data. You talked about pricing data. Talk to me about all of the different areas that you're able to capture that data, because it doesn't sound like that was even possible 20 years ago. to get all of that data and you guys continue to add onto it.

Mike Lukianoff: 16:09

Yeah, no, you're right. it wasn't even possible seven years ago, right? the data sets and even the methodologies, right? being able to, drill down and write and execute an algorithm, that can really, analyze, the, the depth of the purchase baskets, To pick up on these cues that customers are changing their behavior earlier in a purchase cycle. But you've got to start with understanding what the behavior of restaurant customers are. If you think that, a lot of these, a lot of pricing theory in, comes from airlines and hotels. But there's real problems with applying that to restaurants, right? Because those industries assume that there's a capacity problem

Jeremy: 17:00

huh.

Mike Lukianoff: 17:01

and that, and that, when you manage that capacity, that, people are going to accept it. And they don't account for the same emotional attachment that, that comes with restaurants.

Jeremy: 17:16

Yeah. Yeah. and even the behavior, I, one of the things is you were talking earlier, even that I found myself and I'm sure all of us did is we go to the same restaurants because they're part of our regular rhythm of life, our regular cycle of life. And when that cycle of life changes, our behavior changes. Behaviors oftentimes change because of that. Sometimes it is driven by price changes and those kinds of things. But I, in the middle of COVID, it's like, Hey, I used to go to this restaurant every Thursday after soccer practice, soccer practice isn't happening, so I'm not going to that restaurant anymore because COVID happened and, I'm not going to the office every day. So I'm no longer spending next to the office. And but I think, talk to me a little bit about that because you talk about this emotional and this linear equation, even with hotels. Funny enough, I'm looking at, I was looking at a hotel for right around a trade show. And I'm like, I don't know that I want to spend that kind of money based on that, because the price has gone up in the area where the straight show is a hundred percent to what you said. I made a decision. Whereas if I was in a restaurant, I would have bought the combo or I would have bought it without the drink back to your point.

Mike Lukianoff: 18:13

You said something really important in there, right? Which is, there are certain, we make certain restaurants a part of our, a part of our life. right? you build in certain habits, right? And it may be that, I'm going to have lunch at noon every day, and, and then, for a while, my noon lunch was the same place, all the time. And it just became easy. And that's what I was doing. Part of the whole theory. Of, like the dynamic pricing and, what's adopted from, the airlines and the hotels is, I'm going to change your behavior because it's better for my restaurant to get you to come in at 11 o'clock, Or one o'clock. And there's some problems in that, Which is okay. I'm going to make you hungry at 11 o'clock for money.

Jeremy: 19:05

Yeah, probably

Mike Lukianoff: 19:06

right. there's a problem first, but then there's a second problem, which is. It takes, there's different radical diversions of this, right? But it takes somewhere between 30 and 90 days to develop a habit. And once you've gotten a customer to develop a habit, do you really want them to break it because once you get them to break that habit, that's a risk. You're risking them, and if you're, and if you're going to get them to break that habit over price. Is that really how you want them to break a habit of coming into your restaurant on a, on a somewhat regular basis, right? That's, there's some problems in that, right? So anyway, I think point being that, when you get really deep into pricing, right? and I think this is important, right? Is that pricing isn't the only thing that we do, but pricing is a really good tool. foundation for a lot of what we do because in order to do pricing the way that we do it, you really have to understand the foundation of the demand at a really local level, right? because once you understand that, then you understand, then you can discern what's price, right? And what's promotion and what's weather, right? And what's, your competitor, right? that's pulling the demand away from you, right? And that's really what our goal is and bringing in all of these other data sets, right? So foundational models that are understanding it. what's the demand that we expect each location to have so that we can discern, what's coming from price, and, I think that's some of the problem is that when I see, restaurant companies during a highly inflationary period saying we have no price elasticity, the definition of price elasticity is, 1 percent increase in price. is 1 percent decrease in traffic. You tell a restaurant executive, that your 1 percent increase in price is going to result in a 1 percent decrease in traffic? Or your 5 percent increase in price is going to result in a 5%. That's a less that is an elastic condition and anything lower that is not elastic, right? so what level are they actually using? Because we're not using the textbook definition

Jeremy: 21:37

Yeah.

Mike Lukianoff: 21:38

because we know that's not acceptable, right. For this industry,

Jeremy: 21:41

and I'm certain we're going to get into this because this is one of the things that I always, I actually laugh about because I think, restaurant operators, oftentimes, even if the data is right there in front of them, don't believe it or do it the way that they've always done it. we have a very famous national brand, that's called That their CEO, their founding CEO does a hundred percent of the price changes. And he does it based on gut feel. And I don't get it. And even if we say that their group has proven to him statistically, and he doesn't care, he's going to do it the way he's going to do it based on what his own. Feeling is, and what is somebody willing to pay for this dish versus that dish? and, I think it's, I think it's funny you mentioned Mike, that seven years ago, you could even get a lot of this data. How are you guys storing this data? Talk to me a little bit because it's just, it, more data is being produced.

22:27

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Jeremy: 23:12

I don't know the statistic off the top of my head, but I know more data is being produced. Now than ever, and it's only going to continue to expand. I heard some, something last week or two weeks ago where by 2050, 2055, we're going to run out of Silicon in the world. by the end of, within the next a hundred years, we're going to run out of Silicon to even be able to create the chips. And so people are trying to figure out better and smarter ways to store things. How are you guys doing that? You mentioned AWS earlier. I know there's lots of people that are dealing with this. Talk to me a little bit about that tech portion of it, because I think it's critical. To understand as you guys continue to add data sets, what does it mean to both the results and how you guys get there?

Mike Lukianoff: 23:52

yeah, we are. Our tech stack is a combination of, a W. S. And snowflake. and, I was, as I said, I was an early adopter in the Amazon cloud. but, I started using snowflake. boy, I guess it's seven years ago now.

Jeremy: 24:10

my gosh, you were an early adopter of Snowflake then too, because it's not been around that long.

Mike Lukianoff: 24:15

It's been, I was pretty early. Yeah. it's been around longer than people know. it has actually been around quite a while, but it didn't start getting popular until probably the last, four or five years. but what they've done is. it's very different because it's designed for people who are analyzing data, right? As opposed to people who are maybe, a lot of these databases are prime for people who are maybe, building an app, right? Or building an interface. And without getting too technical, right? It's, it's, it's a columnar database, right? there's this eternal, head butting, that I got to do with for most of my career with the, with the folks who always want to, Normalize the data sets, right? So you've got like lots and lots of tables. So all the sophistication that you've got to do is written in pages and pages of code that are joining the tables in analytics. You don't want to do that, right? Because the sophistication that you're doing has to go into the mathematical code that you're doing, and you have to see the changes right in the tables. In order to make sure that you're able to QA every single step. So in Snowflake, being able to do really wide tables with all of the data that you have without, and being able to have that. With really cheap storage so that you could have really big tables, With, slowly changing dimensions, completely changed the kind of work that I do. trying to do that in, redshift, 10 years ago for, I tried.

Jeremy: 25:56

you weren't able to, you were just having to write a ton more code. It was funny that you say that I was talking to our CTO recently about, the advent of all of these just different data sets and these machine learning logarithms. it's insane how much faster we're able to get to at least hypothesis and then test those hypothesis against, because of tools like you talked about with snowflake, which is awesome. I. I'm sitting here listening. I'm live in this world, but now I'm an operator. I'm an operator listening to this call. what does this mean to me? Talk to me a little bit about what SignalFlare can do for an operator. Why do they care? What does it matter? why is it that you exist? And if you're, if people aren't using your tool, compel me to understand not just the bits and bytes, but what does this actually do for an impact to the business?

Mike Lukianoff: 26:43

Thanks for asking that question. Cause, cause I think also, okay. I've been an operator. I've been working with the industry and operators and even franchisees for a really long time. So I get that, people don't really care about what's under the hood. Some people do, I think it's really important, that. what we do works and, anybody from, from anywhere can look under, who has, who, who understands the statistics and the machine learning and all that, we'll stand up to, anybody else's algorithms. But, sometimes it's, it's the most sophisticated things underneath. That's what's required to get the most, simple and easy to use results at the end. And what I know about, the folks in our industry is that, they need to focus on their industry and they just want to know what to do and how it's going to affect their business. And that's the difference between what we are, which is decision intelligence. And a data company, right? A data company is just, stacking up visualizations, And graphs and saying, you take it from here, We've got data for people who want to access data, but really our goal is to bring it right through to that last mile and say, look, if you do this, as opposed to that, then the impact on your check average is going to be this, the impact on your traffic counts are going to be that. The impact on your promotions are going to be that, right? So that's really our true North is saying, how do we really make you, how do we really help you make a decision and how do we make sure that our algorithms are constantly learning? So that once you know what that decision is and you implement, That we're constantly going back to make sure that the simulators are getting better. So we're building everything in that last mile into probabilistic simulators, where, where you can actually input, we'll make a recommendation, right? And say, this is what, this is what the optimal solution is. But what I know from working with restauranteurs is that they're not going to do what I tell them to do, right? They're going to do their own thing and that's okay. But they need to know what the consequences are going to be. they may be willing to take more risk than what I think they should take. But what's that going to do to your potential, what's that going to do? You check average, right?

Jeremy: 29:13

Once again, good to do your traffic and all of the things that you talked about, I guess breaking it down simply, you're going to tell them whether the cheeseburger should be 11 or should be 12 and 50 cents. And they can then choose, Oh, I want it to be 1175. based on your data and based on, and is it store by store? Is it store plus, talk to me a little bit about how granular you get down to that. and do I have to be a thousand store chain in order to exercise these things? Cause I think that's, you talked about your McDonald's days. A lot of people go, Oh, that's great for McDonald's and pizza hut, but I'm not McDonald's and pizza hut. I own 30 stores and I need to figure this out.

Mike Lukianoff: 29:52

Yeah. So there's a couple of different models, right? so I think with the largest chains were, full white glove service, right? Where, where we've got, consultants and analysts who, are also helping them with, special. one off questions as well, right? Where, where we're doing risk models and, telling them what the prices of every menu item, on or off menu, what, what store should be priced and how to manage their pricing tiers and so forth. We also have completely the other extreme, which is, I think, our smallest, customer has eight units, and they don't, they're not really looking for, item level pricing, but they want to understand how much each store. can take and they want to know what their competitors are priced, right? So At that level we're able to say look in your market because here's how things are priced This store has the ability to take about, two and a half percent, this one can take five percent This one has no room at all, right? You've got to you got to hold that one steady and here's how your prices are You are, are priced, versus your competitive items, right? for that level, Very cost effective way for them to be able to say, we'll take it from here, And do our own item level pricing. now that we've got the guidance that tells us, how much price, we can take with those same level, If they do want to pay a little bit extra, our analysts can help them do the final, the final pricing as well.

Jeremy: 31:27

yeah, I love that explanation. It's so simple. And I think, back to what kind of you've been trying to solve for so many years, it's just that idea. I'm going to go a slightly controversial, Mike, and I'm going to talk about. Dynamic pricing, surge pricing, all of these different words that are out there. And I'm somewhat teasing. And for those that are on video, they watch my smile at me, but at the end of the day, we talked a little bit about the emotional attachment to restaurants. if I'm at a concert and I'm in an Uber and I need to get home and there's less Uber drivers, I get surge pricing and restaurants taking surge pricing and or discounted pricing. we've been doing this kind of pricing for happy hour for as long as I've been alive and as long as you've been alive, likely, just, that's just how it's been for discounted pricing, but at the same time, surge pricing, talk to me a little bit about why the word dynamic or surge pricing really doesn't work for restaurants. And I think, and I say controversial because there was a whole bunch of hubbub in the last few months, where some large burger brand across the country was talking about doing surge pricing is how the article got written and. and I think it's wrong. And I think, you would agree that's not correct, a correct way to consider it.

Mike Lukianoff: 32:30

Yeah. I get a little sticky about semantics. I think that, it's crazy about the English language. we got a word for everything.

Jeremy: 32:38

That's funny.

Mike Lukianoff: 32:39

To your point, we've had happy hour forever, right? I wrote an article, about okay, going back, when did differential pricing start? and I think the first blue plate special, that was recorded goes back to the 1880s. there's nothing new about pricing things differently, by meal period by day, there's nothing different about creating automated discounts, right? we've been calling that, marketing automation for 20 years, that's been happening as well. dynamic has a specific meaning too, right? Which means constantly changing.

Jeremy: 33:16

huh.

Mike Lukianoff: 33:17

Now this idea now that, like differential pricing has morphed into dynamic pricing is just, it's incorrect. So it irks me a little bit,

Jeremy: 33:28

Me

Mike Lukianoff: 33:28

but I feel like, but I feel like I'm swimming against the tide.

Jeremy: 33:31

flipped out about it too on social media. It's I'm not going to go in and get a burger and it's 10 today. And it's 11 tomorrow because they just happen to be busier that nobody would accept that, at least in the food service industry thus far, they may accept it as time is going on. It went from 10 11, but they're not going to accept Tuesday, it's one price and Wednesday, it's a different price. If I'm interacting with that brand in a different way, on a regular basis, it should stay pretty static. and so dynamic, I agree with you. The whole idea of dynamic pricing is, is I don't say a myth, but I don't think it's feasible and I don't think consumers will stand for it.

Mike Lukianoff: 34:05

that there's a better way to think about it and to look at it. and I've done. I've done work, on some other, technologies, in, in the background, on displays and so forth. if you take the idea of dynamic and look, I think going back in 2017, I actually did a price, I did a presentation at one of the conferences. talking about what dynamic pricing is coming, but what I described as dynamic pricing is much different than what it's become, right? Because what I was saying is, look, when dynamic pricing comes as at least what I anticipated it being, was being, starting with a baseline of solid econometrics. And then thinking of, the total spend as being the price, right? Not this, ticker tape, the item prices is changing in real time or item prices changing, during the peak, how do you. How do you change the bundle, right? Or how do you change, how do you incentivize people to, gravitate toward, the higher priced items, right? Or to, how do you merchandise things, right? Because if you think about the opportunity in what the technologies are, this makes a lot more sense, right? working with a lot of large QSRs over time, I know that you can change, if you changed an item price. By 5%. you're going to get, if you change all your item prices by 5%, you're going to get 5 percent lift, right? But if you take a particular, bundle and put it into the center panel, you can raise your check average by, 25, 30%, that's an opportunity, not just because you just increased your, your check average, but also because you did it with people opting into something that they want more, right?

Jeremy: 36:03

more value in whatever that item is that you're presenting them with.

Mike Lukianoff: 36:07

And because you might be able to do it while you're managing other KPIs, right? Because maybe you chose that item because it's a higher throughput item, right?

Jeremy: 36:16

Yeah. It's faster in the kitchen. It's lower food costs. Yeah. Those are so many different factors. Like you talked about that might make you want to drive that.

Mike Lukianoff: 36:23

So if there's a dynamic discussion that needs to be, had in the industry right now, it should be about, total, dynamic, menu management, right? Not just dynamic pricing. And look, I've done, I've spent so much of my life, and, teaching people about pricing. So you'd think that I, and look, we could, we can easily, help pipe, pricing buckets and we integrate into, POS systems, helping to, support, dynamic pricing for anybody who decides that they like it. That's not, that's not a difficult thing for us to do. but. But I feel like it's a much bigger service to the industry to go the next step. And actually do something that's going to take us to a higher level. And an offer when you create something that is offering value to the customer, while you're actually improving, the service and the metrics, right? That's the kind of thing that the industry really needs. At least that's my opinion.

Jeremy: 37:22

and I've been calling it. I think you would agree based on our conversation today. it's data driven pricing decisions that you're making. it's based on you're making conscious decisions based on the data and what is coming in as well as, and the data doesn't necessarily have to be ones and zeros in the computer. It can be other data that, that you guys are capturing Mike. How do people learn more? How do people engage? How, I'm again, now I'm sitting here, I'm 40 some minutes into listening to the show and I'm like, I need to know more about this. How do I learn more about SignalFlare? How do I get engaged? How do I start to figure out if this is right for my brand and engage with your team to, to see, see what you guys might be able to do for them.

Mike Lukianoff: 38:00

You can come to our website, signalflare. ai. you can email me, mike at signalflare. ai. You can email Tammy and, you don't even need to know her email. you can just, look at any conference website. she's everywhere.

Jeremy: 38:16

She is, she's a big deal, that Tammy lady.

Mike Lukianoff: 38:19

that's right.

Jeremy: 38:19

I'm grateful that, I know we were teasing about it cause I've known about you for quite some time and I've heard all these fantastic things. and, while I tease Tammy about it, she was a hundred percent, right? you're, very, wise in this area of, of pricing and price, price elasticity. And, and again, I guess I would ask you to give me one last encouragement for brands that aren't down this path. Mike, what are they going to miss out on if they haven't already started down this path of working with you guys or somebody else to evaluate these data elements and they're still sitting there going, I see a menu, I think people are going to pay 1895 for chicken parmesan. So I'm just going to put 1895 on that, whether it's the right decision or the wrong decision. Again, I'm just picking a random item. What are they going to miss if they haven't already gone down this path?

Mike Lukianoff: 38:59

I think there's a few different levels to it. So one is that, again, in, In decades now of using a more sophisticated, way of pricing, there's a very high return on investment to it, and look, we've gone through the scrutiny of every major brand. with testing control and all of that. And it's proven that when you use these kinds of methodologies and the one that we're using now is even more effective than the ones that I've used in the past, and you're getting at least double the flow through, of doing it on your own. so there's that just the pure ROI on it now. What I'm building though, is not just a pricing engine. we're, we are doing this because we are the first. decision intelligence company in the restaurant industry. So all of this data that we're ingesting and creating for these, pricing models is really the foundation of understanding what makes your demand tick. So as we're building the pricing models, we're starting to understand what's driving your marketing, what's driving your promotions, where are your customers coming from? and all of this is driven by, not to jump on a buzzword, it's all machine learning algorithms underneath. And that means that the longer you do it, the smarter they get. once you adopt these types of technologies, they get smarter, right? So you get on it now and you continue with it, what you know, and what you can do and what you can automate. Is going to be much better a year from now than it is today. So the longer you wait, you start to slip behind and you can see these kinds of case studies, in industries across the board where, the ones that waited too long, find themselves, way, way behind.

Jeremy: 40:53

Yeah, no. And I say it all the time, if you're not doing it, likely your competitors doing it. And so you better watch out because they're going to figure out how to win that the game. Cause they've invested in tools like what you guys are building. So Mike, thank you for educating us. Thank you for, even. Waiting into the discomfort as I, as I threw out some of those buzzwords. It's fun to, it's fun to finally meet on the show. I'll have to thank Tammy after the fact for, for introducing us. And I'm, excited to, to have our listeners out there, learn more about you guys, to our listeners, guys, we know you guys have got lots of choices, as I said, so thank you guys for spending time with us and to Mike. thank you and to our listeners, make it a great day.

Speaker 2: 41:28

Thanks for listening to the Restaurant Technology Guys podcast. Visit www. RestaurantTechnologyGuys. com for tips, industry insights, and more to help you run your restaurant better.