Full transcript of Turning One-Time Visits Into Loyalty: Guestologie’s Ryan — episode 344 of the Restaurant Technology Guys podcast, with Ryan Volberg, Guestologie. Speaker labels preserved; lightly edited for readability.
What this episode covers. Ryan Volberg of Guestologie on the problem of not recognising your own returning guests. The premise: restaurants sit on data spread across reservation systems, POS records and past visits, and almost none of it reaches the person standing in front of the guest. Ryan covers what guestology means in practice, how the system builds a playbook for an individual guest, the SAPS model of loyalty (Status, Access, Power, Stuff), why most loyalty programmes give away too much, and how they prove an experience actually changed behaviour.
- In this transcript:
- Getting Guest Data Into One Place
- A Billion-Dollar Industry Nobody Is Serving
- Why Recognising Returning Guests Is So Hard
- What Guestology Actually Is
- Pulling Data From the Reservation System and POS
- Building a Playbook for the Individual Guest
- The SAPS Model: Status, Access, Power, Stuff
- Proving the Experience Changed Behaviour
- The Numbers Behind It
- Advanced Guest Segmentation
Jeremy Julian (0:01): Welcome back to the Restaurant Technology Guys Podcast. I thank everyone out there for joining us. As I like to say, every single episode, you guys have got lots of choices. So thank you for hanging out today. Today I am joined by a multiple-time founder, entrepreneur, guy who’s got a million ideas, and it’s been fun to get to know Ryan over the last uh last little bit. Uh gotta get a shout out to my friend Alan Heyman who uh gave us the original intro. I don’t know if you remember that, Ryan. That’s where uh that’s where we got connected, but uh Why don’t you introduce uh yourself for those that maybe have uh been living under a rock and don’t know who Ryan Bullberg is ’cause you’ve been in the space for uh a week or two. It’s uh at least if I look back at the uh at the bio.
Getting Guest Data Into One Place
Ryan Volberg (0:39): Yeah, awesome. Well I guess there’s there’s probably a lot of rocks because uh I’m not a household name, but uh uh I’ve had the pleasure of serving this industry for a long time. So um, you know, just in terms of introduction, I think, you know, my first entry into this uh restaurant technology space that I that I know and love so much was with the creation of a company called VivoNet, which was in uh really was founded in nineteen ninety-nine. Uh and our mission was to create the first cloud-based POS and You have to remember that back in 1999, that terminology didn’t exist. I don’t know when cloud came out, but we called ourselves an application service provider, an ASP. I’m really dating myself. I think that’s gonna happen a lot on this podcast. But um and so, you know, we were just trying to solve a simple problem, which is if I had a hundred restaurants, I had a hundred separate POS systems, and then I had to try to
Jeremy Julian (1:14): Yes.
Ryan Volberg (1:32): get all that data into one place and then, you know, God forbid I needed to change prices because I had to change it in a hundred different places. And so wouldn’t it be nice if everything just was all in one place and we could get the reporting and change the prices and have an agile business. So we built that product. It was called Halo, uh, and it was the first enterprise cloud based point of sale system. And we caught the attention of Micros. And that’s back to Alan Heyman. Alan Heyman was um was uh I think the head of sales o over at Micros in those days and Paul Armstrong was the CTO and they saw what we were doing and they got interested and uh they said, hey, we think this is the future of point of sale and we’d love to invest in your company. And so they did. Uh Micros invested many millions of dollars into uh into VivoNet and we collaborated on a variety of products. Um a product called IPOS was was our product uh branded through and sold through Micros and MyMicros.net we influenced and a whole variety of other things. So it was a tremendous uh education with them. And uh ultimately VivoNet won on uh went on to win some of the largest POS deals in our industry, most notably a 5000 location deal with Sedexo, which was kind of interesting because inside of this one system we had um you know maybe an office building cafeteria that was doing eight hundred dollars in sales a day. And then in the T D Gardens, the stadium, which during a hockey game would do eight hundred thousand dollars in an hour. Um and so it was a it was a great education.
Jeremy Julian (2:59): Uh-huh. That’s awesome. Um, and I know that that uh your your tech space didn’t didn’t stop there. I guess I know, you know, post Vivo Net. I think you you left there. Um I guess talk a little bit more about your career because I think it really culminates in where what you’re doing now. And I’m super excited to talk a little bit about that. But I think you had one or two stops beyond that, right?
Ryan Volberg (3:22): Yeah, you bet. And so the next thing we did from there, that company got sold to private equity and and then ultimately got bought by Infor. Um so I’m really proud of the fact that that that product still lives today and is serving thousands of customers. Um and I went on to be a co-founder of a company called Instant Financial, which was really the first company to do daily pay for hourly hospitality workers and also uh digital tips distribution. Um and so we actually had a real Simple problem. And I think my career, I’ve always tried to be the white hat guy. Like we’ve always been on the side of the restaurant industry, right? And so we looked at the restaurant industry and said, look, there’s two forces here. Force one is restaurants are chronically understaffed. Like they can’t find and keep the workers they need to fully staff their restaurants, and the turnover is enormous and there’s a lot of costs with that. And over on the other side, you’ve got this payday loan industry, which is this predatory $85.
Jeremy Julian (4:18): Awful.
A Billion-Dollar Industry Nobody Is Serving
Ryan Volberg (4:19): billion dollar industry that you know honestly is taking advantage of these hourly workers who couldn’t make it paycheck to paycheck. And we said, look, if we could provide a solution that restaurants could use to give their employees access to their earned pay. Like they worked today and they could get part of it on the day they worked, it would ease a lot of financial like maybe you just needed five dollars in gas money to come into work the next day or whatever it was, keep you out of the payday loan cycle. But the real you know, advantage was it would make the employer an employer of choice. If you were a restaurant uh chain or or whomever and you could offer that daily access to pay and your competitors couldn’t, you would probably get more employees that wanted to come work with you and they would stay with you longer because they would get used to that advantage. And so that’s what we were trying to do. And I think at Instant Financial, uh and to this day we’ve taken the high ground, which is we we would never monetize the employee. We never charge them for access to their own pay. Unfortunately, that’s not uh the case with many of the the companies that are in that space. But instant has gone on to do very well. I had two co-founders in that business. Um and ultimately um when my first business sold um from private equity to to Infor, that was uh a time for me to take a step back and kind of reflect on what I wanted to do next. And so um I stepped away from Instant Financial and I was Trying to figure out what to do. I was gonna take a long motorcycle trip from Vancouver to Key West, Florida, and actually was probably a month away from from jumping onto the bike and going when I got a call from Savneet over at uh Parr and said, Hey, we’re uh we’ve got some grand plans for turning Parr around and you know, really getting that company to live up to its potential, but there’s a lot of challenges. Would you be interested in coming and being part of the team and helping us turn parr around? And um I went down and And looked around a bit at it and I kind of fell in love with the opportunity. And so I spent the next two and a half years of of my career uh working with that team to to you know really restore PAR back to its sort of leadership position in the industry that it you know historically had enjoyed. And you know, I’m proud of the fact that the the day that I joined PAR, I think we had a $400 million market cap. And the day I left it was $1.81 billion. So um the whole team there did a tremendous job under Savneet’s leadership. And uh that was just a great part of my career. And that’s kind of where I stumbled into this um, I guess, identity as a turnaround guy. Because the next thing you know, I was getting, you know, inbound requests, hey, could you come help us fix this company or fix that company? And so for the next few years, um I did that and I worked with a variety of different companies. Um and the last one was with uh a company in Florida um called uh T-Rock, the Revenue Optimization Companies. And in a partnership with Google, we were working on really cool uh digital labor solutions for tier one uh retailers and tier one brands. And um and that was all based on Gemini and the Google Cloud stack. And that’s where I really got a bit of an education on how to unlock business value from these you know generative AI solutions. And that’s when I was able to connect the dots. into guestology, which had been um a product I had had on my mind for a long time, but up until this technology becoming available, I didn’t really think there was any way to do it. And so uh so that’s brought me to guestology today. I’ve I’ve uh been incubating that on my own for for you know a good part of uh 2026 and then uh jumped out uh or I’m sorry a little bit into twenty twenty five and then really jumped in full time uh January twenty twenty six to bring this uh company to market.
Jeremy Julian (8:02): I love it. Uh and I’m excited to talk about it. Uh funny enough, uh uh I don’t know if Tall was one of your co-founders, but he was on the show back in twenty twenty four, Tal from Instant Financial. This the the acting CEO, and uh he was on in twenty twenty four and I’ve had Sav Need on the show twice, I think. So funny that uh funny that that that some of your uh your former coworkers have also been on the show. So for those of the listeners out there want to learn more about what uh what they’re doing at Brink and a little bit of what they’re doing at Instant Financial, uh Really cool episodes back on uh in the archives. Let me know uh let me know what your thoughts are on them. But uh Ryan, before we you know, before we get any further, we we’re eight minutes in. I love the background and the and the part that I wanted you to share is just there’s so much technology influence that you’ve had. And while you might not necessarily be a household name for for many, it was amazing to me when I got introduced to you just how much influence you’ve been able to have to truly help. And the thing that I love about our continued conversations is that you always put Both the guest and the staff member, the restaurant owner at the heart of the solutions that you’re building, which is really kind of where I want to dig into guestology. So at a high level, what is guestology? Because I do think it’s something unique in the space. And why I wanted to have you on the show is because I I really, really find what you’re doing as a innovative, different way to think about uh guest engagement and guest recovery and and all of those kind of things. But why don’t you give me what’s the elevator pitch on what guestology is?
Why Recognising Returning Guests Is So Hard
Ryan Volberg (9:26): Yeah, you bet. Um, I’m gonna I’m gonna tell you a little story, a very brief story, because I love the problem. I’m I’m obsessed with with the problems. The technologies themselves are just sort of like the way you solve the problem, but in order to understand guestology, I’m gonna give you an experience that I had that is repeated thousands of times every day throughout our industry. And it looks like this. My wife and I would go to dinner uh at a particular restaurant. every Friday. It was our date night place. And and you know, we were emotionally attached to the place, right? We was just, it was our place. Um and it was a wonderful restaurant, but you know, a lot of staff turnover feel like every time we’d walk up a little bit, you know, different host maybe a lot of different servers. Uh but that’s okay. Um one day we go in and uh we sit down and we server comes over and she’s perfectly lovely and says hi my name’s Joanne. I’m your server tonight. Um welcome to restaurant. Uh is this your first time here? And I I just pause and and I looked up um and I didn’t say it this way to her, but I was like, no, this is my 50th time here. 50. And I realized right at that moment that you know the investment that we make in these restaurants is often sort of not returned to us. And it yeah, that’s the word. And and it’s not because the owners don’t care, and it’s not because you know the ethos isn’t there.
Jeremy Julian (10:41): Reciprocated, yeah.
Ryan Volberg (10:49): It’s just because it’s so hard to recognize these guests as they’re coming in. And so I went to the COO of that company and I said, you know, here’s what my experience is. And is this a problem for you? That, you know, you have staff and there’s pretty high turnover and they just don’t they have the ability to recognize a 50-time guest from a first-time guest, from a three-time guest, and whatever it is. He goes, Oh my goodness, it’s a huge problem for us. I said, Well, you know what? I I think we could fix this. And so that was the that that was really what we set out to do. was to provide a system for restaurants that would help them create lasting and deep relationships directly with their guests. um And so if we really look at what guestology does, and what makes us unique from every other system out there, is that you know you can look at a variety of systems like Open Table or a lot of loyalty systems out there. uh There’s uh you know customer data platforms, CDPs and marketing platforms and all these things. But mostly what they do happens after the guest has arrived. You know, so open stable job is to bring you a guest, right? I mean, they even bill you that way. We’ll charge you a couple of dollars if we can bring you a guest. And after that, it’s kind of done. Um, and then a lot of loyalty programs will, you know, once you’ve left the restaurant, you accumulate your points, and then they’re marketing to you to try to get you to come back. But what nobody was doing was actually working with the guest while they’re in the restaurant, right? To build an experience just for them. So hyper-personalization, but I actually think there’s a much bigger proposition here. And it’s and it’s really contained in this one statistic. The National Restaurant Association published just a few months ago uh an article that said table service restaurants have 36% guest churn per year. So you gotta just think about for a second. 36, one-third of our guests that are table service restaurants on average. are just not coming back. And so obviously that begged a follow-up studies like, well, why is that? So they s went out and we talked to those guests and said, would you used to be loyal somewhere, but now you don’t come back? How come? Number one reason, 68% said due to staff indifference. And and so what that means is is that the staff they can’t recognize you. And frankly a lot of times they just don’t care. Is is just there’s just this lack of of connection. And so what’s happened is is the guests are going
Jeremy Julian (13:08): Uh-huh.
Ryan Volberg (13:13): They’re starting to get comfortable at a place, but then very quickly that connection isn’t, they’re not being seen, they’re not being connected with. And so what happens is they just go somewhere else. And so this is a new stat just out by Tilster, I think this week, saying uh 45% of diners have switched their favorite restaurant chain in the last year. 45%.
Jeremy Julian (13:35): Yeah, you when you shared that with me the other day, when I was like, what? That’s insane.
Ryan Volberg (13:38): Right. So here’s what what the upshot is. We have never had more technology around loyalty and we’ve never had less loyalty. So where does loyalty come from? I think loyalty is built in the dining room. It’s not it’s not built in an email and it’s not built in a dashboard. It’s built in the dining room room when you are in my restaurant and how I treat you says more than you know some email offer for 245% off uh whatever it is down the road.
Jeremy Julian (13:47): Mm-hmm. Yeah.
What Guestology Actually Is
Ryan Volberg (14:07): And so that’s what guestology is. It’s a system that allows at scale the ability to recognize guests and to treat them personally to do one of two things. It’s either to retain a high value guest or to grow more visitation from a high potential guest.
Jeremy Julian (14:24): Yeah. Well, and I and and I love, and I I know we’ll dig into this Ryan here in just a few minutes. Um, the idea that that you can do it digitally within the store while you’re in that transaction. But I have two experiences, one on the negative side and one on the positive side. Our normal Friday night restaurant is in town. We go there almost every Friday night. My wife likes her margaritas on Friday night, and she’s not here today, but she’d normally laugh down the hall and go, Yes, I do like my Fri Friday night margaritas at this little Mexican restaurant. And the owners are always really sweet, but They’ve had turnover in their staff. And the last two times we’ve been there, they didn’t recognize us. They didn’t have any idea who we were. And we literally are there 40 weeks a year, I would say, 35 to 40 weeks a year. So it’s like it’s and it’s been five years running. So it’s a it and it was really disappointing the last two times going, why haven’t they why don’t they recognize us? We missed a couple of weeks. This graduation season right now, and you know, lots going on. And so it was a very frustrating thing. On the flip side, there are certain places that we choose to go to because I know. We’ve got the Cheers effect where you walk in and they know who you are when you walk in. Your drink might already be sitting there waiting for you. I was talking to a couple last night. They go to a different Mexican restaurant because they they literally can do the order for their entire family of four because they’re there all the time and they know who they are and they make sure that they get the right people. So I’m saying this all to say, I think we’re all trying to figure this out. And I think everybody, it it’s not unique to the idea that says we want to treat guests the way that we would want to be treated. But now let’s talk a little bit more about how you guys have figured out digitally how to figure out who that guest is, Ryan. Not the secret sauce per se, but how do we how do we identify that without it being creepy? Because I’ve seen people go, We’re gonna have cameras and facial recognition, saying Ryan Bullberg’s walking in. I don’t even need the the the blocking and tackling, but talk to me a little bit about kind of what the guest experience is because I think it’s it’s it was a very novel idea that you brought to me when you you shared this product with me that says we can. digitally represent this guest in a way that you’re gonna drive them through the value chain the way you guys did. And I’d love for you to kind of talk a little bit about that without it coming off as, hey, we’re big brother, you know, watching over every single guest and know, you know, know all of those things. Cause I yeah people say that, I’m sure.
Ryan Volberg (16:30): 100%. Yeah, it it listen, I like to think of this from the perspective of I am the owner of a 20-table restaurant. Right? Like, you know, so our customers right now are multi-location operators because we’re really leaning into the enterprise side. But I when I think of what our product is and who we’re building it for, it’s for that owner operator that’s there. And we want to be able to scale that perspective. So When someone walks into the the door of any restaurant, what would the owner want to know? Two things. Who is this guest to me and what should I do about it? Right? And that that that is it. So if we talk about what get guestology does, we’re pulling data out of the existing systems at the restaurant. So this is, I think, a a real uh important thing to say, which is restaurants are already burdened with a lot of technology. And what we don’t want to do is like create another layer of it. So we’re pulling.
Jeremy Julian (17:09): Yes.
Pulling Data From the Reservation System and POS
Ryan Volberg (17:27): data out of the reservation system or the table management system, which is really the system of record for visitation. And then we’re combining that with POS data out of the POS. And we do a segmentation. And so we’ll look back a couple of years if we can. And the idea is going back two years, we now have a complete picture of who the guests are, at least for that last two years, right? It’s a pretty good picture. And so when a guest walks into a restaurant that’s using our system, they say, hey, I’m here for my seven o’clock reservation or I’d like a table for two, please. Once that that data is entered into the you know, say open table, the guest is marked as arrived, our system goes to work. So the first thing we do is we surface uh who this guest is. Uh so what I mean by that is what’s the value of this guest? Okay, so it’s the first thing that we should know. And so we do that, our nomenclature is uh you could have an elite, a VIP, a loyal, a prospect, right? So these all sort of internal nomenclature. So the idea is Ryan walks up, I’m here for my seven o’clock reservation, and our system says, who is Ryan? Oh, Ryan’s an elite. Now, just to give you an example of how important this data is, an elite is somebody that usually makes up about 0.4% of all guests in a restaurant. And one elite, 0.4, less than 1%. And one elite is typically worth 100 regular guests in terms of customer lifetime value. So my proposition is this.
Jeremy Julian (18:41): Point four, less than one percent.
Ryan Volberg (18:53): If you’re a restaurant operator, you sure as heck better be able to recognize an elite when they walk in. But there’s a problem. There’s more of them than you think. So for a pretty medium scale, medium to large scale casual dining restaurant, you might have 200 elites. And so you’re, you know, even your best managers are not able to pay, probably recognize more than 60 to 70 people at site. And so what’s happening? Right, exactly, exactly.
Jeremy Julian (19:18): That’s with zero turnover. That’s with zero turnover, right? We we just talked about the turnovers topic.
Ryan Volberg (19:23): So I think this is an example of why this problem is so pernicious, is because humans actually can’t do it. There’s too many people, but it actually gets worse. Our next segment down we call the VIPs. You know, and these are people that are coming between six to ten times a year. They’re they’re quite loyal to us. Um, there could be a thousand to eight hundred of them. So they’re invisible. Most of them are invisible. And so what our system is doing now is when someone walks into the door and says, I’m here for my seven o’clock reservation.
Jeremy Julian (19:45): Mm-hmm.
Building a Playbook for the Individual Guest
Ryan Volberg (19:53): The first thing we do is we say, what is the value of this person to us? We’re like, ah, this is an elite or it’s a VIP. Great. The second thing we do though is I think where the magic starts to unlock. And that is, I think a lot of the problem with segmentation is we treat all elites the same or we treat all VIPs, you know, all segments the same. We don’t do that. We do what we call a loyalty trend assessment. So what our system is doing is it’s looking in and saying, okay, Ryan’s an elite, that’s nice, but what’s his relationship with our brand? Is he gaining in loyalty? He’s starting to come more and more. Is he stable? Or in this case, is he starting to decline, which is right, which is where this churn comes from, right? Across all segments. So now Ryan’s walked in. He hasn’t even been seated at his table yet. And guestology has provided you with these two very important points to answer that question, who is Ryan? And it’s this, Ryan’s an elite, and we’re starting to see less of him. So automatically at this point you have an advantage, right?
Jeremy Julian (20:51): Yeah.
Ryan Volberg (20:51): So there’s a second thing we do. So that’s the who is Ryan equation. The second piece of it is what should we do about it? So everyone talks about the sexiness of AI and all that, and then actually also the criticisms of AI and all of those things are warranted. But really at the root of our system is machine learning. Because what our system is doing is looking at patterns and getting really, really intelligent about how to make decisions for these guests. And so what we do as the third piece of this is
Jeremy Julian (20:58): Yes.
Ryan Volberg (21:21): We now build a playbook just for Ryan. So we know Ryan’s a high value guest. We know that we’ve been seeing a little bit less of him. And so Ryan’s here sitting in our waiting room and we’re about to seat him. How should we approach Ryan? And really, there are two uh main strategies, right? Strategy one is we want to retain a high-value guest. That’s it. And especially if they’re like a very, very high value, like someone who’s uh ultra regular, a VIP or a an elite. It’s actually obnoxious to try to get more visits from them. Right? And I think that’s the problem with a lot of these email campaigns that are sort of brute force going out, is this like, guys, I’m already, you already got me. This is not relevant. So the first thing that we’re trying to do there is either retain a high value guest or get more visitation from a high potential guest. So this is where our system goes to work and it builds that playbook. So if we rewind now, we know that Ryan’s a high value guest. We understand that we’re starting to see a little bit less. Maybe he’s getting bored, right? And so we build a playbook for Ryan. And maybe that playbook is surprise and delight. Or maybe we’re going to do something like um we’re going to recognize his status. Or maybe it’s just our everyday best because we don’t have to do something special every time. But the idea now is behind the scenes, we have a playbook for this guest. Every single individual guest is treated individually. And so now the restaurant, I think, is built a very strong advantage. Uh but we need to do one thing more, because we understand that in the restaurant industry execution is tough. And so what we do is we now surface specific what we call loyalty actions that instruct the staff what to do. And so those can be executed by management, they can be executed by servers, the host team. All of this is sort of built custom to the brand. But the idea might look like this: our system says, ah, Ryan’s a very high-value guest, but
Jeremy Julian (23:00): Mm-hmm.
Ryan Volberg (23:16): We’re starting to see a bit less of him. Our playbook for Ryan today, because we want to retain him, is surprise and delight. So what it does then is it’s instructing the staff, we’re going to drop two glasses of Prosecco at Ryan’s table. And so that sounds a little bit like this. Server walks over or manager walks over, it doesn’t matter, whatever works for that brand. Say, Mr. Vollberg, it’s nice to see you here again today. We really value you being one of our most important guests. And we just thought we’d drop off these couple of glasses of Prosecco to start your evening off right. So our system orchestrates that for the hundreds and even thousands of guests who are invisible. But when we put our system to work, we make the invisible visible, and the results are really pretty dramatic when you’re able to engage all of those guests in that highly specific way to drive retention or increase visitation. So that that’s really the guestology system in a nutshell.
Jeremy Julian (23:54): Mm-hmm. Well, and I I wanna I wanna talk more about um a little bit you you mentioned it that that it it was almost impossible and I and I know it’s not completely impossible, but was very likely hard to do without some of these uh advanced compute machine learning logarithms that you can now get to. So I’d love to to talk about what that unlock was there, Ryan, ’cause I think There’s people that have kind of a playbook that just says, hey, I see this guy, or you know, it pops up on their loyalty and now they get a free dessert or whatever else. And again, I think we’ve all experienced this where it’s just kind of, you know, it’s generic, and you know the guy that that you know potentially is spending, you know, 10% of what you’re spending might be getting the same dessert just because that’s the what the system told them to do. Whereas I feel like um you guys have have had an unlock that not only do is it predictive It also is very relevant to that guest and what you think and what you learned and what you grow with versus kind of the blanket emails that just says, Hey, you get a free dessert. It happens to be my birthday this week. So the amount of emails that come into my mailbox from all of the loyalty programs saying, Come in and get your free dessert because it’s your birthday this week. And so across the board, I think that’s kind of the generic way. But I feel like you guys have, through machine learning and through the these ideas, you guys have unlocked something that’s very different. That’s, you know, I guess just Just at a higher level. So I’d love for you to talk a little bit about that, Ryan, before we keep going.
The SAPS Model: Status, Access, Power, Stuff
Ryan Volberg (25:31): You bet. I want to talk about the SAPS model because if if we think about this and we’re saying, look, what are we trying to do, right? We’re trying to solve that 36% churn problem where guests are coming in and and they’re, you know, they’re just not loyal, right? And so you actually have to pause for a second and say, well, what actually drives loyalty to a brand? Hmm. Okay. So there’s a model out there called SAPS, S-A-P-S. And it was uh created by a guy named Gabe Zikerman, who um who’s a really brilliant guy who figured this out. But SAP stands for status, access, power, and stuff. And so, in order, those are the things that create loyalty to a brand. So the number one thing is status. And what is status? It’s how you make me feel, right? And then and then access, power, and then the bottom is stuff. So what is stuff? It’s free things. It’s a it’s a free cake on your birthday, it’s points accumulation, it’s discounts, it’s half price red wine night. It’s this, that, and the other thing. So what Gabe’s work proved was that the number the thing that creates the most loyalty, which is status, is also the cheapest. So like restaurant people pay attention because it’s like, holy shit, we’re the wait a sec. The thing that causes the most
Jeremy Julian (26:47): They give away too much stuff when all they need to do is acknowledge their guests, but they need to be able to do it at scale back to your point. And without digital, it’s almost impossible.
Ryan Volberg (26:51): Well it’s right. So this is why I got so excited and I and I was like I I felt like I was like Raiders of the Lost Dark and I and I found you know I found the treasure, right? It was like I’d been on this quest and it was right there all the time. Is that the thing that causes the most loyalty is the cheapest, but the thing that cause cause causes sorry the least amount of loyalty is the most expensive. And it feels like for the most part, that’s where our industry operates. Is that, you know, what do we wanna do? We we wanna get the guests to come back. So well, how are we gonna do it? Well, we’re gonna go send them some cheap offer. And I think group on is the most
Jeremy Julian (27:22): Yes. Well, because it’s attribution, because they can prove that they said that and the person came in. But at the end of the day, now with some of the machine learning, and I’m sorry to cut you off, but it’s like now we can actually know who that guest is today if you look at the data appropriately. Whereas 20 years ago, everybody’s still running that old playbook that says I’m gonna throw out a coupon and people are gonna drive guest behavior. But at the end of the day, we now know who that guest is so much more often. So I apologize. I I get passionate about it because I hate these brands that just
Ryan Volberg (27:38): No no. Exactly. No no.
Jeremy Julian (27:56): cheapen who they are by throwing out all of these discounts and ultimately they get into the discount loop where people won’t come back unless they have the discount and it’s ruined more fantastic brands than any of us would ever even imagine.
Ryan Volberg (28:03): Well and Well, it it is. We all know it, right? And yet but there’s no system to actually do the status part. And so that’s what we built. So so to get to the machine learning piece, right? Uh what our system is doing, and this is a really I’m glad you brought this up because it’s an important point, is our system is saying we’re gonna do these things. So what are what are you know we have monetary and non-monetary experiences that we orchestrate within the restaurant. So what are we doing? We’re acting as a co-pilot very intelligently, and actually we’re taking over Part of that discretionary budget that all restaurants have to give away the free thing here and there. We’re saying, why don’t you let us allocate that with intentionality, but also with provable ROI? And so here’s what it looks like. Our system says, you know, we’re gonna do a manager visit here, we’re gonna offer these people a premium table. There’s a whole variety of like experiential, like some of our customers are just incredibly creative. Like some of the higher-end places are doing things like a kitchen visit. One of my favorite actually is a choose your own steak. So if you have like a customer in our system sees what’s going on and it says, uh, you know, you just ordered a tomahawk, um, our chef would like to invite you into the kitchen uh so that you could pick your own steak from among the ones that we have available. These are experiences, right? And the way to do it is they’re exclusive. It’s not for everybody every time. But our system today singled you out and said, we want to give this experience to you. Why? Because you’re so important to us, right? And so
Jeremy Julian (29:18): That’s pretty amazing.
Proving the Experience Changed Behaviour
Ryan Volberg (29:34): When we’ve allocated our system has said, you know, recommended these experiences, we check off when they’re done. This is an incredibly important piece of it, right? So you give away the Prosecco or you do this intentional manager visit or gave them a premium table or whatever it was, you check that off. And what happens now is our system is learning and watching. So there’s two things. Thing number one, we saw the guests pattern. We we intervened. We we sort of
Jeremy Julian (29:41): Mm-hmm.
Ryan Volberg (30:01): changed their experience. We we created this experience. Now what happened? Right? Did it did it change? Are they coming more often? And so our system is learning and saying, well, these are the things that are working. So it starts to do those things more. And the things that aren’t working, it starts to do those things less. But the other piece is is it gives us this all-important um attribution. And so what happens is we are able to say, look, here is what the guest behavior was before. Then we intervened. The behavior changed. What’s the value of that behavior now in terms of increased revenue to your business? And so, you know, the one thing I’m very proud about in terms of our business is we’re able to prove to our customers the down to the penny of the additional value that we’re creating. And I think that’s really important because there’s a lot of I don’t know, I guess there’s a lot of snake oil out there. There’s a lot of AI hype, there’s a lot of bullshit. And what we’re saying is like, don’t pay us a penny. If you if we can’t prove to you that we’re actually bringing you tremendous value and and you know, our our one of our lead customers right now is for every dollar they invest with us, they’re getting nineteen dollars in top line revenue back and we can prove it down to the penny.
Jeremy Julian (31:07): Yeah. That’s incredible. Um, that’s incredible. Before we dig into kind of how you guys are going about doing that and what it looks like to engage Ryan, talk to me about those guests that are kind of at the bottom of the spectrum. You talked about the elites and you talked about the VIPs, but now how do I bring a guest that’s kind of in that they’re an occasional guest or they maybe they’ve you’ve seen them once in the last 18 months or twice in the last eighteen months, but I now want to create a another reason for them to come in and I’d love to to talk about because I I know a lot of people are also looking to try and solve that problem too. So I’d love for you to talk a little bit about how you guys are solving for that guest that you know a lot less about ’cause they’re not your VIP or they’re not they’re not at that level.
Ryan Volberg (31:44): Yeah, this is one of the areas where we’ve invested a lot. So, you know, we have 25 machine learning models, and and some of them have very specific tasks. And this is one of those tasks, which is to find signal from people that we maybe have only seen once before. So what we’re able to do now with about 70 to 80 percent accuracy is to to determine after two visits if a guest has the potential to be in your top 10. And so we do this. Um by looking for patterns. And there are a great many different types of signals and patterns the system’s looking for. But I’m gonna give you a very simple example. And when you hear this, you’ll be like, oh, that makes sense. So the idea looks like this. After two visits, you are able to see somebody who looks a lot like one of your best customers look like on their first two visits, right?
Jeremy Julian (32:35): Oh, so you’re doing the lookalike campaign but in through the data. I love that. That makes so much more sense. Okay.
Ryan Volberg (32:39): Right. Yeah, so it’s a bit of a simplification of how ML works because there’s a lot more going on, but that’s what’s happening. So oh a guest visited us once, but now they’re here today, and our system is looking at this and saying, based on the time of day, based on the group size, based on a whole variety of things, we’re starting to see a bit. Now it’s not a precise picture, but it’s a good enough picture that what we’re saying is we think we should invest in this relationship and see what happens. Also the same with guests who are like, I would say pretty infrequent. So we have a segment that we call prospect. And within it’s about 65% of the total customer base. It’s it’s the bulk, right? And these are people who are coming once or twice a year. Inside of that big segment are are people who are probably they dine out a lot, but they’re just not loyal to us. And our system has gotten incredibly good at picking these people out. And I’ll I’ll back that up with a with a number in just one second. So what our system’s doing is it’s looking inside the prospect group, and as a prospect shows up and we seat them at the table, our system says, you know what? This prospect, we’re going to lean into this relationship. We’re going to invest because again, we see signals that there’s potential here. And so what happens is, and it’s really counterintuitive because you’ll have your elites and your VIPs and they get all the attention. But our system’s saying, you know what, we’re going to do a manager visit for this prospect, and we might even do a little appetizer or a little something. And we just and it’s a warm feeling we’re creating. You know, manager walks over, just say, you know, I I I you know I just want to introduce myself. It’s nice to have you here. And I just wanted to drop something at your table. Wow. Now that makes a big impression. So what our system is is looking at is then what was the effect that that had on the behavior? And what’s happening in some of our customers, actually most of our customers, is kind of an average now, is we’re getting so good at it that we have a 77% increase in return rate. And so so right, so like this is the power, and again, like I don’t want to get into the AI hype stuff. A lot of this is machine learning, which is looking for patterns. And then what we do is we stick the AI on top of it, right? To sort of help bring this information, like the summary, and and sort of make it digestible and and and personable in the dining room. But what’s happening is our system said, let’s not this guest, but but that one over there.
Jeremy Julian (34:31): That’s and just printing money, man. It’s incredible.
Ryan Volberg (34:59): Let’s make this investment and see what happens. And so when we make that investment, like I said, um we’re getting so good at it now that that you know we’re about a 77% increase in terms of how fast these guests come back. So um that mounts up to huge increases in revenue. So some of our customers right now are getting across the board um a 3% uh chain wide lift in in gross revenue, which is obviously massive. Um
Jeremy Julian (35:27): Yeah, which is huge.
The Numbers Behind It
Ryan Volberg (35:28): I’ll give you another stat. Um, when we did some analysis for a customer, what was happening is we we we pulled in all their data and we did a segmentation and we showed them and said you are losing 18% of the revenue from your elite tier. And they freaked out, right? They were like, What do you mean? Like, we’re really good. And I am like, you are, you are really good, but this is the math, right? It’s what it is. And so they actually had their own data scientists go in and look at the data. And she came back and said, they’re right, that this is what’s happening. It’s an 18% loss in this revenue. And when we put our system in, um within four weeks, we broke away from the control group. And after about 12 weeks, we had 101% revenue retention from the elite group.
Jeremy Julian (36:15): Oh my gosh. That’s huge.
Ryan Volberg (36:17): So what’s happen so like I know people maybe listening to this is like what what you know Ryan’s from Vancouver on the West Coast, he’s probably smoking something. But it’s it’s actually super simple, like in the concept, which was once you put our system in, what was happening is is these elites, first of all, we have the segmentation that identifies an elite. This is not a tag and open table that says VIP. This is the math. It’s frequency, time spend.
Jeremy Julian (36:25): Oh.
Advanced Guest Segmentation
Ryan Volberg (36:45): Into a pretty advanced segmentation model that basically says these are who your elites actually are. And then what our system is doing is when they’re in the restaurant, we’re surfacing that. And then we’re we’re acting as a co-pilot to help the staff identify them and then to engage with them. So there what was happening was they weren’t getting missed. Each elite was being, you know, sort of seen and valued every time they showed up. And so what happened is is we didn’t get that 18% churn. They just kept coming. Why? Because it felt good. Right. And that’s status, how you make me feel. Right. So uh, you know, I think that’s really the core mechanics of this. What we figured out is how to do this at scale and execute it in the dining room in a chaotic environment with servers and managers who are super busy. It’s like how do we make all this work operationally? And and that’s what we figured out.
Jeremy Julian (37:34): Well, and the funny thing is is uh I think we’ve all experienced this in a positive way when we run when we’re in a dining room with a guest focused staff that knows who you are, but you can’t do it at scale. And that’s the part that I think you’ve you’ve really unlocked. So so Ryan, talk to me w how do people engage? Uh you they’ve sat through thirty five minutes of us, you know, talking about restaurant tech and what you guys are solving for. What does engagement look like? How you how how can they learn more? How can they stay in tune? How can they decide if this is something that uh that you would be willing to put it into their brand and figure out if it makes sense for them.
Ryan Volberg (38:09): Yeah, absolutely. I mean, I I wanna I wanna talk a little bit about like why we’re doing this, right? Um, like I’ve said, I I think if you look back at my career, I I’ve always and and I think it goes back to the fact that you know, I did dishes at my dad’s restaurants. I’ve grown up in this industry. Um, I don’t think it’s any surprise that I’ve dedicated my entire career to sort of coming up with tools that that help restaurants succeed. So our motivation is is very important to me. And to uh to our staff and our team, which is we want to uh help restaurants be successful. We want to build stronger relationships between restaurants and their guests. Why do I bring this up? I I think it’s because it’s not obvious that that is the case out there in general. And and I’m I don’t want to I don’t want to vilify anyone at all, but you you know, there are there are there are platforms out there that that I think are competing for the guest relationship. And so
Jeremy Julian (39:03): Mm-hmm.
Ryan Volberg (39:04): You know, and I it doesn’t make them bad, right? Like they’re running their business and they’re doing their thing. But but what we’re trying to say here is like our motivation is that one we want to help restaurants build deep and lasting relationships with their guests. So like if that resonates with you, then we should talk because we just happen to have some technology that does it. But it’s the technology is like the second piece, right? The why is is because I don’t think restaurants who can’t recognize their elites when they walk in the door. Who can’t personalize service between you know Ryan who’s been here 50 times and someone who’s here two or three times but is showing signs of like maybe falling in love with the brand and we would really want to invest in that. They’re all very different journeys, and our system has the ability to pick that out. And so, you know, if this makes sense to you and and and you want to have a chat, you know, go to guestology.com, um, g-u-e-st-ol-g-i-e.com. I’m sure it’ll be in your show, your show notes. Um and and uh or you can reach out to me directly. It’s just Ryan at guestology.com. We would just love to chat with you. Uh we’re not a high pressure outfit. Uh we just we want to have a conversation about what you’re doing, and if it fits, it fits. We we love to do pilots and prove um you know exactly the results that we can generate. Um we’re really starting to pick up a lot of momentum right now, which is which is great. But uh we’ve said no to a few brands that that you know we didn’t necessarily think were were right for us. So, you know, um We just want to work with um, I think really phenomenal operators that care about their customers and uh build some magic together. That’s that’s what we want to do.
Jeremy Julian (40:40): I love it. Uh and uh I I know longtime listeners know this part of why the podcast exists is because I am passionate about watching restaurants succeed. I watch too many restaurants do some really dumb stuff. And so bringing people on like yourself and others that have just really changed the restaurant industry to help us to serve the guests that we need to, to serve the staff members and give them the tools to be successful is what I’m passionate about and part of why I think you and I uh resonate so well with each other, Ryan, when I uh when I continue to uh to talk with you and um If I haven’t already said it to you, thank you for creating this because I do really think it’s uh it’s an opportunity to to change the story that’s going on within the restaurant industry. So um uh for our listeners, guys, go check out gastology.com. It’ll be in the show notes. Um if you guys want to know a little bit more, hit me up. If you haven’t already subscribed to the show, please do so and make it a great day.
