Jeremy Julian - Restaurant ...: Welcome back to the restaurant technology guys podcast. Today we are joined by a SaaS company that is really doing a lot in the way of customer retention, data enrichment, and understanding what is happening with your consumer. And the guest shares a really cool story where they took a brand from knowing about 20 % of their guests to over 80 % of their guests. So Welcome back to the Restaurant Technology Guys podcast. As I like to each and every time, I know you guys have got lots of choices. So thank you for hanging out with us this week. Today is going to be a fun episode. quite honestly, our guests and what they do as a business is something that so many restaurants need to figure out. And so I'm excited for you guys to hear a little bit about what Lauren's going to share. But before we jump into that, Lauren, why don't you introduce yourself to our audience a little bit, and then we can talk about what you get to do for a living. I would encourage all of you guys to check out this episode with the group from Bridge. Lauren and I talked quite a bit about how today's day and age, it is almost impossible to know who your customer is in offline commerce unless you're using a tool like what they've built. If don't know me, my name is Jeremy Julian. I'm the Chief Revenue Officer for CBS Northstar. ⁓ We at the Northstar point of sale solution for multi units. Please check us out ⁓ at And now.

Loren Piparia: Great, sounds good. Really excited to be here. I'm Lauren Paparia and I'm the product lead for a SaaS based identity resolution and data enrichment company. So ⁓ we do is we help enterprise brick and mortar businesses ⁓ able to convert all of their anonymous in-store customers into known customers ⁓ enable more effective marketing strategies and ultimately more profitable businesses.

Jeremy Julian - Restaurant ...: onto the episode. I love it. Talk to me a little bit about your background. How did you get into this world? Because you said a lot of really cool stuff and I know we're going to dig a lot into this and genuinely am excited. I always love these episodes because ⁓ a data geek and I'm a restaurant ⁓ that's been around for a long time. But ⁓ guess talk to me a little bit about your background. How did you get into a product role at a data company that's ⁓ online and offline data and aggregating it and helping people make better business decisions? I'm the path is not a linear path, but I'd love for you to share a little bit with our listeners.

Loren Piparia: Yeah, yeah, it never is a linear path, is it? So I started my career agency side. So doing a lot of platform development, marketing strategies, advertising. And ultimately what I kept seeing across verticals, across startups, Fortune 100 companies, the whole gamut is we kept running into challenges with the data that was available. Right? ⁓ No matter, thereof. ⁓ no matter what size company, no matter what vertical, ⁓ all had the same problem where they couldn't get the volume or the quality of first party data that they needed ⁓ drive accurate and ⁓ insights. And ⁓ they were all pouring millions of dollars into marketing programs that ⁓ couldn't perform at the level that they wanted and needed.

Jeremy Julian - Restaurant ...: Mm-hmm. Or lack thereof.

Loren Piparia: because the insights were skewed. So that's really where I started to get interested in the power of big data. And eventually that led me to pivot my career to work more on the tech side and working at Bridge Now, right? We're sort leveraging big data to help deliver those improvements for marketing and advertising across clients. So ⁓ I guess can say the rest is history.

Jeremy Julian - Restaurant ...: I love it. Well, and so talk to me a little bit about kind of why I guess Bridge even got created. You you talk about the, recognized it from the agency side. You were working with brands and I think ⁓ funny because I talk with different restaurant brands on the show. I talk with different technology providers and there's a couple of places in the restaurant industry where people know who their guests are. ⁓ it's kind of in the pizza world ⁓ you know, so much of that's online and some of that, so much of that is, you know, either an online order or a phone order and they know some way to get that guest data, but you talk about it, e-commerce, most of the time you have a login and you have an address and you have a credit card number and all of that, but in restaurants, I guess, why has it been such a challenge before we dig into kind of how you guys are uniquely going about solving it?

Loren Piparia: Yeah, great question. So, you know, what Bridge really identified was a gap in the ability to identify more of these in-store transactions, right? So restaurants, QSRs, retail businesses, where they're still doing a lot of business within their four walls. You don't get the added benefit that you do of ⁓ companies that have really robust loyalty programs, which most Restaurants don't, right? They have very low loyalty penetration and you're not getting any first party data from those customers, right? You could have ⁓ that's shopped with you for years very regularly, ⁓ if you don't have any data from them, ⁓ don't have the ability to recognize that they're a highly loyalty customer ⁓ ⁓ of market to them and keep them engaged. So. ⁓ Bridge really specializes in that in-store offline identity resolution component that you don't see from most of the other providers in the marketplace today that are relying more on online signals. And that's really, we talk to a lot of our clients, this is something that we're able to offer that complements anything else that they're doing with other providers ⁓ really removes that in-store blind spot.

Jeremy Julian - Restaurant ...: Yeah, and so talk to me a little bit about kind of, guess, how are you guys capturing that data? Because it can't be easy. You sit on the product side and identifying me as a guest, you know, I've got, you know, and I'm a middle class, you know, father of four and I have, I don't know, probably four or five different credit cards in my wallet and I go to different brands at different times and different occasions. So I'd love for you to kind of talk about what are the data elements that you guys are trying to capture to try and triangulate that guess? Because it is. It is something that you guys do really, really well. And I'd love for you to kind of talk through, how do you figure out who this guest is? And then we can talk through more of why that's so important.

Loren Piparia: Yeah, so Bridge has an identity graph that we've stitched together from multiple other data providers. And then ⁓ we've built over the last decade is a proprietary data layer of point of sale data and ⁓ data, right? So ⁓ within those more traditional signals that you might see from other providers, ⁓ have a graph ⁓ 12 billion transactions and counting. ⁓

Jeremy Julian - Restaurant ...: Bye.

Loren Piparia: that are integrated into that graph. So when we're partnering with clients, we are taking all of their first party data, know, known customers, store location information, ⁓ point of sale information. ⁓ then we're running our identity resolution algorithms against that graph ⁓ that we can ultimately help to identify who the customers are behind those transactions. and link them to those known identities that exist in our graph. And part of the benefit of that, right, identifying the customers is one step, but then our clients are able to leverage our first party data so that all of those customers now become reachable through marketing tactics. So think about the millions of customers that you don't have any PII for that are non-marketable. Now, by supplementing our data with the client's first party data, we're able to now make all of those reachable through Omnichannel marketing.

Jeremy Julian - Restaurant ...: Yeah. And I want to talk a little bit about that, but can you define PII? I know what it is, but for our listeners that may or not, may not know the inside language, what does PII stand for?

Loren Piparia: Sure. Yeah, so personal identifiable information. Think about email addresses, ⁓ phone numbers, address data, right? These are the type of elements that you might get if you're checking out online or if you're utilizing ⁓ an application. But for in-store shoppers, none of that data is collected at the POS terminal.

Jeremy Julian - Restaurant ...: Yeah, very much so. I think I appreciate you sharing that. And so let's talk a little bit about kind of, so you guys are aggregating data. You're throwing it into this data tool to be able to identify one of the stats that you guys had put into the notes that we were talking about that I think is worth mentioning is just what a large percentage of people don't come back to a restaurant after a first time guest. We've had loyalty companies come on. And I guess for those that maybe this is their first time listening to the show, Talk to me a little bit about why it's so important to understand who your guest is and how making sure that you understand who they are and can communicate with them is so critical.

Loren Piparia: Yeah, that's a good question. So I think you take a step back and sort of look at the big picture, right? The fundamental challenge that we're seeing for restaurants and QSRs ⁓ a customer intelligence gap that is driven primarily by a data problem, right? ⁓ we talked a little bit about low loyalty penetration for ⁓ businesses. ⁓ restaurants will see ⁓ best about 15 to 25 % of their total customer base as part of a loyalty program, right? Which means that all of their other customers are anonymous ⁓ they aren't able to extract accurate insights when they're looking at such a small percentage of that customer base, ⁓ Then you combine that with things like economic factors where we've got rising menu prices, changes to consumer dining patterns, right? A shift towards takeout and delivery, for example. If don't have those insights into your customer base, ⁓ ultimately makes it very, very challenging to be able to develop a more sophisticated marketing program. ⁓ So see a lot of these businesses having to rely on narrower insights from a smaller percentage of their customers ⁓ and using to try to capture a broader portion of their customers and get them to return to store. often happens with that is that you get higher marketing fatigue because you're sending them messages that may not be very relevant to them ⁓ you can't see what they're buying, when they're buying, and how they're interacting with your business. ⁓ ultimately,

Jeremy Julian - Restaurant ...: Well, other thing that I, that I guess, sorry, I'll try to cut you off Lauren, but the other thing that I think I've heard from other marketers in the space is you often also get a skewed set of data because that may or may not be representative of the entire base of customers. You know what? I'm a loyal customer and I always go into this brand for this item and so many people do that. And so it might skew your marketing efforts and even your product. innovation efforts towards those people that may or may not be indicative of the other 80 % of people that you're not evaluating. So sorry to cut you off there, but I just thought it was important to say.

Loren Piparia: Absolutely. No, absolutely. And that's just it, right? ⁓ Of course, your highly loyalty customers are going to behave very, very differently than your average customer. So really being able to capture a larger percentage of those customers and better understand who they are, how they're interacting, that will change your entire segmentation strategy and ultimately all of your marketing program.

Jeremy Julian - Restaurant ...: Yeah, and I appreciate you sharing that. Let's talk a little bit about what a non-bridge customer looks like. What is the typical stack and how many different data silos do they have? And, you know, so I've got a loyalty program, I've got 20%, you know, penetration of loyalty, and I've got my POS data and I got a data warehouse and I might have my online ordering data. And so let's talk a little bit about kind of all the different data elements and in a non-bridge customer, who does that look like? And then let's talk about what does it look like once they're able to... know, layer your guys' data elements on top of that and be able to make better decisions.

Loren Piparia: Yeah, so a typical customer will have probably multiple different ⁓ of POS data, right? Many of them have ⁓ POS platforms and processors for ⁓ versus online versus kiosk orders, right? ⁓ yes, yes. ⁓

Jeremy Julian - Restaurant ...: This is my life. I'm sorry. This is what I live every day. Uh-huh.

Loren Piparia: So think like four to five or sometimes more streams of just POS data, right? they've got their loyalty platforms, ⁓ which by the way, many of them have a legacy platform and then one or two or three ⁓ platforms where they're also needing to aggregate all of that loyalty data. On top of that, many of them will have some type of a CDP that they're utilizing. And then they'll have all of their activation platforms that they're using for activation. So really tech stacks that we're working with, right? And ⁓ really data coming from many sources where they need to unify it, which of course makes ⁓ data normalization and sort of standardizing all of that data very difficult for them today.

Jeremy Julian - Restaurant ...: Very much so. And we actually, and I'm not, this is not a plug for our system, but I've pulled together multiple PLS data and it's amazing how many different versions of Coke are in there. How many versions of the same burrito are in a Mexican concept across six stores? They might have 12 of the same exact burrito just because different people have managed it. And it makes it really, really challenging to make better business decisions, I guess, is kind of where you guys are at. So, Lauren, talk to me a little bit about how do you guys take all of that data and

Loren Piparia: Yeah.

Jeremy Julian - Restaurant ...: do you guys aggregate it into a place that it makes it actionable? Because ⁓ for data sake is worthless, but data that's gonna ultimately help you drive better decisions in a way that's meaningful to your bottom line and potentially your top line is huge. So I'd love for you to talk a little bit about how you guys do that.

Loren Piparia: Yeah, so one of the things that Bridge has done a good job of is making sure that we're very easy to integrate within these really sophisticated tech stacks. So typically what clients will do is they will either provide access to data in their system or we'll aggregate all the data for them and pull it into our system so that we can normalize data across all these different sources that we mentioned, right? From there, ⁓ run identity resolution. That is ultimately what unlocks all of these previously anonymous customers. And then each of those customer profiles are enriched with all of the data that we have available for those ⁓ individuals. So think that we have available that make it easier to market to different types of cohorts. Think about ⁓ demographic and socioeconomic attributes, right? That they may not have themselves. So ultimately, what we provide back to clients in their cloud environments are the identified data all of these additional attributes appended to those profiles. ⁓ then from there, we can utilize their existing activation platform so that they can onboard ⁓ wherever it is that they are already running marketing campaigns. One of the things that Bridge also does is we've made sure that data privacy is at the forefront of everything that we've built, right? So they get the added ability of the PII that Bridge has available in our identity graph without ever actually exposing that data. think about, I'll give you an example. We have a QSR client who had, when they came to us, they had about 30 million identified customers, which was only about 20 % of their total transactions, annual transactions. After with Bridge, ⁓ they have 190 million known customers. So that's a 5X increase in known customers, and they're now identifying roughly 70 % of their transactions. ⁓ So take that type of an increase and think about the

Jeremy Julian - Restaurant ...: Wow.

Loren Piparia: the richness that that adds to the insights that you have around your customers. They rebuilt their segmentation strategy off of that. And now for all of those customers where they don't have PII in their first party data, they're able to utilize the supplemental PII that Bridge has to be able to market to all of those customers that were blind to them previously.

Jeremy Julian - Restaurant ...: Yeah, so I want to talk a little bit. You talk about your identity resolution. Can you just dig a little bit deeper on that? Because again, you're a marketer, a marketer turned technologist. What does that even mean? And what is this identity graph you've mentioned a couple of times through the conversation? Because I guess ⁓ for those that are listening on audio, they're on their run in the morning and them paint a picture of what exactly that looks like. ⁓ And what ultimately the end result of ⁓ how you can use the data that you guys are pulling to drive the behavior.

Loren Piparia: Yeah. So identity resolution or IDR is really just about identifying who the individual shopper is behind each transaction. Right. So again, where many businesses lack that first party data in their own systems, we are able to utilize all of the data that we have in our network to help ⁓ fill the holes, right, that businesses have in their own data. So When we're running these algorithms, we're really just looking across all of the data sources from a client compared to the data sources that we have all integrated and interlinked within our graph in order to identify that this transaction belongs to this shopper that we've identified, that we can see shop across all of these different businesses and our. you know, is a woman that is 35 that is married with two kids.

Jeremy Julian - Restaurant ...: love that. And I got to hear a presentation by a company that does something similar kind of in the credit card space to you guys. ⁓ it's amazing how much data you can ascertain and really even kind of, guess, the birds of a feather flock together. ⁓ know, that same mom that's in this income bracket with this amount of kids probably also shops at these types of stores in this demographic. Is that kind of ⁓ you guys are able to create lifestyle? ⁓ conversations with them and then be able to market to them that says, if you like this, you're also going to likely like that an offline commerce ⁓ Because I think a lot of times, restaurants will say to me, I want the Amazon effect within my brand, but they have no idea who their guests are. ⁓ think it feels like you guys are trying to ⁓ that back to your example of this, you know, going from 20 % of the guests to ⁓ to 80%.

Loren Piparia: Absolutely. Yeah, I think, you if you think about some of the common use cases, a lot of restaurants have invested heavily in new customer acquisition where they have a hard time is converting those one time or several time visitors into more active shoppers. So, you you think about things like looking at trends and how often they're coming. ⁓ We're to help our clients identify. Hey, this person usually comes in every month. They haven't been in for six weeks. Maybe it's time to give them a nudge, right?

Jeremy Julian - Restaurant ...: So I'd love to talk a little bit about that because I literally was just talking to my wife about this. There's a Mexican restaurant. live in a small little town. We go to this Mexican restaurant every Friday night. We used to have a server that was there. He was always our guy. He would always come over the table, even if he wasn't waiting on us. He became our guy. We had the cheers effect. We would walk in and they'd, ⁓ where's this kid or that kid? It was always this conversation. We've not been back in six or eight weeks. We've not been to that brand in six or eight weeks. And I said to her, It's surprising to me, because I live in this world, that they haven't reached out to say, hey, what's going on? We're part of their loyalty program. They have our, you know, they're on a point of sale that I pay digitally so they know who I am the QR code, but they still haven't figured that out. And so I'd love to ⁓ through a little bit of that because you know what? Every week it's a hundred dollars that they're missing out on in sales on a Friday night. Cause my wife likes margaritas on Friday nights and we would go ⁓ Friday night, literally for four years we've been going, three times a month probably on a Friday night and we haven't been back in six or eight weeks. Some of it's been the holidays, some of it's been travel, whatever else, but it was interesting to me. We literally were driving by that restaurant yesterday talking about it in the car. And so I'd love to talk, what does Bridge give them the capability to do to be able to change that behavior?

Loren Piparia: Yeah, so the challenge that you're describing is that they can't see that the person that ordered online is the same person that's coming in every Friday into the restaurant, right? And so ⁓ Bridge is really able to do is we're able to sort unify that view of customer across online and offline channels so that can then see that you are the same person and recognize that you are ⁓ exhibiting a behavior that doesn't fit your normal pattern where marketing might help to get you to revisit the restaurant. And that's really what it is, right? they can't see that that's the same person making those transactions, ⁓ have no way to tailor the way that they're speaking to you. ⁓ you sort of fall into a more generic marketing message that gets blasted to their whole customer base that is likely not gonna be very relevant to you.

Jeremy Julian - Restaurant ...: Yep. so the pivot then? How would this restaurant, so say they signed up for Bridge and three months from now, same behavior is happening. What would change? How would they get to a place where they'd be able to know who I am and be able to speak directly to me? Because again, Margarita on the rocks with salt, you know, is what my wife gets knowing that that's on my transaction count. I typically get one of two items. She gets one of two items. Like again, understanding the POS data, the credit card data, the frequency. understanding all of these data elements can make a huge difference. So if they were in your guys' platform, what could this brand do if they needed to, to get us back in?

Loren Piparia: Yeah, so what the brand would see in our platform, they would see all of those different transactions linked to a profile, right? So they would know that of the transactions are coming from the same customer ⁓ they'll have all of the segmentation data. ⁓ of these companies also do their own segmentation strategies, right? ⁓ they would have all of this tagged in the system where they're able to very easily activate a campaign for. ⁓ all of the people who are lapsing, let's say, right? If they wanna do it frequency based or if they wanna do something that is more product based, right? They're running like a margarita night or something. They have all of the skew level information available on each of these profiles. So they know exactly what you're purchasing and what you're not purchasing. You one of the things that we see happening a lot with restaurant operators when they don't have this full view of customer, ⁓ is that they have to rely on a ⁓ promotion heavy marketing strategy, right? And exactly, and it's because it's the only effective way they've found thus far to drive repeat visits. Ultimately though, what that's going to do is it's going to hurt your lifetime customer value.

Jeremy Julian - Restaurant ...: Yes, or a discount strategy to get me back in, right?

Loren Piparia: if you're constantly discounting products that you may not need to do to get them back in. So this will help them really take what is a one size fits all marketing program and create really targeted personalized marketing for each of those different types of customers. Again, could be lifestyle, it could be demographic information, ⁓ product purchase history, frequency, you name it.

Jeremy Julian - Restaurant ...: I love that. you talked about security and I get the people that get scared to death and it makes me laugh because they give all of their data to Google and Facebook and all these other places for free. But they're like, well, but are you going to know exactly who it is that it's me? And I'd love for you to talk a little bit about that because I recognize, but I'd rather you share with our listeners, how do you make it so that you're ⁓ fair to ⁓ consumer and still allowing the brand to get to that customer, but not necessarily knowing that it's Jeremy Julian with four kids and a wife and makes this amount of money, that kind of thing.

Loren Piparia: Yeah, yeah, that's a good question. So I think the easiest way to think about it is that everything in our system is double blind. So for every individual customer, there will be an anonymized identifier assigned to them. attributes that are being provided back are sort of aggregate level category attributes coming from these data providers. ⁓ And All of Bridge's proprietary data, as well as all of the client's proprietary data is all kept hidden and obfuscated in the system. So you think about the ability to, let's use the lapsing customer example from before, ⁓ be able to run a marketing program against them. ⁓ will be flagged as lapsing relative to their original behavior. ⁓ have the ability to tap the PII in our system. and to send that PII to these activation platforms like Trade Desk or LiveRamp, whoever they're using, in addition to their PII, without actually making that PII visible to either party. So everything is kind of encapsulated and kept secure in the way that our system has been integrated with these other platforms and in how everything is hidden in the platform itself.

Jeremy Julian - Restaurant ...: Love it. last thing before we kind of talk a little bit about how people engage with you guys is we've had loyalty providers on the call before. We've had people that are like, ⁓ just get people to sign up and then you can do the same thing. What sets Bridge apart and makes it different than any of the standard loyalty providers? Because I think ⁓ guys are doing some unique things by taking data that you guys are acquiring other sources and tying that to this. And so I'd love for you to talk a little bit about why does Bridge set itself apart? from just kind of a standard loyalty package that you've got this 20 % and how are you guys getting beyond kind of that?

Loren Piparia: Yeah, so I think the way to think about it is that we're enabling loyalty tactics for non-loyalty customers, right? ⁓ Yeah, so, you know, especially with restaurants and QSR businesses, there's a lot of loyalty program fatigue that is happening. They just don't want to have to sign up for a program like that. And many customers are becoming more wary of sharing their data in the first place. So,

Jeremy Julian - Restaurant ...: Love that. That's such a tagline.

Loren Piparia: This really allows you to extend beyond that view that you have already. All of the data that we're utilizing, like you think about any of these other leading identity companies, right? These are all reputable sources. It's pulling from places like census bureaus, et cetera. So all of this is data that has been provided elsewhere that ⁓ new data being requested for the customer, right?

Jeremy Julian - Restaurant ...: I love that. So what does engagement look like with you guys Lauren? You know, I've got a 40 unit brand, I've got a hundred unit brand. I'm running three different point of sales, two different loyalty packages, four different processors. What does engagement look like? How do they engage with you guys? Walk through what they can expect and then kind of how quickly can you guys help them see results and drive the behavior?

Loren Piparia: Yeah, so we've sort of got two different business models depending on the need, right? ⁓ one case, ⁓ we have clients who don't have the resources to sort of aggregate all of that data on their end, ⁓ can help handle all of the data unification and the data normalization across these systems. So in that case, we'll work with these businesses to set up feeds for the data so that we can pull all of that together on their behalf so that they don't need to ⁓ sort of incur any tech debt to be able to handle that component, right? And then all of the processed results are delivered to them back in their cloud environment where they can with any other systems that they have, plug into analytics tools, and then plug into all of those activation platforms. The other thing that we're working on and that will be a big focus for us in 2026 is starting to expand our cloud native capabilities. So this will be an offering for businesses who aren't comfortable with PII exposure and moving their data and who would rather be able to run identity resolution on their own in their native environment. So that's something that'll be coming here in the next couple of months.

Jeremy Julian - Restaurant ...: Interesting.

Loren Piparia: will be more of a self-service model for businesses that want to keep all of that within their four walls.

Jeremy Julian - Restaurant ...: Awesome. Last question before we ⁓ for contact info and how they engage is we're 29 minutes into this recording and we have not talked about AI. That might be a first. Typically it's within the first five minutes and obviously I'm talking to a product and a technologist. ⁓ I had lunch with our CTO and we laugh about AI because we've been doing so much of this stuff with computer learning and all of these different systems for a long time but ⁓ now the knows about AI and so. How is AI impacting, I guess, what it is that you guys are doing now that everybody's kind of thinking about AI? We've probably been thinking about it for a really long time because we've been doing these things to be able to aggregate data. But how is it influencing and impacting what it is that you guys are doing as you guys go out to market?

Loren Piparia: Yeah, well, I think one of the obvious ways is that customers today really expect a very, very personalized interaction with any company that has their data where they're actively shopping. And so you think about the requirement on the marketing program, ⁓ need to not only understand your customers, but really you need to be able to do predictive modeling ⁓ that you can start to anticipate and drive ⁓ behaviors and the interactions that you want to see. ⁓ all of that goes back to the data quality. Right? So in order to run predictive models, you have to have an accurate view of who those customers are. And you have to be able to have more sophisticated integrations where you're able to pull in these data elements and run these models ⁓ that you can create those multi-pronged marketing programs.

Jeremy Julian - Restaurant ...: Thank you for sharing. And I love that you guys are in that place. think as time goes on, I think you guys probably start to see a lot more self-service too. You're talking about kind of your guys' new product launch. People want to be able to say, hey, who are my last customers that drink a lot of margaritas? We have a margarita night coming up. And be able to do that without having a whole bank of data analysts sitting there trying to figure that part out. They can just ask the question of the system and have it spit it back out. so, how do people engage? How do they engage with you? How do they engage with the team? How do they dig in deeper? They're sitting here 30 minutes into this call going, need what Bridge can do. How do they engage and what can they expect from there?

Loren Piparia: Yeah, yeah, for anybody who's interested, can be found at bridge.com. B-R-I-D-G, no E. And yeah, we've got a ⁓ team of folks. I'd be happy to talk with anybody else interested in learning about in-store identity resolution. One thing we've not mentioned that I think is another factor really affecting restaurants and QSRs today is ⁓ increase contactless payments, right? ⁓ As we to see more and more folks

Jeremy Julian - Restaurant ...: ⁓ yeah.

Loren Piparia: tapping their phone or tapping their credit card rather than traditional swipes. That is something that Bridge has built a solution around that is exacerbating the data problem that we've talked about in the space. So that's another area where we can help.

Jeremy Julian - Restaurant ...: Yeah, I forgot to mention that, but I do remember us talking about that on the pre-call. It's again, tying that customer because they're not identifying themselves, but you're identifying them through their behaviors as well as their payment data. Knowing who that person is for online, on offline is huge. And so I love that you guys are building this. I love that you guys are continuing to dig in to help these offline businesses. The problem is very, very clear as to why you guys have created what you guys have created. And I guess I would urge. any of our listeners out there to go check out what Bridge is doing, because if you're not doing it, likely your neighbor is doing it, and they're going to hand up on you or leg up on you in the data enrichment and the ability to talk to their consumers. So, Lauren, thank you so much for your time. Thank you for what you guys continue to build. to our listeners, guys, ⁓ I said at the onset, I know you guys got lots of choices, so thanks for hanging out ⁓ make it a great day.

Loren Piparia: Thanks so much for having me, Jeremy.