This podcast was previously published on the Cisco website, as part of their Coffee and Conversations podcast series.

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With the advent of AI and big data technologies, companies are now more than ever relying on computer vision to provide data for trustworthy insights to help them make smart business decisions.

Join in on this discussion between Cisco's Mark Scanlan, Danny Vicente and Sabrina Maria Gonzalez, with meldCX's Raffi Vartian.

Featuring:

• Mark Scanlan — Global Business Development Manager for Retail at Cisco
• Danny Vicente — Global Marketing Lead - Sports, Media, and Entertainment at Cisco
• Sabrina Maria Gonzalez — Global Retail Marketing Manager at Cisco
• Raffi Vartian — VP, Business Development & Strategic Partnerships at meldCX

Transcript:

[00:00:00] Danny Vicente: Good morning, good afternoon, good night. Thanks for joining us on today's podcast of Conversation and Coffee with your co-host Gary Senna. And I am Danny Vicente. Thank you for listening whenever, wherever, and however you are joining us.

[00:00:22] Mark Scanlan: Conversations and Coffee is the place where we share a cup of coffee and allow our curiosity sit in the driver's seat and explore topics in your industry.

[00:00:33] Danny Vicente: Everything from technology to leadership, to innovation, and so much more. So grab your favorite cup of coffee, sit back, laugh with us while we dive into the topics. Keeping you up at night.

Well, good morning, good afternoon, and good evening. Thank you for joining another conversation and coffee. I am your host Danny Vicente, and as mentioned last time, we do have a new host on the podcast. This is her second go round. Sabrina, why don't you say hello to everyone. .

[00:01:07] Sabrina Maria Gonzalez: Hey y'all, thank you all for joining us today.

We're really excited to kind of go and dive into what I do for my day job at Cisco, which is on the retail side.

[00:01:17] Danny Vicente: I love it. I love it. And some of you may, if you are watching this podcast on YouTube versus just listening, once you hear his voice, if you're listening, you're gonna know exactly who this man is.

We are joined again by Mark Scanlan, who the infamous Mark Scanlan is joining, and we have a new voice to the podcast, and I'm gonna let Rafi introduce him. And, and what brings you to the podcast?

[00:01:37] Raffi Vartian: Sure. My name is Rafi Vartian. I'm Vice President for Business Development at meldCX. We're a partner on the computer vision side with Meraki specifically on the intelligent cameras.

We're on the podcast because we focus predominantly in retail for our verticals. And I've got a background in it and I talk a lot for a living. So I think that's part of it as well. I'm pretty used to talking ,

[00:01:58] Danny Vicente: you, you and I both. And Mark, actually, as we're going through introductions, even for those folks that do know you, why don't we remind them what you do and, and why you're here.

[00:02:07] Mark Scanlan: Sure. And I may even have changed roles since the last time we talked Danny. So I am the global Lead for retail in the industry Solutions group here at Cisco.

[00:02:16] Danny Vicente: Love it. Love it. Well, you are always the global lead in my book, right? .

[00:02:20] Mark Scanlan: So sweet talking Devil .

[00:02:24] Danny Vicente: Well, mark so, so those of you that don't know Mark and I have a long history of doing recordings, whether video or audio with Cisco, and I am notorious for going off script.

So I do want to show him, I have the script here. We will remain on script Mark. There's an absolute no way I'm going off script whatsoever. And , so those of these that don't know, we did a, we did a recording in New York for NRF and that is actually how I broke the ice with Mark. We had a script down. I threw the script in the air and we winged it , and it was one of one of the best videos we've done.

So we're gonna do a very similar thing here, mark. So I'm gonna, I'm gonna kick it off with a very easy question, guys. And that is, that is what is meld. What, what are we gonna talk about today and what's its, what's its motion in, in retail?

[00:03:07] Raffi Vartian: Sure.

So I don't want to do the elevator pitch and stop at every level, so we'll go as high level as we can go.

meldCX is headquartered in Australia, founded about three and a half years ago, focusing on technology within the retail environment. One part of our business focuses. Device, peripheral and application management. So we've got a middleware application and the other part focusing on computer vision, which is a very broad term that t that basically takes video signals and turns it into data.

And that's a, that's also a pretty big bucket, right? So in, in the retail environment, we're learning that there is a very large gap. Retailers have of what's actually happening within their environment. They've been operating maybe many for dozens and dozens of years. But they have found that during the pandemic in particular, that there's a massive lack of understanding of what's actually happening on an ongoing basis in their environment.

So we're trying to teach them. To utilize intelligent technologies to learn more about how people behave, how their own associates behave in those environments. How we can do things like look at in stock or out of stock. Cuz that's been a massive problem within the grocery market in particular.

And so we're kind of trying to uncover all of the data that's hiding in plain. and you can't do that with some level of intelligence, without some level of intelligence at the edge. And because it's going into the camera, we found out that we can get very scalable very quickly. And so we've been in some very high level conversations incredibly fast.

Hope that, hope that helps. Is that a good overview? Absolutely, it does.

[00:04:47] Mark Scanlan: It does. Absolutely. And. So I, I, I simplify it even more. So if we, if we think about the e-com world we've always known exactly where the customers come from, where they go on the website, what they look at, how long they look at it what, whether they consider it and put it in the basket, whether they.

you know, move on to something else. Does a promotion on the webpage make a difference? You know, to, to conversion All of that stuff has been kind of intrinsic to the platform within e-commerce, but not so much in the physical environment. We knew when people walked through the door, or rather how many people walked through the door, we didn't know anything about them, and we know how many people checked out at the end.

and that was pretty much it. It was a black box inside the store, unless you had some intern standing with a clipboard doing random interceptions, you know scribbling things down. This changes the game from, from a retailer's perspective. And there's, there's studies that show that personalization within the shopping journey can drive a, a 40% larger basket.

To be able to personalize. You first need to understand the consumer, and that's what computer vision and, and meldCX is able to do for us and with us.

[00:06:02] Sabrina Maria Gonzalez: Definitely Mark, and you and I have also discussed more of like that video analytics portion, which is like that use case mainly that meldCX does offer.

So with more of that video analytics, but also that behavior analytics that goes kind of behind with that software as.

[00:06:18] Raffi Vartian: Well, let me take a, let me take a bit of a step back to say one thing very clearly, because there have been implementations of video within retail environments before. It's predominantly been focused on things like loss prevention.

Okay? Loss prevention is a very nice word, which is are peoples stealing things? . And if so, how do I identify that they're stealing things? And then can I go backwards in time and bring that to law enforcement? So video has traditionally been backwards looking effectively, that says if something has happened in the last 90 days and I've recognized that some semblance of crime or something that bad has happened in my environment, can I go back and.

Effectively. Right. So, and that, and that's a, that's absolutely the right use case for folks that are looking at loss prevention from like a law enforcement perspective, which a lot of LP professionals are, right? Former F B I, former law enforcement, things along those lines. The question really is, is what could you do if you had information in real time about what was happening, but you weren't looking for?

individuals, meaning that we don't look at the face of the person. Right? But if you understood more about metadata around an individual, and by metadata I mean is the person wearing a Nike shirt or basic demographic information, right? Or the times of days they come in, all the rest of those things, does that data actually provide things that are valuable?

And what we're finding is the answer to that is yes, it does. So we're at that point. That, that proof of concept stage with a lot of retailers where they're I, I don't believe that this can actually be real. And then we say, well, we'll prove it to you. Right? And they, they go, okay, prove it. And we say, okay, well what are we proving?

That's the first thing we have to define what the thing is that we're proving. Is it that we're gonna utilize cameras to look at cars going through a drive-through, right? And look at that. That use case, is it gonna be, that we can bring, we can look at gaps in the shelf to do planogram, compliance, things along those lines.

And so we try to take a narrow approach and say, let's prove the narrow thing, but then we can look at the data. Around that narrow thing that gives us insights about what's actually happening within that environment. And Mark, you speak a lot about this and I think it bears repeating about the idea of going from kind of insights to action.

Maybe you want to kind of touch on that for a second cuz I think it's very valuable. Yeah, sure.

[00:08:49] Mark Scanlan: It was a, a phrase or a series of words, I guess that somebody used in a meeting a couple of years ago, and it, it stuck in my head. Visibility, insights, and action. So we're able to gain visibility with a camera.

Fairly straightforward. It is no different to any other sensor. It just happens to have a lens on it. It's a super sensor, if you like. The AI models that meldCX produces provide the insights from what it's seeing. So from the camera's perspective, it's, it's an object. It doesn't know whether it's a car or a person or a can of soup.

But the AI models help interpret that data and make inferences. So I'm trying to think of a good example. But being able to look at a situation and derive a. Need or, or concern that it highlights. And then there's the action piece. Okay. So what are we gonna do about that? Actually Raffi mentioned looking at cars in a drive-through.

If the, if the line is growing, what does that tell you? and what can we do to prevent the, what's eventually gonna be a drive off bulk or abandonment of the line. So what action are we gonna take? Are we gonna dispatch an associate with a handheld to, to try and triage the line? Are we gonna, you know, offer promos to get them the customer to select certain things that are gonna, is gonna shorten that service duration?

Or are we gonna try and divert them to curbside and triage the line that. So that visibility, insights, and action, a a approach I think can be applied to many, many things. Obviously video analytics is what we're talking about today. Right, but it, it is something worth banging in mind.

There's no shortage of data in retail. Is it relevant data? Can we derive some insight from it? And then what the heck are we gonna do about it once we do?

[00:10:37] Danny Vicente: So guys, I have to ask, because every time I, I hear these type of things my immediate response is, oh, who, who the heck wouldn't do this? But I'm sure there are people giving you pushback.

I'm sure there are people saying, that's not for me. What, what, what, what are, what are you hearing from customers that are saying that? Sure. What is our, what is our counter to that?

[00:10:55] Raffi Vartian: Sure. Well, I think the first pushback is cameras are creepy. I think that's the first one that we hear a lot.

[00:11:04] Danny Vicente: You know, I got one in my face right now, I can attach

[00:11:07] Raffi Vartian: There you go. Exactly. It's, it's, it's looking at me right now. Luckily this one's not intelligent, it just has a lot of pixels. But the, the idea that someone is watching, right? That idea that someone is, is, is kind of viewing it kind of live, if you will, and trying to look about you and trying to figure out what you're doing.

It's an interest. Approach or it's an interesting kind of feedback because we all have phones in our pockets that are giving an enormous amount of data away on an ongoing basis. The reason why retailers have the opportunity to create personas, I think that we've talked to, you probably probably talked about this before, right?

We all have lots of personas that we're associated with, right? There's a whole sub-practices within agencies that talk about persona mapping effectively against individuals, right? If you buy. Like I have, and I'm a proud minivan owner, I should say, right? . So if you're a proud minivan owner like I am, you are likely to have children.

You are likely, if you're in the Midwest that like I am and you've got an all-wheel drive, you're probably traveling a lot, right? You've got family in Michigan, right? So there's all these kinds of things that you can assume. Based on those personas, and because we have those devices that are essentially broadcasting an enormous amount of information about a on ongoing basis, that there's a lot that the agencies and marketing technology companies have been able to build off of our profiles.

Now, a lot of this has changed because if you are an Apple user, right you are partially responsible for the loss of about 60% of Facebook's market capitalization over the last. because you hit that button that says, do not track me across multiple applications. Okay. It's getting a little in the weeds, so I apologize.

I'll, I'll bring it back to, to, to, to something that's a little bit more relevant here in a second. But They used to have ability to say, if you log into Facebook on mobile, on your phone or whatever, you could, they could track you across all kinds of applications. So that's why if you go and, you know, look at something on Amazon or whatever mm-hmm.

And you pop over in the other browser to cnn, all of a sudden there's an ad barking at you. That is the thing that you just looked at. What the heck is that? Well, yeah, because they're tracking you across multiple applications. It was true on your phone as well until Apple. change things and Googled it as well.

It says, do not track across my, my phone effectively. Right? So, although it's a little bit less intrusive, there is nothing more intrusive in the world that's gonna be as your phone. effectively, right? Because it's got location data and all the things around you. So that's why we go to great pains to explain to customers that the camera is a sensor and we treat it as a sensor.

So although it is video and it is being deployed predominantly for loss prevention, i e video storage, so that folks that are in law enforcement can look backwards in time to see if something has happened. What we're doing with our AM AI models is in. the video in real time to drive out those insights, right?

And to take video terabytes of data and turn it into ones and zeros kilobytes of data. Effectively, right? So we are not tied, the way that the technology works is it's not like we're pulling and taking in tons of raw video on our side in the cloud, and then have a lot of people looking at it and trying to figure out what's going on.

We're quite literally doing it at the camera where we can't even see the video, so we don't see it on our side. All we see is metadata being extracted out of that video, and then we're being able to inference that information as. There was a bit of a tortured explanation, but I think it's important to say why cameras can be great sensors and also be privacy compliant.

[00:14:54] Mark Scanlan: Yeah, there's, there's always been. This concern around privacy, whether you're talking about, you know, your digital assistant at home, your, your Alexa, or your Google, I've gotta be careful what I say here cuz something's gonna spark up. And, and there's this misperception, I think as with cameras, that somebody's sitting there.

watching it or listening to it or whatever it may be. And, and the reality is it's a, an ai that's looking for specific triggers or, or patterns. And really it's not invading in your privacy. You know, somebody isn't listening to your personal conversations that you're having in front of your digital assistant.

So it's, it is one of those perceptions we have to overcome.

[00:15:38] Raffi Vartian: Yeah. Yeah. So privacy is one of the biggest pushbacks I would say.

[00:15:42] Danny Vicente: Guys, we are at the -

[00:15:44] Mark Scanlan: That's a short answer.

[00:15:45] Sabrina Maria Gonzalez: Yeah

[00:15:47] Raffi Vartian: We've got more pushbacks I can give to you, but if, if you wanna move on to another topic more than happy to.

[00:15:51] Danny Vicente: No, I mean, I, you know, I'm, I'm, I'm came to go as deep as we, we all feel comfortable and, and, and want to.

Okay. Folks, I, I do wanna remind everybody that There are gonna be a numerous links down below this video or this audio podcast that you are listening to. So if you have any further questions or you want a deeper look into anything you are hearing, please click those links below. And, and, and feel free to browse.

There is also going to be an email address down below, mark. This is something new that wasn't on the previous podcasts that you know about. But if you have any questions, please feel free to shoot an email to us with us with those questions, and we will try our best to answer that on an upcoming podcast.

[00:16:27] Sabrina Maria Gonzalez: Thanks, Rafi, and, and I kind of wanted to also touch on that license plate usage to recognize the cars as well. So we'd love to go into that and get your thoughts on.

[00:16:34] Raffi Vartian: Sure. Well, there's two lines of thinking that we've been solving for, for retailers. One of them is to utilize license plates to be able to create a more frictionless system of.

Grocery pickup or license plate to pay in a drive through. Things along those lines. Right? So there's a line of thinking, which is, which is a, a, a thing that we're pursuing and we're implementing, which is that co consumers want convenience. Right, and they're willing to show, tell you what their car is and what their license plate is, and things along those lines, and verify their identity so that when they get to their grocery store or they get to their drive-through, they can effectively just drive in.

They get recognized and they drive out right. You have to ensure that the security is protected and there's a lot of things that you've gotta do in order to meet the security requirements that the retailer has put aside. That's absolutely a use case that we can kind of go through, and there's ways of doing it.

There's all kinds of technical ways to do it. Real time, near real time, all kinds of different things. But there's another line of thinking where there is. Wanna collect data again, metadata. So there's a customer that we're working with that had required us to not use license plate information. Okay?

So going back to this idea of training our models and utilizing an ethical approach to artificial intelligence, we said, okay, we're gonna basically fork off our model where we can look at the make and the model of a vehicle, but not look at the license. And we're gonna use the pixels that are available to us to take a picture, right?

And then be able to track that picture from camera to camera in a drive-through to be able to get those statistics and that information. But we basically can see the license plate. I e, the camera sees it, but all it sees is a collection of pictures that it's sort of like a fingerprint more than. And we bring those through in the drive through.

We collect a little bit of data. We ensure that that is tokenized. So it's encrypted and secure kind of at the edge. And once that car leaves, that token is destroyed. And we don't have that information. We don't know who that person is. So we can kind of solve for both use cases and both use cases are valid.

And it just kind of depends on what the customer's really asking. Whatever.

[00:18:57] Mark Scanlan: In a lot of cases, it's, it's gonna come down to whether the, the, the consumer opts in if they see value, hundred percent. Generally there's a, there's a, a, something called the Plain Site Doctrine. If, if you can see it in the streets, then you can use it to identify somebody and, and license plates, generally a public record.

However, I completely understand that some retailers and other industries, may have privacy concerns around that and moving jurisdiction to jurisdiction not just in the US but globally. It can vary. So it is definitely something to be cognizant of, but being able to identify, make, model, and color of a vehicle it's, it's perfectly ethical but also as, as Rafi was suggesting before you can actually infer certain things from, you know, types of vehicles.

Oh, my camera just shuts off. Sorry.

Am I still with -

[00:19:54] Raffi Vartian: Yeah, see you, but we can see you fine, Uhhuh. Sorry. Sorry about that. No worries.

[00:19:59] Mark Scanlan: Oh, I ran outta space on my hard drive. Excellent.

How did that happen?

[00:20:08] Raffi Vartian: Your deck before you start? Right? Well, it's a, it's a, it's a, it's a great point. Markets, it's this idea of what are people prepared to. . Mm-hmm. . Right? And the risk that they're, that they, that they think about when they look at kind of technology deployments. This is really net new stuff.

You know, we, we are at the, the, the cutting edge of this, this market right now, but it's only gonna increase. I say that, but I think that there's barriers. and I think that there's some unethical behavior that's out there. There's some problematic technology that's been implemented. So I think that what we try to focus on and the reason why we're so invested in our relationship with Cisco is can you get any more trusted from a network perspective than Cisco?

and I think the answer is, is no. Right? Like, you know market leaders and we want to be seen as a market leader. And we also, during our process, when we talk to, you know, some of the, the top end, you know, fortune 500 is we are here from the grace of the platform. We are an application that sits on top of the platform.

If we do anything that violates anything from a Cisco perspective, they can turn us off and turn us off. So we, we, we are disincentivized from, you know, having any kind of bad behavior or running afoul of any kind of security protocols cuz it would cripple our business if we did it that way. So that's a pretty good incentive to stay in the, stay in the lane and over the long term.

[00:21:38] Danny Vicente: So I have a question because both you and Mark have touched on this you know, you talked about delivery systems, but both of you have touched on the. You talked earlier on about people being in, in line with their cars and then taken off because it was taken too, too long. So, so how are we optimizing drive-through?

I would imagine that's something that we're doing.

[00:21:56] Raffi Vartian: Yeah, absolutely. Well, I think the first part is we're collecting data about what's going on, and we're showing insights that we're previously unavailable. So the, there's a big shift that happened in the pandemic, a lot of. Kind of traditional kind of QSRs, if you will, like the McDonald's of the world and folks that have been at for a long time, they've always had this 70 30 split, or about 70% of their businesses going through drive-through about 30% is kind of going in retail.

It turns out now that that's effectively industry-wide, if not higher in some places. So where people have expected a 50 50 split, it's gone to that seven 30 and it's really not coming. So what, there's a lot of folks that found themselves earning an enormous amount of money throughout that process because they had that drive-through that was available, but they also don't know what's going on out there because their point of sale data is basically an on-off switch, somebody ordered and then somebody delivered.

Right? And all the data in in between is just the big, big blind spot. So the first thing that we're doing is we're identifying what the blind spots are, and then secondarily, we're looking. , what are those balk and abandonment rates? When does the congestion happen? Right. So it's almost like there's this idea wasn't an economist, but I'll have to look it up, of, of the accordion effect.

I don't know if you've guys have heard about that, but it's, it's related to traffic and the accordion effect is effectively, if you've ever done a long distance trip and there's an accident, you know, a quarter mile down the road, but there's nobody blocking. The lanes at all, you're gonna incur counter a slowdown cuz the first person looks in rubber necks and then the person behind them has to hit the brake and then they rubber neck, and then it creates this congestion and then that congestion opens up ahead.

So this kind of back and forth, which is an accordion, right? So that's what we're seeing in the drive-through. We're seeing that accordion effect where you got a tremendous amount of congestion and then it opens up. But then we're starting to look at, okay, what, what were the drivers behind those things?

and it turns out that the drivers could be a lack of an efficient payment system. It can be, believe it or not. Large dogs that are in the car that come out and try to poke their head out the, it's, it actually happens, believe it or not, right? That, that then the person that's serving wants to interact and they're having this great.

you know, interaction human, you know, canine interaction, and then it, it slows things down. Right? But the biggest one is like, when, when things become inaccurate or you've got a, a , I'll go back to the minivan here. You got a minivan with like six kids in it and everybody's got a different order and then it slows it down, and then you people start blocking away, right?

Yep. So there's a, there's a lot of things that are going on that we can say when we look at Congest. Go back to the example of kind of vision and insights and actions. What are the actions that we can take? Some of the actions that we're looking at is integration with many board systems where you're promoting not items that are, you can traditionally think of, quote unquote upsell, right?

I'm gonna do something that's a little bit higher margin. You're trying to figure out what are the areas that are the easiest to make of which you have the most inventory to be able to get that line moving. because throughput is critical, right? In those really kind of peak times within those restaurant environments.

So it's a, again, it's a long answer. I, I'm sorry I'm not giving you good sound bites here. They're, they're longer answers, but there's a lot of complexity within those answers. I love it.

[00:25:22] Mark Scanlan: I love it.

Raffi's example you know, in terms of the, the, the promo, if you've got line growth and you have a, a bunch of people in the line that are gonna order a, a hot sandwich that, you know, maybe is pretty packaged, but it's still got to sit in the microwave for 60 or 90 seconds and you can send a promo targeted to the, the individual who, you know, excuse me, you know, is in the line already for, you know, a cold danish.

Straight outta the cold cabinet, you've just collapsed that line by 60 or 90 seconds. And if you, if you can do that multiple times shorten the line overall and then you will see a re reduction in those people pulling out of line. And, and while we're talking about drive-through, it's actually no different to the, to the line in the grocery store.

Exactly. You know, we're talking about cars, people are objects too. That probably sounds bad. . I am not an object. perfectly don't objectify me. But you know, it, it's, it's from, from the video's perspective, it is an object with a predictable movement pattern and predictable actions that can come out of you know, what the camera's seeing.

So, you know, you walk up with. cart with $300 of groceries in, and it's five deep at the, the register. You, oh, forget this. Walk away. And you're not just losing the revenue that's in the cart. You've now gotta pay somebody to put that back. And there's also the potential for spoilage. You know, if there's frozen goods in there and it takes them, you know, an hour to actually wrestle that cart and, and get the stuff back to where it should.

Potentially it's defrosted, you know? So, and, and you can apply this across multiple segments of retail where you can look at the behaviors that are gonna occur. I, I've, I've done this in, you know, big box stores where I've, I've walked away from a cart because I couldn't make a phone call. So there's all sorts of reasons where you can potentially.

Things like Abandonments not just in the drive-through or at the register, but somewhere else in the store. If a cart is there and it's been sitting there for, you know, 15 minutes, chances are somebody has walked away and just left that product there. And you know, you can dispatch somebody to, to go and deal with that.

[00:27:42] Sabrina Maria Gonzalez: Mark, I think additionally from that is even brand loyalty, right? Like if I, if I know that that store is gonna have a long line every time for lunch, I'm gonna go somewhere else for lunch or I'm gonna go do my groceries somewhere else. Yeah.

[00:27:54] Mark Scanlan: And, and, and thank and thank you. Because this is an area that's so frequently.

Skipped. You know, we, we look at the, the, the direct costs. You know, what's the revenue lost and what's the cost of labor to put that product back on the shelf. But you've got the soft costs as well. And if you go to the marketing department, they will be able to tell you what, you know, a, a point of loyalty costs for that particular retailer.

And each time I abandon my car to walk out the door you know, how many points are being lost in that process. That's gonna impact not just the single shopping trip, but the lifetime value of the customer. If they decide to take their wallet to the, the grocery store or the home improvement store down the street because they're not happy with the service they're getting on a, on a regular

[00:28:36] Raffi Vartian: basis.

And we've all, we've all done that, right? I know I have. Oh yeah. You know, I've, I've been to, I've been to a Home Depot and I've looked at the quality of the lumber and I'm like, no, wait, this is ridiculous. You're gonna charge me for that. And then I go to Lowe's, and then the next 6, 7, 8 times I'm going for it.

I'm thinking about Lowe's first, then I'm Home Depot. Right. I'm just using that as an example. It's not to say that the retailers are, they're both great retailers. Thank you to, to both of them if you like . But, but the point, the point is, is that perception is real, right? And it's hard to figure out how you measure those different things and what the long-term impact kind of on your businesses.

We're working with one retailer that they, they were saying we, we, we always ask the question of what are you currently doing? because they look at like, what's the art of the possible with this new technology, quote unquote new technology? And then we say, well, what are you doing now? And they go, well, we do survey.

They go, oh, interesting. Right, okay. You're doing surveys for people and how long they're waiting, and they're like, I, we go, how accurate do you think that is? And the answer is not accurate at all because there's a perception about the amount of time that you spend in a line or whatever, and that perception could be colored by, I didn't have enough caffeine this morning, or I need to use bathroom, or, you know, whatever.

Right. There's all kinds of things that can modify perception and what we're trying to show is extremely. , right to the subsecond level of data and what does that data give you and what actions that you can take on those.

[00:30:04] Mark Scanlan: And, and with surveys you're trying to extrapolate a very small sample size.

Mm-hmm. and the thing to remember with, with retail, particularly in large geographic areas like the US or Europe The how the brand is perceived is gonna vary significantly from area to area. So a format that works particularly well in the northeast of, of the United States. Put that in the deep south and you'll see a completely different behaviors in there.

I remember. , years and years ago when I first got into into retail somebody telling me that when you see promotional signage outside stores, there has to be varied by market because two, for the price of one versus 50% off when you buy two is perceived differently. It's the same math, but it's perceived differently by different demographics, different geographic locations. And so on.

[00:31:04] Danny Vicente: And this is just a quick reminder for everybody listening. If there is anything that you want a deeper dive in, be sure to check out the links below. They will have lots of information on everything you are hearing here. Guys, I, I have a, I have a question for you and, and, and we're talking about all this and, and, you know, simplistic Danny Brain says, this is pretty cool and futuristic, but there's gotta be something on the horizon.

Where, where are we headed? What's, what's, what's in the future for us?

[00:31:29] Raffi Vartian: Oh, Mark's better at predicting than I am.

[00:31:35] Mark Scanlan: If, if, if I did, that's, I'd be in Vegas Raffi. Boy, it is, it's a. Difficult one to answer because technology is moving quickly. AI is moving quickly. Typically, retailers don't honestly move as quickly. We, we saw, you know, a very rapid forced innovation cycle. During the pandemic. We saw lots of Retailers running out to either deploy or enhance their curbside and, and drive through capabilities.

And, and to an extent, that's why Raffi and I tend to probably focus heavily on, on the drive through because we saw a problem during the pandemic where, you know, lines were backing out onto the street. And, you know, the, the, the market was desperate for a, for a solution. But , we we're seeing a slowing of that innovation and kind.

Retailers are now taking a step back and going, okay, we, we deployed a lot of stuff during that period. Is it still valid? And how can we in some cases kind of shore up or reinforce what we've done? We've, we've decided it was the right. Now how do we consolidate and, and refocus on that?

So I think we're gonna see a lot of the use cases we've talked about and things we've, we've seen in the last two and three years become more prevalent within the industry. I think you'll probably get fewer. Innovators kind of leaping ahead. But that said, you know Rafi, I, I don't know how much detail you can go into.

We, we, we were having an interesting conversation the other day about Hyperpersonalization. Mm. How we can get to a point where we're, we're not just looking at, you know, historical spend data in the, in the CRM or loyalty system but we can look very specifically at how consumers are interacting with the store, with fixtures, with the objects in the store being able to do targeted Engagements and I, I, I want to get away from thinking about promos cuz you know, promos sounded like Yeah.

We were just trying to sell you more. Right, right. It's, it's, it's really about engagement because I think ultimately the, the, the winner in this whole environment is gonna be the retailer that serves their customer best. Mm-hmm. . And it's going to, as Sabrina brought up, it's gonna drive that loyalty because consumers will pay a little more for a superior experience.

And in a, in a very vanilla world is those retailers that stand out are, that are really gonna succeed to do that they need to understand their customer better. Rafi, I don't know if you have any thoughts on that.

[00:34:10] Raffi Vartian: Well, I, I would say, you know, I mean it's, it's, it's a dangerous business to be in long term prognostication, but I would say short term, the bet that we're making, , right?

Is, is exactly to that point. Mark is that if we're looking at how everyone is looking at how much square footage they own, whether it's a retail environment or a corporate environment or whatever, right? They're trying to figure out how much of the, the space we need. If you wanna take a grocery store, for example, how much of we need to be able to carve out to make effectively micro fulfillment.

So that we can keep the fresh stuff fresh and then bring it out to be able to put into a car that's actually gonna get delivered, right? And that stuff just gets, it gets wobbly that, that, there's no straight lines in any part of this business where it just kind of goes, it goes month to month, quarter to quarter, and consumer shopping and behavior happens it's like a whiplash.

It goes back and forth and happens all the time. So our big bet is that real time actionable data and having that data available, Is really the, the new kind of gold rush because that 10 billion and the 60% market value that came off Facebook, it's not like marketers are not spending that, those dollars in different places.

like we've seen, I used to be in the digital signage business and, and to some extent we kind of still are because we're looking at, you know, the efficacy of signage. But the digital out-of-home business in particular is growing like gangbusters because when people are outside, it's showing you, there's a big screen that's in, on the side of the road.

People look at it and it influences behavior. Right? But if you can't really target and look at that phone and be able to do those targeted conversions, Buyers have been spending the better part of a decade doing, they're looking for other things that they can measure to be able to show a, a real.

Physical, click to convert, if you will, right? A mouse click in a, a physical environment, a mouse hover. Those are things like where people's hands are and how they're engaging. And do they put it in the basket? Because we can look at say, okay, do you wanna give us the point of sale data? We can say where people have moved in, the things that they've interacted with, and did it actually end up being paid for or not?

Right? And that level of data is, is much more than you would get when you're online. So our, our. In the, in the short term, and I guess short to medium term is that data is the currency of retail and that's where we wanna be.

Yeah. And I, as you were talking there, Rafi, I, I was coming back in my head to how I generally open up a presentation when I'm, I'm talking to a retailer and it's really about how can you be an Agile retailer?

Mm-hmm. Because. As you say, it, it, it's, it is the whims and vagaries of, of consumer demand. But it's also the business landscape nobody could have for foreseen. What was gonna happen over the last three years and. . So you had the business landscape change and on top of that and in part, main part because of that the consumer demand changed as well.

Mm-hmm. So being being able to get that realtime or near realtime data helps you be agile and respond to those customer demands and, and changing landscapes, whatever the next, that's way you've made whatever the next thing is.

Yeah. Yeah, exactly. Hopefully nice.

[00:37:35] Mark Scanlan: Yeah.

[00:37:38] Danny Vicente: Well guys, I know we are up against the time slot and I wanna be conscious of, of the, the allotted time that you gave us.

And we are approaching my favorite time of the podcast. And Mark, you've already joined the podcast, so you know the secrets out of the bag. But Rafi, this is new for you. And so I, I like to tell everybody, if you are like me, you join podcast and you the night before, think in your head of all the crazy questions that I may ask you or that Sabrina may ask you.

And because we throw scripts on the ground in the very beginning of podcast, I don't ask those questions. And so this is your open opportunity. What should the audience know that maybe I haven't asked you? Or that one nugget? If there's one nugget that they should walk away from this podcast with, what is.

And Mark, same question to you. So get ready cuz we're coming to you.

[00:38:24] Raffi Vartian: I would say that we are fanatically committed to finding the places that are a little strange where maybe a vendor hasn't gone before and that we can spin up new use cases around. We love to find those little niches within markets that have been previous.

Uncovered or unexplored, and we can find them and then deliver immediate value out of. We have the ability to move very, very, very fast. If the resources were committed the right way, and we can find those kind of nuggets of information that allows us to unlock a tremendous amount of business value.

So we try to focus less on product. and more on the solution. And I think that that's what it is. I mean, we, I spent a lot of time talking, like I mentioned before, and there's a reason, because if you don't have an honest communication going on with your customers and you don't ask about what problems they're facing, and you can't get that level of trust, you're never gonna find what your value proposition is.

So that's, that's what I think is important about us, is that we're veterans and that we really look to find the places where we can drive the business value that may not have been uncovered previously.

[00:39:36] Mark Scanlan: Beautiful. Well said. I, I should have known it was coming. Danny and I, I didn't . So I think I, I come back to something we, we, we frequent.

Say when we're talking to, to customers and, and they're, they're trying to wrap their heads around what the heck is video analytics and computer vision? And there's a lot of expertise out in retail but it's generally not systemic or institutionalized. I can't even say it. Institutionalized. So if you were to take one of your most experienced manager, and set them up on a shelf at the, at the front of the store and, and look down on the store.

What would they see? What would they notice? This gives us the opportunity to, to some degree, institutionalize that knowledge so that it becomes just part of the process. Nobody even thinks about it or notice. If, and, and Rahi can correct me if I'm wrong here, but generally , if we can see something, then we can probably analyze it.

And if we can take that knowledge from those experienced professionals in, in the industry and be, actually I should, I should add a piece in here. You know, the number one challenge today in the, in the retail market is, So, you know, not only are we losing associates, we are losing the experienced, you know, store leadership, et cetera.

So we need to be able to take that knowledge and replicate it through the systems so that other people don't have to. That's great. Love it. Love it, love it.

[00:41:17] Danny Vicente: Well guys, I, I want to thank you for your time and, and conversation. I think I learned quite a bit, like, I won't speak for Sabrina. She's far smarter than I but God knows I learned quite a bit.

I think our customers did as well to our customer. To the folks listening in the audience again, just one last reminder. If you have any questions, please feel free to email us and we will try to get that in an upcoming podcast. And if you want a deeper dive into anything you. Click the link down below and take a tour on whatever URLs are down there.

Guys, thank you so much again, I hope to have you both on the podcast. Once again, Sabrina and I thank you all for listening.

[00:41:54] Raffi Vartian: Thank you very much.