In this episode
Angie Westbrock, CEO of Standard AI, explains why retail media spend has climbed faster than retail media measurement: in a physical store, brands can see that an ad played and that a transaction happened, but they cannot see who was exposed and didn’t buy. Standard AI converts footage from security cameras already in the ceiling into structured, non-identifying shopper data — what Westbrock calls Google Analytics for a physical store — which lets a retailer run exposed and unexposed cohorts in the same store at the same time instead of inferring lift from a 40-store control panel. Greg Kihlström and Westbrock work through what that unlocks operationally: metrics that didn’t exist before, like time to first contact and resilience to crowding; pilots that return confident results in weeks rather than a 16-week trial; and why AI-distorted digital search is pushing budget back toward the aisle, where the shopper is already at the point of decision.
Key takeaways
- The unmeasured half of retail media is the non-purchaser. Physical retail could always correlate ad plays with transaction logs, but never see how many exposed shoppers walked past without buying — Westbrock calls that missing denominator the core problem.
- Computer vision replaces inference with observation. Standard AI taps existing ceiling security cameras and converts raw video into a structured dataset, with no PII and no facial recognition — the underlying representation is a three-dimensional stick figure that shows position, facing, and gaze but not identity.
- Individual trips, not heat maps. Older in-store analytics stopped at density and dwell time; holding each individual trip and aggregating afterward is what makes true exposure-to-conversion attribution possible.
- The A/B test moves inside a single store. Four cohorts — saw the ad and converted, saw it and didn’t, didn’t see it and converted, and so on — can be measured in the same store on the same day, removing the weather and outage noise that corrupts multi-store control panels.
- New metrics come out of the pilot, not the product roadmap. In a high-service category, distinguishing employees from customers produced “time to first contact” — how long a shopper waited before staff approached, and whether that changed conversion.
- Resilience to crowding is a brand-strength signal transaction logs can’t produce. Stronger brands hold conversion when a fixture gets crowded; a shift in that number is an early read on demand, and a fixture with poor crowding conversion makes the ads counted against it look better than they perform.
- Speed of decision is the operational payoff. Customers reach confident results in a few weeks where a comparable rollout test would have taken sixteen — which matters when retail media means running electric to a physical screen.
- Teams are converging on shared metrics. Standard AI launched its vision platform in 2024 into disjointed marketing, merchandising, and operations teams; in the last six to nine months those groups have started working from one set of baseline definitions.
- The funnel is shifting back toward the aisle. AI search is muddying digital attribution while many retailers still report more than 80% of sales from physical stores, making the moment of decision in the store a more defensible place to spend.
Chapters
- 0:00 The shelf is still the least instrumented part of the journey
- 1:30 Angie Westbrock, CEO of Standard AI
- 2:36 From CPG manufacturing and supply chain into retail tech
- 3:35 Exposure to outcome: the gap retail media spend hasn’t closed
- 4:47 Two investments, two teams, two scorecards
- 6:44 Baseline metrics as the way to unify merchandising, ops, and marketing
- 7:48 What changed technically — and how the data is actually collected
- 9:01 Individual trips vs. heat maps, and four-cohort attribution
- 11:45 The pilot phase: configuring metrics to the retailer’s strategy
- 12:40 Time to first contact
- 13:25 Resilience to crowding, and what it says about a brand
- 17:14 There’s no such thing as a crowd on a website
- 19:48 Test and learn inside a physical space
- 22:14 AI search, the distorted funnel, and the aisle as the point of decision
- 26:01 One year out: predictive experiences in the store
- 27:10 Staying agile — and the discipline not to be too agile
Why retail media spend grew faster than retail media proof
Investment moved into retail media without the measurement layer following it. Westbrock’s framing is that the industry still has a lot to prove between exposure and outcome, because in physical retail the information simply hasn’t existed. Practitioners correlated transaction logs against known ad play times and made timing assumptions. What that method can never surface is how much a given consumer was actually influenced, or how many people saw the ad and bought nothing.
What ceiling cameras can see — and what they deliberately can’t
Standard AI works from the security cameras already installed above the sales floor, using AI to turn raw footage into a usable structured dataset. Westbrock is explicit about the privacy architecture: no personally identifiable information and no facial recognition. The system distinguishes one person from another, tracks which way they are facing and where they are gazing, and does not know who they are.
Why individual trips beat heat maps
Earlier retail analytics products leaned on inference because they couldn’t retain individual journeys — they measured density and dwell, which caps out at a heat map. Standard AI observes each trip and aggregates afterward. That distinction is what makes online-style attribution possible in a store: how many people saw the ad and converted, whether they also entered the aisle and engaged with a display, and what the conversion rate looked like for shoppers who never saw the ad at all.
The A/B test that runs in one store at one time
Because all four cohorts are visible simultaneously, the test environment collapses into a single location. Westbrock contrasts this with the older approach of standing up a control-versus-test panel across forty or fifty stores, where one power outage or a week of bad weather in one region compromises the whole design. Same store, same day, ad on and ad off — plus the question of where in the store the ad sits, and whether its placement changed the path shoppers took.
Metrics that didn’t exist before the data did
Two examples came out of customer pilots. In a high-service category, the ability to tell employees from customers produced time to first contact — how long a shopper stood there before someone approached, and what that did to conversion. In a category with viral products, a still-unnamed metric around resilience to crowding measures how quickly shoppers abandon a crowded fixture or whether they buy anyway. Stronger brands hold their conversion under crowding. That has a direct retail media consequence: a fixture with poor crowding conversion is generating viewed-ad counts that aren’t converting.
Why the funnel is tilting back toward the store
For two decades digital was the easier place to prove return, so budget went there. Westbrock sees that swinging: online is saturated, AI search is muddying attribution, and many retailers still report more than 80% of sales coming from physical stores. Her argument for the aisle is about intent — online shopping involves a lot of browsing and ads served against products the shopper wasn’t thinking about, while an ad encountered in the aisle reaches someone already at the point of decision. Her caveat is that in-store advertising has to earn the same personalization quality that made online ads tolerable, and it now has measurement arriving alongside it.
FAQ
What problem does Standard AI solve for retail media? It closes the gap between ad exposure and in-store outcome. Transaction data shows purchases but not who was exposed and declined to buy, which leaves brands unable to state real conversion rates for in-store media.
How does Standard AI collect in-store data without violating privacy? It uses the security cameras already installed in store ceilings and converts the video into structured data. There is no personally identifiable information and no facial recognition; individuals are represented as three-dimensional figures with position, orientation, and gaze but no identity.
How is this different from older in-store analytics? Earlier systems couldn’t hold individual journeys, so they reported density and dwell time — effectively a heat map. Observing and then aggregating individual trips allows exposure-to-conversion attribution across multiple cohorts.
What is “time to first contact”? A metric developed in a high-service retail pilot that measures how long a customer is in the space before an employee approaches, and whether that delay affects conversion.
What is “resilience to crowding”? An emerging metric for how shoppers behave when a fixture gets crowded — whether they move on or still purchase. Conversion holding up under crowding indicates brand strength, and a decline serves as an early demand signal.
Does in-store computer vision replace existing CPG measurement? Westbrock says no. Broad measures stay useful for baseline and fleet-wide reads; the vision data goes deeper and earlier, letting teams optimize in flagship locations and reach confident results in weeks instead of a sixteen-week multi-store trial.
About Angie Westbrock
Angie Westbrock brings 20+ years of experience in retail, CPG and tech to her role as the CEO at Standard AI, the retail analytics startup valued at $1B that’s helping retailers and brands understand shopper behavior so they can improve the customer experience and their bottom lines. Angie specializes in scaling teams, culture, and business operations to support massive growth. She’s bucked the mold of what’s expected of women since the start of her career; she was one of the first brewing managers at Anheuser-Busch, one of the youngest women to run a plant at CPG giant Sara-Lee, and helped lead Lyft’s global Covid-task force with a young family at home.
Angie Westbrock on LinkedIn: https://www.linkedin.com/in/angie-westbrock-a2581111/
Resources
Standard AI: https://www.standard.ai
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Transcript
[00:00:45] Greg Kihlström: [gentle music] Hi, I’m Greg Kihlstrom, host of The Agile Brand, and here’s a question for you. What if the moment where the buying decision actually gets made is still part of the journey we understand the least? Agility depends on seeing what customers actually do, not just what they clicked, and being able to act on it while the promotion is still running. Today we’re talking about the gap between where brands are putting their media money and where they can actually see it working. Retail media budgets are climbing, stores are getting reinvested in, and yet the shelf itself remains one of the least instrumented parts of the customer journey. We’ll be covering why the surge in retail media spend hasn’t automatically produced better measurement, what it takes to connect a digital impression to what a shopper
[00:01:30] Greg Kihlström: actually does in the aisle, and how marketing, merchandising, and store operations have to work differently to act on any of it. To help me discuss this topic, I’d like to welcome Angie Westbrock, CEO at Standard AI. Angie, welcome to the show.
[00:02:27] Angie Westbrock: Thank you. So happy to be here.
[00:02:29] Greg Kihlström: Yeah, looking forward to talking about this topic. Before we dive in though, why don’t you give a little background on yourself and your role at Standard AI?
[00:02:36] Angie Westbrock: Yeah, so I’m the CEO of Standard AI, and my background is kind of great for this conversation. I spent the first half of my career in CPG and retail, on the manufacturing and supply chain side. And then a little over 10 years ago, I switched into the tech side of the, of the industry over here. And so this was really, joining Standard AI several years ago was really an opportunity to bring those two together and, help, you know, bring innovation and new technology into the retail sector.
[00:03:07] Greg Kihlström: Yeah. Yeah, love it. Yeah, and, definitely some, some interesting stuff you have [laughs] going, going on, so I know we’re gonna dive in here. And I want, I wanna start really where, you know, we’ve, we’ve talked certainly about retail media a bit on this show, and it’s attracting enormous investments, but a lot of it’s being pulled out of other channels. Where do you see the widest gap between what brands believe that’s, that spend is buying them and what it’s actually delivering at the shelf?
[00:03:35] Angie Westbrock: Yeah, I, I think we, there’s still a lot to prove in terms of what happens from exposure to outcome, right? And so it’s, it’s why there’s a lot of interest in what we’re doing today. We, we bring this technology to help close that gap because especially in physical retail, that information just hasn’t been there. So we’ve had to do a lot of correlating between transaction logs. You know, we’ll know when an ad plays. We can maybe do some assumptions based on timing of purchasing. So, you know, there is some data available, but what you don’t have is how much were you able to influence that consumer and how many of those consumers that saw
[00:04:21] Angie Westbrock: the ad didn’t purchase. Like, you know, this, this, gap in not knowing who didn’t purchase has been a big problem in physical retail especially.
[00:04:28] Greg Kihlström: Yeah. Yeah. Well, and, and certainly there are, a lot of retailers are investing and reinvesting heavily in the physical stores. I mean, for all the, the talk of e-commerce, it’s, it’s important to know that physical stores are very much alive and, and a, a critical part of, of retail.
[00:04:46] Angie Westbrock: Absolutely.
[00:04:47] Greg Kihlström: Are, are those two trends, you know, the, both the reinvestment in, in physical stores and, and the investment in, in retail media, you know, are they actually maybe the same investment being run by two different teams against two different scorecards? You know, what… where, where does all this kind of net out?
[00:05:05] Angie Westbrock: It’s interesting that you say that because, you know, we, we launched our vision platform in 2024, and in that year especially, we saw still a lot of disjointed teams to where we were trying, you know, we were talking to multiple teams within a large, you know, large retailer brand that weren’t r- necessarily talking to one another. That has really changed in the last, I would say, six to nine months, where you’re starting to see is they’re very quickly realizing that there is– I mean, this is a huge difference between and what makes physical retail so cool, in my opinion, right, is that you’ve got the, these really important intersections between operations, customer experience,
[00:05:51] Angie Westbrock: merchandising, store layout, and then your, you know, your entire ad campaigns and, you know, whatever you’re doing for retail media. The more that those teams are working together- And it’s also one of the value adds for our platform is, is that we understand that and we can provide metrics for all of those teams on one platform because they really do need to be talking to one another and understanding each of their impacts on the overall, you know, purchase and consumer experience, so they’re investing resources accordingly. I mean, that’s a, that’s a huge piece of it. And I think this, you know, for me, I’m a, I’m an operator, I’m a big data nerd. I think, you know-
[00:06:29] Greg Kihlström: Yeah
[00:06:29] Angie Westbrock: … I think this is true not just for different teams with inside the store, but also working closely with their online teams and their digital teams as well. Because certainly what you’re doing on online or in store is impacting the other, right?
[00:06:44] Greg Kihlström: Right.
[00:06:44] Angie Westbrock: So we really, for me, being a data nerd, I really believe that the first way that you can start to unify these teams is by getting some baseline data and metrics that we can agree on and measure the business in the same way. Once you align on that, and you get them thinking about the same definition of success, they naturally are gonna be working together to drive, you know, performance in the direction that they want to.
[00:07:10] Greg Kihlström: Yeah. Yeah. I mean, ’cause at the end of the day, we wanna make a sale. It doesn’t-
[00:07:14] Angie Westbrock: Yeah.
[00:07:14] Greg Kihlström: I know there’s teams that are incentivized to have their channel be the, the winner or whatever, but, you know, at the end of the day, a brand, you know, it’s the, the goal is to sell product, what- whatever those are. And yet, you know, closing that loop that, that you’re talking about, I mean, that’s been the Holy Grail for years is, you know, understanding how a digital ad influences or, you know, translates to an in-store purchase. What changed technically that makes that possible now and, you know, where, where is this connection still, you know, more inference than an actual observation?
[00:07:48] Angie Westbrock: You know, this is, this is what’s really exciting about computer vision. I mean, all of these advancements have been really in recent years. You’ve seen a lot more computer vision tech companies out on the market, but especially for us, you know, we’ve been around almost a decade. And so we are approaching it differently in that because we don’t use facial recognition, and maybe I’ll just spend, 30 seconds talking about how we collect the data-
[00:08:15] Greg Kihlström: Yeah. Yeah
[00:08:15] Angie Westbrock: … and generate the data. I think it sort of helps understand, is that, you know, we’re tapping into the security cameras that are already in the ceiling. So we convert that raw video footage. That’s what the AI is doing, is, is turning that raw video footage into a usable structured data set. So, you know, we say i- it’s easy to understand to think about us as like Google Analytics for a physical store.
[00:08:41] Greg Kihlström: Right.
[00:08:41] Angie Westbrock: We are, we don’t use PII or any personally identifiable information. We don’t use facial recognition. We do all of this through effectively, and you can see this on our website, like, the data is basically like a three-dimensional stick figure that we’re seeing. So we can distinguish people from one another. We know which way they’re facing. We know which way they’re gazing. But we don’t know who they are.
[00:09:01] Greg Kihlström: Right.
[00:09:01] Angie Westbrock: You know, we don’t know the, the, what they’re doing. But what that allows us to do is, you know, early on in analytics and some of the older, analytics types of companies in retail, where they’ve had to do a lot of inference is because they can’t really hold onto those individual journeys. They are more looking at it like density measurement and dwell time through a store. So think of a heat map, right? They can’t really get more granular than a heat map. What we do is actually look at every individual trip and then aggregate that data. So we know how many… You know, this is a great example when you’re thinking about why is this different. Well, now we can tell you real attribution like you would online, because we can tell you how many people saw the ad and
[00:09:47] Angie Westbrock: converted.
[00:09:48] Greg Kihlström: Yeah.
[00:09:48] Angie Westbrock: Did they also go into the aisle and interact with some display? And maybe that was the secret combination to get a higher conversion rate. We can also tell you the conversion rate of those that didn’t see the ad and still converted, or vice versa. You know, there’s like basically four different cohorts that we’re monitoring. So your, your AB test environment is really in the same store at the same time. You know?
[00:10:11] Greg Kihlström: Right. Yeah.
[00:10:11] Angie Westbrock: Versus historically, because we’ve only had transactional doc- transactional logs and transactional data, what that means is you’re trying to set up some, you know, control versus test environment. Maybe it’s like 40, 50 stores. You know, someone has a power outage, or there’s weather in one area for a week and your entire, you know, control system-
[00:10:32] Greg Kihlström: Right. Right. [laughs]
[00:10:33] Angie Westbrock: … is messed up. So because we are seeing this, you know, in near real time with actual customers in an organic environment actually shopping and then making changes, playing the ad, not playing the ad, you know, doing that display. You know, big question we get at the same time is also, you know, not just w- whether you’re playing the ad, but where is it? Where is it in the store? Did that actually change the customer’s path that they’re going? And then how does that create new environments and places for you to advertise or do new displays within the store? So really interesting information that you can have by getting that deeper layer of granularity that is, that we’re able to do because of the advancements in computer vision.
[00:11:18] Greg Kihlström: Yeah. Yeah. Well, and then, you know, to your point, if when, when, brands and, and stores are able to understand the change in either the effect on behavior or change in behavior, what, what does this look like? you know, how do, how do they act on this? ‘Cause I, I would imagine this is a relatively new set of data or insights to get, and so like what, what do you do with this exactly to, to optimize?
[00:11:45] Angie Westbrock: So I’ll give you a great example. The… We’ve done into this with a few customers now because we also know that retail is incredibly dynamic, right?
[00:11:53] Greg Kihlström: Yeah.
[00:11:54] Angie Westbrock: So every retailer really has their own strategy, their own customer base, you know, the things that they’re, they’re launching or that they care about. So what we do is during our pilot phase, that’s really when we go- We go deep into the setup with the customer to find out, what are your key strategic initiatives? Like, what do you wanna learn? And during that pilot phase is when we establish their key metrics that they’re gonna have going forward. And when we talk about scale afterward, those metrics are really configured for that industry, that customer. And we’ve h- we’ve come up with new metrics. I mean, this is the part I love. It’s so cool. Like, metrics that we hadn’t even considered before we’ll be asked to do. So a great example is in a high-service industry where,
[00:12:40] Angie Westbrock: you know, we can tell the difference between an employee and a customer. We can, we can do things like how long was that customer there before they were appro- approached by an employee, and did that have an impact on conversion? And now that metric, which we call time to first contact, becomes this very important operational metric for them. Another thing that I think is, like, a great just way to think about it, and, this was, you know, s- really interesting for us, is we’re working with another customer who has a lot of viral products and, you know, in, in this kind of industry. We realize that there’s a metric which we’re, we still haven’t really named yet, but it’s something around, resilience to crowding. So we could tell by
[00:13:25] Angie Westbrock: pr- like, brand and fixture, we can, we can give this metric of, like, when you have crowding around that fixture, how quickly are people to move on, or will they still buy?
[00:13:35] Angie Westbrock: And does that conversion get impacted? So the stronger the brand, the higher we see conversion maintained, and it doesn’t drop off during crowding. But this is something you would never be able to measure through the transaction logs. And so because we’re measuring in the ceiling and we can see this over time, now we can give brands an indication of when they’re starting to see that number shift, which is saying, you know, maybe, maybe we h- we’re seeing a consumer trend here that we weren’t anticipating, you know? They’re-
[00:14:08] Angie Westbrock: And we can, you know, help the retailer understand what are those, fixtures or those brands that you want to be near overflow, things like that. But all of that is really important to understand what’s happening when you’re running that ad at the same time. So, you know, layering in retail media on top of that, if you have a fixture that has very poor conversion drop-off during crowding, and you’re, you’re claiming that as viewed ads, it’s not helping, it’s not helping you convert, right?
[00:14:38] Angie Westbrock: But those are the types of insights that we can provide that help brands and retailers work together to create the highest, you know, best customer experience. You know, understand where to allocate more space, where they should be placing more ads, you know, how are those consumers navigating that space? Where are they going? Where are they looking? And then leveraging that to create better campaigns and better outcomes.
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[00:17:14] Greg Kihlström: [gentle music] This is fascinating to me because, you know, I think a lot of the digital measurements come from… They originate from digital outlets like websites. And, you know, you think about impressions, you think about a conversion, you know, g- equivalent of a click or, you know, something like that. But you’re talking about physical crowd. You know, there’s, there’s no such thing as a crowd at a website unless there’s so many it crashes. But, like, the, these are, these are things that, you know, people in the physical retail space understand and, and know but didn’t have a way to measure in this way, right? So it’s, it’s, this is sort of where the digital measurement kind of bridges a, a different kinda gap, right?
[00:18:00] Angie Westbrock: It really does. Like, we started being inspired by all of the digital metrics, and I think that’s really the lesson that we’ve learned, is that it’s just as you were saying earlier- You do have a combination of operations, merchandising, marketing all working together.
[00:18:17] Greg Kihlström: Right.
[00:18:17] Angie Westbrock: And understanding how each of those is influencing in the moment is super important, and you don’t necessarily… Of course, like you said, there are parallels online if you have a broken link, you know, or, or something that is a negative experience.
[00:18:31] Greg Kihlström: Yeah, yeah.
[00:18:31] Angie Westbrock: But it’s also much easier to go back and research what’s happening there. When you see that drop, drop-off in traffic on a site you know that there’s something, you know, that you need to go in and fix, where it’s not as obvious in a physical store. Whereas now we can provide all of those types of insights and that, you know, one of the things that we hear our customers saying is that it’s allowing them to start thinking further upstream, you know, before purchase on what they can influence, and getting leading indicators of what’s happening versus-
[00:19:06] Greg Kihlström: Right
[00:19:06] Angie Westbrock: … lagging, right? So all of those things are what help them just be more empowered and making faster decisions. And exactly as you said, there is just a lot to take into consideration in a physical environment.
[00:19:19] Greg Kihlström: Yeah. Yeah. Well, and so what do, what does this look like then for, you know, a CPG brand that’s used to using, y- you know, some, some I’ll just call proxy metrics to some of these in-store things? You know, obviously it’s not a, you don’t switch one off and o- the other all the way on, you know? What does that, what does that look like, and is it, is it just that you measure everything [laughs] or, you know, what does, what does that transition to this kind of measurement look like?
[00:19:48] Angie Westbrock: Well, we- the way that we think about it is it’s kind… Once again, if you think about the way we’ve been handling media or growth, growth marketing online, you’re not doing… You know, y- you have your teams that are doing AB testing, right? And so you’ll run ads to certain segments at the same time, or you might have two different versions of a website that are running, and you see which one converts better.
[00:20:12] Greg Kihlström: Right.
[00:20:12] Angie Westbrock: Those things are happening all the time, but they don’t have to be 100%, right? So the way that we kind of think about it with teams is we’ll have those operational metrics that are probably gonna be adopted across the fleet, but then there’s also some, like, flagship locations where we’re, you know, working with very specific teams where they’re testing out. So think- imagine, and we just really haven’t had the ability to do this, with really strong data behind it, but now you can actually have sort of a test and learn environment within a physical space to optimize the performance in a couple of locations before you scale it. So some of those broad metrics, like, are still very good for baseline measurement and for across the whole f- I don’t
[00:20:57] Angie Westbrock: think we need to replace all of them. But what we can do is go much deeper and, you know, and earlier in the planning process to do optimization, get a ton of confidence there. We’ve seen several customers do that, where they’ll test something and they can, they can get very confident results within a few weeks-
[00:21:19] Greg Kihlström: Right
[00:21:19] Angie Westbrock: … versus maybe a 16-week trial across, you know, how many stores they would’ve had to do previously. So, you know, that’s really where it comes in, is I, I don’t think that it necessarily is replacing everything that they do, but it is just giving them more data, more confidence, more information upstream to be able to have a ton of confidence and move faster. I mean, that’s really, I think, one of the hardest things that physical retail has had is, is speed of innovation and speed to roll out. It’s very challenging. In order, you know, when you’re gonna commit to any kind of… If you’re gonna commit to retail media across your stores and you’re putting a digital screen in that requires electric and you know, physical person going in there, this, this is, this is not as easy to deploy as when you make a change on [laughs] digital site, right? So, so, being able to know that those investments are gonna be worthwhile is very, very important.
[00:22:14] Greg Kihlström: Well, and the, this i- this move to, for physical retail to be able to understand intent and actions a little bit earlier in the process, I mean, it’s kinda counter to, you know, another big thing going on in the world of, of e-commerce and, and, and on the digital side, is that funnel getting, being distorted by AI-based search and, and stuff. So in other words, people entering the funnel a lot later. So it’s nice to hear a story about [laughs] getting, you know, getting a little more understanding of consumer behavior, at least in the physical space a little bit earlier on. So how should brands be thinking about that? You know, they’re kinda looted- losing some territory on the, on the front end in the digital space, but they’re maybe gaining some in the, in the physical retail space.
[00:23:03] Angie Westbrock: It’s, it’s a really interesting tension that’s happening right now, and I think it’s why you’re hearing so much more about retail media in the physical world. Because for the last, I don’t know, 20-plus years or whatever, you only had data online, and it was the more, reliable is probably the wrong word, but it was, it was an easier place to prove out your investment and to have that return on spend. and you could use that data to justify your budget, right? And physical retail couldn’t compete with that, and that’s why so many dollars went into digital. Well, that is starting to swing a little bit because, one, we’re just getting more and more saturated online. Two, the introduction of AI search, as you said, is now muddying that a little bit. It’s, it’s not so clear that that’s gonna be the best place for me to put my money. And now you go back into physical stores, you know, as you mentioned, we’re still many retailers still report more than 80% of their sales coming from physical stores. And I don’t know if you’ve noticed this, especially in the last six months, but, like, I took my daughter shopping this weekend. We couldn’t find a parking spot, and I was like, “Yes,” you know? [laughs] Like-
[00:24:13] Greg Kihlström: Yeah, yeah. [laughs]
[00:24:13] Angie Westbrock: … it’s coming back. Because for the last several years it’s been, it’s, it’s gone down, but people are interested in getting back into the physical environment again. And because of that- Thinking about advertising in a physical spa- when you’re in a physical store, that is the point of decision. You know? And when you think about when you’re online, you could just be, I mean, you could just be brow- like, it’s a lot of browsing, it’s a lot of, like, you know, I get surfaced an ad when I’m not even thinking about that product. Well, getting surfaced an ad when you’re in the aisle or in, like, there to purchase that thing it is suddenly a really important moment to be engaging with that customer, especially if you can do it in a way that is going to feel personalized. And, you know, one of the things I always think about is, like, back 20 years ago when we were starting to get ads on the internet, they were so spammy, right?
[00:25:11] Greg Kihlström: Right. [laughs]
[00:25:11] Angie Westbrock: Like, it was, it was awful, and it made you… They, they were a nuisance. They weren’t helpful. But because of the level of personalization that we can experience now online, they’re very helpful. Like, I don’t mind them. I’m like, “Hmm, yeah, that’s kind of… I’m really interested in that item that was just served to me.” If we can create those, that type of feel and personalization inside a physical space when you’re there, captive, you’re at the point of decision-making, this can be a very reliable and effective place for brands and retailers to invest in that media. And now that we’re gonna be able to add measurement on top of that to help them validate and give them confidence in that spend, it is becoming a much more attractive place than what it had previously, you know, and especially as it compares to online.
[00:26:01] Greg Kihlström: Love it. Well, Angie, thanks so much for joining today. Got two last questions for you as we wrap up here. First one, if we were having this interview one year from today, what is one thing that we would definitely be talking about?
[00:26:14] Angie Westbrock: You know, the, what I’m excited about is right now we’re talking about how computer vision is unlocking all of these measurements and, and data sets that weren’t available before, but inevitably we’re headed toward, towards a place of being able to unlock new experiences, predictive nature of AI, getting better at starting to predict those kind of things. And I’m just excited about what kind of customer experiences we’re gonna start seeing showing up in the physical environment, because that’s really what we’re doing is by unlocking these new data sets and metrics, there are… it’s also allowing us and others to build on top of it new ways to engage the customer and, and new ways to make physical shopping really interesting and innovative.
[00:26:57] Greg Kihlström: Yeah. Love it. Yeah, can’t wait to, can’t wait to see what’s in store there. [laughs] No pun intended,
[00:27:10] Angie Westbrock: I love a good pun.
[00:27:10] Greg Kihlström: Last question for you, what do you do to stay agile in your role, and how do you find a way to do it consistently?
[00:27:18] Angie Westbrock: You know, it’s funny, I was thinking about this question and I’m like, in a early-stage tech startup, we actually have the opposite problem. We’re too agile, you know?
We, I think, very curious, we love new tech, we’re constantly researching. We’re, you know, constantly interested in building the next thing or that new thing that the customer asks for. And so we are sort of the opposite, where we have to have discipline to not be too agile. [laughs] So I think, you know, I think, naturally we try to stay very focused. We try to make sure that we understand what technology is really gonna drive a return on investment and isn’t just cool for the sake of, of tech sake, and being very disciplined around defined success metrics and not getting so carried away with, like, the stuff I just described that could, [laughs] that we could build. but we know it’s coming, and being excited about that, but still staying very focused on driving value and, you know, making sure that this is a, a long-term journey that we’re on with the industry and not something that we’re getting ahead of ourselves. So I would say for us it’s the, it’s the opposite, which is staying disciplined. We are quite agile [laughs] in our in our applications.
