Mobile Dev Memo Podcast, Season 7, Episode 9: RecSys and internet commerce (with Michael Komasinski)

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Welcome to the Mobile Dev Memo podcast. I'm your host, Eric Seufort, and I'm joined today by Michael Komasinski.

Michael, welcome to the podcast.
Hey, Eric, thanks for having me on.
Well, it's good timing because Cateo has been in the news recently, so I'm I'm happy to have you on during this period. Before we dive into our our conversation, which will unpack all things agentic commerce, can you please introduce yourself to the audience?
Yeah, sure. So, uh, Michael Kamasinski, CEO of CRIO, been in this role for just a little over a year, and what a year it's been from sort of pulling back from cookie deprecation to, you know, changes in the retail media landscape, the emergence of agent commerce, and now as of this week on Monday, ads in Open AI are in chat GPT. So it just keeps going faster and faster and it's awesome to be in this space right now.
And prior to you you're the CEO of Denu.
Yeah. So I spent really kind of 10 years between Merkel and Densu and you might know Denu acquired Merkel but yeah was the CEO of Densu Americas and then also

ran global data platforms for the group and before that was the global CEO of Merkel where I'd spent eight years between Europe and the Americas and then eventually running the whole company. So it was a great run, a lot of great colleagues back there and now they are an important partner of ours. So everything goes round in this industry.
And then kind of a couple hops before that you you'd spent quite a quite a stint at Neielson. Is that correct?
Yeah. Yeah. I was at Razerfish. Neielen back in the day, which was a great grounding in kind of data analytics. I always like to say like Neielson is kind of where I grew up as as a as an executive. just a very rigorous company, really great on sort of product development and and just the way that they run the place yeartoear. I was there during the take private when Dave Calhoun took over. So it was really transformative time in the company back then.
You've touched kind of a number of of different call it vantage points across

the sort of digital advertising landscape and and kind of the advertising landscape more broadly. Your perspective is is fairly multifaceted I would say. Yes, it is. It is. But, you know, as you do, you kind of find your way to the right place in a career. And I'm really happy with where I've ended up here. I believe in today's age being platform side at a platform like CRIO that has the data assets that we do sits in these kind of fast currents like performance and retail media. I think we can accomplish a lot here. And so, yeah, I think I've yet again sort of found my way to the right place at the right time.
Well, the time is the era of what what people are calling Aentic Commerce, and I I'm I'm really happy to to have you on to talk us through your perspective on that. So, so Cout recently launched the Aentic Commerce recommendations service for AI shopping assistance. Can can you kind just talk me through that? What what is that, you know, what's the kind of commercial model? How does it feature

in this in this landscape?
So, you're right. We launched that a couple of weeks ago and and you can think of it as like a commerce intelligence layer for AI assistance. And what it does is it powers product recommendations inside of conversational environments uh with highfidelity purchaseoriented signals. And what's interesting is we've brought it to market. We do sideby-side testing offline against LLM environments and we see on average a 60% uplift in product relevance and accuracy. The reason we launched it is we really wanted to demonstrate the value of our access to real-time purchase data and our AI models to turn that into relevant recommendations. And I think it's something that you've written about as well in terms of the underlying models that power these different systems, right? And so LLMs by definition are semantic models and

they're great at language, but Rexus models need to be built on data loops and reward algorithms at high volume and scale. We very much believe in that. The mccrio backbone is really built on a Rexus platform. And so this uh service is really a way to sort of give a front end to that in a way that can be tested in a partner environment and allows us to you know sort of prove out the efficacy of that data set. So it's it's super interesting, right? And we believe the future of great commerce recommendations in discovery platforms is going to be powered by a hybrid integration of semantic platforms and Rexus platforms. And so again, this is our sort of way to push that agenda, put our capability out there, make it testable, accessible. It's early, so we've not focused as much on the monetization path yet, but

eventually that could be SAS, that could be pay-per-query, and more importantly, it could support both ad and non-ad use cases. But right now, it's just about proving effectiveness, validating use cases, and driving partner engagement. So, monetization will follow all that.
Got it. Because that was going to be my next question is how do you get paid for this? I I guess the the phrase agentic commerce I think is is it's almost like AI in a way because it can be it can be so vague and it can mean so many different things that it can be unhelpful in some cases. So so so when some people talk about agentic commerce and this is kind of I push I' I've pushed back pretty forcefully on this idea taking shape. They're talking about I've got an agent and it's just buying stuff for me like in the background. some some stuff's going to show up on my doorstep tomorrow and I'll be pleased that it did, but I will have no knowledge that that was purchased on my behalf. There's just some agent, you know, fulfilling what it it it anticipating my needs and fulfilling them with my credit card. Right. Then

there's there so that's kind of the call that like the the robot buying stuff model. And then there's the the model that OpenAI had had sort of implemented, which is the instant checkout model where they're going to allow retailers to slot into your your chat and just, you know, based on what you're discussing, it'll suggest products that are that are relevant and then, you know, you'll you'll check out right there. That they they sort of just it was the information reported yesterday that they're probably walking that back. They're going to do that more in the underlying apps that they're on boarding onto their apps platform. And I had pushed back on that idea too, just because I said that's not going to work as well as ads does. And it sounds like what you're what you're what you're and let me know if I'm, you know, and I've read the press release and I understand, you know, how you're positioning this, but just kind of, you know, feel free to like flush it out more. This sounds like this is Rexis for the the chatbot environment, but but it sounds like this really could be Rexis in in a number of different environments where there's some sort of agent being invoked. Is

that is that am I kind of understanding that correct? So there's there's there's advertisers, there's there's there's retailers who would like to have their stuff recommended where it's relevant and where it's like additive to to some interaction and that's what you're facilitating. Is that is that the right interpretation?
Yeah. No, I think you I think you said it well. Yeah. It's basically where the fidelity of product recommendation is an important use case to the overall experience, right? That's probably the simplest way to describe it. But look, I' I'd love to go back on a couple of the points that you you started with there that I think are super interesting and and and a good way to like sort of get foundational. I completely agree with you on the autonomous versus agentic nomenclature. We also are not big believers in the autonomous use cases at CRIO and that shapes then kind of our worldview of how we think the ecosystem evolves, right? Because we see agentic platforms really more as augmented or assisted, right?

Like the word agentic gets kind of used, it gets used in confusing ways. Sometimes people mean agentic as autonomous. Sometimes people make mean agentic as in augmented or assisted. We definitely subscribe to the latter definition. So like you, we are skeptical of the autonomy use cases. As a side note on that, you you might like this. So, it's become one of my favorite sort of dinner party questions. Like at work events, I'll go around the table and I'll ask people sort of what they will or won't do when it comes to autonomous commerce. And I'll start very deliberately with that autonomy word. And two funny things kind of happen. One, everybody automatically defols to to the toilet paper reordering question, right? It's unbelievable how consistent this is. And then everybody always ignores the autonomous rule of the game and they immediately go

to augmented and assisted and start talking about how they would use agentic to do these things as long as they get to approve the final purchase or the final step or whatever. Like wait, you're not playing the game the way it was set up. But nonetheless, it is remarkably consistent when you ask people their sort of personal, you know, preferences around that. But I agree with you. I'm not a big believer in the autonomous workflow. And again, that that shapes our roadmap, which is how do we surface great products so that people get a better experience because they're still going to have agency in that experience. And also that there will be customer journeys that will continue to touch on multiple formats. They'll land in e-commerce. They'll land on publishers to do deeper research on cited sources. and they'll ultimately do shopping and basket building in brand contextual environments. And so all those things kind of shape our roadmap and how we are moving the business forward. I like that rhetorical trap

there. I'm going to use that because yeah, it's pretty easy to drift outside of the autonomous use case because I think in theory people love the idea of the Jetson's made robot just buying you a bunch of stuff, but yeah, the that there's sort of like a bright red line past toilet paper and paper towels and air freshener refills.
It all sounds good until your small closet in your New York City apartment is overflowing with stuff that you haven't used yet,
right? or you know a new car shows up on your front porch or your your your driveway and uh so I like that I like that rhetorical trap but I think also like the way this is positioned you get to be friends with everybody right another kind of point I made in this piece that I wrote aentic commerce is a is a mirage was that you know if you don't get Amazon on board here it's a non-starter and I mean people push back on that but I I still think it's true I mean it's 25% of US ecom but but in in that sense if you're sort of like Rexis

for everything and in these contexts where traditionally you know Rexis hadn't been applied like the contextual use case in a chatbot well then Amazon could be your friend theoretically anybody could be your friend because they want to have their stuff recommended right is that right am I thinking about that right
yeah yeah that is true that is true and as you think about where where multiple people can be friends you know Amazon's obviously got a great commerce graph but so was critio and they are distinct actually. So our graph has intelligence about people's interactions with products in different environments in different contexts than what Amazon has in their graph. And so those two data sets are the two most powerful commerce data sets in the industry. And yeah, depending on sort of the surface that you're trying to uplevel, they absolutely could sit side by side.

That LTV model you're using to optimize your campaigns, even the most accurate one in the world won't [music] guarantee better performance. Google and Meta run on signals. Developing and deploying them at the right time in the right format for every platform, [music] that's its own discipline. One that has to learn alongside the auction, not behind it. Open Door, SEM Rush, Miro, they all work with Voyantis for exactly this reason. Because the model [music] was never the hard part. It's everything that comes after. Signal engineering, testing, maintenance. If you're ready to close that gap, visit voyantis.ai. [music] That's v o y a ntis.ai. Talk to me about this kind of matchmaker role. If we would move into this agentic purchasing paradigm, how do you maintain the matchmaker role if if there would be agents shopping versus versus the human doing the shop? Does that change at all? Is that a meaningful difference or or no?

Well, I mean, I think it I think I think it depends on the level of autonomy. But in the in the worldview that that we prescribe and I think you could see this in the change that chat GPT made like you said this week to pulling back on the instant checkout platform. Humans want to remain in control of a lot of the purchasing decisions and I think a lot of that's based on the fact that retailers continue to own fulfillment trust brand contextual shopping environments and so with that there are going to be multiple touch points in customer journeys and so you know jumping ahead a little to you know chat GPT's focus on ads and being the discovery surface chat GPD scales discovery CRIO optimizes downstream cross channel conversion. I guess it's kind of the simplest way I've been able to sort of describe the the the partnership there. And just to maybe sort of go more

broadly, even as AI assistants like narrow options, somebody still has to rank products by likelihood to convert, right? Somebody still has to measure outcomes across channels. somebody still has to verify purchases and optimize towards ROAS and and that's our lane right we help determine what actually sells and we help drive and optimize and measure that cross channel
you mentioned that I I write about Rexus a lot and I think one thing that people don't really appreciate is that ads is applied Rexus right I mean it's just a very specific application of Rexus really yeah and that's where a lot of the AI research in the the sort of cutting edge research that's that's happening now and and being monetized. This is what drives me insane is, you know, you read these articles, AI is it's there's this bubble and it's not really, you know, driving any sort of material gains for these companies that are deploying all this money against research. And I'm I'm just like, do you not

do you do do you not understand the ad sp this is everyone is is pointing their their their research bazooka at this because this is where all of the frontier research is yielding these incredible gains.
Yes.
And it's just it's so frustrating because it's like you just just listen to these earnings calls. Where do you think these gains are coming from? They say it it's really frustrating. But talk to me about so this this kind of idea is like well we've got you know some some some big set of candidate things that we could show to this person right and then we've got to whittle that down talk to me about the where maybe the line blurs or where Rexus becomes ads targeting right because I think there's a specific moment where where that distills into ads targeting this is ads target we're targeting ads now but fundamentally this is like a rexis algorithm or you know what this this this system that we built does that at scale. Talk to me about where the baton gets handed off, right? And when that becomes ad targeting.
Yeah. Well, to be clear, for for for us,

there's like 80% overlap between these things because that's how CRIO drives performance outcomes, right? So, it's really a great big prediction engine that's looking for the optimal impressions to serve across whatever channels were pointed at to drive the campaign outcome that that that's in question. And so, you know, we've been sort of doing this for 20 years. It's a it's a little known fact actually. The company was founded 20 years ago. And believe it or not, when CRIO first started, it was a recommendation engine for DVDs, believe it or not. And and they couldn't figure out a great revenue model for that 20 years ago and quickly pivoted towards the retargeting use case on the open web back in 2005, 2006. And that's where the company has its origin. So

back then that was all machine learning. But you could imagine over 20 years like that prediction engine has just gotten bigger and more powerful. We learned how to normalize SKUs, learned how to build up shopper graph intelligence. And so again, the recommendation system or the the commerce recommendation service is just a like a front end on the Rexus system that underlies the whole company. And you know, I mean, I guess if you look at if you really get specific and look at the similarities and differences, they they both rely on intent signals, both use predictive models. They both aim to match demand with supply, but maybe the differences would be traditional ad targeting noncr would be like audience-based. Aentic recommendations are more like product level decisioning inside of a live interaction. I think that's a really key difference. And you know, Critio has always been a lot less about placing an ad impression as it has been trying to

drive to an outcome.
Got it. And and so you know, these things converge kind of depending on the context, right? And you know, obviously like there's tremendous amount of overlap and and a lot of the original research like if you go to, you know, YouTube developed the two towers framework for instance, and that was 2016, right? So that was that predates transformers but that was just for video recommendations and then a lot of the ad stuff got was built on that which is just the kind of dual encoder architecture which is you know become a lot more complex now but so we've got creto is kind of you call it rexus for for all right I mean is is ac the the acronym you're using aentic commerce recommendation service or is that is that a thing is that
you know it's funny we haven't acronymed it it actually still gets spoken to in its full glory but I'm sure it'll find an acronym soon and maybe it will be ACRS. I don't know.
Maybe that's the that's the difference. That's the difference between Rexus and ads targeting. If if you if you if you articulate the whole phrase, the whole name, it's it's Rexus. And if you

acronym it, it's it's ad targeting. But so let's say that ACRS you call it like it's Rexus for anybody. Anybody can get the benefit of that in their own environment, right? In their own in their own product, in their own in their own product context and get these these products recommended to them. Is that kind of how you're viewing this? Is that the how you slotted into the landscape? Yeah. Yeah. So like let's take a couple of examples, right? So like if you were to take travel, like you could ask an assistant for a long weekend in Barcelona, like it could summarize options, but like which flights get surfaced, right? Which hotel, which package is most likely to convert. How is any of that based off of actual price availability or real demand signals? Like that's the same optimization problem that we solve in retail. Or take like restaurants, right? If you ask for the best Italian place nearby, the assistant can list options just by retrieving things across the open web, but like ranking them based on likelihood to book, real-time availability, historical behavior patterns, like that's decisioning and

that's that's Rexus, not sort of semantic or or or you know, real real augmented retrieval augmented generation and so on and so forth, right? And it all comes back to having high quality structured data, real performance signals, um, and then being able to optimize against outcomes at a high scale so that you get data loop learning. And yeah, just to sum it up, like if if there's a decision in structured behind and structured data behind it, like this model can apply to bring relevancy and personalization to any user experience. Can you share examples of companies that that you're working with already like just to just to give a sense of like where where this is being applied? So we've kept a couple of them confidential just just because that's the way that partners have asked us to do that right now. But we are in testing with two large platforms. Those tests keep getting progressively more sophisticated and I think continue to prove the uplift that that that we've shown in our own offline testing. And I think that that partner list will

expand. You know, it could be commerce platforms that are starting to get into, you know, targeted product recommendations and ads like in the say like the post purchase space like checkout that needs a service to like create the right recommendation out of a product catalog or a feed. So, we've got I think a few different paths. Um, but right now it's the two large partners that we're testing with and and we've kept those unnamed for now.
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discount for the first 6 months. Where does this scale to in terms of like product categories beyond I mean ecom is kind of the obvious I mean it's obvious one right but where else does this scale to like what other product categories what are the verticals yeah so I think I think it could go in a lot of directions right like think think about like financial services if somebody is looking for say like best rewards credit card for travel right that's a that's a structured comparison problem you could optimize based on profile intent probability of approve approval or activation and that engine could play a part in that. So yeah, I think there's a couple different examples, but right now it's probably more directed at partners that own sort of high volume use cases where product is essential.
Got it. And then but I mean are you what about the modality? I mean, is are you thinking like web kind of is the starting point or it could be in in consumer apps because I mean I guess

it's it's yeah it would be we wouldn't be tied into any specific format there
is the commonality here because I mean consumer adoption of of your chat bots is is obviously been you know really rapid right and and I think that has become kind of a new functional norm in terms of doing research right I mean that's just that's kind of what you do now you're probably your first stop is it whereas before it would have been just a query based search. Now it's probably more of like a conversational based chatbot experience. And I think you'll see a lot of those those use cases proliferate to be vertical specific, right? So like I you can imagine when you're buying a car going forward, it's going to be through some sort of like conversational interface because like here are my requirements and you know give me give me some options, right? Is that kind of the the interface that you're looking to target? I mean that it's it's it's it really is meant to be this sort of semantic you know chatbased interface that this best accommodates.
Yeah. Exactly. And and the starting point clearly is anything more tied to our current catalog and and shopper

graph, right? Which is going to be more in the kind of fastmoving consumer goods categories. It could scale to automotive and things like that architecturally, but that's obviously not where we've got a lot of our IP currently just based off of the legacy of the business and where we have those sort of high volume of advertising situations.
Right. So, but but the the idea being that kind of broader idea is that like the way I do shopping discovery will be conversational going forward essentially across any category. as his example, but [clears throat]
you're gonna keep and you're going to keep coming back to interfaces that give you high quality recommendations that are complete, accurate, and have intelligence of around likelihood and suitability. When you don't get that, you'll move on to some other experience or service. And so that's, you know, we're we're we're designing this to to uplevel, you know, those types of platforms and where they're competing

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All right. So, kind of shifting gears, talk, talk to me about Commerce Go. What is that? Can you walk me through that? Commerce Go is our new self-service platform that launches at the end of the quarter, so just in a couple of weeks. And what's different about this is it's our it's our first true self-service product for fullfunnel cross channel performance. And really what we've done

is we've made the application like really really simple to focus on the SMB marketplace. And so it's self-registration, super simple, and then it's actually like five clicks to campaign is is sort of the uh tagline that that we help to position it with. You know, a small medium advertiser can be up and running like literally within 15 minutes. Once they get registered, they can pull in creative direct from their domain. We'll auto tag or check for their tagging situation and we can actually do that automatically for them. they put in a budget, a campaign objective, and they can be off and running like that fast. So, I think it takes advantage of a couple of different trends in the marketplace. One, the kind of democratization of SMB advertising. It also takes advantage of the sort of compression or convergence between

performance and brand. And we think there's a real need out there for that SMB segment to get access to cross channel performance which this enables. So it's open web social and eventually we'll have other channels included in it as well. It's been great. We've actually been transitioning our small clients to this for the last 6 months. And what's what's new here in a couple weeks is like a full launch for net new and selfredge. But in the conversions that we've been doing the last six months, we've had great results and we've got three or 4 thousand clients live on on that platform now and they get like 20% higher rorowas. Well over a third of them are taking advantage of the cross channel setup to include social. They churn less, spend more. Um they get better performance. It's a it's a really great product. So we're excited to get to the the the full net new launch here pretty soon. and

then continue to build on that with additional channel expansion over the course of the year.
How how do you manage the the measurement for that?
So that can be through whatever tag management system they're using today. So we can detect like Google Analytics tags as an example and it can just integrate like right into that. And so we've got sort of an auto tagging capability and ability to detect that. So what it does is it takes sort of that technical friction away from a non-technical buyer or whatever extra cycle times that that would typically introduce into coming onto a new platform. So a lot of that's automated behind the scenes and like how how do you see that like so so for like an SMB like how do you see that sliding into the kind of existing suite of like automation tools that they already might be using? Right? So you think about like kind of advantage plus and you think about PMAX. Is this just kind of sits alongside those and covers just the

programmatic inventory. Is that is that kind of how they would approach this?
Well, I think what's different about this and yeah, admittedly there are other products out in the market, but they're almost all dedicated to whatever walled garden they came from. And so what's unique about Go is the cross channel setup and the ability to hold constant performance across. So we think that for marketers that are coming onto platforms for the first time or some that are looking for maybe a simpler cross channel setup, we think that Go will be attractive to them. So I think that the the segment is still expanding and although we're certainly not first to market with this, it is unique in the cross channel nature of it. All right. Can you give me like a picture of the the upcoming road map? Like what what are you working on? I mean, this obviously is coming end of quarter. So, this is sort of the the self-s serve is on on the road map, but what what else like what do you have in store for 2826?
Yeah, sure. There's a lot. I always

break it down into three buckets. Talk about agentic, talk a little bit about performance media, and then retail media. So in agentic and we've not talked about this yet maybe we'll come on to it again but would be scaling our open AI integration that we announced on Monday continue to expand the partner use cases for the reco service and we are in pretty advanced testing with a few of our retailers on conversational shopping experiences shopping bots conversational ads things like that and then we have been deprecating user interfaces across our whole product portfolio in favor of MCP enabled frontends whether that's audience campaign management etc. So the MCPificification, if you will, of our whole suite is is a big agenda item. And then in performance media, we're going

to be moving up funnel and launching a discovery ad product in the second half of the year, hopefully a beta in the first half, and continuing to look at new supply paths that we need stronger access to like CTV. And then in retail media, it's really about scaling like auction based display, shoppable video ads. We just launched conquesting and continuing to bring our retailers new monetization paths and and scaling the demand side. So a great new package rolling out for Commerce Max, the demand side tool for for retail media, new insights package, better cross retailer support, better measurement. So it's busy. like there's a lot going on in the company right now and yeah, we're excited about where it's headed.
I I do want to get to the OpenAI topic. I mean, I know it's it's kind of it's news, right? I know maybe there's not a ton you can share, but maybe before that, can you talk to me about just like the MCPification? Like what what what

motivates that?
It's to make the company more scalable and and to to reduce the reliance on the managed service component. It's as simple as that, right? Like I think managed service just doesn't allow you to scale a platform as as quickly as you would otherwise. And so we think you know automating those things or or in some just making them easier to access and and and not getting hung up in a lot of front-end UI and workflow. the trend in sort of how all that in in like workflow in media planning and activation is towards essentially agent-based or promptbased instruction and and and interaction. So I would say we're just in some ways modernizing the front end of all these tools, but what we'll get is a lower cost to serve and certainly make them more scalable and and that's the direction that the markets move in.
Got it. Yeah. And I think I think it'd be a great place to end just to talk

about your recently announced partnership with with OpenAI. Maybe you could just kind of you know it was it was big news on Monday. We're recording this on Friday. So couple days to digest, but like for for people who haven't heard about it, maybe you could just kind of provide some some sort of like broad strokes outline of of the partnership and what you'll be doing together.
Yeah, sure. So Monday was great. really excited to be open AAI's first advertising technology partner and uh repeating a few things I guess but the integration's live and what it enables is brands can come through CRIO's you know ad platform and and get access to this new contextual ad unit that chat GPT is opening up for their free and go versions and the the the inbound from clients this week has been incredible like just level of interest like nothing I've ever seen. And I think it stems from, you know, people know that this is a powerful surface. You know, our our studies show that the traffic that comes from LLM platforms like Chat GPT, like

they convert at one and a half times the rate of other referral channels. And so it's a it's a no-brainer to get into now a scaled access program through a pretty broad-based platform like CRIO and start to test how that impacts, you know, traffic levels. But we're really excited. We think it really starts to scale this sort of new discovery layer and we share Chat GPD's values and principles around like it's got to be grounded in experiences that are additive, relevant, built on user trust. And I think this first unit that they've rolled out is very true to those principles, but there's like a lot of learning to do and certainly the product is going to scale and evolve pretty rapidly over the course of the year. Yeah. And I just I just checked and as opposed to pretty much the entire market, CO's up today. Today was a kind of a down day broadly, but you know, it's it that's obviously been warmly received the partnership among, you know, other things.
It has. I mean, yeah, it was a good week in in what is kind of an odd market, but

it it also speaks to like broader dynamics. I mean, the the sort of TAM or the market for discoverability is expanding and because of how OpenAI is approaching this, you know, companies like CRIO now have access to that TAM that we didn't have before. So it's it's purely incremental to play in the discovery layer in a way that we couldn't, you know, even two weeks ago. So you know, if market participants are considering that factor when they evaluate us, then, you know, they've got at least part of it right. Michael, this was fantastic. I really appreciate you taking time on a Friday going almost kind of weekend here coming up on 4:00. So I appreciate your time and I appreciate you sharing your road map with the mobile dev memo community.
Thank you, Eric. We're big fans of mobile dev and the podcast. So really honored to be here with you today. Thanks again.
Yeah, cheers. And I appreciate you making this happen. Thanks so much.
Thanks.