The Post-ATT World

Hi everyone.

Welcome to Grey Matter, the podcast from Greylock, where we share stories from company builders and business leaders. I'm Mike DeBeau, partner here today. I'm excited to welcome Matt Osman, Rishabh Jain and Eric Suefert. Guys, I've been really excited for this conversation. I think for those of us who have worked in growth marketing or E commerce, the past couple years have certainly challenged many of our assumptions and earned learnings along the way. For some advertisers, this has unfortunately meant that growing a business is no longer viable and has been catastrophic to some. For other marketing friends, we say things have been getting fun again. So the goal for this conversation is really to help illuminate what is exactly happening in the world of performance marketing with a specific lens around E commerce. Ideally, founders and other builders in the space will walk away with a actionable set of lessons. So it's okay, and this is a warning up front, it's okay to get into the weeds. Our guests today come at this problem from a different set of vantage points. I'll do quick intros and we'll ask you guys to do the same. Eric's an independent analyst, consultant and investor at Heracles Capital. He's really become the go to follow on all things ad tech. We had the pleasure of meeting back in the day when I was at Stitch Fix and he was at Network, working on mobile advertising and Programmatic. Matt Osman's the CEO and co founder at Treat, a Greylock portfolio company using AI to generate, personalize and deploy creative for large E commerches. And Rishabh Jain is the CEO and co founder of Fermont, another Greylock company. Building a distributed commerce network, helping brands drive conversion at the point of discovery. So guys, thank you again for joining. Before jumping in the conversation, could each of you take a minute to share your background and maybe what led you to dedicate this chapter of your lives to this set of problems? We'll start with you, Matt.

This is my second company. My first company was a natural language processing company in sort of the first wave of AI, but specifically in biotech. Biotech document processing. So I kind of got to know the AI lens of this through that and then I got particularly fascinated in the ways that language becomes basically a summoning technology with LLMs and diffusion models and thought there were some very interesting applications in kind of customer acquisition post attention. So it's almost a continuation from the last company from a technological perspective. And yeah, it does feel like building in the age of miracles. I would like there to Be one week where there isn't an industry changing release or announcement because I need a break and it's starting to get quite exhausting.

Rishabh, in addition to the current company, I would say that the lens that I come at this with is basically from a company called Liveramp. So I was there for six and a half years and it was basically the largest provider of digital identity. And so for better or for worse, we knew everything there is to know about online tracking. And so when Apple first made its announcement, we were sitting at Liveramp saying like, holy smokes, Eric Seifert is right. At the time I didn't know him and basically the writing was so clearly on the wall for me starting in the summer of 2020 and then for sure by that December, and I'm sure we'll go through the history, but that's when I basically decided to leave. Liveramp is, you know, this is like a change that is going to affect the entire consumer Internet. But the first place in today's topic is going to be E commerce. Right. And so, yeah, it was just that vantage point of building technology for tracking in the open web that I come to this with.

Yeah. So first, appreciate the invite. It's great to be, to be speaking with such an accomplished and impressive panel today. I come at this as an operator, so I spent my career in performance marketing for mobile apps, mostly games. Kind of grew up in the sort of primordial soup of the App Store and kind of got that sort of front row view of the evolution of Facebook and Google and all of these powerful technologies that were brought to bear in the service of distribution. Right. Distributing apps, getting products, physical goods into people's hands. And so was concerned and troubled when, you know, sort of ATT was announced. Although I think just, just in terms of the impact and the preparedness of companies to adapt to that. Right. And so kind of in contrast to Rishabh's journey, when that light bulb moment happened for me, I didn't start a company. I started a venture fund to invest in the brilliant founders building the tools that would sort of enable companies to adapt to that, like which I'm very pleased to be an investor in.

So let's rewind back to around 2016. You know, Facebook ads were hitting their stride for many of us who were starting to deploy them at scale in E commerce. Shopify was on its rapid ascent kind of on the heels of this. I remember at the time, like the OCPM algorithm and Facebook targeting became so darn good that merchants could really hit scale with very few people in house. And a few years later this all kind of just blew up. Many point to Apple's privacy efforts in catalyzing this wave of change. Eric, you just referenced att. Maybe take a step back. Can you explain to us what happened here?

The best place to Start here is 2017. So it's ITP, right? So Apple introduced Intelligent Tracking Prevention in 2017. That's a Safari browser feature that did a couple of things. But you know, the most prominent privacy safeguard in that was blocking third party cookies. So Apple rolls this out and this game of Whack a Mole ensues. Right. So Facebook, the Facebook pixel, which is the component I think, that allowed Shopify to grow so precipitously and allowed E commerce to grow so precipitously. The Facebook pixel was a third party cookie, Right. That just means it was signed by Facebook but you would embed it in your own website. Right. So it wasn't of the same domain as the website. And what that did was that it just served as a transmission mechanism for conversion artifacts for data about these conversions to flow back. And so when that was blocked in Safari, Facebook said, okay, well we'll just reimagine the pixel or reposition it as a first party pixel. So developers here, you just have to sign it with your domain that's going to have the same sort of functional purpose, but it's going to be signed as your domain. So it's first party and not blocked by itp. And so then what Apple did was they said, okay, we see what you did there. And so they limited the amount of time that a first party cookie could send data back to the true owner for. Right. So it just limited that timeline. And so you know, my sense is what Apple took away from, from this sort of back and forth exchange was that hey, there is no way to fully prevent this behavior through policy. Right. The way to fully prevent this behavior which, which Apple calls tracking, which is kind of mixing the third party and the first party context, it's, it's commingling that data is to just starve these companies of the data. There's no way to sort of ask them to abide by this policy. Unless you remove the data from that transactional frame, then it'll be used.

Yeah. So I guess following on that, like Rishabh, why did most people not see this coming? You were in the ad tech space at the time, like what was going on in the background there?

I think that the way that Eric was describing people's Behavior to ITP is actually a pretty good indicator of what happened with att. So essentially what happened was that people were basically saying, hey, we're going to do all these steps to get around this action that you have taken. Right? And so the same thing happened with ATT between July and December of 2020. So there was this progression basically in that six month time horizon where we went from people believing that actually what was happening is that the specific identifier called IDFA was being turned off. Then a clarification was issued saying, hey, it's any identifier. Then an additional clarification was issued that it's actually a policy statement. And finally Facebook said, hey, you know what, we're actually going to abide by this. Because for a brief period they said like, we're not even going to abide by this. And then it was pretty clear that they needed to abide by it. And so in December of 2020, that's when clarity sort of emerged. However, the big thing that all of Adtech recognized in that moment and it was sort of, it was this strange gentleman's agreement that if we admit that there's no solution to this for the next six months, our stock price is going to tank. That's just true.

Okay.

And I wish there was like another sort of like collegial way of saying this, but this is actually what was true, right? Criteo. I mean the company that I worked at at that time, trade DAs, there's so many companies who have a dependence on this that basically everybody was issuing statements saying, hey, we have a solution or we are working on a solution. And so that led the agencies to saying, hey, don't worry about it, our tech partners have a solution. And that led the brands to saying, hey, we're not worried about it. Our agencies, Facebook, they're all working on a solution. And when you get that comfort that okay, the people who know what they're doing are working on a solution, you basically assume that that's true. And so what was happening is the early part of 2021, everybody assumed that like, hey, we're working on some magic solution. So also we didn't actually know when implementation was going to happen. I should clarify that. So in January actually did not know when implementation was going to happen. And then Apple said, okay, in 14.5, that's when implementation is going to happen. And that was in the summer. But Apple does this thing that every update doesn't necessarily get pushed. So 14.5 was not a push. Update 14.6 was a push update. So there was another head fake where 14.5, again, people thought, okay, it's not as bad as we thought because 14.5 has happened. But actually most cell phones didn't. Update 14.6 was the push. And then finally through the end of the year, then it happened. And so it caught people by surprise because first there was industry storytelling, then there was this notion of like, hey, when is the actual impact going to happen? Is it 14.5, 14.6? And then the last step is there's a 90 day look back window issue. So there's a lookback issue on when is the actual measurement going to happen based on the identifier that's coming in and the impact you're targeting. And so that had yet another 90 day delay. And so by the end of 2021, then it became like all of a sudden people were just like, wait a second, this thing is like absolutely demolishing our campaigns. And the reason you can tell that people did not expect it is there was a precipitous drop in stock prices of publicly traded companies. Right. And you're just like, wait a second, like, shouldn't have this been known? But I guess it's like, you know, there was a series of events that actually prevented people from fully appreciating what impact was actually going to be felt over what was basically a year. One of the most interesting things to watch happen over the course of 2021 is just how it played out in the industry.

Yeah. I mean, for those of us who were sitting on the marketing side of the table, it seems like the response of the ad networks was just to take control away from us. Why was that the case?

I can give what I think the reason is. So at the risk of kind of simplifying too much, what ATT and the things that led up to it I think really have done is moved us from a deterministic world to a probabilistic one. By deterministic I mean that the postback link that Eric described gives you as close to a perfect mapping as you can get on the consumer Internet where you're seeing kind of convergence data that are happening on the brand side and you can pair that with performance on the ad network side and that gives you this kind of great flywheel and allowed Facebook now meta to be kind of this engine now because that's severed functionally and there's been this kind of game of whack a mole leading up to this, we kind of move to a probabilistic world where Honestly, I think the ad networks have made the call that the only people who can really measure correctly or solve attribution or even attempt to are those with very, very large machine learning staffs who understand the space exceptionally well. And so I think what you see on the product side is that the move to in house a lot of marketing activities. So like Advantage plus, which is Meta's rebranded kind of automation suite, pmax on the Google side is an attempt, as you identified, Mike, to take control away from the market to a degree because the ad networks have the resources and let's face it, the economic pain that they need to solve in order to keep their stock prices high. They're highly motivated and with the resources to do it. So at a very high level, I think that shift from the kind of world that we're in is leading to the product decisions that we see from from the networks.

So Matt, you're kind of bringing us to a topic that I wanted to spend time on and we're getting into like the impact section of the conversation topic being attribution here. And look, I'm cognizant. You get four marketers together, you could spend hours just geeking out on attribution. So I want to limit this part of the conversation a little bit, but it is an important topic to get into. So, you know, as you hinted at, ATT moves means that the move to probabilistic attribution is going to become necessary. And in that world, you know, there's much need for like large ML and data. The big question here is do smaller merchants have these tools and understanding, you know, they used to just be able to rely on the Facebook platform to be able to handle measurement as well as like incrementality testing. What's happening right now for smaller merchants.

One of the things that you can obviously see is that there's a gap in the market that opened for like smaller attribution players to emerge. So Triple well and North Beam are probably the most obvious ones that have been pretty successful and I'm not sure would have been successful pre att. I think there is a begrudging acceptance now, at least amongst the brands that we speak to, that there's no one source of truth anymore. And the kind of the big topic is triangulation and using a multitude of different techniques to try and get to the answer.

I think the real question is for small and medium sized merchants, what are they actually doing right now? And actually I think there's two big problems that they're facing. The first Problem is that attribution has become much more difficult because there's signal loss, right? So when Apple said like, hey, you have to get a consumer to opt in, by definition a certain set of those consumers are opting out. And so you no longer have that signal. So you now need to, like Matt has been saying, you need to move to probabilistic models. But the bigger problem has become you cannot scale a given channel as quickly anymore. So it used to be the case that off of just Facebook alone you could build a brand up to like a $30 million a year brand. Now there are brands who are going multi channel at like 5 million, right? And this is actually like a big, big change in the requirements of attribution and the complexity and sophistication of that attribution, right? So I talk to merchants all day who are like, man, if I can just keep my current ROAS return on ad spend and just scale it, I'll be happy, right? But the problem is I'm hitting a wall on Facebook. If I scale any further, then my ROAS goes down. And so I need to go multi channel. The moment you go multi channel, you need to increase your attribution capabilities. And so then they turn. You know, you can use in platform as long as you have like one channel, maybe two channels, but you simply can't once you have multiple channels because then you're competing for that attribution. And so that's the moment where it's become much harder. And candidly, I don't think that there are good tools for small and medium sized businesses. That's why they're using multiple tools, right? I don't know a single merchant who does not triangulate, like Matt was saying, between their ga, their third party attribution tool, whether that be Rockerbox, Northbeam, triple whale, whatever, you know, pick your third party attribution tool at this point and the in platform data and they're trying to co measure that with what's actually written on their Shopify backend or whatever their backend is, right? And so everybody's trying to triangulate across these channels. And there's also a need for better education at this point because like at the risk of getting bashed on Twitter, it's like there's no shortage of opinions on attribution on Twitter either. And so it's like it's getting harder and harder for people to understand, like, hey, what is the best way to triangulate how I actually do attribution for my brand? And there is, there is actually still a gap on tooling and on education. In my opinion.

What Facebook brought to bear for advertisers, right, was that that sort of like a priori knowledge of, of any given person's like predilection to engage with certain types of content, right? Like they did personalization for ads, right? Using third party data to like the same degree of efficiency as they do personalization for the in product feed, right? And I mean you can see like how well reels is done is just as a result of that, of that technology, right? In certain senses that was problematic, right? So especially in the D2C landscape, what that enabled was was this like false belief that. Okay, well, this incredibly quick path to TAM meant the TAM was very large, right? So like to your point, Rashad, like you, you could, you could scale a DTC brand to like maybe not 30 million, but like, I don't know, a million in revenue or something in the first year. And that velocity of growth led you to believe, well, that chart continues to go up and to the right for a really long time, right? And so I'm going to raise money on that basis. And the reality was you had already started to saturate like that was your total tam. It just, it just explored that very, very quickly, right? Like I wrote a piece about this in, in 2017 called the power and Peril of Facebook's Advertising Platform. And there were like these stories that were sort of starting to emerge at that time, like these DTC brands that raised money on the back of that, like very early success because they extrapolated that growth out, right, to you know, year two, year three, year four, when in reality they had already exhausted like the sort of entirety of that, of that tam, right? But to your point about like why this is challenging. So like, you know, if you're using multiple data sets, right? I'm using like on platform data set which is just sort of on platform clicks basically and all sort of like ad engagement data. And then I've got, you know, some sort of attribution system that's making available like the kind of purchase data. And, and then I've got on top of that probably some sort of like probabilistic model, like an MMM or sort of incrementality measurement tool that's trying to tell me like, well, within the context of this historical spend, how much conversion effect do we think we can assign to each channel, right? Like that is out of question for all, but like the largest advertiser having that system and then the expertise in house to like, to like interpret it and to integrate it like that is just not accessible to the vast majority of advertising clients that previously didn't need any of that. Right. Because it was either all deterministic or they were so relying on Facebook that they didn't really need to explore any other channel.

Eric's absolutely right that there's this now like sophistication mismatch. Brands doing 2 million bucks a year having to behave as if they were doing 100 million in terms of like the sophistication, but with exactly the same like headcount and level of funding as before, which causes a lot of issues as you can imagine.

So if you're a smaller brand here, like what do you do? And I'll share, I think as you alluded to earlier, I think there's some of its in house capabilities which can be solvable by software, but there's also a scale of data problem. And like I go back to my time at Stitch Fix, like, you know, we evolved over time as we started to spend more going from a couple million to 100 million plus a year on ads, moving from a simplistic kind of last touch to some kind of version of multi touch, which was probably false precision eventually to incrementality being our North Star. And then we would kind of like calibrate last touch models to incrementality multipliers, which was actually like quite, quite useful. But I recognized that was a privilege of scale. Like what do you do right now if you're a subscale brand, even if you have the capabilities, you just don't have the data. Right. Like what's the optimal method for a sub $10 million brand?

My candid point of view on this is like if you're sub 10, try not to diversify channels as the way you get some level of scale, try to find the one or two channels. Because the truth of the matter is that there are certain approaches that are only possible when you do have scale. Not only because of financial constraints but also just because of data constraints, like you're saying. Right. And so I actually do think, and most of the successful brands who I have seen grow very quickly, they have found the one or two channels that they're able to scale and that they're pretty disciplined about being not exploring additional channels. They don't like take the wall that they are hitting for granted. They're like, okay, I need to now ask questions around, hey, how do I actually make the most out of this channel before I start expanding to other channels? And so like, my best advice and what I have seen work with other small brands is actually spend the time to really build out your understanding of the one or two channels that are working for you. The thing that has become very popular recently as a consequence of this is retail, right? Like the channel that people have like the highest level of quote unquote certainty around is just selling through retail.

Right.

And so like the way that this is exhibiting its behavior in practice is like more and more D2C brands early on are establishing a retail presence in addition to their D2C presence. And so that's what I'm starting to see in terms of like how you actually approach this problem and what do you actually do in practice to get past basically like the 10 to 15 million dollars mark?

Yeah, for sure. I mean, I think as an investor now, like as well, seeing many both brands go into wholesale early, much earlier in their journey than they would have otherwise, as you alluded to. And also solutions that are almost like as easy it was to spin up a Shopify brand, getting into wholesale kind of, you know, can be just as easy. Fair being a good example, right. Of kind of one of those flavors. We're kind of moving into the third part of the conversation which is on, you know, new solutions that are emerging from all this. And so just to frame it, it feels like in the Facebook heyday there was an optimal strategy just to go open targeting, let the OCPM algo do its work, just feed a high volume of great creative and great data pipelines, good measurement and kind of, you know, just let things happen. You actually needed very few heads to do things kind of in a best in class way. This is starting to change, right? And so to me this signals opportunity both to be a great kind of savvy marketer, but also if you're building software, there's kind of a new paradigm to work within here. Let's talk about some of the new methods emerging and where software builders such as yourselves are poised to benefit. Maybe let's start with some of the stuff going on in the Shopify world. So Matt, I'm calling on you because we've talked about this specifically a bunch. Like, you know, there's clearly a huge effort on them building out their audiences product now and helping merchants with TR generation, which is historically a problem they've like stayed out of. As they evolve to a model that looks kind of, dare I say, kind of marketplace esque. What are some of the downstream effects and opportunities there?

First off, I think that the solution that Shopify has come up with here is actually pretty elegant from a technical and strategic perspective in terms of kind of helping their users achieve their goals because obviously they're highly incentivized as well to try and solve exploding CAC problems. We are starting to hear that Shopify audiences is beginning to show early promising results. And I know they're betting on it pretty significantly. It's come up a fair amount in their recent disclosures and calls with analysts that I know has been covered by Eric, amongst others, but it kind of does. It strikes the balance of being, I think, compliant with ATT both in spirit and in practice. Maybe there's a debate about the spirit, but certainly in practice, and obviously it solves a pretty important problem, which is that Shopify really can't be using sites of brands that run on Shopify as inventory. That would not be acceptable to nearly any brand that I know. And what they've managed to do is to monetize in a pretty high margin way for Shopify as a business like first party data, they have access to. And I think we know that it's considered to be first party based on what Apple said. Although it's sometimes difficult to pass the tea leaves.

Yeah, so you're hitting on maybe more of a meta topic on brands better leveraging their first party data. What are other novel approaches that any of you guys are seeing here? And is that something you think people should be even more strategic about than they are right now?

I mean, so what you've seen happen. And you know, to Matt's point, Shopify kind of exists. It was basically like a middleman. Right. They allowed you to operate the storefront and then they could, you know, process the, the transactions and they have the transaction data. And so that data's data is theirs. Right. In the same way that the itunes data is Apple's.

Right.

Apple doesn't run the apps that you, you know, make purchases in, but it gets first party privileges to the data because they run itunes. Well, Shopify can say the same thing. So I think the sort of compliance argument from that angle, I think it's fine. But you know, it doesn't itself operate a storefront on which it can run ads. And I, you know, to Matt's point, I think it would be unacceptable to the retailers if it tried to do that on their websites. Right. But a lot of companies do, they do operate the storefront. Amazon, Walmart. Walmart said it expects to get more profit from its advertising business than any other part of its business this year. Right. And Walmart's advertising revenue went from 2 billion in 2021 to 2.4 billion in 2022. Right. I mean, that's, that's incredible growth. So what I think we're seeing now is this explosion of what are known as retail media networks. Right? Because if I can't go to the everything store for ads, which was Facebook, Facebook was the everything store for ads. You want to sell anything, you go to Facebook and put your ads there. Well, now you go to the stores for those specific things that are contextually relevant with the stuff that you want to sell, right. Or where the transaction happens. And so, you know, if you can't facilitate really targeted direct response display ads, well, then you go to the point of purchase and you just try to stick an ad in front of someone's face right before they're about to buy something. Right. And then just hope they choose you. And you know, you can make the arguments about whether that's incremental or not. I think a lot of the people that are, you know, you know, that have, that have dove headfirst into these networks, they probably are capable of doing incrementality measurement and like, let's hope that they are and they're actually getting incremental benefit from that. But I think that's behind the dynamic of what I call the everything is an ad network phenomenon, right? Everything becoming an ad network. Because, well, Facebook's not the everything store now. And hey, that's an opportunity for me to spin up pretty meaningful super high margin revenue.

This is a topic I want to stay on. And Eric, you know, I know you've written about, this is one of the more interesting areas that I think is going on around ad tech right now. And even like if I date back to you brought up Walmart as an example, but if I go back to 2017, it was amazing to me that Amazon could just flip the switch on a new business line and like in its first year be at 2 billion, then 10 billion in second year and now like whatever it was 38 billion last year. And so, you know, there's one thread of conversation which is shit. You know, media buying on these plays is very like antiquated, yet they're still able to get scale. What opportunity is there around that?

I mean, that's a great question. I think any marketplace, app, platform that has a lot of audience data is in a position to capitalize on that now, right? And they already own the data. Spinning up the ad tech is actually not that complex. Ad tech people like to Sort of, you know, claim credit for like exploring this like incredible innovation. But the reality is it's mostly commodity tech. Right. Like the value is the data and how you extract and how you extract, you know, insights from that. Right. But if you have a lot of first party audience data, you can utilize that right now. And you don't necessarily even need to build your own ad tech. Yeah, you could just monetize the audience data. That's a lot of these like so called retail media networks don't actually have any impressions that they sell. They just partner with DSPs to make their audiences available for buyers that are similar, that are exploring customers in that space. Right. So that, I mean they're, they're literally just handing data over to a DSP and allowing that to be purchased against. So I mean, but any, I mean like, and you've seen this happen, I mean Uber just the end of last year talked about this new advertising initiative. You know, Instacart is expanded, has been expanding their ad platform for a really long time. But I mean you could look at companies like Ulta Beauty. I mean they don't have that many audience profiles, but it's enough to where if you're selling beauty products, you want to activate those in an open programmatic environment. So it's like a list is literally as expansive as every company with a decent size set of audience profiles.

One interesting thing you said is the technology to actually launch your own kind of retail media network is actually not super complicated. And I'm curious for Matt and Rashab if you have a point of view on that as well.

I have just a quick story about like one of our customers interacting with a retail ad network that kind of touches on both the current state of play and also the first party data. So this company realized based on image data. So like who is responding to what creatives that they probably had people who were like really into golf and they didn't sell a golf product, it was an apparel company, they didn't sell anything involved in golf. So they went to Dick's Sporting Goods and said, okay, we want to advertise against your ad network. At the time, this was a few months ago. I think the sophistication of the buying process was a little bit steak dinner, Madison Avenue, 1970s. But it's getting significantly more sophisticated because there are some kind of like third party things that you can spin up.

Yeah. All right, I want to talk about your companies a bit. So Fermat is kind of indicative of a broader paradigm of distributing through content Maybe to get into Fermat Rishabh, take a step back and explain maybe the evolution of influencer marketing.

Yeah, for sure. So like I was sharing at the start, the reason to start the company was actually because of the problem statement that you can no longer track people from one website to another. And so the question that we started with was what does commerce need to look like if you can no longer track people? So let's just pretend no matter what you do, there's no tracking. Basically somehow just the evidence of a transaction has to somehow tell you something about where the person came from. The only way that you could accomplish that is that every store is one to one with every piece of content that you launch. Right? That's, that's like sort of the most extreme version of reality is like, hey, if you launch an ad with this creative, it points you to this store. If you launch an ad with this other creative, it points you to another store, so on and so forth. And so there's a unique full store for every piece of creative that you launched. And then that was basically where we started. We said, hey, why can't we do this? Is it like actually that hard to do? It's work, but not super hard, right? So that's actually what we ended up doing is we ended up making it possible for a brand to launch a unique store for every piece of content that they launch. And at the time the thesis was, okay, the most engaging content right now that is actually attracting buyers to D2C brands is creator content, UGC content, this type of content. And so basically what we did was we said, hey, why don't we create a mechanism to make it basically trivial to have a store that makes it feel native to that type of content and have the consumer feel like they're engaging with that content all the way through to the transaction. And importantly, on the back end, if the transaction happened, you know with certainty that it came from that particular ad and there's no need to track in the first place. Right? And so that was the basic idea and that's sort of like where we're going. And it turns out that when you do this, not only are there tracking benefits, but there's also conversion benefits because people actually like the fact that they're in a shopping experience that's native to the content. But yeah, the entire reason for the company to be is because especially with the third party cookie going away, once we get into next year, you're going to have to have systems that are self contained and closed loop, right? That also, by the way, just to very quickly talk on the previous topic, that's also what makes me super optimistic about retail media networks is because it's an entirely closed loop system. The question is how do you make more closed loop systems available on the open web? And we're just going to be a participant in that.

One issue I've had with some assumptions around different, for lack of a better term, influencer marketing plays I've seen is they neglect the fact that influencers are also subject to the same headwinds that other brands are facing on kind of hitting their full extent of their reach. And so there was a world where actually organic distribution was a real thing there. Now it's a very small sliver of the distribution that one actually earns as a creator. Like, I don't know, how does that factor into your thinking and building?

So the two main plays that we end up getting used for with our customers, one is basically UGC style content or creator content or influencer content, and then the other one is branded content. So within that influencer world at this point, everybody basically does either what's called branded content ads on Facebook, which the previous version of was whitelisting, and then it's had many name changes ever since. And so they do branded content ads or they do Spark ads on TikTok, which is basically the same thing. It's like you're paying for reach. And the reason you have to do this is there's no other way to get scale basically. Right. Like the number one problem if we were to just sort of quote UNQUOTE Rewind to 30 minutes ago, like Brand's biggest problem right now is, hey, how do I get scale in my business? And if organic reach is also not possible anymore. Right. Because of changes in the algorithm and the actual ad networks needing to monetize more, then you have no choice but to scale your influencer content through these sort of like VCA or Spark ads.

Yeah.

All right, so I want to ask Matt and Eric about this topic of creative and specifically AI and its role in all this stuff as well. So Matt, you wrote a piece recently that kind of underscores a lot of our core beliefs with Treat. It was called the Coming Generative AI, Arms Race and Ecom. So let's go here for a few minutes. I mean, I think most can agree that image models are going to give designers superpowers. There's a bunch of new apps being marketed to marketers. Given it's such an obvious kind of use case with a quantifiable ROI of all this stuff. What are your guys view on how marketers should sift through the massive influx of tools being marketed to them here?

My view generally is that with any new technology like generative AI you have to think about whether the opportunity is going to lie with the incumbents or with the startups. And kind of our thesis is that a lot of tooling around generative AI is going to end up in kind of incumbent design tools that is going to make the production part of creative much easier. You saw Adobe's Firefly suite was launched yesterday. Figma is going to have stable diffusion natively. So you're probably not going to change designers workflows. They're just going to be able to use that technology and the tools that they already use. Where I think that generative AI is really going to be powerful is in the kind of the ideation phase and this is borrowing from a framework that Eric has written about and I think it's like completely on the money which is that's kind of where the bottleneck is in terms of like getting high performing creative. Particularly if creative becomes one of the primary targeting levers. Like you can start to do a kind of targeting by doing like rapid creative variant testing. And as the cost of content creation, both images and copy kind of tends asymptotically to zero, which is basically what we're riding right now, the value of knowing what to create and why increases proportionally in the opposite direction and it gets super, super important. And so I think we're about to go into this world of like content abundance. And the tools that are most exciting really and if I remarked I'd be evaluating are the ones that help me understand like what creative concepts and visual elements to be pumping into these systems that kind of like just before the prompt level for folks who kind of played around with these tools and not in the, in the kind of the pure play production process because I think that will just end up in Photoshop and figma and in the tools that the design creative teams are already using.

Yeah, I think just to expand on that, Canva announced a suite of AI supported tools. I think broadly my sense is that you know, generative AI actually underscores and highlights the value of human ingenuity, human creativity.

Right.

I wrote a piece about this recently called Exoskeletons Not Cyborgs. Right. And I think the beauty of these tools is you know, exactly to Matt's point, like as just content gets commodified. Right. And the value approach is zero. You see that the true sort of like source of value generation is in the creative input. Right? Is the thing that separates the human element from the machine element. And so my sense is that, you know, marketing teams adopt these kinds of tools sort of like most efficiently and effectively as efficiency unlocks for individual members of the team. Right. Like, there are always a lot of risks with total marketing automation, you know, even prior to generative AI. And my sense is, first of all, you can't just hand over the ad production process to Facebook or to Google. You can't do that.

Right.

Their incentives are different from yours. Right. And you will see that, you know, they were, they will very quickly iterate themselves into ads that, that may not be in, that may be totally inconsistent with your sort of like brand guidelines, brand standards. Right? You just, you can't outsource that. You have to own it.

Right.

Or you have to use a third party tool that integrates with them. Right. I've just seen a lot of people immediately jump to this sort of like logical endpoint of generative AI allows us to completely automate everything. And I think that's a mistake. I think these things still need to be somewhat siloed and integrated. Right. But with human oversight. And my sense is like, with ad creative, like Mike, you're exactly right. Like that is probably the most obvious commercial use case for generative AI. And I would just be very, very careful about a, outsourcing that to any sort of third party that also incorporates that into the media buying process because their incentives are not aligned with yours. Now you can use a third party tool for generative AI creative. I think that makes a lot of sense, but I want to anchor that into the actual ad network itself. And then the other thing is, I think it's just going to showcase exactly how valuable creative directors, heads of ad creative, right. How valuable they were in this process and how sort of like competitively advantageous a really good one is.

Yeah, I completely, I completely agree with that. And I think also just to piggyback off that and maybe take it one step further is that, and again, this is going to sound like such a love, and I'm just going to quote Eric one of his posts, but the opportunity here really is to take the intuition out of the ad creative production process and allow everyone to sort of specialize in what they're great at. So like, I'll give you an example. Like we build a tool called, called Lookalike Creative that will basically take every single image that's ever been run by A brand, including statics, video, including copy and pull out all of the visual elements using like a bunch of tagging and captioning techniques and stuff to identify patterns and correlate that with goals that you care about or demographics that you care about reaching. So it might be that images with beaches in tend to perform very well with women 18 to 25 living in the Midwest, for example. And one of the things that we found is that the model is like particularly good at identifying something. It's just very hard for a human eye to perceive, which was that we were working with a beauty brand and there was one specific kind of marble bathroom and it was a specific kind of marble and it just happened to be like really, really eye catching for the machine. That was like highly correlated with ctr, which was the thing that they were trying to optimize for. And armed with that, the creative team can kind of do its magic and like focus on the stuff that they're good at. But the pattern matching across I think this was like across 15,000 creatives. That's just not something a human really should be either manually tagging or reviewing themselves. That should be kind of outsourced. So I view as basically being a little bit at a crossroads where we're going to allow the growth marketers and the design team to specialize in what they're great at. Put on the plate of the machine some of the tasks where machines are just clearly stronger, potentially layer on things. You could think about layering on some kind of data co ops as well that would allow brands in a similar way to an agency has kind of eyes on accounts. You could allow people to opt in to share visual elements that are working for me with other brands to create some kind of shared intelligence. But the value I think is going to be in that specialization.

We certainly had this meeting, it was a weekly creative growth sync where it was delivering kind of insights from all the creative that whatever these five designers were producing and saying, here's the stuff that worked, here's what didn't. People actually love tying their efforts to business metrics, which was oftentimes not the case beforehand. I think the optimal amount of human in the loop is not zero. You could probably make that process a lot more efficient by what Treat and others are working on here.

Yeah, absolutely. And I think really understanding the why behind visual elements and why they're load bearing or not and then also being able to rapidly create tests at scale that hit multiple different goals or multiple different demographic groups because time was a constraint for content Creation that's not going to be as real a constraint anymore. And the other point that Eric made I think is important, which is that I have heard some kind of marketers say, well, you know, well surely this will just be done by Meta because they have the best generative AI people on the planet. And this is kind of be rolled into the tooling that Meta has. And I think that's a critical error because as Eric mentioned, if you're a DTC brand or an E Comm brand, your brand itself, as in like your brand identity, is one of your core pieces of ip. It's not strictly ip, but like one of your kind of core pieces of value. And to outsource that would be like pretty foolhardy or like very brave depending on how you thought about it. Particularly given the like the incentive mismatch that's been identified. And you kind of see that strange creative race at the bottom that we saw in like some mobile gaming creative strategies caused by that exact effect. And most of the brands that I know would not allow people to do that. So there's this, there's this interesting window where I think a lot of folks looking at the market, investors, operators, people who are kind of just generally interested, looked at the last wave of kind of ad tech and what tended to happen was like a sort of reverse whack a mole or maybe it was kind of whack a mole which was that a company would be launched, it would have an interesting lens on improving the media buying experience or some aspect of paid media. And what would happen is that the social, the ad networks would incorporate that functionality, they'd kind of build it out and a lot of like very well funded venture company, you know, venture backed companies got kind of destroyed that way. There's a unique spot right now, I think where there are some things that the ad networks just can't build and wouldn't be allowed to even if they could. And so it's slightly, I hate to say it different this time, but it is slightly different this time for some of these tools because of the constraints around ATT and kind of like brand identity.

Let's maybe move to one last loaded marketing term and then we could ask a set of closing questions. But personalization, right? So I think marketers have been talking about this for years. I ran performance marketing for a startup that was supposed to be a personalization company. I don't think we nailed it. There's a bunch of different reasons why now might be different, I guess, and I understand it's A broad topic, but Eric, I'll direct it to you to start, like, how should people be thinking about personalization? Given there's much new promise in this new world of ability to get more precision on this, how should folks think about this topic?

I approach this from the perspective that we've been talking about with respect to how Facebook did that with two different use cases, right? It was the ads use case and the content use case. Right. So the ads use case, what broke that? Why can't they personalize the ads in your feedback with the same level of efficiency as they could before? Well, because the feedback loop is broken. They don't get the, the data that drives that decision making was not theirs. It was generated by a third party and they was, it was being sent to them in sort of like an unfettered way. And that's no longer the case. Right. The reason that the, the personalization in the feed didn't break is because that's their data. It's in a closed loop. What about the retail media networks? It's a closed loop and that just gives you first party access to the data and you can use it as an input to the personalization. Well, okay, so me as a, as a DTC marketer, an E Comm marketer, right? I can no longer send my conversion data back to these platforms. They can't use it to personalize ads in the, in the feed or wherever the placement exists. Right? But I can do the personalization on my website or in my app, right? I have all that data. That's a closed loop to me. And so what I should be thinking about is okay, CAC has gone up and there's no avoiding that. There's no getting back to where we were. There has been a permanent dislocation to this market. We're never going back. The targeting will never be the same.

Right.

Okay, so once you accept that, then the CAC for call it a relevant user, a high quality user is higher. It's systemically higher. Right. Well, how do I attack that problem? I raise my ltv. Right. So I'm no longer personalizing the ads or I'm no longer personalizing the experience of the ads layer and bringing the person into a universally consistent product experience. I need to have more generic ads, less relevant ads. And that's by that, that's out of my control. That's a privacy thing. And then I need to sort of personalize the in product experience such that I'm driving up aov, I'm driving up ltv. I'm driving up conversion rate such that, you know, I'm matching that increased CAC with increased ltv. And that's where I think personalization comes into this, this new dynamic. It's I need to ingest that into the product experience. Super serve the good users when I acquire them for more money such that they spend more in my product.

Yeah, there's a great opportunity to plug two portfolio founders who aren't here builder that does on site personalization then postscript on kind of downstream SMS marketing as well. So yeah, I definitely believe like if you follow on this logic, like there is a thesis that brands should be investing a lot more in kind of like downstream both conversion and retention. So there's obviously been a rich conversation on a bunch of different opportunities that are emerging from this chaos here. The headwinds are still very real for advertisers. Right. But I are on the side as an investor of being excited about new platforms and business models that can emerge. Is now a time for folks to get more optimistic on ad tech as a category. And I think a lot of investors over the years have kind of underestimated how massive some of the outcomes were. I referenced the trade desk, but for every one of those you have dozens that were crushed by platform risk or something else existential. How do you guys think about opportunity here as mainly through an investor lens but also as entrepreneurs on kind of the broader topic of ad tech?

I would think that there's basically like a couple of very interesting opportunities. So the first one, which we haven't spent a lot of time on is like what's going to happen when the third party cookie goes away from Chrome? Because basically ITP took it away from Safari in 2017 and we actually know we can monetarily ascribe the value of that loss because every ad tech platform gets 30 cents on the dollar for Safari versus Chrome. So like you kind of have a pretty good long set of data to like describe how impactful the loss of the cookie is going to be for Chrome. Right. So basically your ad effectiveness is going to drop 70%. That's a lot. Right. And so I would say that like the very first thing in terms of opportunities, especially with AI and machine learning models getting better, is hey, what are things that are totally cookie independent in terms of serving relevant data that you can possibly start to train on right now while the cookies still exist for the next nine months? So if it were me, I would just be like, hey, who's out there getting as much signal as possible to train the Best possible models such that they're strategically advantaged at the end of the year when this thing gets turned off. The second thing that I would be doing is I would be asking the question we were talking earlier about like all these retail media networks that are popping up everywhere. It's actually non trivial for a brand to plug into one of these. So let's just say I come to you and you have a retail media network, Mike. And I'm like, hey Mike, I want to plug into the Mike retail media network. And you're like great. It'll take you two months and $50,000, right? This is like sort of the average conversation that a brand will have with the retail media network. And I'm like man, that's a lot. And then I have to go to Eric's retail media network and this is like extremely irritating. And so the second opportunity I would think is somebody who makes it possible for these mid sized brands to actually kind of very easily plug in to these retail media networks, which is very different than just plugging into a miracle and then having your product feed go to a retailer. It's not the same thing to buy media. And so I would say that those are the two opportunities that I would be thinking about if I were an investor.

I think this is a new golden era of ad tech. I think we're going to see many companies that build the tech to fill the gaps that have been left in the wake of ATT and that are going to continue to widen with future sort of like privacy disruptions. It's not just att. ITP was the tip of the spear. Apple's been ahead on this, but anybody who was reading the tea leaves knew something like this was going to happen. And when it happens with regulation, it's going to be more far reaching and probably more disruptive. Right. So I mean I don't know if you saw that the TikTok hearing, but I mean that to me is that's going to presage a national privacy bill. We have no idea what's, I mean it might look like the one that's on the table, it might be different. But, but anyway that everyone is going to have to adapt to this and it's just going to absolutely change the status quo. And I think many new ad tech companies will emerge to service advertiser needs. Like there's been no demand destruction here. That's the good news. The good news is people still want to buy stuff, Right? So companies will emerge to facilitate that.

Yeah, I agree. I mean I Think people are just going to have to basically rebuild their entire ad tech stack from the ground up. Because if you think about it, Facebook was doing. We're singling out Facebook, but I mean, for obvious reasons, but you know, they were doing so many jobs, right? They were the everything store for ads. They also basically took on an agency role in part of their automation tools. Like, they really built out their tooling pretty successfully. So I think there's a huge opportunity caused by this dislocation. And I also, I think that one of the things that if I were an investor and an operator, I'd be looking at is what are the second order effects of content being free? So the thing about the Internet that was extraordinary and broke a lot of people's brains at the time who were trying to apply old school business models to what would happen on the Internet is that distribution was free. And so all the case studies that you would have at Harvard Business School and Weber would be like, oh, it's like a newspaper, but on the Internet. Oh, it's going to be classifieds, but on the Internet we have a web page. Now everyone has a web page. And then some weirdness really happens when distribution goes asymptotically to zero. And if the same thing is about to happen to content, broadly images and copy, some weirdness is about to happen. And that's a good place to make money sometimes. Yeah.

All right, so my final question, and it's really an extension of this, is we've kind of been deep in the weeds today. Some stuff grim, some exciting, some tactical. For the closing question, and I'm leaving this intentionally vague, what's one idea that you're most optimistic about in the years ahead around all this stuff?

Matt, you gotta go, man. I feel so inspired. After the way you close the last one, it's like you got.

That was going to be my closing thing. The one thing I'll just say is like, I'm obviously very in the weeds in like image generation, more so than copy generation. And I have consistently been amazed at the pace at which things move. There are things that were on our product roadmap where I sat down with our head of machine learning and said, yeah, we should think about getting to that, you know, in a few months time and then someone will do it on Reddit. So I think we're very close. A tactical instantiation of generative AI technology that's going to come very soon, like much sooner than I thought. Is short form video, like text to short form video is much, much closer than we thought like I thought it was a year away, I give it two months at this pace. The runways demo yesterday kind of blew my mind and I'm just dealing with the ramifications. So I'm like broadly exceptionally positive on a bunch of different tools for creation and now we need to probably have better filters and better upstream tools to work out what to create.

I actually got asked a question on Monday. So we do a meeting every Monday where we try to zoom out a little bit and ask questions about the business. And somebody on my team asked me, he's like, hey, can we take an inventory of where all in the business we're using AI and I'm just going to try to keep this relevant for ecom operators, but I'll just share the personal anecdote. I had never done this before and I'm glad that this teammate of mine asked us and I was just shocked at all of the places where our workflows were already impacted. So our sales team was already using AI for call note generation, our marketing team was already using ar, engineering team was already using AI product. It was just everywhere in our workflows and I could see the pace with which it was improving our operating velocity. And so like one of the things if I were an E comm operator that I would be asking is yes, you should be thinking about it in the context of your ad creative, like a specific workflow. But really something to really be thinking about is where are all the non obvious places in my business workflow that it could impact the way that I do my work? And ask the question, hey, should I just be using some of these tools to improve my margins? Because basically yes, look, CAC has been permanently impacted. And so the next set of questions in order to actually get strong EBITDA as you're growing is how do you actually improve the overall efficiency of the organization? And it's worth inspecting and asking where are all the places that I can create leverage in the organization by using some of these tools? I think that's a like I was shocked when I took inventory on Monday and so I think, you know, it could surprise people as they do this.

Yeah, I would say that. So if you think about ad tech, there's kind of three buckets of use cases, right? So there's targeting, there's measurement and then there's ad serving.

Right.

The ad serving I talked about, that's pretty trivial targeting, permanently degraded, not much you can do about it. Right? Measurement. Where I'm optimistic is that there will be technologies, tools, methodologies that are probabilistic, that emerge, that reach the same level of performance, of efficiency, of precision as we had in the deterministic environment. Right. I'm hopeful that that happens. I'm optimistic that that will happen. Right. And that solves a lot of problems. That solves a lot of problems that were masked in the previous environment where everyone thought this was totally deterministic. It wasn't. Right. And so I think if we actually invest more into those methods, into those tools, into those technologies, we'll actually be superior in some ways to than what we had before. Another targeting, again, can't do anything about it. But the measurement side, I do think I'm very optimistic about the things that can be achieved with that.

Thank you guys so much. It's been a great conversation. We appreciate it.