Meta’s Andromeda: The Biggest Shift in Facebook Ads Since iOS14

[00:00:00] Richard Gaffin: Hey everyone. Welcome to the Ecommerce Playbook Podcast. I'm your host, Richard Gaffin, Director of Digital Product Strategy here at Common Thread Collective, and I'm joined on this Monday afternoon as I often am by Mr. Taylor Holiday, our CEO here at Common Thread. Taylor, what's going on today?

[00:00:16] Taylor Holiday: Well, I think this is a pretty big week for CTC and content, CTC content as it relates to. I'd say of the things that we are most well known for, I'd like to put like marketing and finance connection up there, but whether I like it or not, I would say that we have been deeply integrated into a very specific form of media buying as it relates to cost caps in particular.

A lot of my internet arguments over the last few years have been related to this topic. We're not here to abandon that entirely, but I would say that we are undergoing the largest fundamental shift in the way that we think about and structure meta ad accounts that we've had at a long, long time, and I'm excited to chat about it today.

[00:00:56] Richard Gaffin: Yeah, so the context for this, as I'm sure most of you are aware, is meta rolling out Andromeda, which is sort of the next gen of advantage plus automation that incorporates AI into into kind of the algorithm or, or sort of the general way it works. And so I, I think what we wanna talk to through today is a kind of what this is, what it means, and then what the implications are, particularly for the way that we think about.

Campaign structure, general media buying, all that type of thing. And I, I should note here up front that we're putting out a video on Friday, another Sharpen Your Skills, featuring your friend and mine, Taylor, about this specific topic that'll go a little bit more in depth, but we wanna kind of tease that today and give a little bit of a summary about what's coming down the pike.

So let's let's get into it. Taylor, talk to us a little bit about what Andromeda entails, sort of generally.

[00:01:43] Taylor Holiday: Yeah. So I'm gonna cover a, a few things including Andromeda today that I think are. Real changes to the way that Meta is designing their ad system to serve its advertising partners. And how then in light of that, we are restructuring the way that we think about designing ad accounts to align with those changes. And unsurprisingly, the biggest piece of this to understand comes from changes as it relates to Meta's relentless focus on ai. So for many people, I think the important thing to understand about what AI offers is just insane amounts of computational power packed into individual applications of elements.

So, like I imagine, I. When you sort of query a question on Google that what is happening historically if you search for something is that you're getting an index presented back to you, right? This list of things that match your query based on a. Contextual data elements contained within different websites on the internet.

And Google did this monumental task of sort of like contextualizing the entire internet, indexing it and providing it back to you in the, you know, the 10 blue links, right? The very famous thing. But what AI really is, is just a massive step function change of not just presenting you back the choices, but actually consuming all of the material of the world and synthesizing it down for you, right?

And so you can imagine. Just like a, this way to sort of think about computational power. Sometimes I like to just think about like the illustration of, let's use Richard's books behind him,

right? The amount of mental energy and time that we go into. Richard reading all of those books, consuming the information and responding back to a question about which one of them is best, would just take a monumental amount of time and brain power. But what AI is functionally doing is compressing all of that down to an instant, right? And so when you hear these stories about these clusters of giant compute centers that all of the magnificent seven, the Metas and the Googles, and the the, the X and all the big businesses in the world are building these giant data centers to house. These Nvidia super clusters, it's all about this computational power to consume mass amounts of information and instantly and quickly synthesize it and deploy it on behalf of you, the user in the case of chat, GBT, or in our case, you the advertiser. And so what Meta is doing with this product, which is something that Meta is so good at, is that they're, they're productizing technology in a way that allows us to enter into it.

So Andromeda. Think of it as a marketing ploy.

It's a description that allows advertisers to begin to understand what all this underlying technological infrastructure investment is gonna do on their behalf. And so it's really important. I, I would say if you're in e-commerce, you have to understand how this then affects how you should think about advertising in your account. And so we're gonna go through today. A little bit about andro, which I think is a really important change, as well as then some of the specific things that it enables in ad accounts that meta is beginning to bring to life in different form factors.

[00:05:14] Richard Gaffin: Yeah. Okay. So let's talk then about, again, digging a little bit more into roa. So you talk about the way that sort of AI functionality gen generally has sort of transformed or will transform the way that, I dunno, the internet is used or whatever, but there's one specific way that we talked about previously hitting record here, which is around like the aperture or window of time that.

The algorithm can use to sort of track or judge your behavior and then serve you ads. So talk a little bit about how that kind of complicates things or just makes them sort of more complex and useful.

[00:05:46] Taylor Holiday: Yeah. So if you think about you as a user of meta ad products of of Facebook itself, of Instagram, of ig stories, of WhatsApp, of threads, if you happen to be on there too, you have a long history. Behavior. You have a long history of clicking around on websites that also have Facebook pixels on them that you could imagine in your head if you close your eyes and think of like a big data visualization map of everything you've ever done on the internet. As scary as that might be for some of us to imagine being released into the world that exists, right? And if you think about how that information could be used to decide the next ad to deliver to you. You start to understand is why we often get this sensation or phenomenon that meta's listening to us.

It's actually just the sort of complexity of the map of information they have about us from location to the apps that we use, the things we browse on the internet, to the things we've clicked on on Instagram. It's really is true that they probably know more about you than your significant other or closest friend or whatever it might be. But what has been true historically is that the amount of information that they would bring to bear. In order to allow the ad algorithm to consider that context to make ad optimization decisions has had to be fairly small because it requires an immense amount of compute power in order to have a really large context window, right?

So if you've ever noticed in chat GBT, like this idea of context, windows or memory are often very limited. Because it actually, if they were to store all the information about every user of the product, it would just overwhelm the system in terms of the amount of computational power that that requires. But this is why meta is investing so much, is so that they can increase the personalization to every user based on larger context windows and more data. And that's what roa enables. And one of the things that they tout specifically is that by leveraging advanced deep neural networks unprecedented parallel processing and real-time personalization allows them to overcome traditional bottlenecks related to latency, memory, bandwidth, and computational intensity.

And the benefit is in 8% improvement in ad quality for the user, and a 22% increase in ROAS for advertisers adopting Advantage plus tools. Okay. So that that's the benefit of all of that increased computational power on behalf of both the user and the advertiser.

[00:08:11] Richard Gaffin: Right. So I mean, ulti ultimately, like the quick way to say that is that it'll just serve people better ads and we can kind of expect a better return from that. I mean, I'm like here in the in the kind of article about Andromeda that's on the Facebook engineering site, and

[00:08:25] Taylor Holiday: Awesome.

[00:08:26] Richard Gaffin: I mean, we can throw this up Corey when we produce or actually put this out, but there's a very difficult to understand graph here that I'm looking at.

[00:08:34] Taylor Holiday: Of that

[00:08:35] Richard Gaffin: you, Hey, I guess the question is, do you understand? Understand what this is trying to say, and is that useful to talk about, I 

[00:08:42] Taylor Holiday: Yeah. So, so this gets the, that visualization and we should put it on the

screen at this point, Corey, and we'll put it in the show notes is a reference to the second really important benefit. So think about what I just described as like meta personalization. Think about that as like further emphasis on the elimination of you specifying audience targeting and more allowing meta to sort of continue to handle. Delivery and targeting for potential users for your product. Okay. That's, that's like really the end benefit of that, which is more of what a advantage plus shopping is pushing anyways. The second most important thing, which is something we've talked a lot about, which is the emphasis on creative volume. Okay? So what this is saying is that if you think about the idea of the ad corpus as being like all of the potential ads that might matter or be able to be referenced by meta for the sake of then considering delivery into a user.

If you go back to the old days of like BAU, they would tell you that there was a real limitation to the number of ads that you could put into consideration for the auction. And now with Andromeda, what meta is saying is, no, no, no. We're actually gonna give preference to brands that provide more creative options because you are actually giving us more levers to create more pairing between user and ad. Okay? The more options you give us, the more likely it is that we can create a match between ad type and person. It's just an even a further emphasis saying that Andromeda is enabling us to consider the possibility and delivery of more creative volume on behalf of the advertiser so that it can, this like new retrieval system.

The way they phrase it is this, they say Andromeda employs hierarchal. Indexing to efficiently handle an explosion, their words, explosion in ad creative volume driven by AI generated content, enabling

advertisers to scale campaigns without performance degradation, which is them pushing to say. Take these AI tools to create more variations of ads, different headlines, thousands of different backdrops, thousands of different headlines, thousands of different creative audio overlays and allow us to find the perfect match between user and creative.

And that is, this is like sort of a, I feel, reinforced by this realization, which is to say that the dream scenario is that there's sort of like infinite potential variables to match to each unique person. I. You could process through all of those options to find the perfect pairing, right? It's this idea that there's a soulmate of an ad for every person, and if you had infinite options, you could find it.

And

that's sort of what they're getting at, is that we can now process more possible relationships between users and ads to find the right match to get you the best results.

[00:11:17] Richard Gaffin: Right. So I mean, it kind of sounds like, so every time like something like this rolls out it feels like the upshot tends to be. Creative's, the most important thing, make more creative. And it sounds like this is in some ways just like the most intense version of that so far, and maybe the kind of like apex of that that movement now.

So what, what's different about this other than just an order of magnitude? Like, yeah, go ahead. Let's get 

[00:11:41] Taylor Holiday: So, so very practically if you think about our media buying structure at CTC, there's a video on the internet. That you could go look up, which is a reference to what has really driven our account structure historically, which is called the pipes methodology from CTC. It's this idea that we think of every campaign as its own unique pipe that you're trying to get delivery through or flow through, and that the cost control, control is this like lever that controls the constraint on how tight the expectation is and how much flow can get through.

And then the way that you increase flow through an ad account or dollars spent is by launching more campaigns. And that was historically true because there was a limitation to new creative existing within the same campaign or ad set. But what now they're saying with an a SC campaign is that that's no longer true.

You can sort of add an endless amount of creative into this campaign and medic process through it and find the delivery. So it's changing for us, it's bringing a more consolidation. At the campaign level and more ad creative volume per each campaign. We used to have this relationship where it was like three ads, six variations in each campaign, and that was like this defined constraint that's now gone.

It's out of our system. We don't even use the language concept anymore, which referred to a campaign with three ad variations. So we're now unbounding the relationship between campaign and creative. And in fact, if you have a high performing campaign around a core offer that's going to exist for a long time, maybe one of your best views.

If there's actually gonna be a repeated process of consistently adding new creative to that campaign for the sake of elongating its ability to continue to scale and spend.

[00:13:18] Richard Gaffin: Mm-hmm. So, so the basic I mean to kind of recap a little bit of what you're talking about or contextualize it our creative sort of structure has been for a little while, look at least a year and a half, maybe two years. This idea that there's offer angle and audience, any combination of those three equals a concept.

A concept is new campaign. Each campaign contains six ads, whatever. But it sounds like essentially what this is. It sounds like this is doing away with BAU. Is that basically

[00:13:42] Taylor Holiday: exactly.

Everything is gonna move to default a C, so there's BAU going away as a premise.

That's, that's the end tail of this, this sequence of actions that Met is taking.

And really that's just about getting AI enabled into all of the ad product delivery more than anything else.

So that, that's the piece of it.

The other, the other, things that this does. AI also has allowed meta to begin to introduce more complex optimization settings. Okay. And this is really where, what I get excited about for the future of advertising is, is that if you think about why we have fought so much around cost controls, there's a

very simple reason is that they res, they represented. The ability for an advertiser to assert their desired business outcome into the algorithm in a way that did not exist before when you were bidding and your only choices were lowest cost of acquisition. Okay. Or highest value of purchase value. Those is the only two options. Do not, do not include the ability for you to optimize around the business outcome.

I know people actually care about,

which is the efficiency of those results. So if it's lowest cost of acquisition, there's always an inferred or necessary relationship to a OV, which is a OV divided by C equals roas. So the efficiency of that acquisition, and if it's value. Then there's also the infer, the missing cac, or the cost of that value, and it's all, it's actually the relationship between those things that the cost, the cost control, or the target ROAS was always inserting.

[00:15:14] Richard Gaffin: Mm-hmm.

[00:15:14] Taylor Holiday: But what Meta is doing now. They started by saying, Hey, we will allow you to optimize for third party metrics. First they said, you can optimize for a last click result if you want, you could optimize for a North Beam Ross. You could North optimize for whatever third party metric you care about. That was the first additional optimization result they cared about. But they also recognized that that's not actually the, the solution either. Those are still just proxy metrics. And so now the next set of things, we're in betas for both of these. We have a large scale study going live for one of 'em around one. Incremental revenue creation. So this idea that where they take this process of geo holdout and measurement allow you to optimize for incremental results. Is one. And then the final stage, the godfather of the SA is profit optimization.

Okay? So they're actually gonna absorb the cost data for your products and present back to you a marginal return number, and then ultimately the final boss of incremental marginal return. And, and

that's where this is all heading.

And guess what that frees us from? We don't have to use cost controls at that point

because now the optimization setting actually matches the business outcome. And so we, we can remove needing to assert for ourselves. That, that cost cap that's defining that result for the business.

[00:16:27] Richard Gaffin: Right. So in other words, the cost gap is essentially a stopgap measure in a lot of ways. It's like it's always going to push towards one of those two objectives and the cost gap artificially inserts this restraint in order for us to kind of jerry r it to get to the profit goal that we want. But at a certain point, that's just not gonna be the case anymore.

[00:16:43] Taylor Holiday: Well, you know, assuming like if you think about the idea of incremental marginal return, there's still gonna be this need to define what term you would accept. And so maybe,

maybe we still have to say greater than zero, you know, like, or whatever it might be.

But, but, 'cause there's some point at which people are always willing to spend to a loss at different scenarios.

So there, there's probably gonna be some version of it still, but the idea is it's moving closer into alignment. And this is all enabled by increased computational power, where the idea of connecting the client's data and additional information combined with the ad delivery and optimization is enabled by the power of the additional compute through ai.

So, so all of this is really exciting and opportunistic, but it just changes the way that we bid a lot.

The other big change for us is that historically we have been really rigorous around the optimization setting. That we would choose in terms of meta right now offers one day click,

seven day click, seven day click, one day view, one day click, one day view.

Those are the only optimization settings. It used to be 28 day click, one day view. That went away and there's now only these four settings. And so we used to push really hard to move everything to seven day click only. And now what incrementality and measurement as a baseline does is it allows you to say, Hey, I, I, don't actually necessarily. Care which one we're using. We're gonna use a incrementality study in either case to help build the relationship back to the actual business outcome that you care about. And then we can factor a seven day click, one day view, or we can factor a seven day click. And in more cases, we're actually getting comfortable. With factoring against seven day click, one day view, because that adds the most signal back to meta. Now there's all sorts of reasons. It, it can, you gotta be careful and there's different cases, but it's just something we're holding looser than we used to, I would say. So we're fewer campaigns, more consolidated, more creative per campaign?

Still broad targeting because we believe that's the way that. Andromeda and their, their understanding of context windows is better than mine. And so we're gonna allow for that breadth of delivery. And then we're gonna have ideally we're gonna use incremental optimization or profit optimization as those things become available and are tested and validated for their efficacy. But if not, we're gonna use. Maybe seven to click one day view, but we're gonna pair it with a measurement test and then deploy an I ROAS goal into that system. So these are big changes in terms of the way we think about the structure.

[00:19:04] Richard Gaffin: Yeah. And so one thing I don't think that you mentioned that it's actually gonna stay the same or rather the most important, important holdover is the idea that each campaign is built around one expected A OV right? Or one expected CAC or whatever. So whereas previously we would change out or build a new campaign for a new angle or a new audience or whatever the case may be.

At this point, it's basically dumping a creative into one campaign, one ad set, and then having, but making sure all of them are pushing towards the exact same offer. 

[00:19:30] Taylor Holiday: So, or the same things that are related to the same margin profile. So,

'cause then you could use a t row s on a value optimized and you could get to the same marginal result. Right. So the key is, yeah, as long as, and the other reason we would separate it out is if there are inventory needs based.

So in other words, I want to make sure we have some clients that are like, we need a certain amount of budget every month going into women's versus men's. Okay, cool. Well, let's make sure that we're getting budget into both of those areas separate and not just conceding that allocation to meta in those cases. So it's either inventory demanded or it's separate offers or designs. Our reasons why that we would be changing them out. And then the, the last thing is, is related to exclusions.

Like in an A SC, we are setting up our campaigns where you're now defining the audience exclusions at the, at the account level. So you're defining what an existing customer is. You're defining what an engaged audience

is, and you're defining what a new customer audience is at the account level with a SC, we're doing that in a unique way, which is we're using new customers to mean what you would expect it to mean. But we're using engaged audience. A lot of people do this as like remarketing. I don't really care about the distinction in remarketing. What I care about is the distinction in active versus lapsed customers.

So in engaged audience, we're gonna define as your active customer base, and that's who we're gonna want to exclude. And then your existing customer base are gonna be. Your lapsed customers, the ones that are not engaged functionally. So those are distinctions where we're gonna allow for those people back into the funnel in most cases in order to inform the delivery.

Because for a lot of, especially larger businesses, we're gonna wanna make sure that those people who very much are not responding to you via your free marketing channels are getting, are making their way back into the educational funnel. The other thing we feel a lot more freedom to do now with incrementality and measurement is to bring in other campaign objectives.

You

wanna try add to cart, you wanna try reach, you wanna try video views. Those things are easily enabled. I. By pairing it with a holdout study that allows us to look at, okay, what is the impact of some of these more higher funnel actions? People love this phrase, so I'm just gonna use it. Video views, add to carts page view, et cetera, where we can run that. We compare it with a holdout study and we can see the incremental impact. So that's actually usually very easy to do 'cause it's a true holdout where you're actually just running it rather than turning it off in some regions, you're actually turning it on in just a, the very narrow select set of regions.

You can run the test and see that result. And then we have a, a, a measured way to apply a result against that, which has historically been very difficult to do with a traffic study or a

reach campaign to really understand what the result or impact is.

[00:22:10] Richard Gaffin: Well, I mean, this really is revolutionary if, if I'm hearing you say that you might build an add to cart campaign or a video view 

[00:22:17] Taylor Holiday: Yeah. Now to be clear, I think that I would be very hesitant

to tell you that I'm very confident in the result of that impact, but I, I don't actually, I want to be less and less. I want to speak only in I. The evidence that I have available to me as much as possible. So if I've run 400 studies of X, Y, Z, I can let you know.

But if there are cases where people have seen add to carts be incremental, then I, I'll hold those as appropriately and as many as I have and as repeat consistent the results are, I wanna speak through the lens of that confidence. I think we're in a very new era of meta advertising, and so a lot of the norms and heuristics we have to allow to evolve into the present and to understand that what might have been true on

meta in 2019 may not be true on meta in 2025.

And we should do our best to update our priors and to reconsider the present as best as we can and make the best decisions for today.

[00:23:16] Richard Gaffin: Yeah. All right, so let's talk about then real quick the what the timeline looks like on this. So as this is, we'll be coming out on Tuesday, the 25th of March, what will be happening already as we're listening to it? What's coming down? Like what do we know is coming in the near future, and then what's a little bit more vague and what sense do we have on the timeline for those things?

[00:23:35] Taylor Holiday: Yeah, it's a good, it's a good question. So, many of these changes have already rolled out for meta in that they, as this changes to advantage plus shopping are already underway, it's now the default way that you would build a new campaign. It's gonna opt you right into that from the start. So on that core underlying Andromeda algorithm change, that's sort of in place some of these other new optimization settings.

Like I mentioned, we are right now running a 10 client agency wide. Incremental attribution study across a series of clients to understand the incremental impact of that optimization setting. Same thing with profit optimization. We have that in a couple of places where we're trying to be diligent to work very closely with the metadata science team to release those things. And then go from there. And then as is Meta's case, there's also a lot of other exciting things coming. There's WhatsApp ad placements that are down coming down the road. The very, they actually just announced this past week. Notification ads. So there's a new ad placement now in meta. If you go into Facebook and look at your notifications, you're now going to get ads in there.

That's a new, a brand new net new placement that'll be coming from Meta. They've, they're launching omnichannel ads, which is the ability to. Deliver ads. If you have first party store ownership. So let's say you're Travis Matthew and you have your.com, but you also have retail store where the ad can both allow the user to click to the nearest map location and it will track and report on those purchases, or they can click to the website.

So that's, there's some new, creator content related ads that allow you to run collection ads alongside creator content. So continued evolution of the ad product for sure. But it's exciting. There's a, there's more changes to meta and, and I think in a, in an era where AI is so rapidly evolving, that's what you would hope to see out of an ad product.

And now we can continue to lean in and figure out how to deploy these new tools to the best result.

[00:25:29] Richard Gaffin: All right folks. So, this Taylor's episode of, of Sharpen Your Skills is coming out on Friday this episode on Andromeda. So you can dig a little bit more. He'll go deeper on this particular topic. And of course, as always, if you guys are out there, you wanna work with us you want us to put some of these changes into place for you comment thread co.com, hit that high risk button.

We would love to chat. Taylor, anything else you wanna hit on this?

[00:25:51] Taylor Holiday: I, I would just, the last thing I would say is I, I know I am a broken record on this creative volume thing, but I really think that nobody has really figured out how to do this well yet. Like it has reframed their brain around, like, we're not talking about 50, we're talking about 5,000.

And the way that you enable that is with. Meta is offering you the tools and so many brand leaders are hesitant around like, oh, what about if I don't like the music over the

top of my ad? And I just think if, especially let's say you're a brand that might be struggling a little bit, if you're willing to just deploy these things, absent that and allow the process of the user's response to the creative to be the end all, be all. There's going to be gain in this new system for people who lean into what Meta wants out of it and what they're telling you it values.

So, I just think that continuing to think about your supply chain of ad creative and how you could 10 exit a hundred exit is really important. And then to allow for the least human intervention is possible to what?

Here's, I'm gonna say a thing I've said this before on thing, is that I believe that. 99.7% of all media buying, including media buying that happens at CTC in many cases, is a net negative on the results of the business. What do I mean by that? I mean that humans in the way that we interact with a system like that, it is so hard for our brains to map computationally what's happening with what's happening inside of the, the system of complexity that is meta and to make decisions. That we think are affecting things in ways that they just aren't. And I really believe that this is all going the way of hedge fund and

stock trading is that this is like flyboys is coming for our industry because just not good at it. Like we, we right now, the studies will someday show that just like most portfolio managers don't outperform the index. I think that's true of most media buyers too, and that, that we have to allow for some of this automation to continue to press forward. And we're gonna get better results as we do that. And so, your job is to be clear on your business objectives, to be clear on your inventory, to be clear on the offer design, and then to fuel, fuel this machine with as much quality creative as possible.

If you reframe like a very simple media buying structure that says we're not gonna spend all our time and energy. Worrying about this structural design. We're gonna build a SC campaigns, we're gonna do it to broad audiences. We're gonna do it around the right offer design with a clear target, and then we're gonna build the rest of the system around deploying as much creative volume and thought into that as we can, and then go build the marketing moments and branding exercises that we talk a lot about. Those businesses, I think are going to drive immense value off this platform in the next 12 to.

[00:28:45] Richard Gaffin: That's right. All right, cool. Well, we'll have to do a, a pod on. On creative volume enablement as it becomes even more important moving forward. But alright folks, well good chatting and we will talk to you again next week everyone. Goodbye.