Brent Peterson (00:02.57)
Welcome to this episode of Talk Commerce. Today I have Nils Jessen. Nils, go ahead, do an introduction for yourself. Tell us your day-to-day role and maybe one of your passions in life.
Nils Jessen (00:14.014)
Yeah. Hi, Brent. Thanks for having me on the podcast. It's a real pleasure. Um, I'm Nils, I'm a co-founder and CEO of Mabel AI. We're a German software as a service startup. And we've basically built a tool for e-commerce brands to do the conversion tracking that you need if you run ads on Meta ads, Google ads, TikTok, Pinterest, and so on, um, when you use these app platforms.
You have to have the tracking pixel in your shop. And since iOS 14.5 and so on, the data quality has gotten increasingly worse to a point where the algorithm of your ad platform is missing the data to really understand your target audience. And therefore, your targeting becomes worse. And you pay the money, but you pay too much because you show the ads to a lot of people.
people that are not in your target audience because the algorithm doesn't understand it anymore. What we've basically done is built a completely new architecture of how the tracking script is loaded into your shop, how the data is captured. The data quality is way more complete again, that every single event is captured with as much parameters as possible. Then the data is sent back to your advertisement platforms via the conversion API so that the algorithm gets all the data input again that it needs.
And then automatically all your campaigns become more profitable because the algorithm can understand your ideal customer profile way better again, out of this conversion data, and then target your ads so that like the learning phases become shorter and the campaigns over the whole runtime become more profitable. And yeah, like from my background, I'm a mathematician and computer scientist, but like right out of uni I've...
started a bunch of startups, learned a lot, then built a consulting company. And out of this consulting company, basically our consulting clients had the same issues in summer of 21 when iOS 14 launched, and then we build a solution for our consulting clients realized, Hey, basically everyone who runs conversion ads needs this, we stopped the consulting company and started Mabel. And so I'd say my biggest passion is basically
Nils Jessen (02:34.298)
acceleration. I like fast cars, but I also like taking a good idea and just accelerating and scaling it.
Brent Peterson (02:41.93)
That's great. Thank you for that. And you're in Germany, so having a fast car is good because you can actually drive as fast as you want. So before we get into content and maybe talking about fast cars really quick, I wanted to tell you a joke. And all you have to do is tell me if that joke should be free or if at some point we should charge for it. This is called the free joke project. So I have a joke I'm going to tell you. So just
Nils Jessen (02:51.528)
Yeah, that's true.
Nils Jessen (02:57.757)
I'm gonna go.
Brent Peterson (03:10.786)
Tell me your reaction at the end. Here we go.
My dad just texted me. I can't wake up this morning. I feel like a bicycle. Yes.
Nils Jessen (03:20.714)
Wait, Brent, you were gone. You said to me, you have a joke, and then I couldn't hear you for a bit.
Brent Peterson (03:26.646)
All right, we'll do it again. All right, here we go. This is the free joke project. Ready? My dad texted me this morning. I can't wake up this morning. I feel like a bicycle. Too tired.
Nils Jessen (03:28.085)
Yeah.
Nils Jessen (03:42.726)
I like it. I like these kind of dead jokes with wordplay.
Nils Jessen (03:50.482)
I think if there was a platform to pay like a subscription model to get jokes, I'd be okay with this being like a paid job.
Brent Peterson (04:00.65)
Wow, all right, good. Thank you. All right, let's talk about Mabel. And let's talk about, I know specifically you work with Meta, right? Give us a little bit of background on that.
Nils Jessen (04:13.258)
Yeah. Yeah, sure. As I said in the introduction, the goal of our software is to capture the highest quality conversion data in the shop and then send it into your ad platforms through the conversion API to make your ads perform better. Basically all your campaigns become more profitable and also you as a marketer get better insights and make better decisions when creative testing or scaling campaigns. And when we started out.
our MVP, because we said marketing, like everyone on earth who runs conversion campaigns basically needs better data, but that's not a good target audience for a startup. So we decided to like focus on e-commerce and inside e-commerce start with a Shopify app. So that's how we started out as an MVP. And then on the other side, which of the app platforms needed the data the most. And we realized if the Google algorithm doesn't know who to target, they can still just like randomly pick anyone who searched for the keyword.
And that's going to be like one out of a few 10,000 maybe. And that's way better than if the meta algorithm just randomly picks one, because they don't have the keyword as a to go off of. So they would pick anyone randomly out of 3.5 billion to exaggerate it. So meta got hit way harder by this loss of data. So our MVP was Shopify stores, like an app for Shopify stores that tracks the data and sends it back into meta so that the meta algorithm can do its thing.
By now we've added more shop systems and also Google and TikTok and Pinterest, but that's how we started out. So very early on Meta got like interested in what we're doing because they realized, Hey, these people are making Meta ads more profitable for e-commerce drops. And of course Meta is also interested in that. So we talked for a while and they said, basically, there's a lot of things we can do together, but before we can start off, Meta wanted to check and to verify that.
what we claimed our software does is actually true and that it works. So we did a huge study, which we published this fall and in September of 23, we were for like several months, we like worked together with 23 different, German e-commerce brands and they did very clean AB tests where they basically ran one campaign with two different ad sets and in one ad set.
Nils Jessen (06:35.862)
They used their old pixel that was integrated the normal way, always with browser and server-side tracking, but not with the Mabel architecture. And then the other ad set was identical. The only difference was it optimized on the data that was tracked through Mabel. And then over a course of at least 14 days, we tested these against each other and then looked at after 14 days, how well did each of the ad sets perform. And in total across this test, there was a...
Six figure amounts and only on AB testing budget by these 23 brands. And in the end, we could like prove that the ad set with the Mabel data on average had 108% higher rows. So return on ad spend was hugely improved and Meta internally built a huge kind of simulation to check how likely would it be to just randomly see these results.
Or how sure can you be statistically that they were caused this uplift was caused by Mabel and they came back with a very clear result that obviously Mabel was the differentiating factor that caused this uplift, which was a very, very cool result for us because Meta as a third party and with access to all this data, basically verified that our tool does exactly what we claim. And now there's a lot of things of course we can do together with Meta.
Brent Peterson (08:03.202)
That's great. And it's not just for Meta Facebook, right? You have it now enabled for some other platforms.
Nils Jessen (08:11.562)
Yeah, exactly. Of course, everything that's in the meta advertising cosmos. So if you run Instagram ads and so on, it works for all of this because everything goes off of the meta business manager, but you can now also use Mabel to power your TikTok ads, Pinterest ads, and also Google ads. Well, I still have to add Google ads are still in our beta and like probably end of January, mid February, we'll reach a point where we plan to
deliver the same level of uplift in Google. But right now there's still some optimization left for Google Ads.
Brent Peterson (08:46.691)
Got it. I know this ad space is super competitive, and there's a bunch of other apps out there that are doing something like this. I think Trackify or Triple Whale, how do you differentiate yourself from those other tools?
Nils Jessen (09:01.93)
No. Yeah, that's a very good question. These other tools mainly, they come from the same problem that iOS 14 reduce the data quality, but what they aim to do is give insights back to the marketer. So you as a marketer after iOS 14 and then so on, you lost a lot of like a base data for your decisions. Like which ad set to scale or which creative...
performs better than the other. And the route that all these other tools are taking is giving you like a third party attribution dashboard or like a Chrome extension that lays data over your ad manager. And then they claim with their third party attribution logic, they can tell you which ad set to scale and which to shut down. But the advertising platform itself, like Meta, Google, and so on, they don't ever like...
get any of the data that these third party tools basically show you as a marketer. So the only chance for an uplift is if you take their insights and then make better decisions. While Mabel is taking a different approach, that not so many tools or basically no other tools go that way, where our goal is to basically really give the best possible raw input data, the raw conversion data, back to your app platform.
so that then the attribution logic inside of your ad platform works better again. So you don't need a third party dashboard to look at your data. You can just trust the data that is in your ad account again and make your decisions based off of these insights that Meta and Google show you. And then on top of that, the algorithm also gets the better data. And even if you make the exact same decisions as before, your ads will still become more profitable when you use Mabel,
algorithm itself and the automatic targeting optimization, they start working better again.
Brent Peterson (11:25.102)
I know you had both, you had mentioned Shopify earlier, and then I think I read that you're doing Shopware as well. Why did you choose those two?
Nils Jessen (11:40.006)
Shopify is the biggest single ecosystem or biggest shop system out there. So it was obvious to start there. Also, we've seen that a lot of more startups and newer e-commerce brands are on Shopify where they rely a lot on single channels like Meta ads or Google ads as their main source for new customers where they don't have a huge brand yet. So for them, the
relevance of our tool was very high and we could often talk to like the founder or head of marketing easily and have very short sales cycles where we learned a lot very quickly. And now we're progressively targeting bigger clients and then bigger brands. And as we already discussed, we're like a German brand and where we started was a lot in the German Austria, Switzerland, and then Europe region.
Now we're expanding also to the US, to UK, and so on. But in Germany, Austria, Switzerland, and Europe, Shopware is actually the second biggest shop system. So that was the logical next move. But we're also currently already developing integrations for WooCommerce, for Magento. And then the goal is also to offer a custom SDK.
so that you can basically integrate it into any shop, even if it doesn't build on one of these shop systems, but it's just custom built by your developers.
Brent Peterson (13:14.478)
I know that some of the data problems have come out of data compliance and also the iOS, the different changes that are happening on Google and Apple. How does this work within that realm and how can it help, I guess, what is it for a consumer and then how can it really help a merchant?
Nils Jessen (13:43.878)
So, especially in the EU, there's the GDPR, which is like the data protection regulation, which says every website has to collect tracking content first. So if you enter an online shop or any website in the EU, it will always give you this pop-up where you can click either yes or no. Yes, I accept my data to be used or no, I don't want my data to be used. And that's a purely legal thing.
So if someone clicks, no, I don't want my data to be used, then there's nothing we as a tool can do because it's a legal decision. But if the customer says, yes, I allow tracking, then there's the next hurdle where are you technically able to track them? You're like legally allowed now, but can you track them? And what Apple did with iOS 14 and 14.5 and now with iOS 17, they became more hurdles is-
They try to technically block you from doing this. Because iOS, like Apple says, it's in the name of data privacy of their customers, which the customer, at least in the U, already has the option to say, no, I don't want it. So that doesn't make so much sense. I think behind the curtains, it's more like a move where Apple realized
Maybe we're going to lose our revenue stream from the App Store royalties because there's some lawsuits there. And what do other big digital ecosystems that have billions of users, how do they make their money? And they looked at Google and Meta and realized, oh, they make their money from ads. Of course, Apple won't start showing your 15 second ad before you can use your iPhone. That wouldn't make any sense. But what they have is they control
ecosystem where the data is and these other platforms need the data in order to run profitable ads. So I think Apple's game plan is first build a wall and cut the others off from accessing the data and then later basically sell access to this data. It gets a bit deep when you say, okay, but selling the data wouldn't fit their narrative then that they protect the privacy. And then you can look at things like what Google does with the
Nils Jessen (16:06.122)
federated learning of cohorts where basically they say we don't sell the data of a or give access to the data of a single user anymore, but just to a group of users. And why, for example, Google is doing that is if you use Google Analytics and you get all the nice raw data, then you can take it to any other app platform and run your ads there and Google doesn't make any money. But if Google starts to only show you like...
You don't know, okay, this is the user that accessed my site that I want to target anymore, but they just say here is like the cohort ID of the user that access your site and the only like entity on earth that can go back from the cohort ID to the single user to target them is Google themselves. Then they basically give you no other option, but to target the user through Google and this way kind of Google built the wall and made sure that the asset, the data, which is super like valuable.
that you can only go to Google for this data and pay them to run the ads. And I think Apple's going to do something similar where they don't sell the raw data because that would go against their narrative, but basically something group-based, which protects the privacy of the user, but still make sure that they get paid. And what we believe is that in the end, it shouldn't be like a handful of huge corporates that control this very, very valuable and powerful asset, but instead
Every business themselves should be able to control their own conversion data. So that if you run an online shop, you should be the entity that controls who gets access and you shouldn't be like dependent on a few big players that they are like so nice to give you this data. Um, but instead when you use Mabel, you control the data flow, you control the tracking and then the like dependency, uh, inverts and
Basically the ad platforms are dependent on you as a shop owner that you are so nice to give them the data that they need to like provide the service for you of running ads.
Brent Peterson (18:08.551)
If I was a merchant or a store owner, what would you tell me, sort of recommendations around tools and how I should implement them and then I think more importantly, what types of areas should I pay attention to?
Nils Jessen (18:26.682)
Yeah, I think it depends on what stage you are. If you're just starting out with your first brand, just in the very beginning, just focus on the basics, like have a great product, find out who your target audience is, who your ideal client is, and then show it to them and don't think too much about all the tools around it. Because I feel like bigger brands for them, it makes sense. And if you just start out in the very beginning, there's a lot of noise out there that might distract you.
bit bigger brand if you spend a few thousand dollars per month on ads already, then it's really important to get data quality right in your ad accounts. And I think everyone who's been in the game for a few years has seen the drop after iOS 14.5. So you intuitively know that it's somehow important, but there's a lot of like, it's a bit of a black box, this tracking part. And
There's some people out there, experts who can build you like server side tracking setups that are way better than just using the, the pixel. And so that's the first step that you can go, but it's also always expensive and it can break easily. So it's high maintenance. So if you find a tool that you can use that basically does it for you to bring the good data quality into the tools that you use into the app platforms, then yeah. That's.
I think easier to use and then better in maintenance. If you, for example, use Shopify as a shop systems, there's not only Mabel, there's also, for example, Elevar as a tool. So like, of course I wanna pitch you what Mabel does, but yeah, there's one other tool that we know and it does something similar. What they do well is basically, what Elevar does,
well, basically is to connect to a lot of tools. So with Mabel, as I said, you can only connect your meta ads, Google ads, Pinterest, and TikTok ads. And in these tools, we know that we have the best data quality on the market, where you can run an A-B test between Elevar and us, and we will beat Elevar. But if you have like 20 different other platforms that you need your data to be in, maybe it's an affiliate network or
Nils Jessen (20:53.966)
a price comparison site where you run also some PPC ads or something, then it makes sense to also use Elevar to bring the data there because it will be way better than just implementing only a browser pixel because that is very fragile and can be blocked easily by Apple and other browser extensions, for example.
Brent Peterson (21:14.478)
I know that AI is such a huge topic right now. And how is AI being deployed? I'm sure you're using it across the spectrum. But tell us a little bit about how are you using it and are there any issues or concerns around the privacy when using AI for users as well?
Nils Jessen (21:36.45)
I think the very obvious AI that everyone thinks about and has on top of their mind right now is like chat GPT and everything related to that. But if you're in the ad space, you have to keep in mind the algorithms by Google and Meta and so on who do the targeting. They are some of the most advanced and biggest AIs that mankind has built and they've been there for years. It's just not like as...
flashy maybe or as easy to see how it operates for you as a person. But if you run ads on these platforms, they've been AI powered for years. And just like chat GPT and any other AI, these AIs are only as good as the data that they are fed for learning. And that's like, we are called Mabel AI. It's not because we build an AI ourselves, but because our job is basically to provide the best possible training data.
like your individual businesses training data that you can provide to the AIs of Meta, Google, and so on, so that their big AI can learn your target audience the best and then deliver the best marketing outcomes.
Brent Peterson (22:53.983)
If I'm a marketer and I have a client, what would I be looking for in terms of what are we seeing ahead of time, what trends are happening in this community, in the marketing community and tracking? What's the best way to prepare for that type of thing?
Nils Jessen (23:13.626)
Yeah. Um, I really feel that having the best possible data quality is going to be a huge differentiating factor in the future for you as a brand, like it is, I think having good data and using AI well is a, it's going to be a differentiating factor in any industry, but we're like specifically talking about marketing. And so, um, in the past, when there was just a browser pixel,
like pre 2020, 2021, everyone basically had the same setup and just basically worked well enough that this was not something to like differentiate yourself. Over the last few years, you were like, it was really important to have the best creatives in the market and the best hooks and so on, but looking forward over the last maybe five, six years, we as especially performance marketers have like given a lot of what we used to do ourselves.
As a task to the algorithm, like maybe five, six years ago, if you remember, we were all like building these 38 different ads, uh, campaigns with the different audience target things, and then running tests here and seeing, and like three stage retargeting funnels and all that, and a lot of that has vanished. And right now, most brands just have like an always on campaign and then like a second creative testing campaign. And that's basically it. Everything's like broad targeting and the algorithm will figure it out.
And this trend makes a lot of sense because now we focus more on the marketing message instead of like some, uh, like abstract settings and some dashboard. And also the algorithm can always target the single customer while you as a marketer can't operate, like you can't decide for millions or billions of people, if they should see the ad or not. Like you can always only operate on audience levels, which is groups and therefore like inherently less.
So it makes a lot of sense to give these tasks to an algorithm. But as I said before, the algorithm was only always as good as the data that it is fed. So looking as a trend into the future, I think controlling your data and making sure it's the best possible quality as like step zero, that's going to be very important because if you as a brand provide worse data to your algorithm, then it can like only do a mediocre job for you. And then.
Nils Jessen (25:39.894)
You will always be less profitable. That's differentiating factor. Number one, the, like the, the trend of. Bring the best data quality to your algorithms so that they can do the best possible job for you. And then the next trend is in my opinion, once you control this data flow to the algorithm, you can like tracking stops being this fixed, uh, unchangeable thing. And you can start playing with the data because
Imagine these algorithms in your ad platforms like a dog that you're trying to do like some tricks. Right now the algorithm is just some like sled dog that runs in one direction and never changes. While maybe you as a brand have like a subscription product that you want to sell. And if you let the algorithm optimize on the best possible like revenue return for your ad dollar and you also like not only sell your subscription but also like a
one-time trial package and the like if you purchase a product as a customer as a one-time purchase maybe you pay $50 but if you get the subscription then it runs for 12 months but every single purchase is only $30 anymore then if you get a new customer through MetaAds the algorithm will get like a new customer that brings $30 if you have a new subscription sold but a customer that brings $50 if you sell your one-time item.
So the algorithm will see, hey, cool, these kind of people who purchased the one time, only one time purchase, they bring $50 while the others only bring $30. So the algorithm will inherently optimize to bring more people who do the one time purchase. But you as a brand, you usually want to sell your subscription product because you have a way bigger customer lifetime value and way better margins there because you don't have to remarket them for the second to 12th order.
And so what you can start doing where the tracking and what the algorithm does, as we just discovered, is misaligned to your actual business goals. If you start like changing the tracking and like tweaking the data, and instead of just the like purchase card value, you start sending like customer lifetime or predicted customer lifetime values as the conversion value to your app platform. So for the one-time purchase set of $50, it's maybe.
Nils Jessen (28:04.946)
I don't know, $54 or whatever your analytics say as predicted custom lifetime where you have someone purchases the one-time purchase product. Or instead of $30, it's like the 360 at least, or maybe $380 that your analytics say, and you send like instead of 30 and 50, you send 380 and $54 as conversion values to your ad platform because you control the data flow at that point. The algorithm starts to understand, Oh, this is what
each of these like purchases or sales are actually worth to you as a brand. And then the algorithm can like understand it and we'll start searching way more for people that are likely to like buy the subscription, which aligns way better with your business goals. So I think controlling this data flow and actually proactively using this to tell the algorithm what you want. Basically you control. If you.
look back at this algorithm as training a dog, you control whenever it gets a treat and how big this treat is, and in which case it gets the treat and which it doesn't, you can basically train it to do exactly what you want. And this is gonna be super powerful over the next two to three years, specifically training your ad account, your algorithm, to do exactly what you want with your individual business goals for your brand.
Brent Peterson (29:27.57)
Wow, that's incredible. So I just want to go back and just briefly talk about the algorithm and AI and how AI is such a thing. I like what you said that it's been around forever, right? And now it's only getting better now. What is it that is so impactful about maybe this, what you talked about, the new customers versus how other platforms will convert those customers?
Does it make it a game changer, say, for merchants that are really targeted at Facebook?
Nils Jessen (30:04.222)
Yeah, I think like.
Nils Jessen (30:09.794)
For each of your ad platforms that you use, it's important to understand where in your customer journey, does this play the biggest role? For example, for a lot of brands that use Mabel, they see meta ads, especially also TikTok ads, all of the social performance ads, a lot of it is a new customer channel, like a channel to drive new customer growth, whereas Google is usually somewhere in the middle of the funnel.
Or to convert users that already very much know about the product and then just want to purchase it, but not so much the discovery phase. And then your email marketing campaigns are a lot to drive retention and then get business from your previously existing customers and so on. But if you generally run Meta ads, the AI in Meta, they don't know that. They just like...
As I said, it depends on what treats they get. And if you optimize for purchases, they just, the dog gets a treat for any purchase that happens. So if you have like a campaign that has two ad sets, one of the ad sets generates, I don't know, 120 sales, generated 120 sales in the last week. And the other ad set generated 180 sales in the last week. Then you would say the one that generate the 180 sales.
is probably the better converting one that you should put more budget into, that the algorithm is gonna show more and so on. But if your goal for Meta specifically is to use it as a channel that drives new customers, like maybe if you had the info that you would know that out of the 120, that the one ad set generated, maybe 90 of them, so three quarters, were actually new customers, while out of the 180 purchases that the other ad set generated,
30, so 1 sixth out of them were new customers, and the rest, that 150 remaining purchases were by existing customers. And if you don't know that, you might scale the wrong asset for what you actually want this specific AI to do and what you want this platform to do in your customer journey. And so, for example, with Mabel, we've just launched the feature before Black Friday, where it allows you, when we track a purchase order in your shop.
Nils Jessen (32:35.722)
We will not only send the purchase event to your ad platforms, but we will start querying the backend API of your shop and basically get the whole list of historical orders that this customer ID has ever done. And if there's any orders in this list, we'll immediately know this is a returning existing customer. But if this list of previous orders by the same customer ID is empty, then we know, oh, this is a new purchase. Like this is new customer that purchases in your shop for the first time.
And when we realized this is like first time new customer, then we send an additional event to Meta and so on that says new customer purchase. And through this feature, suddenly your algorithm in your ad account gets this info also. So you as a marketer can start seeing this in your like a campaign view where you see the different ad sets in the results. Tap, you will see. Oh.
Out of the 120, this ad set generated 90 new customers and the other only 30 out of 180. So if I want my meta ads, for example, to drive new customers, I should scale the one that cost the 90 new customers. So this is the first thing that you will see, but even more importantly, your algorithm also gets this information. So if you like as a campaign goal, when creating new campaign, instead of selecting purchase as the campaign goal, you would select new customer purchase, which
now is fed into your Pixel, into your ad account through Mabel, then the algorithm starts only getting a treat when they actually cause a new customer purchase. So if they find someone new who's not previously purchased. And this is, as I said, controlling the treats that you give to the AI with the reinforcement learning. What is a good thing? When does the algorithm get a treat and can feel good and say, OK, I want to do more of these?
If you, for example, say you only get a treat when you bring me a completely new customer, this is how you kind of teach and train your MetaEd account to fulfill exactly the role that you want it to be, which is a channel that drives new customers first. Because like you can reactivate your existing customer through email, which doesn't cost you anything. While if you show MetaEd to existing customers, that's way too expensive to reactivate them.
Brent Peterson (34:56.734)
That's an interesting overview. So last question for you. You had mentioned that Shopify is a good target because there are a lot of smaller shops. Do you have a sort of a minimum size that you would recommend as a store owner, maybe in either ad spend or revenue? Let's just say for ad spend. Is there a minimum size for ad spend?
Nils Jessen (35:20.606)
Can you repeat the question? I just heard, do you have a min? And then you were gone. Sorry.
Brent Peterson (35:23.83)
Sorry, as a merchant, is there a spend amount that makes it a good platform to use? Maybe either ad spend or even revenue.
Nils Jessen (35:42.658)
I'd say if you spend less than one to $2,000 per month on your performance channels, then, as I said in the beginning, first focus on finding the right match between a good product and your ideal customer. Once you spend more than $2,000 per month on your ads, that's when it starts to matter and to have the better data.
per month on meta ads or Google ads and you start using Mabel, that's already when the performance uplift that you get automatically from the better data will automatically pay for the additional cost that Mabel brings you. So it's already like basically a no-brainer because the increase in revenue that you get back from your ad spend already covers the cost for the tool.
Brent Peterson (36:37.858)
Got it. Good. Jen's it's been a great conversation. We've gone through quite a bit of time here. As I close out the podcast, I give my guests a chance to do a shameless plug about anything. What would you like to plug today?
Nils Jessen (36:54.174)
Yeah, I mean, obviously, I'm going to plug our own software. So if you have a Shopify store or a shopware store, then head over to Mabel.ai, just book a demo call. It's completely free. And then you can also like talk to our team. And if you run any ads, it will always make sense. You get a full free month of testing. You can just install it as an app into your shop, connect it to your ad account, create a new pixel. So.
None of your existing campaigns, none of your existing tracking is affected in any way, you can just like test it out, run an AB test, like we do with almost all of our brands, you will just see within the month, exactly how much uplift you can generate with better data. And then after the testing period, you can decide if you want to keep it and slowly start shifting your existing campaigns to the better data or if it's not for you, then you don't pay anything. You just uninstall it and yeah.
Brent Peterson (37:47.67)
That's awesome. And I'll make sure I put the, the Facebook study that you have, the meta study, as well as the links in the show notes, uh, Niles Jessen. Thank you so much for being here. Um, founder of Mabel AI. Thank you. Thank you for this, uh, great conversation.
Nils Jessen (38:06.25)
Yeah, thank you, Brett. It was a lot of pleasure, a lot of fun. And thank you for having me.
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