How To Work Around Walled Gardens And Reach More Customers — Reeto Mookherjee | Why Traditional Attribution No Longer Works, How Brands Use First-party Data To Improve Targeting, Why Predictive AI Unlocks New Marketing Power (#375)

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In this episode, we explore how to supercharge your marketing performance using predictive AI in the era of increased privacy regulations and data restrictions. 

Our guest is Reeto Mookherjee, CEO and co-founder of Angler AI, a company pioneering predictive conversion software for digital marketing. Reeto shares insights on improving marketing efficiency and discusses how brands can adapt to recent changes in digital advertising platforms.

Start a 30-day free trial of Angler AI today. Click the link:  https://www.getangler.ai/free-trial?utm_source=podcast&utm_medium=referral&utm_campaign=ecom-coffee-break

Topics discussed in this episode:  

• Why privacy changes demolished marketing performance. 
• How iOS updates doubled customer acquisition costs. 
• What predictive AI does to restore ad targeting. 
• How first-party data powers smarter marketing. 
• Why old attribution models stopped working. 
• How DTC brands are improving ROAS and reducing CAC
• What makes the 'movable middle' crucial for growth. 
• How brands achieve 32% better media efficiency. 
• Why AI combinations amplify marketing results. 
• How to implement new tech in under 10 minutes. 
  
  

Links & Resources 

Website: https://www.getangler.ai/
Shopify App Store: https://apps.shopify.com/angler-ai-app
LinkedIn: https://www.linkedin.com/company/angler-ai
YouTube: https://www.youtube.com/@GetAnglerAI

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Transcript

[00:00:00] Hello, everybody, and welcome to the eCommerce Coffee Break Podcast. I'm Claus Lauter, and you're listening to the podcast that helps you become a smarter online seller. In today's episode, we find out how to supercharge your gross marketing with predictive AI. Joining me on the show is Reeto Mookherjee he's the CEO [00:00:20] and co founder at Angler AI.

So let's dive right into it. Hello and welcome to another episode of the eCommerce Coffee Break Podcast. Today we want to share and find out how to reach more customers. We want to talk about walled garden and what that is and what all of this to do has to do with predictive AI. With me on the show today is Reeto Mookherjee.

He is [00:00:40] the CEO and founder of Angular AI, a company pioneering predictive conversion software for digital marketing. With over 18 years of experience in AI and machine learning, he has held a senior leadership role at GoodRx, NBCUniversal, and IngroMicro, and he is an Informs Edelman Laureate for his groundbreaking [00:01:00] work in pricing optimization.

So let's welcome him to the show. Hi, Reeto. How are you today? Good. Uh, really nice to do this together. Cool. Let's get started. So performance marketing used to be very, very simple years ago, has changed a lot, and it's not that easy to get through on, um, what see results on Meta, Google, TikTok. And so [00:01:20] tell me what has changed.

Yes, sure. A lot of, you know, actually the fundamentally, I would say the shift has started in around 20, 2017, when there is a, you know, there are a series of regulations come start coming in. What started with GDPR, uh, in Europe, uh, continued at CCPA, all of these [00:01:40] regulation changes. Then, you know, then there's a major shift happened when, um, One of the major tech player, Apple, made their privacy policy changes.

They're saying that, well, platforms, it's okay you can track users on the, when they are using your product. It's not okay, or you need to ask for explicit consent for track the same users when they are not [00:02:00] using your product outside, uh, and they're going onto the internet. And that essentially disrupted the way the entire architecture, entire digital marketing ecosystem was in.

It's essentially, they, they needed to have data. And, uh, and data about unrestricted view of what people are doing on inside their app and what they're [00:02:20] doing when they're living it. So once, you know, and that, that line of sight almost got broken for, you know, all the iOS users. And when you think of Apple, it's a 50 percent or more than 50 percent market share and in some of the markets like us, that's where most of the buying power comes from those devices.

So that disrupted the way digital marketing was [00:02:40] working. And that happened around 2020. Now, I think a lot of merchants are very aware of that. And then some workarounds came up, um, conversion API from Facebook and so on and so forth. But obviously the access to the data, to the details that are really necessary to target your audience was not as good [00:03:00] anymore.

What are kind of challenges, um, came out of that for brands and how did they try to work with that? Yeah, so, you know, as you can imagine, that was a major disrupt. Uh, and when, when the, when Apple started rolling out and, uh, everyone, you know, there's a, there's a question that I'm, well, how many people will [00:03:20] give them consent?

And it turned out is the very low number, you know, latest number stats I've seen is 88 percent users have opted out, you know, it's almost become. Defacto. People thought, well, if I ask for permission different ways, maybe people will give consent. It didn't work out. So what happened overnight is, you know, the, you know, if you're thinking of CMO all the way to a [00:03:40] hands on keyboard media buyer.

Your media efficiency suddenly on the surface got lost, like your, you know, media cost, uh, or acquisition cost or efficiency, if you're running direct, direct, direct response playbook, your cost of action, desired action, which is usually a revenue event, cost per goes up, uh, went up by 100, 200%. [00:04:00] It's like, It's like everything feels like broken.

And then there are, uh, you know, then there are a lot of solutions started happening. It's like, no, no, no, it's an attribution problem. Let's solve the attribution problem. Your ad is working as fine. You just cannot see the results. Well, then that was the, you know, then a lot of investor VC money and [00:04:20] a lot of company attribution focused companies.

Okay. Let's, let's see, maybe work ad is working equally well. You just, you just cannot see it now, but in all this experiment. Prove it out to me. That's actually not the case. Yeah, you can, attribution, you can build a better mousetrap outside and you can get some of the efficiency, but it's still not adding up.

If your loss is [00:04:40] still like 50 percent plus efficiency gain, uh, efficiency loss, they are reporting. They're like, okay, what the, what is problem? The problem is, and if you, You know, and platforms have been very tight lipped about this, right? They could, they cannot tell how severe this is. Like almost like a, if you look at any world garden, like of course I'll say meta, if you [00:05:00] look at their, they're almost like two universe exist within engineering teams.

There is a 700 plus engineers working on attribution solution. There's a very few set of engineers working on auction. side of things. Auction basically means is, you know, it's a very efficient auction platform. So who do I need to show the next ad to from this advertiser [00:05:20] then so that, you know, it, it win, win, win for everyone, you know, uh, user has a better experience.

Uh, advertiser gets the desired outcome and it's, uh, and the platform also makes more money. So for that very complex decision. that auction systems suddenly were starving for data. They were flushed with [00:05:40] data, now they are getting, only they could use 30 percent or less than 30 percent of data they could use for auction trading.

And then, and then the, the, the very poorly understood concept is, it's not that they didn't have the data. Brands are still sending data. They are collecting, they are anticipating these changes. They are collecting more data, like asking [00:06:00] survey, quizzes, before you become a customer. All of those data, data collection tools, CDP, customer data platform, all those investments went in.

So not that they're getting more data, they're sending all the data back to ad platform, but the auction engineers, their hands are tied by, not by that data, by the legal team. Legal team says, no, no, [00:06:20] no, you cannot touch that data. If it is, if user has opted out from my detailed tracking and iOS, you cannot touch that data.

So it became like, well, I have the data, I'm dropping the 70 percent of the signal on the floor, and I'm, my line of sight suddenly is broken. I can see some users through the, all the way through the [00:06:40] cradle to grave, but majority of the users have no idea when they're leaving my platform, what they're doing afterwards.

And it creates a major disruption. I think this was the best explanation of the situation that I have ever heard. So congrats to that. And I think our listeners should listen to that twice because that really explains the situation what has happened in the last year [00:07:00] absolutely perfectly to the point.

Now also everyone opted out and then people were surprised that they get completely random ads in their news feeds and get annoyed by that as well. Now you came up with a solution to help with that and we want to talk about it. Predictive AI solutions on that side, um, talk me through how does it help?

How does it [00:07:20] work? Right? So at the core, when you think of it, uh, is that the, you know, before these changes happen, iOS 14. 5 and all the changes happen, uh, the platform was doing certain work on advertisers data, you know, so you send the data, they were doing some, you know, think of it as a data [00:07:40] pipes going in, but they will.

Enrich that pipe before they use it for auction, and they could do it because they have unrestricted permission to it. But you as a brand, when you think of it, users opted out from platforms, tracking. They haven't, you know, platform. You know, if you are a brand, you know you, this is your [00:08:00] visitor, your data, of course you have a consent form and everything.

Most of the users are okay to share that data with, uh, with their brand that they're buying from. So you as a, if you own the brand, you still have all the data at your disposal. So what you, we do is we sit in between that data flow. So think of the [00:08:20] data supply chain or data flow as a simple pipe, like where you transmit all the data as is.

We intercept that flow. We do some extra work in the middle. Then we send that data. You know, then before we transmit the data, so in a court, what it means is and the communication mechanism is essentially, uh, is a [00:08:40] conversion API or API system that each of the platform has that's basically saying this is how this is the standard protocol for you to send me your transaction data or your browser data or your offline transaction data.

That's open for everyone. You don't need any permission. So what we do is we get all the data. The enhancement that [00:09:00] happens is we, at the core, is a predictive AI. So the predictive AI that does, you know, uh, we use a deep neural network. We train, we, in the early days, we had about, you know, two years and a few months into the journey.

But first eight months, we've spent a lot of time and energy. being a former data scientist [00:09:20] myself, you know, we make sure that we build the system, this prediction in as robust possible at as general purpose as possible so that we can cater to a lot of different customers prediction problems. So the prediction that we do is based on what outcome you as a marketer wants to do.

If you want to identify a new customer, we'll find high value. [00:09:40] Visitors, visitors who are likely to be, you know, have high propensity for conversion. And we send that data back to that platform. So then telling Meta, okay, your line of sight is broken. I get that. But can you only focus on this, this good stuff?

Don't focus on the less, less of the fluff stuff. So we basically bring inefficiency [00:10:00] back to that mechanism. If someone says, well, I'm going to acquire. Customers, my whales, the customer who will repurchase in the next 90 days. Or if you have a subscription brand, the customers that never churn, okay? These are the good stuff.

Auction, find more of these, like these people. So that translation service that we are doing on behalf of brands, [00:10:20] uh, when, you know, if the brand has their internal data science, machine learning. You know, all the technology skill sets, they could do it themselves. What we find is, you know, most of the brands, even if the brand does, you know, billion revenue plus, uh, the, the, the skill set is not as simple as, well, I hired a data scientist and they do it.

[00:10:40] It's a different skill sets in a very super specialized skill sets, a lot of know how on ad tech, plus data science, machine learning, plus maintaining the system. Plus changing constantly evolving as platform change their requirements. So it becomes a lot of under undertaking and we are platformizing all of that.

Do it once [00:11:00] and you can distribute the cost amongst. More users on the platform. I think that's a very smart solution there. And as you said, you need a lot of skills, knowledge, and expertise to build something like that. Now you said you're pulling the data from different platforms. Now we have listeners who are on Shopify, who are on other platforms, who lose using other [00:11:20] apps.

All of this data can be used. Is that right? And how can you use it? That's right. Uh, so when you think of it, we have customers. Uh, we are very lucky actually in the early days. Uh, for a journey, we actually got some tough problems. Like we have some customers who are using completely homegrown solution.

You know, they don't use any of the Salesforce, BigCommerce, [00:11:40] uh, Shopify, uh, you know, any of the WooCommerce, none of this. They're like homegrown. Uh, or some of them using, you know, Shopify, but you only use Shopify backend, Shopify headless. So the technology standpoint, when you think of it, we see it as a data in, data out.

Data in standpoint, you know, we, we, we make it easiest, easiest [00:12:00] way for, for our customers or prospects to get us data. So if they're using Shopify, of course it's very straightforward. Under 10 minutes, literally takes four clicks and creating an account and we, that app installs and we start, we are, we are, we are, we're all set to get data from.

Same thing happens for if there is a commerce platform like Salesforce, commerce [00:12:20] Cloud, BigCommerce, E-Commerce, all this, we have apps or plugins. Now, let's say there are, there are some, some customers, maybe not like just having homegrown, but they have invested in customer data platform. Great customer data platform like Segment, Telium Segment or Amperity or other, other platforms.

We actually have built [00:12:40] in connections connected for it. So we have a destination. Angular is a destination from the CDPs. Or if it is, if not, none of this works, like if you're something, some other ways, or you do not have CDPs, then you still have a mechanism we can connect directly to their cloud data warehouse.

If they're using Snowflake, Redshift, or other places, or they can deploy Angular [00:13:00] tag. Directly on their JavaScript or tag managers. So we make it data ingestion as easy as possible and as customizable as possible. Based on the need from the client. And then on a destination side, of course, we work with, you know, all those world gardens we mentioned.

And we're building more destination for programmatic web and in the outside of world garden [00:13:20] as well. Uh, but, uh, but yeah, that's how, that's how we, we tackle the data in and out problem. Mm hmm. Let's, let's talk about the distribution side, about the ad platforms itself. Um, how do you measure the attribution, the results?

What kind of reporting do you supply? Right. So, so the biggest thing is [00:13:40] when we started on, on this journey, you know, uh, you know, my co founder and I had two things, which is, can we build the technology where it is very easy to experiment? While experiment, uh, when you do the experiment, because the performance market in this day and age, you need to run a lot of experiments, a lot of tests, and you need to [00:14:00] have a at scale running those, getting inferences, what's working today may not work, uh, you know, two weeks later, and then you constantly, uh, evaluate and testing.

And then taking that same team, when someone, not a customer, just from the piloted. We didn't want to, uh, you know, we'll be as less intrusive as possible within their [00:14:20] marketing operations function. So the way to imagine if customer working with us or prospects working with us install their, our app, then we do not want to mess up any of that existing setup.

They're already having a way to send data. They're optimizing it. It's all fine. The way we say it is. Set up a new pixel new data sets, you know, so give us [00:14:40] a new container completely fresh We don't have all the history your legacy setup has years of data all the data We have we barely started getting data from you last minute, you know And now let's start pumping the data with with this new technology into this into this into this data set then after a week We are ready [00:15:00] for a test.

So when you think it like one, one hand, we are already starting on the disadvantageous position because your legacy pixel has years of data. We barely have a week and some cases even less than a week. And we're now saying, well, okay, you run an A B test. A B test means, you know, in platform A B test. So Facebook has a A B split test.

So they're [00:15:20] making everything being equal, same creative, same copy, same audience even, you know, but only thing is. Those campaigns are optimizing with this new way versus your legacy way. Everything being equal. Only thing is that difference. And then sure enough, if you run it, we have run over 200 A B tests.

96 percent of the cases we won. We won at the statistically [00:15:40] significant test level. That means, if you run this test again, very likely, you know, 70, 80, 90 percent chance that you're going to find the same winner. And then when you think of it now, it's like, well, you're already, you're winning already on a, You know, disadvantageous position because you have, your hands are tied.

Now what happens, natural question is, okay, [00:16:00] what happens if you start sending all your new technology data into my legacy pixel, then I can use all my history plus going forward better data. Then you see another state function improvement. But by that time, the You know, the technology is somewhat proven because they already tested in their data.

And then, uh, then clients start using it. Then they see the [00:16:20] step function improvement in their overall KPI. So overall KPI, maybe, you know, uh, marketing efficiency ratio or blended CAC, all of these things started improving, you know, that you're, you know, it's that it becomes, you get out of this attribution noise.

That, you know, uh, because attribution last touch attribution [00:16:40] in this DNA doesn't work as well. So you might win. It might something may look very good on the surface on the click, but you may win. You may lose the performance marketing, you know, uh, the battle overall. So in this way. When you, when you start, you experiment, you prove it out.

When we start, uh, being the system of record, then you see a [00:17:00] state function improvement and that performance stabilizes. And it also gives them optimization capability or experimentation capability of test out new things. You have a hypothesis, very likely platform can support with some clever event orchestration.

So we orchestrate new event. You want to start optimizing on it. Does it work? Great. If you work it, [00:17:20] then you, you found an unlock. If it doesn't work, let's find iteration on, on those things. So it becomes a, you know, we, we pretty much create a smart data loop that work complements all of the creative, all of the strategy, all of the things are very much still fundamental, fundamental of performance marketing.

So we see like three pillars of [00:17:40] performance. Marketing is in a strategy, uh, creative and copy. And data loop. We, we take care of the last pillar, very essential pillar. We make the data loop smart data loop. As long as you have the first two pillars, you know, you should be getting step on. It's a significant unlock.

On average, we have seen [00:18:00] 32 percent improvement. So your 100 spend suddenly works like 132. I think the performance marketers listening to this episode will have a big smile on their face right now. I want to dive a little bit on privacy issues and maybe data limits. Is there anything from the platforms where they cut you off or where you see a risk?[00:18:20] 

Actually it's the opposite of it. So when you think of it. The, the platforms like Meta and others, we are actually working pretty close with them as they are rolling out new offering and product because they just realized that someone needs to do this translation job and they cannot do it. And, uh, and after ChemDesign Analytica and all of the data [00:18:40] privacy changes, they are not.

We need to take that, uh, chance and we actually, you need someone who is a marketing service provider to the first party, you know, to the brands, you know, that data and we take privacy and security very seriously, you know, my, my, in my, in my past lives. I, I implemented HIPAA systems, you know, for health tech [00:19:00] companies.

So, you know, and, uh, working other regulated, uh, industries and entertainment where there's a, uh, VPP, a Video Privacy Protection Act. But we, you know, uh, we build Angular. As privacy by default, so the way you think, think of this is it's not a not your data contributed to other data. So strictly [00:19:20] non contributory.

So we are a marketing service provider. If a brand stops working with us within 30 days, all their data is gone. All the trace is gone and then when within the data itself, the way we handle is there is very sensitive data. We handle it like customer information, customer data in this. So the way is, you know, again, we [00:19:40] take very good best practices from this thing is which is, you know, decouple sensitive data as early as possible.

Use it, you know, keep it in the vault and only use it when you need it for transmission side of things. When you look at prediction, I cannot tell any, you know, if you go and look into Angular systems, you, it's completely ID, ID based. I [00:20:00] cannot tell you whose data it is, you know, because I don't need to.

Machine learning systems or deep neural network doesn't need to know it's, it's clouds or it's retail. It needs to know this is the user ID and this is the content ID, this is the product. So that's how we abstracted, you know, concept. And then, of course, when we communicate back to that platform, then we need all the data again.

We go back to the vault. We [00:20:20] get the only permission based access and very few human being actually can access it. I mean, some, some of the data vault, I even cannot access it, you know, and we did it by, by default because that's how, that's how important it is, the security and privacy, you know, at the, at the forefront.

Um, and so we are building it for the future. You know, we [00:20:40] expect more regulation to come. Uh, only requirement is as long as brands have permission and use of their data, their consented data, that all goes into the platform. Yeah. And I think that's the way it should be. Once you have the content from your client, then it's in the best interest, in the best service level for the client, and then you can use it.

Can you share some success stories [00:21:00] or case studies of brands that you have worked with and what kind of results they saw? We have many use cases. We You know, uh, we, we unpack. So when you think of it, uh, try before you buy band as a one table in the one of our early design customers, they are like stitch fix competitor when you think of it.

So they were optimizing the early customer and they're like [00:21:20] homegrown. So they help us build this, all of the technology flexibility. So they, they have been seeing, you know, over 30 percent improvement in, you know, their new customer acquisition cost. And then what they started seeing is about. in 11 percent improvement in their retention of the customer.

So it's not only your, your acquiring customer cheaply, but you're also, [00:21:40] you know, those customers taking or ordering more retention instead of behavior. So almost like that becomes a free cashflow on top of. your acquisition cost efficiency gain. Uh, then we have, uh, we have a customer, uh, you know, called, uh, Bortesian.

So they are, uh, they are Shopify. They are actually, they recently became, you know, the top 10 [00:22:00] innovative brand. They are, you know, think of their Nespresso for Uh, cocktails. So they make a machine, espresso for cocktail, very innovative brand, you know, grew really fast, you know, in, uh, in the last few years, uh, and they had an existing set up what they started using and we are seeing, you know, uh, [00:22:20] close to 90 percent improvement in, uh, marketing ROAS on the Northstar they, they, they care about, which is the, uh, New customer acquisition selling them first first product.

We have a woman apparel brand Where they are seeing, you know, they're interesting is they are a they're a holding company pe backed holding company And they before they started with [00:22:40] angler They had this constant battle between the in the cmo and the board about new customer acquisition because the numbers they see They saw in platform didn't match with You know, uh, what they were seeing in their overall system, financial reports.

And, and even though they are following to the T's, all the best practices out there. Their data sanity, data [00:23:00] practices was the best in class I've seen. You know, but it was still not working. And it became like really a perfect, you, you study is what is, why this is not working or what is broken? And how we come in into, into, into fixing that.

So they, uh, that's a Christie Don. They saw 33 percent over Improvement, [00:23:20] uh, you know, over I think 35 percent improvement in new customer pack, you know, go on. I mean, we have some brands, you know, years and which Facebook actually came up with the case study. There's a digital landscaping service, uh, you know, and they recently has an exit.

But, uh, when we started working with them, there's a digital landscaping. So we 1500 class digital landscaping [00:23:40] service. So people don't buy on impulse, right? People come back over and over again. So the way they they are using paid marketing is, you know, they buy leads. And then, you know, those leads convert, you know, then they have different mechanisms, CRM, telecells, you know, all of the cells forced to convert.

With Angular, they got same cost per lead, but those leads converting [00:24:00] 55 percent better. You know, and become a case study, uh, from, you know, and it's not us telling us it's like platform meta published that case study with the agency and the brand and, and, and, and then they're in the mix. Uh, so things like that, you know, we have some, you know, outer as a, you know, furniture, digital, uh, long consideration cycle and a [00:24:20] very innovative brand furniture, outdoor furniture brand.

Uh, they were using, you know, when they started using Angular, uh, they saw immediate impact on, you know, uh, bottom of the funnel. Then they started expanding to the middle funnel, and it become a full funnel optimization strategy. Beauty brand's Wonderskin has great use cases of that, [00:24:40] identifying the movable middle for their unlocking growth.

Like, you know, think of it, beauty is such an impulse driven by, and, but this is also the category where there's a lot of competitions out there. You know, there are so many brands out there. Even Sephora has a lot of companies. A lot of people are conquesting Sephora audience for their purposes. And [00:25:00] in that category, the growth actually comes from not the in market.

You know, interestingly, we will be like what we call the movable middle, which is the, you, you, you talk to the right people. They may not be ready for, you know, an eyeliner. yet or whatever product or skin care in a cream that you're selling, they may not even know you exist as a brand, [00:25:20] but once you engage with them, then they are likely to convert in next seven days or whatever, whatever time they will take, whatever in the usual window is.

And when you tap into that audience, interestingly, your competition is very much less that point your media cost. decreases, but that actually, our media, you know, impression cost decreases, but [00:25:40] those users also convert, not in the same session, within the next seven days, they will come back as a different session.

They may come back from another channel, but your first engagement, you're driving from that, from the channel, uh, that will drive to the ultimate conversion. And it's, we're seeing a steep function improvement in their overall final efficiency for a billion, you know, one in this. Very [00:26:00] long consideration cycle, niche market to mass market, you know, impulse buy.

This technology works because all we're doing is segmentation. And segmentation with the data that brands has and the business model they have. Sounds a little bit like the good old times, um, when that was working from the platform itself. But with your solution, I think we're coming back to that. [00:26:20] Now, walk me through the typical onboarding process.

What steps are involved? Is there any kind of homework to do before you can get started? How does it work? Right. So home onboarding process for most of the platform. So if you, if someone is a Shopify merchant or they're on Shopify or big commerce or any of the commerce platform, the onboarding process literally takes under 10 minutes.

So [00:26:40] if you, I'll use the Shopify as an example, uh, going to, uh, there is an Angular app. You can find it. Uh, you install the app. Then you connect, in the process, you connect to the destination. So we, you know, you'll see all the channel destination, like Meta, Google Ads, TikTok, Snap, Interest on the platform.

Let's say you say, well, I'm going to start with [00:27:00] Meta because that's my, you know, most of the spend going in. So you connect there. And that's it. That starts, that starts the process from the back end. So we start listening events, we start listening, exporting the data. Uh, we, of course, in your workspace, then you, you start getting about three or four emails from us.

Like, as the each steps complete, [00:27:20] well, we are data ingestion complete, just ignore. You don't need to do anything, just, just, uh, when progressing is the, you know, the pizza oven, right? So the pizza is made, and we just move to the Next stage, last stage. What happens after, you know, after the last stage is done, we are already starting events in the, in, into your, into your new setup.

So not [00:27:40] disrupting anything. And then that point, our customer success person, uh, or team will reach out for a follow up. So in that follow up, what we discuss is essentially, you know, this is what we learned. It's not a. Black, we don't want to make it as just a, you know, magic. It's like, this is what we learned.

This is what's going to happen. You, you are now ready for a test. So we shared a test plan. [00:28:00] Uh, very lit, literally like any hands on keyboard marketers. It's like, we never spend more than two minutes, maybe five minutes on a test plan. Because they're like, oh, this is straightforward. I know it. You just clone your existing and you optimize in a new way.

Just drop down, select it. You, you run your own experimentation now, usually two weeks [00:28:20] later, we come back and many cases actually before two weeks platform will say, well, I actually find an early winner and 96 percent of the time it's us. But again, it's like, well, under, after two weeks we reconvene. What did we learn here is the thing by that time, you know, that point we already have a business case for it, for the, you know, how, how we're going to be [00:28:40] helping and, uh, what does it mean for this?

top line and bottom line impact. And that point, and then we talk about the services, you know, our, our service fee becomes one of the easiest sell from the internal selling standpoint, because this is the technology that will give you at least 10x ROI from the services and everything you, you, uh, you know, [00:29:00] factor in.

Okay. That sounds very, very straightforward. Let's talk about your pricing. How do you charge for your service? So we miss, you know, uh, our pricing based on, you know, size of the brands and how much media they're going to optimize through us, you know, to the channel. So we typically, there's a minimum fee about, you know, a [00:29:20] thousand dollars, uh, minimum, uh, platform fee, but then, uh, usually, you know, what we try to do is about, we try to get about 3 percent of media that optimized by us as our fee.

That's our target. Of course, uh, we, we have some really big spender where You know, we'll be making a lot more money, you know, uh, if we, if they're paying [00:29:40] 3%, but there's a ceiling. So if there's a below, you know, there's a, uh, there is a floor, there is a ceiling. And we try to strive for about 3 percent of media spend optimized, you know, and 3 percent may look like, you know, it's like becomes non non, you know, very trivial decision.

Uh, from the standpoint, because if we are delivering 32 percent improvement, getting you [00:30:00] 3%, you know, you can take your 3 percent budget cut. So you can keep your flat budget and you're going to get 20, you know, 29 percent of the benefit. You know, it becomes very, very easy sell from that standpoint. Okay, that's very easy to calculate for a market, marketeer and to put into their budget.

Reeto, [00:30:20] before our coffee break comes to an end today, is there anything you want to share with our listeners that we haven't covered yet? Um, Oh, so I would say the landscape is changing very fast, you know, and there's very exciting time to be in industry, you know, and with all our JNI. LLM the innovation happening on the creative and copy and what we're focusing on an [00:30:40] angular is the predictive AI.

So when you think of the JNI innovation and predictive AI on that thing, when you join these two things, almost like a left brain right brain. Coordination and that's where the magic happens, you know, so that's why it's a very exciting phase You know, we are creating a smart data loop performance marketing is just a wage for [00:31:00] us we think you know, we we focus on performance marketing because we are passionate about it if we had a founder market because we've been working with marketers we saw firsthand how problem it is and we think it's really it's a It's a it's a it's a painkiller, not a vitamin.

You need that solution. But then possibilities once you create the smart data loop, you [00:31:20] can use the smart data loop for site personalization. You can do better retention. You know, there's endless possibilities on this and even collaborative commerce. So that's why we're excited about. You know, what we have built and what we are learning with our customers and design partners together and it also opening up what is possible once we, you know, the [00:31:40] extensibility of the platform.

So, and we made it very easy to test out, you know, I know there's a lot of claims out there, you know, on the what is technology possible and we are making also very bold claims and I think we believe is the best way to prove it out, prove it out for every account in a very easy way. In a simple way, [00:32:00] uh, so that it does not require a lot of investment on anyone's time, uh, to see the results.

And that's why we built, so we're, we're willing to get, take on more, tackle more hard problem. Is that in a use cases that we can solve, we use, use cases we solve, we think it, it helps everyone. It helps us, [00:32:20] it helps other brands, we're trying to solve the same problem. So, we're definitely looking for, uh, people who are, You know, questioning the status quo, not, if you're not happy with your performance marketing, and if you think your data loop is okay or not okay, even if you think it's top notch, still, still, I think there's an improvement opportunity.

So give us a shot, give us [00:32:40] a chance and, and we'll, we'll have a lot of fun along the way. Yeah, I would totally agree to you. Um, I'm in digital marketing for 25 years. I'm a dinosaur and, um, exciting times are actually on yet since AI came around the corner, things became much more exciting, much more productive than they were before for a couple of years.

So I would totally agree. And I think there are [00:33:00] so many opportunities right there. And also with your solution that marketing will become better, will be more, create more results, better results than it was in the last couple of years. And I have a lot of people reach out to you. Where can people go to find out more about you?

Sure. Uh, our website is Gate angler.ai, uh, gate [00:33:20] angler.ai. Uh, and, uh, you know, of course, uh, you know, uh, you can find me. We have LinkedIn presence and we mm-hmm . Also, uh, you know, uh, you can start a trial in without talking to any one of us. You can start the process, complete self-service, or you can also book a demo, you know, and we will be happy to, one of us will be happy [00:33:40] to talk to you and walk you through the steps.

Excellent. I will put the links in the show notes as always. Then you are just one click away and I hope a lot of people reach out to you. Reto, thanks so much for giving us an overview and, um, I hope to talk to you soon and see what's in the box for you and our listeners. Thanks so much. Thank you.