Stop Losing Money On Ads! Try This Insane AI Hack — Mati Ram | Using First-party Data In Ads, How AI Optimizes Ad Campaigns, Using AI for Better Google And Facebook Ad Results, Importance of Dynamic Segmentation for Targeted Ads

In this podcast episode, we discuss how to tap into the power of AI to create ads and manage ad campaigns for your online store across multiple channels, all in one place. Our featured guest on the show is Mati Ram, CEO at AdScale.com.

Topics discussed in this episode: 

• Why third-party cookies are dying and its impact on Google and Facebook ads
• How first-party data from your store can improve ad accuracy
• How losing third-party cookies do impact ad budgets
• How merchants can use first-party data in ads
• How does AI optimize ad campaigns
• Why dynamic segmentation is key for targeted ads
• How merchants can use AI for better results in Google and Facebook ads

Links & Resources

Website: https://adscale.com/
Shopify App Store: https://apps.shopify.com/adscale
LinkedIn: https://www.linkedin.com/in/mati-ram-0930517/
X/Twitter: https://twitter.com/adscale


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Transcript

Claus Lauter: Welcome to the e commerce coffee break podcast. Today, we revisit the episode with Mati Ram, CEO of AdScale.Com, where we discussed the power of AI to create ads and manage ad campaigns. So let's dive right into it.

Voice Over: This is the e commerce coffee break. A top rated Shopify growth podcast dedicated to Shopify merchants. And business owners looking to grow their online stores. Learn how to survive in the fast changing e commerce world with your host, Claus Lauter, and get marketing advice you can't find on Google.

Welcome. Welcome to the show. 

Claus Lauter: It's a difficult topic for a lot of merchants. And when AI came out, or basically launched big last year, I was thinking about, okay, that's finally helping us with advertising and making things easier. And we want to dive into this topic a little bit more today with me on the show to discuss this.

I have met him, he is the CEO of AdScale. A leading ad tech company, revolutionizing e commerce advertising with AI driven solutions. A tech entrepreneur with over 20 years of experience, he's also passionate about bringing innovative solutions to market to solve real world challenges. Before AdScale, Matti was the founder and CEO of Dynasec Ltd.

and continued to run the division after it was acquired by Checkpoint Software Technologies. So Matti, a really expert when it comes to advertising in AI, and I would like to welcome him to the show. Hi, how are you today? Hi 

Mati Ram: Claus, nice to meet you again. Pleasure to be here. 

Claus Lauter: Great to have you on the show.

Let's dive right into it. Ecoverse advertising, all these different platforms, we see that third party cookies are dying. Um, there's a big shift in there. Things are not getting easier. Tell me a little bit on what's happening in the market right now. 

Mati Ram: I think it's all started in March 2021. That was the exact month that, uh, in my opinion, at least the, uh, advertising at the core, if you want to call it, uh, change forever.

And, and what happened on that month is that iOS 14 was launched. Apple kicked out Facebook from, you know, from getting data from the apps, unless people give consent. And I think that it was the first time where we've witnessed the death of the third party cookie. We, maybe we need to spend some time to explain about the third party cookie.

So basically ever since the internet was invented, the third party cookie served As the main vehicle to track user behavior. And from the early days of internet marketing, Google, and later on also Facebook, heavily used the third party cookie to offer personalized ads. Basically, they could have known anything about any click, any page we visited, any product we've added to the cart, everything we've purchased, like, Everything was very clear and very open.

That made advertising very personal, more accurate. And if you look holistically, I think that's one of the main drivers why budgets shifted from TV and print and radio to the Internet. So Google and Facebook became what they are today. And it's all around the ability to track user behavior. Now, following the, um, uh, privacy regulations like the GDPR and the CCPA and some others, the usage of the third party pixel started to, to, to be limited.

So it started with Safari and Firefox who banned the third party pixel usage. And then, you know, the issue with Apple and Facebook had to narrow their attribution window to seven days. And Apple declared that it would, it's gonna ban the use of third party pixel in 2022. Now it moved to 2023, probably will move to 2024, but eventually it'll happen.

Uh, and they also declare two years ago that they are gonna ban the data also from the Google Play, which leaves us in a totally different world. It means that Google and Facebook are collecting less data, which means that they are less accurate. And that is the main reason why everybody sees drop in ad performance ever since.

March 2021, and it's going to be even worse once Google will ban it from Google Chrome, which is 70 percent of the browsers. So I think that's a huge, um, that's a huge issue in the industry that, uh, everybody heard about it. People know about it, but I am not sure that everybody grasps how dramatic it is when it comes to ads performance.

Now, if you look at that clause, you can see that, uh, you can see that Google and Facebook are becoming more and more black boxes. Like, Google came with the PMAX campaigns. Which is basically, give us your budgets and your assets and we will advertise for you. And Facebook came with the Advantage Plus, which is basically the same idea.

And the idea is, use our own Google and Facebook first party data. We will get you the audience that you want and the people that you want. And we will get you the results. And I think that what we need to discuss is first party data. And how first party data can help advertisers To be more efficient with advertisers.

So what is first party data? First party data is the data that we collect from our customers orders. It includes the order information, the customer information, and the product information. And the nice thing about first party data is that, first of all, it is our data. Nobody else has it. It's not something that either Google or Facebook have because they are not connected to the store.

So they don't know what is the average order value, and what is the customer lifetime value, and what is the repurchase frequency, and what is the repeat customer rate, which products are frequently brought together. All the huge information that you can extract from a store is not something that Google and Facebook have because they are simply not connected to the store.

And, more than that, the pixel gives you broken data, and that's why everybody is so busy with trying to implement analytics, uh, and getting the data. There is duplication of conversion, and everybody is trying to store the data. That's an issue. But the first party data is a very accurate data. It is my data.

I know exactly who are the people, what do they buy, when do they buy, which products. Everything. So it's a very accurate data. It's much more extensive than the pixel data. Think about it. Pixel data is what, 30 days long? The first party data is, is, is forever. I mean, we could use the entire history. So it's much more expensive, extensive.

Having it much more extensive means that statistically it's much more significant. There is more data. With more significance, see, so if we can take that data and we can make Google and Facebook more accurate, we might we might be able to get more results. in, in advertising. And in order to turn the data into that, I think we need the AI 

Claus Lauter: part.

Okay. I like that you basically summarized what every merchant went through in the last two years and everyone's can see declining results, increasing ad costs, um, having the problem that the beloved audiences are not working anymore. Lookalike audiences are not working anymore. So I think everyone had.

Very good feeling on what was good and a good golden times when everything of that was working and marketing was easy and now things are becoming more difficult. Now, obviously, collecting your own data, um, with third party data is a great thing. Um, tell me a little bit about the implementation. So how to do that.

And then the step going from your own data back into the advertising platforms, because obviously then you need to connect that somewhere to make it work. How does that work? 

Mati Ram: So, uh, it actually works in several steps of the process. I think that what connects the BI, I look at the first party data as two layers by itself.

There is the raw data, which we collect from the store, and then there is what I call the BI data, which is the, um, insights that we, uh, Learn from the data because there's a lot of data and there's a lot of noise and and we need to Summarize the noise into actionable things that we can use. So I think that's the first layer now what connects the BI data Into the AI advertising are mainly the segments.

There are customer segments And product segments. Now, what are customer segments? Customer segments, basically, everybody knows that a group of people with similar buying behavior, buying patterns, which we group together in order to offer them the relevant offer. In the relevant time on the relevant channel.

And if we can inject this customers, if the AI can help us find the correlation and say, okay, this is a segment of big ticket spenders. Let's offer them big tickets products. This is a segment of, let me give you some very interesting segment that I see quite often. Segment where the AI finds correlation for people that are more likely to purchase soon.

Based on their repurchase behavior, based on the products that they are buying, based on other people that are buying similar products. So the AI takes that into consideration on these features. And comes back and say, well, here is a list of, I don't know, 3, 000 people. They are more likely to create a purchase soon.

Now, once you have that data, you have a competitive advantage. You can target these people, and people like them, we will discuss it in a second, before your competitors will do. Which means that you have more chances to get them as customers than others. So, the segments are connecting the BI data, BI first party data, to the, what I call AI advertising, and maybe later on we can discuss what is AI advertising, because It's a buzzword that I throw right now, but I didn't explain what does it mean.

So segments are definitely the answer, and maybe to elaborate a bit more about that, it's important that the segments will be dynamic segments. What do I mean by dynamic segments? We want to create segments that people get in and out of the segment based on their behavior, and based on the entire people's behavior.

So a segment of lost customers would be people that The last purchase was more than two standard deviations than the average, for instance, okay? That's something which is dynamic, it goes with the store, it lives with the store, and it means that everyday people are getting in and out of the segments automatically, but you offer each and every segment what's good for him, in the right time, on the right channel, and that makes the advertising much more accurate.

All with data that Google and Facebook don't have. 

Claus Lauter: Hey, Claus here, just a quick one. If you like the content of this episode, sign up for our free newsletter, commerce Smarter Shopify Merchant. In just seven minutes per week, we create content from more than 50 sources, saving you hours of research and helping you stay on top of your e-commerce game with the latest news, insights, and trends every Thursday in your inbox, a hundred percent free.

Join now at newsletter dot eCommerce coffee break.com. That is newsletter dot eCommerce coffee break.com. And now back to the show. A very important point that you mentioned before is that Google and also Facebook introduced dynamic ads there where you throw your money into and you mentioned that very nicely into a black box.

So it's like, okay, you do it and you don't have any control. I think at scale is a little bit different because you have the control, you know, what's coming. And. Coming to the AI, which I'm very interested in and how you do it, there's so many levels where I can think AI can help you with doing your running your ads.

Give me a bit of an overview of how it works, what kind of features you have and what's the best scenario for immersion to get started. 

Mati Ram: So the AI advertising is actually divided into three stages. Stage number one is the data training. We somewhat discussed it before, where we, you know, connect, uh, at scale to the store, we learn all the first party data and we inject the, the insights.

The insights are segments, but also some more advertising related insights that can be very helpful for us. As we just mentioned that interest, gender and age as targeting doesn't work anymore, right? You don't get the good results. But if you can take the segment and you can Looking like a segment and they narrow it with Interest age gender you might get better results because you make google and facebook more accurate So you need to prepare that data that's in the first stage and you also need to look at business Kpis like aov because sometimes you know There are businesses that they will never get to the relative to the expected return on ad spend Only because their aov is too low.

So you need to find ways to increase the aov. Otherwise The correlation between cost of click and conversion rate and the cost of the product will never work. So we need to see how you increase the AOV. Sometimes you need to shorten the repurchase frequency. So you also get some KPIs that are business KPIs that you say, Okay, in order to improve that business, we need to do one, two, three.

And then how, how, how do we achieve that with advertising? So that's like the first stage, analyzing the business and understanding which things, uh, the advertising can help with in order to achieve the, the goals and, and what will move the needle. I think that's, that's the main point where to shoot, what will move the needle.

Stage number two is the campaign creation. And within the campaign creation, you have also two types of AI. You have the mathematical AI, which is getting everything that we've gathered in stage number one, and inject it automatically into the campaign creation stage. So not just give the data and say, okay, success.

But when you create campaigns, you know, say to the people, well, these are the products you want to target on these channels. Here are the audiences that you want to target. You need to work on new customers, acquisition, remarketing, and retention. Here are the segments for that, here are the segments for that, here are the segments for that.

So, and here are the, you know, the geographical places, and here are the keywords, and let's exclude these things. So, all the things that you usually do manually are now being done with AI that analyzes the data and helps you to improve the conversion rate. So, that's One part of the AI in the campaign creation stage, which is a mathematical AI.

But then, and that's one of the nice things, you know, in the past, systems like AdScale had suffered from the fact that you, from the fact that you also need some soft skills. Copywriting, creative. Now we have generative AI. So it makes things much more easy. So for instance, with AdScale, we have added, uh, ad copy with ChatGPT.

Not only ChatGPT, ChatGPT plus a version that makes sure that what you get Is relevant for the different, you know, lengths that you are allowed to use and meets Google and Facebook criteria, so you will not be blocked, which is a big problem by itself. So, ad copy can be done with the system. Ad creative, video ads, image ads, it's all something that the generative AI can do.

Part number two is generative AI, or AI campaign creation that includes generative AI plus mathematical, uh, insights that are injected into the campaign creation stage. And then comes the, for me, uh, the most important one, which is the optimization stage. Now, if that's okay, I would like to spend a few minutes on that because that's super important.

In the beginning of all days of Google and Facebook, optimization was like a big thing, right? We all optimized the budgets and the bids and play with that. And then it was too big and too complicated. And Google and Facebook came with their own, you know, optimization. And most of the people use that. I agree with that to some extent, but the idea is to optimize, not only optimize Google and Facebook is to optimize the entire portfolio.

So how do you optimize your. advertising. There are three ways to optimize. It's budget optimization, bid optimization, and campaign structure. You know, download this, uh, stop targeting this interest, add new ad, all kind of these things. And here, AI can substantially help. Let me give you an example of how, for instance, we do budget optimization, because I think that's very interesting.

Let's just understand the goal. The goal, usually, is to maximize the revenue in the given budget, which means that the advertiser gives AdScale a budget, call it X, And says, give me the maximum revenue that you can. Now, we are in a world that there's a walled garden between Google and Facebook. Google sees only Google, Facebook sees only Google.

Who sees the client? We sit on top of them. We use AI, and I'll explain what is the AI about it, because AI could be a lot of things today, but We use AI in order to optimize the campaign. So in order to optimize the campaign, what do we do? First of all, the idea is to generate a distribution for every campaign in Google, because that's the budget holder, and every ad set in Facebook, because that's the budget holder over there.

And the distribution generates the regression line. In the distribution, we actually try to learn the correlation between the budget And the revenue for each and every campaign. So we get a, we get a lot of data. We draw the regression line. The regression line is, is the line that represents the minimum variance between all the optional lines.

So it's like the, call it the average. Probably. Once we have the regression line, we can now predict because we know in a decent level of certainty, what would be the outcome of revenue for every given budget we give based on the historical data. So if we can predict that, and now you, Claus, as a customer come to me and say, Mati, here I have 10, 000 penned to me in the best way.

I just throw the number into the input. You give me 10, 000 and you say I need a return on ad spend of 500%. Okay, that's the input for the model. Now I use your input and the model looks at all the possibilities. It has all the, you know, curves. So it knows what we will get, probably we'll get from each and every campaign.

Now there is a mathematical model known as the knapsack problem, uh, that tries to find what is the optimum of all the optimums. So each and every campaign has its own optimum, but we want to optimize everything. And that's how we divide the budget. And then, the AI listens to the Google and Facebook auctions 24 hours a day, and sees if the prediction is right.

If the prediction is right, all is good. But if the prediction is not right and there is an anomaly or trend, then it automatically reacts. Now, Google and Facebook are, you know, 24 7 live auctions. So imagine we discussed about Facebook, let's discuss a bit about Google. Imagine that at eight o'clock in the evening, you know, it's evening, everybody's home watching football.

The agencies are home, the customers are home, everybody's home. But the AI that is there 24 7 identifies that. The correlation between the CPC, the cost per click, and the position in Google is much better now than it is usually in the same hour in previous days. It means that our competitors are out of budget.

They are out of budget for today. Which means that if we increase the budget, let's take some budget from Facebook, and drop the bids, we can collect all the impressions for Almost no money. Now that's like an interdate opportunity that you can exploit with AI. So that was a very big and maybe some mathematical explanation to how the AI can help you get better results by actually sitting on the auction and managing your campaigns 24 7.

Claus Lauter: I think it gives a good overview of how strong AI actually is because it helps you in so many different levels. The campaign set up, um, the artwork, The copy and so on. And then when you mentioned that, and I think it's, it's very important to point that out for our listeners is the bit management because who has ever worked with Facebook knows they go in, you waste a lot of time in there because you're trying to manually find out what works best.

And as you said, a, even a very proficient ads manager can Goes home at some point and then whatever. And we all had it Google or Facebook, the algorithm goes crazy. And your ad spend goes through the roof with no results. Now you have the AI tracking 24, seven, what's happening and adjusting accordingly to what's happening on.

The ad platform. So I like that a lot. And obviously that will give you much, much better results. Now, what is the kind of homework that a merchant needs to do before they can get started and what kind of timeline, how long does it take before the first data really kicks in and the AI kicks in to give optimal results?

Mati Ram: The good news is that once the AI gets in, uh, like when, for instance, we connect our system, we have all the history open for us. So if you have data, we can use all the historical data both in, in the store and in the ad platforms. If you have zero data, then the main advantage at the beginning will be the ease of use and the automated, automatic connection, collection of data.

But the learning, learning curve will be longer because we need to learn the data. Okay, that's, uh, when you have a new store. If you already have a store and you're running it for a few months and you have sales and You know still things starts evolving or even if you're big and you have a lot of data basically the more data you have the Shorter time you'll see results results coming in now it's very hard to give a concrete answer only because Uh, the variance is very high, so you know, some people see good results fairly quickly, some people see good results with, requires some more time.

But uh, I think that using AI in advertising, in, in general, if I have to, you know, give a ballpark figure, you can see about 30 percent increase in revenue or decrease in cost per acquisition, depends what you want to. Within one year. Now, along the year, it could be either more quickly or less quickly. It really depends on the store.

It's also dependent on the time of year. Like, Q4 is a very strong quarter for e commerce, so usually results are better. Q1 and Q3 are lower, so it depends where you start, when you start as well. But I think that as a ballpark figure, 30 percent in one year is doable. 

Claus Lauter: Okay, that's, that's a huge result there.

How does the onboarding process look like for a Shopify merchant? 

Mati Ram: So the idea is, uh, to, first of all, connecting AdSkill with Shopify is just to download from the app store. We are in the app store. So it's one or two mouse clicks, and then there is the integration with Google and Facebook. So the system guides you through the process.

You must be an admin in your assets in Google and Facebook so you can connect up. So the system just guides you through the process. Click here, give us an access. It's about a five or six clicks process. And then we are connected, uh, both to the shop and then to Google and Facebook. And if you're an admin and you have no, sometimes people are not admins, so they need to get the password, but if you have the rights, this process should take not more than two, three minutes.

Thanks. If you don't have the rights, get the rights and then do it. But usually it's an easy, uh, an easy process. 

Claus Lauter: Okay. If you don't have the rights, find someone who's better than you. Exactly. 

Mati Ram: Or call, or call the people who have that. You'll never believe how many stories we hear of people with enormous amount of assets in their, in their Facebook page, and they don't have the admin right and nobody knows where it is.

It happens. 

Claus Lauter: What's the pricing structure? How do you charge for the service? 

Mati Ram: Our positioning is to help people to, you know, increase the revenue and, and be much cheaper than any other option in the past. You have two options, either to go for an agency, which is, Has pluses and minuses or to do it yourself where you need a lot of time and knowledge And we're coming back with a third option and we want to be very competitive.

So we are basing our pricing on on the ad spend simply it's uh, So the minimum is 129 dollars per month and it goes with with the ad spend but you pay us a few percentage of your ad spend and In any case it will be much cheaper than in any other solution that you have in order to manage your, your ads.

So pricing is very competitive. 

Claus Lauter: Before we come to the end of the coffee break today, is there one final thought that you want to leave our listeners with? 

Mati Ram: The most important thing is to understand that we are living in a different world and whatever you decide to do with your advertising, first party data comes first.

The only way today to increase the cost is with just to increase the results is with first party data. There was a very interesting study of Boston Consulting Group. I think it was even in 2020. They've analyzed organizations that are using first party data for marketing and advertising and, you know, compared them with others that didn't.

And I think that the average difference was that the people that used first party data was. 2. 9 better, 2. 9x better results, like it was like almost three times better than the ones that didn't. And that was before the third party cookie, uh, uh, was starting to die. So my number one advice to everyone, first party data comes first, and AI really helps.

Claus Lauter: Yeah, makes total sense. And I think that's not really an option to, um, ignore first party data because it's, it's the one way to go. Where can people find out more about you guys? 

Mati Ram: Uh, on our website, www. adscale. com. Or if you're a Shopify merchant, we are just a quite popular app in the, in the app store.

Claus Lauter: Okay. I will put the links in the show notes. Then you're just one click away. Marty. Thanks so much to give us basically a masterclass on how AI helps with advertising. And I think that's the way going forward. I don't think there's any other way around and I hope a lot of people will check your system out.

Thanks so much for your time today. Thank you so much. Claus. Hey Claus here. Thank you for joining me on another episode of the e commerce Coffee Break podcast. Before you go, I'd like to ask two things from you. First, please help me with the algorithm so I can bring more impactful guests on the show. It will make it also easier for others to discover the podcast.

Simply like, comment, and subscribe in the app you'll use. Using to listen to the podcast and even better if you could leave a rating. And finally, sign up for our free newsletter and become a smarter Shopify merchant in just seven minutes per week. We create content from more than 50 sources, saving you hours of research and helping you stay on top of your e commerce game with the latest news, insights, and trends.

Every Thursday in your inbox, 100 percent free. Join now at newsletter. ecommercecoffeebreak. com. That is newsletter. ecommercecoffeebreak. com. Thanks again. And I'll catch you in the next episode. Have a good one.