The Scariest Ad Mistake Shopify Stores Make — Drew Smith | Why Paid Media Is Harder Now, How AI Changed Google Ads, Why Most Products Stay Hidden, What Makes Performance Max Tricky, Why Big Data Sets Matter, How One Brand Grew

The Scariest Ad Mistake Shopify Stores Make — Drew Smith | Why Paid Media Is Harder Now, How AI Changed Google Ads, Why Most Products Stay Hidden, What Speaker 1: Could we predict the probability of an item selling and then could we calculate the cost of selling that item so we could then work out the return on investment? Speaker 2: Hello and welcome to another episode of the Ecommerce Coffee Break podcast. Today, we want to talk about how you can make paid media pay. Now paid media was a bit of a pain in the neck in the past because a lot of manual things you need to do, things you need to learn, algorithms changing all the time. Now with AI around for quite a while now, things have changed quite a bit. We want to dive into this topic and with me on the show, I have Drew Smith. He is the co-founder of Up.ai. Here's a big background when it comes down to helping a lot of merchants with their strategy when it comes to paid marketing. So, I want to welcome to the show. Hi, Drew. How are you today? Speaker 1: Thank you for having me. I'm really excited to be here. I'm looking forward to the chat. Speaker 2: Drew, what are the biggest challenges retailers face today when it comes to managing paid media campaigns? Speaker 1: Oh, that's a big topic. I think there's quite a lot of, quite a few challenges, I think. I mean, if you look at it from the interaction from the consumer side, first of all, you've got more channels than ever. So you've got platforms like Google and you've got Bing and then you've got social media platforms like Meta and Instagram and TikTok and then you've got marketplaces like Amazon and they're all competing for the audience and they're all competing against one another. They're all evolving their own technologies. They're all advancing their own AI. That means that for paid media managers, they've got definitely an extended buying cycle where they're trying to advertise to the same audiences in different locations across different devices through different media interactions. You've then also got for the paid media manager themselves, they've got to constantly be ahead of the curve around the adoptions of technologies and the approaches and the best practices. And then, as always, I think the biggest challenge that paid media has managed to have today is everything is becoming – paid media is becoming such an important and integral part of a retailer. It's driving typically anywhere between 40 to 70% of the total traffic to a website that The media spend has become so important that it's really important that paid media managers align their business requirements and add to their media goals and objectives. And that's actually one of the biggest challenges that we often see is there's so much to learn and there's so much in the process and the kind of treading water scenario. That actually kind of ensuring that the strategy and the business objectives are deployed successfully is often sometimes overlooked or just there's not enough frequency of adapting in that environment. Speaker 2: Like perfect sense. Now, as is mentioned, there's a ton of different platforms. Today, we want to talk about the one that is around for the longest time. It's Google. Google is probably also the biggest player in the game. They came relatively early out with Performance Max, I think about two years ago, if I'm not completely wrong, and were relatively early to the table when it comes to AI. Let's talk a little bit more about that. What has this shift towards AI-powered internet advertising done to the landscape for ecommerce businesses? Speaker 1: It's done a huge amount in terms of the evolution of AI and how Google kind of adopted it. If you go back only four years ago, it was still quite of a kind of analog manual experience for the PPC managers. And you had to kind of really have a depth of knowledge and specialties around kind of like you're doing bids at a manual level. It was highly complex, highly configurable rules-based programs. And then, you know, 2019, they released Smart Shopping, which was the first kind of walk into reinforcement machine learning. And I remember that time, there was a lot of, obviously, talk in the market around it. And, you know, with AI and automation, there often means things like kind of black box scenarios where Paid media managers weren't becoming aware of things like, you know, they lost transparency around where products, adverts were being placed in terms of devices. They were getting kind of limited access to understanding of audience insights and how these automation programs were deciding things around things like bidding. But what we did see pretty much 12 months later in kind of 2020 that most paid media teams and most retailers had adopted smart shopping. And we've seen a similar thing with Performance Max that at first there is often quite a concern. People kind of jump in, jump out of using the technologies. But what you can kind of see again is Google has delivered a product that actually ultimately outperforms the old ways of working and provides a more simpler and sophisticated kind of tool. Although there are the drawbacks that I think some of the challenges that paid media managers have is they're kind of You know, you've really gone from a kind of rules-based content world to a mathematical problem and it's a real kind of data science problem and that's quite a gap in kind of learning and expertise a paid media manager has to go through. I think there is very limited resource in the market today to really teach and educate paid media managers of how to work with a machine learning driven product. Most of these, the background of a paid media manager isn't from an engineering or data science background and they've kind of been thrust upon with these technologies. As I say, although actually fantastic and actually if you look at the end result Most people are really happy with what Google have produced, but there definitely is the fear around, how do I control it? How do I make the most out of it? What could I be doing? And there is very little education around these technologies, which I think is a challenge for them. And a lot of the kind of old ways of working have pretty much been fully automated as well. Speaker 2: I have been with Google Ads for, I don't know, 20 years, and I think most of my white hair are coming from that. And as you said, it was rule-based, so a complete shift there. And as you mentioned, not everyone is a data scientist. So tell me a little bit more how Performance Max campaigns work and why are they sometimes hard to manage? Sure. Speaker 1: So I think if you go back in time, paid media managers, especially if you're a retailer, you've got thousands of products, products in different states, you've got new products being launched, you've got promotions going on, you've got products you might be end-of-lifing, you've got competitors. Paid media managers were able to really control things quite concisely, like right down to the product level. They could choose exactly what products they were bidding on and the level of bid and the volume of the bid and exactly how much spend was going on. And with all that control, though, it became very, very complex. And if you just think about the mathematical problem around, you know, you've got 2,000 items to sell all different states, it's a really easy reason to understand why Google said we can provide a solution that automates all these kind of laborious mathematical tasks. So I think, you know, for the long term, it's a great solution. I think the challenge with that, though, is that, yes, it's become quite black box. And how do I do the right thing when I use a Performance Max solution? So for anyone who doesn't know, Performance Max is a This is a solution that for the first time actually from Google works across the whole of the marketing funnel. So it goes all the way from your top of the funnel with display advertising and brand awareness right down to the kind of shopping campaign performance. Pro Process, it heavily focuses on also using the Google ecosystem so it uses YouTube advertising and Gmail and everything else. So it is kind of the campaign to rule them all and that does mean actually the barrier to entry if you're a kind of up-and-coming retailer or you're kind of deploying the first time onto the search world that still does happen. It makes it a lot easier from that point of view. The technology that Google essentially is using is reinforcement learning programs. And Google recommend that you put products into a campaign with the asset contents display video. And the reinforcement learning will essentially work off the objectives you set in a campaign. So you'll apply a budget to it and a return of investment target, maybe a ROAS, return of advertising spend target. And that system will essentially go and work out how to become successful. Reinforcement learning, for anyone that doesn't know, it really works through a guest program in terms of pass and fail. So Google's system will basically take that inventory within a campaign and go through testing that inventory. Does it meet the ability to spend against that return of investment? And for the segments of content and inventory that does, it will repeat that process. And essentially, we'll look to achieve the objectives that you put into the campaign. Now, while that completely pretty much works, the limitations that come with that is that the clue is in the name. With reinforcement learning, what becomes successful remains successful and is focused on. Whereas other parts of your content or inventory become ignored because they failed during the testing process. So what you see is an outcome. It's kind of a very typical kind of Pareto law scenario where 10 to 20% of the content inventory is focused on and a large majority of the inventory is ignored. And we see this time and time again when we're kind of auditing accounts. And you end up with basically a scenario where you kind of plateau in performance because Google becomes hyper-confident on certain audiences and certain product ranges and it starts to ignore others. So the questions a paid media manager will often go through then is, how do I get more inventory active? How can I get Google to kind of relearn? And they've got trading teams and leadership teams saying, we need to push this promotion that's going through. We've got dead stock in the warehouse we need to clear. And these challenges around business principles become very, very difficult to know how to interact with Google's Performance Max campaigns. Speaker 2: Hey, Claus here. Just a quick one. If you like the content of this episode, sign up for our free newsletter and become a smarter Shopify merchant in just 7 minutes per week. We create content from more than 50 sources, saving you hours of research and helping you stay on top of your ecommerce game with the latest news, insights, and trends. Every Thursday in your inbox, 100% free. Join now at newsletter.ecommercecoffeebreak.com. That is newsletter.ecommercecoffeebreak.com. And now back to the show. So, I understand at App.ai, you have come up with a concept that's called Product Data Platform. Tell me what it is and how it basically addresses the technology. How does it address all the challenges that you just mentioned? Speaker 1: Through our learning and as we saw Google develop these technologies, I think the really simple analogy to think about it is the platform like Google is the best customer data platform in the world. It's the best CDP. We are constantly, all of us, training those algorithms and it's getting smarter and smarter. And also, you know, with everything that's going on around GDPR and consent mode, you know, audience data is becoming more and more difficult, actually, for most retailers to obtain from the platforms. So we kind of see the shift that, you know, the last 15 years, it's all been around audience data with ad tech and marketing. And there's some great technology to do that. And it makes complete sense why you would put the customer at the center of the universe when making decisions. What we could see was kind of, okay, well, the platforms are really going to own that space and they're doing a great job, but how could you impart other data sets? And were there other data sets that were kind of being ignored? And we thought about it around, well, what about product data? Product data is fascinating because one, It's unique to the retailer. It's actually highly active. If you think about a retailer and a purchase cycle, you might have a customer buy, if you're lucky, kind of six times a year. That's not actually many interactions with your business, but inventory and product data is constantly active. You're changing prices, you've got different stock levels, you've got sales volumes changing, and you can kind of see that activity all of the time. So that was really interesting. So the first question we actually asked ourselves is, Could we predict the probability of an item selling and then could we calculate the cost of selling that item so we could then work out the return of investment? And by understanding that, what we were able to do is actually look at kind of bringing the concepts of a customer data platform into the product world and create a product data platform. And that's essentially what UP is. It's a product data platform that works out How often is a product going to sell? What's the cost of selling it? And what will my return be? And by understanding that, what we could basically do is use all of the great technology that Google is using in Performance Max because it's fantastic, but also bring in this other data set that Google largely doesn't understand around not only what's going on in the media world and what's going on in the Google landscape, but also what's going on in the e-commerce world and what's going on in the commerce world. And by bringing inventory data and performance data alongside our customer data, it's kind of a marriage made in heaven from that point of view. Speaker 2: Okay. That sounds for me like there is a lot of data points that you have to process to get this match right, to show the right product in the right moment to the right customer. How much kind of a learning curve is there for your clients or basically for the system to learn the clients' SKUs, products, and the media strategy? How long does it take? Speaker 1: Sure. I mean, so with all of these AI systems, the crucial thing is data. And for that reason, typically a customer that fits kind of perfectly with that, they need to be spending around a million pounds a year or greater on a platform like Google. That's really because on the media world, spend kind of creates data for you. And you can then really start to see patterns in the behavior. In terms of actually the learning process, what's quite unique about Up because of the AI world is the bigger the account, the bigger the data set, the quicker it is time to value. So an account spending greater than $2 million is seeing a return of investment typically in 14 days to 30 days very quickly. And then what we see is a smaller account will take slightly longer. Speaker 2: Okay, that's very quick. Who's your perfect contact in the corporate world to talk to? Is it a media manager? Is it the marketing department? Is it a data scientist to whom do you usually speak to? Speaker 1: Typically, we're talking to chief marketing officers, CMOs, head of paid media teams, paid media managers, also sometimes CEOs because paid media has become such a crucial thing for most retailers. We're often talking to quite senior stakeholders within those businesses and they're often, to our surprise, they have a great level of knowledge. Actually, the senior leadership team, it's not uncommon that a CEO will really understand the latest trends in Google and paid media. So, you know, it's kind of typically quite top of mind and people really understand the ins and outs of it. Speaker 2: Yeah, I think that has to do with what you mentioned before. It's pay-to-play. Most traffic is, unfortunately, paid nowadays. So, I think you need to have a mindset and a bit of experience on how that works. Who's your perfect customer? Are there specific industries or niches that you work more with than others? Speaker 1: In terms of verticals, we pretty much work with all the verticals. So, from any kind of vertical of retail, from sports to health and fashion and electronics and home and garden. So, we're quite broad in that sense. We're even kind of looking outside of physical product into things like holidays and travel and that sort of thing. But to work and really get the benefit out of it, you need a fairly large inventory, so multiple thousands of SKUs and our largest customers have multi-millions of SKUs. And like I said before, you really kind of want to be spending a few million pounds a year to really get the benefit of that. Because what we're really saying at that point is there's so much data to analyze. Really, you're kind of at that level where The other options today is continue to build out your paid media team, add further and further heads, which becomes, I think the challenge again in the market is expertise are hard to come by. And also to keep these people in-house is quite challenging. Alternatively, you could go to an agency, but again, sometimes the challenges really depends on how good that team is within the agency. So you kind of have the option of adding people to the problem. Or some of the more laborious tasks and day-to-day, that's exactly what Up is here to do. It's kind of allow Up to deploy your media spend and where we've actually found and what we really try to do with the paid media managers who are kind of our main user, is actually help them move into a world of strategy. If they can focus on actually ensuring that the strategy is defined to make sure that their business objectives are deployed correctly onto platforms like Google, And they can spend more time doing that and doing that analysis. Up can take care of managing the campaigns and managing the media spend. Speaker 2: I think it's much more fun to do strategy than to sit in front of your ad account manager software and staring at numbers the whole day. So I think it's a perfect match for AI. Could you share some kind of success stories or case studies? You don't need to name brands of companies that work with you and what kind of results they saw. Speaker 1: We work with some key brands such as Charles Tewitt who have been with us for a number of years and they've seen amazing results. We also work with, as an example, one of the major DIY home retailers in the UK. They're a great example from that point of view. Since deploying up, I've been able to really focus on strategy and what they are trying to achieve out of the channel. From their point of view, why did they join up? Again, it's around that problem that we saw that they were spending 20 plus million a year, but actually only around 3% of their inventory was active prior to joining up. Since then and running up for nine months now, they've seen product visibility go up to around 84%. They can see that year-on-year revenues have increased by about 28%. And actually in Performance Max, we can see that spend has been able to be increased by 30%. So they're able to spend more at a higher efficiency and more inventory is active, which is kind of the perfect combination for them. Speaker 2: That's a perfect and very sound outcome there. What's a typical onboarding process for a new customer, client or viewers? What kind of steps are involved there? Speaker 1: Yeah, I think with most technology and knowing kind of how it feels to be a reseller, the biggest fear is always kind of onboarding and the time and the prep it takes and whether or not this is an IT project. So from the early days, we always made the decision that we have to make it really, really simple and an enjoyable experience to onboard with that. So actually, the ecosystem is already there. So we actually try to make it quite plug and play. So to integrate, most clients will typically take 5 to 10 business days to actually go live with that. We integrate across the Google estate, so Google Ads and Google Merchant Center and all the analytics platforms, SA360 and GA4. And then also, because I'm focused on the kind of e-commerce and commerce world, we also integrate into web platforms such as Magento and BigCommerce and Shopify and ERP systems like NextSuite. We have already pre-built configurations, so it's actually pretty quick. And then we have, as typical, kind of public APIs for other usage. But as I say, most clients will go live within 5 to 10 working days, so it's quite a light implementation. Speaker 2: Okay, that sounds good. Tell me a little bit about your pricing structure. How do you charge? What kind of range is that? Speaker 1: Sure, so it's pretty straightforward. We actually charge a percentage of media and that's typically based on the size of the media spend. So essentially, The larger you are, the lower the percentage. And as that customer grows, that will change. We also try to make sure that we work from a seasonal point of view as well. So we work typically on a month-in-a-year program where peak season, Q4, a lot of retailers are spending a lot more. Maybe in summer months, you might be quieter. So we find that way works well. And also, the whole idea with Up is that our customer success is our success. So we really do try to align with the customer around even the way we price to make sure that we're here to make sure the customer grows successfully and that's how we also charge on that basis as well. Speaker 2: As we come to the end, I want to ask you about what do you see coming up for the next 12, 18, 24 months? Will AI completely take over or will there still be a human component within the whole marketing? Speaker 1: Now, I don't think we're at a point where AI is going to fully take over and it's not Skynet yet. I think we're all going to be fine. But I think it's really exciting, actually. I think that famous quote that for paid media, especially for paid media managers, is the ones that, you know, you won't lose your job to AI, but a paid media manager that knows how to use AI effectively, that's how you could lose your job if you're kind of agnostic to that and kind of refuse it. The paid media managers that I get to work with where they're constantly curious about the next evolution. They're really excited what they can see from Google with things like the Gemini project that they're working on. They really get why Up exists and the time saving of the performance improvements. Those are the exciting things. I think what it allows People do is focus on actually high-level, high-value projects. And that's always been the way of technology, right? It's being able to save time and improve a process and a customer experience. But for the pay medium managers that we work with, it enables them to actually do much higher-value work, get work done. I explore other opportunities and other channels. Most customers that we speak to, they may be kind of on two to three paid media channels, but they kind of typically have one single strategy. One channel will be leading the way by a long way. Removing some of the laborious tasks from the more kind of day-to-day tasks allows them to start to really pick up on the other opportunities that they have in market that prior to that they just couldn't get to. Speaker 2: Okay. Before we come to the end of the coffee break today, is there anything that you want to share with our listeners that we haven't covered yet? Speaker 1: I think the exciting things from UpToWorld as well is that we are going multi-channel. So as we see consumer journeys change and evolve over time with AI development and with the battle that's being played out across TikTok and Meta and Google, the world's becoming more and more complex and more diverse in terms of the opportunities that are there and how retailers take control of those opportunities. So for us, we've historically always been kind of Google focused, but we're also launching into Microsoft and Meta. And we believe there isn't a great solution out there at the moment that really helps product advertising effectively. And so we really want to be the one-stop shop for a paid media manager to put all of their product advertising through and make it a really simple, transparent experience for them so they can maximize all of the opportunities across their estates. Speaker 2: Where can people find out more about you guys? Speaker 1: Yep, so the website is www.up.ai. That's our website that has all of our context on there. We also run algorithmic media health reports that customers can fill out and that really helps customers understand where they are in terms of AI today and how they're set up and how their media is set up. It's an educational tool. It kind of works like evaluating your credit score and it will give you a good understanding of actually how you're currently set up and your current ways of working. We find that a lot of our interactions at that point really start to help educate what the best practices are, where today is and where people need to focus and improve their accounts. That's free to use on our website and people log in. Speaker 2: Okay, perfect. I will put the link in the show notes, then you will be just one click away. Drew, thanks so much for giving an overview what AI can do for media managers out there. I think it's a massive step forward. Google has the biggest reach out there, and I think everyone who has a good budget and is looking into AI should just check you out and see if there is a match. Thanks so much for your time today. Great. Speaker 1: Thank you very much. Speaker 2: Hey, Claus here. Thank you for joining me on another episode of the Ecommerce 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 also make it easier for others to discover the podcast. Simply like, comment, and subscribe in the app you're 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 online seller in just five minutes. We create content from more than 50 sources, saving you hours of research and helping you to stay on top of your ecommerce game with the latest news, insights, and trends twice a week in your inbox, 100% free. Join now at newsletter.ecommercecoffeebreak.com. That's newsletter.ecommercecoffeebreak.com. Thanks again and I'll catch you in the next episode. Have a good one.