Claus Lauter [00:00:00]:
Welcome to episode 297 of the ecommerce Coffee Break podcast. Today it's all about AI and we talk about how to optimize your revenue streams and grow your digital marketing with AI driven analytics. Joining me on the show is Brian Warrick, head of revenue at Baresquare.com. So let's dive right into it.
Voice over [00:00:20]:
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 Lauter, and get marketing advice you can't find on Google. Welcome, welcome to the show and welcome.
Claus Lauter [00:00:46]:
To another episode of Ecommerce Coffee Break podcast. Today we want to explore gross revenue options within AI. So how can AI help you to grow your business? And what kind of tools, what kind of strategies and tactics will help with that? For that, I have Brian Boyd with me on the show. He's the head of revenue at Baresquare.com. He's an experienced marketing and technology leader with over 15 years track record of driving success for startups and Fortune 35 companies. Leveraging his expertise building authentic partnerships for customer success, Brian has helped notable brands such as JetBlue, the New York Times, Sanofi and Metlife achieve their marketing tech goals and unlock significant revenue growth. So let's welcome him to the show. Hi Brian, how are you today?
Brian Warrick [00:01:29]:
Hi . I'm doing well. How are you?
Claus Lauter [00:01:31]:
I'm very well. Brian. You're talking about AI. That's a relatively new topic. What was your first experience with AI?
Brian Warrick [00:01:38]:
So my first experience with AI was probably the same awe that a lot of people saw when the image generators came out and just the quality and it was just amazing what could be done with a few simple text prompts. I can remember going back and forth with a CMO friend of mine on a daily basis, just sharing the latest of what was created and just unbelievable.
Claus Lauter [00:02:07]:
Yeah, AI is obviously moving very fast on that. AI is also transforming marketing and e commerce business in general. Which areas do you see changing the fastest so far?
Brian Warrick [00:02:17]:
I think given that we're now about a year into the post GPT era, I think we're seeing text content really coming on. Although what we're seeing with video is also pretty amazing. Same as the image generators were not too long ago. So certainly around content production, whether that's product descriptions depicting the product in everyday life, we're probably not too far from a place where you can really see products at work in your home. And we've seen some of that with paint colors and things like that in the past, but truly being a more immersive type of relevant experience I think is going to be key. But there's also, I think, a huge shift on the data side. We collect a lot of that as martech professionals, and how much of it gets used varies from place to place. But it's safe to say that not a lot get used, or certainly of not 100%, no matter where you are, and the ability to go through all of what's collected and start to generate either meaningful insights, which is what Baresquare does, or start to really personalize for shoppers.
Brian Warrick [00:03:37]:
And it's going to be amazing. And I think if there's one last one, not to make too long of an answer of this, but I think search is going to be huge. I've got the brave browser that I use on my phone and I'm already getting summaries of multiple sites into one cohesive answer for certain searches. And I think when we start to think about that in terms of commerce, it's going to be huge. In terms of the most, the highly rated, the highest quality product or item that you're potentially looking for could be surfaced nearly immediately.
Claus Lauter [00:04:20]:
I think you touched a couple of very interesting things there. So first of all, yes, we saw that coming up. More and more apps are coming out. More and more tools are coming out for ecommerce merchants, for online sellers, for DTC brands, providing them with more and more data. And then obviously very quickly, you come to a point where you're completely overwhelmed with all the data that is available. But then reading out of it mentioned search engines and that for sure, and I'm with you on the same page, that will change. I think Google will have to change because we already see the first results coming out there. Now I understand@baresquare.com.
Claus Lauter [00:04:54]:
You help with sort of creating clarity around data and finding a better way to deal with all the data that's coming in. Tell me a little bit about it.
Brian Warrick [00:05:04]:
Oh, sure. Happy to. So Bare Square really got its start, you know, 15 or so years ago as a data consultancy, mostly around marketing data. So deep knowledge in analytics, in, you know, a lot of the bi and reporting platforms and how to take what data is collected and create the appropriate dashboards and things. And as most kind of consulting companies do, you start solving similar problems time and time again. And specifically what we uncovered was there's a gap in the market in terms of whether you call it site merchandising or category management and revenue and understanding all of the things you can do to affect revenue and how successful those are. I mean, that's a full time job for multiple people in most organizations. And we were helping to solve that from a visualization standpoint and realize that, hey, we build upon that and generate a product.
Brian Warrick [00:06:16]:
And so our revenue accelerator solution does exactly that. It's kind of your AI analyst that reviews category performance and tells you, hey, this category is well above plan or well below plan. And sometimes this is probably not talking too far school for most of your viewers. There isn't generally a good revenue plan anyway. And Bear square can actually calculate an expected revenue based on all the activity that's happening and then use that for comparison to say, hey, we're performing well against these revenue targets or not.
Claus Lauter [00:06:56]:
Now, most of our listeners are on Apple podcasts, on Spotify. They don't have a visual idea of what's happening. And you talked about categories. Can you talk me through what that means and how can I visualize? And I learn visually. So for me, that's actually a really interesting topic to talk about. It's like, how can I imagine the data being visualized?
Brian Warrick [00:07:17]:
We pride ourselves on not only being able to present visuals, but everything. All of the insights that are generated on Bare Square are actually shared in plain text. So they're very easy to read. It's clean language. It's very easy to understand what's happening, why it happened, and what to do about it. Again, you're going to get a nice clean text summary of that. But in addition to that, we have some tools that visualize expected performance over a period of time. So a nice kind of graphical way to show almost looks like a river moving through time.
Brian Warrick [00:08:00]:
In terms of it gets a little wider, gets a little smaller depending on activity to show, hey, this is the expected range of revenue for a given product category. So when I say category, I mean things like shirts, dresses, tops, to use kind of an apparel metaphor. But in addition to that river I mentioned, we also have, you know, kind of the stream, which is the actual number overlaid on top of it. So you can see real performance is inside an expected range or outside of it as well. And then the last piece of data visualization that we do is a full funnel analysis. So every step of the commerce funnel, from viewing a product to putting it in your cart, to checking out all of the steps within that, we visualize the impact of what's happening in each of those steps in terms of revenue performance for that given product category.
Claus Lauter [00:09:00]:
Now, when it comes to revenue optimization. There's a lot of different data sources coming into that. What kind of data sources do you support and how do you connect to them?
Brian Warrick [00:09:11]:
Yeah, we can connect to just about any time series set of data, but the primary ones that we use are going to be your analytics platform. So your Google Analytics ga four Adobe analytic. We can connect to aggregates of those data sources as well if some people are bringing them into any kind of bi tool in terms of that. But about 75 80% of the insights we can generate come just from Linux data. There's so much that's captured that we're able to do that, and then we enhance that with a number of additional qualitative or quantitative data sources as well. Everything from a revenue plan, if it exists on spreadsheet somewhere. Promotion plans we can look inside the email promotion system to see, hey, this is when these things, these messages are going out to people and what in them. In addition to that, we can look at the news, we can look at weather and see what is this happening on the site performance.
Brian Warrick [00:10:19]:
So it's pretty interesting.
Claus Lauter [00:10:22]:
Hey , here, just a quick one. If you like the content of this episode, subscribe to the weekly newsletter at newsletter Dot e commercecoffeebreak.com dot I score and create 50 news sources so you don't have to saving your hours of research. Grow your revenue with ecommerce news, marketing strategies, tools, podcast, interviews and more, all in a quick three minute read. So head over to newsletter Dot e commerce coffeebreak.com to subscribe. I said 100% free. Also, you will find the link in the show notes. And now back to the show. That's definitely very interesting.
Claus Lauter [00:10:50]:
So as an ecommerce business, how can I leverage all this data effectively to make qualified decisions going forward?
Brian Warrick [00:10:58]:
I think the thing that holds most e commerce companies back is from the time that you know that there's something you want to work on, there's a revenue deficiency in the shirts category. We'll use that example again. From the time that you know that to the time that you done the analysis to understand why the market have moved on, you may have missed your opportunity to do anything about it. And since we do this on a daily basis and you're presented next morning with what's happened as well as recommendations on what to do, it's pretty easy to take advantage. And if there's an error in the email promotion, let's say it's pointing to the wrong product. Most people know that, hey, people aren't going to go and then search for the product you're promoting and you've lost that customer. Well, now you can know that that's the case and submit it. Send a new email or an update.
Brian Warrick [00:11:57]:
You can be cheeky and fun about it if you want to. However, you as a brand want to react to that, but you have that ability to react, which is important. The other thing we do is we can tailor how many insights are surfaced depending on the size of the team you can focus on. Hey, I only want the high priority ones or I want to see all, no matter what the discrepancy is. So it's tailor made to the organization.
Claus Lauter [00:12:30]:
Do you have a real life example on a campaign or on a use case that one of your customers must have used?
Brian Warrick [00:12:38]:
Yeah, I do. We work with women's apparel brand and they're one of a portfolio of brands under management. So as you can imagine, in that scenario, there's generally a pretty resources are generally spread fairly thin on this company. Man, everything exceptionally well. But I can recall it was a Friday afternoon, we had just got connected to the data and I was like, great, what a good way to end the week. This is going to be fine. We'll start to generate some insights and have a conversation sometime next week. First thing Monday morning, took a look at the data.
Brian Warrick [00:13:13]:
I've got people from our team knocking on my door like, hey, you need to see this, and we need to get on the phone right away with this particular company. What we spotted was revenue was down for multiple product categories. And the AI agents that are running within our platform, they identified the same root cause for each of them. And essentially it was kind of a dual factor. Two causes. One was an email that had been scheduled to go out for the weekend, did not. And the other was that a promotion that should, a promotion code that should have lasted through the weekend also did not had ended on Friday. So you've got two kind of factors happening at a fairly significant drop of new.
Brian Warrick [00:14:09]:
So we saw that and we said, okay, hey, we need to flag this because we just started looking at the data. We thought we had a couple of days before we would talk to them. So we need to talk right away and come to find out with that company. We looked back and we saw this was the same thing occurring over like five weekends. And the cumulative impact of all of that was about 5% of annual revenue. So it was no small thing. Turns out Sunday is their biggest revenue day for that company. So it was pretty tactful.
Brian Warrick [00:14:44]:
And just one of those things where you're like, we're doing some cool stuff here because we're able to help people spot some of this stuff right away.
Claus Lauter [00:14:51]:
Yeah, that's a huge impact. 5% of the annual revenue. So you talk about, you found that out very, very quickly. Now, as far as I know, AI usually needs some time to learn from the data that is available so that the algorithm really can kick in. How does it work from your side? What's usually the average time before you really see the maximum results coming out from AI?
Brian Warrick [00:15:16]:
It's great because we connect to the system as if we're a user. There's no plugin or pixel that needs to be put on the site, so that speeds things up. There's no time to collect data. We can read from what's already been collected. And because of that, within 24 hours we start generating insights in terms of revenue performance, day over day revenue performance by category or other timeframes as the customer wants to see. If they want to see week over week, then obviously we need to look at a week's worth of data to see. But generally we can look back whatever timeframe the customer would like to see it in and already start to compare today to a week ago. Well, so it's pretty immediate from our standpoint.
Claus Lauter [00:16:04]:
No, it's obviously bigger organizations. You have different people in marketing departments and fulfillment departments and so on and so forth. How does the system of Baresquare.com work with that? So that the right person gets the right information or message at the right time.
Brian Warrick [00:16:20]:
We're able to map the categories, the product categories, to the individual responsible for that category. So the merchandisers or category manager, if it's a team of people, it can go to the team as well. And we're able to share the results with productivity or products like slack teams, Jira workfront or others. We can actually output the results and say, hey, here's something you should take a look at and then have them come back into the tool to see what the result is. So we fit right into a company's main workflow. Could be sent via email as well. Whatever's necessary.
Claus Lauter [00:17:07]:
Who's your perfect customer? Are there specific industries or verticals that are easier to adapt to such a system than others?
Brian Warrick [00:17:15]:
Right now we're focused pretty much on e commerce. Perfect place for us to be chatting with you. It's generally a multi product or multi category retailer. So if it's a single product, bare square is still beneficial, but probably not to the full extent that it is across a larger landscape. And it's those retailers that are doing in the tens of millions of year or higher in terms of revenue. And I put a caveat on that because it's also a matter of how much promotion and marketing are you doing as well. So if you are a starting business and you've got low revenue, but you're really investing in media buys and email promotions and things of that nature, well then there's activity there that we can help to influence. So it's really kind of a matter of, you know, the activity that an organization does to generate interest in traffic and sales.
Claus Lauter [00:18:27]:
Now AI is moving very, very quickly. There's new features coming out all the time. And I understand you told me before, you are in the works of a big update for Baresquare.com dot. What does that involve? What can we expect?
Brian Warrick [00:18:40]:
Yeah, so when we first started building out the platform, we sat down with our team of like 40 data analysts and said, okay, given a result, what would you do? And we turned those into AI agent. So that kind of analysis that our people would do, we basically were able to turn that into an AI agent. And sometimes they run multiple times for a given result. So the platform update coming out in about a few weeks at this point will have an expanded library of those. It looks at additional data sources. So we're able to look at not just a broad base of kind of competitors, we can look at named competitors for a given retailer. So if you really want to know what X, Y and Z competitors are doing, we can look at those and see what's happening in the news and see what effect that might have. And one other thing that will be included is we'll have the ability to do session replay as well, which some of the larger quality platforms out there are able to do.
Brian Warrick [00:19:58]:
But we'll be able to do that as well. So if there is a result and you're interested to see what the buyer journey was like, we could replay a lot of those sessions, which is a big update for sure.
Claus Lauter [00:20:12]:
Now you're at the top front of artificial intelligence and what's happening there? What's your forecast, what's happening within the next twelve months? I know it's very difficult to say, but will it cut jobs, will marketing departments fall away or what's, what's happening?
Brian Warrick [00:20:28]:
I don't think marketing departments or analysts are ever going to go away. I'm very impressed with what AI has become over the last twelve months, and I'm sure it's going to grow by leaps and bounds. But at the end of the day, we're still talking computers that are programmed by people. And no APIs are 100% perfect, no data feeds are 100% perfect. There's always things that we're going to need to do to kind of make sure the machines run the way that we want them to. So I don't think that jobs are going to go away as much. I think we'll take on new skills and learn to interact with data in a more human way. So being able to ask questions, being able to have a conversation, if you will, with the data, that's another thing that we're working on.
Brian Warrick [00:21:15]:
We see that coming fairly soon. Speed, right? Speed is just going to continue to increase. So I think those are big things. I think my biggest prediction for the next twelve months is really going to be around adoption. We're, I think, coming out of the testing and trial mode of where does AI fit into my organization. And in the next twelve months, I don't even think that's going to be a question because it's going to be prolific and it's going to touch upon almost every aspect of the business. So it'll just end up being everywhere. And who knows, maybe the hype around AI as a term will start to diminish a little bit because it'll just be like the Internet, it's just around.
Claus Lauter [00:22:09]:
No, I totally agree. I think you need to adapt to AI or as a business, you have a problem going forward. Walk me through the typical onboarding process of a new user. What steps are involved? How long does it take to get up and running?
Brian Warrick [00:22:21]:
Yeah, it's pretty quick. As I mentioned previously, we connect to the analytics platform as a user. Again, there's no pixel, there's no tracking tag that needs to be installed. So we don't site performance at all. What was it, the Heisenberg principle? The minute you start to measure something, you start to affect it. I think that holds true for a lot of tags that get placed on things. But once we're connected there, the system will do an automated kind of quality review just to make sure that the data is in the way that it needs to be. Sometimes we need to have the customer make a few tweaks to tags or things of that nature, but again, usually within a day or so, insights just start being generated.
Brian Warrick [00:23:15]:
And we do like to take a look. There's some calibration that can be done, certainly according to customer preference, but within a week where we're generally showcasing the first results and able to get that going. So it's a pretty good. I think that's the other amazing thing about AI in general is there's so much automation inherent in it that long deployment or implementation cycles or things of that nature are going to start to become a bit antiquated.
Claus Lauter [00:23:52]:
How does your pricing structure work?
Brian Warrick [00:23:54]:
Yeah, so as you can imagine, we're turning through a lot of data, so there are some volume bricks that come into play. So we typically look at the volumes that we're looking at and revenues as well, because we're in the mode of we want to help as many people as possible and we're able to do it fairly cost effectively. So we try and make sure that we're not gouging anyone in terms of price. Right. It's a fair price for the value delivered and it's a pretty easy equation from that standpoint.
Claus Lauter [00:24:29]:
Okay, excellent. Before our coffee break comes to an end today, is there anything that you want to share with our listeners that we haven't covered yet?
Brian Warrick [00:24:35]:
If anything, I would say don't be afraid to experiment. There's lots of great tools out there outside from Bare Square, and it's an exciting time to explore new things and. And be surprised and just hold on to the wonder of it as you see it because, you know, it's easy to get overwhelmed or be like, wow, this is going to place me and I don't see that happening.
Claus Lauter [00:25:02]:
Yeah, I think you said hold on to the wonder of it. It's a once in a lifetime thing that's happening there and I think we should fully embrace what we're seeing coming up there in the future. Where can people find out more about you guys?
Brian Warrick [00:25:15]:
Baresquare.com so it's b a r e s q u a r e. So not bare as an animal. Bear as in without something. So that's where we are. We're on LinkedIn and social media as well. So our website or LinkedIn is probably the best. I think you'll start to see us at some of the relevant trade shows and things as well as the year progresses. So that'll be exciting also.
Claus Lauter [00:25:43]:
Okay, I will put the links in the show notes as always, then you just one click away. And for our listeners, there are some good videos on the website. I watched them. It gives a very good and quick overview of what the system can do. And I would just say, go there. Have a look. Brian, thanks so much for your time today. I think we gave a good overview of where we are right now with AI when it comes to ecommerce marketing and how it can help business owners to grow their business thanks so much for your time today.
Brian Warrick [00:26:07]:
Thank you. Pleasure.
Claus Lauter [00:26:09]:
Hey, here. Thanks 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 make it also easier for others to discuss, 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. Thanks again, and I catch you in the next episode. Have a good one.