Guessing vs. Knowing: How Can AI Actually Understand Your Store
Every independent retailer has heard some version of the promise: AI is going to change everything.
But between the hype and the hesitation, a real question goes unanswered. What can AI actually do for a store like yours, and where does it still fall short? In this week's Indie Insights, we're pulling back the curtain on AI advancements for indie retailers, including our own advancements to Retail ORBIT®. We will spotlight Ask Indie™ the plain-language assistant, built into Retail Orbit, that answers your toughest merchandising questions using your own numbers.
See how our private, closed AI engine trained on 40+ years of proprietary data from 1,000+ independent retailers turns daily POS data into confident buying, markdown, and open-to-buy decisions. Whether you're already planning with M1 or just exploring what AI can do for an independent store, you'll leave knowing exactly how to put your data to work.
You'll learn:
What makes Ask Indie™ different from other general AI tools.
The real questions you can ask and the answers you'll get back.
How AI forecasting, Retail ORBIT Health Score™, and AI session recaps work together.
Why "private and closed" matters for your store's data.
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Guessing vs. Knowing: How Can AI Actually Understand Your Store — Webinar Transcript
Dane Cohen: Hello, everyone, and welcome to this week's Indie Insights Live. Just a reminder to everyone, and I think we're catching on here, so I appreciate everyone's attention to detail, but Indie Insights Live will be every Thursday at 1pm Eastern. So, the nice part about this is you know that weekly, you could come here, you could sign up, and be introduced to a new topic, a new speaker, a new presenter, and that's gonna be every Thursday, 1 p.m. Eastern. So, we appreciate you all being here. We have a really interesting discussion today. we're gonna take things in a bit of a different direction, and that is because we have a incredibly exciting launch here at Management One, and I think it's really important to be able to discuss it, and to be able to discuss it in what seems to be the hottest topic in the industry. probably in the world right now, which is the developments happening in AI and how quickly they are changing and advancing business, and of course, with retail being no exception. So, I brought on today Nico Cabral, who is our Head of Marketing here at Management One. To share some perspective and insight on what's happening on the marketing front, and the PR front, and how AI is helping there. And then, of course, I brought on Marc Weiss, who is our CEO, to talk about where the retail industry has been, where it's going, and all of this will be unified through the lens, and I just want to be direct here, of this incredible launch that we're having here at Management One, which is the launch of Ask Indy. And AI is the engine now that will fuel our planning products. So, as always, please participate. We want as many questions as possible in the chat. Please, this is your opportunity. Ask us questions, interact. If you're a client. this is your opportunity to kind of ask where the company's going, where AI is going with Management One, and if you're not a Management One client, this is your opportunity to get to know us and our products better. So, without further ado, Marc, Nico, how you doing?
Marc Weiss: We're doing great, thank you.
Nico Cabral: Doing great. Happy to be out of the chat room and up in the camera here today.
Dane Cohen: Well, you look fantastic, Nico. You're camera ready. Okay, you too, Marc, of course.
Marc Weiss: I just don't have your, your guys, I don't have that, jug-masculine-looking, hair growth, so…
Dane Cohen: The Millennial Stubble, here we go.
Marc Weiss: That's what it's called.
Dane Cohen: That's what it is. Okay, so, where I want to begin, and Marc, I'm going to start with you here, and, you know, let's view this all in context and kind of expand into the bigger picture, which is… You know, where we're at right now feels very reminiscent of, I'm sure, many other eras in retail. You know, when the computer systems were being put in first to the retail stores, and we're, you know, going back, when the internet and cloud-based services came into retail stores, and it feels like we are on the precipice of never going back, right? AI is here to stay in every form of business. It is going to continue to impact industries. again, retail being no exception. So, let's start with the exciting stuff, right? What's your excitement about AI in the context of right now when it comes to retail, and through that lens of where these technology shifts have come in to play before in our industry?
Marc Weiss: Right, it's a great way to start the conversation, Dane. You know, you talked about e-commerce, and you know, we were at the forefront of when e-commerce started, and we were fortunate at the time we had some, First adopter retailers who, worked with us, to help us develop, to measure their sales, measure what was going on with e-commerce. And, we really got an opportunity to test and play around with all that data, and that was really helpful, and they were really beta sites for us, so we began to learn early on. you know, how e-commerce could impact business, and this goes back from the very beginning. And the same thing happened with Facebook Live, and when we had live selling, so we've gone through these technological changes that have, you know, that some early adopters… we've always been very fortunate to have early adopters in part of our cohort of clients, so that's been helpful. But this is different. This is, this is, This is bigger, this impacts not a group of retailers who decide to go into e-commerce or build websites. This is everybody, and everybody's playing around with it in their own lives, bringing it into their business, so it's a completely different, it's a completely different, challenge and opportunity. And I think, you know, we'll speak of some of the fears and issues that are there, but when I came back last August. One of the things that I was most excited about being back It was the work that Adam and his team were doing. Adam's head of product and planning for Management One. he really shared with me the work that they had started doing, this is over a year ago, on building the AI platform within Management One, and the outside expertise that we got was just… they really built a brilliant roadmap for us that we're now finally being able to launch, but this doesn't happen overnight. You just don't plug into Claude today and get the answers you want. We've done massive amounts of testing, learning, building in an internal system that's closed, that's specific just to our industry and our niche. So, we'll talk more about that over the course of this conversation, but I want to emphasize how important that is, that there is a difference between general AI and a closed AI system that we've built at Management One that's continuing to learn every single day. So there's a lot of things that we've done that we'll talk about that will maximize the opportunity. And of course. you know, my core focus since, you know, I started the company 40 years ago was how do we help, you know, independent retailers compete at a level that they, can compete with the big boys and people who have lots of money? So, AI is just another opportunity for us to bring a level of expertise that the world has never seen before into the hands of an independent retailer they're doing $250,000 a year or $100 million a year, they now have the opportunity to compete at a level that, you know, because of AI that didn't exist before. And we can provide that information to them more specifically than they're going to get from using Claude on their own, or ChatGPT.
Dane Cohen: Yeah, and Marc, I just want to quickly zero in on something you said, which is, you know, the development that's gone in, because I think we're going to talk about this in two different ways, right? What is the, kind of, the back-end power that it has allowed us to unharness in the business, and what does that mean for a client, right? And Nico, we're going to talk about some of the front-facing stuff in terms of general AI and what that could do for a client's retail business, and then, of course, what our closed system can do. But, Marc, I just want to zero in for… zero in on, you know, the year that Adam and the team took in development, right? What does that mean for the end user, right?
Marc Weiss: Yeah, I'm gonna answer that with a quote. There was a… just a great article in the New York Times this week, an opinion piece by Julie Avaril, who was the CIO for Lululemon and Nordstrom's, and, you know, she talked about the pros and cons of AI, and I thought she said it brilliantly. And I'll start off with what she said, AI can't solve an issue that has never been taught. So, that's a critical comment, because what we've been doing is teaching our AI what we've learned over the last 40 years. And, you know, even after a year, and even all in its ability to, amass all the massive… the tens of billions of dollars worth of planning and data that we've done over 40 years, even with that, we still have to add the wisdom. and the knowledge and everything else, so we're feeding all of that into our AI so that it is a moat, so that it is 40 years of retail experience that you're not going to see anywhere else. One of the things that MIT did this year was they did a program, a project called NANDA, and they used it to help identify in pilot programs whether AI was giving the kind of results that, you know, clients expected, and what they found out was 95% of the time it wasn't. So, you know, being able to build an AI system that actually has the wisdom, the knowledge, the intelligence, the mistakes that have been made, the corrections that have had to have been done over a period of time, is what leads to better results, and that gets compounded every day. So, you know, when you ask that question, it's really about how you build and ask AI questions that really matter and make a difference, and I think that's… that's… I don't think that's where we've really gone with it. So when we go back to where we started, you know, what we got in the beginning was maybe 10% right, and then it was 15% right, and then it was 20% right. I mean, we didn't even ask… we didn't even launch this until we were over 90%. And now we've got humans, who help, you know, we've got our clients, we've got our consultants, we've got our planners, we've got our team of 30, over 30 people, who are all every day going in and filtering and making sure that the results we're getting are right, and then, you know, you know, in continuing to compound and build on that.
Dane Cohen: Yeah. Right, it just keeps… it keeps getting smarter the more, and especially in a closed system, right? The more data you feed it, the more information, the smarter the brain gets.
Marc Weiss: Well, you know, you have a really great example, Dane. You know, we're building a deterministic system versus a probabilistic system, and we'll go into some examples of that, but you've got such a great example of that, that I love when you share it about the recipe, right? And I think that's a good way to tell the audience about that again.
Dane Cohen: Well, because let's get real, right? And I want to say this, and Nico, Marc do not take offense to this, but the average person doesn't want to hear about deterministic versus probabilistic AI, right? These are heady concepts. They want to know, at the end of the day, how could this drive more business to my store, right? How could these tools drive more business to my store? And this is why I give this example, because I think it just really highlights exactly what we're trying to talk about when we talk about these more headier concepts with AI, and that is a deterministic system. Let's look at probabilistic first, okay? I'm going to compare that to, you tell Claude, or you tell ChatGPT, I have some ingredients in my house, I want to make a recipe, right? Give me a recipe. I have some beef, I have some vegetables, help me make a recipe, and it gives you a recipe for this Tuscan beef stew. Oh, it looks delicious, what a great recipe. The only problem is… you gave it a few vegetables, and it's assuming you have other stuff. It's assuming you have beef broth, it's assuming you have tarragon spice, you know, it supposes that you have chili peppers, whatever it may be. Deterministic AI would be Claude, or ChatGPT, in this case, Management One and our Ask Indy, knows every single ingredient you have in your house. It knows how much salt you have, how much pepper you have, what type of spices you have, so that when you ask it for a recipe using beef. right, and vegetables, it's gonna give you the exact recipe that meets all of your inventory pun intended, inventory that you have in your pantry and your refrigerator, right? So, that is… is kind of the really simple way of touching that example. That one way you're getting kind of assumptions baked into your question and prompts. The other way is a full understanding, and it's grounded in your. data. And then it may even tell you, hey, there are 30 households like yours in the neighborhood that have this ingredient that you're missing. We would suggest you add that to your pantry in order to really beef up your… your recipes. Okay, I'm done with puns, I promise. Alright, Nico, I kind of want to go over to you, because I don't want to frame this conversation just as general AI versus, you know, internal AI. There are obviously so many ways that a retailer right now is using more general AI systems, and I think a big part of that is through the lens of marketing. So, you know, both how are you and how are you seeing retailers really jump on that?
Nico Cabral: Well, I think the main thing is you look at the daily content grind, you know, especially for independent retailers, they have a very small team. They can't always be sitting at the computer writing product descriptions, going on social media, writing posts, writing marketing emails. And AI has drastically reduced the amount of time it takes to put good content out there. If you can… like you said before, if you can teach it your brand voice, and you can teach it how you want yourself to be represented, it can spit out some pretty good content. Now, you still need a human eye to… vet what they're saying. You want to make sure that you don't want to just hand your entire marketing team over to AI, but not only that, the amount of… not the amount of content, the quality of the content. When you look at video generation now, just in the last 6 months. retailers used to have to spend thousands of dollars to get really good videos of their products, of their stores. Now you can do that in AI and have something that is pretty usable in minutes, as opposed to hours or days, and it just reduces that amount of time for them. That's massive. Right, so.
Dane Cohen: This could be everything from marketing copy, product descriptions, but.
Nico Cabral: I agree.
Dane Cohen: And then, and then we're talking, right, you can have a picture of a product and actually bring it to life in video.
Nico Cabral: 100%. And you can iterate on things, you can test. If something's not working, you can change it across multiple channels in a much faster time than you could before AI was here, so… As far as efficiency, I think it was… it's a massive shift, and we used it here, too, at Management One. So, AI has been a huge lift for marketing teams. here and across all retailers. I think it's a massive shift. We definitely don't want to downplay the benefits of it. I think we're… retailers can get bogged down a little bit, though, is, number one, becoming over-reliant on it. You see a lot of channels on social media, and you can tell right away if there's a lot of M-dashes in the copy, if the the visual looks a little bit off. You don't want to become too over-reliant on the technology. And we see that if you look at feeds. across all channels. The AI slop is definitely real, the slop problem is real, so you don't want to become too over-reliant on it, but it is definitely an efficiency game changer.
Dane Cohen: Yeah, and I think that that's really important, and I think this is something that we're gonna see as a thematic part of this, is the human lens. is still super important. Like, I scroll and, you know, now I can just… by the way, I see individuals using AI slop in their captions, you know? And what we mean by that is there's these very specific cadences that AI talks in, there's some very specific wording, so, you know, not to go too off-topic, but, you know, you see people writing personal letters, right? You see people writing cards now with AI, and that, you know, so you want to stay away from just putting it in there and having it wholesale, do all of your copy for you, but it is a power user in terms of being able to get through.
Nico Cabral: Speed to content is probably the biggest impact, for sure.
Dane Cohen: Yeah.
Nico Cabral: And brainstorming. It's a great brainstorming tool. I mean, AI can come up with 30 different topics. You might have… in your head, you might have had 5 or 6 of them. The other 24, you thought, oh, that's actually a good one to explore. Let's go down that rabbit hole and see if that's worth exploring. So, it's a fantastic brainstorming tool, especially for small marketing teams of 1-2 people.
Dane Cohen: Yeah, and that's where probabilistic AI is very helpful, right? Because it's bringing in outside information to help you brainstorm. But Marc, let's flip that over to data, and when it comes to… Numbers, and data, and actual, risk that you're going to be taking with dollars in your business, right? Marketing's one thing, you know, that's a… obviously a revenue driver, but we're talking copy and product descriptions. Now we're starting to talk about monetary decisions you're going to make based off data. And Marc, I want you to share, you shared this with me yesterday. the kind of test that you ran through ChatGPT and Ask Indy.
Marc Weiss: Oh, yeah, I mean, it was… I asked ChatGPT, take a women's store, a branded women's store in Cleveland, Ohio, that's doing a million dollars a year. How much dress business do you think they'll do, and what would it look like? What would the inflow look like over the course of, over the course of a year, and what percentage of its business would it be? And, you know, then I compared it to, you know, the reality of what we deal with and what we create, and the consequences were staggering. the, information was, wrong. It would have cost the client thousands of dollars. In this one example, the way that, the inventory flows in ChatGPT, it really has, It really has a flat line, so you never stay fresh. So I'll just point out, we have, first of all, it said that 20% of the business would be done in dresses, when in reality, we would know it would be 7% to 8%. It had it in the wrong months. It had inventory peaking at the same time it had markdowns peaking. It wasn't adjusting for markdowns. in the right way. It assumed that markdowns would run a straight 10% across the months. And when you reach your number one month in sale, according to what its plan was, it was missing the opportunity of when the real peak was, so there were just thousands of missed dollars. Plus, we all know what's fresh sells. On the Management One plan, you're always fresh. at at least 80% or better, or 76% or better with ChatGPT, in your peak, most important regular price selling month, freshness was 56%. So this is, this is just where having wisdom, knowledge, the experience of planning over all these years that's built into our plans, our algorithms, and our AI, it doesn't compare to what you're going to get with ChatGPT. However, when you read the results of what ChatGPT said. If I were, you know, a person that didn't have this internal knowledge, I would say, oh, this looks good, I feel good about this. But if it's planning 20% of your business in dresses when it's 8%, that means you're missing… also missing business and other classifications. So if you add up all of the reasons why the plan is wrong, it's significant. So this is where AI looks good, but it's not… it doesn't have the internal knowledge to know what we know about what the nature of that business is. And I was able to compare it to a store in Cleveland that does a little over a million, and we have many stores in Cleveland that are… that fall into this category, so it just… this one store wasn't that unique, but this is just one example of that, so… I think…
Dane Cohen: Well,
Marc Weiss: Go ahead.
Dane Cohen: And I think what's super interesting here, right, on the face of it, right, we can obviously see, and I pulled up this little graph. that we did use AI to build, this little grub…
Marc Weiss: I actually used our Claude system to compare Management One's plan versus a general AI plan, and I said, which one do you think is right? And then our Claude system said, well, to start with, some of the assumptions are wrong from General AI and some of the formulas. So, that was significant in and of itself. Go ahead.
Dane Cohen: So if you see… no, no, no, if you see on this graph here, obviously, Management One is represented in purple, this is a percent of freshness versus ChatGPT being represented percent of freshness in black. So the freshness thing is really easy to see, right? That's the, you know, on the surface, we can see exactly where that, is gonna hurt a retailer. But here's where I think the most interesting thing that you said, is that ChatGPT assumed that if a markdown rate of a retailer of that kind is 10%, that that 10% holds. throughout the entire year, right? That there's not ebbs and flows in that. So, that is something that's going into the back end calculations that you aren't even seeing happening. And so, if you're not asking the right questions, the right prompts. it's going to go off on, again, those assumptions, and you're not going to even be able to see, because I love that you said that, right? It's going to spit out an answer that sounds super smart, right? That is what it's designed to do. It is not.
Nico Cabral: I'm confident.
Dane Cohen: Exactly. It's not designed to speak in ambiguities, it's designed to speak with confidence. And so…
Marc Weiss: a year.
Dane Cohen: And tell you what you want to hear. Okay, let's stop sharing that. And, okay, so, Nico, here's now the… what kind of gets exciting for me, and we talked about that freshness and understanding your data, and so, you know, that planned data that's coming through. our AI and our plans and, you know, your ability to interact with that, how could retailers use that In their marketing.
Nico Cabral: Well, I mean, without proper data, a marketing calendar is really just a guess. You're guessing on what you want to sell, you might have a general idea of what your hot ticket items are, but when you're laser-focused on what the high margin items are, and you're laser-focused on what product is coming in. how long it's been sitting in the store. You can design a merchandising calendar, a marketing calendar, that fits that cadence much more clearly. So I'm talking about things like… think about in-store. Where do you want to put items that you want to push out? You're going to re-merchandise those in a separate spot in your store. What about on your website? It allows you to change where those products are going on your website, what products you're gonna push out this week on social media. When you know exactly which items are selling the hottest, and exactly to the dollar which ones you're getting back. That's gonna give you much more… clarity on how you want to market them and when you want to market them. The other one is sale items. You know, you see a lot of… Labor Day's coming up, you see a lot of blanket, 25% off this whole category. If you have a high margin item, or a high margin category. do you want to lose that 25%? You want to make sure that you're keeping as many dollars as you possibly can in the items that you're selling. So, AI is very good at spotting which items to maybe hold back from that discount a little bit. I think that's probably one of the biggest powers for it, as far as the data. But then, look at the data on your website as well. If you want to see which pages are performing well, you want to see all of the different marketing statistics that show up on your website, AI can now help retailers disseminate the plain English answers to the questions that they want. You know, on a Monday morning when they're meeting with their team, what are our top five priorities? Do we all have time? We're a two-person, one-person marketing team. Do we have time to sit down and go through Google Analytics for 45 minutes? No. But now with AI, you can get a very good list of what are the top 5 things I can tackle today that are gonna move the needle.
Dane Cohen: Yeah, and I love that we said plain language, so I think that's a good bridge trans… plain language, and I think that's a great bridge transition, Marc, into Ask Indy. And I… listen, I am so excited about this, because… and it goes beyond just Ask Indy, it's the idea that right in your most… in the most important tools in your business, the ability to now speak to them in plain language. You know, I was giving you the example, Marc. If we're talking in spreadsheets in 2026, it's like recommending a VHS to someone in 2026, right? You're not going to say, go to Blockbuster to get your… the movie of the week. So, give us the thought process on how Ask Indy kind of came about, and where we are now with Ask Indy, and then we'll do a little bit of a… we'll do some live demos.
Marc Weiss: Well, I mean, I think this really comes from our clients, right? I mean, 10 years ago, you know, probably… the one… the one thing that always bothered me is, one of our clients once said to me, I love your numbers, I wish, you know, you could just give me more info and tell me better how to spend it. And that's really where Ask Indy came from, right? So I finally feel like we're finally at a point where we can take the class planning and bring it down to what really happens at the brand, vendor, and SKU level. And that's why we've spent the last year modernizing our integration so that we're now picking up data every day at the brand and SKU level, because I think that now we finally have the opportunity to marry planning at the class level with how to take action at the more granular level. And we're very blessed to be working with some really talented retailers who've got, you know, merchandise teams that are helping us build and develop these products right now. So, I think that what you're seeing now, Ask Indy, is the first iteration. The next thing is marrying it to the SKU level. The important thing here is to remember that we're also learning, and one of the reasons we wanted to get Ask Indy out is there's a thumbs up and a thumbs down. So the thumbs up is, you know, we're glad you like it, tell us why, you know, you feel like something isn't right, there's a nuance in here we're not picking up. tell us that, and we have the ability to learn from that, and change it, and fix it, and make it right, so everybody knows that MLLs, you know, machine learning, is all about learning and growing, so because we've got a large client base, because we've got a large database, you know, we're going to learn faster and quicker for our own clients. Also, we're picking up conversations from thousands of meetings, and I think that that's being built into our database as well, and will be built into Ask Indy. It's not yet, but it will be. But I really love that, because in one of our webinars you did a couple months ago, it was about renegotiating your leases, for example.
Dane Cohen: Mmm.
Marc Weiss: And that webinar was just, you know, it was off the charts great. And then… but we found out, through all the meetings that we're keeping track of, is that one out of every six one of our retailers is now renegotiating their leases, and, some of that emanated from that webinar. So this collective community that we're building with, which is one of the powers of AI, We can literally build a community of information to raise the level, so… there was an interesting… I wrote a blog about it a couple weeks ago, about sovereignty of data versus sovereignty of decisions. And the guy that, you know, runs Palantir said, you should have your own data and not share it. And that's okay for if you're Walmart, but if you're a small retailer, the opportunity to share your data, With all others, it's all anonymized, nobody knows where it's coming from. But we aggregate it by vertical, by type of store, by location, by… we've got all kinds… we've got a whole hierarchy. I couldn't even go in and tell you which one is which, because it's all anonymized. But we do… we do filter all of that information for your benefit, and then we can begin to read, and we send out a client briefing every week… every month as well, about what our retailers are saying, what's on top of their mind, what's moving the market, what's trending, what's happening. All of that is because we're now aggregating all this data through AI, which we couldn't have done before. And that's really the beautiful, the part of it. So, you know, rising tide raises all ships, so if you're part of this group, then you get to participate and benefit from it, and you still have sovereignty over judgment, over the decisions you make, over the marketing that you do, over the way you want to use the data to your own benefit. So I think that's really the exciting play here.
Dane Cohen: Yeah, and so we have some questions, that came from the audience that I just want to quickly run down, because there are some great questions here. So, I'm happy to answer some of these. Marc, Nico, if you want to jump in. What's the base model for Ask Indy? This is Matt Lucas. Matt, our shoe dog… resident shoe dog here today. What's the base model for Ask Indy? And is it running on your own infrastructure or something else?
Marc Weiss: No, it's… it's running under, we've developed our own… we use Claude, we also use OpenAI, and matter of fact, we could switch to almost any… if another AI system came available, we could switch over to tomorrow. We built our own internal infrastructure, our own closed system. So, it's accumulating all the knowledge of what we have. And then it's delivering those results. So we have an AI forecasting system, we have results from all the meetings, we have now all the information that clients and our consultants are feeding us, all of that goes into our own system, and it's sealed off. That does not go into general AI.
Dane Cohen: Yeah, and so it sounds like the closed system is.
Marc Weiss: So it's used, yeah.
Dane Cohen: the AI planning model, but Anthropic is the large language model that is a closed system that backs our Ask Indy. Okay, Kelly asks, what about data inventory entry? I'm gonna, take this one. This is a little more complicated, Kelly, because there is so much unique mapping that goes on. And so, we're not at the point yet where AI is fully operationally independent enough to be able to do complete data entry. However, we do have a lot of partners that are supporting our clients with creating those maps, and then letting AI take the wheel. So, for instance, SKUflow is one of our partners now that's handling AI order entry and purchase order entry, especially into Shopify. They work with multiple point-of-sale systems. So that is an incredible way, but you need that mapping first, right? Because your store may… your POs may spell black, B-L-K, B-L-A-K, there may be all different abbreviations, and it needs to know where to map all of that. And AI just, like, weirdly isn't there yet with a lot of that stuff, or if you have different categories and you spell you know. t-shirts with a T dash or a T no dash, that's still weird stuff that AI, like, isn't fully processing with data entry, so we have a partner SKUflow, that can help you with that. I would really book a meeting with them. We can send you the details, Kelly. Misty, hi, Misty. Yeah, Misty, one of our regulars here on Indie Insights Live. The AI event invites drive me insane. It all looks the same on social media, right? We want to avoid that, you know, everything kind of looking that homogeneous, same, same, sameness, what we may refer to as AI slop now. Caroline asked, Marc, I think you might be…
Nico Cabral: Let's touch on that, Dane? Because that's something that you can at least massage into your AI. It's only as good as the information that you feed it, so if you feed it everything you possibly can about your brand voice, you can start to get away from that. So I think some retailers, all businesses, not just retailers, if you're getting lazy about how you use the tool and you're not feeding it and directing it properly, that's where you get the homogenization.
Dane Cohen: And that's a great point, Nico. I think you've done an incredible job, you know, you built a, you know, over the years, such incredible brand guides, and color specifications, and the way our logo appears, and once you feed that into AI, it then can have those ground rules set. And by the way, set your AI to remove M dashes. Please and thank you.
Nico Cabral: Sweets, please. I'm actually curious in the chat, too, while we get to the next section, I want to hear from the people in the audience how they're using AI. Just throw one thing that you're using in your business, you're using AI for that you're excited about. I'm curious to see that.
Dane Cohen: Great, yeah, put that in the chat. What did we say, Nico? One thing you're using AI for? Let's get that in the.
Nico Cabral: One thing that you're excited about in your business using AI for yet?
Dane Cohen: Great.
Marc Weiss: a big question.
Dane Cohen: Alright, Kelly, we shared that SKUflow link, and this is an incredible question. Marc, I'm sure you're gonna be able to, wax poetic about this one. What are your thoughts on customers who are against AI for political reasons, and that they're actually boycotting retailers who obviously use the technology?
Marc Weiss: you know, You know, the political polarization, always seems to… you can't avoid a conversation without it, right? You gotta do what you gotta do. I mean, you're in to build your business. I mean, people come around. We've had, I think part of it is just education. I think there's, you know, people have necessary fears. I mean, AI is both wonderful and creepy at the same time. I get that. I mean, there's parts of AI that I don't like, but, you know, you can't stop what's happening. And, the adoption… so one of the things that, you know, had I thought the adoption rate of AI was going to be what it was this year, you know, I probably would have, you know, even poured more money into it, and we made a huge investment in it, we continue to. But it has, you know, we would have been quicker out to release if we could have. But I think that, you know, you're not… the trains left the station, and there.
Nico Cabral: Yeah, exactly.
Marc Weiss: There's always going to be a group of people. You can't, you know, it's like people who make their decision because they read too much about the economy. Yeah, you've got to pay attention to your own business. And if you've got customers who are… are… you can't let the customers drive that for you, but what you can do is educate your customers and let them know that, you know, they're… you know, we get that question all the time. Is my data safe? I mean, we hired another… we hired an outside company just to make sure that our data was safe, just to make sure that we were following best practices. So I think part of it is just educating your customers and handling those objections one-on-one, and making the customer feel well. But you can't stop what's happening, and you can't not not use it. I mean, I don't want to use a double negative here. I sound like an attorney, which I'm not. I think you've got to… you've got to use AI doesn't have to control you, you can control AI. You can use it as a tool to build and develop your business. I mean, we use AI as a tool to make our business better, to be more efficient, to do all the things like what Nico said, but we still have planners, we still have consultants, we still have people, we still ask it the right questions, so it's not like AI has taken over. and is better than us. AI is just a tool that we use to make us better at what we do, to our ability to aggregate information and use things. So I think if I were a retailer, I wouldn't stop using it. I would embrace it, but not let it derive all my decisions, but use it to make… to use information in a way that I can be more… I can be… more… I can provide… what does the customer want? They want great service, they want great product, they want great selection, they want to be taken care of. I would use AI to find ways that I can provide better customer service, that I can make better assortments, that I can be more on top. So I think my answer to that customer would be, we're using AI to serve you better, so your experiences with us are enhanced, and that.
Dane Cohen: I'm gonna…
Marc Weiss: Using it.
Dane Cohen: I'm gonna ask… strike what you just said, though. And, Caroline, I'm gonna… I'm gonna kind… I'm gonna agree with 90%, but here's the 10% I'll differ on. It's good, we got some healthy…
Marc Weiss: Friction is… I love.
Nico Cabral: Covering.
Dane Cohen: I think that the idea of driving your business through efficiencies using AI is… is just mandatory at this point, right? There are time efficiencies, analysis efficiencies that you just have to do to stay competitive in 2026 and beyond. However… I think where customers are getting upset, and rightfully so, is when there is inauthenticity in the images that you are producing, or the copy, or the posters, or the sale flyers, or the marketing materials. And Nico was talking about that before, and I've had to say to a few people, a few customers that we work with, clients that we work with. Saying. I know that this is AI, it's so egregious that it doesn't even look like your store, and the most beautiful thing about independent retail, not to sound cheesy, is you, is your perspective, is your point of view. So if all of a sudden you went from being this quirky, fun, interesting local store, to now putting on these whitewashed uninteresting AI graphics. your customer is going to respond to that, potentially in a negative way. So, when it comes to building out business efficiencies that are ultimately going to serve your customer. Ding, ding, ding. When it comes to remaining authentic to your brand vision, and you as a person, and what you're trying to achieve in the vision of your local independent store. let's keep some authenticity there. So that would be my… my caveat.
Nico Cabral: The biggest word in that was the obviously part. If you're making it obvious that you're using AI, you need to change how you're using the AI.
Dane Cohen: Oh, what?
Marc Weiss: Authenticity and everything is what matters, and… You're still a people… we're still a people business. AI is a tool, it's not us.
Dane Cohen: Yeah, and just some fun little ways that our people on the live today are using AI, short social media video descriptions, some funny, some educational, using it to generate ad concepts for marketing, which we then adjust. Brainstorming event details and ideas, love that. AI research what other retailers are doing. For instance. Mahjong. Misty, that's a great one. Looking to see what others are doing locally as I develop my events. By the way, Misty, we're gonna get to this in a second, knowing what other retailers are doing, the benchmarking power of AI in Ask Indy. Use it to review website product, top products, top search. So we have all types of way that it's working. Marc, do you mind if I get to some Ask Indy? I would love to show a little bit of what this looks like.
Marc Weiss: Yeah, and I just want to… don't want to forget, since it's also a good segue, is we want to talk a little bit about the launch of Indy Insights next month, and the power of our knowledge of the product that's selling across North America.
Dane Cohen: Yeah, so first I just want to actually show what Ask Indy looks like, and so this is now built into all of our clients' Retail Orbit. And let me just say something with a lot of strength here, and with a full throat. If you are not logging into Retail Orbit, and you are a client, and you are not, interacting with the platform at least once a week, you are missing out on such rich information. So let's look. This is a children's store. We have it as a training store, but this is real data, so I want to know what my top 10 priority actions are this week. We have built-in prompts, these are our built-in prompts, you could obviously ask it whatever you want right down here in the chat. But let's go to my top 10 priority actions this week. and let's see what it produces. And what's going to happen here, right, is this is where, instead of having to comb through multiple reports and compare different reports and I mean, who remembers VLOOKUPs and, you know, pivot tables? I mean, that alone, the amount of time… you know, we have a planner on our team, shout out to Jess Vishnik. who I worked with in my previous role. She was a planner for the business I worked with. And Jess, every Sunday, had to pull all the reports and all of the data, put them into pivot tables, merge them together when she could have been at her son's soccer game or, you know, doing family time. Instead, she was doing pivot tables on a spreadsheets. So let's see what it came up with. For our top 10, actions. So, one of the first things, mark down girls' clothing. largest, and sales are down, and we have too much weeks of supply. Action number one. Let's go to clear outerwear. This is a great, we have 167 weeks of supply, we have 38,000 of inventory. There's dead stock there, we're overbought. we're entering into a new season, we need to clear out the old season, right? And so instead of finding this months later, or waiting till, you know, you kind of comb through reports, it is right there, for you. And let's keep going to what you should chase and invest in. Toys. We're seeing this with the resurgence of… I'm gonna get this wrong if anyone wants to jump in. Nico, you're a dad. What's that, toy that everyone is talking about? The… the squishy, or the…
Nico Cabral: Nitinos.
Dane Cohen: Nito, right? We have the Nito explosion, we have the Jelly Cat explosion.
Nico Cabral: Yeah.
Dane Cohen: Look at this toy business right here. It's.
Nico Cabral: The whole thing.
Dane Cohen: 45%?
Nico Cabral: 45%, all things squishy. And so now we know that we have $85,000 in open-to-buy spread out across September and November. This should give this retailer the confidence to keep going hard in the paint when it comes to these categories.
Dane Cohen: How about that for a sports reference from Dane Cohen? And… And then books, right? Reorder books. And I love this right here, right? How much time you can save right now with someone telling you to reorder books? You have 921, go place a reorder, you have $1,000 to spend, don't overthink it, we have it right here for you, in very plain language. Protect and replenish blankets and Lovies. Again, could not be more direct. You're lean, you only have about 13 weeks of supply, you have a little bit of dollars open to buy, and then you have more coming in September, October that you haven't placed orders for yet. So… This is some just great, easy stuff to really be able to speak to your business in plain language.
Nico Cabral: Yeah, and going on marketing… planning out your marketing calendar for the next 3-4 weeks, this is a perfect structure for that.
Dane Cohen: Right, and then we could ask it, you know, any questions, right? So if we want to see Sorry, what is my open… to, by looking like, for November, And… December. I guess it still can't correct my spelling. Doing great here, Dane. Let's search.
Nico Cabral: And to Marc's point earlier, we have retail coaches that our clients work with as well, so these are excellent conversation starters to get that human-to-human interaction going, because the coaches have the decades of wisdom to really disseminate Based off of this output. how you're going to direct your efforts, where you're going to direct your efforts, what they've seen in the past, and other clients that they worked with. So I think it's a fantastic conversation starter there.
Dane Cohen: Unfortunately, I can't see this without being angry, because the fact that I did not have this when I was in the business, I mean, the fact that we could pull up, you're open to buy in November. right here, what are your plan sales, what's your open to buy, buy category, I mean, within, you know, 30 to 45 seconds. And then, not only that, we're gonna be able to get the context behind it. So, Toys has the biggest open to buy in November, which we discussed. Girl clothing and boy clothing account for $50,000 of that open to buy. Swim, right, we're getting into cruise season. We gotta love that for kids, going on vacations in December over President's Day weekend. We need to get in swims starting in November.
Nico Cabral: We're going to Magic next week. I mean, wouldn't you like to know exactly how many dollars you have per category to peruse the halls in Vegas?
Dane Cohen: And then look… great example, right, going right into market. And then look at this, and this is so interesting, because this is the context that AI needs. And when we talk about general AI versus this closed AI, this is the context it needs. So, you have open to buy in November in sleepwear and underwear and even outerwear, but both currently have dead stock territory. So they have old inventory in that, so before you spend that open to buy, you have to clear your current inventory first before committing to fresh inventory. So the fact that it can contextually put those two things together, right? You have open to buy of dollars to spend, however, you have dead stock right now.
Dane Cohen: Marc, that's pretty good stuff. That's pretty good stuff, Marc, place.
Marc Weiss: Oh, it's brilliant. It's like having a teacher.
Nico Cabral: I need this.
Marc Weiss: It's at your fingertips. You know, we have 10 minutes, and I'd like you to also… and by the way, I just want to say, Ask Indy is being updated every single day, and it's, you know, it's learning, it's learning from you, it's learning from your, your questions, it's learning from you know, what you tell us, this doesn't look right to me. Why? Because every store has its own nuance. It'll learn your nuance for your store, both from that and eventually through your client meetings. That hasn't been looped in yet. But we also use AI in a lot of different ways, and one was Jeremy created, the Retail Health Score, and I think that's a good barometer. We, you know, it's kind of played off the idea of a FICO score, FICO score, and I'd like you to pull that up too, Dane, because I want to talk a little bit about how AI moves the needle for you on that. So the Retail Orbit Health Score really you know. takes a look at those elements that really grow your business, and, you know, what makes you healthy. And this… trends over time, you're not going to see huge changes, but it looks at the components of what makes your business healthy. Your gross margin, your sales growth, your markdowns, your annual rate, your inventory freshness. And all of those will reflect itself in your cash flow, in your performance, and those kinds of things, and they're also going to be looked at in benchmarking. If you… you look at this one. They have a high Retail Orbit Health Score, which is great, it's, like, at 86%, but they have a turnover of only 2.1, and the benchmark's 3.5. So this is important because If they had… fresher… if they… they're selling… what this tells me, what you know from this, is that you're… they're selling a lot of the new stuff that comes in, so they're managing to stay fresh, but they're not getting rid of their underlying inventory, because their GMROI is 2.2, and their CMROI is 2.7, which means when your GMROI is higher than your… when your CMROI is higher than your GMROI, it is a good thing because you're cashing out of inventory, but your GMROI tells me you've got a lot of unsold product on the walls, so… or on the floor, or on the… on your shelves. So, the Retail Orbit Health Score is a way for you to measure your own progress and to have conversations with your retail coach about what are the things that I can focus on, what are the priorities that I should have? Let's now build and develop a strategy. And then you've got your sales, your inventory, your margin, your markdowns, and your turnover rates right there for you to look at. So I think that, this dashboard is showing you this is on the Shopify app, but this is also now embedded in Retail Orbit. It's the same thing, and your cash margin, we just use 40%, but you can… you can edit this and put your own cash margin in, and watch how your cash margin is changing over time. again, our ability to do all this is because we have invested in AI to be able to aggregate and accumulate data, measure you, measure you against your own vertical, measure you also against, you'll see it in Indy Insights, against, you know, your location, your geography, and all those kinds of things. So,
Dane Cohen: Marc, I just want to, you know, highlight this for a moment and really have this jump out, which is… think about retail and how you analyze your numbers, right? It used to be you basically analyzed your numbers against LY, right? That was the barometer for everyone. What did I do versus LY? What did I do versus LY? Then, you start getting involved in open-to-buy planning, and if you're an M1 client, you're looking at things against LY, and you're looking at things against plan. Now, we've added another layer, which is the contextual layer, which is your markdowns are at 7.2%, right? But the industry average, the benchmark, is 10%. So, you know you're actually running a little too lean on markdowns, or even your sales increases, right? You're up 15% against an industry benchmark of 6%, and the reason we know that is because we can aggregate data across Across independent retail clients and across verticals, so that you are comparing yourself in the context of not just you know, industry numbers that are taking into account Amazon and Target and Walmart, but what is happening in your vertical, in your In independent retail, even in your region, if you want to see it that way.
Marc Weiss: Well, she'll…
Nico Cabral: That's the biggest difference.
Marc Weiss: Kelly just asked a question about, you know, how does this backend differ from what Shopify offers? So, Shopify is a, e-commerce platform that does POS work. What we're doing is we're collecting all of your data out of Shopify, and then we can compare it against all other data, on Shopify. So, and then match you, marry you, to stores like you. So, we've gone and done The heavy lifting so that you don't have to do it, so you're not just comparing against yourself, but you're comparing against others like you.
Dane Cohen: And then, Shelby, I just want to acknowledge one other thing and how this differs from Shopify, or any point-of-sale system. Your point of sale is your central nervous system. It holds all your inventory data, it holds all of your sales data, all of your markdowns, receiving, purchase orders, but it only it only looks backwards, right? It's only showing you backwards data, or what you're doing today. what Retail Orbit is able to do is blend this with your forward-looking forecast and your open-to-buy plans. So, we are working Simpatico with your point-of-sale system in order to take that data and put it into forward… forward… forward forecasted actions. I do want to just call out one other question we had. Robin, you're on Retail Foundations. Well, talk to your coach. We would love to see you transition into planning after your, kind of. you know, worked into your Retail Foundations program, that's exactly why we created Retail Foundations, to get your base layer and your data in your system, in your POS, correct and organized, so that you can move into planning with ease. So definitely talk to your coach about that. We'd love to see you make that transition into planning, and we love that you're using Retail Foundations. And hi, Robin! Okay, so Jeff.
Nico Cabral: Dane, I wanted to go back on your, showing the benchmarking on that turn rate, because in the context of this conversation, if you ask general AI, alright, I have a 2.1% turnover rate, is that good, or what's the industry standard? The benchmark they're gonna come back with Is gonna be an arbitrary number that's based off of whatever publicly available, large retailer knowledge they have. We didn't invent the number that goes right down there. That's actually pulled across all of the retailers that are connected to our system. So it's a much more valid number, it's a much more real number than anything you're gonna get from general AI. And it changes as the data from our other retailers change.
Dane Cohen: Exactly, exactly. So, Marc, Nico, we have 2 minutes left. I just want to end on any kind of, final thoughts that you have, anything that you want to tell our, audience today, and then I have one final, beautiful news that I want to share. So, Marc, how you…
Marc Weiss: just to continue to look for innovation for Management One. I mean, you know, we are going to be launching our… we're going to be taking in, Ask Indy to the SKU level for those point-of-sale systems that we're collecting SKU data from. That's coming out soon. Indy Insights, portal's coming out soon. We have a growing peer-to-peer group, if you're interested in being involved with that. I'm going to use this time to be a commercial. Because I'm so excited about the community we're building, and your entry point into the community can be in many different places. So, you know, stay in touch with us. Call us, contact us, ask questions on anything. And tell us how you like the product.
Dane Cohen: We'd love to hear from you. And by the way, that Indy Insights benchmarking portal, that's gonna be a great entry in for a lot of folks, you know, to really understand their industries and even traffic data that's coming from your regional neighborhoods. So that's going to be incredible.
Nico Cabral: That'd be huge.
Dane Cohen: We can't wait to show you. And Nico, final thoughts as we wrap up our session.
Nico Cabral: A blend of AI and humans, so Pamela in our chat here, she said… we asked how she used AI. She recently created a pencil drawing from a customer-provided photo. of a dog that passed away. Printed it on a photo paper, and included it with a handwritten note.
Marc Weiss: Oh, that's beautiful.
Nico Cabral: Perfect. Perfect use of the tool. Going back to the, the other user that was talking about users boycotting AI, it's a tool that we can all use to create things like this. The limit is only your imagination on how you can use it, so the tool is not going anywhere. If people want to boycott it, they're just going to leave themselves behind, that's their problem.
Marc Weiss: Yep.
Dane Cohen: Well, Nico took my beautiful ending, I was gonna shout out Pamela, and I just wanna… but I do wanna just go back to that, because it really did bring a tear to my eye, you know, I have my husky, my baby girl, Lila, sitting behind me right now, sleeping and snoring. And, you know, that… those are the human touches, that we could really make. And, you know, as we blend together the data that we're gonna get, the efficiencies we're gonna get, the forward, you know, the forward advancements that we're gonna get from AI, especially if you're a client and using our systems, it's still all about the human touch to it. And that's why you have a coach. to put this all in human context, to help you strategize, hey, even to help you vent and be a therapist for you at times, right? Because the burden of being a small business owner is real. The burden of being a retailer is real. And to have the right tools and the right people in your corner make all the difference. Final thoughts, if you would like a more in-depth, you know, view into the power of Indy Insights, talk to your coach, reach out to me. Marc, I'm gonna offer you up as well. I'm sure Marc would love to always talk shop and talk through this with you. Let us know how you're using Ask Indy, and as we depart. Patrick, people plus platform always win, and Robin can't wait to share this with her team. Pamela, thank you for that example. Thank you for joining us on Indie Insights Live, we will be out of office next Thursday at Magic in Vegas, so we are not going to be on next week, but we will be following up the week after, with a special edition for you coming on Thursdays at 1pm Eastern. Okay. Signing off.