From Spreadsheets to AI Agents: The Ecommerce Data Playbook

I could think that list of names give it to an agent and say, "Build me a database with a photo from Wikipedia, a brief bio and a summary, and 10 minutes later, I have the database built." To look up the company's snowflake, it's huge. Multi-billion dollar businesses make a lot of money. Data warehouse is huge because when you have all your data clean in one place, you can make better decisions. Once the agent is there, you just have to change the way you work, right? This is just about resistance to change. To just get yourself over this change resistance. And if you're not lazy, it's a game changer. You get up in the morning, you open your slag, you just say, "Adverture for a FAQ. How will my sales yesterday?" You get an answer. If you see that the sales number in a particular country are below what you think should be, you can just ask a follow-up question to sales seem low. What happened? Someone's going to need the essential repository, and it sounds like that could be the the Sarah's IQ agent. All of these are ready to talk and bring data into you, and then someone has to synthesize it, clean it, and then give the rich version to a rich executive to make a decision. And it's just a very exciting future. If you're not making decisions on data, you're just using your gut, and honestly, that's just overconfidence, period. There's so many people that are just like literally flipping a coin. That's one of the things that I've been doing internal for the last 18 months. Doing these AI audits of workflows and processes in tournaments, documenting every step of the process, and then looking at each step of the process to see whether that step is AI replaceable, or that step is AI assisted. Get your AI foundation ready today. Agents are coming. You will see a serious unlock in productivity. Don't let your team struggle with spreadsheets. Get the model foundation. Give them their time back on these dear creativity to that core of your business. Welcome to the operators podcast. My name's Mike Beckham, and we are proudly brought to you by Fulfill, Aftersell, Rich Panel, NorthTheme, Sarah's Analytics, and PostScript. We are a community for entrepreneurs that are building things. And if you want to be a part of this community, you can listen to the podcast, but you can also go sign up for our newsletter. A ton of awesome information in there. We also partner with e-commerce fuel. I form for you to connect with other entrepreneurs that are building businesses where we can learn from one another. So without further ado, onto the pod. Dealing with Big Box retailers means EDI connections. And that's often a trigger for needing an ERP system. We've been using EDI connections to Costco forever, and the only way that we really solve that problem to make it seamless is through Fulfill. EDI adds complexity to everything you do, and Fulfill solves that complexity with their connections to their systems. You need Fulfill to move from being just a D to C brand to being a true multi-channel brand, because Big Box retailers are going to require you to connect to their systems using EDI. Let me tell you, it's way easier if you do it with Fulfill. Krishna, we could do this in person. All of us are in LA right now. How's how are you drawing California? I'm doing very well, Sean. Last couple of thanks, we did this. It was 10 pm for me, and right now it's 7 am for me, jet lag. But yes, looking forward to getting started with the conversation excited. Would have been loved to have this in person, but nevertheless. Dude, you know, when the operators podcast calls, if it's 10 pm or 7 in the morning, we get it done, guys. Jason, how are you feeling? I'm very happy to be here. I'm not feeling great because I've had a cold for like the last week. So I'm going to be going on mute while I hack up along. But Krishna within my office on Monday, he had his team in, we had a bunch of meetings. We're cracking the whip on Saras. They're doing some pretty cool work for us. So I'm excited to have Krishna back on the pod. Okay, I love it. Today's episode is all about data, data integrity, the importance of data. The reason why we're doing this episode is because with AI rolling out everywhere, we're the 20 initiatives that bridge this year's AI everywhere. We're finally getting validation that like all the data we've captured is important. So we have Krishna here to talk about what to do if you're a $15 million brand or below, what data is actually important, what you need to be focused on, and then what do you unlock with AI when you get all this data ready? So, you know, if you're a brand right now and you have no data integrity set up, if you're just not tracking stuff, you don't even download and CSV's from Shopify, this episode's for you. We're going to help you unlock things you can do and really try to demystify all the AI stuff. I think there's two camps for people right now. You either hand wave that AI is going to do everything or you hand wave that AI totally sucks. Obviously, there's something in between there, right? It's not doing every job in my business right now, but I am making ads and I can show you guys some amazing AI creative if you guys want. I'm crankin those out myself. So we'll do something in between to figure out what's really going on in the world of AI right now. Sounds like a good episode. I love it. And just Sean, just real quick, like, you know, you and your organization have been, you've been super focused for months now, you know, pushing people to get into it and I to really leverage AI. And I think people are doing it. I'm actually pretty impressed. I'm starting to feel better about it, especially with the new Clawed Release. People are actually embracing it and are trying to do cool stuff with it. I think, you know, for me going forward, when I'm every hire I look at now, it's like, are they going to embrace AI or not? We just hired a new Middle East country region manager because we're selling and Dubai now. Before we made the offer, I said, I need you to send me two things. I just send you a five-year plan. And I need you to because people are doing like these two-year plans and like it's not that exciting. Let's see what it looks like over five years. And then I said, explain to me, your feelings on leveraging AI for the business. And you know, he basically wrote like every every wrote sort of a position paper on everywhere within the e-commerce stack like we should be using AI. And just taking the, just taking the making the effort to do that, I thought was really nice. And so it's just, it's got to be like table stakes for everyone at this point. Talking about table stakes, Jason, so I've been on this AI journey personally for the last 18 months. So as a technology company, we have to transform internally. I have to want people to team that I have to get them to adopt AI, think AI first. The culture in the company has to change because the customer expectations are changing, right? You expect things to get done faster, better with the same, same set of people. So there is the internal aspect of it, which is, how do we change culturally of the company? Something that I've been experimenting with for the last 18 months. And externally to customers, customers are going to expect AI output from us, right? So so for us, it's on both sides, one is on the people's side and the internal processes and how we can do more, which less by becoming AI first as a company internally. And the second part is, what do we put in short of our customers so that they can become AI as well, right? So having gone through this journey, it's been interesting, right? Because not everybody is adopting AI at the same place. There are some early adopters, there's some non-believers still, which is very, very surprising. So having gone through this journey and getting the teams to transform, there are some learnings that I would like to share if time permits. But yeah, happy and looking forward to chat about what we could be doing together with AI and how exciting times are right now. Yeah, you know, the non-believers, let's start there, right? I think most people in this probably have used AI. I mean, everyone definitely has, right? Even if you don't want to, there is going to be Google Knowledge Box summaries at the top of every search. I can't believe someone being a non-believer in 2026. Like if you used JetGPT or Dolly four years ago, I totally like, you know, this is really cool, right? And then moving on with your life, it is totally different now. It's a different product, calling it AI is actually like unfair, putting it all into the same bucket. I'm using agents now. And agente at work is awesome, right? And I can give you guys an example of what that is. I got a list of famous people from Italian agencies. These are people we want to book a commercial with, right? And it's, let's call it 100 people. I don't know any of them by reading their names. I can't picture their face, right? So I could individually Google every person and look at them. Or I could take that list of names, give it to an agent and say, "Build me a database with a photo from Wikipedia, a brief bio and a summary. And 10 minutes later, I have the database built. It could be a thousand photos. It could be 10,000 photos, right? Like it is doing the work of, you know, junior level college employees right now. Yeah. When I say non-believer search, Sean, I'm talking about folks who are still not convinced that they are going to be talking to agents first before they talk to humans next. If I look at the roadmap or service analytics over the next two years, I can assure you, or even the next one year, I can assure you that most of the first conversations or interactions that customers are going to have with my company are going to be with the agents that we build first. And then if the agents are not delivering the right answer, then pull in a human, right? So, so from my standpoint, I'm looking at non-believer as somebody who is still not convinced that they are going to be agents are going to be frontending them and they'll be interacting with agents. So, it's not necessarily all whether they're using AI or not. If that makes sense. They're not convinced yet. Right. And they're either going to be convinced. And if they're not, it's because of either to either they're lazy or scared. Right. They're lazy because they just don't want to take the initial initial time and effort to to figure out how to use it. And it's it's actually so easy. Right. Like once the agent is there, you just have to change. You just have to change the way you work. Right. And you literally just need to spend. I think like like 10 minutes, 30 minutes to just get yourself over this change resistance. And then and and if you're not lazy, I mean, it's it's a game changer. We were looking at. I mean, this is always what I've wanted. I've always I've been saying this since I was a banker. I used to use huge databases as a banker, fact set, capital IQ, to get information about companies. Right. And we would have to build these massive models. I had these people build a massive models, massive power points. And and all I want to do was be able to just like write a question to this database and like spit it out. Right. And that's exactly what's like what's finally happened. And this is what I've been talking about with Krishna like from the beginning with Sarah's what I want back when I back when it was when it was at at fact said in FinTech when back when I was a banker, it's like what's a solution for like sea level executive to like really easily get answers. And that's what this does for me. Like it's it's it's just like absolutely insanely good at this point. I'm I'm so excited about where where all this is going, Krishna. If you're scaling any commerce brand today, ads alone aren't enough. Afterself focuses on the one moment that every brand already owns after checkout and turns the post purchase moment into more profit monetize every order with post purchase offers and thank you page experiences without disrupting checkout or hurting conversion enterprise grade tech used by gap ticket master mases and target now driving results for brands like true classic hex clad ridge and Jones road. I would know this is the reason I ended up buying three pants from hex clad instead of two after sell is already generated over one billion in additional revenue for ecommerce brands revenue that doesn't require more traffic or higher cat. So check out after sell and tell them that the operator sent you absolutely yes. So I'll give you some real life examples for us. Maybe I can talk about stuff that we're doing internally and then we can talk about things that we're doing with customers externally internally. For example, I have a large chunk of my team sitting in India and very often customers ping us at 2 a.m. in their time 3 a.m. in their time asking a clarification question saying hey my my numbers in this dashboard seem a little bit off or how do you calculate this metric right now to also that question I need to have a customer success engineer who is trained up on our technology can take that question understand how to find an answer to that question right or consult the right people in house and then get back to an answer get back to the customer with an answer right. So the average response time so we have we built our own internal data lake so whatever I go talk about building a thing source of truth for brands applies equally to every single business no virus the scale of the business right. So we have our own internal data lake where we have you know all the interaction data everything that we are loading up into the system. What we have done is we have measured what is the average response time across all the questions that we are getting from customers and the average response time of questions that we get during our work hours which typically are on to midnight is less than 30 minutes or 30 minutes to one hour but if the questions come to us after 2 a.m. the response time increases to 8 hours right. So we are going from being prompt at 30 minutes to one hour in terms of response time to being not present when the customers perhaps sitting in a in a board meeting or in in an executive meeting trying to do numbers and they have a question that comes up and here we are not able to respond to them on time. Where we are going and we are testing this in beta internally we have some agents that we have built you call it IQ engineer and IQ analyst. So you can ask the same question are agents respond to you in real time or in real time in less than 60 second response later see all these questions around okay how did you calculate demand revenue for instance right. It does not require a human agents can answer that question and if the conversations go beyond a certain point then a human can get engaged right. So that is just an internal example an external example that I can think of is I recently met a product team and the product team is trying to understand for the product that they are designing or the product that they have come up with what are the conversions on the site how many how much traffic am I getting to these web pages how much is my marketing team spending on campaigns to drive traffic to these products how much revenue are we getting what discounts are the giving etc and they just don't have answers to those questions all of that would become a single text prompt with answers coming back to these users in 60 seconds right imagine what that can do to your business where any stakeholder in the company can ask a question get the answers of their data without waiting on their data analyst their data team etc but instantaneously so that they can take a decision that will follow yeah you know let's go to start with the internal of example first I think we can all agree and all the listeners can agree in 10 years you will not hire customer service reps like it is so obvious that email based customer service where is my order free commerce orders that will not be a human job in 10 years and then it's just in your or maybe even less than that actually yeah yeah yeah but I'm warm everybody up right so I'm saying it we all agree 10 years and then it's just how aggressive are you is it five years I would say yes is it four years is it three years is it by the end of the year you're never hiring another cx rep right it's like the technology is moving so fast you can just so obviously see that work being done it's the same thing with self-driving cars open this up all the time it's like you're not going to drive your car in 10 years we can all agree that it's like the cars can already drive themselves and it's just like where on that curve do you think the technology actually happens and with any repetitive knowledge based work like customer service I think I think it's literally by the end of 2026 you're not hiring new people for that role that the external thing let's talk about that first we should we should I don't think we've ever done a good explainer what is serriss analytics so it's a data warehouse okay and serriss analytics as a company comes in and they clean and set up your data warehouse okay so you have data in Shopify you have data in wholesale you have data in fulfill your ERP you have data in amazon the problem is all that data is not clean what I mean by clean is my title for amazon for a gun middle wallet is like best wallet ever you know it's an Amazon title right so if I if I just try to figure out how many gun middle wallets I sold I would have to go in there after manually think about that right so it's gonna come in and they're gonna map those things and clean it up for you and then they're gonna give you a box that is all of your data updated every hour in real time that is clean and custom design for your business Christian is that right is our tirsion analytics does yeah that's what we have been doing for the last 10 years we are taking a couple of steps further than that Sean we are looking at far as more often the i foundation and any i will flue engine for for brands and agencies and the data warehouse is becomes a foundation of anyone here right yeah yes let's let's let's let's let's talk about the future yet I just want to give everyone familiar with why they would need a data warehouse and then we can talk about what we could build on top of it right so like at the at the very base level if you're a brand having a data warehouse is important because as soon as you start selling on more channels as soon as you have a couple of years of historical data somebody changed the price way back someone changed the skew way back it you have to get all that information in one spot and right now it's you know ancestral knowledge like you just know because you did it right but when you're gone and you hire somebody else it's hard to translate that information the data warehouse makes that easy the advantage of having a data warehouse in the AI age is now you have all your data in one spot you have your marketing data in there you have your spend data in there and now you can start building agents on top of it right or reports on top of it or query into it right and now Krishna then you tell us about you have the data warehouse where where is the future of Sarasana lyrics going so future of Sarasana lyrics is agent fiction so I imagine a user coming to Slack and asking a question saying okay I just launched this product last week what's my performance what's the performance of the car right and I expect then to get a summary of the product performance since launch on wider parameters that can be used to assess whether the launch went well did not go well or doing average all in a matter of seconds what does that mean from a practical standpoint right if you think about it when I launch a product the orders are getting logged in short of five and I'm on so my saves data is in these two platforms at least when there is traffic advertising campaigns or money is being spent so that's data is sitting in Facebook and Google and all these other places Some click on the add, lands on your website, the conversions on the page does. activity on the page is locked in GF4 or Edge mesh or one of these tools. So for a product owner or for a marketer or even a CEO for that matter to understand how did my product launch go? You need data from all of these systems to understand what is my spend? What is that spend leading to from a conversion standpoint? And what does that mean to us from a revenue standpoint? And how does that pace against the targets that we have set for ourselves? Right. So that getting that summary today is so hard. It takes multiple people going to multiple systems. All of that is going to get reduced to seconds actually going forward. And that's the direction that we are taking our customers. Every SaaS company says they are AI powered but very few can explain what it actually does for the revenue of my brand. This is why PostScripts approached it out to us. They don't just build AI for demos or buzzwords. They built it to drive real incremental revenue. PostScripts AI called Shopper. It shows up inside of SMS at moments with real buyer intent when shoppers are likely asking questions, hesitating maybe even about to drop off. Shopper can answer product questions instantly answered questions about fit availability recommendations order issues that kinds of stuff that people usually bounce for. This means more conversions higher of less lost demand. So you are driving more revenue and doing it more efficiently. Check out shopper from PostScript. We use it at Pila, which is why I am telling you to check it out. You need a data foundation underneath your AI, right? That's fundamentally, if you don't have a good data foundation under your AI, it's usually why quantitative people have been less interested in really adopting AI, until Claude's most recent release and you could really power Excel with Claude and do really good stuff in Excel with Claude. But even then, there's so much time going into compiling and scrubbing data. It's a very manual human task. If you are running on more than just shopify, even if you're only running on shopify, you're probably still using NorthTheme, right? Or using another MTA tool. You're getting more getting metrics out of that. You have an ERP system. So I've been a big believer in data warehouses for 15 years. If you look up the company's snowflake, like these are huge, multi-billion dollar businesses make a lot of money. Because this is in the financial markets, data warehouses are huge and basically all throughout large-scale businesses, data warehouses are huge. Because when you have all your data clean in one place, you can make better decisions. It's all about making good decisions. So for us, we pull in shopify, Amazon, Costco, NorthTheme and many other sources. And it's all in a great clean state within the data warehouse. And then we pull it out into dashboards. We pull it out into daily email, summaries, data summaries. And now with AI, you can get the real power, the real leverage is actually querying the database through AI to just answer questions that you have. You just, you know, I'm constantly going to chat and asking questions now. But now I'm just going to be doing the same thing, asking questions about my data. And it's just going to allow me to make way better decisions. It's like astonishing how good this is going to be. Yeah, a simple workflow there, Sean. Jason, for you, like the way things are going to look very soon, once we launch IQ for you is you get up in the morning, you open your Slack, you just say, "Adverture for FACUE. If you see that the sales number in a particular country, you know, are below what you think should be, you can just ask a follow-up question saying, "Sales seem low. What happened?" And the agent is actually going to get into your data, understand how the marketing team, marketing performed previously. What could be some of the reasons why the numbers are lower than expected? And it will come back to you with a better than usual, like better than expected answer. Sometimes you get surprised at the quality of answers, the AI, and the lengths are able to produce with the right set of context and training. You can actually ask a simple follow-up question saying, "Okay, why if the sales slow, you come back to you with a summary, so you can start your day interacting with an agent, getting to understand the current state in a matter of a couple of minutes, and then get on with your day, you know. What is the traditional workflow there you wait for an analyst to put all of these reports together, usually manual. They are on leave one day, you're not going to get that report. And even if they are working on that day, you probably have to wait for them to compile everything and then ship that five to you, you get an Excel file and you have to open it, read it, and comprehend what's in there. Imagine swapping that with a simple question and a nice summary that you can read in a couple of minutes and that comes to speed. So that changes the decision velocity at a business and having that power to any team that may not have budget to hire their own analyst is in my opinion going to unlock seedless productivity for brands. Yeah, and I just want to keep highlighting how people need to experience like a genetic work to understand what's capable, right? So right now you can use Manus. Manus got bought by Meta. If you spend money on Meta, they'll give you Manus credits for free, ask for it, and then just ask it to do something. It will go on the web. I mean, I probably did this on the podcast a year ago. I had order me Chinese food, but like now I can do like I told you guys, I had a task that would have taken someone an hour to do and I'm like, hey, Manus, just go in there and take photos and screenshots of all these celebrities and compile me and then make me a little web app for it. Like there is so much that can actually get done with the actual agents doing work right now. And I just want to make sure people experience that because it sounds like, oh, I'm just going to talk to the AI bot. Here's an example at Rich. We have Sarah Stonelette, etc. And we put all of our marketing data in there, right? And it's very easy when things automatically port, right? So Meta spend, Amazon spend, Google spend, like there's APIs. You can pull the stuff in, but what do you do about podcast spend? What do you do about TV spend through Atari? Right? So I give Atari a budget and they spend the budget and then sometimes they don't spend all of it or they only spend so much of it. I mean, I have to know what they spend per day. So they email that to us. Our agents just going to ask them that like, our agent will bug them every day. Hey, where we spend yesterday? And so Tari and then that agent will get that information and it will put it in Sarah Stonelette for me. That is a human remove from that loop. There doesn't need to be a human in that loop. It is getting data that is harder to find, but they can give it to an agent to actually understand that. And then if the Tari was smart, they wouldn't have a human email my agent. They would build their own agent. So my agent's talking to their agent and just get into the spend data. And you just start seeing how more and more results and actions in a company reduce down to an agent can probably do that. Right? So I just want to highlight agents. They're way different than normal LLM's or AI. And I'm getting used out of them right now. Krishna, why don't you go and show us Sarah Syke here? Yeah. So let me give you a quick quick sneak peek here. So here's an example of life at a at a typical brand. So if you are a founder and executive, you're getting spreadsheets. This is usually what your team is struggling with. They might not be coming to complaining about it, but they're definitely unhappy that they're doing some of this work. Right? So you have data sitting across all of these different systems, advertising, marketing, marketplaces, analytics, TPLs, etc. You have your either your leadership team or your analyst pulling data manually from these systems. And then sending that to folks like Jason and Sean who are then asking you questions or asking you questions about data quality or asking you questions about how something was measured. And this is a tedious process. Right? So someone goes on leave your stock. What we've been doing all these years is we are giving these analysts and the leaders, operators in companies their time back by building what we call as the data foundation. Right? So what we are doing is we are pulling data from we have connectors to on the plus platform. We pull data automatically in a real time from these different systems. Load that data into a data warehouse, clean it, transform it and certify it so that your data is ready for reporting analysis or whatever works being that you might have. Once you have the data foundation, what are the kind of questions that you can answer? Right? You you'd be able to understand your contribution margin by skew, by campaign, by product, by product category, etc. So that will help you understand which are your top performing products, which are the products that are leaking you contribution margin, which campaigns are performing which ones are not so that you can take decisions. Right? You get some trackers that you can use so that you can understand pacing, projection and your actuals. You get a consolidated view of your inventory, you get customer cohorts so you can understand like value metrics, etc. So once you have all of these, so this is your data foundation, right? This is your 101, right? Every brand in my opinion should or every company for that matter should invest in a data foundation where you have the single source of truth. Once you have the single source of truth, that's where the agent declare starts to kick in. So we have a product called Sarasaiq. In Sarasaiq, it's actually, what is the goal? Any stakeholder, whether you're a data engineer, or a data analyst, or a CEO, or a CMO, we want you to get to the right answer from the right agent in the right language, without you having to know how the data will start to move. All in a matter of seconds. How do we do it? So IQ actually comprises of six agents, each doing a different function. So there's an IQ analyst, who is answering questions like what happened in my data. When you ask this question, IQ analyst, agent is dipping into your AI foundation, that we build for you, and comes back to you with a response. IQ engineer is troubleshooting a data pipeline break, these APIs change sometimes, you have to latency, you don't get the data. IQ engineer kicks in when you say, in my data looks suspicious, can you go check what happened? IQ engineers, scientists is predicting what might happen in the future. Data quality, again, we want you to trust your data. Only if you trust your data is when you're going to use it, and only if you use it regularly is when your decisions will, you know, will be influenced by. So these are some of the agents that we're working on. They're all under the IQ agent work force. And the way this works, if I were to give you a quick demo, so somebody who was signing up for IQ would get an interface like this, where you can ask a simple question, right? So for example, give me sales for the last 60 days, broken down by Capically. Essentially, what is happening behind the themes is IQ is taking your query, connecting to the data foundation. It's picking up the business logic that we have trained IQ on, specifically for your business and your business context, and generates the query, and it generates a summary that you can quickly read and understand, or get an answer to here, right? So typically, you'd be either trying to find an answer to this question on Excel, or you could just come here, ask, and get a simple summary out like this. - Okay, Christian, but, you know, the pushback I'm gonna have for you is, why are you building IQ from scratch? Why don't I just take the data and just put it into cloud 4.6 and have cloud do reading models on top of it? What's the advantage of building the agent yourself? - Yeah, that's a fantastic question, Sean. In fact, IQ is built on top of cloud, right? So we use cloud internally along with a couple of elements. What cloud is a general purpose tool? IQ is a vertically specialized tool for e-commerce, right? IQ understands your business context, and those are the agents that you're building. So we're building agents that understand your business context, what the metrics in your business are, how they're defined, what is the data saying about your business? So we build all that context into IQ. We then pass that context over to cloud, and then let cloud do its magic, right? So cloud is generating the SQL for you. Cloud is helping us generate this visualization. Cloud is helping us generate the summary, but what IQ is doing is teaching all of those workflows together, passing the business context so that we get achieved nine out of 10 questions, right, when we answer a question. That's one. The second thing is, if you ever see any of the announcement that come out from, let's say, a chat GPT or part, where they're launching a new model, the way the launch process works is they basically write a series of tests, and they test a new model, and the old model against those same set of tests, which are called benchmarks, and only after the benchmarks are passed is when the model gets promoted to production or general purpose use for it by users. In IQ, every time we get a question right, we actually ask the user to just hit a thumbs up. The moment you hit a thumbs up, this question gets locked into our evaluation framework, saying, OK, we got this question right. So the next time, let's say, today, there is open 4.6. Cloud comes up with open 5. And we want to make sure that Cloud is actually giving you the same answer. There is no guarantee that Cloud will give you the same answer. But by hitting the thumbs up here, we create our own testing suite, where we know what is the question that got asked, what is the right answer? And when we are trying to upgrade our models, we run parallel test against the old models, the new model, and only upgrade to the new models when we are getting the answers consistently, right? And beyond a certain threshold, you cannot do that if you are on Cloud, right? That would be a second reason. The third reason is user management and commissioning. If everybody is using Cloud on their own desktop, it's very difficult to build and maintain context across everybody. So a stable example of that could be, let's say I am downloading sales from Amazon. My colleague at Ridge is also downloading sales from Amazon. And both of us are coming up with NetRavenue. My formulas are different. My spreadsheet numbers are going to be different. The same thing can happen in Cloud as well, right? Because people might be feeding in different contexts, and they might be getting a different answer and the confusion increases. By going through IQ, you avoid that confusion, because now you have a standard definition of what electric means for a business or for a department. And that is governed by IQ quality. Agents just to ensure that you get the right answer. So these are just a couple of examples that I can give you. Why IQ right out of the back gives you a little bit more than what you could get with Cloud. But the beauty of the AI foundation that we build for you is if you want to use Cloud, you love it. You can absolutely connect to the data foundation and have it. What's up, operators? Welcome to the Rich Panel AdRead. Rich Panel has been a sponsor for over 12 months. It's been a paying customer for over 12 months. And guess what? I just renewed to pay again for another year. We have cut our SaaS bill in half, and automation dropped our cost per ticket by 70%. Our CSAT has also improved from 88%, which is still really good, to 96%, best in class, all powered by Rich Panel. I told them last year, hey, you guys need to do the same thing with returns. And now Rich Panel has a returns portal. It's built to cut down your tickets and convert more refunds into exchanges. They do the heavy lifting, data import, self-service retention flows, team training, all of it, and it'll be live in two weeks. If you want to save 30%, guaranteed on helpdesk, and now returns, book a demo. Yeah, it kind of sounds like you've built the skill that is e-commerce analysts for Rich into the agent, right? But it's not really doing a gentick work right now. It is, I guess, it's going to your database and inquiry. But it's not going to go out there and do work on the open web for you. It's not yet. So it is in our roadmap. So I'll give you a simple example. Let's say your campaign performance is dropping. And we have one of the IQ agents detect that your campaign performance has dropped. What do you do next? Do you pause the campaign or do you reduce the spend threshold? There is some decision you might want to take, right? From an IQ standpoint, by the end of this quarter, we will have a few agents that we give you this signal, saying, hey, this performance seems to be off. But we don't necessarily have the setup today to go take an action on your behalf. But that could be an extension of IQ down the line. Yeah, I have to speak with Connor, right? About where the world's going, because you know, met up on Manus to turn it into a ad buying agent, which will do a gentick work, right? And a house is building an agent. And I'm sure Northean's going to build an agent. And what does this mean is that all of the work and the ad that it have will have someone full time to someone. It's going to have an agent full time prompting you to do what it thinks it should do, right? And right now those prompts are going to go to a person. It's going to go to Connor or Jimmy, my VP of Marketing. It's going to go to one of these people. And there's going to be a market to build the Ridge agent, who will take in all of these inputs, and then actually summarize them for you. Because house might be saying I should spend more money on YouTube. And Manus from Meta is going to say, I should spend my money on Meta. And Northean's going to say, ah, you know, they're both kind of right. You should probably do this. Who's actually going to be processing and rocking all that information, and then giving you the unbiased thing to do, right? And you know, it's going to be the Ridge agent. It's going to be the Hexclite agent. Someone's going to have to build an agent to receive all these inputs. Think about the inputs as APIs. Someone's going to need the essential repository. And it sounds like that could be the the Sarah Syq agent. All of these are ready to talk and bring data into you. And then someone has to synthesize it, clean it, and then give the Ridge version to a Ridge executive to make a decision. What I just described might sound like nonsense to so many people on this podcast, but I'm telling you it's where the world will be in three years. Is the 100% the future? What you're talking about is 100% the future, right? like what you need to do is figure out what agents should be doing for you, right? And how to implement that over time. You know, until it turns out that that kind of workflow system has changed. But once it's sort of set up that way, just like, you know, you type in www.inabrouser, you know, the internet is done through a browser. AI is going to be done through an agent. And your workflows are going to happen through an agent. That's just like the way it started. And that's just where it's going to go. And what we're, what we're should be all figuring out is like, what agents do we need in our, in our, absolutely. That's what's happening. Yeah, absolutely Jason. That's one of the things that I've been doing internally, actually, for the last 80 months is doing these AI orders of workflows and processes internally, documenting every step of the process. And then looking at each step of the process to see whether that step is AI replaceable fully, or whether that step is AI assisted, right? Or that is something only a human can do. What we have identified is that for many of our workflows, the simple example I gave you around customer success is the first step a human does is actually go and tell the customer who they start saying, give me a few minutes. Let me check and get back to you, right? That is a step that requires a human involvement that could be fully replaced by AI, right? So we are doing these AI opportunity or audits constantly across team, documenting these workflows and then going after these workflows which have high ROI if we introduce AI and AI agents and going about doing that. And we are seeing some incredible success. There's something I'm happy to share with any of your listeners, if you're interested in talking to me about. >> Okay, so let's summarize, Sarah's son, let it so far. It is a data warehouse solution. So you guys are going to come in here and it's going to clean your data and that's been the business the past 10 years is it's going to take all your data from all your e-commerce sources, it's going to make sure they all are accurate, they speak to each other, it's going to bring in your marketing information, it's going to bring in your cogs, it's going to give you the truest sense of daily profit, daily contribution margin and it's all set up on great infrastructure like Tableau and everything else. They're going to come in here, it's something like $10,000 a month, maybe a little bit more for our wonderful listeners if you're too big and they're going to set all that stuff up for you. That's the Sarah's son, the past 10 years. Right now with the building is Sarah's IQ, which will take all of that data and just make it easier to access. You'll just be able to type in, hey, summarize this for me. Just like you do with cloud, it's built on top of cloud, just like you do with chat GPT, it makes an LLM out of your data and your database. Just for you, just for your team, so it's proprietary, it's safe and you can get your information back quicker, easier with maybe more detailed prompts. The future is the Sarah's IQ agents coming out, right? Where they will start doing work on your behalf, answer questions on your behalf. And I'm predicting it's going to go fight the meta agent for you, it's going to go fight the house agent for you. And it will end up getting the cleanest truth across all these different data sources. Marketing is not a solved problem. And that's probably why it's such a big industry, is that it's a trillion dollars a year of marketing spend give or take, right? Between fees and everything else, it's a big, big part of the economy and half the twisted. It's like it's the only part of the economy that is totally inefficient in that way, right? Like if you talk about oil and gas, their loss rate is 0.01%. Talk about meta ads, half of them are just letting mining on fire. And in the next five or 10 years, we will solve that inefficiency. We will remove that, but it's going to be through agents fighting each other and really eliminating all the waste fraud and abuse. Jason, what's your response? I mean, you look at the nail in the head, right? It's exactly right. And it's funny. You've seen tech stocks like getting hammered, right? Sess, company stocks, getting hammered because the people don't understand, like, or are concerned about the impact. And that AI has-- what is it going to do to these Sass businesses? And the right Sass businesses, just like the right humans, are going to learn how to leverage this, right? So you look at the disruption in the workforce that people are afraid of, the people that are good at their job, and leverage this to be even better, are the ones that are going to be the winners, right? And the companies that are really embracing the Sass software companies or tech companies that are really embracing, like, how do we integrate and just make it a part of our DNA are going to be the winners. And I think that's the real-- so you have to make that decision. And then it's like, how do you go do it? That's the hard part. How do you go do it? You can talk about this for a while. You can see it. You can see that. I got a demo when I saw agents working, but that Sean's point about that agent looking out versus looking in is a really good one. It's just like the speed of the rate of change here is so geometric. It's crazy. It's just-- it's wild. Long time sponsor NorthBeam is launching incrementality later this quarter. This means that you can now have the trifecta of marketing measurement all in one platform. That is, multi-touch attribution, medium-ix modeling, and incrementality holdouts all inside of NorthBeam. You can automate that lift testing end-to-end, unify results with your MTA and your MMM. This is a lot of letters, but if you know, you know. And you can start to cut what doesn't work. And you can scale what works. And you can do this all with confidence. This is why this is such an incredible ad to NorthBeam. NorthBeam's incrementality measures what results marketing is actually generating. Not just what they're claiming, credit for. As a CEO, that's like music to my ears. A side up now, and you can lock in 50% off, unlimited tests for the year. [VIDEO PLAYBACK] Yeah. And an earlier point you had Jason was that if you were lazy to learn this, like, you know, it could be a detriment, if you're lazy, you should be at the cutting-- you should be at the front line for agents. They do the work for you. Like, I cannot stress that enough. Like, I'm bad at a lot of stuff. The agents are good at stuff. I tell them what to do when they do it. It's like every CEO's dream, right? And now everyone can be their own little CEO. So if you're lazy, I really hope you embrace agents. And if you want to taste, look, right now, I just have this tweet. It is kind of like GeoCities building agents. And what I mean by that is like, it's the early internet, and it's not going to be very useful. And it's going to be replaced very, very soon. There's going to be commercial great agents coming out from the Sarasana Linux of the world, from the Metas of the world, whatever. But you should still do it and play with it, because it's a good hobby to have, right? Just start using Manus right now. You should get credits from Manus. You should jump in there and start seeing what is capable, what the work you can do. Do a fun little hobby project. And it is just cool what it can get done. It's a great point. It's a great point to hobby project. Mike was tweeting about-- Mike told me. I reached out to Mike about OpenClaw, because he was tweeting about it. He was all about it, right? And people were talking about security issues, et cetera. I was just wanting to know from what his thoughts were, because he was really all about it. And he's like, I treat it like a pet. It's like, you know, it's like his little pet project. And what you're saying, Sean, is like, you have it like a little hobby. Like it's something that is going to-- it's like a self-improvement. By doing this stuff, everyone's focused on their health, right? Well, this is actually going to really help you. This is like a health thing that you need to do. It's like, make this a hobby. Nine months ago, I was really not concerned about AI at all. And we talked to Craig. He came in, Craig Fold came in, and we just started talking. And he just showed us me like that. This is crazy. I have this totally wrong. I was totally wrong. This is changing my life. And it has. And you just have to put the time in. And I think it's the one thing that I'm getting out of this conversation, Sean, even though it was a parent to me, but not as a parent as it is now. I was like, everybody needs to have the word agent in their head and be thinking about how to contextualize and integrate everything into that term. Yeah. I'd say, I think agent first, and then human next. At least that's the mantra that I'm following here at SARS. And that's been a health thing. Yeah. Joni was saying something. I just want people to try an agent today. So go to chat, GPT, go to Claude, go to Manus, all these have agentic flows. Have it or do you launch? Just see that it can make action in the real world. I think that is a very eye-opening thing. And then once you start doing that, then you can start understanding what happens if you set up an open clause. And I will be super clear. The tools right now are not really production grade. You don't want to be rolling them out across your business. Within three months, they will be. In six months, they will be. It's going that fast. Right? Like the stuff that they're actually capable of doing. So for right now, the takeaway from this episode is get your data foundation layer set up. So, status analytics is a partner of the podcast. I pay for them, Jason pays for them. It is a reasonable fee for them to set up and manage your data warehouse. Now, you're not locked in. It is the same infrastructure that every major Fortune 500 company has. Right? to set up there could maintain it, they're going to work really hard to keep your business. There's nothing, I mean, it is, it is blue chip data infrastructure. Then they're going to lock you in because you're going to fall in love with the Sarah's IQ features, the ability to actually query and get answers back about what's happening your data, your sales every single day, set up a gentick work flows inside of there, right? Every single day tell me how many phone cases I sold yesterday, right? You could do all that right now in Sarah's IQ. And then eventually they're going to have agents out there battling the rest of the agents. Krishna, what else do you want to say on the phone? Let me jump in for a second because you know, when you guys sent out the invite for this one, the reason why I was excited is it says in it leadership and decision making, right? And then, you know, we're talking about leadership and decision making. This is going to relate back to everything that we're talking about here. It's like where does good decision making come from? You know, it comes from, I mean, the first thing is good data. You know, the second thing is good judgment, right? And then it's, you know, taking decisive action. Like these are the, these are like the, so like you've got to have the data foundation to do it. And you know, sure, you can go to Shopify and just like download stuff or look at stuff, but like you just, you have to have this data foundation. And then you have to apply, you know, you have to apply judgment. Like data doesn't make the decisions for you, but you just have to have it. And it's all like relates back to leadership because leadership is knowing what matters, right? In the data. And particularly like understanding that the data is is probably not never going to be totally complete, right? It's like all models are wrong. I say this all the time. And, but you have to have like, you get the data as clean as you can. Like you get it to like, Sarah's is going to get it to like 95%. And then you're making this, if you're not making decisions on data, you're just like, you're just using your gut. And, and honestly, that's just like overconfidence period. Like the level of overconfidence that's out there is, it's, there's so many people that are just like, they're like, just making decisions on their gut. You know what they're doing there? They're like, they're literally flipping a coin. Sean here, tell you about Sarah's analytics and Sarah's pulse. Ridge is profitable every single day. And we've taken that super seriously since we built this business. We track contribution margin by day. We look at the skews. We sell every single day. And we have to do this manually up until Sarah's on XK now. We take all of our skew level data. We build it into the data warehouse. Everything that goes into making a true PNL, I get on a day-to-day basis. Sarah's pulse gives you clarity. So your CLO and your CFL and your CMO start speaking the same language. Contribution margin shifts teams away from hoping profits survive the season to manage them in real time. Book a walkthrough with the Sarah's pulse team today. Click the like the description and thank you Sarah for bringing you this show. So I'm, I'm Q and it's actually from a leadership standpoint, how are you getting your company to TNKI, leverage AI? What are some strategies that you're adopting? I'd be interested to know. Yeah. How do you get buy-in across an organization for AI? It's a great question. You know, Jason brought up Craig Folls from Chatwall office. Craig, friend of the pod, he probably has AI operators coming out. He's going to be in the sphere and ecosystem. He was at Crocs setting up AI for them like in the 2020s. Before AI was where it is today. He's seen it from the ground floor. He's like a training course thing called Chatwall. That's the first thing is get it for everybody because we have people who from 18 years old to 75 years old who work at Ridge, they're going to have different levels of proficiency. You have to teach them what is an LLM, what is Clawed, how do you log in the basics? Make everybody take that course just to start off the rip. Then the second most powerful thing is show them what can be done. So we did this in the past. We did game building days where you put teams of five people and we're like, hey, we're going to use Clawed. We're going to build the game in an hour. The game is actually fun and engaging and pretty exciting once you can do it in an hour in Clawed. Then you show them a demo of what can be done. That's not a game. Today, after this, I'm going to do stand up. I'm going to show everybody the cool, man-ess, agentic workflow thing I did. I'm like, hey, check this out. I post the creative I make in AI all the time. I make amazing ads in AI and I'm posting them like, hey, guys, this is what can be done. Then once people are there have to be interested. They have to opt in to one and to learn this stuff. Then we have a bunch of resources. We have an amazing VP of internal projects, Adam, who's like the best with AI. We have a guy, Jewels, who's amazing with AI. They're sending up any workflows and they're very much like they are super nerds in the cutting edge of what's happening. They're there as a resource, but if they have people excited to actually try this stuff. That's what we're going to people to opt in. I'm going to force people to stand by setting up a bit of something like the AI demo day and bring your best ideas and demos. Are you going to let people opt in? I got them asking more like a founder CEO or the head of the company. How are you thinking about it? Yeah. We don't start with the AI demo day. What we start with is we're going to build a game. So everyone has to build a game. You're forced to do that, but that should be fun. That's like, and we do a couple hours of this every month and the winner gets $1,000 or whatever. If you built the funnest game and then people are like, there's money on the line and like, it just get people's hands in the tool to build a solitary competitor or whatever. I built mine sweeper, but it's random or something. You just build these little games or whatever. You get people excited. Then it's like, hey, listen to what we did. Bring us your least favorite thing to do. On stand up in real time, we're going to build this an AI solution to solve it for you. We had a guy Antonio. He is in charge of through a matching inventory shipments. What showed up at the warehouse? What the vendors did they left? What did the docs that they had? Does it all make sense? Is it all the costs? That job sucks. We built AI to do all of that. We did that live and stand up. It just shows a cool idea. I can replace parts of my job that are bad. That's how we get people excited about trying it. That is awesome. I'm just going to say that's totally the way to do it. You're literally gamifying it. Both ways. One is incentivizing people to build stuff, but you're also gamifying it because you're asking them to build games and get excited and start using tools. Great advisor, Sean. I think that's where the high school and middle school and how much school is going to look like in the future as people build this up an AI. Jason, you said something earlier about the data projects never done. It's because if the data project was done, your business was over. The data is ongoing. It's a box that keeps building and building and building. It's never only in SkyScraper because as you grow every day, you have more data coming in, every month, you have new products launching. We should just talk about that. There's going to be ongoing maintenance. This is your entire livelihood. Everything that ever happened. Your brain is a very powerful agent and you've worked on this business exclusively for five or 10 years or whatever. You just have a lot of decisions and data you have to unpack. And teaching it to somebody else so they can write it down and put it into tablo and code and everything else. It is a journey. This will take a couple months to get started to build your data warehouse dream. Then every day after that, there is a little bit of maintenance going on to it. But the power when you finish it is you end up getting something that tells you contribution margin every day. Jason, if your marketing team comes to you and they're like, hey, we want to run a 30% off sale. We think revenue is going to go up like this. They never think about contribution margin. But now you can be like, well, I can just going to say, I'm sorry, I keep what happens to my contribution margin if we run that 30% off sale. So it is, you want to lock a next level of thinking, but work does have to go into it. So Jason, you want to unpack it? Yeah, we're like, we're we're kind of defining like, what is the next good offer? Right? That we're going to run for some offer period. We've got lots of different options. Right? We've got gift first purchase. We've got buy more save or there's like, all or do we just like go with our best discount our best bundles? Do we just do across the board discount? Like we can we can actually look at the data from from previous sales periods, right? And do that. Like how are you going to do that properly without a data warehouse? You know, and like you've got to load all that data in there, right? That's that's actually that's dirty work. That's like dirty hard work. That that's why you need someone like Sarah's to do it, right? It's just like that's really not an AIable problem. It's that's it's the foundational data layer that every real business, you're not a real business unless you have a foundational data layer. And it does take time to set it up. So this is there's a little bit of a process here of just having a little patience at the beginning to get it all going. It's not like it's not like oh I flip on Shopify and I start you know seeing numbers come come through. Well dude you don't get clean data unless you label it. So what you're talking about is labeling the data to actually make sense and ingest it. But Krishna, final words for the podcast, my man. Sorry, Sean, I wasn't ready for that, but. - Final words for the. - I love that. Yeah. Final words for the podcast. Get your AI foundation ready today, because agents are coming. You will see a serious, serious unlock in productivity. So don't let your team struggle with spreadsheets. Get them on a foundation. Give them their time back on these dear creativity without growing your business. You hear it here first, guys. The agents are coming. Krishna, I appreciate you coming on the pod. Thank you for all you do. Thank you for being a proud operator supporter. Jason, great seeing you on the pod, brother. Anything you need for me, you hit me up. We're here. Keep rockin'. Talk to you guys later. So yeah, do I talk to you about a few? [Music]