Meeting Title: Brainforge x LMNT Retail: Discovery call Date: Jan 8 Meeting participants: Uttam Kumaran, Russell Broere, Shivani, Ashwini Sharma, Robert Tseng
Transcript:
Them: Hey. Wish.
Me: Hello.
Them: How are you?
Me: I’m good. How about you?
Them: Okay, I think. That. Getting some light in here. There’s a chance that Russell not going to be able to join, so. Hello. Hey. How are you? Hey. Good. I think there’s a chance Russell not going to join. He just let me know that he’s feeling kind of stressed and so then. I have pinged. I have pinged Will that inertial officer, Right? The executor of the team. Thinking that at some point you could meet him anyway, okay. Yeah. In worst case, we can use this time for if you’re still have this time. We can basically talk through the other work streams. Yeah, we’re going to discuss that in the next meeting. Anyway, plenty to talk about. So I think it’s fine. And I will know if he happens to be free at 1:30, that we’re around. Do you guys want to do the four box together? Just. Yeah, we also had that teed up for the next meeting, too. I don’t think. I think we can just jam. There’s plenty to talk about, so I think it’s, like, top of mind for me right now, and I think we can crank it out pretty quickly. Yeah, let’s do it. Yeah. And what I told Awaish is like, we can always. At least depending on when you need it, we can do a first draft. As part of this weekly call. Yeah, that sounds ideal. Flow.
Me: Yeah, I have already filled a few things. In those slides. I copied over to our slides and then filled those out.
Them: You could just type it on directly. Await. I think I gave you comment access by accident. Let me. Because the initial version. Control. Hold on one second. Let me open the right one. Okay, I’m going to show edit access. And that way you have it. And then. Hey, Robert. Okay? So you see, I, like, put some stuff here, right? Yes. And then, like, in terms of, like, the weekly, like, what has been done, I thought we could go through this in particular, and I know some stuff, it’ll be done by tomorrow, but, like, in terms of, like, what the wins are for this week, it’d be helpful to name. Cool. So, yeah, I think await. Let’s copy some of our things in there. So blocker is like API rate limits. It’s not necessarily like. It’s just that it’s slowing it down, right? Yeah. So I can put, like, Flowing ingestion. Yeah. Okay? Just like. Yeah. Tell me what form I think is best. Like. Like sewing a rejection. Sorry. Ingestion to, like. You’re like, this is now going to take six weeks longer than we planned. Or, like, four? Like, where? How much did it delay the timeline? Yeah, maybe await. I’ll let you answer.
Me: Okay? Yeah. I can surely comment by seeing the patterns, how it goes for other clients, but we don’t exactly know because it depends. On the shopify. And the volume of the data. It may be like, it might take maybe two more weeks to finish.
Them: Yeah.
Me: Full ingestion.
Them: That would. That’s what I was going to say, too. I think we expected it to take two weeks. It probably will take another two weeks. But again, it’s not. It’s not like. It’s not necessarily blocking us from moving. It’s just like a flag. Yeah, Like, I think it’s like, dependencies. Flat. Like, it’s more of a, like, fy. Right? Yeah, yeah. Blockers. Like, I need, like, the tech team to, like, unblock something, you know? Okay, so then, yeah, I mean. Really the. I would say we still haven’t gotten the spins. API information. Like we signed the doc. The countersign came in. I can. I mean, I. I can also go ahead and bump them again, but. Yeah, they need to just get us spins API. Keys. Yeah. But. We’re waiting on spin specifically. Yeah, they. They need to give us, like, API keys as part of that. Agreement. I’m happy to just ping Matt. Again on that thread. Yeah, I mean, that. That sounds good. I think you should. That’s the action that you’re taking, right? And you can just do that right now if you want, but, like, they. They honestly could have sent this to us. Like, it’s been a mod. It’s been a month, yeah? Okay. Yeah. The action I’m going to take is. Is follow up, and then we can escalate if it. If it doesn’t happen by next week. Okay? Did we? The wholesale G sheet. Okay? We haven’t ingested or. Oh, did you ingest that between. Yes. Tuesday.
Me: I don’t think we got the access.
Them: Yeah, we did. We got. We got the version of it in the. In the drive.
Me: Okay?
Them: I didn’t put it. I didn’t make a copy. I actually just gave you direct access. Oh, yeah, that’s right. Which, like, you tell me, because I’m like, they’re going to keep updating the document. So if I like either, I do import range. And put a version, but I just. Yeah.
Me: We got access, then we can start on ingestion. So we have ingested the Shopify. For wholesale, but Google Sheet needs to be ingested. You can say in progress.
Them: Would it be helpful? Shivani to. Should I put, like, a wholesale section? Because we also made progress on, like, dbt, GitHub, snowflake. That’s more like data engineering. Yeah, go ahead. If we didn’t ingest that, I’m going to just delete that note then. Are you guys on the plus plan for Shopify, or is it a advanced or basic? I don’t know. I don’t think we know. I assume it’s at least plus. Data engineering on the BI side. I would say we’re going to do the demo for Omni today. So I feel like Shivani, that recording of that is something that you can totally share. With Phil Demolati will be walking through like. Sort of end to end. So we’re still going to. We’re still kind of going to progress there. And then additionally today, one thing I mentioned to Robert is we have had these, like five or 10 questions that I know. You’ve mentioned a few times, like about retail cannibalization and a few others like that. Right now as we’re landing some of these data, I did mention to him that, like, if we can we discuss, like if we can get some analysis wins out of that. It could be good. So that’s something we were going to discuss in our later meeting. It’s more of, like, probably next actions is like, identifying. Low hanging fruit. Analysis wins. Or x? Yeah. So this. This we can do even prior to having, like, a bi tool. Another next action here is. Collaborate with Wholesale Team. On. Basically, like, providing, like, supporting them with sort of data, so. Yeah, I guess. We also have, like, discovery with cx. Like, we have, like, stuff for next week. I’m not trying to just write out every step coming up. So, like, I think mentioning to Phil that we are our first stakeholder is to solve. Is wholesale. Because their process is extremely manual and actually much simpler and, like, kind of more isolated. Like. I think that’s a good. Share out. Because it’s what again? I have it written somewhere else. What did you just say? Oh. It’s. It’s extremely manual. And fairly like, isolated to just that team and just to one source, which is shopify wholesale. So it’s not like we don’t have to work with multiple teams. It’s just one set of data, and it’s just like two stakeholders to basically help them with this spreadsheet process. Cool. I’m just putting it here. I think that’s fine. If people are reading it, then it’s like we’re figuring this thing out, but I’m just kind of like, if it, like, I’m like, yo, the plan for this sprint is here. And like, if there’s like additional call outs, we can have them. But I’m like, I don’t want to put every next step necessarily, but I think that. And it’s like, where possible, if the next step has a date, but, like, it’s fine. We received Emerson in the element Snowflake incense also win continued. We said 90% adjusted. Great data engineering set up with initial data models executing it sounds very vague to me. Well, this is where I feel like Phil was aware last time we talked to him about, like, DBT, GitHub, Snowflake. So I was like, Maybe he’ll and like dbt is snowflakes. Dbt. They’re separate. Well, I guess, like. Yeah, separate. Wait, what do you mean? DBT, right? No, no, no. Well, I guess technically it is 5 trans PR1, but has nothing to do with the 5 train product like we set up. DBT is. Is what’s orchestrating the data models. So at some point, somebody was. Andy was asking a lot about dbt, right? Yeah. So that was what we walked through yesterday. Okay, so maybe I wasn’t. That’s. I just don’t think. I don’t know if we said the word dbt. Wait, I mean, we. We explained it, I think, in a call a few weeks ago, but. Yeah, DBT is the frame. Is it the framework by which we are writing all the. SQL models. Yeah. So has a contract now with dbt as well. No, it’s an open source. They have an open source and a cloud hosted version. We are just using the open source. Version for now. There’s not. There’s not much. There’s not much difference. So we, we didn’t. We didn’t really flag that for, like, kind of handle at this phase, basically. Yeah, perfect. So I guess I can change the initial data models. I can kind of put, like, Shopify like customers, etc. If helpful, Wiccan. I can also put in Shivani, like, a screenshot of, like, what’s in Snowflake. I don’t know if, like, this. If it’s only limited to four slides and maybe there’s no. No, no. Like, like, we can. Because that may just give some, like, visual of, like, add whatever. Like, there. Like, if you want to add a slide, you can add a slide. This is what, like, questions. A sort of a sequencing with atomic exploration. I’ll talk about that visibility in a Shopify Insider bundles. Did you guys land that plane? Andy was like, I now have contacts. And I was like, I’m going to let you guys handle it because I didn’t understand. Yeah. What. What I recall is that it doesn’t have. I feel like my takeaway was that it didn’t have anything to do with us, but I let me ping him again today and double check because it was kind of confusing because Jeff pinged me and he was like, FYI, for a data team. I’m trying to get this app. And I was like, do you need the app if, like, all you need is, like, detail around what an insider bundle includes? And he was like, He was like. That would be useful data for, like, E. Commerce and whoever else was. Yeah, eventually the order table will tell you. And Andy also was like, Don’t think about netsuite in operational use cases. So I was like, okay, yeah, I mean, we’re not touching. We’re not really touching that at all anyway, so. I can send. I can just say it out loud again in our tech team channel. Like what we heard. What the decision is like. Is that? Am I on the same page? Okay, that would be great. And then. And then we can either delete this because it’s, like, cleared or whatever. This one is an open question. And I can just say, like, context. Retail discovery call. Also needed to be rescheduled. Yeah. Okay? Maybe we could delete this. I’m going to delete this for right now because it might be cleared. I’m just going to add it to the notes. Formatting, and then, like, we can. Whatever. Decide if we. Is this going out? Though tomorrow. Okay, so then what I can do? Also, after our omni training, I can throw that into a loom. For a video. So you have it here. If you can actually just throw that in and then link it. Yeah. That would be great. Okay, great. Okay? Okay. This is awesome. I think. Okay, I kind of want to do a zoom out now. Does that sound good? Yeah. Okay? So the let me actually go to the. And I’m just going to put in that picture of Snowflake. So just. That’s the last thing I’m doing. I created the template that everybody’s going to use, but just sort of context the projects that would be using this. Did I write them out here? I don’t know if I did. So the projects include. Let me stop sharing my screen. The shift to Netsuite. That’s one. Okay? Okay? I’m going to just put them in the chat one. The erp. Okay? Two. The like shift away from Emerson. And towards a new 3 PL. Okay, I think that one has less relevance with us. Then there’s supply, demand. Planning. That’s the Exploration Atomic. Then there’s budget process. And lastly, there’s. Okay? These are the five projects that if you can see in the chat that. That Phil feels like he’s, like, the kind of executive over, and he was like, for his brain, he was like, I want a consistent project management format. And then. He’s like. And he’s trying to understand the mapping of, like, how do these all relate to each other and what are the dependencies and things like that. Okay. And you would describe this as. This is like business operations, like 20, 26. So it’s not to say it’s all encompassing. Like, we have business operations. That element also includes, like, people and talent. So it’s like there might be some projects that surface, but I would say these are the big. Kind of more cross functional related projects versus like pure like biz ops doing something people orientation might just be kind of siloed to that versus like all of these have like implications for like supply chain and commercial kind of in the business, right? Great. Okay. So. The ERP is, like, getting kicked off. And then Phil was saying, it’s like. I also wanted to talk about, like, the timeline. Like, we talked about a Gantt chart. For the month of January. That was a very helpful call. While we were, like, planning the sprint, And then, like, theoretically, like, the project is a three month contract, right? So I think I. What I flag to Phil was like, I want to loop them into more things. To set up, like long term success. And so we basically said like, like, let’s act as though you’re staying here longer than February right now just so I can start sharing context with you that will be relevant for sure, versus like, feeling like you have to get a surge of information. End of fab. If we choose to, like, continue kind of thing. And our goal that we can work on together is by whenever, by like early Feb. Is to have to go from this to the Gantt. And basically, however far we can get to show that that roadmap. So already internally, that was my goal. Is probably in the next two calls. I was going to say, hey, we’re already looking forward to planning beyond. So with this context today, We’ll start to kind of throw that together that way also, as you’ll have that Gant to kind of, like, poke at over the next, like, two months. Of course, like, we’re going to still try to get through the bi stuff, but, yeah, that’s, that’s, that’s our goal. So internally, our goal is, like, by mid Feb, to have a good sense of, like, what’s beyond. Looks like, okay, perfect. So, like, when we think about beyond, right? It’s like, what are the one. There’s like, even without looking at the projects right now, there are the obvious, like, functions of the business that we aren’t really touching yet. We’re saying let’s ingest gorgeous data, right? Like, let’s do discoveries start that ingestion of gorgeous data for cx. But the place that we’re not really like getting have a process right now for collecting data is on the supply side of the business. Yeah, right. So, like, yesterday, when they’re like, oh, we got spreadsheets sent from these three PLs, it’s like, what’s the process to get that stuff, like, ingested regularly into our warehouse? Okay. So that’s for the future. But then the ERP like what Phil was saying. Like, maybe it’s good for Brainforge and our implementation team for the ERP to get to know each other. And so I want to figure out, like, when does that touch point make sense for you to start, like. Like being able to weigh in on. Anything that might be relevant related to netsuite and data and analytics combo. Great. Okay? Then. So, like, if I go back to the next steps, right? Like, that we were kind of working on together, it might be like a new next step is, like, find time. For mhi, the implementation partner and Brainforge to connect by, you know, end of February. Or, like, talk to Jacob, who’s overseeing, like, Like, find time for mh to. To connect. And I’ll discuss with jacob. In finance. Brainforge. Yeah. And to tell you, like, part of the benefit here is if they stand up a version of NetSuite and the core objects, really, we can already begin to, like, get pipelines in place. For when things go live. Like we don’t have to wait. So there is some parallel processing that can happen, which. That’s the primary. That would be the primary benefit of getting in touch with them earlier. We are the down. We are downstream consumers of, you know, their setup. Yeah, but of course, like, The adoption and the success of their platform hinges on us being able to report on it. So it’s, like, pretty symbiotic. Yeah. Okay? I think that that makes sense in terms of. Yeah, I think that’s, like, you’re aligned with what Phil wants, right? Which is, like, at some point, like, we got to get you guys. To know each other and, like, be. I don’t know exactly tactically, like, what it means, but I’m like, at least they should know that who you are, like, what we want to get to long term vision and stuff like that. Yeah. I would also love it just while. While I say the words long term. Vision. If you can read this later with them and tell me if it aligns with you and, like, how I articulate it. But we don’t have to do that right now. Okay. Okay. Okay. Yeah. So then. Okay. Will says you can hop on in five minutes and like. Like, I think it would be helpful for you to get to know Will, and we have plenty of time elsewhere to talk and like. Like, can we just run through for the next few minutes? Like, what did you want to talk about in the retail discovery call? Yeah, so. There’s two kind of big components. One was on the data. On the data side. I just want to clarify all the sources in addition to SPINS and Emerson. Like we want to talk about all the retailers specific reports. I do want to ask Will. Questions that we had yesterday, which was how it spins being used, benchmarking versus, like for operational use cases. I know a lot of our questions that we heard from you were around pricing and velocity. So if we do want to go down one of those rabbit holes to mainly hear from him how he thinks about that and for us. What that indicates is, like, what type of reporting he’s looking for, what are the sources, like, how he’s solving those today. So I’m less interested in like, what is elements pricing strategy more interesting, like what inputs support. Like the creation of a pricing strategy. Does that make sense? That’s great. And then do you have questions about, like, now that you have Emerson Target and Walmart data, Do you have discovery questions on that side? Yeah, I mean, I would like to. I mean, we, we, we know what data we have in there, so I’d like to understand, like, if he’s like, if, if it basically matches what he’s seeing in, like, a ui or if he has access to the Emerson, like, I don’t believe he’s going into Snowflake. Right. So sort of understanding what he has access to within the UI for both Emerson and Spins, because he’ll describe, like, what is the reporting that he’s getting out of there? I mean, consequently, I’m going to say, what reporting are you not being able to get either from? Individual sources or combining. Them. And that will sort of start to dictate, like, what is our roadmap for supporting, you know, Will and a retail team. We also have questions on each specific retail partner. It may be too much to, like, go through. You know you have a document you’re looking at right now? Yeah, I’m looking at an agenda, Doc. You want to just pop it up on the screen? Yeah. So this is like sort of like we want to sort of get an overview of the retail business, map out all the sources. Do want to get a sense of, like, if he also has like a spreadsheet, like a okrs or metrics. I do want to talk about, you know, spins usage, the benchmarking sort of product versus, like, information that we may or may not be also getting from Emerson. This is where we’re going to talk about, like, pricing strategy and velocity. And so there’s, like, questions about, you know, sort of each of these here. I don’t know if we wanted to get into this. This is a question that, like, we’ve been sort of, like, hovering around, which is, like, how he thinks about retail versus E Commerce versus wholesale. And then the last piece, like, and I don’t know if necessarily he may just say, like, everything’s important, but it’s just going to be talking through, like the, the kind of the, the roadmap, like, what are data capabilities that would be super, super helpful. Like what’s on the roadmap for retail coming up. Trying to envision, like, what a retail dashboard, you know, would be so. We don’t have much time. So again, kind of like the other calls that I kind of see his get a technical understanding and then see really for us, the core thing to understand is get a canvas of all the sources. Again, a canvas of what he’s using you source for. We can do that. Like it’s. A pretty big win today. Yeah. You know, you’re a high level Excel spreadsheet with all the sources at Element that like, you know, that one, that might be one. We want to just like flash up and say, like today we wanted to. He’s about to enter, so I’m going to introduce you guys, but that might be, like, if you want to stop sharing your screen and, like, that might be one that we can, like, flash up. Sure. Hi, will. Thank you for being willing to join us. We were supposed to do this call with Russell, and we were just jamming about other things, but I was like, if there’s an opportunity for you to meet this team, I would love for you to start getting to know the Brainforge folks. So they had some kind of specific questions about retail they wanted to go through today. But let me just take a moment to introduce you. So we’ve got Uttam on the line. We have Awaish and Ashwini. Utam is the founder CEO of Brainforge, our data analytics. Kind of company that we’re working with. And so they’ve started to ingest data. Like, I would say in December. We started working together pretty quickly. We landed on some tooling to figure out what kind of warehouse we want to use. We’re going with Snowflake and what kind of etf. Like what tool we wanted to use to actually ingest the data, and we’re using something called polyatomic. And so we started building components of the data stack and started ingesting Shopify data. We’ve now linked Emerson data to our snowflake instance. As opposed to, like, a shared snowflake instance. And what else are we starting to ingest with them? Amazon Recharge where to go Stored. Yeah. Building. The building. The pipes. Over here. And one of the items that we haven’t started ingesting is, like, SPINS data. And so that open question is, like, spin says a lot of data. What do we actually want to ingest from spins? Spins is the hard work for me to say, because I just got Invisalign yesterday, so I’m lisping and really hating it right now. Spins. Okay, so spin Zeta is tbd. If we’re trying to get the API connector set up and everything. And then Uttam just has a series of questions that he was going to go through with Russell in particular, but we thought we could get you to get to know each other and maybe go through some of their specific questions if you’re game. Yeah. So just to make sure I understand where you guys started. And so Brainforge is ETL and bi. Is that how I should be thinking about it? So Brainforge is like our data and analytics team basically right now. So in terms of actually helping us build the data stack, Ingest the data into our warehouse and then eventually learn analytics. Build the clean tables. Build the table so that we can actually have a foundation for future BI tooling to be very effective. So they’ll help recommend a BI tool that we’ll use. Whether that’s I see it’s not a software provider. It’s. It’s a consultancy that’s doing the etl. Structure set up around etlbi, making recommendations there. Made recommendations on the database. I got it. Okay. All right. Exactly. I’m up to speed. And I guess we’ll like. Like, high level. Like when I’ve kind of mentioned this to Phil. There’s a possibility that one day when we start building out our own bi. Analytics and everything source medium data may become less needed when we can actually start like building our own versions of dashboards that are useful to us versus, like, what they have kind of like as off the shelf. Products for CPG companies. So that’s something that we don’t have to talk about today, but could be like a puzzle piece that fits into like the longer term view. Yeah, I’m sorry. I’m. I’m in Arizona. We have storms in. Our Internet connection is really bad right now. Can you hear me? Can you hear us? I know your visual is going in and out a little bit, but you can hear okay? Yeah, I can hear fine. Yeah, and I think that makes a lot of sense with Source Medium. I guarantee you we are big. We are Source medium’s biggest client by 10x. It was not billed for someone of our scale. I would agree. And so it’s. I think we’re well grown past them, but the tool is still directionally valuable for the revenue team, so. But I think we need to outgrow that pretty quickly. Aligned. Cool. Okay, maybe I’ll let you take. Sure. Take it away with some of your. Yeah. Yeah. It’s great to meet you. Well, yeah, you have our team here. As part of background. You know, we do data and AI work for host of companies, many of which sort of in cpg, E. Comm, like Omnichannel, retail. So kind of very familiar with the stack of tools and the types to work. Also big fan of the company, but I always say I’m using a lot of like whatever Costco sells because they sell a bulk. So. But once you are in Costco us, I will start buying. I’ll start buying element, which hopefully we can help facilitate, you know, some of this data. But yeah, maybe I’ll tell you a little bit like what we already know about retail. We’ve spoken to E. Com Wholesale, like a host of different folks in the company. So I do know that, you know, from our, from our last sort of conversations, you know, you’re selling into Target, Walmart, Costco, Canada. We’ve heard Vitamin Shop. Maybe Sam’s club. Roughly. It’s like, we’ve heard 35% of, like, total revenue is maybe coming from retail. You know, we haven’t got a chance to talk with Dan yet, but I know we’ll talk to him a little bit about, like, how, you know, he’s recognizing some. Of the revenue from this sector, but that’s a little bit about what we know. We have had access to Emerson and so we’ve looked through all the Walmart and target point of sale data. We have yet to get access to spins, although our team has worked with spins with, with a few other clients before. So we are familiar with what they give, but I do know that they they also provide both like benchmarking and sort of competitive data, as well as also some sort of point of sale order information. So we’ll be interested in talking to you about that today. And then roughly, I think a lot of the questions that, you know, the team and the element team is asking of us to help facilitate is like, how did, how are the dynamics between retail, E comm and wholesale? Like, how are we acquiring customers across each and how do we continue to sort of grow and grow with, you know, sort of data driven way? So that’s like, what I know. I think really today is we’re really interested in just hearing from you about, like, the retail side of the business, about, like, your role and especially you versus Russell, and then hearing about there’s kind of two core objectives today. One is I’d like to get a canvas of all of our retail like partners. But in particular our reasoning is mainly just to get all of our the data sources to give you a sense of like why we’re sort of mapping out all of elements core data sources. So here about we’re using spins, we’re using Emerson. But I know we also had talked about vitamin shop. So getting a sense of, like, what all of those are will help us make sure that we can integrate all of that data. And then also getting a sense of, like, what reporting looks like for you today. Like, where are you getting data? What are you getting from different sources? So maybe I’ll pause there. That was. That was just a lot, but that’s a little bit of background. Yeah, I mean. Could. I want to be useful. Can you sharpen it to some specific questions that I could answer? Yeah. Can you walk us through? Just all of our core, like, retail partners, you know, today? Actually. I mean, you. You got it. So our existing retail partners are Vitamin Shop, Target Walmart and Costco Canada. Okay? Yeah. And then we do have. We do have a few. International partners, which tends to be more e commerce oriented. And that’s, you know, uk, Australia, we’re just shipping palaces sticks over there. And that’s not, it’s not a significant. I shouldn’t say that. It does pretty well. It’s not. A very significant amount. And then I don’t know if when you talk to the E. Comm team that we have a partner in Canada that is selling on Amazon up there as well. And so will we have. Obviously, we have the Emerson data for Target and Walmart. How do you, how do you get the data for Vitamin Shop in terms of, like, the results that we have with Vitamin Shop? Do they send it to us specifically? Yeah, they send it to us weekly. Shivani. And that’s, like, just a spreadsheet that they send. Exactly. Yeah. Okay. So when we have sources like that we were even talking about, like, the three pl sending us some of the three pl sending us spreadsheets weekly or regularly. Like, what is the pathway there? Yeah. So there’s a couple of paths for retailers that we’re used to. I mean, one is it depends on their sophistication. Sometimes they will have the most sophisticated, which is they have an API that we can hit. Sometimes they’re able to facilitate sending us that data through a couple other means, whether S3 buckets or just continuing to share that with email. So the goal would be for us to just get in the middle and, you know, so if you have a tech team or an account manager relationship there, we can help draft an email to send us, say, hey, we’re, we’re starting to centralize our data across the retail footprint. Do you guys, are you able to facilitate various methods of getting this data? If so, what are they and we can work directly with them. Yeah, yeah. We probably position it Awaish, we’d like to unburden you from having to run manual exports and sending us the data. What other things could we support that would take this burden off your plate? You know, I want to sell it a little bit. Exactly. Yeah. And really in other ways, like, we’re. We want to do more business with you, and so we need to. We need to show how you’re crushing it compared to other folks. So we can, we can sell the story. Yeah. So really tricky one at this point is going to be Costco Canada. And, and that is because. They refuse. I should probably say to send us data. Unless you buy their data package. And I’ve heard that. I’ve heard that it can be 100 to 200 grand kind of thing. Like, it’s a really expensive thing. And I don’t know at this point if that means you have access to all Canada or all Costco data. If we added additional regions or if it’s just for Canada, it might just be for Canada. So that’s. I actually had this conversation with Russell this week where we’re a little bit unknown. But we. That’s a bit unknown. And we’re. We are flying a little blind because unlike the other accounts, we don’t. We don’t get data. And then I’m sure, I’m sure you’re aware that. You know, Walmart has evolved over the years where now they have much more sophisticated data platform that you can purchase. You know, I’m sure you guys are very familiar with that. Yes, we haven’t hit that scale yet, or, you know, how to need to do that, or as concerned about baskets and, you know, those kind of granularities at this point. So we haven’t, we haven’t entertained that yet. But that might be on the rise, and at some point, I’m sure you guys are familiar with that? Yeah. So for the Costco Canada, for example, Yeah. If you want us to go work with the Costco folks to kind of get. Figure out what. What’s in there, what. How that fits in, and, like, kind of put a sense of, like, what. What the ROI could be, like, we could also do that. Okay. You know, and again, our lens is we’re making sure that we have a unified view of all as much retail data as possible. So some things are going to be worth the squeeze versus the other. That may be because we’re starting to go into a new retailer. That may be because a retailer as a percentage of revenue is growing. I did tell Shivani that, like, retail reporting this is sort of like still somewhat in the stone Age compared to digital, of course. And so it is sort of something that we have to go retailer by retailer and they, they gatekeep a lot of this. And so, yeah, it’s, it’s something that if. If it’s. If it’s easier to leverage us to kind of take that burden to go figure out. What’s in that Costco package, how much it costs, like, how that fits into our overall, and, like, kind of give a recommendation. Like, we could do that. Okay. Yeah. I think the only thing I just want to be a little careful about is if we’re talking with these retailers. To get them to do something for us. I want to be sensitive to the cadence of the relationship because if we are also trying to get some other commercial initiatives across the line, we don’t want to burn or favor because you guys are. We just want the data. Totally. We’re also trying to get other stuff over the line, and we want to fit that into the whole account management so that we don’t play. We don’t play a wild card that we needed to play in another. Another thing that we’re working on. Totally fair. So we’re following entirely your lead. You know, if we want to continue to go through you, we can kind of give you the things we need. And you can. You can totally make the judgment call. Like, even if they’re helping, like, just, like, draft questions for you, Will, you’re like, hey, like. If I want to because I don’t think Utam needs to be communicating. Yeah, we’re totally can work with what however we want to do it. So I totally am aware of like kind of like how what the dynamics can be so happy to work with but if you ever like I just want to establish the line of connection. Because if you’re ever like, hey, there’s a data thing coming up. Can you help me understand what questions to ask? This team can help you, like draft a detailed breakdown or whatever it is. Okay, that’s great, because that’ll be really helpful when we start taking a look at this, this Costco stuff, and if they’re, they’re selling it, I don’t think, I don’t think we’re going to get a favor across the line with something that they were like, great, buy it from us, you know? So, yeah, I think that’s one where we’ll probably just get you plugged in there and you can ask all the questions directly. But we’ll lead and then pug you guys in. That sounds good. And then can I also hear about upcoming, like retailers that were going into. Yeah, I think we’re under NDA. I’m sure. And also. We’re about to close a deal with Sam’s club. Okay. I think I may have heard that in another meeting, so I had it in my notes. Yeah. So Sam’s and then. Very likely we’ll have Heb and Wegmans this year. And beyond that, it’s hard to say. Costco US is a really very unknown right now. What’s going on there on the drink mix side, on the can side, on the RTD side? Russell with me, sent essentially our kind of our final offer for a region in the US So it’d be Texas and California for the sparkling product. So that could be one that comes up this summer as well. So all this to say the most likely ones are Sam’s Club. I. I think we’re going to get Sam’s club for both sticks and cans. HEB for sticks for sure. TBD on cans. Wegmans for sticks and cans, very likely. And then Costco, a region, maybe two on cans is kind of what we’re looking at for 20, 26. Okay? And then, of course, we only had a partial year of Target, Walmart, Costco, Canada. So that’s going to be a big chunk of revenue. This year. Right. I think your numbers are pretty close. You know, probably 30. 30, 35%. 33, 35% of revenue from retail is kind of what we’re looking at. Will continue to grow. And then can you talk to me about, like, maybe we can talk about spins. About, like, kind of if you could talk to me about that engagement. Like, what data were we looking to get from them? What are you getting today? You know, we’d love to hear about that. Yeah. So for spins, we have, you know, you pay by category. So we’re getting about, I think, three or four different categories. Obviously. Hydration functional Beverages waters in there. Energy drinks. Don’t recall if we have soda. I don’t think so. But the functional bev space is really the key one for us. And you know we have a multi year contract for that. So we’ll continue to get that data. And they, they send us, I’m sure you know this. They send us updates, you know, on a regular cadence, and their tool is gone. It used to be more downloading a workbook, you know, Excel workbook, but now it’s pretty much all in one and. You can build it yourself. I think where we find it valuable is assessing our velocity in stores. To see what kind of progress we’re making versus the competition. Identifying what hurdle rates are required for success. So those. Those things are super helpful. Having some visibility into the features spend of competition. And how we are relative to that. That feature spend, you know, our spend. And then I found 1p data from target to be more useful in evaluating the ROI and some of the offers that we deploy there. It’s a little harder to see it in the SPINS data, but you know, an example. For a week. We ran a buy three, get one free offer at Target that resulted in about 20 to 25% lift in revenue. That was really helpful us to see if we can understand more with, you know, Shivani and, and you know, we have a new cost account getting, getting those, getting understanding the cogs, so. That we can see actually the contribution margin of an initiative like that and see, like, I think the real big next lever that we need to understand is what is the ROI for trade spend. And that’s an area where I, you know, I set it to Shivani a few times. I feel like we’re flying a little blind. But we also, this is our first rep through the cycle, and we, you know, I think getting more granular on how profitable my role fundamentally in heading up revenue is identifying where to spend the next dollar. And so I think from a tradespin perspective, really identifying if that money is well spent is very important for us. Great. And when you said target 1P data that’s coming through Emerson. I’ve actually asked for that in this example, when I asked for. I wanted. I wanted Target’s data. They sent it to me directly as a spreadsheet. Just said, hey, this is what it looks like. It was like, oh, I know this is a lot of data, but it was kind of funny. It was very basic Excel, but I was able. That’s usually how it is with the retailers. It’s funny because the digital world is like, almost also overcomplicated a lot of levers. I’ve yet to meet a buyer who knows how to make a pivot table, to be honest with you. And maybe we can talk about Emerson. So we’ve seen the Walmart and the target data. That’s great information in there. Are you leveraging, like, Emerson’s UI for kind of similar things. And if you could talk about what’s what you’re leveraging it for. You know, versus versus spins, potentially. Now, I haven’t gotten into the Emerson data. In the Emerson data that you’re talking about, is it showing store level by store? Is it showing store level data? Okay, I haven’t gotten. I get the Omega email every day from Emerson that shows trailing. I think it’s like trailing month over month progress. So it’s very high level, dashboardy kind of stuff. But I think actually I mentioned this to Russell on a call yesterday. I think having some granularity on store performance to hold Target accountable. Particularly for things like end caps. So we did an end cap last year. It didn’t have a lot of facings on it. We’ve increased that by about 40% this year. So there’ll be more facings. But I couldn’t tell you how many times I was getting pictures from people that end cap just being empty. Because there were, there were certain areas. I mean, you go into Bozeman, where element is huge. Bozeman, Montana. It’s where our headquarters is. That friggin end cap was always empty and it drove me up the wall. And so I would like to be able to hold target accountable for making sure that. They’re getting the right inventory levels. To the stores that have higher velocity. To make because I don’t think they’re getting into that level of detail to make sure that their replenishment is that when we do something that does really well, like an end cap, that they’re supporting it with enough product. And we send retail detail into those stores. But sometimes they go in, they’re like there’s no more product in the back. Well, everybody lost money there. You know, it’s really annoying. Yeah, maybe if I can just flash you up like this is something. When we were going through the Emerson data, we kind of put together, which is like, you can see all the SKUs, the locations. So what SKUs are being sold at what locations? The what? The daily inventory data of the item at the location, at the day level, and it’s, like, pretty clean. Meaning, like, we’re kind of sitting on this right now. So this is something if. If it is helpful for us to put something together short term, for you to kind of, you know, have that conversation with them. This is where in the past, if you’re able to come to the retailer or, you know, partner like this, with that on our side, it’s, like, irrefutable, you know, but we saw the Emerson data is actually really, really rich. You know, similarly, on the Walmart side, we were able to look at, you know, all the stores that we were in, what items are selling in which stores, you know, both in store and online, all the digital and, you know, omnichannel, you know, sales. So there’s a lot of good stuff in there. We are getting access to that via Snowflake. So you’re getting that by skew. And by skew, I mean not. Not, like single. You’re getting by flavor. Is that right? That is. Yeah, I believe that’s. Oh, you know, like, what the specifics are.
Me: Yeah, I think it’s by flavor.
Them: You’re on mute.
Me: Sorry. Yeah, I think it is by flavor.
Them: Okay? Okay, that’s really, like, if we can get it at a flavor level, you know, obviously that’s called variance and on Shopify, but, yeah, I think these are going to be. Yeah, it’s going to be SKUs, because these are going to have different SKU numbers. You know, sku level data. Yeah, these are different skus. There’s significant inconsistency on velocity by flavor. Oh, great. Okay. Shocking. You know, like, let me give you an example. We will. For variety pack. In in sticks. We’ll sell 10x more than grapefruit, for example, you know. Yeah. So that’s something I think await Awaish we can follow up now that we have a little clear use case. What’s in there. But that’s actually good to hear that, you know, I think there, it’s. It’s really rich. So I’m sure that we can find some stuff in there for you to use, you know, a lot shorter term, and it’s, you know, that you’re not getting. Through the Omega report that you mentioned. I think that’s great, Will. Just to give folks a sense of like, what are the quite like we have now that we’re like we realize what data we have, it’s like what are the questions that we want to unlock? Eventually we want to tee up like what bi tools we want to use across the board. What are all the dashboards? We want to build for different leaders, but in the meantime, Brainforge is saying, like, if they’re pressing, if there are questions that we can start to answer, just to give people a sense of how this data works, then that’s great. Just to, like, build an ongoing like feedback and trust loop with our data analytics function too. And given. Oh, go ahead. Well, I have a question around. Omnichannel. That’s an area for me. Particularly when it comes to halo effects and geo targeting. On performance spend. That I think is going to be really important for us this year and in coming years. So, you know, we’re right now working on a national ad campaign where. We ran some surveys, actually just ran a second, second iteration of an existing survey to see what kind of share of household penetration that we have and, you know, assisted on assistant brand recall, like a very standard survey with a pretty good firm. With the national ad campaign. We will be investing in things like outta home, you know, billboards, more TV ads, things like that. A key reason why I’m focused on Heb as an example. Heb is a Texas. Based. Yeah, I’m here in Austin, by the way. So, yeah, there’s Heb, like, two minutes up the road here for my house. The only reason why I’m keen on HEB is we have self distribution in a warehouse in Austin. We just bought a second truck. And the reason why I’m prioritizing HEB is it’s pretty rare to have a grocer of that scale that’s contained to a specific state. Correct. It’s not. It’s like Albertsons, maybe in California, kind of similar. Safeway. Southern California. Yeah, Safeway is. Safeway is. Yeah. Now, yeah. Albertsons multi state. He pretty unique and we do really well in Texas. I mean obviously it’s, you know, one of the bigger states. Population just kind of screams. But with us doing self distribution there, I would like to invest in out of home billboards. There. I’d like to invest in geotargeted performance. Spend there around TV ads, maybe radio, some things. And some things, but to be able to understand if this spend is actually influencing sales at HEB or if it’s actually driving increasing our velocity at convenience store chains where we’re doing self distribution. For me, that’s a little bit of the Holy Grail. And I look at HEB as a bowling pin strategy. If we can identify a way to do well in Texas in collaboration with HEB and be more confident on top of funnel influence on bottle bottom of funnel performance, Then we have a pretty good directional playbook to expand across the United States. So I’m kind of looking at it as, from the scientific method, as a controlled experiment. But it’s going to be very difficult to see if. Okay, what we did, we spent a lot for the these two months here. Did our velocity increase at heb? Did we get more convenience stores? Did we, did our awareness increase there? Like that starts to get into the Holy Grail kind of territory for me as a CRO? Yeah, I think. Yeah. Maybe two pieces there. One. I worked for this company, Flow Code, a few years ago. I was like, kind of led their data team. Flow Code is a QR code company. Sort of started, kind of pioneered a lot of QR code on linear tv, a lot of out of home linear. TV type stuff. And this is exactly the kind of work we did, which was bringing something where you can kind of see the conversion lift. And we did a lot of work with, like, Nielsen and direct data from those folks. It’s like, it’s worse than the retail data that you’re going to get. You’re not going to get anything from them worthwhile. And so the second part of this is actually, like, how do you structure this test? How do you structure this test? How do you make an impact? How do you isolate like the variables? And this is something that broadly across element like this data work is also just like. How do we have a mechanism to run a test like this and clearly see it to some confidence the impact one way or another. Did you talk about data at all with with the performance team. I don’t think so. This is another data source that’d be very interesting to me to are TV by. Oh, great. Okay. And so the way that Tatari works is I chose them. I was running the TV stuff for the. For the team a couple years ago. And I chose them because they do a lot with remnant. Remnant ad buys. So you can get some pretty good TV spots. Good price. In the way that they operate is they drop a pixel on our website and shopify. They have an attribution window and they can actually identify if people go to our website after seeing an ad. You know, so many people watch TV with their, their phones out. We didn’t put a QR codes. I’ll be honest with you. I know. I know the QR code stuff. Not a huge fan. No, I don’t work for them anymore, so, you know, I don’t mind. But like to tell you the reason why is, of course, we, we saw that more than this is, you know, back. In. More than 50% of people have a second device while they’re watching T. It’s very easy to do the scan. And so we built. My team, built a lot of the tech to identify the fact that when you go through qr, we put query parameters in a pixel, and then when they go to the landing page. You can attribute it directly to that one ad. But, yes, you do have to put a huge QR code. You have to make it really big, because people are, like, at least six feet away. Like, there’s all these dynamics that the creative has to be pretty good. All this to say There were times where we ran some TV spots that came out in a really, like, lovely times. The one that I remember which was meaningful was it was the McDonald’s All American game. Brnny James got subbed in immediately. Oh, great. Immediately sank a three pointer, cut to LeBron on the sidelines, and then they ran an element commercial. I actually saw a big moment. I remember this moment. I saw it in the data that we actually had a bump on our website. And so that’s kind of how we’re navigating with Tatari. So I think to add to your list, Atari data would be another one that’d be useful for us. How much, like, are you. Is it a. Is it a big spend center right now? I’ll probably spend a million dollars on TV in Q1 here. Likely to ratchet up after we have a national ad campaign which you know will be probably look to spend eight figures over 12 months on that. Okay. Yeah. So, I mean, Tatari would be great. And I mean, yeah, I’m familiar with these folks. Like, Live Ramp worked with a lot of, like, identity stitching kind of companies, so if, like, I would love to see their data, and we can pipe that in as well. Yeah. I’m kind of curious also when we kind of. When we talk to them, just kind of what data you’re getting for them and what you’re seeing, but perfect. Yeah. I know. Do you have a hard stop? Will. Let me see. Yeah. Who am I? Who am I upsetting now? No, I’m okay. Oh, okay. Maybe just a one note, like, since Will sits over all of. All of commercial, all revenue. But then maybe you can describe how we’re kind of like narrowing in on the wholesale team, the Sprint, and just give him a sense of what’s to come on that side, which I know is not, like, the biggest revenue. Lever, but it’s where we’re kind of trying to practice, like, getting. Getting teams, like, good, clean data. You want to just. Yeah, happy to. So, you know, we’re kind of doing two things simultaneous. So one, we’re building, like, data infrastructure. So that is getting access to the data sources etl. So it’s like kind of landing it into snowflake, modeling it, so creating clean customer order transaction shipments, tables, and then leveraging. That for reporting and leveraging that for, you know, operational use cases. One thing that when we started talking to every team, understanding everybody’s sort of spreadsheet or mix of ways that they use data, the whole Sale team in particular, Really went through a ton of growth, and they are managing quite a bit. And there’s a lot of manual processing that they are doing in spreadsheets that, like, is super ripe for us to solve. And part of the reason why it’s actually easier to solve for them is they’re purely just one Shopify source of data. So from our data teams perspective, it’s one data source and a couple of data models that powers them. And, but then from their perspective, you know, they’re really, they’re, they’re really managing quite a bit of operational use cases, and some of the questions that they have, you know, they’re not able to really answer today on the wholesale side. So that’s really like why we’re focusing on them. Part of the other reason is it’s a good test or end to end pipeline, right. With, with in our, in our world, like it’s with the stakeholder who can use that make decisions and start to give us feedback on the data models. Compared to some, some supporting teams that maybe are more cross functional, that’s who we’re kind of going after first. And so we’re sort of, you know, looking to solve a lot of their, their major pain points right now. That’s great. That’s great. And self distribution is under roadmap, too. Or. No, that was another. Yeah, I was going to ask about that. If we had any time about. Yeah, if. About self distribution as like a percentage of revenue and, like, what’s the importance there? I kind of told Shivani that difficulty here is, like, we have to support, like, the nth retailer, the nth source of data, and there is, like, diminishing returns. So part of our understanding is like, what are the core data sources? But would be great. Yeah. To understand. You know that side of the business. So it’s not currently like we have a discovered call set with Jeff or any. Anything like that. He’s obviously so very new. So I don’t know who the right person to do the discovery call with in terms of like, I had done one with, I think Paul at one point. Right. And he was saying he looks at spins data to try and understand which geographies he might want to go into next for self distribution. And I was telling with them at the beginning, once we actually start getting the omnichannel view of data, you can say, I want geographic data that that is like, encompasses retail. E Commerce wholesale and everything, right? So you could say, like, how is New York performing across all my channels versus just spin data, having retail as an example. So we’ve done like, a little bit of like, what is this Omnichannel view? And how could it support. We’ve talked a little bit. How could it support the self distribution decision making of where else to go but haven’t like formally set up time for discovery with that team yet. Yeah. So when you had that conversation, if you could have with both Jeff and Paul, it’d be great. Perfect. Okay. Okay? One other question I had is I’m sure you’ve talked to our tech guys then, right? Yeah. So we’re working directly with all the whole tech team. Because that’s always been. Just been such a weird gap in our middleware. That we pass to where to go now stored through the middleware data. And there’s. There’s always been this one gap that we don’t have in understanding what goes into an insider bundle. Obviously, it’s required for fulfilling a package. And you know what I mean when I say an insider bundle. Yeah. And I think this is their discussion we even had yesterday, Shivani, about what’s in the insider bundle, how to break it apart. Okay. And obviously Stored needs to know that to fulfill it, but for some reason, we don’t have visibility that in Shopify doesn’t have that bundling feature. And that that’s always been really tricky for us because when we’re trying to make some. Yeah. Let me give an example. We do an lto for hot flavors with we did a chai. We did Chocolate chai. Caramel and a mint chocolate, right? We weren’t able to see how much of that product was actually purchased in these insider bundles. And so when we had to make some decisions the next year about which flavor to do, we couldn’t figure it out. Like that data was not available in Shopify. It was really friggin annoying. So yeah, this is something common. You know, when we talk about, like, limitations and reporting from source systems, this is like, exactly the trouble. We have a lot of other clients who are doing like, kind of like subscription advanced subscription bundles or advanced bundles where we have to break. We. We break everything down to the order item level and then almost rebuild. It again in the data warehouse. So at any moment, we. We want to support insight into every single item. The cogs, the shipping, like every component of an individual order, add as much dimensionality as we can get from an individual source. And then when we build the Omni channel view, really we are limited by how much dimension, how much of the same dimensionality do we have across all sources? Like, can we do everything at the day level. Can we do everything with. With what geographic, you know, dimensionality do we have? But this example of breaking out the insider bundle, you’re right for stored, which, which is the operational sort of fulfillment use case. They will need to know that. But again, like, there’s an integration between Shopify and storage. So it’s all been back end. They do have access as part of reporting though. We will solve that, you know, through our. Through Snowflake to provide that. That insight. Okay. Okay. And then Shivani. You guys ready to talk to Sharon and Esther then as well? So we’re doing a discovery call with Esther and Landon next week to start ingesting gorgeous data. I’ve chatted with Sharon a little bit, but I think I pinged Esther, and she said, I think the discovery call could be with her and Landon. So we have that set up. Phil was saying it could be nice to start ingesting CX data, even though that might not be, like, the top priority. For, like, data modeling. Yeah, I think the two things I’ll name that Esther might not be is plugged into in Sharon Wood is. I know Sharon’s keen to revisit our customer journey. That’s one thing. And then. We don’t do as well as we could on retention. Which is totally tied into the customer journey. And we almost do nothing on recovery. So when I say that you know customers who’ve gone dormant. So I think those are on our radar, but I think. Sharon would probably be a good resource. Has there been a retention, you know, recovery team in the past, or. It’s sort of all been bundled around. Retention in. Actually, conversion rate have always been kind of tricky here because it sits in between CX and tech, and revenue like, unfortunately, fall between the cracks a little bit. We had a real big conversion rate issue that I think. I think we figured out what was going on, but it took. Too long to figure out who owned getting that solution. Getting that figured out. Yeah, I think we talked about that conversion rate topic a little while ago. Yeah, we did it. So that was the exact example of, like, how would a data platform have helped the team triage and solve faster? And so that’s like a, that’s like, that’s another use case. And then, yeah, this is. I would say very, very common is like, you know, folks focus on the dollar in, and then it’s like a leaky bucket problem at the end. But it’s also good, you know, if we’re able to uncover the numbers around folks that have churned and give it clear path for marketing to reactivate. Or put them in a, you know, in an email campaign or kind of get them back in the flow. It’s a great win win here, you know? Yeah. Okay, so how do I start getting access? Like, it sounds like you guys have built some dashboards and built some things already. Like, how do I see what you’re doing? Yeah. So as of now, sorry, Shiva, you can go ahead. Yeah, I was going to say, no doubt we were getting the data into Snowflake, but I wouldn’t say there was any, like, dashboards built yet. And so, like, the deliverable and wholesale that I was mentioning is really, like, within Snowflake trying to get really nice, crisp, clean table of orders and customers that can then be a foundation for dashboards in the future. And that’s like one use case because it’s specific to shopify data as well as, I guess, The CRM Google sheet is like their other data source that they have. I was thinking more because I. You mentioned the Emerson data that’s gotten pulled into Snowflake. I haven’t seen that. I would love to see that. So you know, that’s like logins for Snowflake, but that might be like a starting point. I would also think we’re going to talk about it. Maybe it is worth us just doing like. Thinking of a couple angles from what we heard from Will today and just putting together a deck and just running doing an emerson like what we found on the couple those questions like we do a lot of strategy and analytics works as well. So we can just do that. That was on our list to discuss anyways today. What this is going to solve is like it’s going to take, well, probably another month and a half for us to get a bi. Like thing set up and going and there we are starting to land and model data. But what I mentioned is Shivani, is if there are wins sitting there, we would love to surface them. The best method is just going to be us putting together like, just typical, like strategy decks with, with analysis and, and then presenting that and. Then we put together a primer on like, what does the Emerson data have, which they just flashed aside up showing it. But like, what do we have with this Emerson data? Taking inventory of what the data provides us with and like what level of granularity. And so that’s a write up that I can I can share with you, Will. And then we can make sure that if you actually want to poke around in Stowflake, that we can make sure that you have access to do so. Okay. That’d be great. Yeah. So we can talk to Jason about that in the next call. Great. Awesome. Well, thank you for taking. Thank you. This is great last minute joint. Really nice. Yeah. I was actually really surprised when I saw you said, oh, yeah, this time I’m like, wait, that’s five minutes. I’m available. I saw your calendar that you had some availability, but I was like, this is a, you know. Roll the dice situation. So we’ll see. Thanks for joining us, Will. Yeah, it’s really nice to meet you guys. Really excited to dig in with you. And I, you know, my background is tech, and I love some of the. I love navigating by data. Honestly, I. You know, not exclusively, but I love getting into that stuff. So really, that’s the art, right? That’s the art about this. So we’re all engineers, and so our angle is this, but there’s a lot of art you have to go with your guide. But as much as you can have as fast as you can have it and make the decision, you know, so for us, we always describe, like, can you make more decisions and can they be more accurate on average? And, like, I think, you know, that’s, that’s the big thing that we try to fuel. Yeah. Yeah. Looking forward to collaborating with you guys. I appreciate it. Perfect. Okay? All right, thanks. Thank you. Bye.