Webinar: Category Manager’s Toolkit — Drive Profitability Through Smarter Assortment Decisions

All right, it's one after um I'm sure we'll uh continue to have people trickle in, but let's get started so we have plenty of time to get through the content of today's webinar. Welcome everybody. Um we're so appreciative uh for your time today uh this morning or this afternoon wherever you are in the world. Welcome to the Leafio Assortment Performance webinar. Uh I'm going to quickly talk about what we want to cover today, introduce ourselves, and then we will uh certainly get into the fun details. So I'm Brad Mitchell. I'm the vice president of North America here at Leafio and uh you know help our clients achieve great results with our our platform and our solutions, one of which we're going to focus on today. And then this is Victor Hart. Victor, please introduce yourself. >> Hello everyone. Good morning, good afternoon, good evening. Victor Hart. I'm the senior account executive uh for Leafio North America and I'll be walking us through the demo of our assortment performance module today. >> Yeah, that's right. So, in terms of

agenda, what we're going to do today is um I'm going to take us through a little bit of history about our company so you know, you know, what Leafio is and what Leafio does. We'll um we'll talk about, you know, the customers that we serve and how we serve them. We'll talk about the products that we offer uh to our our customers and then we'll um we'll focus on assortment performance and what you know why we have assortment performance, what it does specifically and how it interacts with um our other solutions and modules. I will keep the presentation bit very short because I know it's much more exciting to see the actual uh platform in action and then I'll turn it over to Victor who's going to take us through some of the the great features and functionality that we have within the actual solution. With that said, let me pull up my screen and share the presentation. Oh, I should mention before we get started, certainly we've got a great audience today. Um, and appreciate

everybody coming. You know, hopefully you guys have great questions throughout the the conversation that Victor and I are going to have. Please, um, use the Zoom question and answer feature to log your questions. We've got Julia Bellow on the on the line as well, who's going to answer some of those questions on the fly as we go through the webinar, but also um, I think there will be plenty of time at the end for Victor, Julie, and I to field some questions as well. Um, and, uh, you know, give us the hard ones. you know, you you don't have to worry about stumping us. We want to hear your feedback. We want to hear your great questions and uh we'd very much appreciate that opportunity. Okay, so Victor, can you see my screen? >> I sure can. >> All right, great. So, as I said, we are Leafio AI and Leafio, uh we've been around for many years, been working with retailers for over 15 years. We've implemented you know hundreds of projects throughout the world with many different types of retailers um both large and small um all the way from you

know normal few store type retailers all the way to you know retailers that have hundreds or even thousands of stores across the globe. And so we use that vast experience and knowledge that we've learned along the way to continue to serve our clients, bring our expertise and our solutions to them to improve their you know supply chain and merchandising environments. Um here's a look at some of the customers that we serve and uh of course this is not everybody but um you know will hopefully give you a good idea of the industries and the types of of partners and clients that we work with. We work a lot in you know grocery retailers. We work a lot with sea store, convenience store and gas stations and also have many clients in sort of the DIY pharma, pet space, health and beauty so on and so for so forth. And so you know wanted to share some of our beautiful client logos here. In terms of what we offer at Leafio, um

we have a single integrated platform that helps our clients manage the end toend supply chain and merchandising process for their business. So you can see I call this our great product wheel and I love showing this slide but this is a uh a look at the different uh modules that we have under the platform. Today we're going to be focusing on this sort of top right portion which is assortment performance. And assortment performance uh is a great place to start when we think about our solutions. It's a great place to start because it answers you know maybe the most critical question that we have to answer as a retailer. What should we sell and where should we sell right? What should our assortment be and should it be different in this sector of retail uh locations versus this sector? so on and so forth. So as a retailer I think this is a very critical thing we have to be able to ask and answer. Assortment uses our assortment

perform performance module uses AI to make data uh driven recommendations uh for changes in the assortment and uh saves an enormous amount of time for our users and we'll talk a little bit more about that in a second. The other parts of our platform are also very critical to the retail um client that we serve. If we think about shelf efficiency, this answers a different question for us. It answers okay, we have the assortment. How do we visually display our products in a beautiful way so that our customers when they go through the door are delighted. they see the product that they have demand for and ultimately uh you know this planagramming solution drives sales and profitability for our stores. So it answers the question how do we display it and then on the left hand side here on our slide our inventory optimization solution answers the question that's very hard to answer. How much should we stock? Right? How do we replenish our

stores to make sure that our assortment and our shelves are full um with the right products at the right time, but we don't have tremendous overstock. So, we really want to align our inventory al with our assortment and alongside actual customer demand that we face. This is a demand forecasting and replenishment tool. We have other aspects of the software as well that we're not going to focus on today uh around promotions and how we manage and gain intelligence from our promotion environment. A retail execution application and a customer loy lo loyalty application as well. All of this sits in one single platform. As I mentioned, we typically integrate with the ERP system of our clients. Um and uh you know together these can be a very very powerful mix of of modules within our platform. As I mentioned today we're going to talk about assortment. So let's talk about the problems with you know assortment planning in in the clients and prospects

that we typically serve. Well naturally there is a very high cost to having an assortment strategy that's misaligned with demand. Right? Um, very critically, if my assortment at the retail location level is not aligned with demand that our customers, you know, have coming in the doors of our stores, we're going to lose out on sales and profit, right? Our our sort of critical cornerstone KPIs that that we're looking to optimize around. And, you know, not just at the aggregate level, but at the location level, we've got to make sure that we're providing the right products in the right regions at the right stores to drive these metrics. One of the key reasons why it's difficult to align the assortment with demand is because typically we're slow, right? We're slow to react to changes in seasonality, trends in the market, holidays, you know, um other market dynamics that drive different demand patterns. And the reason we're slow is

because it's impossible to manage our entire assortment across what is often a very large and complex retail footprint. So if I've got, you know, hundreds or even thousands of stores, I've got thousands or dozens of thousands of SKs potentially, it's very, very hard for category managers to really stay on top of, you know, assortment decisions at the item and location level to optimize our business. And that causes us to be late for, you know, these trends in market dynamics. Even if we're really good at this, right, and we are changing our assortment on a regular basis to align to demand, it often is due to a tremendous amount of manual work and analysis by our category managers. So, you know, if you are a category manager and you look like this gentleman here on the right side of my slide, you know exactly what I'm talking about, right? Your job is to make sure that your category is performing at its highest

level. And typically that involves, you know, being buried in manual analysis, uh, Excel spreadsheets, things like this to make sure that you're staying on top of changes in in the market. And this can be very cumbersome. And then lastly uh you know I think one of the other key challenges that our assortment of performance module addresses is that typically even if we stay on top of it right we do the work we do the analysis we make changes to our assortment it's really hard to enact them in our business um it's not enough to say yep we want to replace one item with another that has a lot of implications across our supply chain and merchandising processes that involve sort manual intervention or manual communication in order for for those things to take place appropriately. So, if I make a change, I've got to go tell our merchandising team to update the planagram. That has to roll down to the store level. We've got to change the shelves. We've got to

make sure it's compliant. So on and so forth. And more on the inventory supply chain side, if I make a change, I've got to make sure that I've got the item um that I've changed at the store or I'm removing the old item. I've got to update my purchasing strategy at the central warehouse level as well um to continue to align our actual inventory and the working capital we have tied up in inventory to our assortment strategy and ultimately to demand. And this is very difficult. Luckily, that's where our assortment module comes into play, right? This is the reason why this module exists for our clients. When we uh think about our assortment performance module that Victor is going to show today, the first and foremost thing that we think about is you know having the system instead of us you know chasing changes via analysis. We want the system to come to us and recommend to us changes uh that we should make based on datadriven AI. Right? Our

system is going to be looking across your entire product landscape, across your retail footprint to identify pieces of the business that we can optimize related to the assortment and uh recommend those changes to our category managers. Now their job is to you know enact those changes, accept those changes uh and then you know use their valuable time in other places in the business. Um by doing this clearly we are looking to have a more balance balanced mix of assortment. You know making sure we have uh the right assortment available to to satisfy the demand removing sort of these dead slow selling uh SKs that aren't helping us uh you know optimize or drive our key KPIs and um and so this is a major result of having these recommendations. I talked about how hard it is to, you know, uh, have our assortment changes flow through our our entire environment throughout our business. And by using

our assortment module connected with our inventory in our shelf solution, this really becomes a seamless process where I make a change to the assortment. uh it rolls directly through the planagramming solution, updates the planagram, that gets down to the store level, so on and so forth with with just one click of of the button in assortment. At the same time, on the inventory side, if I make a change to our assortment either now or in the future, I can rest assured that the inventory uh will be there. The replenishment will take place to supply our new items, replace our old items, update our purchasing strategy accordingly. And this, you know, removes the manual intervention, the manual communication, the phone calls to our our purchasing and and replenishment team that we often have to make to make sure that our assortment decisions actually go into practice at the store level. We'll talk a little bit today about new items. Um, but one of the great things

about our solution is, you know, I'm not just managing the assortment for today potentially, but I could be planning a new item months in the future. I can go ahead and set that up in our solution, set it and forget it and and again rest assured that it's going to be taken care of when the time comes to replenish and put that item on the shelf. All of these, you know, outcomes from using assortment performance result in an absolutely different level of agility and performance in our business. Um, this is going to cause our business to react faster to changes, right? be more aligned with demand, boost our sales and profit, free up a tremendous amount of time for our category managers so that they're not, you know, thinking about what they need to change, but instead they're looking at our category performance at a higher level to understand, you know, more strategically how they can add value and uh and create better results for their categories. Um,

and so when we talk about dashboards and BI today, sort of Victor is going to show you how we track performance, how we monitor these dashboards. Um, now that we have so much more free time and we're not chasing our tails trying to manage the assortment itself. Uh, to recap a little bit, the solution, you know, it's a single source of truth for the assortment strategy. So, the changes that I make here, this is where my assortment lives and breathes. We're using AI to not only cluster our stores um to make sure we're we're making decisions regionally, but also to um manage price segments, to recommend changes to our assortment and uh and keep our our business up to date with with what's going on in market demand. uh we've got a tremendous amount of business intelligence and and and BI data that will help our assortment managers keep track of their category and report on their category up to the

executive level. And uh all of this is built into this this brilliant solution that's connected with the other elements of our business. With that, it's a very quick overview. With that, I will get us out of the slides because I know everybody is probably very eager to see the actual software and I'll turn it over to Victor to get us into the demo. >> Fantastic. Thank you, Brad. >> Sure. And while he's pulling that up, just a reminder, please uh you know, let us know what questions you have as Victor's going through this. Um, we're going to move a little bit fast so we can show a lot of software, but want to make sure that uh you leave with a good idea of how we actually, you know, use this in practice with our clients. >> Great. So, Brad, can you see my screen now? >> I can I can see it. It looks great. >> Excellent. Um, so, um, folks, what I wanted to do is give kind of a high-level overview of our assortment performance solution and how it helps

bring to life some of those concepts that uh, Brad was uh, leading us through there. As you can see, like Brad mentioned, we're a cloud-based solution and we simply integrate with typically our clients ERP systems or their point of sale systems. um just depending on where all of the relevant data is going to be housed so that we can leverage that in all of the automation that I'm going to walk through with you here in just a moment. Um I wanted to start at kind of the foundation of the solution which is going to be our um assortment matrix. And so I've got that opened up here and you can see that I'm looking at a couple of different um uh categories that I have access to. And one of the key concepts of the software is um being able to control access for individual users. So as an example, if I'm the category manager for T, I can see my category here, but I can't see the categories that Brad is responsible for. Whereas maybe Julia is our manager, and

she needs to be able to see all of the categories. And so we can control that kind of access in terms of read write permissions just depending on um how each individual client needs to set that up. When I'm looking at my um T assortment here, a few things that I want to mention here. We've just got by line item each individual item that fits within that assortment. And then you can see there's a lot of additional information that I can add in here which is going to be custom based on my preferences. So, what's important for me to be able to see and then I can adjust um that information as well. So, if for example I wanted to add in my revenue for the current month, I could do so. I could um drag and drop these columns around just depending on what I want to see. And then over here, I'm going to be able to see the status of those products by my different, in this case, store clusters. We can view this on a clustering basis or by individual

locations. Just depends on how each individual client is structured. Um, if you've only got a few stores like some of our clients, maybe it makes sense to look at it on a storebystore basis. But if you've got hundreds or thousands of stores, we need to group those together. Otherwise, it would just be simply impossible to manage in an effective basis. And so I've got my environment set up to automatically cluster my stores based on the size of the stores as well as the price points within the stores. And so we can see I've got a premium large, premium medium, and pre premium small store, basic large, basic medium, basic small, etc. And that might impact um which items are we offering in each one of those stores. Uh maybe for your environment though, rather than um clustering based on those price points, we want to do it on a regional basis. So like in the US, we've got our northeast cluster, our southeast, our northwest,

etc. So that's going to be customizable for each individual client. And then lastly, we can see our statuses. So I've got a number of different statuses that I've established in here that help me manage each one of these items. Um, you're by no means limited to just the statuses we see here in my demo environment. So, different clients will establish different statuses again just based on how they manage their items. Um, in terms of updating these statuses, I could do so um manually from here. And we can see that I'm managing my assortment as of today. But like Brad talked about, we want to manage these items out in the future so that we can tie them back into the planagramming side of the business as well as the supply chain side so that we can be sure that we order the products far enough in advance um to actually get them into the shelves when we want to be selling them. So, as an example, let's go out to let's

say start planning for next year. And we can see that I've got a Valentine's Day gift pack here. That's one of the filters that I've got set up. And so, I could filter and group around just my um special gift packs if I wanted to. Um, but we want to, in this example, start getting this um, item active in our stores so that our customers have enough time to start purchasing them prior to Valentine's Day, of course. And so, I want to go through and set these items to active here. And so, I could do it by individual clusters or I could select all the clusters that I want to activate and then simply update the status here. Now, one of the things that we'll notice here is that I've um getting a alert that in one or more of those clusters, um if I activate that item, it's going to be exceeding the capacity that I've got established for that store. And basically what we mean there is that

based on the strategy, we only want to be offering X number of SKs that fit the different parameters here. And if I activate that, then we would be in excess of some of those. And so I can review that here and I can see that in my premium small stores I would be going in excess of that. So maybe I don't want to activate across all of these clusters. I'm simply going to select the clusters that it's going to work with and then set those to active. And now the beauty especially with our unified platform is that at the right time that's going to feed into the merchandising and planagramming component of the software feed into the inventory and replenishment side of the software as well. So again it's all going to be connected so that any changes I make here in my assortment strategy is automatically going to pick be picked up in those other areas of the business. Yeah. Uh wanted to pause there for a second, Victor. Um and and just have our

audience, we've got a great audience on, you know, think about how you would make this change today, right? And and actually, if you're if you're willing to share, we'd love to hear your feedback in terms of if you need to make an assortment change for like an upcoming holiday. How would you do that today? Can you do that today for a a holiday like this in the future? Um and what that looks like for your business. Because you know for me this is a great you know visual indication of my assortment within this category. I can plan today I can plan in the future. Um it's easy to make changes. The system is obeying constraints around that we've set up based on a strategy um around how many items of each category or subcategory I can have with within each store cluster and uh and really allowing it us to make an easy easy change. uh for this upcoming holiday period. So we'd love to hear sort of from the group in the in the questions and answers panel. How do you do this today? What kind of view do you have? Is this done in your ERP system? Is this done in

Excel? Do you make these changes in email? So on and so forth. Um would be very very interesting for us. >> Thanks Brad. So obviously when I was going through this process, it was kind of a manual review, right? Um and so we want to have that kind of uh process available to the users. But one of the benefits of the system be it in the assortment module or any of our other solutions is automation. We want to let the system run and automate as much of this process as it can. So we're not having to do like manual calculations in Excel or something like that. just trust the logic of the system which is then going to free up the endusers time to be a bit more strategic and focus in on the dashboards and the BI reporting and things like that. So from a um assortment adjustment standpoint rather than having to come into each individual assortment matrix and make those adjustments the system is going to make recommendations for me. And so I can

open up this screen here and we'll see all of the different statuses as they stand today. And again, I could look out into the future as well. So let's go back to my example starting in January of next year. And so we can see we've got items where the system is recommending we replace this item in the assortment. So maybe we're launching a new um a new version of this product or we've adjusted the uh the packaging, something like that. And so we'll pick up on that automatically and recommend making those adjustments. Uh maybe the system is recommending we remove this item from the assortment for specific clusters or we've got items that it suggests that we add into the assortment. And so based on the time frame here and all of the different data and the AIM ML logic that we're running in the background, the system is going to make these recommendations. Um, so I don't have to go through the entire um, assortment matrix. The system filters

out that noise for me and gives me these clear priorities um, to where I can say, okay, starting on January 1st with these items, I'm going to dellist them based on the recommendation that the system gives. And so it's very very easy for me to monitor this on an ongoing basis and make the um the different adjustments that the system is recommending to me. >> Yeah. Again, again, today I think most of the the clients that we've talked to, Victor, um you know, they don't have a way to to to drive these recommendations, right? They're they're having to do >> quite a bit of work to get to the answer that we're providing right here in this screen. And this is just a much better way to work, right? The system is saying, "Hey, we've got six items that we recommend some kind of change across our retail footprint in terms of assortment." And um and actually we can dig into the details and the system will kind of show us why we're recommending that change as well. Um and so this is very powerful. It allows the the

category managers to be in the driver's seat, control the assortment, but you know get 90% of the way there via the system so that they can make decisions very very quickly, review the recommendations, etc. You know, to point out as well, this is obviously uh demo data around our T category, right? where there's a limited amount of items, limited number of of store clusters here. But think about how complex your business is as a category manager, right? Maybe you've got thousands and thousands of items um you know, many hundreds of items in a single category, whatever your situation is. And just again think about if I wanted to review my category assortment um on a monthly or quarterly level, what would that process take? How long would it take you to get to this point right here where you have a good feeling about those changes and how accurate are are those decisions that you're making? Um, within the system here, we're setting up the strategy around recommendations to

optimize on things like sales, performance, etc. And and it's recommending to us this item out, this item in, you're gonna you're going to drive better better business results at the retail point of sale. >> Yep. Great point. Great point. Um, okay. So, what we've walked through so far is the various different techniques we can use, leveraging different levels of automation in the system to make ongoing adjustments to the assortment where it makes sense and um focusing on those true priorities and not having to dig through all of the noise um that clients are typically um dealing with in like their ERPs or their assortment spreadsheets or what have you. What that's going to then do is free up my time as the user to focus on the analytics. Um monitor the overall health of let's say my categories, say okay, where are we performing really well? Where are we having some issues? And then dig into um those potential problem

areas to be a bit more strategic in how we can turn them around and make them an area of strength for us. And so there are a couple different ways um that we can access this kind of data. First would be from a dashboard standpoint. And I can look at individual categories or the entire company. Just depends on what level I want to monitor here. And then um the dashboards are going to show me some really highlevel metrics that are going to be important to monitor. And I can select the time horizon and automate this. Um, in this case it's just the the current month, but I could adjust that as I see fit. And then I've got some key financial metrics that I want to monitor. Up here in the top, each user can adjust these metrics. So I could come and let's say I want to add inventory turnover or take out my write-offs, etc. So there's going to be a lot of high-level data that's available just as a snapshot here. And then via the reporting solution we can

dig much much deeper into that. Um down here I can see the dynamics of revenue and gross margin. And this is showing me on a daily basis what my revenue and gross profit is. I could adjust that and look at it on a weekly or a monthly basis. Essentially just making sure that we're trending in the right direction. Is revenue increasing? Is gross profit increasing or not? If not, what's causing that? Here to the right, I can see a list of my top 10 best sellers, my top 10 outsiders, meaning the the worst uh performers in this assortment. In this case, the T category. So, I can keep track of those. I can track across all of the different stores from a sales in terms of units, revenue, or gross profit. And then just like we looked at with these metrics up here, I can see my sales in terms of pieces on a daily, weekly, or monthly basis. So dashboard here just takes some of those key

metrics that I want to monitor over time and pulls it up at a high level. So it's very easy for me to uh to track that. Then we can dig into all of these metrics a bit deeper um via the BI solution. So, like Brad mentioned, we've got a really robust BI tool built into the platform, uh, both in the assortment module as well as throughout the rest of the platform. And we've got a number of predefined reports that we've worked with our clients over the years to build that show some really um, really great insights. So, for example, I can open up my plan fact report. And this is going to allow me to track my performance at a category level compared to what my goals were, compared to what my plans were. So, as an example, let's look back at my T category here and pull this down. And so, then I could look at either sales, gross profit, gross margin, etc. And I've got it set up on a month-by-month basis. So we can see um each month what

my plan was and then what my actual performance was. And so we did really great during these months where we exceeded our plan but then over the past few months during this time period uh the performance wasn't quite uh quite what we expected and what we were hoping for. And then I can dive deeper and deeper to see okay by product as an example um which products weren't quite up to snuff etc. So, that's a great example of a report that clients um have uh loved over the years. Um, another one that we get questions around all the time are how do I identify if I've got items that are supposed to be active, but we don't have the actual inventory at the store level. So, um we can view that very easily here. And you'll notice that I've got a bookmark established. And so there are all these filters that are built into uh the reports and we can create bookmarks to make it really easy for me to with

just a couple clicks find the information that I'm looking for and not have to go back and reestablish all of those filters every single time. And so we can see here is get alerted to any items that are active in my assortment but we don't have any inventory for them. items where they don't have inventory, but we do have some available in this case at a central warehouse. So, some clients will have central warehouses that fulfill the retail locations um and we can get visibility into if we've got that item available in the central warehouse so that it can be distributed down to the store level. Again, if we are combining um the assortment platform with say our inventory and replenishment solution, um these imbalances should be few and far between, but life happens. So, if they do occur, then we'll be alerted here very easily. There are lots of other reports um like

you saw that are already built. However, um let's say that we don't have a report that already shows the type of information you're looking for. Well, then we've got a report builder in here as well. So, you can create your own ad hoc reports and save them for the future. So, let's say as an example, I want to do some um supplier analysis. And so, I can view my suppliers, the items that are coming from those suppliers. And let's say I want to see the financial performance of those suppliers. And so very very easily, very quickly, I'm building out a really insightful report here. And I can analyze this over a given time period. And so as an example here, I can see each supplier, what my gross margin is from working with them, what their share in sales is, etc. I can expand this and then see okay from this specific supplier here are all the items that I order from them and I can see the exact same uh metrics for that as well. So the

report builder here is going to give a lot of flexibility in terms of leveraging all of this data that we're pulling in and allow you as the end user to build your own custom reports based on the information that is important to you um on an ongoing basis. >> All right. So, that was a lot of information in a very short amount of time, but Brad, it looks like we've had some questions coming in if I'm not mistaken. >> Yeah. Before we get to questions, I wanted to do a little quick recap. Okay, so obviously Victor has shown um you know, as as you said, a very high level demonstration of the functionality in our assortment performance module. Um there's a lot under the covers as you can imagine to actually define the strategy, how we segment by price, you know, how we gain recommendations. Um the the inputs and outputs to the system are very critical as well. So there's a lot more we can talk about even on the

reporting side. We've only shown a couple of our sort of um really high value reports. But uh you know, look, the system as I said, it's a single source of truth for assortment. you can make manual changes to your assortment that flow through the rest of your supply chain and merchandising process. The system is also constantly analyzing um your assortment across your store footprint and recommending changes to you that you can as well as the reason behind those changes that you can activate as a category manager to keep your assortment aligned with demand. The system has some great dashboards and really infinite BI capability as it relates to reporting. So, um, not only can I have visibility to see the performance of my category, I can also, you know, save a bunch of reports that I use to report upstream to my my manager or our executives on a monthly basis to to show the great performance that we're we're getting out of it um, out of the solution and and out of our products.

So, this is sort of the scope of assortment and um there's there's much more to discuss. So, of course, if you're interested, we'd love to hear from you. Um we do have some questions. They're great questions and questions that we almost always get on our demos, Victor. Um so, we can dive into this a little bit. The first question I wanted to go over is, you know, what is the logic behind store clustering? So, how are clusters formed and managed? And uh and we can go into that a little bit right now. Victor, if you want to show the the strategy behind clustering. Um, but this is this is a critical part of the implementation of our of our software is to define how we're actually going to cluster these stores. >> Yeah. So, we can go into the clustering strategy here. Um, and I've got a predefined example of clustering here. And so, this is just one way to approach it, right? We can set up different types of parameters here. But we can see um I've got those different um size premium

stores and different sized basic stores as well. And so it's clustering these based on the sales performance um at those stores. So I can see my uh revenue sales in terms of uses, gross profit, etc. or sales in terms of units rather. And then the average price. So just as an example here in my premium large, we can see average um product prices here. If I go to my basic large, we'll see those prices drop down. And so that's just one way that we might cluster stores together. Again, maybe rather than um this type of data, it's based on the different regions that you're selling in. Um whether it's state by state here in the US or northeast, northwest, southwest, southeast, etc. So lots of different ways and different logic that we can leverage to to build those clusters automatically um and then update them on an ongoing basis. >> Yeah. And and we have you know some

clients uh we can define the number the number of clusters as well. So some clients are very simple and they need you know three clusters. Some clients are very complex regionally or based on price etc and need you know 10 or more clusters. So it it really depends on the client. the system does it all for us. Um I I will also say that if you're if you're a company that already has predefined clusters that is a key part of your business and process, we can simply use those as well. So we don't we don't have to use the AI clustering here. Um but it's it's really really good. in terms of price segment. Um, you know, we were talking with a >> large retailer the other day that a big component of their business is liquor and wine and they said, "Well, we don't we don't have, you know, good um uh categories behind our liquor and wine when it comes to price segment." And obviously, that's a that's a critical component. We've got sort of our high-end, our midshelf, and our our bottom shelf liquors and wine. And uh the system will actually do that for us. So we can define the price segment which

rolls into the clusters and uh it makes it really easy for clients to get to you know value in the solution without doing a lot of manual data analysis. Um so hopefully that answers your question and you know would love to hear more uh from you uh Lewis who asked that question about you know your specific use case and what's important to your your retailer or your retail company related to clustering. There's another question on here um related to store clusters. Oh yeah and I've already answered that one. It's you know can we use our own? So yeah absolutely we can. Uh similar question Victor around the actual recommendations that we're that we're generating. So obviously the system is coming to us and saying hey category manager we want you to replace this item with another in this category or you've got to remove um you know this item from this cluster. it's not selling well, so on and so forth. And uh we definitely have some controls in terms

of the strategy that we implement to to gain those recommendations. Would love to just talk through those now. >> Yeah. So there there could be it depends on the item, depends on the retailer, but there are many different reasons why it might generate the recommendations. One that you just mentioned, Brad, is it's not selling well in a specific cluster. So, if we're allocating space to that item that's not selling well, maybe that's taking up space for an item that would be a better performer. Um, so if we can um remove that from the assortment, provide that additional space, we'll see our sales targets increase um because we're selling to that that higher seller. Maybe it's a seasonal component. Um so there are items that we only offer during the summer or the winter or what have you. just depending on what type of uh business you're in. Um uh could be special promotional aspects. So like if

we're a a grocery store supermarket and then we've got special packaging on Bud Light for the Super Bowl as an example, then I could bring that in. Or we talked about those um Valentine's Day gift sets. Maybe there's Fourth of July gift sets or or other holiday based >> um >> based uh characteristics. And so there could be any number of of reasons why it makes those uh recommendations. >> Yeah. And and I think you know going a little bit deeper on that behind the scenes, right? When we go to implement this these this recommendation system at the category level, we are defining as a company what we want the recommendations to be based on. And that's typically a mix of, you know, sales performance, margin performance, or sales volume. And uh it can be it can be one of those things. It can be a mix uh a balance between those things. And we're saying, hey, we want the system to look at, you know, a combination of margin and sales across our store clusters to see what's

been performing better over some specific time period. And uh the system is then you know using machine learning to make those datadriven recommendations based on the the strategy that we defined as a company. What's important for us? So hopefully that answers the question. Another question here from John. Um is the system suitable only for large retail chains? Uh this is an easy one that I you know it's so easy I can take this question. Um, no uh not certainly not John. Um, you know, especially when it relates to assortment. Uh, you know, we have some clients that have, you know, a small amount of stores. Maybe we have 10 stores, right? And in that case, we're often not clustering by store and we're making the assortment decisions at the store level. Um, and I think Victor showed that at the beginning of the demo, but the reality is if you've got, you know, if you are a large retail chain, there's a there's a lot of value in the clustering um, and the AI that that goes into play there. But

certainly, you know, whether you've got 10 stores or, you know, a thousand stores that there's probably some uh, difference in the way demand moves through your stores that you can you can leverage assortment to make better decisions and achieve better results at both the small and large level. Yeah. And and we've got clients that do have just a small handful of stores, but they might be pretty drastically different in terms of size, which is then going to impact the um the assortment at each store. Um at the very small stores, that might be a quarter of the size compared to the largest ones. You're not going to be able to offer the same range of product. And so, um, even though we're not having to cluster hundreds of stores together, there's still great value to be, um, achieved out of that. Here's a great question. Uh, and we may have to bring in Julia to give us a better idea, um, around this question, but, uh, the question is, how does space in the store interact with the range?

Should ranging, you know, should assortment decisions be done based on space or independently not considering the space that we have available? um you know they say especially considering very small or very large retail formats and so I think the question is um you know is does this assortment tool take into account the actual amount of space that I have available in the store to display products or is it making independent decisions and then you know obviously shelf and planagramming is is taking care of the space element >> so Julia I don't know yeah go ahead >> the question and I think it's very important question according to clustering because actually we can use this information while clustering stores and we have ability to add this information while we are clustering stores. So that's why in this case you have clusters for example in our case we had it like uh based on average price and also capacity and uh that's why it's very important for us to know this

information and then to make this clustering methods according to your needs. Yeah. Yeah. So I think the way that looks like is you know if you have a specific category that maybe has a more like large footprint um you know assortment like the products are more large footprint you're obviously going to have an idea for what the fixtures or the space looks like at different stores. And so maybe um maybe our clustering is done based on the fixtures that we actually have available in store. we set the strategy in terms of you know the number of items available that we can have in those different stores because of the limitation of the fixture and that's certainly something we could do as well. Of course when we get down to like practical space planning and the number of facings and how we display them that's all happening on on the uh on the shelf efficiency side. Yeah, great question. Uh just looking through the list here. I think we have answered all of the live

questions. Uh certainly if you have any more questions, we'd love to hear them. As I said earlier, not only if you have questions, but you know, if you're if you're willing to share with us, um I would just, you know, as we think about our market and we we talk to clients, it seems like this is an area in assortment, Victor, where everybody's doing this a little bit differently, right? And there's more automation in some, no automation at others. They're at different places. And so >> we would love to understand how you do it today, right? How do you manage the assortment? How do you communicate between, you know, assortment and replenishment, assortment and and space planing and that sort of thing. So to the extent that you're willing to share, we'd love to hear uh specifically how you do it and talk about, you know, what that looks like in our system. I'm sure many of our attendees today um have additional questions that they haven't asked. reach out to us. We're happy to answer. And you may be thinking, "Wow, this is really cool. I think I can save a lot of time." Um, but I'm not sure if

if my business uh, you know, this is if my business will fit this sort of workflow that we've shown today. Um, you know, this is a very high level demo, but let's talk about it. Let's see the use case that you have and and what you're trying to achieve and see how uh this this platform could fit into your your process. Anything else, Victor? >> No. No, I don't think so. Love the questions. It's fantastic. Would love to have more in-depth um conversations with you folks about, like Brad mentioned, your specific business. Um so, we would be very very happy to hear from you and um just wanted to say thanks so much for the time today. I think everyone that attended will be receiving a recording of the uh of the session, right Brad? >> Yeah, I think so. Um I think so. and that way you'll have a way to contact us. Um, yeah, I think we crushed it, Victor. And thank you, Julia, for for jumping in to help us answer uh that that detailed question. Again, thanks

everybody from Leafio. Look forward to hearing from you and have a great rest of the week.