Webinar: How to Eliminate Revenue Loss from Missing Items

All right, let's go ahead and get started. Good morning, good afternoon, good evening, wherever you are in the world. We are so thrilled to have you joining us today. Uh just a couple of quick housekeeping items. Um all of the attendees are on mute. However, there is a Q&A box that you'll find in the Zoom. So, as you have questions, please input those and we'll answer them as best we can and as many as we can during the uh the presentation today. Um so, just a quick introduction. Uh my name is Victor Hart. I'm the senior account executive for Leithio, and then joining me today is Julia Bello. She is our product expert for assortment and category management. She has really deep experience in category and assortment management across many different verticals within the retail space. And so, I'm really excited to have her joining me for the session today. Really quick, our agenda. For those of you who aren't familiar with us, I'll go

through a very quick introduction of Leithio as a company and what we offer. And then we'll jump into today's topic, which is about assortment management. So, I'm going to go through some of the key challenges that many clients we talk to are dealing with on a day-to-day basis when it comes to assortment management. And then I'll hand it over to Julia to walk us through our AI-driven um software solution so that she can show how we can help overcome those challenges. And then like I mentioned, please put your questions into that Q&A box and we'll save some time at the end of the session here today uh to go through those. So, a little bit of background about Leithio. We've been in business for over 15 years and we've implemented in hundreds of clients of all shapes and sizes all over the globe. And here are just a few of those clients. You can see that some of them are large global companies with thousands of retail

locations around the world. Others are much smaller mid-size companies and everywhere in between. And so the software solution we're going to look at today is highly scalable just depending on the size and scope of each client's operations. And then you'll also see that we've got a lot of experience in many different verticals within the retail space from grocery stores to supermarkets, gas stations and convenience stores, DIY hardware stores, pharmacies, health and beauty, and beyond. So basically any vertical within the retail space you find yourself in, we've got experience working with very similar clients. And what we offer is a single unified platform for end-to-end merchandising and supply chain management. So what we're going to focus on today is called our assortment performance module, which helps to answer the question of what products should we be selling where? And we'll dive in much more detail into that as we go. But there's much more to the

platform. So our assortment strategy can then feed into what's called our shelf efficiency solution, which helps us to optimize the use of space available within the stores, optimize the store layouts, optimize the planogram creation and management on an ongoing basis. And then execute within the store via our retail execution app, which will help us to send those signals down to the store level so that they know how to build out and adjust planograms and then tie that back into the central web application for compliance checks to make sure that the stores implemented the planograms as they were built by the central merchandising team. And then once we know what we're selling and where, how much product we're placing on the shelf, we of course have to make sure that we've got the inventory available at the right time to actually place that product on the shelf. So, that's where our inventory optimization solution comes into play. That's going to

automate the demand planning and forecasting process, automate the replenishment process such that we have the right product at the right location at the right time, so we're not missing out on sales. While at the same time, we're not tying up too much inventory in the wrong product that isn't moving. And as needed, we also have additional AI-powered promotional intelligence to uh really effectively plan uh promotional campaigns to get that forecast even more accurate uh for those promotions. So, the solution is designed to talk to each other. We can offer these um modules individually, we can bundle them together, we can be very flexible just depending on what each client's unique needs are. So, let's jump into the main topic today of assortment management. And there's a high cost associated with an assortment strategy that's misaligned across our network. Number one, the loss of sales and profits. If we don't have the right

assortment down to the location level, it's not going to be mashing up with the demand that our customers have, and we risk losing uh miss- uh missing out on sales um because we have the wrong products at the wrong location. Um the customers don't see what they're looking for, and so that results in a loss of sales and profits. And typically, cus- uh companies are slow to react to trends and seasonality. What that means is that the product reaches the shelf too late at the wrong time. By the time it gets there, that demand is gone, and so we've got product on the shelf that the customers are no longer looking for. One of the reasons for this is that category managers are buried in manual work. They're digging through endless Excel reports that they have to analyze manually, which is of course time-consuming and results in those delayed decisions. And ultimately, this is going to result in a gap between our assortment plan, the shelf plan,

shelf design, and the inventory. So, because we don't make our assortment decisions at the right time, it doesn't make it down to the planogram level in time, doesn't alert the supply chain team in time, etc. And so, this is what our assortment performance module is designed to help with. It's going to result in seamless data flow enabling smarter decisions. So, what you'll see is a smart assortment optimization. The system is designed to automatically balance our assortment mix across our entire network so that it can identify those items that are, quote, dead, that customers are no longer interested in, remove those from the assortment down at the location level, and fill them in with products that customers are looking for to fill in those demand gaps. It's going to seamlessly sync our assortment plan, our shelf plan, and our inventory levels so that the shelves and inventories are always in harmony. And it's going to

help us to effectively plan new items on time. So, plan those new launches in advance, which is going to feed into the shelf component to make sure we start planning those adjustments in time. It's going to feed into the inventory piece so that we start ordering those new products on time to get them down to the stores. What you'll see in the software is that the system is going to leverage AI to give us um recommendations on the optimal assortment based on the actual demand coming in from our customers and automation is going to build in efficiencies such that we're not buried in all of that manual work anymore. We can let the system do that kind of analysis for us, which is going to free up a lot of time to be a bit more strategic and identify potential exceptions, problem areas, etc. and try to resolve those. That's what our assortment performance module was designed to do. So, when we get into the software, you'll see that

it will automatically create and maintain your company's assortment strategy on an ongoing basis. It will effectively manage the different product categories, different clusters of stores on a daily basis based on the AI recommendations that we'll look at and then we'll enable deep analysis via the analytics built into the software so that we're not going through all of that manual Excel work. We can focus on the higher level strategic analysis to make sure that we're always trending in the right direction and keeping up with the goals of the business. So, the tricky thing about assortment problems is that they're invisible. You never get an alert saying you just lost a sale. Dead stocks will show up on a report, but the missing demand doesn't. And by the time you notice a pattern, there are weeks of revenue that have already slipped away silently that you just didn't see. And so, that's what

we're here today to help fix. And let's be honest about how most teams actually work. Typically, they'll do a monthly Excel review. That's going to be manual, backward looking, and by the time it's done, it's already going to be out of date. In some teams, SKUs are going to get added because someone asked, sometimes they're going to get removed because someone complained, but in all three scenarios that you see on the screen here, there's the same missing piece. Nothing is connecting the business goals with the daily um assortment decisions. So, this is the shift that we're talking about. Not asking your team to work harder or review more spreadsheets, definitely not. We want to give them a system that does the analysis continuously, automatically, and in line with your actual financial strategy. So, we'll see store-specific assortments instead of a one-size-fits-all

strategy, decisions that are grounded in data, not just instinct, and goals that cascade all the way down to the individual SKU choices. This is exactly what the new strategy automation and smart recommendations features within our assortment performance module deliver, and that's what Julia is going to walk through for us today. So, without further ado, I will hand things over to Julia so we can have a look at the software. >> Yeah. >> Julia, feel free to take it away. >> Great. So, now I'm going to show the uh system itself. Uh just like a couple of words to remind you that if you have any kind of questions, please do not hesitate to write uh write to us and we will answer them after the demonstration. So, today we're going to cover two like kinds of functionality in our system. The first one is strategy and probably one of the most important ones. So, I think you faced those like times when

you had a question in your mind. For example, if you have enough SKUs in your like category of chocolate bars, if it's enough or for example, I need to increase the number of SKU or decrease the number of SKU. And the main question is if you have the right assortment at the right stores. So for that reason we created this strategy functionality. Let's Let's take a look at it at this functionality and this picture and this page in general. So how it's built? The first part, this left part is decision-making tree. It's very important for category managers to build it to understand how the clients, the guests of the stores are navigating in the store. And for example, now we're looking at the the demo tea category and we can see here first of all our subcategories and it's like a sachet, loose leaf, powder, etc. And we can drill down and see the types of our items. And here we have black tea, green tea and other types of

like exactly tea category. And here we have also, when we drill down to the next level, it's price segment. It's also another functionality of the system and the system can help you to identify if this item is premium or if it's low price segment or any other segments you like to have in the system. And now for example here we have our clusters of stores. It's possible to manage the assortments with every single store and make this assortment matrix to every single store of your retail chain, but also we have a functionality which can help automatically distribute the stores and create the clusters by understanding that this particular store is very similar to another one according to the chosen parameters you decided to choose previously. And here we have probably the most important part. For example, in my category in loose leaf black tea low price segment in premium like big stores, I have five SKUs. And

for example, we have a similar situation here when I have basic store, so So, means that we sell not that um expensive items here. And I have four SKUs in this particular segment. Is it right or not? We don't know for sure. Of course, it's possible to create this strategy, to create this helicopter view of your category manually, but it takes pretty long time, I would say. Sometimes for some category managers, it takes like 3 days of work, and for some category managers, it takes even longer. So, that's why we created functionality which can which can help you to distribute this number of SKUs in the right way. And for example, pay attention that in those basic stores now we have the uh exact like equally distributed number of SKUs. And here I have four, four, and four SKUs. Is it right or not? Let's figure out. So, for that reason we have this like super button. Uh and I can press it, and after that I will be able to see the parameters I need to set up to create my

strategy. Uh so, for that reason we just need to select needed period of time. Let's select um for example, last um last period of time, last 2 months, or even more, it depends on your needs. And after that, uh very important part is like parameters. Now I have gross profit, and it's 100%. Uh like what does it mean? It means that the system will show you that picture, that helicopter view, that strategy which can help you to maximize gross profit. It's possible to change the parameter and choose for example revenue, or even combine some parameters for that reason. So, after applying this um like after pressing this apply button, the system will proceed the calculation for you to see the right picture. And now, for example, I see all the history of my strategies, and some of them even planned for the future. And we can see that it takes not like uh not uh many much time. So, it like took, I don't know, like 1 minute for us to calculate

it. And after I open this like a page, I can see the right distribution of SKUs. And here, for example, probably you remember that here we have we had four SKUs in basic cluster for premium segment. And the system shows us that probably we don't need to sell those items, those premium items in basic S cluster because there is no need for those SKUs in this particular uh cluster. And there is the same situation for premium stores. Here we have like one SKU for low price segment to just to have this representation of this particular segment, but we are focusing on middle and premium segment. So, by this strategy, the system shows you and helps you to distribute the SKUs in the right way and also meet the expectation of market. Of course, if it's needed, you can combine some parameters. And for example, for now for my category of very like both indicators are very uh like

important. For example, like revenue and gross profit or other indicators. It's possible to combine. And also, another thing which is also very important is that the system takes into account all the rules you decided to set up. For example, if you have um the rule you want to have your uh stores like inclusive and you want to your have your assortment inclusive and you want to have no matter what this particular assortment, you can set up the rule for that and this automatic strategy will calculate it for you. So, after this calculation, which like takes I don't know, 1 minute. Sometimes it's longer if you decided to choose longer period of time for analytics. And after that, you see the right distribution of SKUs. But it's not the end of the story. So, now we have number of items in every single store in every single segment. But then, we can save this information and we can go like deeper to understand which exact SKUs must be placed in particular store. So, for that reason, we have

recommendations for it. And here we have three types of recommendations. So, the system takes all the rules you decided to set and also the strategy we built to identify, for example, if there is a trend for this particular, for example, black premium tea in some stores. And after that, we provide you with recommendation. Now, we have in the system three types of recommendations. The first one, SKUs to add. Let's imagine that we decided to create the strategy and in our like premium L cluster, we had not enough SKUs of premium tea. In this case, the system will recommend you to add some of them. For example, like we have here. So, here we have that the system the situation when the system recommends you to add this particular SKU to basic M cluster to like make this gap smaller and even to remove this gap from your assortment matrix.

So, another situation is like with the basic S cluster when we had four SKUs, but the system shows you that there is no need to have those four like SKUs. In this case, the system recommends you to remove some of them. And like for example, here we have the same situation for this first SKU. So, the system recommend you to remove this first SKU from three clusters at the same time. And we have similar situation with this third SKU when the system recommend you to remove it from all the stores because the performance is much lower than other SKUs. And there is another thing, even like probably the most important one is that SKUs to replace. So, let's imagine the situation that, for example, we have our strategy and everything works like very well. And then, after some particular period of time, it would be great for us to know

if there are some other SKUs which would be a great fit for other stores. So, in this case, the system recommends you to combine those two previous recommendations and show like how the metrics must be changed. in this case, we recommend to change one SKU with another one and why. For example, we have an SKU in premium L cluster and it performs with the really good level of like performance. And it would be great to add this particular SKU to premium M cluster, but for example, we don't have enough space. Or, for example, we have already enough of SKUs with the same characteristics. So, to remove this transfer of demand, the system shows you recommendation to change one SKU with another one like this. So, in this case, we show exactly which SKU must be listed, which SKU must be delisted, and why. And here we have this like pretty

interesting information for category managers why we decided to change it this way. For example, in our particular case, we recommend to add one SKU with and like remove another one just in order to increase your gross profit like we decided to choose previously. So, in this case, the system shows you that while you like working with your like assortment matrix and while you are working with your reports, you don't need to waste your time. You just need to like accept this recommendation and after that, the gross profit of this particular item will be increased by the change of items like this. And here I have like gross profit and it will be in like increased. So, in this case we also can customize those indicators the same as we did with the strategy. And how it works. So, we can press this button and we will be able to see parameters. So, here we have a also gross profit in 100% but if I need it, I

can just combine it with other indicators and for example to choose revenue and combine those two indicators. And I can press for example here to change like to change it to like a distribution like 50/50 or I can um like make a priorities like between them. For example, the most important indicator for me is a gross profit and I can change it to for example 80 and um like give revenue just 20%. After that, the system will see all the needed like indicators you selected and also all the needed tools for example that you want to cover all the needs of your guests. For example, to have at least one SKU for every single store or every single cluster. And after that, the system will show you that exact recommendations you need to cover all the needs of the business to increase the profit for example and also to cover the guests' needs to cover all the uh segments we have in our system. So, after saving it you can generate the

recommendation. You usually we do this like one time per week but it depends on your needs. Sometimes our clients they use it like on the daily basis and some of them they decided to use it like one time per month to have a pretty stable assortment. So, it depends on your needs and it depends on your wants of course. And also it's very important part and probably the most asked one that those recommendations they take into account the capacity of stores. Uh a lot of our clients before implementing the system I this issue that category managers, they are not pretty sure while adding some SKUs if they fit the store. So, for that reason, we combine a combined the functionality of assortment performance and also shelf efficiency system, some of them to have this to give this possibility to category managers to prevent the situations when they add some SKUs to the stores and unfortunately there is no

space for them. So, this why the system provides you with the recommendations which always fit the shelves of the stores and also meet the expectation of your clients. Uh so, returning back to the strategy, here we have also a pretty good functionality which can help you not only to react on trends, like current ones, because it's also very important, but also to plan your assortment in the future. And for example, here I have many strategies and here I have many types of um like parameters and different decision-making tree depends on the stage of my particular category. So, for that reason, we have those strategies planned. And here we have, for example, already planned strategies for the next quarter, for the last quarter of this year, and also I can create any kind of them. And also very important question from our current clients is that if they can

use different like properties, different details for every strategy to build this decision-making tree. Of course, you can. For example, in my case, while I was like building the strategy, I decided to use three of them. As I told previously, like subcategory, T-type, and price segment. But of course, it's possible to add some other like properties or indicators. They can be imported from your ERP system and also they can be created on our side and you can add this information like straight to the system. And for example, to create another strategy or create a strategy for I don't know, back to school period of time or for example, some seasonal seasonal assortment. You can just basically select needed properties. You can select any of them and after that the system like will build the strategy for you and you will be able to see the right distribution of items and like use

it for the future and use it like for other system if it's needed. Uh yeah, there are some questions if I'm not mistaken. >> Yes, we have a few questions coming in, Julia. >> Yeah, we can answer them like right now. Yeah. >> Perfect. So, the first one can clustering be done on any type of attribution? For example, I want to I want clusters to be done based on product attributes that determine if an item is healthy, global, Hispanic, etc. >> Uh yes, so there are like different types of segmentations and for example, for clustering when we are talking about clustering and those cluster we have them here like premium L, like premium M, basic L, etc. They were built using two types of parameter. The first one is average price of goods sold and another one is capacity of stores. So, also in this case we use not only some like indicators,

but also information about your shelves to understand if this like particular store or cluster is the capacity of it is like pretty big or for example, it's a small store. But also while creating clustering, we can basically choose needed parameters. For example, also here we have the same situation as with the strategy, the manual one and automatical one. And when we choose this automatical one, we can like choose basically any like parameters we we need. For example, in this case we can choose, I don't know, some indicators like a revenue, gross profit, sales, like gross margin, etc. And also we can choose some kind of a description. For example, stores group or for example additional information or regions, etc. So that's why if you have this information or you can provide it, we can build this clustering based on the parameters you want. Usually we can suggest to choose like not all

the parameters because in this case the clustering will be like pretty big and there are a lot of clusters can be created. But we recommend to choose like up to five to have a like clear picture of your clusters. But usually we discuss it after first part of implementation because to recommend something we need to know all the ins and outs of your business and all the ins and outs of category manager management especially. So answering the question, probably yes, but yeah, we need to deep dive into your processes before recommending something. >> More context to be discovered, for sure. >> Yeah. >> I think you answered Julia the second question. They said, "Did you just say that the assortment recommendation is space aware?" >> Yeah. Yeah, definitely. And by the way, here we have not only recommendations which like take into account the space. Because here we have information even

while adding something manually. For example, here I have my assortment matrix and I can basically change some statuses and for example, as a category manager I want to add this particular SKU to all the stores I have. After I click in like and change the status, the system show me that there are some problems. And here I have this situation where the system show me that, for example, I can't add this particular SKU because of strategy, by the way, which we built not long ago. And after that, I can see that, for example, this particular SKU it doesn't fit at the premium stores, maybe because like it's we already have like particular SKUs in this segment. Or, for example, another reason is that we have this price segment that the price is too low for those like clusters, and we don't want to make a cannibalization in inside of those clusters. So, that's why the system shows you not only during the

recommendation like while recommending you something, but also here you can see like capacity shelf with like supplier supplier's rule and strategy. That's our validation types of validations. And, for example, when category managers manually, and I understand why, I can do some can make some mistakes, and it's obvious because it's very difficult to keep in mind some agreement with suppliers, this strategy, pretty big table, yeah. And also the like available space, it's very difficult. So, that's why the system does it on the side of the system and shows you in case you have any kind of problems that you can't add this SKU before because you don't have enough space. So, like this. That's why, like answering the question, yes, we have those validations. And for like this manual adding, the system works this way that it shows you that there is some there are some problems with the this item and shows you why.

And another thing is that we don't basically recommend you to add some SKUs in case we don't have enough space for that because it would be not that good, you know, to add one SKUs which much like bigger instead of another one which is pretty small. It's It's never like it's It's never worked this way. So, this way the system take it into account and shows you the right recommendation. >> Great. Thanks, Julia. Yeah, it's particularly powerful when the assortment performance module is connected to the shelf efficiency module so we always know how much space is going to be needed, how much space is available, etc. And then when it's connected to the inventory optimization solution so we can be sure that we're replenishing in the right quantities at the right time. Again, connecting all of those different aspects of the business that are typically siloed, now by implementing this single platform, they're automatically talking to each other on an ongoing

basis. Um Julia, another question. How do category managers receive and process specific requests from individual stores regarding additions to and deletions from their assortments? How do store humans feed into this process? >> Yeah, very interesting question and I would say that it's not um very popular one. So, for that reason we have different roles for the system and I can go like to this settings and I can show you that here we have all the like properties for example and here I have all the roles I have. And for example, here we don't have any kind of limits and you can create any kind of roles if it's needed. And here somebody already tried to use this this role store manager but they decided to make this word shorter. So, how it works. You can basically change those user roles and create your own one if it's needed and then show the

needed parts of the functionality. For example, if you want if you allow your store managers to see the dashboard for example or analytics usually it's very sensitive parts of the system. So in this case you just can hide it from from stores managers. Or for example, if you want them to see your assortment metrics or add some comments for example, in this case you can give them permission to see assortment metrics and to and add some additional information. So it's can be it can can be done like here. So it's quite easy. You just need for that reason to create any kind of name of the role then save it and then change those permission if it's needed. So in this case we save the information because like it's very sensitive and those roles those people they need to have a permission specific permission for that. But at the same time we give this possibility to communicate inside of the system. And even some of our clients

they added marketing managers to the system for them to be able to see uh for example some actions category managers do. For example, if they add some uh SKUs to a folder it's a special functionality of ours and then they want to have those SKUs on the website on the main page for example or they want to see the list of items after clicking the banner. So for that reason they gave this like permission for marketing managers to enter the system to log in the system and after that they have um like permissions to some particular stores or some particular uh pages of the system. So it's possible. It's also depend uh it depends on the your needs like and uh what exact um functionalities you want to allow managers or managers to see, but yeah, in general it's possible, but also we need to know some details of it. >> Great, thanks.

The next question was entered in Portuguese, so I used handy-dandy Google Translate, so apologies if Google didn't translate the question exactly how how you had intended, whoever input that. But the question is, in the space management process, what do you do first? Do you handle macro-level space management, defining the layout and equipment, or do you address the micro-level space first and then define the layout and equipment? And I think this is more asking about the shelf efficiency solution, and like many cases, the the question really depends. I think a lot of our clients that are the brick-and-mortar retailers, they start at the macro level. So, they either upload their floor plans or build the floor plans in our solution, and then start placing the equipment, and then once they have the floor plans built, then they start actually building out the planograms, and there are tons of automated rules

that we can leverage to build the optimal planograms at individual stores, as well as kind of templated planograms that you can extend across any number of stores. But we also have a lot of clients that are the suppliers to the retailers and are only building planograms for their products, so they're not concerned with the macro space of the entire floor plan, they're just concerned about the space that their retail partner allocates to them. And so in that case, they would start from the micro level. So, different ways that we can approach that, it really depends, and I'd suggest we set up uh input that question, I will in the chat here put in a um a form that you can fill out to provide your contact information so we can get in touch and discuss the the shelf efficiency solution in more detail um to to talk about what your specific use cases are. We'd be very very happy

to uh have that conversation with you. Um Julia, another question here. Say there are plenty times when merchants tell us they want a product in particular stores even when the data shows it shouldn't be there. Do we have the ability to override the system's decision or even feed those overrides at the start so that they are prioritized from the beginning? >> Like also, it depends on your needs because not all the time we can't like rely on uh subjective like information from uh even store managers. I wouldn't say that it's not important or whatever, but uh sometimes it depends on them goals of the company. If the main goal, for example, for us for this quarter or for example, for this year is to increase gross profit, in this case, I would rather suggest you to stick to the plan and to follow the recommendations. But for example, if uh

the those requests, they are stable and we understand that the reason need and for example, in it can be also uh suitable in the case for example, we didn't have uh particular segment in our store and it can be I don't know in for my tea category can be I don't know matcha and we didn't have it, but our store managers or like other people from who who like communicate every single day with the our guests, they ask uh and they keep asking us to add this particular segment. So in this case, even the sales is not that good, we can like force the system not to remove though those SKUs from our system. For example, we need to return to the strategy for that. Um let's imagine that for example, I have here, if I'm not mistaken, this third quarter, I decided to set up a the limits. If there are no limits, we'll we'll set it up. So, here uh to combine some um like

behavioral economics parts, and also to combine this performance-driven uh recommendations, we can set up some limits. And for example, some of our clients, oh, I have one, by the way. So, some of our clients, they want to cover all the needs of the guests. So, in this case, they want to have at least one SKU in every single segment. For example, even even if I know that uh matcha has really like bad level of uh sales in my particular store, but anyway, I want to have it because it's my priority for the next um like for the future, for the nearest future. Or for example, I know that people who uh like buy this particular item, they usually buy not only this uh item, but also they have a really like uh big transaction, and usually they buy something else uh because they they bought already this uh particular premium tea or whatever. So, in this case, we can set up those

limits, and the system will combine uh the approach of showing you the recommendation, and showing you the right number of SKU in this particular segment, with the behavioral economics, let's say this way. So, and for that reason, for example, in this uh for this uh particular strategy, I decided to set up the limit like this way. And how it works, for example, I told the system that no matter what, in all the clusters I have for this particular subcategory, and it's loose leaf for black tea, and now it's it it doesn't like make any sense, for example, to remove some of the like items from my strategy. So, I decided for all the price segments to have at least one SKU. And here I put this like equal or more and one SKU. So, in this case, even even though the performance of those items will be less than we expect, the system will never recommend you to remove some of them because it's important for us to keep them in our assortment.

So, for that reason, like answering the question, it depends on the situation. If the stores are asking and keep asking you to add some SKUs and you understand that it's a strategic like decision, so in this case, you can set up those we can call them walls for the system. And after that, the system will like act inside of those wall, inside of that structure you decided to stop. So, in this case, if you understand that it's a strategic decision, you can set up this limit and create a new strategy. Or, for example, if you understand that those like requests from from the stores or from merchandisers or like other roles, they are not that relevant, you just can stick to the recommendations. And in this case, we understand for sure that the system will increase your performance depends on the like indicator you selected. So, it can be

revenue and gross profit or usually it's a combination of them. >> Great. Thanks, Julia. Another question from the same attendee, I believe, who is asking about the space management. They ask, "Can you show a planogram example?" What I would suggest is we set up time to do so one-on-one with you so that we can learn a bit more about your specific business and then walk through the planogramming process because there are different ways that we can build and maintain the planograms depending on the products you have, the the types of equipment you you have, your business type, etc. and then the automated rules you want in place. And so I'd highly recommend we connect offline and I'd be very happy to walk you through that process. >> Yeah, great. There are some other questions.

>> I think that's all the questions that I see. So if there aren't any other questions, I want to say thank you so much to everybody for attending and for your great questions. That was really awesome. Obviously, this was kind of a high-level overview. There there's going to be context, there's going to be nuance in each one of your businesses. And if you'd like to dive deeper into the assortment performance module and how it can impact your specific needs, your challenges, etc. Or if you want to dive into any of our other solutions, we'd be very happy to have that conversation with you. For those of you that are already clients or partners, feel free to reach out to your account managers and we'll be happy to set that up. For those of you who aren't, you'll notice in the chat I input a link to a contact us page on our website. All you have to do is fill in your email, your name, your company and then a quick description of what it is that you're interested in learning more about. For example, I sat in on Julia's amazing

webinar and would her to talk to me more about my specific assortment challenges. So if you would like to connect with us, please fill that out and we'll be in touch with you very shortly. Additionally, this webinar was recorded and so all of the attendees will have access to that recording shortly after the session as well. So, if there are no more questions, again, thank you so much for everyone for your attendance and we look forward to talking with you soon. Hope everyone has a great rest of your day and a great rest of your week.