Good morning. Good afternoon. I'm Imran Choudhary, VP of E-commerce here at Crown Peak. Thank you for joining us today. So today we're going to give a walkthrough, a high level walkthrough of our product discovery solutions. This is primarily for our customers and our partners who I thank you for attending today. Now in terms of Q&A, happy to take Q&A. What you will notice with zoom is there's a little Q&A and chat window. Please post your questions there. What I'll try and do if I can see them and I can answer them quickly, I will do otherwise I'll look to address them at the end or as follow up post this session. This session will be recorded and if for some reason you need to drop off early, do not worry, we will send you the recording and all the other assets to go with it. So let's begin. So just as a starter for ten, just as a reminder for our customers and our partners. So Crown Peak are a digital experience provider and what you see here on the right hand side is all the different components. We have our composable tech stack. And today in particular we're going to be focusing in on product discovery. Now before we jump to product, what I do want to highlight is a little bit of market intelligence and data. So last year we commissioned a independent report with the London Business Research Group. And what we wanted to do was understand what are the challenges that our e-commerce market was experiencing, and how were they looking to pivot or potentially prioritize their investment and time, what with the economic climate into 2023? So it was about a 300 odd, uh, actual merchants actually surveyed. And we asked a series of questions just to try to delve into that topic of the challenges and how they were prioritizing. Now, what was interesting is how product discovery. So when we talk of product discovery, that's how people find a product, how merchandises are sort product on the side. And then how do we look at building relevant recommendations for convert. And out of those 300 that we surveyed, what was starting the interesting was that 87% of those merchants felt that product discovery was both an opportunity and a challenge, especially for those that were operating in an international setting for real success in terms of their revenue and their growth. So during the course of today, we're going to pick up on some of these statistics, some of these data points, hopefully to reemphasize the importance of this topic and where the focus is happening with a lot of the e-commerce market and the teams associated. Now, in terms of international, international is a very popular topic for many of our customers. Most of our customers have a our enterprise and operating across multiple regions, and they take a concept of thinking global. They're acting local. And anyone that's in e-commerce, we'll know that each different region has different preferences. And what we found with our analysis in that base is there are different kind of nuances and challenges that we're having to address. So it's beyond just the language. There are cultural nuances, cultural context, which we need to understand and adapt to how we're positioning our products as well as our content, beyond just the typical things of language and synonyms and variants. What we also noticed is around preferences. So we see different shopping behavior and different priorities in how people are purchasing with regards to different regions. So a classic example is whereby price is less of a sensitive topic versus let's say price in the UK, which is more of a sensitive topic. So when you think about how these purchasing behaviors kind of have a reaction as to how you should be merchandizing and where you start seeing success, it allows you to really start to think about the strategy of how you are sorting your products for the but the benefit of improving that conversion rate. And we call equally we see kind of different shopping season. So obviously in the UK, the temperature and the product catalog you'd be showing in the UK would be very different to that in Australia. But equally in the US, East Coast and West Coast, you'd be looking at promoting different products and having a different assortment and these are challenges. But a lot of our e-commerce customers and the market see, but we look to address for them and that allows them to scale to that demand, be it that regional need, but more importantly, being able to adapt to that regional need without a lot of manual time, effort and investment. Now, what was interesting again in this survey was about 50% of those that were surveyed actually did say that their current tech stack would not meet their needs for international. And that's an area that we look. To help our customers with and what we'll be touching on today. So very, all very important topics right now. So let's talk a little bit about product discovery. So for us product discovery fits into across three kind of tool sets or or features. You could say. So how someone searches for a product how they ensure relevant results and personalized results to them. So what we'll start to see today is how search has expanded into more complex terms, and how we're adapting for that. Then how are we merchandizing? So we talked just a moment ago about how the merchandizing need in one region should differ to another one to improve performance based on those regional requirements. But equally, how are we promoting the right product assortment, the right content, the right recommendations that are relevant and are going to help not only drive that conversion, but also drive a higher AOV and as a headless solution, what we are also very mindful of is we want to encourage customers to reengage with us, to come back for that second, third purchase and improve that lifetime value. So how are we looking to drive recommendations in our retargeting and our email or marketing channels? So this is kind of where we cover the the whole widespread kind of end to end journey and recycling of journey with regards to the shopping behavior. And there's some examples here as to where we fit. For those that are familiar with our content management solution, obviously content is underpinning a lot of this in terms of that experiential journey. So let's have a look at search. Let's jump into search. So a little bit more research. But we wanted to share here today. And what we are seeing is that search is reemerging as quite a core and important topic. Shoppers are seeing virtual search. They want it to get to the point and to the product as quickly as they possibly can. They want a logical site. They want to be able to navigate it easily without complexity. And what we are seeing is that shoppers are actually putting preference to those that are actually providing a good search experience. So I choose online retailers to shop at based on the quality of their results. Nearly 70% are saying that that is true. But the challenge with this is that traditional tools or traditional e-commerce search, you know, it is reliant on manual effort. And this is kind of why search has kind of not been at the forefront, but now is because there is an expectation from the shoppers and it is a very, very quick and easy win in certain respects in order to recapture some of that online revenue that maybe you're missing right now because you're not able to optimize the rules or optimize the engine to adapt to customer queries. So this is going beyond just the basics. So let's just touch on the basic before maybe we go to the complex. But you know we talk of brilliant basics. So that is your spelling correction. So for example here we've got a spelling correction on jeans. And what we're getting is is basically not the right result. Now these are really basic things. But again it takes a lot of time and effort to be building these corrections, building these synonyms and these associations. And with our solution, what we're looking to do is, is address these brilliant basics as a phase one, as we look to help optimize your search results and your search queries and overall, the search engine. The other one that I just want to highlight as well is dress. So here I'm searching for dress. And because traditional e-commerce and traditional search terms are kind of looking at the keywords and prioritizing them based on product data, the attributes, the description, or even the title. In this example, what you're seeing is probably not a relevant result, because actually I want to be looking and seeing my dresses, but actually you're showing me dressing robes. These are very often things that we see with the market and customers that engage with us as part of discovery, really kind of simple things that should be kind of corrected. But we look to address and and fix for them. The challenge is, is it's just generally quite hard using manual tools. But the problem we also see is complexity. So we're seeing that users are more used to searching via the likes of Google. So if you look at Google, the majority of search queries going on Google right now are four words or more. And we're starting to see that start to ripple into e-commerce. Consumers are expecting that retailers should be able to understand these complex search queries and the data that we, um, but we have been able to get from our study with sit in a market, another independent study kind of highlights. But this is an expectation. And the challenges is that if you're not meeting that expectation, unfortunately, you know, shopping. Consumers, they have plenty of choice. They can just go to another retailer. So what you want to make sure is that you are meeting their expectations and helping them find the right product at the right time. All that usual good stuff we talk about every time. So let's see an example of what we think of as complexity. So this is what we talk of in terms of the long tail of search. So in this example here on the left I've put in long blue dress. And what you've been able to return again, what we see often with very traditional e-commerce solutions is kind of yes, it's pulling on the keywords or the product attributes. And in this case it's not understanding the intent or the context very well. What it's doing is it's giving me the long sleeve hooded sweatshirt, when actually the product catalog does have these products of long blue dress in stock, and it should be suggesting and populating the result with the one here on the right. The challenge, though, is because these traditional search terms, these traditional search engines, are just pulling on the keywords rather than looking at the intent and the context. Now why this is important. We generally see out of all search queries, 20% of those search queries falling into what we call this long tail, which is here. And why is that important? Well, when you look at search queries and we look here okay, so here we've got really simple search queries. Maybe someone's still in that, you know, that early early side of the funnel in terms of trying to discover something. So it's kind of one word terms like shoes. Maybe they start to be a little bit more specific. So men's shoes when you're here this is low intent. They don't really know what they're looking for yet. But equally that's where you see the highest volume in terms of these types of queries. And that's this kind of area here in the orange. Then when you start to get a little bit more finessed in terms of, okay, men's shoes or women's shoes, um, you start to see an improvement in terms of they starting to know what they're looking for and their intent to purchase a little bit higher. This is where we're in this green zone here where the money is, and that 20% of all search queries is this long tail. Okay. So that's a really descriptive terms. So red Nikes men's running shoes I know exactly what I want. Help me find it because I'm ready to buy. So where we see this complexity people know exactly what they want. They want to find it really fast because they're ready to buy, and their intent to purchase is equally the highest. The problem is, is they fall in here, they fall in here in this kind of bluey purple zone. Because the search query is so unique and complex, the volume and frequency of them is extremely low. It makes no sense using traditional tools for Merchandizers to be optimizing for these queries, because the frequency of them is just so high. So traditionally, as a business, um, what we do as e-commerce traders is we focus where the volume is the stuff that we can see, and arguably the stuff we'd get shot or sacked if we weren't focused on on because that's where the volume. But the problem is, is and it makes sense to do that. And even our solutions ensure that, you know, our customers can do that. But the problem is, is that that's usually where the low intent is. The high intent, where the money is is here. It's these really descriptive long tail queries, which is where the money is because they're very descriptive, but they're really hard to be able to give a relevant result. This is what we fix with our AI solutions. So our AI solutions allow us to understand this context and understand the product catalog, understand past behavior. And typically this zero search results, as I say, 90% of all search queries we pretty much eradicate on average by about 98%. So it's a really easy, quick win to recapture that opportunity with our solutions. Now, one of our most common customers who have been very vocal about the solutions and how they've been able to address this long tail is pretty little thing, uh, how a fast fashion retailer that operates globally. So not only are they addressing the language barrier issue that we spoke of earlier, but they're also looking at how they merchandizing differently per region based on preferences. And what you see here is 97% reduction in zero search results using our AI solution. So outside of just that stat, you can see some really big improvement as well by with a 20% increase in conversion rates. And that's predominantly because they're being able to merchandise and assort their products better based on local preferences. So they're improving that propensity for people to convert and add to basket, purely by understanding the local preferences and the local trends and being able to dynamically change the assortment using our solution to be able to meet that need. Let's have a quick look at merchandizing and recommendations. Now. So again, back to a little bit of stats. Um, out of those 300 merchants, we kind of asked, where was you focusing your time and effort over the course of 24 and 20 year 20, 2024 and year 2025, in terms of driving growth for your business. We're in the spectrum of merchandizing. Uh, and what you see here is kind of for the big stats. So they want better control of their merchandizing. And we're going to talk about what that looks like and the why they want to create visually engaging, uh, experiences for their customers. But being able to do that in a way that tries to mimic the in-store experience. So that might be how they're curating collections, it might be how visually it looks in terms of color or style. They want to be able to do that very well, to engage customers visually online as they would try and do in-store the age of 1 to 1 personalization. I mean, we talk about this every. Day. We have for many, many years, to be fair, but being able to do it right in a 1 to 1 way across, um, you know, multiple customers across multiple regions, you know, it's quite hard. So you need this. I and what we are being able to develop with our solutions is build AI capabilities to improve merchandizing and visual merchandizing in this 1 to 1 personalization. And we take the heavy lifting. Yeah, we take the heavy lifting away from the merchandizers and those manual processes by leveraging AI. But we allow the merchandizing team to do it to be able to control it. And we're going to touch on that right now. So if we think about merchandizing traditionally with a lot of e-commerce platforms, um, your assortment is ranked based on maybe 1 or 2 data points. So the typical one might be click through rates or bestsellers or maybe even highest conversion rate. Yeah. But the problem is that if you're limited with the data points that you can pull to assort your, the sophistication of your merchandizing strategy is also limited because you might want to, uh, have this top line. So the really important row of your product listing page, you might want that. That says the highest conversion rate. Okay. But equally, there's no point in actually promoting highest converting products if you're spread of sizes is really thin. So what's the point in actually presenting in this top spot? Let's say your number one best seller is highest converting, but there's no point if you've only got it in Excel. It's a wasted opportunity. So being able to build real strategies that's pulling in multiple data points. So that could be conversion rate, availability, even margin, let alone the local preferences as well. And be able to weight that into a strategy. This is where we're seeing real class leaders, um, having success in building sophisticated strategies that actually align to their business objectives. So let's give you an example from an international setting. So here so the classic one of Australia versus UK very different markets in terms of seasons and therefore product catalogs at any one time. And what we see very often is that each of these row of the product listing pages, um, each of them may have a different strategy, again, different strategies to try and engage in different points based on the customer's kind of buying preferences or their kind of stage in terms of the buying journey. So here what I've done is I've built out a strategy for the first row. I want to show everything that's new, everything that's getting the highest views and everything that's actually seeing the highest level of purchases. Now, really, I should have added an inventory data point there just to be safe. But what I've done is I've added it in the second one. Okay. So I want to try it for that first row, share everything that's new. And I'm just going to, to a certain extent assume I've got everything in stock. I just want to get the most trending stuff on that first row. That second row, that's the one where I want to make sure that I'm not only showing the best sellers, but I've got decent inventory and I want to improve my margin. So I'm showing an A sorting based on margin as well. That's my killer row in some respects. In terms of my the one that I want to see performance. So my first row is kind of the most exciting stuff, the most trending. My second row is a case of this is a step I want to kind of get out the door, and I know I can get out of the door, and I've got inventory and it's decent margin. And then the third one and we often see this in fashion or across various different verticals in retail, whereby a brand will promote a certain row or a certain page on a site. So in this one, I'm just showing the ability and the flexibility that actually could have a certain specific brand that is sponsoring me for a month. And I'm also aligning that to a data point such as Most Trending. So I've got my strategies per row. But what you can see is because I'm also pulling in the preferences from different regions, Australia and a warmer climate that the UK. I'm dynamically changing what actual products are being presented based on those local preferences. Okay, so some examples of of our customers that are doing this really well. So Adidas has been a long standing customer. Many, many countries that they are using our solutions to dynamically change their merchandizing assortment based on their strategies. And what you see here is UK versus Germany. Now the strategy is kind of the same over when taking in the local data to a sort based on preferences, and it's not dynamically different. You know, when you look at them side by side, you know very similar styles of shoes, but you see some slight changes. And those slight changes are based on not only price but also kind of color. The more dramatic one is Hermes. And what you can see is, let's say in the Netherlands, it's more of a casual kind of look and a casual preference versus one which is more classical in France. The challenge is being able to do this in an automated way, especially if you're doing, you know, 20 plus regions. And that's where our AI and our ability to build strategies with you. So you can automate some of this and really helps our international retailers. Now when we talk about merchandizing, we also should think about the visual merchandizing element. If you think about those stats I spoke of earlier. Visual merchandizing really does differentiate you as a retailer, but it also drives in engagement. So if you are in that discovery versus you know exactly what you want, you're in that discovery. You know, you're shopping for an outfit for an occasion, and what you want to do is be able to merchandise as you would in store. So you have that kind of experience. And one of the classic ways that we do that is taking not only those merchandizing strategies. We spoke of, but adding, adding in a visual component. So in this case it might be based on color. So I've gone red, blue and green. But you're trying to create this experience online. And why? What does this sophistication look like and why should you do it? So here what you see is a snapshot of some common ones that we have implemented with our customers, with good success. So you have kind of the top line strategy okay. So new in or trending or highest converting or sale. And what you've got is the different data points that we're pulling in in order to create that sophisticated strategy that actually really does convert based on your business objective of I want to be pushing my new in or I want to be pushing my highest conversion or my most trending. This sophistication is what we're seeing are class leaders being able to achieve with our solutions, but because we're building in the eye to take the heavy lifting, they are defining these strategies in a very similar way like this. They're doing the weighting and then the algorithms and the AI is doing the heavy lift for them. Now, what this is enabled is that many of our retailers have been able to grow internationally very successfully, but without being able to take on that manual burden. So in general, we see 60 to 70% of time saved by building these strategies and then letting the AI do the automation. And it's one of the secret ways, in some ways, the secret sauce as to how a lot of our customers have grown so successfully internationally across many, many different regions. Now, we wouldn't be wouldn't be fair. Talking about merchandizing without also talking about recommendations. So two very common areas of recommendations that we see. So yes, you want it to be 1 to 1. But there's also a hygiene piece here. So very often customers come to us and they're in this situation. So I'm looking at this men's shirt. But actually their existing e-commerce or recommendations platform is giving highly irrelevant, um, recommendations in terms of products. So that might be, you know, here I'm looking at in a men's shirt, but you're recommending female items generally. That's because a lot of e-commerce platforms are just populating any best sellers within a certain category, or just anything that's getting the most attention. Okay. Um, they're not truly relevant, or they require a lot of manual effort to get right. In this example here, what we're doing is looking at kind of a shop to look analogy. So I'm looking at these men's long johns. Really what you should have seen here is this item. If I'm looking at the men's long johns really I should be looking at also being recommended the men's thermal top okay rather than the women's kind of jacket. So again these are just general hygiene things that we see whereby recommendations are really being done a disservice. But you're also missing an opportunity to create a better experience for the customer, but also drive better cross-sell and AOV. So some classic examples of customers that have done this really well using our technology. The first one is for revenue. What they were able to do is basically building visually similar recommendations. So I might be at this dress, but actually this dress might not be right. It might be a different price point, but I want to spend or actually maybe a different fit. And what we're able to do is automate visually similar recommendations throughout the carousel. And what we've actually seen is being able to do the cross-sell there, which is ultimately remove the dependency on the merchandizing team doing it manually. We're able to use the AI to drive more relevance, and therefore we've been able to improve the AOV for, for for revenue by about 20%. Now, on the right hand side, we've got super dry, another great international customer of ours. What they wanted to do was drive better cross-sell with shop the look a lot of their imagery, their product imagery. So here I am on the jacket is head to toe imagery. And because it's head to toe, there's an opportunity to actually sell more than just the jacket and want to be able to promote the top and also the jogging bottoms. Now, again, traditionally, with e-commerce platforms being able to build this shop, the look capability, you're having to manually create and associate those products. Now, if you've got hundreds of thousands of products, being able to do that is quite difficult and it requires a lot of manual process. Our AI technology understands the image, understands the product catalog, understands those associations around user activity, and we can automate this. And what you see here is super drive. Being able to automate shop the look now and basically improve the efficiency by about 60%. Okay. So, um, let's continue. So what does all of this look like? So if we were to try and bundle all of this into one classical shopper journey, what could it all look like as we go from search to merchandise and into recommendations? Now, what we've done is we built a very, very simple, uh, kind of video demo. I will say it's a little bit rose tinted in, but what we're doing is taking a lot of great examples of use cases our customers are doing and brought it all into one journey, but hopefully it gives you a feel as to what we as Crown Peak are enabling our customers to achieve in terms of building these really empowering shopping experiences for convert. So here we are. Um, I'm bought from this website before I highlighted that I'm male, but I've downloaded that app, so I'm in another channel. But because we understand me as a person, the content through our CMS solution is already personalized to my buying preferences, my gender, my interests. Now, in this example, I'm going with a relatively simple way, which is blue shoes, but actually I'm still not found what I'm looking for. I want to go a bit deeper. So what I'm now in is a chat solution. We understand natural language, and therefore we can understand the complexity of me trying to basically get to a more specific product. In this case, I'm looking for running shoes that are good for muddy trails. We understand that I my preference is blue shoes. We understand now that I'm looking for running, uh, running trail specific shoes. Therefore my search result changes. Now note here we're injecting some content. We're injecting content there because we understand the intent of the journey. And we're able to do that with our integration with our CMS solution to simplify that whole building of such an experience. Now I'm going to go and add these to my basket because I've added it to my basket and we understand a little bit more about me. I can look at doing some top ups. So how can I get my free delivery by adding a relevant recommendation at my checkout? Kind of another quick win, um, in order to get my free delivery. But the retailer in this case increasing their average order value. Now, it doesn't stop there because we understand me a little bit more on my purchases. What we can then do is in our future email marketing again, ensure relevant content and also more relevant products are being recommended to again, reengage me back to the site and hopefully get that second, third or fourth repeat purchase across the year. Now, I know there are many folks on this call today who are existing, uh, First Spirit, uh, CME's, uh, customers. And what I wanted to share today was a really high level, uh, demo, as it were, of how we've injected such product recommendations within our content creator studio. So those that are familiar with, um, our first solution will note this demo of Smart Living. So here I am in the Smart Living, uh, demo right now, and I've built a page around home security. Now, what I'm doing here is I'm looking to inject a widget, which is basically a recommendation widget for products based on this home, um, home security kind of landing page. But I've been gone and created. Now I'm taking a strategy which is my most popular. Um, and I'm going to be using my most popular strategy to then build my recommendation widget here. The challenge for here, and this is where the control element becomes really important. And what we do very well is, yes, these may be the most popular products in my catalog, but actually this page is really specific to home security. So what I want to do is adapt my recommendations to more personalized and be more relevant to the category that I'm shopping in, so that actually the products that are being promoted are actually home security on that first row. But I'm still not right. There's still something that's not quite right here. So what I want to do is I want to now go straight into my recommendation, uh, window. And I want to adapt this even further. Okay. So what I'm going to do is I'm going to go and apply a rule to ensure that even though they're 1 to 1 personalized, they are still sticking within the category of home security. So I apply a business logic here. I add a tag, which is the required tag of ensuring that anything that's populated back in that widget is within that category of home security. And I'm going to go and update that now because of our integration. What we can do is make sure that when that integration pulls through the strategy within our first bullet, CMS is also updated. And what you can see here is that the product recommendations have been changed to be more relevant based on what I'm interested in, but more crucially, the category of the landing page that I've just created. Okay, so we are drawing to a close. I think I had half an hour for today slot, so why Crown Peak so fundamentally we are a class leader within product discovery. We dramatically improve product findability. But fundamentally, without putting huge burden on your team, we empower your team to use AI to take the heavy lifting, but allow them to drive that scale of growth, be it through international or improving their ability to scale to things like 1 to 1 recommendations. Hopefully, what you've also seen is a snapshot of how we've built some of the integration with our existing CMS solution. So for those customers who are joining us today from First Spirit, hopefully you will have seen a very common interface where you could easily build such things into your landing page creation or any creation on any of your digital channels using our tools. Now, this has been literally a very quick walk through today. What we are more than happy to do for our customers and prospects is, you know, we've got 20 years plus in product discovery and in retail e-commerce, okay. And we are more than happy to provide a free site audit for our customers and for prospects where we can actually have a look at your site and benchmark it against what we know is, let's say, the top 30 test points of what makes successfully ecommerce, enabling customers to find your products really well, merchandise effectively for highest conversion, and then build really good, relevant recommendations in different stages of the website and in different parts of the journey of the customer journey. That is, to make sure that you improve that AOV. We are more than happy to provide these free site audits. What you will see at the end of this session is you'll get a follow up email, what with this recording, but also an offer and an invite to, um, to submit your site for a free site audit. That's where consultants in our team will review your site against those 30 different key points, and then they'll come back to you with an analysis, a light analysis of areas for improvement or potentially in some cases, it may be you're doing a really good job. Um, but at least it's an audit for to benchmark. How are you doing? Is there any kind of missed opportunities that we can help address for you and to improve your conversion, your AOV and your sales over the course of the remainder of this year? Okay. Um, in that case, I will just check on any Q&A today. Let me just check. I don't. -Think we've got any. -Direct. Okay, so it looks like I'm only five minutes over, but I hope you've stayed with us. Hopefully this has been interesting. As I said, there will be follow up and as always, please feel free to reach out with any questions or groups. Thank you very much.