Welcome, Hunter. So happy to have you here with us today. Hey. Why don't you tell everybody, first of all, who you are and what do you do at ADI Global? Sure thing. Hey, everybody. My name is Hunter Brady. I'm one of the digital product managers here at ADI Global, specifically covering product discovery. So that's inclusive of anything from SEO all the way down to the user gets to search results pages, PLP, and I'm also in charge of navigation. Been a product manager for the last two plus years prior to that. A lot of my experiences around data analytics, data storytelling, so deeply ingrained within the whole user journey, user behavior kind of flow within the ecommerce space. Excellent. And what makes your catalog and the buying journey unique as a global distributor that you are? Oh, gosh. It's very unique. It it presents a lot of awesomeness because ADI specifically so we're a business to business distributor focused in a multitude kind of streams. So that's inclusive of both like audio video, both residential and professional, as well as fire safety, video surveillance, smart home technology. So we span a lot of different kind of industry verticals and thus within that, customers can interact and behave a lot differently. It presents a lot of uniqueness throughout not just the whole funnel, but specifically within the search experience as well. Brilliant. And know a lot about the journey and what you've achieved so far, but why don't you explain and start telling everybody before Coveo, what was the biggest search challenge, especially with the B2B buyers? Oh yes. So there are several, but one in particular that has always stood out was given the volume and, as I mentioned, like a lot of the uniqueness within the search behavior, it became a lot to try and manage. And what we found, what was happening, is we were trying to diagnose it by symptom by symptom. And so this would lead us to create maybe redirect rules or synonym rules one by one. And while it may have addressed like that specific use case, it could have had negative consequences to other search behaviors as well. And so it felt like trying to plug a hole in a ship with a thousand different holes. And so that was our experience of how do you get your arms wrapped around this type of problem without dedicating a large amount of resources or thus inadvertently kind of burying yourself within just this rulescape that could kind of take over any type of search engine regardless of which one you're on was the biggest challenge, just trying to manage all those rules as they came in. Quite a scary analogy there. So how are you using Coveo then today across your global sites? You know, what capabilities? What's life? And and working? Yeah. Yeah. I mean, so we are, as you said, a global distributor. So we have ten plus sites, four four mobile apps, multilingual. We're on the Coveo headless React SSR Commerce implementation. And so within that, we have a lot of enablement within the Coveo platform. We're using things like the automatic relevance tuning model. We're using dynamic navigation experience. We recently went live with Coveo recommendations, and we're we're currently pursuing Coveo for service as well as a few other things. So as we got implemented with Coveo, we quickly wanted to start integrating and attacking with all the additional bells and whistles that y'all's platform has to offer, and it's been a huge, huge value add as we have progressed. Yeah. Big up personalization as well, Hunter, in there. Oh, most definitely. I mean, that that was one of the huge, like, opportunities. And as I was alluding to was that it was so hard to manage a lot of those symptom by symptom issues and things like with the automatic relevance tuning model, it comes and kind of assumes that workload for you. So rather than having to self inject a lot of rules or synonyms in order to help address some of those underlying issues, the model of automatic relevance and tuning kind of helped bridge that gap for us and thus the dependency on needing to rely on individual manually created roles because obviously the machine learning is taking all of those data points and able to apply it to future sessions as well as other compatible sessions. Amazing. It's fantastic, really, and every time I hear you talk about it, it's exciting. It's amazing. Tell me about the results that you've seen since they go live, for customers, the revenue, but also for the teams. Yeah. So we addressed it from three different kind of result gathering approaches. One of that is going to always be quantitative, qualitative, as well as internal team member usage. From a quantitative standpoint, we saw a drastic, drastic reduction in null search rates. So that was one of our key pillars is we were over ten percent null search rate occurrences. So that's essentially users would type in a query and get no results back. We saw a degradation of that of close to from double digits all the way to single digits. I think we're sub three percent null search occurrence rate, is absolutely fantastic just from a general user experience standpoint. And we saw that reflected from a qualitative standpoint as well. Right? We're hearing from customers. It's well, it's the best case I can explain it is prior when the search was bad, you would hear about it all the time. Once search was good, you don't really hear a lot about it, but that's always a good thing. Right? So if you don't have issues, it may get quiet. So that's kind of like what we derive from it. And there's also been some feedback through surveys that we've done post implementation where the rating and satisfaction rating of the search engine itself had gone up drastically. Okay. And then the third one I mentioned is team members. Right? So we had a lot of digital merchants and team members that relied on the search experience in order to perform their their roles. And so we had a lot of time saving opportunity that we observed through things like the Merchandising Hub, where they're going in and creating, you know, page campaigns or doing boosting rules. It is something to the tune of multi hours per week saved through leveraging, like, the Coveo Merchandising Hub platform. So it's a a try kind of impact that we were able to observe since go live. Unbelievable. And and I know the numbers, you know, multiply and and have Oh, yeah. A huge impact, for your business. So you've done a lot. You're still doing and experimenting and bringing into other parts. But what's next? What's the biggest priority in this AI journey that you guys have embarked in at ADI? You know, oh gosh, I love that question because it's so fun. And what I revealed through this whole journey is we are really just getting started within product discovery. I think now that our US sites specifically has been on Coveo for a little over a year, and through that it's enabled us to explore how else we can leverage the Coveo platform to really improve the experience. It's enabled us to integrate features that help improve the speed and the your ability to make confident decisions. Right? That's what it all comes down to is how can we equip our users with the information they require to purchase confidently. That's what we're all striving for. Right? And the Coveo platform specifically has enabled us to do that through the data that it presents to us as well as the features that it allows us to to leverage. One in particular that we're currently doing explore exploration on is relative generative answering. Right? I think that's the next logical step is how can we equip our users with a intent box where they can gather information. Right? Because our customer is varied. As I was alluding to earlier, it's like it could be an installer in the field. It could be a sub one in their own corporate offices. So we have to provide an experience that addresses each of those different streams of persona. We think that RGA may be the conduit in order to help bridge those gaps where they may be not be able to get that information specifically or even just like from a quickliness perspective on the experience today. Yeah, definitely confident that it will help. So we have a lot of people listening in. Distributors are a big group. What advice would you give those other distributors starting or scaling from where they are today in this journey in commerce in particular? Yeah, this is such an important question and it's something I thought about really carefully early on. And I always go back to it's absolutely critical to understand your users at a very deep level. The best way that I can recommend taking your first steps at this is starting slow, Right? Don't come in with a lot of business rules or pre existing notions of how you think that a search system should be set up. It's like really letting the machine learning kind of take stride and inform you then on maybe some decisions you should make after. So that's like the first point. The second point, as I was kind of speaking to earlier, was understanding them at a deeper level. The best way that I can kind of explain to accomplish this is to get really nerdy with the data. Right? Like, it's understanding not just the individual search terms as they come in, but applying some level of classification logic is the best way to kind of wrap your arms around how your searches or search types, how your users are performing. Right so I'll give you some examples from the ADI experience. It's users are often gonna either search by a part number, they may search by a category, may search by a brand, or they may search by an attribute. Right? So those are some critical elements. Then instead of looking at your tens, maybe hundreds of thousands of search terms each distinctly, you're able to group them together and thus see where are the deficiencies within my search from classification standpoint. Right? That's a lot more digestible, not just for, like, a search analyst, but also, like, an executive as well. Because it's gonna be hard for them to maybe see, like, here's our top ten thousand searches and how can we action that? That's hard for anyone to try and address. But whenever you can apply some level of classification to it, it suddenly becomes a lot more digestible. And that way it equips you with the ability to really understand your search going into the implementation with Coveo and maybe where some opportunity to manipulate the the configuration or the conditions that you're using. But then post implementation, you use that as your levers to understand are you trending in the right direction or are there opportunities distinctly within each of these classifications that we then can go address. It's so important. And again, I'm going to go back to that point where it seems easy. Like you see a problem with a search term. Okay, I'm going go address it from that one single search term. It's really not the best way to go about it because what will happen is you'll run into a very similar situation. A year from now or two years from now, you'll have thousands of search rules just from trying to diagnose them independently. If you start to think about it and just take a step back and address it as a whole, right, like how can I fix searches for part numbers, or how can I fix searches for categories, you start to think more creatively rather than on a case by case basis, and it'll have a longer term impact overall for teams to action? I know that was a little long of an answer, but I'm really, really passionate about It's absolutely fantastic. I think it's great. Thank you for giving the detailed advice to so many people that I'm sure is resonating with with many of them. So thanks again, Hunter, for sharing the story. Thank you for, you know, making it a success. It wouldn't we wouldn't be here talking about it without the work that you've put in and and also the partnership between ADI and Coveo. So I really appreciate you and taking the time to share with all of us. Thank you. Thank you very much. It's my pleasure. Thank you, Priscilla.