Not Just Smarter - Connected: How AI Makes Retention Work

Not Just Smarter - Connected: How AI Makes Retention Work Speaker 2: Welcome back to another episode of Chew on This, which is brought to you by Klaviyo. Today, we have a guest who's actually been on the podcast already, Jared, who is the Chief Digital Officer at Symbiotica. Thank you for being here today. You know, the last conversation we had was absolutely incredible. Symbiotica is one of those brands that if you are an advertiser, a meta, you've definitely seen them. And if you're a consumer, you've definitely seen them as well. So today, there's a few things that I want to chat through. Mostly around like, you know, AI and how brands are actually leveraging, you know, there's so many tools that are coming out every day, I feel, right? But before we kick off, for those who may not know you and your background, we'd love a quick introduction. Speaker 1: Yeah, absolutely. Let's see. So I joined Symbiotica about five years ago, August of 2020. And it was a really small team at the time. We had a handful of people, all DTC through our store. And my role was to open new channels, different marketplaces, different digital channels, and retail. And as our team grew, the need for technology and sort of I would say more innovative user experience and retention tools sort of led me to lead technology. I have a background in software development, and so we put together a team in-house of developers. Now we have an AI team as well, so we're investing in heavily. Before Symbautica, I was involved in a couple early-stage startups, but Symbautica has been a heck of a ride. Unknown Speaker: Before we dive in, a quick word about today's sponsor, Klaviyo. In 2025, retention isn't optional. It's a growth engine for any brand that's serious about scale. That's why at Avvi, we use Klaviyo for all of our email and SMS marketing needs and to know what our customers are talking about. We're not just using Klaviyo to blast everyone the same promo code, but to make sure our message actually reaches the right people at the right time based on real customer behavior. Klaviyo combines real-time customer insights with AI to help us design smarter flows, create dynamic segments, and personalize every touchpoint based on what our customers are actually doing. It saves us time and helps us drive way more repeat business. Want to know more about your customer than ever before? Go to klaviyo.com slash chew on this to learn how. Now, let's get back to the episode. Speaker 2: I mean, the question, let's get right into it. I mean, you mentioned that there's a whole AI team at TempBiotica. What does that mean? Like, what did you feel like there was in this market and in this climate where you're like, you know, we actually need a full team to kind of cover this? Walk us through that. Speaker 1: I'll give a lot of props to our CEO because he's been really adamant about making sure that we're on the front lines of this technology and there's tons of tools out there and a lot of them we've tested and we're not thrilled about. So having an in-house team, it's more like an incubator. It's more like a lab where these are Developers that have experience with the language models and in different ways of leveraging them. So a lot of what we do is testing out new strategies, testing out personalization, but a lot of it is also internal optimizations and efficiencies. By leveraging multi-agent systems, there's a lot of ways of sort of just automating routine tasks, especially around CX and copywriting and other areas of the business. Specifically around retention and customer acquisition, it's all about personalization and we have a lot of kind of balls in the air right now that we're juggling. Speaker 2: I love that. You mentioned, you know, trying to figure out what processes that you can possibly automate, you know, starting with customer service, maybe even in like finance. I know a lot of Brand owners are looking to AI to help with the marketing side of things, like copywriting, creative strategy, brief building, customer service for sure. Obviously, there's a lot of questions that come in on a day-to-day basis that's very routine. It's the same questions like, where's my order? How do I take the product? This and that. I feel like with AI, you can cut down a lot of the response times, which We'll build that better relationship between the brand and the customer. I don't think people realize how important that is. I'll give you an example. Last week, my wife had ordered something. But she ordered the wrong color of some water bottle, right? And she's like, oh, I gotta cancel it, but then I still wanna place my order, because I have this coupon code. And she's like, oh, just email them. And in my head, I'm like, yeah, you'll email them, but they'll probably get back to you in two days. Literally five minutes later, she gets a response. And I'm like, wow, that's crazy. And she's like, oh, they're on it. And I was like, no, it's definitely AI. But yeah, I mean, I'm very curious. I wanna kind of dive into some of the more tangible examples of how you guys are Implementing AI, I mean, obviously customer service very, it makes sense, right, kind of responding back and forth and obviously giving it a database to chat through it, but where are some of the other places that you guys have found implementing AI to be very, very tangible for the brand? Speaker 1: Yeah, I mean, so I would start with understanding your customer profiles. Every single customer should have some sort of object that essentially can Say first party or third party data across every single touchpoint with your organization. So, you know, from the first time they reach out maybe or when they land on your page or even the ad that they click on, there's ways you can start to collect data. And then as the browsing experience sort of progresses for the user as well, there's ways to start to collect this data and then put together a customer object that you can use AI to sort of reference when you're Either retargeting or, you know, trying to implement different retention strategies as well, either through email or SMS, which has been something that's been effective for us. Personalization is, I think, the future for e-commerce and AI is critical for that. Speaker 2: So, the consumer journey for most people is, hey, they've seen some sort of Marketing material, whether it's on Facebook or TikTok or Google, they see an ad, they click on the ad, they're brought to a landing page, they go through information, they learn about it, they see the price, they like it, maybe there's a pop-up, they drop their email, maybe they buy, maybe they don't. Walk us through, we have this entire journey, how are you guys leveraging AI to personalize that journey for the consumer? Speaker 1: I think a good place to start is to think about most e-commerce brands. They have a quiz, right, where you land on a page, and it's like 10 to 20 questions. It takes a couple minutes, and they complete this quiz. At the end of the quiz is product recommendations. I think most consumers have gotten used to that, and most consumers are familiar with it. It doesn't feel super personalized anymore. I think there's an opportunity to personalize the browsing experience for the consumer as they're shopping. And the way that we do that is through micro-surveys. I sit there on a page and I go through these long questions. As I learn on the PDP, for example, we ask maybe one question or maybe two quick questions and that'll help us dynamically update the product page to deliver them relevant content that we know that they're interested in because they just told us. So rather than going through a long quiz, at the end of it's, you know, some product recommendations, we try to dynamically update the content on our pages That's relevant for the consumer. Speaker 2: I love that. I would imagine in the quiz, you're probably asking them for quick information like their name, obviously the pain points that they're dealing with, and then when you do get to maybe the landing page, it's like, hey, so and so, here's your recommendation. And I feel like getting a little bit closer to that customer where it's like, Oh, this was created for me. I feel like really can improve that conversion rate, improve that journey. And honestly, like LTV starts at the initial touchpoint, right? It's like, do you have a great experience? Is it tailored to you? Is it personalized? And then obviously, you know, the product works great. But I think LTV initially starts with that first touchpoint. Speaker 1: Yeah, absolutely. I mean, it starts with that first touch point. It starts with trust. Understanding that these products are appropriate for them gives them that trust of knowing that, hey, if I stick with this routine, my goals are going to come to fruition. And that's kind of why having these micro quizzes during the entire shopping journey rather than one long quiz is so important because the pages are updating. If I land on a Symbiotica Vitamin C page, for example, I might be interested in skin health or I might be interested in immunity. And we don't really know what their interests are until they land on that page and until they kind of inform us of what their interests are. And from that point, then we can update this PDP, but as well as other PDPs when they land on knowing what their interests are. Speaker 2: Love that. So with the AI kind of personalizing the different stages of the journey, one thing that I'm interested in understanding is that the messaging That is being used and can be different for everybody. How do you ensure that you're maintaining the integrity of the brand experience while tailoring very unique experiences to everybody? Speaker 1: Yeah, I think it kind of goes back to understanding the customer's goals and interests. Why somebody lands on a product page is so important. And you can also start to collect that information from like a UTM in an ad, for example. So maybe we run two vitamin C ads going back to vitamin C. Maybe we run two vitamin C ads. One of them is more skin health. One of them is more immunity. And we take that information when they click on the ad, and then we store it locally when they land on the page. And if they do give us their email, if they do check out, then we know exactly, you know, kind of what their interests were. And if we're also fortunate enough to collect some information along the shopping journey, then we have this information saved in their database, and then we can retarget them with more personalized content. Speaker 2: So personalization being obviously the keyword here, how are you guys bringing that into maybe the email journey and the SMS journey? Because segmentation is such a massive, you know, element of success for retention marketing. Give us some examples of how you guys are bringing personalization to email. Speaker 1: Yeah. So, you know, Klaviyo has email and SMS that we use. And there are really cool ways of split testing at the times of day when certain customers are more likely to open an email or more likely to click or purchase. And so by just sending like one campaign at one point to everybody, That's not really, I would say, a responsible way of leveraging your outreach. You kind of want to continue to run split tests to know, is this customer more likely to click in the evening or maybe in the morning? Are they more likely to click if it's about an immunity ad or more likely if it's about skin health, for example? And so continuing to run experiments is crucial for The feedback that's required in order to continue that personalization and as the trust continues to build because the customer feels like Symbiotica or the brand is talking to them personally, that's what's going to improve the LTE over time. Speaker 2: And then in terms of like maybe even SMS, is it a very similar strategy to email or is it a little bit different? Speaker 1: I would say it's more or less the same strategy. We try not to leverage SMS too much. There's a lot of fatigue that can come with SMS if you kind of over leverage it. But the same principles apply. You want to know the best time to text a certain customer. It's not a group of customers. It's Jared. What's the best time to text Jared? And that only comes from experimentation. Also messaging and does somebody respond better to plain text or images. So continuing to collect this information is critical. Speaker 2: The team that's obviously focusing on the AI side of things, obviously personalization is a big one. Is there anything around just like data analytics and like maybe even traffic behavior and maybe some of the stuff on maybe the CRO side of things? Are you guys doing anything with AI there? Speaker 1: So yeah, when it comes to CRO, Having this information about specific customers during the shopping journey is, again, really important. Some of it is quantitative data where it's black and white, it's numbers, but a lot of it's qualitative. And the qualitative data is more difficult for humans to process and leverage at scale. When you have 30,000 customers going to your website per day, And they're providing qualitative data feedback, not necessarily quantitative feedback. You need language models in order to process that data and to tag customers within their profiles on what they're looking for and what their interests are. So when it comes to analytics, sure, there's heatmaps, there's CR optimizations, there's split testing, but the really cool opportunity that AI has presented is through the qualitative data that is more, you know, language that people are kind of conveying to us that language models are able to process. Speaker 2: Interesting, okay. Walk us through maybe an example of like the the qualitative you know data that the AI is kind of analyzing and maybe something that you guys had found through the AI that was something that you maybe not have thought of before. Speaker 1: So chatbots are really common these days. I'd say a lot of brands have chatbots. We build ours in-house and ours is trained on our product data. And so when customers talk to this, they're talking to it in natural language. They're using long-form sentences. They're describing what their goals are. They're describing the routines. And so we're able to process that information and then put together these Tags and segments based on certain users that have these similar tags. And that's been really effective for the retargeting. If a customer doesn't convert for the first time, but they're chatting with us, they provided their email, maybe they provided their phone number, we can then retarget based on the conversation, referencing the conversation without You know, like regurgitating the conversation, but just kind of, you know, circling back on it based on what they told us. Speaker 2: More human-like almost. Speaker 1: Yeah, it's all about trust and it's all about making it feel real. Speaker 2: Yeah. I mean, for me, I feel like every day there's like a new tool or a new platform that comes out that's like very AI focused, right? Whether it's a new platform for creating content or videos or, you know, something for customer service or something for finance, right? Has there been anything recently that the team has maybe discovered that was like a wow this is like kind of insane like have you guys had that moment recently with anything new that has come out? Speaker 1: We've been playing around with sort of static ad creation at scale. So there are tools that will do that. Adobe has one that's pretty cool. I think Pencil's another one that's been really cool. We've tested those. I think those can be effective, especially for smaller teams that don't have a ton of resources. And then what we like to really implement are more like attribution tools. Prescient is one of them, and then we also use Segment by Twilio. And so what these tools do is essentially track the customer's journey from the first touchpoint to the point of converting. And even with Segment, we're able to pull in every single touchpoint across the customer's journey, even post-purchase and any type of interaction with us, even with our own databases that we've built in-house. And that's been a really effective tool if you have the in-house resources to leverage that data, which can sometimes be a restraint for some brands. Speaker 2: Yeah, that makes sense. When we moved Obvi to Klaviyo earlier this year, we weren't just switching platforms. We needed a better overall system to manage email, SMS, and real-time customer insights to create long-lasting customer relationships. All under one roof. Unknown Speaker: Since making the change, we've rebuilt our abandoned cart flows, created dynamic segments that tell us who's ready to buy again, and used predictive data to time our offers more effectively. The impact has been higher repeat purchase rates, Better retention metrics and even more control over how we grow our customer value. Retention isn't set it and forget it tactics anymore that used to work. It's a system that needs the right tools and infrastructure. For us and for a lot of top brands out there, we know that's Klaviyo. If you want to turn retention into a growth engine, go to klaviyo.com slash Chew on This to learn more. Now, let's get back to the episode. Speaker 2: You brought up attribution, which I think is in today's world, it's like everybody's talking about, I'm on Meta, I'm on TikTok, I'm on Google, right? Maybe Applovin and Snapchat and then Rocked and things like that. For a brand of your guys' size, obviously you're not just on one channel, right? How are you guys thinking about attribution? Obviously, before the episode, we were talking about, well, you guys have a budget and how to actually deploy that accordingly and understand where to obviously bump budgets or bring things down. How are you guys thinking about that? Speaker 1: Yeah, that's a tough one. I mean, like you said, everybody is concerned about attribution and this is really not a silver bullet. There are tools, like I mentioned. I think Pression is a really common one. Essentially, what they do is they're kind of machine learning that gives you their best judgment on what channels are contributing to your overall acquisition. I think those tools are important, but what I would ultimately recommend is running incrementality tests at least twice a year to understand the The impact of budget and spend across different channels. We recently did one and we found out that a channel had a lot of opportunity to be invested in. And then you go and invest that budget and then you circle back in six months and you see kind of how it was impacted. So there are tools. I would say, you know, I wouldn't rely on the tools solely. You definitely need to run experiments. That'll give you a really strong indicator. Speaker 2: Love that. The test that you guys actually just ran, very curious if you can walk us through that. Because I know brands out there are very focused on like one channel, maybe two max, and incrementality testing is very scary. Because I mean, even for myself, I don't quite fully understand what the right way to carry this out. Carry it out is and obviously working with partners is probably the best way to do it. But very curious what your experience was. What were you guys testing? What was the lift that you were looking for? And just walk us through that process. Speaker 1: Yeah, absolutely. And you're right, I would say if you don't have the resources internally to do it, there's tons of partners out there that can do it. But what we did, and I'll just kind of speak to our experience, is we have such a significant budget through meta, and it's so significant that the other channels, it would take a lot of testing to understand the impact of these other channels. So what we did is we pulled back spend in certain geos on meta altogether. Which might seem scary, you know, to your point, you're pulling back on meta. If you can pull back in these certain channels, you can see the lift or the decrease in sales acquisition in that channel specifically and how much that platform had, you know, on impacting that acquisition. So, for example, let's say you pull back on meta in three cities across the country and you see these three cities and the sales in those cities, if sales plummet, The model is going to indicate, hey, Meta had a pretty big impact on your acquisition in those channels. Now, granted, if you pull back a little bit and sales didn't change much, there's a strong indicator that maybe that you're overspending in those channels. And then also what it will do, if you have enough time and budget, you can start to test other channels as well. And it will give you a certain sort of degree of confidence that you can increase a certain channel spend by X amount and your ROAS or your CAC will be affected by a certain degree as well. So it's a lot easier when you have a large budget because you can do these tests a lot faster. But even for brands that have small budgets, I would definitely recommend it. And it's not across the board in all of your geos. It's a couple small cities. You just see the impact in those cities. Speaker 2: So, I don't know if you can kind of get into the results that you saw from this last test, but what did you see and then what were some of the action items that you guys had as a team to kind of carry out? Speaker 1: Yeah, so it's essentially what the results were. TikTok was a massive opportunity and Meta, we're over-indexed on Meta. Speaker 2: Sounds about right. Speaker 1: Yeah, I mean, it almost seems like a moving target with Meta. So Meta, we're over-allocated. We're under-resourced on TikTok and also Google PMAX, specifically PMAX. And so we've started to sort of, you know, increase budget in those channels. Now, we can't just throw money into them and, you know, hope that it's going to solve everything. We kind of need to gradually roll into it. So far, we have seen efficiencies improve a little bit. Speaker 2: Very interesting. No, that's awesome. Going back to some of the AI stuff, where do you see the growth going? Obviously, you guys have invested early into building out a team. Obviously, the CEO has a vision for this and thinks that this is where everything is heading. What does the next six months, maybe to a year, look like for that specific team? Speaker 1: Yeah, so we like to look at our AI team as an incubator, more like a lab, where they test different hypotheses kind of at small scale. And then when we prove them out, we're going to kind of scale them out and put more budget behind them. One example is that this more of a social feed type of shopping experience where, you know, TikTok is obviously killing it right now, TikTok shop, where people just scrolling and they see something they'll buy. But the Shopify shop app is a good example of this as well, where, I'm not sure if you've used it before, but it's basically, they have these like cards where you just kind of scroll through and they kind of insert like UGC throughout the browsing experience or maybe surveys or testimonials. And it feels more like a natural, you know, social platform type of experience rather than shopping experience. Right now when people show up on Shopify stores, it's more or less all the same. It's very generic. There's collection pages, a grid of products that are just stacked up. You land on a PDP and it's all kind of the same structure. So what we're testing is more of a social feed where Maybe we can load maybe five to ten cards when the user shows up on the page, but as the user engages with the cards, maybe fills out a quick questionnaire, or clicks in and engages with certain products and not with others, that gives us an indicator of what products or what interests they have, and then we'll dynamically load the next cards underneath it so that they're continued to scroll and engage with the brand. That's really going to help with bounce rates, especially. It also helps with collecting data. This is a really small scale at the time and we're not launching this to everybody. We want to prove it out and make sure that's really going to work. We have a strong feeling that that could be the future of shopping digitally. Speaker 2: I love that. Is there anything that you're worried about when it comes to AI? Like, obviously, there's conversations that are like, oh, like, AI is going to take my job. And, you know, I'm nervous about that, which I have my thoughts on, like, I don't think that's probably the case. But I'm very curious, like, obviously, there's a lot of things that are exciting about AI that are coming out. But is there anything that makes you nervous? Speaker 1: I mean, I think there's always opportunity to be nervous when it comes to AI. There's so much unknown. I mean, even for people that are shopping on online stores, AI could get to the point to where it knows what you want and it'll just do the shopping for you. So all of this discussion around human interaction with digital e-commerce brands could be out the door in five years from now, right? Because it might not even be humans that are shopping. But ultimately, I really think that we're going to be able to leverage AI. We're all going to have our own personal AIs that just kind of do stuff for us and we get to enjoy the things that we like to enjoy. So, you know, am I scared about job loss? Potentially, you know, am I concerned about, you know, the way that we interact with the world in general? Of course, but Being, you know, pessimistic isn't gonna help. Speaker 2: Yeah, I agree. For brands that are maybe exploring AI with, like, you know, their current team and, you know, it's like, oh, like, there's a creative tool that's come out, you know, the marketing team will kind of explore it. Do you think building a team around AI specifically is important and how should brands start thinking about that? Speaker 1: Yeah, I don't know if all brands have the resources to put together an in-house team, which we're fortunate enough at Simotica to have that, and also have an aggressive team and leadership and CEO that wants to invest in that. Is it responsible for other brands to do it? I would say probably not, considering the salaries and the resources that go into those types of roles, but it would be irresponsible to not have somebody on the team that's Researching or investigating on all the latest technology and tools that are out there on the market. The build versus buy conversation should be had across every organization. If you have the resources to build, I would build if the tool doesn't exist. But with how quickly things are moving, we could be researching something, we could be testing something that might be a product that we could just buy in a year from now. What I would recommend is having at least one person on your team that is staying on the front lines of the new tools. That way you're not left behind when it actually works. Because a lot of them don't work right now. A lot of them definitely don't work. People get ads and people see posts and videos that are clickbait and they get you to over-index on what the opportunity is. When you actually demo with these products or implement them, you realize that they're not what they're advertising. But you need to have somebody that at least knows what's real and what's not. Speaker 2: What are you using every day for work, whether it's just helping you with tasks and things like that? What's your go-to AI stack? Speaker 1: You know, a lot of what I do these days is just conversations and meetings and talking to people. So, you know, there's note takers and stuff that you should obviously use on any meeting that you're in, especially if it's an online meeting that takes notes. And then you just save that in your own... We can either have a drive for it or however you want to save it. You can always reference these conversations. You can ask Chat, hey, this was my conversation with this team two weeks ago. I have a meeting with them coming up next week. Kind of remind me of the conversation and remind me of things I need to follow up on. And then there's obviously tools like ChatGPT that I use all the time. Deep research is critical, especially for marketing and sort of new opportunities. It's essentially, you know, somebody on your team that would spend a week researching something that could, you know, take 20 minutes. And then we have the in-house stuff that we've built. But, you know, at the end of the day, language models are really effective and you just gotta get involved to sort of see how it works for your workflow. Speaker 2: Love that. Awesome. So, would love to get into almost like a rapid fire. I have a few questions for you and then we'll go from there. Cool? Speaker 1: Sounds good. Speaker 2: Awesome. What's a mistake or failure early in your career that taught you a valuable lesson you still carry with you? Speaker 1: So early in my career, I definitely went after salary over interest, I would say, in my career, which I think led me on a path of not really loving what I did. And when you don't love what you do, I think ultimately that leads to you not being super engaged with it and not really want to I lean into some of the interests that you might have in the role. And when I had this realization, I actually took a step back. I took a few months off. I was kind of getting burnt out. And I kind of went back to the drawing board on what I love to do and what I'm really interested in. And that led me to some opportunities now to where work is work, but it doesn't feel like work. And anytime you want to sort of improve, I do it out of interest rather than out of necessity. And I think ultimately that's a much longer term holistic view on career growth. Go after what interests you and I think that'll pay off over time. Speaker 2: I love that. I definitely love that. Has there been a moment where listening to customer feedback completely changed your team's direction or priorities? Speaker 1: Absolutely. Yeah, so we built a rewards program in 2023 that was based on certain incentives as most rewards programs should be. And we made a lot of assumptions about what those incentives would be. We kind of noticed that some were really effective and some like weren't moving the needle at all. And it wasn't until we actually started talking to customers to understand what they would like as some sort of reward that helped us sort of rethink our rewards program to make sure to offer the right incentives to drive the right behavior that we're looking for. And it really wasn't until we started talking to customers that we understood that. For example, A lot of rewards program will offer like points for doing something, right? You order, you get some points. Maybe you add an email address, you get some points. And the point system is very effective. We had these other offerings, which were these tokens that could sort of Add some like brand equity and sort of be exchanged for certain rewards. And these tokens, for whatever reason, weren't effective. What was most effective were discounts. And so once we sort of listened to customers and once we adjusted our incentive structure, our customers started to respond to those incentives a lot better. And it allowed us to sort of pivot our rewards program. Speaker 2: I love that. That's awesome. How do you personally stay curious and continue learning in such a fast evolving industry? Speaker 1: I mean, I think that goes back to their question earlier about something that I learned early in my career, which was go after what you're interested in and it doesn't really feel like work. And so, you know, I listen to podcasts all the time. I try to go to events, you know, I try to network. There's a lot of online resources out there that you can learn essentially anything. I mean, you know, the four-year college degree I think is maybe something of the past and, you know, learning on your own is something that a lot of people should, you know, have the power to do. So whether it's online courses, you know, podcasts, networking, those are all really effective ways to stay on top. Speaker 2: What's your favorite podcast besides Chew on This, obviously? Speaker 1: I was going to say Chew on This is on repeat. All-in podcast is a really good one. I'm not sure if you're familiar with that, but it's essentially, you know, finance, technology, you know, ecom. A lot of AI as well. Speaker 2: Yeah, I love that. What keeps you motivated on the toughest days? Speaker 1: What keeps me motivated is my family. I had a kid nine months ago. Thanks, man. And, you know, early in my career and as kind of a young adult, I had this drive to like, you know, get a good job and make sure to have a good career. And I never really understood that drive. It's like, you know, why do I care so much? Why am I working so hard? Why am I busting my butt? And it wasn't until I had a kid that it really made sense. And now, because I love what I do and because I have a family to work for, that's what keeps me motivated. Speaker 2: I love that. How do you celebrate wins with your team, especially after overcoming big obstacles? Speaker 1: Yeah, we do a really good job of celebrating at Symbiotica. Our team throws amazing events. We really emphasize relationships and spending time with our colleagues and making sure to appreciate what we have. You know, we go to Padres games a lot. We have team builders. There's actually one tomorrow that our team's throwing. And I think it's just bringing everybody together at some sort of event, making sure to have a couple leaders maybe give a toast and kind of talk about the accomplishment, and making sure to really recognize everybody's contribution because it's not one person that accomplishes goals, it's the whole team. Speaker 2: I love that. I do love that. I do think, and I'm pro making sure that you reward when you can. And even just like that, that job well done pat on the back really does bring the team closer together. Because I mean there are times where it's like we're also like super heads down and we're like trying to get something done and the team is just like... Doing what they need to do and they're crushing it and then sometimes you forget like, you know, they want that acknowledgement and it really does make a difference. Jared, this has been amazing. Obviously, you know, AI has just been on everybody's minds and how to think about it. Thank you for giving your perspective on it, especially coming from a brand that actually is building out a team specifically for this, which is very impressive. But before I let you go, Would love one final takeaway for brand owners that are listening or watching this to take back and implement in their business. What would that one thing be? Speaker 1: I would say get involved. Make sure to research all the opportunities that are out there because there are a lot. And don't take it for granted. Don't take it as a fad. AI is real. It's here to stay and it's adding tremendous value to brands and companies all over the world. Speaker 2: Yeah, love that. Chew on that. If you want more from us, follow us on Twitter, follow us on Instagram, follow us on TikTok, and check out the website ChewOnThis.io.