Shelley E. Kohan (00:03)
Hi everybody. Thanks for joining our weekly podcast. I’m Shelley Cohan and I’m excited to welcome Tim Shea to Retail Unwrapped. ⁓ welcome Tim.

Tim Shea (00:13)
Shelley, thanks so much for having me. I appreciate it.

Shelley E. Kohan (00:15)
It’s a real privilege to have you here. So, one of the biggest challenges that we have in our industry is a lot of retailers and brands are trying to figure out the complexities of our new AI kind of generated retail landscape. And you are an expert and have spent, I don’t know, two, two and a half decades t untangling the messiest data for some of the biggest companies like Pepsi, Nissan, Reddit, the NBA, UFC, LinkedIn, Facebook. I could go on and on and on.

⁓ so you have lots of experience to share with us. You’re also the founder and CEO of Lattice Warp Insights and you do this for a living. You help brands untangle this data infrastructure problem. So and I I just want to add one more thing, Tim, because I find it super interesting. You’re a serial entrepreneur and you’ve raised over a million dollars. So super impressive and I’m so happy to have you here.

Tim Shea (01:09)
Really excited to get into it.

Shelley E. Kohan (01:10)
Awesome. So ⁓ kind of let’s start at the so the biggest problem right now is there’s a lot of data out there and it a lot of it might be ⁓ unfragmented data. There’s a lot of issues with scaling AI initiatives and all that. So let’s start maybe with the current state of data in retail.

Tim Shea (01:29)
Sure, sure, absolutely. So look, ⁓ fundamentally Latticework is in the business of helping ⁓

Retail brands become elite retail brands, right? We use data and technology to get them from point A to point B. We help them see their business more clearly. ⁓ we help them see their business more clearly so that they can graduate to their next stage of growth. ⁓ you know, I I always wanna be ⁓ mindful when I’m describing the company. We talk about you know, crossing the blood brain barrier, right? When we when we describe ⁓ other otherwise nerdy topics like data and technology. ⁓ it really is a big deal.

When companies can figure out how to use data to their advantage. It is often seen as a cost center. We talk a lot about what you would know in marketing as ROAS. We like to refer to it as return on analytics spend. There’s a real sense when analytics is done right, that there is a multiplier on it, that companies spend a dollar on analytics and they get ten dollars back from it. It needs to be done the right way, because a lot

of al analytics has presented as just, you know, numbers on dashboards. ⁓ and you can see how that can easily be framed as a cost center or a boring or mundane, you know, piece of work. but elite brands that do this right see ⁓ wild, you know, spikes in their growth, big inflection points in their growth. but it’s hard work. it’s not just the data. ⁓

It’s the culture in the company. It’s the way they can align all of their stakeholders. ⁓ It’s a real ⁓ change in their DNA as a company. ⁓ so it’s super exciting, but it’s very hard to do. Yeah, as you mentioned, data is fragmented in a million different places. And if you read the newspaper, ⁓ AI is going to just upend everything and maybe make it really easy, or maybe replace human jobs. but there couldn’t be more misinformation out there at any any time in human history ⁓ as it is now. So it’s very confusing.

for folks to think about, well what should we be prioritizing with all this stuff on our plates?

Shelley E. Kohan (03:34)
Yeah, it’s a lot. And I want to go back to something you said about ⁓ dashboards because I know that there’s a lot of solutions out there and everyone has their own dashboard, but now you have a thousand dashboards. So what’s this over reliance on dashboards and how can you make that actual something that retailers or brands can use?

Tim Shea (03:54)
Yeah, yeah. So so I have a framework that I like to take everybody through. ⁓ it is called Smart C. so we focus not just on like getting data into a warehouse and putting a dashboard on top of it. ⁓ we focus on the Smart C framework. So Smart C is ⁓ speed, margins, attribution, retention, and culture.

So what that means is the first thing I’ll do with a retail and D to C brand is I’ll look at the the speed ⁓ that they transact with customers. E commerce brands it’s a well known phenomenon that eighty percent of e commerce customers never come back.

They they

Shelley E. Kohan (04:32)
What eighty percent?

Tim Shea (04:34)
eighty percent and and it’s this is why across all companies. Many companies ⁓ come to us and they say, gosh, this is terrible news. Seventy five percent of our customers never come back and we’ll be like, This is you’re wow you’re way ahead of the game. You’re better than ⁓ most retailers that are out there. But it is true that consumers are ⁓ both very distracted, they’re very overwhelmed by messaging from many different places, and also a lot of customers don’t like your product, right? And so there’s a big ⁓

Acceptance maybe that that 20% of users that stick around need to pay for the 80% of users that churned out. And you pay you paid for marketing to get these 80% of users through the door. ⁓ and the 20% needs to make up for them. So speed and frequency of purchases is a huge thing and it varies. Like some customers buy once a year when you give them a 50% off coupon at Black Friday. Some of them buy once a month. And so it’s super important if people understand the speed.

which people transact. They need to understand their margins. Go ahead, go ahead. Please do. Please do.

Shelley E. Kohan (05:34)
Can can I ask a question about speed for a second? I’m gonna jump in here. So one

of the one of the things that’s really impacting a lot of retailers is agentic commerce. So you talk about the consumers that are going to the websites, but sometimes now that’s even bypassed. So people are going to the agentic commerce and saying, you know, find me the best palm dress that you can find, and then they’re purchasing it. They’re bypassing the e-comm interaction altogether.

Tim Shea (06:01)
Totally.

I look, I always love examples where AI is is real, where there’s actually a real impact. I he I actually hear companies saying something, hey, this has affected us in this way. I think before we they even get to agenda commerce, there’s this issue of discovery, which is that like people search on Google now and the top 80% of the page is just like AI results and then sponsored posts and like you used to talk about like being above the fold and the search results, but you’ll be lucky to end up on the second or third page now because Gemini is answering all the questions.

⁓ I did see something recently where Google announced at their latest conference that ⁓ you can tell it what you are looking for. You got some big gardening project or home improvement project, say hey, I need the following things. And it will go out and fill up your cart.

Right? So it’s sort of pre-loading your cart and it’s making decisions as to where it goes for those goods and services. And that’s a very scary thing for retailers because they used to feel some level of control over how people added things to carts. And now there are these agents in this in the middle who are unpredictable and they don’t can’t really have a conversation with them and tell them, you know, put my product in there and and not someone else’s. So it’s a big wild world that and we are just beginning to understand the concept.

Yeah.

Shelley E. Kohan (07:20)
That’s amazing. Okay, so go to your next ⁓ after.

Tim Shea (07:22)
yeah, yeah, look. So ⁓ people spent a lot of money on marketing. People sa spent a lot of money ⁓ making goods and so margin, right? Margin is the second, you know, thing in our acronym, right? So people need to be very focused on like how much do they spend on CAC.

how much are they spending on cogs, what does the walk look like between their gross and their net revenue. ⁓ and these are non-trivial, right? Because I think again like at big tenpole sales events, people are spending lots of money on discounts and they pat themselves on the back when they have a big, you know, gross revenue. They say, my God, like what we did for Black Friday, I’m like, you just cannibalized October and December and ⁓ and all these people they’re they’re not coming back until next year. They’re waiting for you to give another coupon.

So margins are important, attribution ⁓ is important, ⁓ retention, the sort of customer journey, right? So like are are people when they buy a second thing, what is it that they’re buying? Do they buy a t shirt, do they buy a candle, are they buying a you know

a a Celsius fizzy water. so it’s very important for folks to understand like what are they getting to on the second purchase. It is a it is both an opportunity to sell them more stuff and it’s an opportunity for the customer to have another aha moment to discover, Shelley, what your brand is about, right? You have this great brand story, but customers may not know, right? They’ve only bought one thing. So it gives them a chance to, you know, have a journey with you. ⁓ and then the big one I think this is the the the part of analytics that everybody forgets is is culture.

I want to talk about culture with everyone who’s willing to listen. It’s it’s the and I think maybe this is your dashboard question is everyone says, okay, listen, we’re willing to invest in a tableau dashboard, and you’re like, can everyone agree what metrics matter for the company? Right? Like, I think we can agree that like total revenue is important, but like

Shelley E. Kohan (08:54)
⁓ I wanna talk about culture.

Tim Shea (09:17)
Can everyone agree that we need to shorten the time between two purchases? Can everyone agree that we’re actually trying to increase total lifetime value of our customers or reduce the CAC when we’re spending too much money on Meta? And I I can’t tell you how many times we get into a meeting where we pull up the dashboard on screen and instead of talking about action items, everyone says, wait, I thought we were gonna talk about net revenue this time and and the discounting rate and

And so the the a aligning everyone around, you know, today is Tuesday is like maybe the biggest elephant in in the room. ⁓ and so getting, you know, trust in the data, getting stakeholders aligned so that they can A B test and so that they can iterate and see, you know, grow the company effectively is one of the hardest things in analytics.

Shelley E. Kohan (10:03)
Yeah, and I think the other thing is so you’re describing kind of what I see like the C suite or a couple levels below the C suite and the stakeholders, the boards, all of that. But I think

To really get this kind of culture of data, you really have to bring it all the way down to the associate level. So we look at examples like I hate to keep using it, but I will, you know, Google, that the whole company is very focused on innovation and you know data, and that that’s their whole business model. But it runs from the top all the way to the associate who’s trying to figure out creative ways to use data and all of that. So how do you go from the top level, which you’ve already said is difficult to even

getting it all the way down to the ⁓ lowest part of the organization. I don’t mean lowest, but you know, the entry level people.

Tim Shea (10:51)
Yeah. you know, it’s interesting, for old folks like me, that have been in the game for a while, it interesting ⁓ the difference in questions people have asked me at different points in my career. And nowadays people more and more are asking for leadership.

Right, they want the dashboard, they want the warehouse, they want the predictive model, but really they want leadership. And in many cases they’ve got a whole team of excellent experts. They’ve got data engineers and warehouse people and programmers and statisticians. ⁓ but they’re like no one can agree on, you know, what what to what today’s data is. ⁓ no one can can can talk to marketing in a way that marketing understands. ⁓ no one in marketing really knows when to go to the data team.

⁓ and then yeah, we get all these like young, hungry people with like lots of smart knowledge, but we need to align them around a common way of speaking about, you know, how to make an impact in the company. ⁓ so there’s a lot of talk about upskilling, even before AI kind of hit the world, right? Everyone was like, Hey, how do we get people to use data intelligently? And now everyone’s like, Well, what specifically should we be doing with AI? Like should we be using AI just to generate code?

should we be using AI to ask and answer questions about the company? ⁓ should we just go all in and just ⁓ embrace the agentic future ⁓ and and know that these, you know, the ⁓ artificial general intelligence has arrived and it will lead us into the future? ⁓ or should we acknowledge that it’s going to take a really long time to figure out what these processes are? and so that’s a question I’ve been asked a lot, is like how do we upskill our teams? How do we get people on the same page?

How do we get all the twenty two year olds and all the fifty two year olds speaking the same language, so that the company can succeed?

Shelley E. Kohan (12:35)
So Tim, let me ask you a question. So right now there’s four to five generations in the workforce today.

It’s we have some of the older, you know, generation that are still in the workforce in past decades that might have been retired, and you have the young ones coming in. So you have a vast amount of ⁓ generations in there. So it is reverse mentoring something that might be beneficial for companies. So you have the young ⁓ people coming in that you know just came from college that are very kind of up on new technologies. They lived it, they’re the digital generation, they’ve grown up on it, and you have baby.

a baby boomer ⁓ working if you do it in reverse mentoring is that something that retailers and brands should be thinking about

Tim Shea (13:18)
God, it’s it’s I think this is actually a phenomenal question. Look, I think I I got this talk track around like domain expertise is the new oil, right? Like everyone’s data is the new oil or computers the new oil. Actually people with a lot of experience i are the new oil, but like it’s not just people that are forty, fifty, sixty years old that have a lot of knowledge.

And a lot of domain expertise. ⁓ I I was working with this ⁓ this younger girl, she’s at an ad agency, and we were really trying really hard to get the analytics squared away. The dashboard needs to be polished and the numbers need to be correct. And we’re like, is the content working? Is the content working? Let’s look at the numbers. And she goes, listen, anytime at anytime anyone asks me some esoteric question, she goes, I spin up a burner account on TikTok.

Like, this is this is about this about home living, right? And she she builds a a home living feed on on TikTok and then she brings that into the meeting and she goes, Look at look at what’s trending in the in the home furnishing world. And you’re like, ⁓ what are we doing looking at the you know, the cost per thousand impressions when we could be looking at what’s trending in this particular domain? And that’s a form of new knowledge that a lot of folks that have been, you know, in institutionalized, they’ve been in the building too long, ⁓ are missing out on. ⁓

So yeah, I think it’s important to listen to young folks, who are trying to come up in the game and have an interesting point of view. ⁓ important for young folks to understand like people that have been doing this for thirty, forty years have a very kind of shrewd way of seeing the world and they sh they should, you know, understand what’s going on at the top as well.

Shelley E. Kohan (14:52)
No, I think that’s great. the other thing I want to circle back to is the R O A R O A S model. And ⁓ can you just talk a little bit about ⁓ what you see in that and also how can you tie these data investments into revenue outcomes?

Tim Shea (15:10)
Yeah, a hundred percent. So look, ⁓ I’m out there selling just like every other, you know, founder is. people ask me all the time, like, hey, what am I gonna get? ⁓ Tim, I pay your fee, like what am I gonna get out of this? And we’re like, Listen, you should get like double what you pay me. You should get maybe ten X what you pay me. When you are interviewing a new ad agency, right, you’re asking them, you know, it’s a performance marketing agency, what am I gonna get out of my marketing?

I’m gonna pay you a you’re gonna have a million of my dollars to spend every year. There’s an expectation I’m gonna get one point one million dollars out of that investment. And it’s con it’s ⁓ it’s confusing why those questions are not asked of technologists.

Right. Maybe technologists have not been trained to speak that language. maybe folks have not been thinking about technology in quite that way. And so I I just like to bring a lot of ⁓ you know, case studies and examples to the table. And people say, Well, what do you mean specifically? I’m like, Well, this one company found that their churn rate was about, you know, eighty percent. And ⁓ but but they also had three types of users that are premium, mid tier, and kind of you know, low at low end

consumers and they found if they could get like just if they could reduce churn for the premium users by just one percent if they could get do a second purchase, just get them to buy a candle, get to buy a a t-shirt, that one percent reduction in churn would pay for all of their cack for the last quarter. And so guys, can can we can we actually look at what that number actually means? Can we look at how much money you would make

Never mind save how much money you would make if we could just introduce this incremental improvement in your process. And that is what the analytics is gonna do. It’s going to give you a new lens on the business, but it’s going to give you strategic initiatives that are tried and true. You know, me personally, I’ve been doing this for twenty five years. I’ve seen retail and D to C companies in every vertical. ⁓ and there’s lots of, you know, domain knowledge we can bring to the table that shows people how they can

unlock all this growth. Maybe the last thing I’ll say to this too is like I think a lot of these super smart retail founders see things in terms of a stalemate and they say, ⁓ gosh, our profit margin is X and that’s what it is. And our cost per acquisition is Y and we’ve never been able to make a change. And so we come in and we really try and like look at the company, like we try to turn it on its side and look at it through a slightly different lens. And that can unlock

whole new chapter in their growth and they say, gosh, I would pay, you know, an infinite amount of dollars for analytics if all of those analytical dollars could turn into, you know, two X, three X, four X of our investment. so really try and like look at it as like a you know a growth driver as as opposed to a cost center.

Shelley E. Kohan (17:58)
That sounds very smart and ⁓ it’s interesting that 1% example. Like, I think I think a lot of retailers are trying to solve the 30%, you know, example, and maybe losing focus on what one small incremental change could really just kind of totally help the business. ⁓ my my other question is so you work with a lot of retailers and brands. So, where is IA ⁓ actually AI actually delivering value in retail? Like, where are you seeing it actually delivering value?

value for retailers or brands.

Tim Shea (18:32)
Yeah, so ⁓ this is this is the question, right? I think ⁓ there’s been a trillion dollar investment in AI over the last five years, and they’re gonna need to make at least a trillion dollars back to make it worth their while, right? So there’s where where’s the where’s the alpha in it? you know, I’m a software engineer, I’m very skeptical of ⁓ AI generated code. Skeptical insofar as I will at least read the code, I will at least test the code to make sure it works. But once you have generated and t read and tested the code.

That code is now deterministic. That code is gonna do the same thing every single time. It is not going to hallucinate. And so I think AI-generated code is a huge frontier for a lot of these retailers where they say, gosh, if I if I can never look at another Power BI dashboard for the rest of my life, like please, I never want to create another looker dashboard. I never want to deliver a looker dashboard to my stakeholders. Is there any way I could just introduce like a breathtaking HTML dashboard that just looks exactly

Exactly how I want it. And the answer is yes, they can do that. And they can pull data out of their warehouse and they can present it in just such a way, in an agreed-upon way, that everyone is trusted, and I can deliver it to ⁓ the C-suite, to the board, to our influencer partners, to the marketing team.

And so I can create four different versions of my dashboard that are designed in totally different ways. And I don’t have to worry about Claude hallucinating in the middle. And so I think AI generated code is a huge component. And then not to discount

AI really allows us to explore like this infinite compute space of possibilities. And so you can a you can really push these tools really hard and be like, we’re trying to solve, we’re trying to move our business from a ⁓ one-off e-commerce business, people buy one thing at a time, to a subscription business.

And so we’re it was really valuable to us if we could get people on subscription. Maybe we should give them should we give them a dollar off? Should we give them a hundred dollars off? Like how much money should we give them off to transition them from bucket A to bucket B? Hey AI, can you give me thirty ideas that we could try?

And it and it’ll do it. And maybe twenty five of them are junk, but you know, there’s some gold in what it can give you back and that ability to explore all this new territory that, you know, maybe you got your team doesn’t have the bandwidth for is now possible. And so I think those two frontiers are are really exciting for for retail folks.

Shelley E. Kohan (21:04)
It is exciting. And you have an extensive background across industry. So what can retailers specifically learn from either the tech companies, the media companies, you know, other platforms?

Tim Shea (21:16)
Sure, sure. you know, I think one thing that the tech companies, let’s say like, you know, Instagram and TikTok have the advantage of is scale. They’re digital products. ⁓ they can A B test, they can have a thousand A B tests running in parallel. It’s kind of wild. You know, you you see that phenomenon, like two people are on Instagram and you’re like, Wait, I’m seeing how come yours looks different than mine? And they’re like, they’re A B testing stuff to you. ⁓ for retailers, I think of ⁓ A B testing as a big litmus test. Like

If you’re doing it, you have become an elite brand, right? If you’re not yet doing it, like you got some work to do to get to the point where you can A B test. Now they don’t have the luxury of having 10 million brick and mortar stores. They’ve only got maybe 50 brick and mortar stores. And so the way that they A B test has to be slightly different. but I think like once they’ve gotten to that point, this is the whole, this is the sort of like the big panacea, the big nirvana for a lot of these companies is getting to a

point where we say listen Shelley like I really really think if we use this creative for this particular audience to do to achieve this particular goal it’s gonna work and if I’m wrong no big deal we’ll move on to the next test but let’s actually put that A B test into production ⁓ and see what we see what we learn ⁓ so that’s like a big learning where you know the Airbnb’s

TikToks and you know Facebooks of the world have become the you know imprimators of this, like you A-B testing methodology. ⁓ and retailers can learn a lot from

Shelley E. Kohan (22:45)
That’s amazing. And of course I come from retail for many years. We won’t say how many, but it’s a lot. And A B testing used to take, you know, six to eight weeks. and that that was before, you know, that’s recently in the last, you know, ten ten, fifteen years. But okay, so what should lead retail leaders be prioritizing in the next year, two years? I know two years seems like a long way away, but where do you see the biggest opportunity?

Tim Shea (23:10)
Yeah, I mean ⁓ I think i i it’s super important that look I think in in all of these tech upheavals that you and I have both seen, right? You know, like when there’s all of a sudden there’s dot com and then there’s mobile and then there’s social and then there’s big data. ⁓ now we have AI. In every one of these upheavals, there’s a bunch of problems that weren’t solved in the previous era. People are like, this news thing is gonna solve all these old problems.

And so I always think like we’re entering into this agentic era and it’s super important to have the old problems solved. And so people like every single retail company on this earth has their data stuck in 20, 30, 40 different platforms, right? You you gotta solve that problem first. And so I think you had mentioned earlier there’s like now they’ve got 20, 30 different dashboards, or now they’re doing they’re downloading 20 or 30 different spreadsheets and trying to jam them all together.

And then someone says, what is our LTV CAC ratio? And and then someone says, well well do we mean LTV minus COGs? Or which which which things are we taking out of that equation? You gotta have that stuff solved as table stakes.

they say, well what what should our AI strategy be for this, you know, impending agentic future? You gotta have that figured out. Like how much are you going to be able to lean into AI? And how much of the hype and and the nonsense that’s coming out of Silicon Valley should you just like put in the recycling bin for now? Just as just like i i irrelevant chatter ⁓ about what could ⁓ happen in the future.

Are you going to be doing age AI generated code? Are you going to be gener doing A AI generated analytics? And how specifically? And a and again, I think the biggest one is the culture. It’s like, how do we get this team of twenty, one hundred, a thousand people all agreeing today is Tuesday and we’re trying to increase LTV and decrease CAC?

Like c can we all agree these are the things that we are working on? And if you don’t have those three things figured out, my gosh, it’s gonna get really, really hard for folks as like the amount of competitors sort of proliferates, as the speed, you know, increases. ⁓ companies are gonna need to get really locked in, ⁓ over the over the next let’s go everyone loves eighteen months. For the next eighteen months we have to get like really locked in. So that’d be my advice for any of these retail and D to C folks.

Shelley E. Kohan (25:35)
And what’s interesting, Tim, the way you said it is you have to get the data sorted out back a house, you have to agree on priority prioritization, you have to figure out where you’re headed, and you have to do all that before you do the ultimate hardest task, which is building the culture.

Tim Shea (25:51)
That’s right. That’s right. Super hard.

Shelley E. Kohan (25:54)
Yeah. Okay, so before we leave, I just want to mention one other kind of fun fact about you that I did not mention earlier, but I find it super interesting and I hope I get invited. But you are the founder of Agentic LA. So you had a big agentic LA in LA and then you came back and you did one in New York in May. And I think you’re gonna do another one. So tell us a little bit about that.

Tim Shea (26:14)
Yeah, so I I I appreciate the question. in fact it’s so it’s been quite successful. In fact, so successful we changed the name. So big reveal, we changed it to Agentic X. because we’ve now done it in LA. ⁓ we did three events in Los Angeles. We did ⁓ three events in San Francisco, and we just did our first event in New York City about a month ago. look, I I think it’s a super interesting phenomenon as we get into this new era, right?

Everyone’s tired of being being stuck at home during COVID. And I think a lot of people are just dying to be in person, to hang out with other folks, but also to kind of like cut through the BS and just be like, hey Shelley, like what are you seeing on the ground floor? Like what’s actually going on? What’s what’s hype and what’s reality? So I did it at my first AgenticX event in LA about a year ago. ⁓ was pretty successful.

And as soon as the event wrapped, I had a bunch of people reaching out being like, dude, I would love to get on stage. How do I get my company involved? I want to go to the next one. Can you can you come to Cincinnati? Can you do one out in Cincinnati? And ⁓ so yeah, so we I like to get really smart thought leaders on stage. I like to get really smart audiences to ask them really smart questions. ⁓ I r record it all and put it out on social media ⁓ and generate a lot of dialogue with folks about like, you know, let’s build this community ⁓ of

folks who can kind of cut through the BS and and help each other out. ⁓ so yeah we got another one coming up in LA in a couple months ⁓ and the response in New York was like really awesome. It was really exciting. So we’ll be doing another retail analytics agentic X event in New York probably in the fall. And Shelley you’ll be a a VIP guest of honor at the next one.

Shelley E. Kohan (27:56)
That’s great.

Yeah.

That’d be great if you can invite some of the students from Fashion Institute of Technology where I teach. Get some students there. Yeah.

Tim Shea (28:05)
That’s a that’s a great idea. That’s a phenomenal idea. Yeah, look, I love that young

and energy, young, hungry entrepreneurs. ⁓ and yeah, that’d be a great idea.

Shelley E. Kohan (28:14)
And there’s some big companies there, like AWS was there, McKinsey was there, IBM was there, Snowflake, Meta.

Tim Shea (28:19)
We had Snowflake, we had Meta, we just had this great company

called Theta. ⁓ they’re like the the wrote the literal book on predicting customer lifetime value. so yeah, a lot of McKinsey folks, a lot of BlackRock folks, Citibank, a lot of finance, fintech, a lot of retail. ⁓ so it’s a super, super fun community to be part of and to be building in real time.

Shelley E. Kohan (28:42)
That’s great. Well, Tim, thank you so much for being here. I’m sure our audience learned a lot today, so I really appreciate your time.

Tim Shea (28:48)
Shelley, thank you so much. I really appreciate you having me on the podcast.