EP269: Is AI Making Us Dumber? Tim Shea Thinks So

This is High Voltage Business Builders. Today, Tim Shea, founder and CEO of Latticework Insights, on the data mistakes costing retail brands millions and how to fix them.

Hey guys, welcome back. Got a fun guest here today. I enjoyed our conversation, uh, when we did our pre-call, and so I asked him to come on here and, and do a real call with me. So, Tim, hope you're having a great time today wherever you're hanging out at.

Having a great time. I'm out here in— it's not so sunny in Los Angeles. It's usually a bit sunnier in April, but having a great day. Neil, thanks so much for having me on.

Yeah, appreciate it. Well, I wasn't sure if you were traveling or if you're still back in California today. Doing much traveling where you are?

You know, I do a lot of traveling to San Francisco and New York. I got a couple clients and a couple activities out there. Mostly just keeping it, you know, the COVID routine has sort of stuck and everyone's doing Zoom still. And, you remote work is the default, but getting on the road and in person with people as much as possible.

Yeah, that's awesome. Do you have any particular places you like to go, like travel or on business or any particulars?

I've got some fun travel coming up. I'm going to a wedding in London, just outside of London, which is super exciting. We're taking the kids out. I'll tell you, I grew up in Boston. I've been in LA for 25 years. I love going to New York. And depending on the person you're talking to, yeah, bring opinions on New York City. But yes, I love it.

No, I love traveling New York as well. In fact, I almost moved there in the past a long time ago. Lived in Manhattan for about 8 months and then almost bought a brownstone on the Upper East Side. I love Manhattan. Yeah. Old story, flew back to the Midwest, met a girl. There you go. So New York was no longer on the radar, but I always enjoyed it. Enjoy the energy and the fun. Is there particular things you like to go to? do, or go like to Broadway, or the particular areas you like to see?

Or I love the West Village and SoHo. If I could have a second place, uh, I would have to make a substantial amount more money, uh, to get the place out there.

Yeah.

Um, but I love, love it out there. And then maybe side note, a good friend and I have a new tradition where we go and see like an SEC football game, like, you know, during college football season.

Fun.

Both of us went to really small colleges, so we didn't get that experience. So we've been to like Baton Rouge and we've been to Athens, Georgia, and Alabama's on the— and Arkansas are both on the radar and Nebraska, Kentucky. We have a big list of bucket list spots to go check out a big game.

Fun. Yeah, Baton Rouge is not quite the same as New York, but definitely on the— I remember a trip down there not long ago and it was the first time I've ever seen water hit the sidewalk and just evaporate into more humidity. That was quite fun. I was like, wow, this is not a place for me. I need somewhere a little more moderate temperature-wise, et cetera. But California is very different, obviously, out there. And you've spent 25 years there, so I'm assuming you love it and enjoy it and have a good time there. Yeah.

Yeah.

Yeah. Well, one of the things you do like geeking out on is data science, from what we talked about earlier. And what does that mean exactly in today's world with the changing AI and all the systems and things that are happening? What does that actually look like?

You know, I've gotten that question in many forms recently. I, and I'm not happy about it. I will say my parents are one of the people, my wife is among them, who will ask, gosh, I don't really understand fully what you do for a living, right? As the main salesperson in my company, it's like very frustrating feedback because you want people to understand fundamentally what you do.

Right. Absolutely.

And we're just on some calls even as early as today. Where we think we understand our business really, really well. We do understand our business really well. And so we've got like a really crisp elevator pitch that describes it. But there's so much unpacking that needs to be done for someone that is not an expert in our field. And I think a part of the game of being a founder and a salesperson is being able to explain it in really layperson terms. So like data science is a thing that maybe seems kind of scary and maybe seems kind of like I would wear like a Like a lab coat to work and like a little microscope at my desk. But fundamentally, you know, we're just trying to like use data to help people see their business more clearly. You know, oftentimes maybe in a way that they have never seen the business before, but to be able to provide someone a new lens on their business where they maybe think that it is a stalemate. You know, they're running some successful e-commerce business and they think that You know, their profit margin is a stalemate, or their LTV is a stalemate. And we can come in and say, well, if you turn the problem kind of like on its side a little bit, there's actually a whole way we might be able to blow our revenue targets out of the water. But it does require a little bit of that, you know, seeing things in a different way. People, I never heard data science described in that way. I'm like, well, that's part of what the selling job is, is describing it in a new way.

Yeah. My partner often says that the numbers tell the tale. And as you get into the numbers, it explains the business and the model of basically what you're doing. And at the end of the day, profit and loss is the game. When people ask me similar, what do I do? I kind of just go, I'm in e-commerce. And like, what kind of e-commerce? It's like, oh boy, here we go. Where do I go with this? Usually I try to just dummy it down to I sell physical products. Like pizza makers. Like, oh, you sell pizza makers? That's really cool. Yeah, I just try to find something that's common. I sell an ice cream machine. Really? I love ice cream.

Like, yeah, that's kind of what I do.

Uh, it's really simplified. Um, never that easy to explain it, um, in the e-commerce world, especially entrepreneurial world. I mean, I've been at it for 19, 20 years. How about you? How long have you been at it?

Um, yeah, yeah, I graduated from college in '99, moved out to LA just afterwards. So I've been in the game for 25, 26 years. I've been in E-commerce, advertising, technology, entrepreneurship for a really long time.

Yeah. Long enough to know that the game is always about pivoting, right? And agility and changing times. How does that— I mean, DTC wasn't really a thing 25 years ago. People just called it direct marketing. They didn't really understand what it meant to be direct-to-consumer per se as much. Because we didn't have the systems and ecosystems we have nowadays with social media and other things where you can get into millions of customers very fast for those opportunities. What has been some of the major pivots you've seen in that 25 years?

I mean, it's funny thinking through the lens of AI now. Everyone says, oh my God, this is the biggest innovation, the biggest disruption that we've seen in, you know, in maybe all of our adult lives. But it's interesting to have been through a bunch of these waves before dot-com was a thing. time before everyone having like a website.

Yep.

You know, social and mobile was a huge revelation. We imagine like ordering things on your phone or watching content on your phone was unheard of for many years. And I always love the big data track. No one uses this term anymore, but there was a time where everyone said, gosh, we can collect all this data about all this stuff. The more data we collect, the better, the more line of sight we will have on things.

Yeah.

And the reality was, is it just causes such a big mess. So all these companies, 100% of my clients, they've got their data stuck in 10, 20, 30 different platforms. Every platform tells a slightly different puzzle piece, slightly different piece of their overall story. And it's created this nightmare where now they have to bring all that data out of those systems into a central database, then model it out, then build the dashboard, then train all the teams on how to use the dashboard. And you're like, where was this thing where big data was going to save all of us? It was going to make our lives easier.

Now we're just trying to write exec summaries. That's really what it gets down to. Because it's like, okay, we're trying to just, in one section, we're trying to determine how retail is impacting online. And then we're trying to determine where online impact is overflowing across channels, say from TikTok to Amazon to Target to Walmart. We're trying to see how do those different channels impact the other ones and how much percentage of overflow is there on the additional channel. And of course, each of those channels have their own reporting, their own segmentation, their own use cases and SOPs. And it's like now we're trying to combine it all the way down to the inventory level at the warehouse. So we're now upgrading the inventory management system to actually get the data we need to start combining that into real-time outputs to all the channels. So yeah, it's quite an undertaking and it's quite an overhaul, but it's where scale and economy comes in efficiencies by getting all that done. It's just a lot of work, man.

That's right. That's right. That's right. And look, I think we're living in this era of AI, which I think is like the most poorly branded technology innovation in the last few years.

What's the technology innovation? Things like Claude are just rolling over SaaSs like every day. It's like they're the giant Pac-Man just going around munching on everything every time you turn around.

I was telling a colleague earlier today, I'd be like, imagine, Neil, if I told you I'd found this piece of software and what it does is you can talk to it and it's kind of like a Google search, but you could be a little bit looser, shoot from the hip. It can give you longer-form responses and you can actually tell it like, put it in a spreadsheet for me, maybe write a little bit of code. You'd be like, dude, that's awesome. I would love to try that piece of software. Yeah, but they called it artificial intelligence, and it's, it's certainly artificial. It's definitely not intelligent. And, um, it's another one of these disruptions that's causing a big mess and a lot of confusion in the market. So you're just like, can we just call it like a— let's at least commit to rebranding it. Maybe it's just like a really convenient, helpful chatbot assistant.

That might be better. Yeah, that's exactly what it is, because it uses us a lot, doesn't it? And then to get what it needs, it uses us a lot. So it's not super intelligent. It can do things faster, which is kind of cool. I mean, it can actually put output faster and create cool things faster. Then sometimes you get into the aspect of using it and then you realize you went down a rabbit hole so far you could have just bought the software or hired the service to do it anyways. And you're like, what did I end up building like a million tokens here to get through? That was a great learning curve and I still got not what I wanted out of it. So I guess we'll just go back to the service we were looking for. They call it a SaaS-pocalypse, right?

Is it a bit of a SaaS apocalypse?

In some ways it is, and certain industries are getting disrupted by its ability to create, that is for sure. But then there's the fundamentals. At the end of the day, we can't get past the fundamentals when it gets to the bottom line of the business. You just can't. And I find it fascinating. I don't know if you saw this the other day, but Gen Z is now the lowest IQ generation of every generation to date. We're actually going backwards. So, I'm concerned that AI is making— the systems that led to it are kind of making people dumber in the output because it's the first generation that's never exceeded their previous generation. So, I hate to say it, man, we're Gen X, right? So, we were the last generation to push the threshold to see the '99 and dot-com bubble, to see all that creativity, to see the way internet moved forward to today. But, we created a crux of information and data and just A crutch, I think, is the right word there that this next generation apparently isn't benefiting from. So maybe we do need AI to run some of this in more of an intelligence level.

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You know, I certainly think what we need a lot more of is leadership.

Yeah.

And I think, you know, customers come to me more and more, not so much to build dashboards or to do data science or to build data models. They certainly come to me for those things.

Yeah.

But a lot of those conversations are like, we just need an adult in the room.

Right.

We need to help people kind kind of upskill.

Mm-hmm.

I have a lot of people say, you know, it would be great to help marketers, finance folks, and operators be more data-driven, but let's be fair to them. We also need to upskill the data people, the technology people, so that they can acquire the sort of finesse and the vocabulary to be invited back to a lot of those meetings, to be able to understand what it's like to be a marketer, what it's like to be in finance, And not everybody wants to hear all the super nerdy nuance of what you do. They just kind of want someone with a little bit of empathy and a little bit of domain knowledge about what they're going through. So I think leadership has been a chorus that I've heard across the political spectrum and across the economic spectrum. That's what people are kind of like seeking out nowadays.

Well, anytime a gray area, there's a need for somebody to become a leader in change. And what we're looking at, while we don't like components of AI, I think we agree on that, we like other components that can allow it to work. But right now we're in this bit of quagmire in relationship to the timeframe. I'd say it's somewhere between '99 and 2001 where everything pushed so hard. And then there was this period of about 2 years where everybody just tried to figure out what they just got handed to them. And then from 2000 to 2005 or so, things started to change very rapidly. In the application of the internet, its technology, computers, and everything that really started to race forward. And then I don't think we saw that large of a change. I don't personally see that until mobile hit. And then mobile really created that ability to take all that stuff in your pocket and go crazy with it. And then there was new leadership that raised out of that that allowed mobile to become such a major important component of our everyday lives, literally. And now we're looking at that gray area in my world. where we see that there's going to need to be leadership over the next couple of years to allow this bubble to kind of go through its change and then figure out how you apply or what you apply or what you don't apply, or in some cases don't even use it at all if it doesn't make sense. And then we're going to see that in about 2 years. What are your thoughts on all that?

Gosh, there's so much to unpack there. I mean, it's, it's in a way, it's never been easier. For someone to create a new brand from scratch.

Yeah.

Right now, you and I can go into a garage this weekend and we can come out on Monday morning with a brand new brand, brand new physical product. We have built-in distribution. We've got Facebook and Google and TikTok. We've got Shopify and Amazon. We wanted to get into Sephora or Erewhon. We've got Salesforce. We've got Klaviyo. And so does everybody else. So not only is it easier for you and I to come to market, but all of a sudden we have like 10x the amount of competition out there.

Yeah.

Now all the things that we used to do to get consumers to pay attention to us, there needs to be an enormous effort to differentiate ourselves from everybody else.

That brand, that brand story.

Yep. The brand story is huge.

It is. It's becoming bigger. It was quite a bit more here in the last 5 to 10 years, roughly. And then we sort of went away with it for a little while. I don't know, maybe the last 5, 6, 7 years or so, we kind of went away from that. I think everybody got confused after 2020 as to what to do and what not to do. And it was just kind of a race to get something done. But I think I feel that shift back, that what you're saying is very astute, speaking from experience. Because I think you're right. Anybody can go create those assets, can now throw up a website in a few minutes on a prompt. They can look like something they're not very quickly. But the difference is going to be in the results. It's going to be in the output. And as we're talking about here, it's going to be the brand story. And I think those who are listening to this really need to understand, if you're building a product, you should be building the story with it.

That's right.

It's a huge affect. And where do you coach people on that? Where do you lead them? And that maybe first 2 or 3 steps for them to kind of understand what we're talking about here.

Yeah, I mean, look, you know, I'm not a brand marketer. I'm not a creative director, but I will say like story, you mentioning story is like a super important part of the equation. I think in many ways people are looking for meaning. And a lot of people, let's say like especially post-COVID, are sitting at home alone. They're being bombarded by messages from like every which way. Their Instagram feed is filled With transactional products, transactional brands.

Right.

Click here, 10% off, swipe your credit card, let's get this done. And, I think that there are a lot of brands that are focusing on that story, that brand universe that says, welcome to this. You're into fitness, this is a whole lifestyle brand about wellness and mental well-being, or this is a brand about effectiveness and precision. I think inviting people into those universes is part of the meaning creation process. It's what gets people to come back for a second purchase. And e-com is something like, you know, 80% of customers only buy once, you know. So from a data perspective, we come in and we're like, hey, can we all agree that system-wide every brand has this problem where 80% of your customers churn out after the first purchase? And maybe you haven't given them anything to believe in. You know, maybe what can we do to decrease that churn rate by 1%?

Yeah.

And so I'm coming in with a crawl, walk, run strategy about like, let's figure out what are, you know, where is the water leaking out of the vase? What are the unobvious ways that the business could be optimized? And can we get everyone to agree like today is Tuesday and we're going to focus on increasing LTV or shortening the time between 2 purchases or like reducing the churn rate by half a percent because that's going to have an outsized effect on our P&L.

Yeah.

So just really sensible stuff, a lot of blocking and tackling.

Yeah. And that's definitely, there's 2 sides of that conversation. There's the, I'm started up, I'm interested in, can I get this going? And how do I build a brand narrative and compete against others that can go do that in now a compressed timeframe with tools and technology? And those who are in the, you know, operational throw already looking to maximize revenue or, you know, obviously profitability. And LTV is a great way to do that. Lifetime value, just for those who didn't catch the LTV in case you're paying attention at home. You want to explain more about LTV to everybody real quick?

Yeah, absolutely. So, you know, in many ways, LTV is just a number. You can also think of LTV as a story. So basically, LTV is like, on average, how much money does a customer spend with you? How long do they stay engaged with your brand? And so it could be as simple as, you know, someone buys shampoo at a grocery store. And the question is, well, how many more times will they buy from you? Are they going to buy just your brand? Are they going to buy what's on sale? But this measurement of how long they will stay with you and how much you can expect to make with that relationship over time is important because it affects how much you should spend to acquire them. So the smart growth folks will be like, this ratio between LTV and CAC, how much am I going to make over the course of a customer's lifetime versus how much did I spend to acquire them or to keep them is very, very important. So oftentimes we'll sit down with customers and they'll say, Tim, we think our LTV is $100. We're going to make $100 over the course of their customer's lifetime. We guarantee you it's not $100 because we think you don't have one type of customer. We think you probably have 3 different types of customers.

Right, right. Right on segmentation.

Some people will buy once a week. Some people will buy twice a year. Let's be honest, the people that you gave a 50% discount to on Black Friday might be really fickle. Maybe you were just buying a lot of really unloyal customers. The folks that have bought from you in the last month, maybe we haven't given them enough time to buy again. So putting them in the dataset might be unfair.

Yeah.

And so there's a lot to unpack with LTV, but the basic idea is you're trying to build a good solid relationship with a customer. And if you fail to do so, you can lose money on that first purchase and you can lose them over time and you're going to have to go to your You know, big customers to try and make up the difference.

Right. Yeah, that's a dangerous pitfall to be in, isn't it? Uh, when you're, you know, balancing medium, small, medium, and large customers, um, in that, uh, that can be very dangerous for a service-based company, um, especially if the large ones leave.

That's right.

And you don't have a basis of small. That's a, it's a little insight from somebody who's felt that problem in the past. That's right. If you're, if you're working on the agency side. Where can people— do you have a social media account or something where people can find out more what you're doing, Tim? 100%.

So yeah, my company is called Latticework Insights. Best way to find us is at our website. So latticeworkinsights.com. You can find me on LinkedIn as well. My name is Tim Shea, for folks that don't know yet. You know, I think that like, especially in the era of social media, it's super important to be like collaborative. Anyone that's like commenting on posts, I'm always out there like responding. I'm trying to put out like super smart, helpful information for folks that are trying to up their game in analytics or up their game in terms of growth or help grow a more healthy retail or DSC brand. So yeah, encourage people to reach out proactively and down to be collaborative.

Well, it's been a great conversation, man. I appreciate it. And thank you so much for coming on and sharing a good word with us.

Neil, really appreciate it.

All right, guys, remember, like, share, comment, engage, help us beat big tech, as I always say at the end of this. It's very important for you guys to remember to do something imperfectly, and something perfect will happen along the way. Catch you guys on the next episode.

Bye, everyone.

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