Transcription

• Speaker #0

  You can incentivize customers into bad behavior.

• Speaker #1

  Remember Publisher's Inquiry? Ed McMahon would show up at someone's door and give them a million dollars. You'd subscribe to different magazines and that was your entry. And then the magazines loved it because they got a ton of subscribers, but then they realized... None of these subscribers are actually buying any of the advertiser stuff that advertise in the magazines. The original goal was like, well, they're going to renew. You get this for a year for three bucks or whatever it is. They're going to renew for $29. And very few of them would ever do that. The only time they would renew is the next year when the flyer came in the mail to get it for $3 again.

• Speaker #0

  The thing is, is that if you don't have a really tight feedback loop on observing what those customers are doing, you can get upside down really quickly by getting a customer base that behaves poorly.

• Speaker #1

  Norm, you ever wondered about your conversion rate in your personal life? I know you've been married for a long time, but you know, but what attributed to that conversion when you got Connie, when you got married to Connie, when you met her when she is like 16 or something, right? Or early. That's right. 16. So what actually led to you getting that conversion? You did the final work. It was you and your charm and your wit and your good looks. Of course. They got it. But what led you to get that conversion? Do you know? Was it your buddy John? Was it something that happened? Do you know what that was?

• Speaker #2

  Yeah, I was asking out the wrong girl. I was split testing. I was split testing.

• Speaker #1

  You're A-B split, so you're doing it the current way that most people do, which is A-B split testing. But you know what? sometimes attribution is not so good and you don't actually know. And so I think our guest today is going to be talking about a different way to approach conversion, whether that's how you actually look at it and actually do you want that conversion because maybe it converts to like me, I got conversion on my ex-wife, but it was the wrong conversion. I had to send her back. I had returner. So I had to actually send him back in return.

• Speaker #2

  Negative conversion.

• Speaker #1

  It was negative conversion. So actually, how do you prevent that? Because maybe you don't actually want that kind of conversion.

• Speaker #2

  It's called a bullet. No,

• Speaker #1

  it's called a bullet. So, I mean, when it comes to conversion, I mean, it's, and now with AI and with some of the stuff that we can do and with the tracking, you know, back in the dinosaur age, when you and Connie met, you know, there was just someone had to go knock on the tree. It's in the signal. Now we know everything. We know everything about every leaf on the tree. So it's going to be, I think, a fun talk today and a little bit different and maybe change some people's perspective on stuff.

• Speaker #2

  Yeah, and I can't wait to talk to Matthew. So let's bring him in, Matthew Barnes.

• Speaker #1

  Matthew Barnes. Hey,

• Speaker #2

  how's it going?

• Speaker #1

  Good to see you, man.

• Speaker #0

  Yeah, thanks for having me.

• Speaker #1

  I'm just waiting for you to leap over a... leap over a wall or something you know like spider-man yeah well what's what's that i never heard of it i i was looking you up and i was like okay what is this parkour parkour is that how you stand up yeah you into that and i was like okay that's really cool i watched a little quick little video and i was like okay first thing that came to mind was like american ninja warriors but that's because they jump on all this stuff but then but they have props yeah i have rope we have stuff but you, then I saw a little Ted talk video. And it's the girls like no we leap on walls and we jump over fences and we do all this kind of stuff It looks pretty cool. I was like, I wish I could do that. But I like that to see you do it You know me face plant

• Speaker #0

  Yeah, so I started I started training parkour back in college, so I went to a pretty you know, Purdue University and Back then it was a it was an online sport. So it's like people all around the world who had gotten connected through YouTube. This is like back in like MSN Messenger days of people connecting like in the UK and a couple of people in the US and some people in like Eastern Europe. And we were just sharing these like 480 resolution like videos, training and like coming up with new moves and things to do is like very much like early days of skateboarding like on YouTube. basically. And it's really a mental sport. Parkour is really about training right on that edge of things that make you feel uncomfortable and scared, but are like physically really quite possible. Now, as you train and expand that circle, it can get pretty like impressive, visually like running up walls and doing backflips and all of that fun stuff. But it's a really a fun mental discipline.

• Speaker #2

  My son was not he didn't get right into it. But But for about a year or so, he was getting into it. And I was amazed because I got to see when I was living in Hawaii, it was unbelievable. The guys that were training in parkour, you know, I couldn't even imagine even starting, but it's just a slow progression, I guess.

• Speaker #0

  That's exactly it. Yeah. No, I actually, I taught parkour, for a couple of years and the, the, it was amazing because you'd have these people that would come in that would be like really physically fit. but they weren't like mentally fit at having like a comfort with like how to manage like fear and emotions and that sort of thing and then you would have these folks that would come in to be like a physics major who is like never done a sport in his life and like a year later he sticks with it and he's like become very athletic because it's really uh it kind of weeds you out mentally because there's no amount of uh ego that can jump a gap uh which is which is really the thing i love about it the most.

• Speaker #1

  Have you ever used it? in a real life situation, not where you're like, you know, competing, but we're like, shoot, I need to, I left my wallet in the third floor and the door is, front door is locked. No problem. I'll jump the wall and get it. Or I don't know, something.

• Speaker #0

  I locked myself out of my apartment and scaled up the building up to the second floor roof. And jumped from the building across onto my other roof and then went in through my guest uh window uh one time after yeah that's great that's awesome that's so that's so cool so i see though how it it is but doesn't it isn't there a physical limitation you said it's mind over matter where you think you can't do it but you can yeah yeah so the it's more of like you You can always find something that is mentally. challenging that isn't physically challenging you don't have to like have this like really high peak level of like fitness to be able to find something that challenges you mentally but can stay like quite safe reasonably speaking that applies business too then doesn't it yeah absolutely yeah no so in that mental state in your business it's kind of crazy what you might be able to accomplish yeah it's it's and the the thing that i love the most about it is it's it's it's gaining a relationship with risk rig uh so uh one of the things is like i have two daughters they're uh 10 and 7 and i started training parkour with them uh like i took them out into you know out in the yard teach them how to fall take them out like training jumping on things you know and that sort of thing and it's it's i love it because you know it's we get to share in this thing together where i'm helping them learn to manage their own fear and like assess risk Right. And I think that's like a really, really good skill for anyone. But it does just massively apply to business. I use it all the time. Right. When something something bad happens. Right. You're assessing your emotions. You're trying to figure out what the real risk profile is here. Right. You're like figuring out how to calm down your central nervous system and make a decision like with with clarity, et cetera. It's it's highly applicable.

• Speaker #2

  No, I am. I think that's so amazing. And like you said, it applies exactly to business, family. That's right. It's all aspects of life. And especially when you're dealing as entrepreneurs, sometimes you're dealing all the time, not sometimes, with crisis management. And if you have that mindset, crisis management goes away.

• Speaker #0

  Yeah, yeah, that's right.

• Speaker #2

  And then it just becomes an obstacle you have to overcome.

• Speaker #1

  Figuratively or sometimes literally. It's risk management. I mean, what about injuries? I mean, do you get hurt doing this stuff?

• Speaker #0

  Yeah, I mean, the thing is, is that done well, right? It carries like a similar risk to other sports. You know, in the UK, it's like really, really common for people to break their leg playing soccer, playing football there, right? Because they get slide tackled from the side, etc. And so one of the things we always talk about is you train to fall. So like, one of the first things that I would teach a student is how to fall properly, so they can absorb impact safely without injuring themselves, or how to take a drop from height. uh or how to short a jump uh and we drill that stuff over and over and over and over again so it becomes kind of second nature and i think any any good sport really probably has that component to it it's a martial art isn't it essentially yeah that's it it's like you're you're you are learning to sort of like uh have your mind and body like commune with the your environment how can you even apply this like

• Speaker #2

  i know you can because of the mindset how can you apply this to business

• Speaker #0

  Well, so I guess the thing is, right, is... So setting goals, right. And like, and, and like understanding what it takes to achieve those things and like breaking them down into their components, I think is like really important. Like I had this, so there's a, there's a move that you can do this. Okay. So back in the day, there was parkour and there was free running. Parkour was just the efficient movements and free running was more of like the stylistic, like flipping things. And then over time, those things kind of merged and people just call it parkour now. But there was a thing that I really wanted to do on campus. I wanted to do what's called a Superman front flip. So what that is, is on the second floor of the campus university building, there's like a railing on this balcony. And what I wanted to do was do a dive over the railing, like Superman. And then once you get over the railing, you pull into a front flip and then you land on the ground below. And there's like a lot of pieces of that that come together to build that move. You have to learn how to take the height properly. and be able to go into like a role, you have to learn to clear the obstacle, you have to be able to learn the aerial awareness of being able to open up in a flip like that early, there's lots of different pieces to it. And so, again, just like in business, right, you may have like some goal you want to achieve, you want to do some like high tier thing like that, right? And you have to break down like, okay, functionally, what would I have to have in place to make this thing like happen, right? And so actually spent like six months training for that move. We set up like really similar scenarios in like a gym, like a gymnastics gym and like practice, like learning the timing of like popping out and then doing the role and then took it to other places on campus and like drilled it there so that when I did it, it wasn't just like YOLOing like this, like really hard move. Like for the first time I had done all of the component pieces. Right. And I had like worked toward, building up to this like big thing. And I think, you know, in, in my business, we approach things in like a similar way, which is like, you know, we're trying to figure out like, instead of just like, hey, what would be cool if we just like built this thing, or we did this thing, like, etc. It's like, what are the functional pieces of this right that necessitate precipitating the thing, because I think, you know, in business, but in this as well, like, when we when I think about a goal of something we want to achieve, or something we want to build, like we have this really fantastic software that we've we've built in our business, we didn't start by just deciding what software we wanted to build. right? We found the things that were like useful that would precipitate the thing at the end. So, okay, if we master how we're going to store this data here, if we master how we do analysis, if we master how we deploy the tests, like et cetera, right? We can precipitate this amazing software as opposed to trying to blindly without any context, like roadmap it out, like ahead of time and that sort of thing. And so it's really fast and functional and has worked really, really well at scale because I think we've taken this like non-traditional approach to building it.

• Speaker #1

  That's like a first principle thing, right?

• Speaker #0

  That's right. My background originally was engineering. Engineering,

• Speaker #1

  I can figure out a better way. I mean, that's how SpaceX came about, you know, building the rockets. They applied first principles, engineering, stripping it down to its parts, and like, how can we put this back together in a more efficient, better way? Rather than looking at the whole and like, well, how can we make this rocket faster over here? It's like, no, just take it all apart and put it back together. That sounds like it's similar to that.

• Speaker #2

  I'm so glad you didn't say this to me, Kev, because I would have said, Mr. Campbell, he was my first principal.

• Speaker #1

  He was your first principal.

• Speaker #0

  Yeah, well, okay. Shout out to Mr. Stump over here.

• Speaker #1

  But those things work. And I don't know if this is what your tool does. We're talking about that. But this works in AI, and it just changes everything. And so it's fascinating that from an exercise or parkour to business, to first principles, to rockets, to whatever, this is where you get an edge ahead that most people don't understand.

• Speaker #0

  Yeah, you're completely right, Kevin. So as you mentioned, my background originally was structural engineering, and I was getting a master's degree in mechanical engineering for seismic design. So I was doing like earthquake engineering research. And I started working in e-commerce for the small apparel company. And as I started going through everything, you know, I was like, OK, cool. So like we care about customer lifetime profit, like against like the cost to acquire their attention. And I was like, what? And I was like, you know, like you want to sum up all of the gross profit that the customer like produces. And like, that's the number you use. Right. And they're like, no, we just take like all the revenue from like the first sale and divide it by all the marketing cost. and then if it's greater than two, then we ship it. And I was like, that sounds dumb. So as a, as a mechanical engineer, I was like, that's, that's so gross. Like you have all the data, like right here, like you could just be calculating like. profitability, like do you count returns in that? No. Okay, like discounts, sale, like margin, any of it? No. So I was like, well, this is dumb. I'm just going to calculate it myself. So as I started working in e-commerce and then eventually started doing conversion optimization, we were just like hand-calcing this stuff all the time. I was like, I've taken like four statistics courses. Like this is not hard, right? But we would take it and look at like okay so like the best customers right the ones that correlate with like high lifetime profit they came in they you they bought from this product category or they did or did not use this discount or like all these like kind of leading indicator things and we started reporting on like testing from the perspective of qualifying like what these like good or bad customers were right because i'd come in to like work with these businesses and they'd be like oh it's amazing our lifetime value is, you know, a hundred dollars. and Our cost to acquire the customer is only like 50 bucks. And so we're kind of clapping along. And I would go into it. I start like teasing out these like customer segments. And I'd be like, no, it's not. I was like, when you factor in cost to acquire, you have a cohort of customers that you lose $10 on every single time and a cohort of customers that you're making $250 on. Stop acquiring the ones for negative $10 and the business will make more money. I could murder your conversion rate and just ship profit and then put. a backfed signal back into the meta algorithm for when it's looking at what a customer is, I'm just going to artificially cancel all the ones that lose $10. And that's like the approach that we really started taking with like, I hate the idea, like the nomenclature of like conversionary optimization, because I think the way most people do it is like objectively bad. But so it's like, but you can't say what do you do? I do customer lifetime profit optimization. I feel like I have no idea what that is. So... All the existing software tools out there, they suck because they're taking a tertiary slice of that perspective where they're either looking at just conversion rate or they're making like, here's a WYSIWYG editor, which is cool if your business does no revenue and you just need to ship it yourself. But it's like, especially when you come from apparel or footwear and return rates are like 20% sometimes. You really, really care if you're acquiring customers that just come in, take free shipping, like return the product, et cetera. And there aren't really great tools for that. So we had to build one.

• Speaker #1

  Hey, Norm, I've got a quick question for you. I'm trying to manage all my affiliate and creator programs from Amazon, from Shopify, from Walmart, but it's just a freaking mess. I mean, I've got reporting coming from here and there's all these different Slack messages. You know, if there's like a unified dashboard where I can do this all in one place.

• Speaker #2

  Yeah, absolutely. And you're right. It is a mess. A lot of brands are complaining about that. But there is a place that has a solution. It's called LaVonta. And they let brands recruit partners, track performance, manage payouts, send product samples, and even run creator programs across every major marketplace all in one place. And guess what? Brands can spend less time on tools and more time making profit.

• Speaker #1

  Is that the one that you sent me a link for, like a 10% off coupon, a gold or enterprise plan a few days ago?

• Speaker #2

  You got it.

• Speaker #1

  Oh, cool, man. I think I've got that link here. Was it Levanta.io slash Misfits?

• Speaker #2

  Yep, you got it.

• Speaker #1

  L-E-V-A-N-T-A.io forward slash Misfits. Awesome. I'm going to go. Go hit them up right now and get that 10% off. Perfect. Me too. Well, they say, they say that like a lot. I remember publisher, remember publisher's inquiry or Ed McMahon would show up at someone's door and give them a million dollars during the Superbowl and everybody, those there's magazine subscription. So they'd send out something as direct mail back in the day and you'd subscribe to different magazines and that was your entry. But, and then the magazines loved it cause they got a ton of subscribers, but then they realized. None of these subscribers are actually buying any of the advertiser stuff that advertise in the magazines. And none of them would actually, the original goal was like, well, they're going to renew, you know, when they're, they get this for a year for three bucks or whatever it is. They're going to renew for $29. And very few of them would ever do that. The only time they were renewed is when the next year, when the flyer came in the mail to get it for $3 again. And the same thing happens with like, what's, what's it a Groupon, you know, when Groupon first came out, everybody was raving about Groupon and I'm going to have them, I'm going to give it what, discounts and get people in to get cheap haircuts or whatever it is. And most of those people never came back and they had massive sales and like, yeah, I mean, we were flooded out the door and it was awesome, but it turns into nothing. So I, how, I mean, now with the internet, you can track this stuff, like what's precision. Um, but you still get confusion. On attribution. Correct. So, I mean, there's tools like Triple Whale and some of these that can kind of follow it across Facebook. And I've seen people that, you know, we had someone on the podcast, John Moran, one of the top Google guys. And he was like, yeah, you know, sometimes I'll see people turn off their Google ads because they're like, there's no, I'm looking here at what Google's telling me. He's like, yeah, Google is showing you what they want to show you to keep you advertising, but you're saying you're not getting sales. So you cut it off because Facebook's got all the sales. But then all of a sudden Facebook dies.

• Speaker #0

  Yes. Yes.

• Speaker #1

  How do you, how do you follow that? Is that what's your tool in part? I'm assuming that's in part what it does.

• Speaker #0

  Yeah. So it's a great question. So I think of it right in terms of like a loop, because if you're just doing analytics, you're a historian that doesn't tell you, it tells you what happened. It doesn't tell you what will happen when you change something. So like to, to your point about the Groupon thing, right. It's like, or, or any, any person that is in e-commerce or whatever, right. Like, oh, when we acquire a customer, their lifetime profit is this. Okay. Well, cool. Try acquiring more to do 10 times as many. Tell me if that number stays the same, right. We know it doesn't. And so the question is, right, is, is teasing out correlation and causation. And so again, like analytics without testing just gives you clues about how to form hypotheses. It also doesn't tell you actually like how to do any of it. Right. So like in terms of like what action should we be taking in terms of like development or whatever, what we built is software that actually completes like the whole circle. So it plugs into your event based data from like a Google analytics or something like that. Right. Which is like helpful on turning out what people are doing on the website. Then it also pulls in your Shopify data. Right. But it's pulling in all of the different attributes of the products so that you can go in like segment. them by different product types or categories. You can pull all of the shipping profiles, like et cetera. The subscription cadences, all of that fun stuff. And it pulls in all the customer data, right? So that we can see this person purchased within 30 days, they repurchase. One of my favorite metrics that we invented, we just make up, right? Because a metric is just a number and it usually has a numerator and a denominator. So I hate the idea of like conversion rates because what's a purchase and what's a customer, what's a session, right? We have to define these things. There's no agreed upon definition of that. Does bot traffic count in your denominator? Probably not. How are you going to subtract that out? In your numerator, if the customer never comes back and they purchase an intro product that has a subscription, but their negative lifetime margin on the first order, should we even count the first orders? Maybe your conversion rate should be all the customers who placed at least two orders divided by all of the sessions that weren't bot traffic. How are you going to calculate that? So we built software that literally just does it because in order to run a healthy business, you actually have to understand how the business generates profit and then actually be optimizing for those numbers. Is that conversion rate? I don't really care what you call it. Right. It's just a label. Language is necessarily figurative. So we're just making up definitions for all the all the words, actually. So then we pull in the ad level data. Right. So from Meta, from Google, et cetera. So we can contextualize all the different touch points that we have. We can do our own sort of like loose version of attribution. We can look at, okay, if we gave first touch credit, last touch credit, all of any touch credit, et cetera, where did these things fall? And that tells us more about like, is to your point, is Google actually like very good top of funnel. And most of our meta people actually in, in, you know, interact with Google at some point, et cetera. We can look for some of that overlap, right? Then we also pull in all of your Klaviyo data. So we can look at when somebody signs up for email, et cetera. people have these giant Klaviyo lists. They're not all, all of those customers are not equal, right? You have some people that you have their email address cause they bought something. You have some people that, that, you got their email address and they purchased the same day. So whatever your, your promo is, like when they came into the website, that was the thing that like converted them right there. And then you have a third category, which is people that you nurtured, right? And then you have to segment those things out when you're doing things like running a test for. What should our intro offer be? Or how should we do a pop-up or whatever, right? Just looking at conversion rate is stupid. Even just looking at our door value is kind of stupid because you're giving them a discount, right? You want to look at profit. You want to look at repeat purchase rate. Are you flooding your email account with a bunch of garbage email addresses that never nurture? Or are they absolute gold? We've had some brands that have used our software and have proven that their emails are worth like 30 bucks, like an email that they get in. That massively changes your strategy, right? So our software does. the analysis portion. It also does all the testing portions that you can deploy tests basically right out of the software and have it come back and use the same metrics that you were measuring things like customer lifetime profit as your metrics that you're actually optimizing for. And then it has another component, which is it does actually, it actually does do some of the ID resolution stuff because I don't like the way a lot of these other softwares do it. They either do it really poorly or just a favor, whatever the thing they're selling is, whether it's ads or emails or whatever the thing is, or they just do gray hat stuff that's like wrong half the time and probably going to be illegal in like six months. So the way that we approach it is we have like a first party app that sits in your store. It observes all of those things that are happening. First party. Oh, they opened an email, whatever. Right. The IDs are available there. They came in through Google Analytics. They clicked an ad, et cetera. We assign a global ID like many do, et cetera. And then we also fire those events for our tracking purposes for testing, right? Because again, not just historians into like a Klaviyo. Oftentimes we can match customer profiles for things like add to cart events, like 30% better than like a triple whale or retention.com or whatever the thing is. It's really, it's really interesting, but that's a hard problem to solve, right? Again, not taking the tertiary approach of like doing event-based tracking, we're deploying like WYSIWYG tests, we're doing like... just like marketing attribution, et cetera. I think you were reaching a point in the world of e-commerce with regard to like regulations of like harvesting like third-party data where you really do have to have like a first-party view of like how the business generates profit and the ways that you capture like that attention of the customers if you're going to win the game.

• Speaker #1

  Wasn't it Tesla that had, when they first started selling cars online, it wasn't working because there was like... I may have the numbers wrong here, but something like 74 different ways, 74 different options to ways to do your Tesla. And they just weren't converting. And so they took all the data, kind of like what you said. I don't know if they were using your tool or not, but whatever they did and they figured out. But there's really only three things that people want on a Tesla. And some of these we can combine in the same choice or whatever. So they combined it all. I may have the numbers wrong, but three choices is very minimal. Three choices. and relaunched the website and sales went through the roof.

• Speaker #0

  Yeah, no, I mean, it's so fascinating, right? Like there's a brand that's using our tool right now and they're going through a website redesign and they have one of these like navigations where on the website, where like when nobody could make a decision, they just put everything in. So it's just like this like awful, like list of 60 things. They use our tool basically to look at Where do customers go on the site that they don't land that correlates strongly with repeat purchase behavior and high lifetime profit? That was the question, right? So it turns out writing SQL is really boring and most people don't know how to do it. Our tool goes and pulls all of that data that I mentioned before into a data warehouse and then preps all of it and sanitizes it and sets it up so it can be easily joined together so that you can write SQL against it trivially, essentially. We materialize that data once an hour. And so you can use a prompt, right? Where it turns out having LLMs actually calculate things is the most dangerous thing you could possibly imagine doing. Having it write SQL to calculate things in an existing tool or write JSON to fill in forms like for building out like an analysis, et cetera. Trivial. They're translation machines, right? They literally just take language and turn it into another language. They take English and they turn it into JSON or they take English and turn it into SQL. So that's all we let the LLM do in our tool. You could do anything that the LLM does in our tool manually. It just sucks. So no one wants to write the SQL or fill in the forms.

• Speaker #1

  Through MCP?

• Speaker #0

  They did an analysis. Like an MCP? We just have the conversation. Yeah.

• Speaker #1

  In your tool.

• Speaker #0

  So we have the conversation prompt directly in our tool, and it has access to every input and output field that a normal user has. And it will call those skills or actions in directly. And then it lets you go through and actually review those metrics or segments that it sets up for the analyses, et cetera. So what they did, they built a report. Basically looking at, okay, customers who came into the website, what collections, et cetera, did they go to that they didn't land on that correlated highly with like repeat purchase behavior, et cetera. Now, here's the thing. Job of a historian does not tell us what we should do if we change things, right? So what that did is it led to a hypothesis of we should promote these product categories. We should demote these ones or remove them entirely off the site because they look like they're dead ends. or you know People just come and buy discounted products, like, et cetera. And they administered a test in their navigation that paired that list of 60 down to 20. And it wasn't the 20 that they thought. And there was, like, a reasonable amount of complaining when they went through it of, like, I don't know if we really ought to put this on there. They ran the test, increased conversion rate by, like, 3%. Fine. Increased average order value by like. 10% increased lifetime, like essentially what is like lifetime profit by like 15% because it was driving people away from the sale products. And so this stuff gets really, really exciting because you can, you can basically set your website up using a tool that can complete the circle like that to be literally optimized for customer lifetime profit, to choose the customers who will come back and purchase again. who will elicit a good behavior, like in their first order and all of that fun stuff. And you can do it on purpose.

• Speaker #2

  So when a business is working with you, so I went to your website and you're dealing with all these huge companies. But what about the small, medium-sized company? And how often does the owner or founder have to get involved? Because you're talking a language where they would have no clue for the most part.

• Speaker #0

  Yeah. So the thing is, right, is it's, it's when you, you know, when you are at a smaller scale, right. The, the, your goals are different than when you're at the larger scale. When you're at the larger scale, you have like all of this, like, fine grain data where you're trying to like, you know, get more juice for the squeeze. The thing that I think is really interesting with the, with the smaller brands is a lot of times the mistakes that we see people make. is they will get something that gets traction on paid media. And it's usually some sort of like intro offer into like their, their product that ends up like poisoning the wealth. And so the thing is, is that if you don't have a really tight feedback loop on observing what those customers are doing that are specifically coming through those ads that you're like trying out and trying to figure out which ones you can scale, you can get upside down really quickly by getting a customer base that behaves poorly. So, for example, there's a small brand that we work with, that was using our software tool, because they were running meta ads to add a toothpaste brand, running it to anti-cavity. and they were like, oh man, this is sick. Like we have this, great anti-cavity ad or whatever. Like it converts really, really well. We're like going to increase spend on it, et cetera. Now, when you do, a analysis on customer lifetime profit and plot it like a histogram, Thank you. So for everybody playing at home that didn't take four statistics courses, histogram is where you have buckets of like, they lost you $10, they were neutral, they made you $10, they made you $20, etc. And you see how many customers fall like essentially into each one of those buckets. Usually what people will do is they average it together and they pretend like it's a bell curve with one hump right in the middle and it's nice and concentrated. The thing that we tend to find sometimes with things that like get good traction like that, right? Is it's actually, it looks like camel humps where you've got a whole bunch of customers that suck and then a whole bunch of customers that are amazing. And like consumer packaged goods are like this a lot where it's like, you're not gonna stop brushing your teeth probably. So you're just gonna keep buying toothpaste. So you're basically either gonna be like a one and done person and you're gonna lose me money, right? Cause toothpaste isn't particularly expensive or you're gonna keep brushing your teeth. with that toothpaste for like the next five years. And you're going to be the best customer that's ever happened. Right. So the thing that we found is there was like, they using the software, they were able to like see, right. Even at the small scale that the customers just fall in these two dang buckets. So able to like basically form some hypotheses around like, OK, like what are the patterns of like a one and done person or whatever? Right. Because, again, they want to scale this. But if you scale that stuff too early, Meta is just going to like just home in on these easy to convert, really low quality customers and scale through the roof. And you've already burnt six months of budget before you figure out that none of the people are coming back and buying anything. Right. And they're sunk. So I think the game when you're at the smaller scale is to figure out. how to evaluate product market fit in like the macro scale, like to scale like responsibly, if that makes sense. I maybe I answered the question. I'm thinking about the,

• Speaker #2

  the, the owner founder. Yeah. Small, medium sized business and they go to your site and how do they even get started? Is it just a plug and play or you need information?

• Speaker #0

  Yeah. So the thing is, is that we've, you know, the way that we. built this software successfully is partnering with businesses like as they were like going through that scale and basically like shepherding them through that process uh of uh taking the things that are important to them and then figuring out how to like triage those into like an actionable testable environment so that they can like essentially like again like learn those like first principles of how to responsibly generate profit at scale and then kind of scale it up so So... I think it helps to have, it helps to have, like, a good, operator that understands these things can like utilize that we've seen the whole spectrum, right? We have some folks that are just like absolutely clueless, but really happy to be here. and they've gotten good, they've gotten good product market fit, in spite of themselves and, and need, need all the help kind of coaching and adopting that kind of mindset. And that other people that are like, oh my gosh, where were you five years ago? like Okay, thanks. We'll take it from here. Right. And we go in and chat with some of those folks. And it's like everybody from like, the person that used to do data entry that doesn't do it anymore, all the way up to like the private equity that's even involved in the business is all using the tool, basically running all these queries, like etc. So definitely varying levels of sophistication. But you're you alluded a little bit to like, is it is it helpful to have some help here? Now we have we have done a thing, which I think is like, quite helpful, which is we have a thing we call playbooks. which is there are these analyses that are universally helpful, especially to a business that's just trying to figure this out for the first time. And it's things that we've like battle tested against like a billion dollars worth of like collective revenue where it'll be like, Hey, run this playbook. It'll build all these analyses, comb through your data, sanitize it, ask you a couple of questions and then print out, okay, this is, this is your buckets of email qualification. This is how much an email is probably worth, that kind of stuff. I think that's tremendously helpful. um, Same thing with like setting tiers for free shipping, right? Where it's like a couple of little inputs of like, what do you actually pay for shipping? It can already see what you charge each one of the customers and all the different levels. And then you can do an analysis again, looking at those orders and then set a like, okay, cool. It's here today. If you move it by this many dollars, it'll move, it'll capture 15% of people this way or that way. And it kind of like tees up the test for you in terms of like doing those analyses. because it turns out there's only, you know. There's a finite number of interesting shapes of data in e-commerce. Now, where the rubber meets the road of actually applying it is easier said than done. But getting people started to like kind of build those thoughts, we found the playbooks thing is really, really helpful.

• Speaker #1

  Hey, Norm, do you know any sellers out there that are just burned out doing this e-com game?

• Speaker #2

  You know, I know a lot of people that have talked to us, you know, when we go to events. And it's not only that, they don't know where to start.

• Speaker #1

  Who would you recommend they talk to?

• Speaker #2

  The first one that comes to mind is Quietlight Brokerage. And here's why. They're going to build you up. They're going to understand your company. And at the end of the day, you're going to know how to maximize your valuation. So the very first thing you need to do is go and get your free confidential valuation at Quietlight.com. They're going to ask a couple of questions. You're going to meet up. It's one-on-one with... somebody over there and then you know let the games begin awesome what what was that website again it's quiet light.com awesome i'm gonna head over there well

• Speaker #1

  these smaller guys i mean a lot of them they're they're they're thought of testing is a b testing let's test a again yeah and i'll be one so let's go with be and maybe they may be the more sophisticated ones may be the control and they keep testing against that and that's all they can do but now with AI Especially, I mean, you have it built in, it sounds like, to your system, but with AI and MCP, Norm and I have a rule in Dragonfish that we don't touch software that doesn't have an MCP. Because that way, if it doesn't have an MCP, I'm not interested. I'm not going to go by your dashboard. I want your data and what you've been assembling, but I want to ask it my questions my way and be able to tie them to different data sources. Because if you know how to do that analytics and that tying together, it's like. freaking magic potion. And a lot of people, a lot of business that's beyond the pay grade of most business people and most people using AI, which baffles my mind.

• Speaker #0

  Well, when you get into testing, you get into really weird stuff too because LLMs don't really have a lot of discipline on committing certain statistical fallacies. And so the thing that we see especially at the smaller scale is people abusing statistics not on purpose. So you run into things like the peaking problems, like Simpson's paradox, multiple comparisons problem, et cetera, just like ways that you can use things like an LLM, which can like pull massive amounts of data really, really quickly and then expose you to tremendous amounts of noise is like usually kind of like the flip side of that. So we've built a lot of guardrails into our tool. One, two, you'll ask it to do something that is like not a good idea. hey, I want to look at 30 different metrics for this test. You may not look at 30 different metrics for this test. That would be committing the multiple comparisons problem, your sensitivity to noise now, right? There's a, have you ever, you know, XKCE, do you know this webcomic? No. Oh, you're going to love this. Okay. So it's just a, it's a nerdy webcomic that just has a bunch of like physics and statistics jokes and stuff in it. And there's this one where it's, it's a bunch of panels of these people like study, like, Researchers doing a study of every different color of jelly bean. And it's like, oh, there's a, you know, P95 red jelly beans don't cause acne, yellow jelly beans. And it's like 20 different like colors. Right. So the joke is, right, is when you when there's a 5% chance of error and you test 20 different colors of jelly beans and you look at every single one of them, your expected value is that one of them is going to be noise. And so they ship this like paper and then it's a headline of a newspaper at the bottom. And it says this just in green jelly beans found to cause acne. And people. Yes. And it's and this is one thing I see with AI and like LLMs is they they can't their translation machines. Right. They cannot simulate. They cannot mentalize, which is fine. But you have to know what you're dealing with because they mimic. human beings and language, right? So they, you people very commonly perceive them as being something that has a worldview or has a goal, which they do not because LLMs can't do continuous learning in the nonlinear case. Right. And so what ends up happening is you can ask an LLM and it'll be more than happy to calculate anything you want, and just let you like moonwalk past abusing it unless it has like good guardrails on it. And so I think that's one of like the flip sides of this which is like have access to literally all of the data, calculate all of the things. But when you're making statistical choices, having like some underlying like rigor there that doesn't have you just like chasing infinite noise because you can run really fast, I think is like part of the game. It is really, really funny though, right? Because it's just a, it's, we thought we, we, we, we wanted to build androids, right? We wanted to build androids with AI. Well, we really built with mech suits. So it's like whatever you normally do, you now can do 10 times as much. So if you are just like an absolute loose cannon and have like no discipline in like the general like work that you do, now you can do it 10 times better. It's like I meet these people that are like, I love cloud code. I use cloud code all the time. Like, please stop. Right. Because it's just they're just doing terrible work 10 times faster and just generating like fake problems and like complexity that doesn't need to exist.

• Speaker #1

  If you're a five on the knowledge scale, AI will make you a 10. But if you're a 10, it will make you a five. Or something like that. It'll take you back. So it's, I mean, that's one of the things, like, what you're talking about there. Norm and I, we have a company called Dragonfish that does email marketing for e-com brands. And one of them, we do warm email and we do cold email. And what we've been building with the five beta clients is a brain system. So we have 11 different brains, and they're the guardrails. So instead of telling Chet Claude, say, here, go write a cold email sequence for... company xyz make sure you do this this yes no it's like reference these 11 documents that were created independently and then cross-checked against each other to actually write this and it's super powerful And you don't get the same old slop and you don't get the same old stuff that everybody does. And we have it on competitors. We have it on their keywords. We have it on their brand statements and what they stand for. And we have them do a first principles document themselves that we then incorporate into this. And it's a pretty cool system, I think, that really differentiates what comes out of it. And

• Speaker #2

  I think we'll be talking to Matthew after this call, by the way.

• Speaker #1

  Yeah, I think so. So when we've... We're in beta now, so we've been building. It's almost done and being deployed, but with a touch of a button, we can create any kind of landing page. We can create any kind of image. We can create any kind of quiz funnel. We can create any kind of optimization. It has all the optimization stuff. I mean, it's pretty comprehensive, and it's not perfect. It can be still made, I know, a lot better. But that's what baffles me is people, I don't know, they don't think it through. They just want the easiest way and the most general way.

• Speaker #0

  I gave a talk on this recently. And so I was, you know, we've done this lots of times historically, right? Like if we say like, hey, what's like the closest like parallel to what we've created with LLMs? It's essentially the assembly line, right? Because what it allows us to do is take something that is essentially like unskilled. like unskilled labor and produce skilled results. Right. And it's, it's only as good as the procedure. Right. And so the thing is, is that procedure is cool because it moves everybody to an average. Now, when you have people who are like, it's exceptional, with regard to like their ability to problem solve, like et cetera, they all hate process because it brings them down to like the average. But when you're, when you are setting up like an assembly Ryan line, right. You used to have to say, I want to make shoes. I need to have be go apprentice like a shoemaker for for two years to learn how to make boots really good. Right. But instead, we built the assembly line. And it was like, hey, buddy, your job is to put the the the rivets in the sole and then you punch for the laces and then you attach the you know, and then so it's each of the individual parts. And so the person only has to have knowledge about how each one of their their little pieces works. And they don't have to have a concept about how to do all the skilled labor. And I think when you have something like, it sounds like what you've built. Right. And you talk about how to use like an LLM like successfully, you have to break the context window down to punch a hole in the leather right there. And it'll do it an infinite number of times really, really well. Right. And when you can chain that stuff together, you can get really, really good results. But if you walk up to the assembly line worker and you say, hey, we're thinking about what we're going to do for marketing for new shoes for 2028. Do you have any ideas? That would be a stupid question to ask somebody who punches the rivets into the soles of the shoe. right but going to an LLM and saying, here's my marketing data, what should I do for our marketing calendar right now is equally as stupid, right? Because an LLM cannot mentalize. It doesn't have a goal. It doesn't have a worldview. It can't hold, it can't anticipate the thoughts of something else. It's guessing what the next damn word is, right? And that is a phenomenally powerful thing that will create a trillion dollars worth of value. Please don't ask it things that are just like intent-based, right? There is... There is a whole world of action out there that could be put onto the LLM assembly line and you're asking it to make like critical business decisions. So like if you go into our tool right now and you say like, hey, based on the data, what should I do? It will say, hi, I'm an LLM. I am not capable of actually having any goals or mentalizing. I'm just going to give you an answer that looks like what a reasonable answer looks like. Here is all of the data. You should consult somebody who can actually interpret it. And I think that that's really, really important.

• Speaker #1

  Someone gave me an analogy of Adobe Photoshop. They're like, hey, have you ever used Adobe Photoshop? I was like, oh, yeah, of course. I've gone in and recropped a picture, resized something. Well, have you ever actually designed a piece of art in Adobe Photoshop? I'm like, no, I'm not a graphic designer. I don't think in that way or don't know how to use the tools in that way. It's like, well, that's the difference in AI. Most people go to AI and they use Photoshop. Let's crop an image or make it from a PNG to a JPEG or whatever. the The graphic designer is the one that knows how to use the machine and actually has that background. And that's the difference between AI operators. It's something similar along those lines. And that's what's differentiated or AI systems that are built by AI operators that incorporate that. And that's the big difference. And I hear all these people anti-AI or AI slop and like, yeah, there's a ton of it. The power of if you know how to use this tool is ridiculous.

• Speaker #0

  Yes.

• Speaker #1

  It's ridiculous.

• Speaker #0

  Yes. It's the game of separating intent and action, right? Intent is all of the judgment type things that requires like the expertise, a worldview, an understanding of how other minds work, etc. The action is just everything that flows after that, right? And so that intentful portion in terms of like the entire time needed to accomplish any goal is very, very small. And so in the same way that like, if you are a good manager or you delegate well, et cetera, you, you are kind of obsessed with separating that action and intent and really only having the intent and your job is to express the intent as efficiently as possible and have someone else carry out the action. You really have to treat LLMs like that to be successful as well.

• Speaker #2

  Sounds like this could be a good dating site.

• Speaker #1

  Yeah, I could, it could be. We'll call it Finder. Finder. You did something with Allbirds. I mean, you've worked with a lot of big brands, Red Bull, Allbirds. I mean, your website has a laundry list of well-known brands. But you talked earlier, you touched on the shoes. You know, if you're selling shoes, you need to know the returns. You know this and this and the other. So you don't want these type of customers. You did an experiment with them where you like split hairs, basically, I think, on something. Can you talk a little bit about that and what you learned off of that when it comes to conversion? And like the value of the customer?

• Speaker #0

  Yeah. So, so like really, really interesting stuff with, you know, with shoes, for example, like that return behavior. So you can incentivize customers into bad behavior. So a lot of times what we'll, what we'll see is folks will say like, oh, cool. hey, we want to do this like intro offer for customers. It's this big of a discount on the first order or whatever, right? It'll have free shipping and returns, like et cetera. And these have knock-on effects for essentially like optimizing for tire kickers. And so you'll get folks that will be like very opinionated about like, oh, we have to collect all these emails, right? Like we do a lot of like email marketing, like et cetera, or, hey, this is a really good deal. Like it converts well for us. Our customer lifetime value is X. And when you're applying things in broad strokes, all those things look like really, really true. But you can do things where you can say something like, hey, free shipping on two or more pairs. Right. And that can all of a sudden like really kind of like change things where it's like the split on the hypothesis is like, OK, is that going to incentivize people to just buy a pair that they like and a pair that they're not sure? And then just pick between the two. Are they going to be like on the fence on sizes, et cetera? Or does it like weed out the people who are just tired, kicking, et cetera? We ran a test. for a shoe brand that we work with. And the really, really interesting thing was we actually changed that. Instead of giving a disc or basically doing free shipping on everything and giving like a discount on the first orders. Instead, what we said is like, hey, free shipping and returns on two pairs of shoes. And then we look to see based on how we were acquiring customers, what the net result was. Now, the really interesting thing was there are actually a non-trivial number of people who would come in, look at a pair of shoes, see that it was free shipping and returns, place an order and then just return the shoes. Like, I don't know. not for me whatever the thing is right uh the thing that we found though is that among the folks that were purchasing two pairs of shoes or more they weren't doing it for uh the reasons that you might imagine right which is like oh am i 11 and a half or 12 i guess i'll just buy both and like send it back like once they really got like dialed in they kind of knew what size shoe they were they weren't buying different styles they were legitimately keeping different shoes or buying one for their wife and one for themselves like etc and so So. The really, really interesting thing on that was repeat purchase rate went up phenomenally. Return rate went down a ton. First order profitability was much, much better, right? Because average order value is going up. You're not using a discount to convert those customers. And so you can really change the unit economics of a business when returns are going down, repeat purchase rate is going up, first order profitability is going up, your gross margin after discounts and stuff is going down on those first orders. And it can very rapidly change the ecosystem that you're purchasing customers in as a result because it sends a much stronger signal back to a meta in terms of who you're allowed to acquire. And if you can, with confidence, acquire those customers, even at a higher price point, because you know you're just getting the good ones, it completely changes the business.

• Speaker #1

  I mean, an interesting, just a quick little story on that is I have a newsletter in the Amazon space called Billion Dollar Sellers. I used Beehive to send this out and they just opened up an MCP. So I went into and I did some queries in the MCP to pull some data sets of different data. And based on leads, I've been paying for leads from this one place. And some of these are business email leads and some of these are personal emails. So I'm seeing like which is performing better. The business emails got me 15, much higher. conversions, higher open rates and decent click rates. The personal email has got me a much higher unsubscribe rate, a much lower open rate, but a way significantly higher engagement rate. So it could be missed. If you don't look at the data all the way through, you would say, hey, I want the business email addresses only because it's higher open rate and good conversion rate, because these other guys are in subscribing and much higher, like three times as high. But the engagement rate is seven times as high. So they're actually. Small and better customers. And now I'm like, OK, I need to ramp that up. I'm still going to do the business. Completely. But that was counterintuitive. And their tool would have never been able to tell me that. But I was able to do that with an MCP and a proper prompt using their data.

• Speaker #0

  Yeah. No, we see this all the time with like pop up providers that do like SMS and email signups, et cetera. All of their like internal A-B testing tools are just crap. Because all they've optimized for is getting more emails or more SMS numbers because they make money on the sends. So the incentives are totally misaligned. So what we'll actually do oftentimes is people will use our software where they'll deploy the A-B test within a Klaviyo, for example. And our software actually has a little script that goes and pulls the ID numbers when those pop-ups appear in the DOM on the site. And then we can look at real cohorts and look beyond what they're measuring, which they're like, oh, people open the pop up. They submitted the pop up. You got the email. Here's all the conversions from the emails that were submitted. That is a stupid number to measure. Who cares? I want to know how many people actually bought things on the website. If they didn't need the pop up and they didn't take the discount and bought it, that's better for me than if they gave me their stupid email address. Right. And so we do these like kind of like macro things where we can look and say, hey, you acquired 10%. fewer email addresses, but you converted 3% more people directly on the site exactly in that session. And we can break those email cohorts out and like, look at the quality of it. And it's exactly what you're saying, Kevin, which is that like, people will be like, oh, this is amazing. So many people interact with my pop-up. I get all these email addresses. Look at all this revenue I'm generating from the email. But when you look at the revenue per email address, it's much, much lower and they murder the conversion rate on their own website. just to have to wait to try to nurture people a week later. It's phenomenally stupid.

• Speaker #1

  Hey, Kevin King and Norm Farrar here. If you've been enjoying this episode of Marketing Misfits, thanks for listening this far. Continue listening. We've got some more valuable stuff coming up. Be sure to hit that subscribe button if you're listening to this on your favorite podcast player, or if you're watching this on YouTube or Spotify, make sure you subscribe to our channel because you don't want to miss a single episode of the Marketing Misfits. Have you subscribed yet, Norm?

• Speaker #2

  Well, this is an old guy alert. Should I subscribe to my own podcast?

• Speaker #1

  Yeah, but what if you forget to show up one time and it's just me on here? You're not going to know what I say.

• Speaker #2

  I'll buy you a beard and you can sit in my chair too. You can go back and forth with one another. Yikes But that being said, don't forget to subscribe, share it. Oh, and if you really like this content, somewhere up there, there's a banner. click on it and you'll go to another episode of the marketing misfits.

• Speaker #1

  Make sure you don't miss a single episode because you don't want to be like Norm.

• Speaker #2

  Oh, so we've heard you talk about this incredible software, but we've never met. Oh yes. What is it? Okay. So, here, so

• Speaker #1

  Well, he's got to run up a wall for us, Norm. He's got to run up a wall.

• Speaker #0

  Yeah, hold on. Let's go backwards over this cache. So with Proteus Digital Lab, we internally, as we were building this, we kept saying like, oh, we should name the software. What's its name? And I was like, oh, no, no, we're not naming it. And someone's like, why not? And I was like, because language is necessarily figurative. Once you name something, it tells you what it does. We don't know exactly what this does yet because we want to do the thing that generates the most value. And people are like, that's the most pretentious crap I've ever heard. What's it called? And I was like, it's called the tool. And they're like, that's the stupidest name I've ever heard. Ha, got them. All the people that use it now refer to it as the tool. And I was like, oh, crap. Now we have to call it the tool. So we have a tool that does business intelligence and testing and customer identity resolution. and it's called the tool, by produce digital lab. And, we are, now actually opening up, allowing folks to, to to use the software in more broadcast uh context we did it with a a group of those brands that you see like on our our website uh essentially as we were like piloting it with them and kind of building it over the the years and doing those services with them etc uh but it now exists um by the time some you're probably listening to this dear listener uh in a standalone uh capacity so you can take it first only available by referral by referral only yes yes uh so uh if you're, This is the best bit about reading really, really good is you just only have to do stuff you like. So I was like, how do people work with you? And I was like, somebody introduces me. I don't have to deal with people I don't like. What are you talking about? But if you if you are listening to said podcast, you come to the website. There'll be a form there for you to fill out. And we'd love for you to take a spin and let us know. All right. Fantastic.

• Speaker #1

  And that's just to spell that for those listening, that's P-R-O-T-E-U-S. P-R-O-T-E-U-S, digital. Proteusdigitallab.com.

• Speaker #2

  All right. Okay, Matthew. Thank you for coming on. This has been fantastic.

• Speaker #0

  Thank you, guys. It was a joy.

• Speaker #2

  That's a lot of fun. You can tell you got a little bit of passion.

• Speaker #0

  Casual. Hey,

• Speaker #2

  I got one question for you. I like to ask this to all of our guests or our misfits. You happen to know a misfit.

• Speaker #0

  I do. I think that you would really enjoy talking to my friend Taylor. So we'll see if we can get him on the show.

• Speaker #2

  All right, sir. You have a good one.

• Speaker #1

  This has been great. Thanks for coming on,

• Speaker #2

  Matt. Cheers, guys.

• Speaker #1

  Appreciate it.

• Speaker #2

  All right.

• Speaker #1

  That was great. That's probably over the pay grade of a few people, but it's really cool stuff and it's really fascinating what you can do. and where this is going and how optimization has gone from just a little split testing to now this very scientific engineered approach. It's awesome. Awesome. I love it. I love getting my head into that kind of stuff.

• Speaker #2

  I got, you know what I got out of that? A new name for you.

• Speaker #1

  A new name?

• Speaker #2

  Yeah.

• Speaker #1

  Uh-oh. Uh-oh. A new name? What's the new name?

• Speaker #2

  The tool.

• Speaker #1

  The tool. I think you mean that differently, though. I think you mean in a non-positive way. In a negative way.

• Speaker #2

  I'll print your badge at the Marketing Misfits. It'll be the tool.

• Speaker #1

  At Market Masters, it'll be the tool.

• Speaker #2

  Yeah.

• Speaker #1

  Oh, that's right.

• Speaker #2

  Market Masters.

• Speaker #1

  At Market Masters. We can check, yeah, on the Misfits. It'll say, Norman the tool.

• Speaker #2

  No, it won't. All right. So, Kev, how do people see this?

• Speaker #1

  Well, if you want to see this one and about 100 and something more, we come out every single Tuesday since 2024. Hey, look, I just made a rhyme. Since April 2024, every single Tuesday, Norm and I have been here. We're your buddies in the car, on the subway, on your run, on whatever you may be doing. New episodes come out at marketingmisfits.co still, right? It's not .com. It's .co. It's .co. you

• Speaker #2

  marketingmisfits.co uh those are also posted on on the uh the video sites what's that the tubers and those tubers on youtube we're marketing misfits podcast and if you just like the the short three minutes and under uh clips we have marketing misfits clips and we also have a newsletter i think it's uh what two months old uh yeah about two about three months old now but

• Speaker #1

  Misfits.news is where you go to get that. Every Wednesday, a brand new edition of the email newsletter comes out. People are saying it's pretty good. I think so myself. A little biased. A little biased there, but lots of actionable stuff from the podcast, a little stuff about Norm and I. Norm always has a cigar or whiskey suggestion or something in there. So check it out at misfits.news. And then we'll be back here again next Tuesday, right, Norm?

• Speaker #2

  That's it.

• Speaker #1

  All right. We'll see everybody then.

• Speaker #2

  I'll see you, the tool.