Build How Customers Want to Buy

Speaker A: We can break this mantra that's very pervasive in Spain that says in industry, that says that insurance is not bought, it's sold. So we were like, oh, we're going to go against the grain, uh, against 100 years of common knowledge and try to have our customers do our work for us.

Speaker B: Welcome back to the Thoughts on Selling podcast.

Speaker C: I'm Lee Levitt, sales coach, podcast host

Speaker B: and author of the Second Meeting and together we Win both due out later this year. Today we're talking about what happens when you stop doing things the way an industry has done them for 150 years and start thinking like your customers actually live. My guest is Juan Garcia, co founder of 2eo, an AI native insurer based in Spain. Juan started in engineering, moved through strategy consulting and building new ventures at Orange and then saw how broken insurance really was. So he built something different. He here's what to Listen why the 25 to 55 customer segment was both underserved and unprofitable for insurers and how that became a significant opportunity. How browsing behavior during checkout can predict fraud before a claim is even filed. And why AI will be powered by startups, not large public companies.

Speaker C: Because speed beats process.

Speaker B: If you're wondering how to rethink your go to market from first principles, this one's for you.

Speaker C: Let's go.

Speaker B: Today it is my pleasure to have Juan Garcia join.

Speaker C: Juan and I have been chatting about bicycles and sales and AI and all sorts of good stuff. I'll start with the first question that

Speaker B: I've already prepped Juan for. Juan, who is Juan Garcia?

Speaker A: That's a great question. You could probably ask three different people and they will give you three different answers. We'll probably keep it to just as you said. I uh, just like riding bikes. I used to love playing rugby as well when I was younger or young. Yeah, I'm an entrepreneur. I haven't always been an entrepreneur. I am an engineer by trade. I uh, didn't study engineering, telecommunications. I started working on technology, Cisco systems. Then I moved to the strategy consulting, mostly working in marketing and sales. Hence being here today for a while for technology companies and telco companies. And then I started building the new ventures division in Orange, Spain and that's how I. You could say I fell in love. I would definitely say I saw a problem because we built Orange insurance and I just realized how broken insurance was. Now I'm building with another two co founders. Tuyo. Tuyo is a AI native. As we want to say insurer. We are not AI native since the very beginning. It's something that we stumbled into. We were, we started thinking that there was something that we could do with technology to make insurance more transparent, more friendly and cheaper, which probably all your audience can relate to. And then we're here today to talk about AI and sales and insurance.

Speaker C: Yeah, I love that Juan. We'll leave the personal discussion aside of bicycles because that can be rather time consuming and that might be for another podcast. Today it's all about the world of business. I love the fact that as part of your introduction you talk about insurance as an industry that is ripe for change. Yet most of the players in the insurance industry are 100, 150 years old and they are very resistant to change. I've called on many of them in the Northeast and New England. Risk averse, late adopters, they know what they do for customers. It's not flashy.

Speaker B: Nobody wants what they do.

Speaker C: Nobody wants insurance. Nobody wants to pay for something they hope never pays off. Right. Yet we all do it because it's a risk benefit analysis. And you come along with an idea that there might be an opportunity to do things differently.

Speaker A: Yeah, we all buy insurance because it's a sensible option. It's not because it's sexy option or we started a company in insurance because it was a sensible option, not because it was a sexy option. So basically when I was building or starting Orange insurance on my own and then we can talk about how my co founders stumble into these realizations. Well, I just realized that insurance, at least the detail side was broken. And as broken as telecommunications are, which we all know, insurance is even more broken. I just realized that it was an industry that didn't have end to end data. Like data, uh, like data movement. Most of the times their websites wasn't able to just give a price. Connecting to the core systems was just very cumbersome because they didn't have APIs. For someone coming from the tech side of the world, some of the stuff that we were stumbled onto is like this is incredible, this is just broken. There must be a way to do things better, do things better. And yeah, as you say, it's very old fashioned for a reason. Because it's very risk averse because you have to actually, I mean risk is what you do to manage it very well. But it's also you only have a relationship with the customer when you sell and when you have a claim, there's no a recurrent conversation with a customer. So there's no uh, benefit on having a better experience because I mean, if you have to experience pain and you only have to experience it, what, maybe three times in five years, then the cost benefit analysis is like you just look at, as you said, you just look at other things to invest. And that partially explains why in Fintech, new banks started way before, uh, the rebels of the world, or they started way before than some of the new insurance companies started, because the interactions are just more interaction so there's space to move things better.

Speaker C: So by the way, Juan, insurance companies grapple with that all the time. I opened the Porsche Club magazine and Amica Mutual Insurance is all over it. Right. They've decided that particular demographic is a demographic they want to target, typically slightly older, well, to do males that make lots of insurance decisions. And they want to have their brand in front of that buyer a regular basis. Because from a marketing standpoint, repetition is good. And so there are constant touches by insurance companies because otherwise it's a pure commodity. What's the coverage, what's the cost?

Speaker A: Exactly. And there's this company in the US called Hierarchy. It's focused on the expensive cars and they do all sorts of nice stuff, like they have these, these valuation tools, they have this, this coffee table magazine every two months. They. That's a very nice way of presenting touchpoints without having to actually go into the insurance piece, which they will provide for you because that's how they earn money. When we started looking at insurance, at least in Spain, we started looking to further angles. I mean, we knew it was, well, we knew it was broken. We still needed to find an angle to differentiate from current offerings. It doesn't matter if it's priced, which obviously you love to do, but also try to look at, uh, things a bit differently. And what we found is that there's this customer segment that was grossly underserved, which is the 25 to 55 or older. It's just not, they're not strict frontiers. As a proxy, 25 to 55, everyone can imagine the type of customer that I'm talking about. There are customers that they consume content through Netflix, they buy stuff on Amazon, they even buy their groceries online. Right. So just, it could just like they definitely proof. So we found that these customers, they were puzzled by the actual choice, like if I want to buy an insurance of my house or my car, I have to go to an actual physical store or I have to call someone. There's no online option. I don't want to. There's nobody. There's no online option for that, that segment that just, uh, we Just use. I can do things on my own. I just do it. I don't want to call anyone. I don't want to start to waste 30 minutes of my time just going through all the different coverages when I can do myself on my own tonight. So we found that there was this customer segment that wasn't really being served as they would want to, because at least in Spain and probably in the US and in other countries, what you have is just like insurers. They have just one or multiple products, but they just sold the same way, have these different paths to sale, as we call them. And, uh, that's it. You either go to store or you just call us. And that's the only way of actually buying it.

Speaker C: I want to pause right there for one second, Juan. So we're going down a conversation path about insurance. And I want to point out for the listener that this is not an insurance specific story. This is a general purpose story of just because the way things are doesn't mean you need to do things that way. There's a book that I recently read called Thinking Better, and one of the primary posits in the book is there's two ways of thinking. One is reproductive thinking, which is it's the Kaizen method. If we're doing something today, let's look at doing it 1% better tomorrow, and then the incremental improvement. And then the second is productive or revolutionary thinking. And it's. Just because the industry has done it this way for 100 years doesn't mean you should do it that way today. You've stepped back, Juan, and said, here is the market, Here are the changing demographics and psychographics of people that are in this market. And someone who is 25 years old

Speaker B: doesn't use a telephone for the purposes of talking.

Speaker C: They use the telephone for almost everything, but they manage their finances on it, they connect with people on it, they do their research on it, they use chatgpt on it. What they don't do is talk on it. And so the traditional model of you have to call someone or sit in an office to arrange for insurance is ludicrous to these people.

Speaker A: It is. But it's not only these people. I just realized that I don't have an iPhone and I just don't use it. I barely use it for calling. I, uh, mostly. And even when I call now, I use it. I use Google Meet or teams, just. I just, just have video chat just like this one. So it's just like the experience of the telephone is just. It's getting eight. In a way, when you see all the research on behavior, some people even find it intrusive. They actually interrupt you, what you're doing just to answer the phone. But I digress a little bit. So we saw this customer segment that exhibit different behaviors, but a given behavior doesn't warrant business behind it because you actually have to make money. And we started double clicking and we saw that this customer segment wasn't only a, uh, segment that was underserved. It was also a problematic segment for insurers because you see these behaviors also translated that this particular customer segment is online a lot. We are online a lot and then we browse a lot. We use price comparison websites to compare different prices and different coverages. So you have more financial literacy whenever you're going to buy a new policy. And so you know what you're buying now, you don't need to talk to anybody. So when you see how extensive we are using price comparison websites is that you put pressure on the top line because you cannot charge whatever you want because segment 60 and above, they still see price. Well, we all see price as a, uh, quality proxy, but this is specifically on 60 and over because it's in insurance. This is not a very transparent industry. And now in the 55 below you have price comparison websites. You have websites like trustpilot where you have all the different reviews of a given insurer. So you can actually make a composition of what an insurance looks like from pricing and quality standpoint. So you just have a very nice image to cut through the nose of marketing, which you can even pull up

Speaker C: Gemini and say, this is what I need. Check trustpilot. Make me a recommendation. And so with a simple spoken query, you can get the results of all that work. You don't even need to do the work. You have your, you have your AI go off and do the work.

Speaker A: There you go. And also when you look at that and you look at the financial literacy and the knowledge that we have, huh, that this particular segment had from the products you saw that not only this segment was putting pressure on top line because they wouldn't extra pay for their coverages. They would also use the product more so they would have more claims and more expensive claims because they knew what they're buying. So it also puts pressure on bottom, uh, line for an insurer. So you have pressure on top line and pressure on bottom line. It ends up that this segment is basically not profitable for insurers. So there's no incentive for them to just try to reduce something that's working for their actual profitable customers as opposed to do something for their unprofitable customers. So for us we saw this double, double sided problem. There was a problem in the customer, there was a problem in the industry and we started thinking like, oh, and this is 2021, 2020 when I started looking at it, there wasn't agents at that point. So if we can move these customers to uh, a self service data based platform where they can, they're so transparent and so easy to purchase their policies, then we can break this mantra that's very pervasive in Spain that says in industry that says that insurance is not bought, is sold. So we were like uh, oh, we're going to go against the grain, against 100 years of common knowledge and try to have our customers do our work for us. And that's how we started. And five years later we proved that uh, we could make customers buy insurance for themselves. And this is something that was only natural. Now it's obvious when you think about it because every other industry has it, like banking has it in telecommunications has it. And um, it was just insurance a little bit behind on times.

Speaker C: If I want to go buy a new Volvo, I can go on a Volvo.com and configure a complete vehicle and I think I can even have it delivered as a long term rental. So the consumerization has become nearly complete. That's one side of the equation. The other side of the equation is let's not recreate a legacy business in the cloud. Right? You didn't create the legacy business in the cloud with its stovepipes of quoting and risk analysis and claims management. All that stuff you started from scratch of. How do we evaluate and manage risk from inside? How do we look at behaviors of our customers so that we optimize one, to provide a good and profitable service and two, we don't get taken because insurance is a place where sometimes people cheat.

Speaker A: Well you do it better. That's the ugly reality. When you look at legacy business. You have. Well and we, let's, let's talk about homeowners insurance. You have what, uh, 20 questions that uh, an agent actually move a prospective customer through. That's all the information that you have. Maybe you can, I mean if you're a bit, a little bit advanced, you can go to these different databases and um, plug information from the house. But what we do, one of the things we realized, and this is not currently, this is we could do it from 2020 is you can actually have so much more data because your Interaction with the customer is detailed that when we first started, we started building what we call the customer DNA. We didn't know what we were going to get from that customer DNA, but we knew that uh, data was so valuable that uh, we would probably find things that we couldn't imagine. And now it seems so obvious, but at the point, at the time nobody was doing it. Yeah, we build the customer DNA and we use it not only for pricing because that's the easy part. And I can give you some examples. Apple users, on average our claims are more expensive than Windows or Android users. And if you think about, think around that fact then it makes sense because um, an Apple phone is four times the price of an Android phone on average. So whenever you have it stolen, your cost of pay is going to be higher than other users. So that's something that went straight from the customer DNA to the claims engine and then plug back into a pricing. Because we could prove that if you were browsing, you were purchasing our policy from an iPhone or a MacBook, then you more often than not would have more expensive claims. We were completely integrated along the customer lifecycle. So we just had this information that we could plug back into the pricing engine.

Speaker C: I love that, I love the fact that you're using the device access to give you a window into the psychographics of the customer. And from now on every time I go shop for anything on the Internet, I'm going to pull out my Windows 5 machine.

Speaker A: Yeah, I mean of course, but yeah, but it's not only pricing what you do. Managing risk, for example, we could start seeing and this is segues from your insight, um, about psychographics and some of the behaviors that people were excited in, in the onboarding process would actually predict some of the behaviors that they would have along the life cycle of their policies. And this is very easy and not revealing any deep secrets that anybody that works in detail can actually imagine. We had an aha moment with is that uh, when you have this customer and they are like buying the policy, they're navigating everything else is normal. It may seem a little bit rushed because he's not reading as much, but whenever you get into the quote page and then he just like he scrolls up, scrolls down M doesn't read anything but then he reads one given coverage very carefully and just clicks and see more and then just reads everything and then just purchases. Ah, we saw that more often than not he would have claim related to that coverage in the very first week of his policy. So you shouldn't be using this, for example, this insight for pricing because you don't know that he's going to have it because obviously you have also customers that didn't have anything. It's just that where probably they were burned by a different insurer with that given coverage and they would want to know what are the solutions and all that. But then what we do is we flag it. We flag it. And when you have this particular customer and then he files a claim on the very first week or the second week and then you just look at that claim more closely. Right? So examples of these insights and things that we look at, we have this whole digital claims filing process where you actually go and you tape a video from your phone of whatever is happening. Or you just take pictures, you send us the pictures and. Or if you have something stolen, you send us the police report. I mean there's this whole different process for different types of coverages. And one of the things we look at and we find, and it's not, as you said, we can call it by his name, it's fraud. It's not a large degree fraud. It's just like very opportunistic but it's not very sophisticated just because of the same reason. So some of the stuff that we find is that you find either the video or the pictures were taken, I mean they're real, there's something that happened, but they were taken before they purchased the policy. So you knew that. I mean that particular claim is not covered because it's from before it was covered by you. So either that person didn't have a coverage before or they had a different consumer and then it wasn't covered either. Anything can happen, right? But and some of them, they erase the file name but you still see the metadata, right? So, so there's ways around it. Usually you see it on the uh, file name but then you find more funny stories that you see that the picture that filed it's from um, Google Images, but it's not a Google image from the like the Wet, like the 10th page, which I mean would be a bit more sophisticated. This is like the first result from the first page of Google Images. So you start flagging these behaviors and then you just start looking for different insights and you just do it automatically. More and more.

Speaker C: Thank good. Criminals are so lazy.

Speaker A: They are. We had this funny story the other day that was two weeks ago, but we had the first picture done by AI or at least the first picture built by AI that we actually realized it was AI. The funny thing we Were funny we were laughing about it, um, on our internal chat is that they could at least could have used Banana Nano, Banana Pro. It was just like really bad picture. It was just like you could see that it was like broken glass type of thing and you could see that it was fly a little bit. This effect that's flying on top of the table. It's not really on top of the table. And you can see that different pieces were different sizes. It's like, yeah, this is really bad. Well not really. I mean it's not bad in terms of inexact is that has all the different things that you could definitely see that it was the iPhone.

Speaker C: That's crazy. So again, you're using the tools of the time to create a better experience for the business and for your customers. And I'll argue that what you're doing is actually good for your customers because the vast majority of your customers who don't cheat, who don't try to commit insurance fraud, they want to have the optimal pricing. And when someone cheats, that drives pricing up for everyone. And so the more you can identify the cheaters and keep them out of the system and don't pay claims because you're both a broker and an underwriter, right? You actually hold the risk or you subrogate it, but you hold the risk. And so when you can avoid paying out a claim when because it's been falsified, that actually benefits all of your legitimate customers.

Speaker A: It also enables us to divert the way we treat claims. So let's say there's no flags, there's no, it's rated as something that's clear cut. It's just very, we do it very fast. But if we have something that we have to investigate that that's when we start, uh, we take more time because we investigate more closely. So it's not only help us, uh, all these different behavioral patterns that we've seen not only say it also it saves money, but it also saves time in their time of need. Which is very important that to realize that whenever you're actually filing a claim, you're very vulnerable because you had something happen with your car, with your house. It's a very central part of your life, right? So not before filing a claim was a very cumbersome process because the only data that a given insurer had was just a phone call. Filing the claim. Now because we have all these digital process and we have all these digital tools, you can make it very fast for a customer. If we verify that, uh, let's say the Perfect customer, right? It's a customer from like that's been three or four years with you never had a claim cookie cutter kind of. We have this. The video looks perfect. There's no issues with the metadata. It's just like, oh yeah, we can go very fast with this because that's what we're built for. We're built for people. That is not gaming the system. Whenever you see something that's a bit weird and you take more time.

Speaker C: So a lot of what we're describing from a marketing standpoint would fall under intent signaling, right? It's the activities that a customer takes interacting with your systems or your information offerings without being specifically in a selling conversation. And so one opportunity for the listeners is to, if you're not using intent signaling is. And I sold predictive analytics package 10 years ago to some very large companies. Right. This is not new. We've been doing predictive analytics and intent signaling and sentiment analysis for a long, long time. What I think you're pointing to that's changed is the ability of the organization to hoover up all that data in near real time and act on it.

Speaker A: For sure. Some of the stuff that we're doing that reports more to selling, I mean we focus a lot on claims and we do a lot of nice stuff over there in case, but we do, we have this agent and for us, selling is basically having as many customers go through our onboarding and get put on top right before one, uh, of the quotes and just making them decide that they want this. So we do a lot of work in digital marketing. That's basically our bread and butter, we think, as opposed to other companies that score competency from us. So we don't use agencies, we do it all ourselves. We have this marketing team that's basically. They do. And one of the big changes from 2021 when we started to now is obviously the gen AI revolution because I mean AI was still there in 2020 and 2021. And as Aziz, one of my co founders, used to say, is that, uh, the difference between AI and ML and machine learning is that machine learning runs on Python and AI runs on PowerPoint. AI was there. It's just like a different type of technology. And so we could do customer DNA that we talked about before from the very beginning. But now what we can do is that you can augment your teams and they can do all sorts of things that you couldn't do before because it was the complexity or the time that it would take, it would be too much. Now what would take I don't know, a team, days of analysis. It just happens in minutes. For all your listeners that are aware that know about digital marketing, you know that in, let's say Google, which is like the biggest search engine in marketing now, you have your campaigns and you before, because you couldn't be as granular, you would have reduced number of campaigns and then you would have what it's called broad matching is you have one or two keywords and then you just everything else that has that keyword, those two keywords, then it matches and then it spends money on that. For a human just to go to, I don't know, 100 or 150 different campaigns with 100 with exact matching, that would only be for that exact keyword. Just like it's just not manageable because we are not able to. Or if you're able to just have to continue to be doing this all the time. So now we build this agent, we build this MCP as a protocol just to strike all the data from the ad platforms we use in the Met as well. I think Google is pretty and it paints a good picture of what we do and we generate insights, we reduce basically the time to insight, I don't know in 90% because what we did before in a few days now we do in minutes. So what it does is it goes through all the different campaigns and all the different keywords and it's just like as you say, the skies. It identifies, uh, waste, suggests actions to reduce that waste. And then some of them, we were able to execute them automatically. We have this thing about uh, confidence thresholds. Like the machine would even, it would have a suggestion and then it would also assign a confidence threshold. And if it's very confident, then, well, you can just go ahead and automate it. I don't want to look at it and have this machine that's enabled us to turn marketing into this process of always on optimization in a loop. So we've gone from a very smallish set of campaigns with broad matching for keywords to have a very extensive tree with almost only exact matching. Because the machine can actually go through all of that and tell us which campaigns are performing well, which are not, or maybe even these keywords, you can just get rid of it. Or maybe you are. And before it would go for the broad matching, I would tell us, oh, you have, you're matching for all these keywords. They seem informational, they don't seem inclined to make someone sell. And then you would pull the different customers that had gone through our Onboarding from those keywords, from those campaigns. It's like yes. See your click through ratio is just like not good enough. It's just, I don't know, it's like two times or 1/10 of what you're earning from these other different keywords and they just get rid of those because it's just not profitable for you. And that's how you continuously optimize your marketing expenditure and you get the actual customers that are eager to purchase a policy in the exact time.

Speaker C: So this is crazy, Juan. So when I first was involved in marketing, campaigns would run six months. Now what you're describing is a specific instance of a campaign might run five minutes. You've got that near real time ability to change on the move. And part of what you said in the process is looking at the campaigns that drove engagement and conversion and then looking at the rates of engagement and value of conversion to go back and then retool the marketing, which in the past sales and marketing haven't worked that closely together in much of the world. Marketing does the one to many and they throw some leads over the fence and sales goes, this is crap. We're going to do our own. And like it's never been a happy relationship. And now what you've described is an environment where there's a bidirectional feedback loop of we're converting customers that come down this pipe faster so let's get more of them and back up.

Speaker A: That seems obvious now when we're talking about it. But we didn't start that way. We started like everybody else. We had marketing and we had in our onboarding which was product, we started looking at how we could improve the number of people that would actually go from the ad to our website. But then we just realized, oh, but maybe the signal that we want as the closed loop signal is how many people actually see our price. And then it's just like, oh, we have these many people see our price. Why don't we go further and we start looking at the actual people that are buying because that's the higher LTV that you're going to get from that. If you look at people that actually go from the ad, uh, to the website, then you can run a campaign for five minutes but then as further in the funnel that you're looking for that signal, then you have to run your campaign for longer but. And now we have ended up on week to week analysis. That's the amount of time that we found that uh, it was the better for the purpose of selling insurance and ah, we started only looking at keywords. That's the first thing that the agent did for us. But then we started looking at oh, maybe we look at copy from the ad. I mean we started having different ad groups for that same keyword. And then you started optimizing for the copy as well. And then you start looking at uh, oh, maybe we have the copy, we have the keyword, maybe we expand that. And then you start looking at the website and then now you have your, you have the agent looking at different combinations of ad copy, website and keyword. And then you start looking at oh, maybe we look at the end of the funnel as well. And then now we have uh, we have this machine that enables us to have all these different combinations. It would be impossible for a person to do. And two months after just having this is an unfinished product. We continuously improve our whole marketing system but we also continuously improve what the capabilities of the agents. This is something, it's basically the Toyota uh, process all over again is an ongoing m loop of improvement both from the tool, the agent and the copy. Well, the copy asset, the marketing assets. Marketing assets are uh, not now just the ads. It's everything. Everything is marketing or everything is sales. You could look at it from any of the angles. For us it's the same thing.

Speaker C: Yeah, I love that you've reinvented the process of buying insurance. You've also reinvented the process of selling insurance and you've reinvented the process of managing an insurance company.

Speaker A: Well we, we're still, we're still in process of that, reinventing that. And, and that's the thing, that's the beauty of it. I think one of the, and this is probably we're getting into a different segue of the conversation. One of the things about AI is that I believe it's a technology that's going to be powered by startups or smaller companies or founder led companies. I mean it doesn't have to be startup because public companies are so large, they have so entrenched processes, they are very deterministic. For a project to go live in these companies they have to have a very clear image of success and a very clear goal and deterministic view of where they're going. And sometimes with AI, uh, you just don't know because the technology is so new, the capabilities advance so fast that as I said we started looking at keywords, then we started looking at keywords and copy and then we started looking at keywords and copy and landing pages and then we started looking at the whole process and who knows where we're going.

Speaker C: Now that I've said the company name out loud. What does it mean? Juan? What does it mean? What does TU IO mean?

Speaker A: So Tuyo, it's a, uh, Spanish word. Well, not the one that we use, but there's a word, Spanish word, there's T, U, Y, O that means yours. It's basically, it means yours in Spanish. So when we started we had, we realized that Tuyo, which is our, our name, which is T, U, I O, it's a wordplay on Tuyo. The dot com, uh, was available and it wasn't too expensive. And dot com, um, four letter dot com, for those that know they tend to be very expensive. They also tend to be very trustable. And since we thought trust was a very good, I mean was something that was very desirable for a new insurer, we thought that was one of the signals that we could use, we could leverage specifically if we were targeting very online type customers. So that was very desirable. And then we realized, oh, we can do a wordplay with this as well. So we claim for two years we protect what's yours, which is protohemoslogger Studio, but now we can use our name, which is Tuyo without the Y. And that's how the name came to be.

Speaker C: Yeah, I love that because it signals your context of doing business. Right, right. It's a statement of what the company stands for.

Speaker A: Yeah, we protect what's yours. And what does an insurance company do? That's what they do.

Speaker C: That's what they should do.

Speaker A: Yeah, that's a completely different story. It's what all of us should do. And that's how we ended up on the name. Funny story about the name. We named ourselves Coconut. At the very beginning, the brand in office to sold us that we couldn't because there was already a company called that in the uk. I was like, we were about to launch to market. We were like, oh, we had everything done for Coconut and we have to redo everything. Luckily enough, now that I think about it, it was just before launching to market. But even if you. So because if you've launched to market, I mean that's a completely different ballgame. It was the three of us and two interns. We started like trying to think about names and we started. We had this whole convoluted, very engineer like process to select and say number. It was. It's a kind of like crazy democracy. And the funny thing is that the only person that actually got declaring for the name was one of the interns. She's still in the company now. She's still with the company five years later. It was her first job. She's still with us in the marketing department. We still have the picture of that whiteboard where we all give her a

Speaker C: call out by her first name.

Speaker A: Yeah. Irene. That's Irene.

Speaker C: What's her name?

Speaker A: Irene. Irene in Spanish. Irene. She had the clarity to pick a better name than those that we were trying to pick. I still have the picture of, of the whiteboard where we all started to describe the different names from a, uh, long list of names. And some of them were awful. We're so lucky that she was Vidada and she actually picked that name that.

Speaker C: Because you had come from orange and you were looking for another round edible thing, coconut. Now, couple of things that come to me as a coconut, they're kind of hard on the outside, you know, spiky on the outside. You can't get in. And when they fall on your head, it hurts.

Speaker A: And I hate coconuts. I do hate the coconut flavor. Like whenever there's chocolate with coconut or everything, I just like. And I don't want that. I just hate it.

Speaker C: I guess you didn't go with the executive override and said no, no.

Speaker A: Yeah.

Speaker C: Well, Irene, well done.

Speaker A: Yeah. I mean, the name. Now, if you think about it, so much better, it enables us to do wordplay. It enabled us to buy the dot com name. Uh, it's just like. It's so much better.

Speaker C: Yeah. I had that epiphany when I first saw your email. I love that. So.

Speaker A: Hmm.

Speaker C: Yeah. Juan, we've covered a lot. We could keep talking about business and AI and cycling and coconuts and other stuff, but we've covered some important stuff of thinking differently, not taking what's given as given. Because in today's business environment, customers need us to think differently. Customers need us to serve them differently to their benefit and to ours. Right. Drucker said the purpose of a business is to create a customer.

Speaker B: Right.

Speaker C: It's not to create great widgets. It's not to build tall buildings. It's to create customers. And you've taken a customer first perspective on building the business and how you engage with customers and how customers are served. And I think moving out of the insurance industry, this is a general purpose learning for anyone running a sales organization or, or anyone who's a, uh, chief revenue officer who's responsible for sales and some form of marketing, building systems that serve the business and the customer as opposed to. That's how we always did it. That doesn't fly anymore. So one where can people find you?

Speaker A: One they can find on tu.comt u I o.com they can find it in our socials as well. And if I have to say to give, uh, a final remark, and B2B is obviously different, but B2C, to me, there is no more marketing or sales. There's marketing and sales. And it's. To me, it's just, I mean, the chief Revenue officer, as mentioned before, it makes a lot of sense because it's just the same thing. One and only M. You just want to sell your product to a customer. Everything else is just noise and tools. That's it.

Speaker C: I love that.

Speaker B: Juan, thank you for your time.

Speaker C: This has been absolutely fascinating.

Speaker A: Yeah, it's been great. It's been great.

Speaker B: Thanks. Another deep dive into into the topic of sales excellence and the performance mindset. If you found this conversation interesting, I would appreciate it if you would share the podcast with a coworker or two. And to explore this topic in more depth, send me a note via the contact form on podcast. Thoughts on selling.com or find some time for us to talk at meet.accelorgroup.com Thanks.