Joel Passen is a proven technology entrepreneur & Founder of Sturdy.AI. In this episode, we dive into the future of AI in Customer Intelligence, discussing the ways that customer data can become more accessible and actionable. Just how crucial of a role will AI have when it comes to businesses interacting with & understanding customers? What are some myths surrounding these new tools? Find out more in this episode!
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Episode Summary
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The Journey
Joel Passen is a seasoned entrepreneur with a rich background in growing and founding start-ups. His first venture was a managed service provider, which led to developing technology that became the foundation for his subsequent start-ups. His second venture focused on recruitment and human resources, which was eventually acquired by Paycor, the fourth-largest payroll provider in the United States. And in 2019, Joel co-founded Sturdy.AI, a customer observability platform, with a mission to revolutionise how businesses understand and interact with their customers. This episode dives into Joel’s career beginnings & the story behind Sturdy.AI.
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The Future of AI in Customer Intelligence
Looking ahead, Joel envisions a future where AI agents can deliver insights in real-time, meeting users wherever they are—whether in CRM systems, CSPs, Slack, or Teams. This proactive approach means AI will not only analyse data but also alert users to significant developments, effectively “tapping them on the shoulder” with crucial information. Sturdy.AI is also exploring the potential for voice-enabled data interaction, making it even easier for users to access and act on their data.
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Practical Uses of AI in B2B Relationships
Joel explained how AI can play a virtuous role in B2B relationships by helping businesses understand these relationships at scale. The cost of acquiring B2B customers is typically higher, and maintaining these relationships requires significant operational expenses. AI can help businesses identify risks and opportunities within these relationships, leading to better customer retention and potential upsell opportunities. By analysing customer interactions, AI can provide insights that would otherwise be difficult to obtain from traditional data sources.
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To find out more about Joel, check out our full episode – available on all your favourite channels. Now including YouTube!
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This article summarises podcast episode 112 “Sentiment Analysis & Debunking AI Myths in Sales” recorded by CX Insider.
Written by Octavian Iotu
[00:00:00.77] Octavian: Welcome back to the CX Insider Podcast. On today’s episode, we’ll be talking to the co-founder of sturdy AI, Joel Patterson. We’ll be talking about sentiment analysis, as well as the practical uses of AI in sales and the myths surrounding artificial intelligence. Enjoy the episode, and if you do, subscribe to our YouTube channel for more videos. This episode has been brought to you by ACF technologies global leaders in customer experience management solutions. Now let’s get into the episode. Welcome back to CX Insider a podcast. My name is Octavian and I’m joined by my co-host Ronnie. And we have a special guest that goes by the name of Joe. Can you tell us a bit about yourself, your company, and what makes it different from the rest? Yeah, sure.
[00:00:45.20] Joel: Um, my name is Joel Parson, and I’m actually one of the co-founders of sturdy, um, websites sturdy AI, but, uh, my background is, um, this is my third startup that I’ve, I’ve actually founded. So I come at this from, uh, more of an operator. I’m a commercial person, by and large. My first startup was a managed service provider, technology enabled managed service provider. We ended up developing some technology in that business, and when we exited that business and sold it, we extracted this intellectual property. And I started another startup. We leapt into, uh, another startup, which was an application essentially for recruitment and human resources. We built that company up, bootstrapped that company as well, built it up to a pretty significant size, a couple thousand customers, and ended up selling it to the fourth largest payroll provider in the United States, a company called Paycor. I joined Paycor and played a variety of roles there, and I left and did some work in the VC community. Later was put into a portfolio company, actually in London, and uh, ran global revenue for this company and uh, did some fundraising and that sort of thing. So in 2019, I started sturdy and, uh, you asked the question, what makes us different? I think first it’s, uh, folks that are watching. It’s a customer observability platform, which is kind of a new category for people we look at instead of KCS. We think about it in terms of CI, which is customer intelligence. What makes us fundamentally different is we’re not just a generative AI platform that’s summarizing text. We’re actually an applied AI platform that builds narrow language models to help discover risks and opportunities. So that’s the that’s the back story.
[00:02:17.96] Octavian: Practical uses of AI in Post-sales can be quite confusing. So what are some ways to better understand these models?
[00:02:25.55] Joel: Well, it’s still confusing. Frankly. I think the market, by the way, got really confused when we started to really realize the power of some of these large language models. I was always sort of taboo anyway. I mean, we started working with AI at sturdy in 2019. Building language models. The acronym is NLP or Natural Language Processing, where we’re analyzing language for specific things like behaviors and sentiment and that sort of thing. But for buyers, I think it’s really confusing because it’s early days in terms of practical use. So, you know, there are things out there that are talking about predictive this and predictive that. And I still think that we’re early days in terms of going through large amounts of data, sifting large, large amounts of data, and then making sense out of that data in aggregate. And I think that’s the most practical use of AI today. That’s what it does best. It’s able to go and process enormous amounts of information accurately with no bias. The AI doesn’t care. It doesn’t cover its own butt, for example, and it doesn’t get tired. And the more data that you throw at it, the smarter it gets. And so I think in terms of the way that people look at practical applications is like, you know, what do you want to move the needle on? Is it productivity? Is it revenue? Is it better insights about your customers? I think the confusing thing is there’s a lot of stuff where solutions have raced to market to like write emails for people. And I don’t necessarily think that that’s as valuable as other solutions in the market right now, but that’s an easy thing to build. Or chatbots, chatting, finding knowledge base articles and that sort of thing. I mean, that’s kind of table stakes. It’s kind of the Delta these days, a.
[00:04:01.94] Octavian: Bit of a broad question, but what are some myths that you can dispel about AI?
[00:04:06.80] Joel: I think the biggest myth in the Post-sales world and that’s, you know, the constituents that were probably talking to today and folks that are listening is that this stuff’s going to take your job. And I’m here to tell you that it it may at some point take some of your, some of your job away. But I think in the early days for the next couple of years, it’s more about augmenting your job and and allowing you to get rid of these mundane iterative tasks manually entering data or transcribing data from one system to another, entering notes into Salesforce or you know, your CSP like that kind of stuff. No one wants to do that sort of thing. And so I think the biggest myth is like, oh, this is coming for my job. And I would tell people I’m like, I don’t really necessarily think it’s coming for your job. I think it’s coming for a part of your job that you don’t want to do, and that if you embrace it and understand it and start to learn about it. It’s like anything else. If you have some intellectual curiosity, this is not going away. I is just not going away. And yeah, there’s a hype, there’s a hype circuit on it right now. And, you know, people are probably getting sick of talking about it or hearing about it, but frankly, it’s it’s kind of a pretty large drumbeat. And there’s a lot of dollars that have come into this space and that’s undeniable. And therefore it’s not going away. And Big Tech is driving those dollars thinking that this is a going to take your job, and B, it’s just going to die down are two big myths.
[00:05:31.58] Octavian: Are is used so much recently through all sorts of industries. But when do you think I should not be used.
[00:05:39.47] Joel: Where I would be skeptical to use AI for example? Is making a hiring decision having I make, for example. It might be a good data point to tell me that somebody’s got the right skills and experience, but I think that in the HR work tech vertical, I think that’s where you get into some of this bias and responsibility, you know, and responsible use of AI where it’s really acute. It could create an adverse impact on anyone and particularly a protected class of people. And that’s what comes to mind first. And I had been in the work tech space for years. My first two companies were in that world, The Future of work, and I’d be a little dubious about using it to make decisions. Now, again, sifting data, finding data, um, increasing productivity, doing, you know, the data, the menial, iterative manual tasks that come in work tech, you know, entering things from one system to another. I think it’s fine. I just think when we get into making really acute decisions about people, that’s where I start to get a little leery and say, hey, you know, that’s that would be something I would want to investigate more.
[00:06:46.16] Roni: What about sentiment analysis? We’ve heard this term. What does it really mean and what does it matter?
[00:06:53.30] Joel: I might actually offer kind of a, uh, an unpopular view here, but sentiment analysis has been is not a new thing. Um, AI has definitely facilitated it, made it, uh, probably more accurate and easier to derive to get get our hands on, but it still doesn’t answer the contextual. So, for example, I’m a pretty classically trained CRO. And let me give you an example. First, just to kind of paint the, you know, the way that I look at it and I go into account review meeting with my largest accounts, you know, my post sales leaders, account management, CS, whatever it may be. And I sit down in a meeting and first of all, maybe they’re showing me green, yellow, red scorecards. And by the way, I tune out, I just I’m just being honest because I don’t necessarily trust green, yellow and red. And the reason being is as an executive leadership team member, my job is not to just check the box and say, hey, it’s green, hey, it’s yellow, hey, it’s red. Okay, great. I’m going to take that at face value. My job is to ask questions why? And when we take that same notion and we apply it to sentiment. Ronnie, if you came to me and putting you in the spot a little bit, but if you said, yeah, all of these customers are very up and to the right in terms of sentiment, great. What are we doing different? What contextually are we doing different with that cohort of customers that are in the green in terms of sentiment? I keep wanting to peel back the layers and sentiment is just like surveys to me to a certain extent.
[00:08:18.26] Joel: Like I want to peel back the layers and I want to understand the why. Just giving me an NPS score, just giving me a sentiment score. It’s just another telemetry based data. At the end of the day, without contextual insights. And what I’m really, I think, more acutely interested in is sentiment combined with specific behaviors that created that sentiment. So for example, if you said, hey, this cohort of customers or this segment of customers is unhappy or confused sentiments, just a top layer metric, it’s not the insight that I need to then go and take back to the team to solve acute problems, or change a process, or swap out an Am or make decisions. So I’m still left in a vacuum on what’s causing it, and that requires further analysis. So I think we’re already through sentiment. So a lot of times I’ll tell people is like, I’m not as interested in sentiment as the next person. I’m interested when it’s combined with specific behaviors that give me the contextual clue or the answer to why I would tell Post-sales leaders you should be using sentiment, just like you should be using surveys. And by the way, I’m not poo pooing health scores. Like, yeah, it’s a part of your lexicon of reporting, but it’s not to be relied on solely because context is king in, you know, going back to the AI piece, I can kind of tell us what’s going on inside of those pretty static or or those data sets that are looking in a rear.
[00:09:47.26] Octavian: Does Joe have any successful case studies to provide around sentiment analysis?
[00:09:52.78] Joel: We have customers that use sentiment the like. For example, unhappy sentiment combined with other specific behavioral signals to understand what accounts need attention and what should be escalated to specific account managers. So one example, and I’m doing a podcast on this on my own LinkedIn around a company called Hawke Media, which is the largest independent technology enabled marketing services company in the United States, possibly in the world. And they use sentiment combined with behavioral signals to understand which accounts need their attention most. Therefore, the people that they have in their business that are dedicated to mining their customers, providing customer service and account management, instead of just going through the motions and sending the emails and looking at the customer journey and using these sort of prescriptive motions, they actually are able to use sentiment combined with behavioral signals to surface the accounts that need the attention right now. And what’s happening, Ronnie, the outcome is that they’re seeing revenue lift because they’re creating interventions with these customers in a preventative way before looking in arrears and having to do a postmortem and saying, why did we lose them? So they’re getting ahead of these risk events in real time. And it’s also informing these people’s work. And that’s what we hired people on the post-sales side to do is keep our customers. At the end of the day, that’s kind of like the core piece, like we sold them a product or service, right? We want to keep them using the product or service because we want to enjoy the compounding are that that’s what creates business valuation. So if we can use that data to stave off loss, I think that’s a really virtuous use case. And Hawke Media is a good example of that. And there’s some others on our website, npr.org. Regional payroll providers, companies like that.
[00:11:39.94] Octavian: Hold on. Before we get into the second part of the episode, we have a few words from our sponsors. Just imagine having all your clients and staff member opinions in a smart feedback system. Well, now you can with a powerful tool to improve customer experience. And here’s how they do it. Firstly, they collect feedback through kiosks, SMS, QR codes, call centers, emails and more. Secondly, they analyze the data with detailed reports that help you understand your customer needs and predict their behavior and implementing change with the ability to measure impact in real time. Here’s the key features. Create surveys in minutes. Capture information about the customer journey. Provides simple and intuitive solutions to create feedback. Contextual surveys help create stronger relationships with your customers and improve brand loyalty. Customer feedback solutions by ACF technologies.
[00:12:43.29] Roni: What’s a great customer experience that you can think of that you can attribute all of this to?
[00:12:50.16] Joel: If you think about the nature of B2B versus B2C, right. I look at B2C as foundationally is a little bit more of a transactional type of relationship, right? Lower cost of acquisition. We don’t need as many people to service B2C customers, particularly across the board. Right. If like we bought like some sort of pet food subscription, for example, you know, what’s that subscription worth? Their B2C is about volume and B2B is to a certain extent as well. But B2B, what we’ve done is we’ve spent, you know, the cost of acquisition in B2B is typically higher. So we’ve already sunk costs into developing these relationships. We then have increased our operating expenses or our opex to service those customers, to provide them with humans that can listen to them and resolve their issues, keep them happy, keep them deriving value from our product. That’s what everybody’s trying to do. The reason that I is really virtuous to the B2B cycle is it helps us understand those relationships at scale, and helps us understand the risks and opportunities within those relationships, not only to help stave off loss, but also explore expansion or upsell or advocacy. More references. That’s a really virtuous, again, use of AI in a B2B motion, because we’ve invested all this time, energy and money to develop a better relationship. And at the end of the day, it’s just like a human relationship. Our customers want us to listen to them better.
[00:14:12.63] Joel: They’re giving us the answers to the test. They’re telling us what frustrates them, what processes they don’t like, what parts of our product that are annoying to them or that they like. And if we’re not listening to those cues at scale, at any kind of aggregate, then we’re just going through the motions in B2B and everybody will go back to just adding more operating expense by adding more humans. To service these accounts. So what I find oftentimes is I think key leaders are starting to get the memo that just because we’ve added 100 more accounts this quarter doesn’t necessarily mean that we need to add 20 more heads to the operating expense on the post-sale side of the ball. I is giving us the efficiency to be able to listen at scale and comb through data at scale and process data at scale, and get our data to work together better than we’ve ever had before. Therefore, our margins go up and our people are working on the things that matter most, not just going through the motions. And I think this is particularly relevant in like hybrid work, like there’s so many people that are post sales that are hybrid and we don’t have that connective tissue anymore, where we’re just sitting in big rooms of people and having these conversations around the water cooler. We need better data. And B2B thrives on better data.
[00:15:27.30] Roni: So as far as trends do you see in like the post sales motions, do you see trends that you’re encouraged by?
[00:15:36.51] Joel: I think it comes back to what I described at the top of our conversation in my intro. And the biggest trend that I’m seeing today is this leaders and executive team leaders at large, operations leaders, people that are trying to create a cohesive bond between departments, product support, marketing, sales, customer success. Right. They’re starting to lean away from this notion of KCS. Kcs to me, is customer experience and is equated to telemetry based reports and adding more humans, I think people are starting to understand is that there’s a unique difference, and this is a trend between KCS and CI. When you’re a small company, you can operate on telepathy, right? And as a founder, when you’re small, you’re having daily interactions with your customers. You’re gathering feedback from your customers that it’s really important to combine with other information or data so that you can scale. So for example, like I listen to my customers, I talk to our customers all the time. I have my finger on their pulse like I know what they like. I know when they’re frustrated. I know what they want. We’re listening to them. As you start to grow. You put in KCS. So as a as a founder or as a leader, I start to step away from that KCS. And what I lose is the CI. I lose touch with the day to day touchpoints, like, I don’t know exactly what our customers are saying about our product anymore, or our new features or our new modules.
[00:16:59.52] Joel: I have to rely on KCS to do it. What the industry at large is starting to figure out is that that delta between KCS and CI, when you start to lose the CI and you don’t listen to it anymore and you just put people in place to listen to it, you lose touch with your customers and it leads companies astray. In every single company that I’ve ever seen and been a part of witness been on advisory boards I invest in. They go through this trough of despair, and it’s usually around 2 to $5 million in recurring revenue, where they lose touch with the customer and things go off the rails, and then they have to come back and triage and get Tiger teams together and put in processes to listen better. And I think that’s where AI driven CI is really cool, because fit can keep us in touch with those moments, with those behaviors that help us build better products, that help us better build better processes that help us coach up KCS professionals, better for better quality of answer and better service. And that improves the customer experience. Without data, you can’t improve the customer. You can’t just throw bodies at the problem. And I think I think that’s a trend. People are waking up to that trend. It’s encouraging.
[00:18:12.63] Joel: Yeah.
[00:18:12.93] Roni: It’s fascinating. Brilliant.
[00:18:15.18] Octavian: I had a question. Sturdy I was founded in 2019 right. Yeah I know it’s a big question, but where do you see sturdy AI five years from now?
[00:18:24.90] Joel: So let me take a really big question and let me provide a little context for my very big answer. Mhm. What Ksp’s Customer success platforms and CRMs have been built to do for the last 20 years is gather information. And I think, let’s be honest, most of these systems are garbage dumps. You know, if it’s important, put it in your CRM, got to be logged in your CSP, their dumps. Right. It’s hard to go through all of that stuff and get any insights out of. And so I think the industry building application software has delayed and ignored, for the most part, a fundamental problem that people wanted. They bought these systems to manage their customers better. What they ultimately did is created a database to put their customer information in, and it provides very little intelligence, maybe some quantitative metrics around, you know, how much usage someone has, which, you know, by the way, valuable. Send more surveys. Sure. But at the end of the day it’s kind of table stakes again. And these things are dumps. And so what we tried to do at sturdy when we built this thing, and by the way, we didn’t get into the market thinking, oh, AI is hot, we’re going to go start an AI company. We got into this market to solve the fundamental problem that we had as operators, and we were building companies, which is we wanted to keep the CI front and center to the executive team. So we knew what to build. We knew what processes would work.
[00:19:46.31] Joel: We could train our people better, make them more valuable. And so we built this because we believe and continue to believe that the fundamental problems that a lot of the technology out there that exists today for professionals is really just warmed over. Crm where I see this going is now that we’ve got and can expose a fundamental data set, by the way, by volume, by behavior, by value, a super important data set that’s been really, really hard to get our hands on email, analyzing email, imagine emails, a house party man, people are stealing your spoons and spilling red wine on your carpet all over the place, and you don’t know that it’s happening because it’s locked in these silos across the entire organization. You know, if you’ve got 25 people, there’s 25 silos of data that you are not analyzing in any kind of scale today. Sure, they may be in Salesforce or Gainsight or wherever, and I’m not admonishing those platforms. But we don’t have any any kind of aggregate insight into what’s going on. Ticketing systems and other silo recorded calls like this, for example. There’s stuff being said in here. If we were on a customer call, it’s really valuable chats. By the way, the more surveys you launch, the more work you have to do to analyze the surveys. So what we believed is that the spoken word or the text based word of our customers, the voice, the literal, unabridged, unbiased voice of the customer is a data frontier that no one is really leveraging its scale in any kind of way.
[00:21:16.45] Joel: And some companies are trying to build tools on their own, in their internal teams to do it, but it’s really expensive. It’s really hard. And by the way, data analysts and AI people, they don’t want to do the data wrangling and de-identification, you know, AI doesn’t exist without data. And unless you get that data into a format that AI can use it, it’s irrelevant. It won’t work. And so we wanted to solve the fundamental problem first. That’s what we do today. We get all of this data working together, working with your CRM data, working with your CSP data. We tear down the silos. We get it all into one pipe so we can analyze it with AI. Where we’re going in the next five years is making certain that people can choose behaviors to analyze, and that that data isn’t left to them to go and find and tick away at and pick away at and analyze with reports in arrears that in real time, an AI agent can deliver an insight to somebody in the moment and meet them where they’re at. Could be in a CRM or CSP, could be in slack or teams, whatever. Wherever they want the data to meet them. It goes and finds them, essentially tapping them on the shoulder and say, hey, there’s a brush fire in aisle five, and it’s something that you should pay attention to because it’s in your book, and we’re months from that. And then the next piece of of this is allowing people to actually interact with their data, like they would interact with anything else in the world, like ask it questions, even voice enabled.
[00:22:41.53] Joel: So I could say, hey, sturdy, for example, tell me the in the growth customer segment, what’s the most commonly requested feature in the last 30 days for our customers in this particular segment owned by Ronnie? Then I want you to take that data and I want you to import it into JIRA. And I want you to create a report, and I want you to send it to me in my email. And instead of the hours and days it would have taken to take all of that data across all of those silos and have it delivered to Ronnie, we do it in real time and that’s where sturdy is going. So all of the data across your organization, making it actionable so you can query it in real speak in real time and then have it find you instead of you searching for all the data all the time. That’s as humans and knowledge workers. You got to admit, a lot of our time is spent looking around for data. We’re like smart people and we spend all of our time looking for data. But the data already exists. Why can’t it just find us based on our interests? And that’s the the vision that we have for for sturdy. It’s not building automated playbooks and writing emails. I mean that again, I kind of look at that as table stakes. We’re already there, I love it.
[00:23:49.72] Roni: I want that today.
[00:23:53.14] Joel: Not that.
[00:23:53.53] Joel: Far out. So when we talk about the vision, the thing that makes the project that we’re working on at sturdy, I think fundamentally different from what’s out there in the world is we’re saying, okay, most solutions have left the hard things for dead. And unfortunately, you have to solve the hard things first, which is all of the data wrangling and all of the data cleansing. Before you can even stick it into AI. And then the AI is evolving in such a way that, yeah, you could talk to your data and yes, it can find you based on your preferences, based on your account lists, all of that metadata and data working together is the future. And that’s happening. And, you know, that’s what we think is a really exciting thing to work on.
[00:24:34.63] Roni: Let’s go back in time a little bit. Joel, what was your favorite subject in school?
[00:24:40.63] Joel: Political science.
[00:24:41.74] Joel: Really?
[00:24:42.40] Joel: Not because I’m a politician, but, you know, I’m not a data. I’m not a, um. I’m not a scientist. I’m probably a stronger right brained person than I am a left brained person. You got to know your strengths and be honest about them. Political science is a culmination of some statistics, right? Polling and all the things that there’s a statistical or data driven element to it. For real. There’s a historical element to it. And that’s kind of the sciencey part. And then there’s this hyperbole rhetoric, positioning, kind of a marketing element to it, which is really cool. So I always thought that it was multifaceted in its art and science. And for me, that appealed to me.
[00:25:20.80] Roni: What would you say to students today? What would you recommend that they go into in the science or in the IT space?
[00:25:32.68] Joel: I have a controversial answer.
[00:25:34.45] Joel: If you would have asked me the same question two years ago. It depends on your motivations, obviously, but I know you’re asking kind of a carte blanche question, right? Like kind of an interesting question, but, you know, go get a software engineering job or a data intensive software engineering job. Go get a degree where you’re involved in the data and you’ll always have a job. Fast forward to today. I’m like, hold on a second. You know. Ai is evolving in such a way. I just described the vision of sturdy like where we’re going, data finding you. My guess is that one of the places where there’ll be a lot of innovation, and probably less demand for skill sets, is around processing of data around coding. I mean, you can go online to GPT today. We do this. It’s sturdy. We leverage large language models to help write code. So I would tell people that if you want to pursue an engineering degree, it definitely needs to be aligned with data and AI. You want to be on the tip of the spear with that. But more importantly, I think that our world is now becoming. If you’re thinking about it just in a general way, you have to. The general education of universities needs to focus on collaboration and problem solving and discovering patterns.
[00:26:46.53] Joel: So I think there are plenty of disciplines that can help condition people for what modern jobs will look like for, you know, a college educated people will go and pursue in the future. And a lot of that stuff is. Collaborating with other people. Being confident to advocate for yourself. Being able to use data in whatever job that you decide. So you can’t just say, hey, I’m going to go be a creative writing major and hope that, you know, I get a journalist degree. Every job is going to be influenced by data and the ability to work with other people and and do gross problem solving. And so there are lots of disciplines that general disciplines and general education that will help people pursue the jobs of the future. It might be too large of an answer, but, you know, I think general education get the foundations. And then if you’re passionate, you know, you might find that you’re passionate about being a doctor. That’s great. I still think that you have to have develop, if you’re not a left brain person that can analyze large data sets and understands trends and do trend analysis, I think people will be at a disadvantage to be successful in a professional career ten years from now.
[00:27:51.96] Octavian: Thank you to everyone for listening. I’ve been Octavian and I hope you’ve enjoyed the discussion. Let us know what you think of this episode by carrying our conversation on LinkedIn @CX Insider Podcast. This episode is brought to you by ACF technologies global leaders in customer experience management solutions. Let’s get into some quick fire questions. What’s your favourite vacation that you’ve been on.
[00:28:14.31] Joel: With kids or without kids?
[00:28:16.23] Octavian: Both. Both.
[00:28:19.20] Joel: I love Europe, traveling with little kids in Europe. I live on the West coast in the US, so that’s a little bit, you know, without kids, I would say exploring more European countries. I love food, I love experiencing the culture of different countries through food with kids. Hawaii, because my kids love the water and the sand and the warmth and the sort of the atmosphere of the islands. So Maui.
[00:28:42.66] Octavian: Was there any European country that really impressed you that really stood out to you?
[00:28:47.61] Joel: I love, uh, Spain and Italy again, going back to food Greece. Yeah. For me it comes back to experience again. It comes back to experiencing the culture through food. And I mean, the historical value of the food culture in Europe is amazing at large. Right. But, um, yeah, I think I think Italy, Spain and probably Greece.
[00:29:09.54] Octavian: Speaking of freedom, is there one specific meal that you just love? Like from any cuisine that is your go to your favorite at a takeout restaurant? Anything.
[00:29:20.97] Joel: So when I go out to eat in any country I live in Portland, Oregon, we, you know, despite the challenges of Portland, Oregon, which admittedly is not a first tier US city in terms of culture necessarily, or, you know, it’s not like I lived in San Francisco for 20 years, and I still think that that’s one of my favorite cities in the world, no matter what the problems are, every, every there’s always an evolution pattern in these cities, right? And countries for that matter. But, um, Portland’s got an amazing food scene. And when I go out, I think the test of the mettle of a restaurant is always the. Especially if you’re on the coasts. I like, you know, seafood, I like fish, I like fresh fish. And if a chef can prepare a meal that involves seafood because of the delicate nature of seafood, I think it’s a real test of their mettle. So I tend to gravitate to ordering seafood on lots of menus, especially if on the.
[00:30:10.77] Octavian: Coasts it makes sense. So outside of work, do you have any particular hobby that you love doing?
[00:30:17.79] Joel: Yeah, I tend to be more of an outdoorsy person. I live in Portland, Oregon. Um, the Pacific Northwest is pretty special, so I snowboard 25 days a minimum a year. I also do a fair amount of fly fishing and fishing in general, so I like outdoorsy things. We hike and fish and ski and snowboard and those are the things that I do when I’m not thinking about. The difference between CJ and CI.
[00:30:43.60] Roni: One last question. I asked that various parties, if you could have one superpower, what would it be?
[00:30:50.23] Joel: I mean, one superpower.
[00:30:51.07] Joel: I, you know, listen, being able to predict the future is a pretty strong superpower. I don’t particularly like to leave things with chance. If you think about what my day job is, is using data to create outcomes, right? Being able to predict the future is a pretty darn powerful superpower.
[00:31:10.96] Roni: That’s a good one to win the lottery, too. I mean, I mean.
[00:31:15.10] Joel: You could win the.
[00:31:16.60] Joel: Stock market. You could win a lot of things, I guess.
[00:31:19.90] Joel: Win at life with.
[00:31:20.92] Joel: Great with great power and super power comes great responsibility. So, you know, that might be a little daunting. So, you know, as long as we’re talking cocktail chit chat, that’s probably what I would answer to you. Uh.
[00:31:34.12] Joel: Right.