Numbers and Narratives
Numbers and Narratives bridges the gap between the marketing/customer experience and data - come listen to marketing and CX experts talk about how to use data to better engage with your customers and provide a great experience.
Numbers and Narratives
Leveraging AI for Better Go-to-market - with Aidan Parisian, Connor Group
Hey everyone - this week, we've done a much longer episode of Numbers and Narratives with Aidan Parisian of the Connor Group. We dove deep into AI's role in go-to-market strategies, the importance of data analytics, and the power of resilience in personal and professional growth.
Aidan shared insights on leveraging AI to democratize data and enhance customer experience, the challenges of data unification, and the critical role of consistent effort for long-term success. Plus, he recommended some transformative books like "4000 Weeks" by Oliver Burkeman and "Hidden Potential" by Adam Grant. Don't miss this episode—it's packed with valuable advice for AI enthusiasts and growth-minded professionals! Check it out now!
Aidan: https://www.linkedin.com/in/aidanparisian/
Connor Group: https://www.connorgp.com/
In the length of a tweet or a tweet thread. Can you please, please try to summarize what we talked about today.
Speaker 2:We talked about everything, but we focused on AI and go-to-market and data analytics and go-to-market and then we touched on everything else.
Speaker 1:I'm Ibi Syed.
Speaker 3:I'm Sean Collins.
Speaker 1:And this is.
Speaker 2:Numbers and Narratives.
Speaker 3:The likelihood of a business lasting increases with every day that they last. Right, like it's just to some level. If you can just continue to fight and survive at some point, either, the chances that you actually like have a great moment and grow and have a big financial opportunity are exponentially higher. However, I think if you focus too much on just like, was that a good shot on goal or not, you lose the like. You're expecting a feedback loop that is so short and it doesn't account for like, great things take time, right. So, like I did an Ironman last year my first ever time doing anything related to that that took a year of. I trained every single day. Like there was no point where suddenly, I like shed four minutes off of each mile, or it was like, okay, now I'm ready. Like the day of the race, I was like I still might not do this. I don't know.
Speaker 3:But, like you, I think there's something to be said for like, yeah, it's about getting shots on goal, but you need, if you're, if you're looking for a feedback loop, you have to shorten that window. And, just like you would in a sales cycle, right, if you have a long consideration sales product, you look for those micro conversions and gates to say someone's moved on, someone's done this. Then you can think about shots on goal. But if you want to do something meaningful, you have to kind of think about, keep it in perspective that you're looking at something that's like one to 10 years to 20 years down the road and it's more about showing up, like at some point, the shot on goal is just that you showed up and worked again and not that you got a result.
Speaker 2:Yes, adam Grant just listened to this book recently. Can't remember the name at the moment, but it's about basically all hidden potential. So it's about finding hidden potential in populations and people and why are people successful? And it's a fantastic book. He talks about performance in music and that you would assume that the kids who can play Chopin and are practicing you know, eight hours a day are the ones that are successful. But actually it's the kids who practice like 20 minutes a day because they don't burn out and it's the fact that they keep the love of that. My brother-in-law is a soccer coach. He talks about the kids. He can identify the kids ahead of time who have love for the sport. They identify the kids ahead of time who have love for the sport. They're the ones that do well.
Speaker 2:Because, to your point about radical incrementalism, as some people call it, the doing it every day and the experience it over time and the growing over time it comes with experience, it comes with time. But we're such a society of performance and outcomes. Everything is measured right. I watch my kids' frustration. If they can't master something quickly, they want to stop doing it and we're missing the. You know, you think about Emily Dickinson, right. That wrote for years before anything was ever published. The Bronte sisters right Same thing. That doesn't happen now. You get people now who are music artists and they're on the internet and they're famous six months after they started doing music. Where you used to have bands would grind on like roads, like dive bars, for 20 years before they got famous. We're missing the patience to develop true ability at a craft.
Speaker 1:It's the same thing in startups, right, like I like my, like every single time I have an investor meeting, there's like the, there's this like hidden agenda of like hey, like you've been doing this for like 16 months, like, why aren't you? You're growing 2X, why aren't you going 5X Like, why aren't you going 5x Like, why aren't you going 10x? And it's like you see, all of these I was talking about it this morning Avenir, which is a venture fund, came out with like a great deck this morning. That was just talking about, you know, the recent decline in sort of IPOs and the sort of flip growth in private equity, and me and one of my friends who's an investor there got into a chat about how often you just see there, got into a chat about how often you just see you know companies on LinkedIn that seem to be overnight successes. And, sean, you and I talked about Clay last week, which you know was a 10 year overnight success. Like they were founded in 2015, 2016,. Raised a massive round like two or three weeks ago and when you look at it, you're kind of like, oh, wow, like this company just came out of nowhere and it's like, no, they've been, they've been around for since 2016, which you know it's not not decades, but we're in 2024, like if it's taking you that long to do it.
Speaker 1:A lot of it is just you know, the companies that that that are around and can build long enough to survive. Yc has a really, really good stat, which is companies that go through y combinator and survive till year three have it goes from like a 10 chance of exiting to like a 50 chance of exiting. If you can just like hold on, it doesn't matter what you do within those three years, but just the fact that you've been around for three years. Just obviously, more of those companies just do stuff. But the wherewithal to keep trying, the wherewithal to like not fall apart at the seams when it comes to your culture, all that kind of stuff. I think, sean, going back to what you said, it sounds like what you're trying to avoid finding is like a local maxima, right, like you don't want to like find a local maxima and like sit there, but the more you try to find something, the more possibility you are actually having like some sort of breakout growth and it is that practice.
Speaker 3:The thing is you talk about it took 10 years, right? I'm not that familiar with Clay, but I can say that just through a quick search it looks like their founder kind of got started in 2006 in the professional world, right, so it's been 10 years as a company. He's been a working professional for 20 years. That means for half of his adult life he has been singularly focused on this problem and product. Right, like, regardless of how many changes and pivots, they've made adjustments like, talk about patience.
Speaker 3:Like for half of your working adult life the first half you arguably got the skills to even be qualified to, to see a problem, understand there is a business problem to solve and and develop the skills and knowledge and network to come up with a solution plan. It build it, get it some some mvps and first clients. Uh, so, like most of his working life has just been working on one problem. Like that's a level of patience that, like you know, some people like want to learn how to play tennis or golf. You talked about golf, right, like you go out one time you don't hit a 70. And so you hang up the clubs like, yeah, right.
Speaker 2:Come on Well and think about, right, there's company development. Right, that company is maturing over time and people are coming in and out of the company and things are changing and everyone there is learning. But he as an individual is learning. He's understanding himself better. You know, I think Danny Kahneman talked about the science behind who gets funded from VCs and so much of that is called the personality. It's the best pitch and it's whoever looks like the person who can run a company. Maybe he went and had that conversation or that meeting five, six, 10, 12 times, and it's this last one's the one you read about, because that's where the funding came from. But without that time period and that time on task, he doesn't have that success.
Speaker 1:Yeah, I mean, I worked at Peloton. That was sort of like. One of the guys who hired me was one of the co-founders, graham Stanton, and he told me that they went and pitched for like months before anyone gave them a shot.
Speaker 2:Have you read the Airbnb?
Speaker 1:Yeah, Airbnb is the best example of this. I mean, you talk about people that have been around for a while. Like for HR software, we use this thing called Rippling, which I find amazing. The company is founded by this guy named Parker Conrad, who fully founded a what you would consider a successful HR tech company called Zenefits. He was the original founder of Zenefits, worked on it for years and years and years and Andreessen Horowitz realized, hey, if we get this guy out of here, we can have this company exit and make a ton of money on top of it. This is apocryphal. This is the story he tells during YC. But he fully built a successful company and, due to some weird politics with his investors, he ended up getting almost nothing from it and he had to start over and started, you know, rippling in 2016 and has been just going since then, and only in the last like three or four years, I think has that really like broken out. The guy's been doing this his whole life, which is crazy.
Speaker 2:I like the offshoots right. It's like 3M. You know, the bots dots was invented because the guy dripped paint or something in his shop and he drove in the next day and was like, oh, look at that, there's a and I'll get the details wrong, but there was an article about the hold music on Slack. Have you ever been on a Slack chat?
Speaker 3:and you're waiting and it's got this very chill like huddle hold music.
Speaker 2:So it ends up the guy who started Slack also started. It was a file sharing, it wasn't like Box, but it was somebody else you would know. Basically, he's been trying to build a video game for 10 years and so he tried to build this first video game. It didn't take, but they wrote a piece of software to trade and oh, it was Flickr to share images for graphics files. And he ends up in his failure to build this video game, he ends up creating Flickr and then he goes back to try. He takes his Flickr money, he goes back to build the video game and in doing that the video game is about some sort of weird corporate other world and at some point you have to sit in a waiting room and that's one of the challenges of the game and it's this music, the challenges of the game and it's this music.
Speaker 2:Well, it ends up. The reason it's in Slack is that the video game failed, but they wrote a chat program called Slack. So the guy's been trying to make a video game for like 15 years and he created in the meantime, just created Flickr and Slack on the side as like a side effect. Right, I mean, I had no idea.
Speaker 1:That was what that was. That's insane.
Speaker 2:If you Google it, there's like a Wired article about the hold music and why it's so good, and the guy who wrote it is some like Americana artist from like Nova Scotia or something like it. The whole story is wild. The whole story is wild, but you talk about again. If that guy had taken failure, if he had been measuring to your point. If he's measuring his success as a video game producer, he stops, but he's not. He keeps trying and in doing so he's making the world a better place. There's these externalities. He's creating these companies just by being on task and doing something, and what a great study in just existing and being okay with that and seeing what you can create, as opposed to having to measure things. Now it seems very anti-thematic for us to talk about not measuring things on a podcast called Numbers and Narratives.
Speaker 1:Yeah, no, the other thing that this makes me think about is as a 27 year old, 1.5 X founder of a company.
Speaker 2:Uh, the, the data is really not, but especially when you guys, when you guys are I is one of the topics. Um, I actually I met with the founder yesterday who's got an AI tool and it's a buddy of mine's a CRO at an AI company and the amount of money in the space right now, I mean the math isn't in anyone's favor. He said they went to some Gartner AI show and his sales guy calls him and says, yeah, there's like literally 100 companies with our exact same value. Pitch right, because there's so much money in this market and it doesn't.
Speaker 2:When there's a market this hot, I don't think you have to actually be good. You just have to have an idea that seems like it may have money behind it and especially in a time when you know there's so much cash sitting and waiting, it's not that hard to get funding. I say that having never gotten funding, but it's. There's a lot of money out there to go start businesses. And so I mean you look at just the raw statistics. I think you said YC's got the three-year, but what's the average statistic of a company failure rate? Isn't it like it was like 90%? Yeah, it's 90%.
Speaker 3:Yeah, well, yeah, but to your point, like it's a, it's a booming market, people that's like all the buzz with unknown potential use cases and States and like, yeah, there are some big names in this space, but there's not. Like, it's not like you're going up. When HubSpot became a CRM against Salesforce, there was like already a model. Right, there isn't a Salesforce of AI right now that you're like okay, because there's so many ways to also niche down within the topic of AI. So, like you know, even within go-to-market, talk to 20 different go-to-market leaders, they're going to have 20 different approaches to it and so, if they created an AI that went off of their framework and the way they think about go-to-market, you already have 20 different, very different product flows and feature sets and ways of describing things and things that you want to measure and stuff like that, and we just it hasn't been enough time to know who built the best.
Speaker 2:And the product marketing pitch is so salami sliced, right, because there's so much, so many people in the space, it's like, well, yeah, mine's like theirs, but it's blue, like it's these weird idiosyncratic differences in the marketplace and buyers don't buy that way, right? Buyers are actually very unsexy. Corporate buyers, b2b buyers are unsexy when they buy. They buy based on cost, they buy based on workload and oftentimes I mean, one of our biggest competitors at our last company was just doing nothing because they look at it and they're like, oh, this feels complicated, I don't want to deal with that.
Speaker 2:I sat on a roundtable that was hosted by a large consulting firm up here in the peninsula in San Mateo and they were asking about AI in customer success and they asked all of these companies, large companies in this room, you know, would you or do you use AI in this space? And everybody said the same thing I would love to, but I just don't have the team to go get my data in a place that I can actually use it right, and for every company that has started, so many of these companies are not products but features and they don't realize that. To your point about the Goliath, the Goliath does exist. It's called Salesforce, netsuite, oracle, workday, these companies to go and build AI into their platforms is not that difficult, and so everyone's like, oh well, mine does this and that. Well, yeah.
Speaker 2:But I asked the person I talked to yesterday. Their focus was on support deflection. I said how is this different than what ServiceNow has? Well, support deflection. I said how is this different than what ServiceNow has? Well, it's got a variety of differences. Okay, great, but ServiceNow, I just send them an extra 25 bucks a month and it gets turned on right. That's a simple use case, and so it's a weird space to be in, I think, for so many of these people, because I think they believe in their ideas, right, product managers always love their ideas, right? They look and go man, this is lost and there's.
Speaker 3:I think there's a difference between a great product or a great idea and then one that functionally makes sense, right, like I think that's the other biggest piece.
Speaker 3:Is that part of the reason these startups struggle is that I don't want to change everything I'm doing. Most of what I'm doing is working. I want you to take one specific thing or a couple of things and make that better and easier. And because I'm already using, you know, salesforce or whatever for all these things, if your tool doesn't seamlessly fit into that flow and I have to change my workflow and change my tech stack, there's switching costs, there's risks, because there's a 90% chance you aren't here next year. If this goes poorly, my reputation at work could be screwed. Now we have to go back and do bring back on Salesforce again or whatever, and reintegrate. Like there's so many trickle down effects of that that, like if your pitch is so nuanced and narrow, you have to figure out a way, and I think you know you were kind of talking earlier and maybe this is the segue that gets us on track for what we were kind of planning on talking about.
Speaker 3:Yeah, I was going to say we need to switch gears at some point. We haven't even done intros yet, but there's this guy on LinkedIn that I love, anthony Pieri. All he does is thinks about B2B homepages. That's the only thing his agency works on is homepages, and he posted today that the greatest rebrands in B2B history. And so you know you're thinking companies here and it's all titles or in like functions.
Speaker 3:So sales center, your sales team, becomes revenue team. Hr is people ops. Call center is outbound sales. Salesforce admin is rev ops. Sales enablement is product marketing. Making shit up is category creation. Unprofitable business is VC backed business, you know, and so it's like. I mean, it's like tongue in cheek. It's funny, but at the same time, for all these companies who are struggling to figure out how do I position myself and go to market differentiated, not say we're the same. But here's the one tweak. You said something I think that was very casually super insightful about growth teams will always get money. Other functionality will not. It's something we talk about all the time. You want to sell in retention, you're fighting an uphill battle. If you can sell in acquisition, you are going to make sales all day long, and so you don't even need to change the product. Just give it a better fucking name.
Speaker 2:So we bought at FastPath. We did a whole product analysis and looked at adjacencies and we ended up buying an identity company, and so that led us to go to a number of these identity and security shows, and that's another place that has a lot of funding and has a lot of new companies and has a lot of features that call themselves companies, but the word salad on these boards when you're standing there. We stood there for a minute and we looked at five or six of these things and I finally looked at the guy next to me and I said what do any of these companies do? And he goes I have no idea. And so there's almost an overcook of that right, the people ops, or the category creation, where you're trying to basically Dr Seuss your way into this thing by making words up right, and that's also not practical. We had a conversation with our board about that and they said we're not going to create a market, we're not going to create a category, because that's hard too.
Speaker 2:So I think to your point, there's a sweet spot where you have. Words matter. People have to quickly understand what you mean. There's got to be some sort of lure in it, but you can't be so cute, so smart, right? It's like in poetry and hip hop they always say you know, just because people can't understand, it doesn't make it poetry, right? It's the same concept. Just because it's just because you drop the vowel out of your name and you went from E-R to just R at the end of a word doesn't mean that you're all of a sudden you know better or worse. So there's, I think there's, a balance, and that's a hard balance to strike because, at the end of the day, the softest software is people's brains and how they interpret what your pitch is.
Speaker 1:I like that a lot. All right, we need to for the purposes of time. I think we need to. We need to like, maybe shift it a little caliber of conversation, but about AI for the market. Is that right? Aiden, do you want to start by introducing yourself to the listeners? We've been recording for the last 18 minutes and I'm going to, we're going to we're going to have to go back and cut some of this, but go ahead and introduce yourself to the, to the audience, please.
Speaker 1:Give me a bit of your background and what you're up to now.
Speaker 2:So, aiden Parisian, I am a partner at Conner Group, which is a big four quality boutique field consulting firm. We're national offices in New York and Salt Lake City, san Francisco. Our focus has traditionally been around financial operations technical accounting but we've broadened that out into places like M&A and business operations, and so I lead a practice that is focused on data analytics and how companies can use their data to glean insights. Prior to this, I was head of go-to-market in various roles at Fastpath, so I spent some time running sales, I ran product and then I also ran customer success, and prior to that I was an auditor for about a decade. So I'm on my third cycle here, third category, I suppose.
Speaker 1:Amazing. And what are you? We talked a lot in the last. The last time we chatted about, you know, the overlap between CX and product. We talked about AI. We talked about a bunch of things. You, you know, we have to figure out some way of talking about AI for CX. We've been talking for the last I don't know 35 minutes about how are you guys positioning yourselves when it comes to selling, to go-to-market teams, talking about how they should use artificial intelligence, all that kind of fun stuff.
Speaker 2:Yeah, so twofold. I think there's an advisory capacity which is working with companies to better understand how they can use AI, but the other part of that is kind of the pickaxe and shovel element of how do you get data in a place that you can actually use AI right. I mean, ai so much is talked about like some sort of magic panacea and it's not. It's another tool that needs to have things done in the right way for that tool to be effective. My view on that space is that AI is going to be a pretty big game changer. It does a lot for small companies. You talk about startups startups from a generative AI standpoint. You now can operate like you've got a 15 person marketing team. From large company perspective, you get to create uniformity and better quality in your interactions with customers and you get to reduce the time spent on low value activities for employees, which is great. And then I think a big component of that is there's a great book called Effortless Experience, which is about how customers like to engage with their products and services and vendors, and more and more people want to self-serve right. They don't want to have to call a support person or call a customer success person. They want to be able to go on the website. They want to go into a chatbot and ask questions.
Speaker 2:Hubspot you mentioned them earlier. I enjoyed my experience when using HubSpot because I could go into this little chatbot. I would use it. I think they had some sort of generative AI support deflection tool running and then it was a seamless experience to go then flip into a person who could handle all of my questions without having to get on a phone call or wait for an email, and I think their close rate on most of my cases were within 45 minutes, even on hard issues. That's a great experience because I'm not having to stop my day to go and have a different experience.
Speaker 2:So much of the investment in the CS space over the last 20 years was oh, we need to get a big CS team and we need these people to be very great on the phone and have these greater building customer relationships. Well, I mean, maybe your generation doesn't want customer relationships. You want to just get your job done and move on. You're not trying to talk to somebody in Fort Worth about the weather for 20 minutes while they try to figure out how to solve your problem right. You just want to solve your problem, and I think that's where AI is going to have a huge boon for the CS space and the go-to-market spaces. It allows you to serve customers where they want to be served, which, at the end of the day, is what all this is about.
Speaker 1:Yeah, I mean we just talked to Justin Crosby who runs CX at Tula, which is like a DTC skin well Omnichannel skincare brand.
Speaker 1:But one of the things that sort of came out of that conversation that I thought was interesting was you kind of went on two fronts right. Like you went on the front where there's just a higher percentage of your, you know, a higher percentage of the tickets that you get can be answered using AI. Like there's all the people that are like hey, like how do I change my username or how do I change my password? You can kind of see, you know, how many people are asking those. You can come up with ordinated responses and that just cuts out on the amount of time that a human has to repeatedly answer those questions. And then the second time, the second half of it is that leaves a lot more time for the people that do need hands-on experiences, that do need some of that like personal touch, to be able to actually like step in and handle that Like I think you kind of went out because there's just there's just like a multi. You went out on like a multi-front approach, which is kind of cool.
Speaker 2:And think about the feed into product, right? I mean, if the product team would love to know what are my top five support issues, and large companies have that, but startups typically don't, especially ones that are on scales. You guys talked about Clay earlier, which has been around for 10 years and just got around to funding. We were similar. We were a 15-year-old bootstrap company when we took funding initially, and so we had a lot of processes that weren't super modern because we had been doing this for a long time and you get organizational inertia and things kind of happen the way they happen.
Speaker 2:We were a right use case for this type of thing to build a grower team into scale in a way that fed data in, where we didn't have to create a new rule, right. Especially if you think about private equity. Private equity is not VC. You're not spending money, right. You're purposely not spending money. It's about margin, it's about cashback on the investment, and so in those cases, I think you're going to see a lot of buyers in the AI space because it enables them to do things differently and better than they did before.
Speaker 3:But without that, I wonder, you know you talk about CX and inbounds kind of feeding product, and I think one of the places where that's been a difficulty up until kind of this, you know, ai and big data kind of democratization has been if you want to even report in on that, you kind of have to predefine your schema of what things are going to feed into what sort of topic and what is the exact schema.
Speaker 3:And it's not going to be backwards looking, it's going to be from the moment you create that tag or automation, and then you got to wait to have it go and you need to have it happen enough that you even think, hey, I should create a tag for this because this is becoming a topic, right, and then you're probably not going to go back through the last three years of tickets to see to opportunity, size it and see if this is a big thing. You're gonna be like, okay, going forward, now we'll monitor it. And it seems like now this gives you the opportunity to almost have like an ever omnipresent sync where it's reviewing things and it is connecting the dots and it is coming up with the schema and then you can automate that to some level and it can start to be proactive and I think that's pretty wide.
Speaker 2:Well, and think about the impact of Gong and Chorus and these other tools that allow you to now record all of your conversations with customers. That's additional data that gives you more feedback, and I think the improvements and the insights that Glean's are massive massive. Now, whether or not people know what to do with that, that's a different question.
Speaker 1:There's a lot of people that had like a little bit of an idea of what they were seeing, but most companies, because they're big and because they've been around for a while and because it's changing. One of the biggest problems was, like, what are our customers even asking? We know, we think, that they're asking about these three things, but we also know that they're asking about seven other things that we have no idea how to figure out, and so it's like well, how would the people that are calling us, what are they talking about? The people that are texting, what are they calling you about? The people that are like tweeting about it, right, like, what are they talking about? When I was at Peloton, this was like a. This was like a huge issue. A lot of our, a lot of the brand related stuff was coming through Twitter, whereas the you know health and safety stuff one channel, the order related stuff was coming to another one, and you kind of need a complete unification of all the different data sources before you even start to do anything.
Speaker 2:So well. There's companies now Syncree does this app, orchid, where they're actually. They're working with data where it lives as opposed to trying to aggregate it. Right, I think you said this earlier, that when you're talking about kind of legacy implementation of this type of thing, data had to get moved first. Right, that was always the first. Do you have a master data management program? Have you taken that data and unified it and cleaned it and remediated it? And I mean, man, you're looking at like a million dollars to spend in two years just to get ready to do the thing that's going to make you ready to make the money. That's why boards are going to look at that and go, yeah, we're good, know you sync all that data together. It gives you a much more cohesive view. But then again, the other hard part is to make any sense of that, and that's where AI and machine learning algorithms and analytics are really powerful, because that enables you to kind of distill this information into something that's readable.
Speaker 2:There's a great book called Top Secret America and it was about the US post 9-11. And they started the military and the government started to collect all this data. But one of the problems they had is there was only like nine generals that had the clearance to review the data, and so every time there was an event like the shoe bomber, they'd look back in the data and go, yep, yep, we had the data. We knew he was there, we knew he had a propensity for this, we knew he shouldn't have been on that plane, but nobody had the time to review it. Well that that that's a grand scale, but that happens at every organization. There's always too much data. So the ability to democratize data analytics and to distill that down to usable information, I mean I don't. I don't know that, we know what the impact of that is yet, but it's it's, it's gotta be positive.
Speaker 1:I mean you can also just have AI, like automate it, right. Like a good example is I don't know Starbucks. There's a ton of people that, when you analyze all of the feedback that comes in through a Starbucks, there's all the people that are complaining about hey, my order took way too long. Hey, my order was made wrong. But then there's also the people that are like hey, like your cup broke and it gave me a third degree burn on my leg. Those two things need to go through completely different channels. Right, like they're probably coming in through different channels, but they also need a different level of response and a different speed of response and a different level of your point is you're able to automate the processing or the handling of the information.
Speaker 2:You have to be able to automate it. Yeah, yeah, yeah.
Speaker 1:Yeah, because otherwise you have to. You know, democratization is great, but there's also the hey like how can I automate this faster than I had before, rather than having a customer having to have an actual human in the loop that has to read all these things?
Speaker 2:Well, and that's everyone's concern. Oh yeah, it's going to take my job. It may shift your job, but I don't think it's going to take it Because, to your point, there's still people in the machine, there's still things still need to happen, decisions need to be made. We've done a good job. If you look at the EU's regulation around data privacy, they actually prohibit decisions being made, critical decisions being made, by automated scripts, because what they didn't want is some algorithm running and telling people whether or not they can get a optimized the human race so that nobody dies anymore, and that there's this group of people that have to glean the population so you don't get overpopulated. But the theory in it is that that job shouldn't be done by the computers. That's the job that should be done by humans, because that's a very human experience. It's a very heavy duty example for what we're talking about in AI business.
Speaker 2:But I think the concept is the same, which is that people still matter, right, and as long as people are in the process, unless machines start to sell to machines and we're all sitting around like WALL-E on floating machines and, you know, playing video games, ubi baby I think as long as people are involved, people have to be involved on both sides, right.
Speaker 3:And if that is the future, who gives a shit that your job's gone? Like we're all chilling, so that's not the problem, right, like you should only be concerned about your job being taken if we still have to have functional jobs, yeah, but okay. So getting this back on track a little bit from humans, from Scythe, yeah, you know, I think we just went through a lot of examples of how this can empower CX teams and how this can turn CX into a more data-driven thing, and how CX can feed product. The interesting thing I think about CX inbounds is that you know the verbiage that people are going to use is likely going to be more closely related to your product itself, because they're complaining about experiences they're having with the product. So how do you translate that into go-to-market enablement when someone doesn't know? The nomenclature isn't talking about the feature, but you're talking about the problem it solves, the benefits they get.
Speaker 2:Yeah, and I think this is. I mean not being an engineer or a data scientist or a mathematician, right? I mean the devil's in the detail there and how you programatize that. But I would imagine that, depending on the data set I'll go back to the example I know, which is FastPath we had 18 years of support tickets, 16 years of support tickets whenever we moved to ServiceNow, and that's a lot of information and depending on the ability for LLM identification to figure out what those things mean. Right, all of this is just bridging concepts together and categorizing things and tagging them to be like for like.
Speaker 2:Maybe that's a possibility with that, because people submit tickets in plain language. So, to your point, maybe they're not articulating it in a way that a computer would normally identify. Right? Oh, you didn't use the right term, right? So being at the DMV, you don't have the right form, you don't want that experience, because then that's a poor experience. This is where Eric Olson on my team he's the guy running the functional day-to-day and he's a data scientist and he's the one writing machine learning algorithms He'd probably be able to articulate better what that looks like.
Speaker 2:But I imagine that that's part of the process. If it's not, there's some anecdote I've read about automated phone systems in the 90s when they first started to put those in and how they had to pick up. You know they couldn't pick up Boston accents when they first started it right, because people are dropping their R's and the computer system's like I don't know what you're saying. So this isn't a new problem. This is a problem we've always had. But I think your point is not about the accent or the word that's used. Yours is about reading past the words into the sentiment of what somebody's getting at.
Speaker 2:I don't know again, that's where that would be great to hear from somebody who writes this type of stuff to say what is the answer to that, because I think that's a great question. I mean, that's the crux of generative, you know, communicative, participative AI is that it works right. That's the primary tenet. And, to your point, if it doesn't, then it does, then it fails. I was going to use it.
Speaker 3:What else should go-to-market teams be thinking about when it comes to leveraging AI?
Speaker 2:So this gets a little bit into what our focus is at Karnagroup, which is around making the news, not reporting the news, as Eric on my team would say, which is a lot of data. Analytics traditionally has been looking backwards right, using machine learning algorithms and statistical analysis and forms of regression and statistical ensemble models to look at data and predict trends based on that. That, to me, is where a lot of the value probably resides, especially for sales teams. Is you want your salespeople focused on deals that will close period, Right? You don't want them chasing stuff that's not going to close. So, identifying which deals are most likely to close, based on whatever signals you can get, right. So we did this at FastPath, where we looked at things like location and system implementation and other things like that. You know, I think some of the use cases we have and customers we're working with today at Connor Group may be different, but they're all basically the same concept at Conner Group may be different, but they're all basically the same concept. I actually talked to a gentleman yesterday who had worked at Carvana and they did this when they were looking at new locations where they were looking at census data and they're looking at average income and average education level and all these other attributes that they had identified in some sort of statistical analysis, that they matter. And so I think that to me, from an acquisition perspective, is meaningful, because you want to target, you want your funnel to be right, you want to be at a 70% close rate, because you actually close 70%, not because people aren't putting deals in the pipeline.
Speaker 2:And I think on the customer success side it's the same thing. But there's an interesting idiosyncrasy where we talked a little bit about this last time. Maybe that customer success was huge coming out of Salesforce. Right, salesforce created this concept and it was like everybody bought into it and you had this huge boom in the space and then you've had this retraction over the last 18 months and a lot of that. I think budgets are tighter, cash isn't free, but also customer success folks who are cares of the customer may not also be good about metrics and accountability around metrics and hitting numbers, and that's why a lot of this is moving over to the CRO.
Speaker 2:But there's also a great quote. I'd love to quote him by name, but I can't remember his name at the moment, but he said there's a direct correlation between the size of your customer success team and the misfit of your product to what customers want. That basically, customer success by and large, is a human gap fill, because your product doesn't teach people how to use it, it's not easy enough to use, it's not intuitive, it's not, doesn't have things in it that allow you to do this. So if you think about AI back to that support deflection concept if the tool, if you could generatively say how do I do X, y, z and it tells you every time you don't need to call somebody right the reason you need to do from a product perspective.
Speaker 1:You and I chatted about this last time, but I think that the words that you said were if you have a massive support team like what you're, you don't have product market fit. What you do is you have a bandaid that's trying to like help you get to that product market fit right, like that's a-.
Speaker 2:The biggest cheat software is having an implementation team. Sales teams love that because hey, does your tool do this? It only does about this much.
Speaker 1:But yeah, we got a dude in there who's going to slap a sales engineer on it.
Speaker 2:He's just going to change it Whatever you want.
Speaker 3:And it buys you time.
Speaker 2:It buys you time. But that's also why, if you look at SaaS valuations, they don't value services in SaaS companies. For that reason because they realize what that is that's a temporary band-aid fix and sometimes, if it's large enough, if that scale of revenue is large enough, that might actually be indicative of an illness that you don't see right away.
Speaker 1:Yeah, no, a hundred percent. A hundred percent. One thing that's also interesting, going back to something that you said about, you know ensemble models and you know looking at correlations with things like location. The other thing that you can start to do now that's pretty interesting is you can look for explicit. Going back to what we were talking about on the CX landscape, something that we've seen an increasing ask for on our side is hey, like, look, I really like the fact that you guys can sit on top of our CX software. Can you also sit on top of my gong calls and tell me every single time somebody has, like, given me an explicit signal that they're willing to upsell to another product that they're not happy about? Like there is something to do with you know our product.
Speaker 1:Like the issue that you run into is all of these things like gong and grain. They're very, very general and saying, hey, like Aiden said this on the call, sean said this on the call. But what you, what you can start to get to, is if you're, if you've got like a trained, if you've got a team to be able to train some of this stuff, you can look for explicit signals that are specific to your business. Like if you're a fintech company and your largest account starts complaining about an increase in I don't know increase in hacks or something like that. That's a tier one response. Like somebody that's got to go to the VP of sales and that person needs to then talk to the VP of product and say, hey, like this big fintech account is complaining about security, we need to solve this because, looking at it, next year, this is a really important thing for them. They're going to end up churning that kind of stuff right, like the explicit signaling within things.
Speaker 2:And.
Speaker 1:Tynan is pretty interesting too. Like you can apply a lot of the oh sorry go back.
Speaker 2:Yeah, there could be hard, right. That's again the issue, and that's when I sat on that round table and these were companies here in the Bay Area I won't name them because it was a private conversation basically but they all said the same thing, which is I would love to do that, but my data's in a million places and I can't justify the cost to just do the data work, to get it set up. And that's one of the things at Conor Group that we have as a service is just that initial piece data rationalization and identifying. But there are products out there that are focused on I think I mentioned this earlier finding data where it lives, and that, to me, will be the biggest boon for this space is if you can get an octopus that goes out there and grabs this data, builds an ontology based on where the data lives and you don't have to move it. Well, I mean now your implementation time is a day and a half or two days, and however long it takes for the machine learning algorithm, you know that's pretty meaningful.
Speaker 1:The way we've solved for this in the past is like Peloton and other places that I've worked, drift and stuff has been data warehouses. So, like you know, with the advent of ETL tools like Stage 5, tran, you know they'll sync on the minute frequency. Like you know, new data comes in.
Speaker 2:Well, and there's like we have some Microsoft Fabric stuff using the lake house concept, which is interesting, and stuff using the lake house concept which is interesting, and I think you're right.
Speaker 1:I think a lot of that is is adjusting and realizing the reality situation and making it feasible for, yeah, a lot of these services. The issue that you run into is, like, hey, like one person's running on an oracle database from like 2005 and having like a modern 2026, 2024 piece of ai software running on top of that, it's just not going to be able to handle the workload. So how do we like get into a place where they don't have to do a multi-million dollar migration by hiring deloitte, yeah, um, and they can, you know, bring it all into one place. So that's, that's.
Speaker 1:That's super, super interesting yeah, I agree where do you see the world going in the short term? So we've got you know the world is just this space, because I can go back to site I'm talking about, you know, as company, like there's this like AI race, like all these people are trying to do it. You're going to definitely see, like a plethora of companies, I think, die off in the next couple years.
Speaker 3:People that are early to market that kind of thing.
Speaker 1:But what do you see in the short term as like the biggest problems outside of like data unification?
Speaker 2:As far as challenges to the AI space.
Speaker 1:Yeah, challenges to implementing it, Like data unification, is a huge one, right, but I'm interested to see what else you're seeing is out there.
Speaker 2:I don't know that it's going to have the normal headwinds that a lot of SaaS software does, because it's again part of it is that it is self-serve, right. You turn it on, you point it at a set of data and if it's Um, you know, I think, as you said, uh, there's going to be a huge culling and rationalization of the number of companies in this space. I'm also interested to see what happens from a funding and budgeting perspective, because, um, a lot of this is. You think about boards, right. You think about, um, you know limited partners at VCs or PEs and you think about where the money is and the money flows and why people choose to invest. And you know a lot of the people who have money. If you look at any of the metrics in the Wall Street Journal, a lot of wealth is concentrated in the baby boomer generation, and these are folks who obviously aren't up to date on technology because you know they're in their seventies and a lot of the stuff is very outside their realm of understanding. And it wasn't. Ai didn't become a big thing from an investment standpoint until it became real in the Wall Street Journal, right. So what happens when they lose interest, right? What happens when people realize it's not magic and it's not some panacea and people see it as a toy or a tool that maybe doesn't fit. I think there'll be a rationalization there.
Speaker 2:But I also think the biggest challenge to anybody in the space I think I said this earlier is the platforms. We saw this at FastPath that we sold GRC technology and we would sell against SAP and Oracle. It is awfully hard to sell against an incumbent that already has a much larger budget. Right, you've got SAP, somebody spending two and a half million dollars a year on it. We come in and say, hey, we can do this. They go yeah, sap can do it. Look at what Apple's doing in version 18 of their software that's going to be built in. I think you'll see it. Probably most likely you'll see AI get built into everything. Right, it'll become a feature of every product. That'll truly democratize it, because now it's available without having to buy it. It'll show up as a button or on a release set of release notes saying, hey, do you want to turn on this functionality? And at that point I don't think there is any implementation risk because it's already there. Back to the data concept and what you said about having Gong or Grain or tools like that, and then having Salesforce separate, is that you're going to still run into that issue, that that AI will be very focused on whatever data it already lives in?
Speaker 2:The other side of this is Scott Galloway had a great post a couple months ago about AI and how everyone calls everything AI and how actually they're starting to hold people accountable. Right, yeah, there was a tool that we used and they said, oh, we have our new AI function. I went great, I went in there and they basically just put a GPT on their front end so I could search their help notes and I'm like that's not AI, dude, that's something completely different. So, yeah, this cycle, I feel like should be different.
Speaker 2:I think maybe, if you flip that and maybe you meant this and I interpreted it differently if you think about it from an adoption standpoint, there was an interesting article in the Harvard Business Review about adoption of AI and what they said is, when they researched and surveyed people, the more they knew about how the AI worked, the less they liked it, which is kind of a weird thing, because you'd assume that when you're afraid of something, you want to know more about it. But they found that when somebody said, hey, you submit a number to this machine and it comes back and gives you a number, they're like great. But then if you got in there and explained how it worked, for some reason there was a level of distrust which, again, I have a hard time wrapping my head.
Speaker 1:I mean the thing that this reminds me of is that Amazon, the shopping with the camera AI thing where, like when you pull it off and they ran a study and they realized 80% of it was just an offshore team manually like charging people through some sort of like identity resolution thing, where it's just like it's a bunch of cameras but there's somebody looking at you and just being a virtual, uh, they've taken the job of the cashier and like moved that offshore yeah, I stand for turk to the highest level yeah, assist assistance in india.
Speaker 2:Yeah, that's funny, well, but I think there's a lot of that right. Um, I just saw somebody posted on linkedin yesterday about this that a number of companies that call themselves ai companies but that's always happened I mean, I visa for a long time called themselves a tech company. They may still, because they feel they are a tech company, but the three of us wouldn't categorize them that way.
Speaker 1:We think of them as a credit card finance company I mean the, the crypto and uh, vr hype is a huge part of this. Right like during pen, like facebook literally now calls themselves meta. For this exact reason, they're like oh cool, the metaverse is a thing, now Cool, we're going to rebrand ourselves as a metaverse company and in the last five years, you've seen the rise of metaverse, you've seen the rise of AI, you've seen the rise of crypto and I think you get two of the richest company leaders in the world basically trying to out meme each other right?
Speaker 2:I don't know if you guys saw Zuckerberg's video from Lake Tahoe the other day with his American flag and his tall can. I'm like what is happening? Like it used to be that leaders of industry were like these guys with gravitas and they had like you know, I know I have a PhD and I'm very smart, and now it's like two guys, you know, sharing cat gifs with each other. It's a little discouraging.
Speaker 3:So you know, you talked a little bit about how, for Salesforce or Apple, like everyone's becoming an AI company and all these things are going to have their own version of AI. If that's true, and like you know, every PC and every Windows software is going to have just a default AI app and every iOS device is going to have that, what is the point of any other product trying to incorporate AI? And is does that not just turn every app and program you have into a different structured data set and capture set? That then the one AI to rule them is really? It's just we get back to the same conversation we've been having for 20 years of are you a PC person or an Apple person?
Speaker 2:Well, but think about it from a subscriber perspective. It's all platform wars, right? If you think about, you know, oracle Cloud versus Amazon, warehouse versus Azure versus, you know, all of this is about dollars, it's all about subscription dollars. It's all about retaining as much captive audience, attention and money as you can. That's the whole name of the game. And as much as it would be very generous of those companies to work together and to pick some sort of uniform standard, like an API standard or SCIM protocol, where they say, hey, ai all runs on the same language and these machines talk to each other, that doesn't help their bottom line, right. And if you took that to a board and said, hey, I think, from an Apple perspective, I think we changed direction. I think we work very closely with Microsoft and we build a uniform front. They're going to go. I'm sorry you, what? But why? How does that help us with market share? How does that help us with our bottom line? You know, and arguably this is I had a econ professor once who very clearly articulated.
Speaker 2:He said you know, don't ever come into this class and say that a business is responsible for cleaning up a creek or avoiding killing a, you know, red-bellied lizard, he said companies are in the business of making money. Belly blizzard, he said companies are in the business of making money. Boards hire CEOs to maximize profits and revenue. It's literally legally defined that you are in the business of maximizing shareholder value. It's the government's job to create the rules. That would be nice, and it probably won't be our government. Most likely it'll be Europe that would do this and they'll create some sort of uniform standard to your point, sean, where then now these companies can play together and you're not forced Because otherwise, as a consumer, at some point you get into some of these byproducts of monopoly effect where you say, man, I'd really like to have that health directive, that thing that helps me be healthier, but I can't unless I buy an iPhone.
Speaker 2:I mean, that's starting to get into personal rights and the ability to actually live your life in a maximized. I'd love to have my lock on my door work better, but I don't subscribe to the right ai platform. So, yeah, I I see this more as a modification of the logic, or the way we go about logic with data, and I do think, by and large, you're going to run into some of the same problems we've always had. Right, it's like the concept that computers get faster. Right, data's still a mess. People still miss-enter stuff on the front end. Right, humans still make mistakes. So it's. I was just reading this morning about the amount of you see AT&T's release today that basically call- there's a massive data breach right.
Speaker 2:Call records for a quarter, for three months, for basically they said, mostly every customer we have and it was on Snowflake. It was part of the Snowflake breach and Snowflake rightly so said. We tell you, guys, you should have dual factory authentication set up. We can't help you If you create a one, two, three password. We're not in charge of your password. Like we can't. We can't come in and like help you actually use your system and they didn't do it and you know they got bit. And so again, all the security in the world, right? How much money do you think I should have looked this up so we could have quoted it? How much money do you think AT&T spends on security, data security? I mean, it's gotta be a fortune.
Speaker 1:It has gotta be a fortune, and some yeah, millions, if not tens of.
Speaker 2:And some dude someplace told the IT manager I don't wanna deal with an authentication app, so I'm not with as smart as the machines get. There's a Reddit post from like a decade ago and it was an Ask Reddit and it said if somebody, if a time traveler, came from the 50s to now and you were there when they showed up, what's the one thing you could tell them? That would blow their mind. And the top voted answer was in my pocket. I have a device on which is all of the knowledge of all of humankind and I use it to argue with strangers and look at pictures of cats, and that that, to me, is a great reflection of human beings are still human beings, no matter how smart our tools get. We're all Homer Simpson at the end of the day.
Speaker 1:I think that's a beautiful place to end. Yeah, aiden, okay, I have a couple things that we're going to end it here. I want to figure out how exactly I go through this entire recording. I'm probably just going to feed the transcript to something and extract all of the books that you said, but what are the favorite ones that like what's what's the? What are the favorite books that you've been reading? Like, what are your recommendations? So I read a ton.
Speaker 2:I actually I just took a coaching course to get certified as a career coach is something I do on the side, and somebody somebody joked that they said they had written in nine months. I think they'd written down 15 or 20 books. So the ones right now. 4,000 Weeks is great. 4,000 Weeks is one of those books that is almost a bit of a threat to some people because it's kind of like the moment is now and stop planning for a future that doesn't exist and live here now and be here now and stop trying to be something that you may never be type of thing right. It kind of challenges you to put your life into perspective.
Speaker 2:Back to the concept at the very beginning we were talking about, you know, time and experience and time on task. There's a book that we read as part of this coaching course called Working Identity by Herminia Ibarra and her. It's all about midlife career change specifically, but her thesis is that in order to change what you're going to do or to be something different, it's not an exercise where you stare out the window and take a bunch of notes and think about it. It's actually a go do things. If you think you want to open a restaurant, go work in a kitchen for four months on the weekends. If you think you want to be a doctor, go intern, right, go, actually do it and experience it, because feeling something and doing something is different than thinking about it. And I think, especially as so many of us have become digitized right, we're all on the internet researching things, and you know it's back to Robin Williams's quote in Good Will Hunting, right Of you know, you can probably quote me a song, but you can't tell me what it's like to lose someone you love, right, it's experience is wildly different and I love that topic we were on because it's right, in line with this whole concept of life is experiential and you've got to figure out what you want.
Speaker 2:Those two books together are pretty rad because it's like one of them's like should I get off the pot? And the other one's here's how to not fuck it up. And that combo is really nice because you're like oh man, now I've told I've recommended it to Know a Person. I listened to that book recently. It was fantastic. The one that I'm cheapish to recommend, but I always do, is Book on Tape. It's Greenlights by Matthew McConaughey.
Speaker 3:Oh, I love that book. Audio version is so good. Bumper sticker.
Speaker 2:Yeah, bumper sticker. But this whole concept, the whole theory that life is full of these opportunities and your job is to take it. There's a combo of books. Everybody Lies and then Don't Trust your Gut. Don't Trust your Gut. Talks about data. That's where I read about Airbnb and I think this is the one you and I talked about last time. Maybe he akin to the Matthew McConaughey concept. He talks about companies, the number of companies that are successful, and they did it. Somebody did a research project to say are successful SaaS companies successful because they have better opportunities? And the answer is no, they all have the same opportunities. It's just the successful ones are the ones who saw it as a green light, took it, and I love that concept because it means that everyone has potential.
Speaker 2:And if you go back to Ibarra's book, she has this concept of multiple selves. Right, we always think about oh, this is highest and best self. You know there's a version of me out there that's the best version. And her point is no, there's not. There's a hundred versions of yourself, depending on which pathway you take, but every time you make a decision, you shut a couple of those down, that combination. I like to read books and combinations, because you get this kind of holistic view, that combination of that life is a series of opportunities and life is a series of choices and there's multiple versions of yourself that are that are great. Talk about a growth mindset shit. If we get everybody to think that way, we would stop arguing with each other on the fucking internet man, that's.
Speaker 3:So I got a couple ones. So first, uh, there's a book I just started called 10x is easier than 2x. Um, and in you know, I literally just started yesterday could become terrible, who knows. But. But the intro was all about Michelangelo and the first big sculpture he did. He was 17 when he started, he was 19 when he finished it and they're like he was a different human being afterwards To figure out. He became so obsessed with human anatomy he was sneaking into mortuaries and by candlelight dissecting human beings to understand how muscles and tendons were, and that was a punishable by death. So if you got caught, yeah, but it was all in, it was all in, so that that one seems pretty cool.
Speaker 3:What you were saying about the, the idea of like there's, you know, a million versions of us and you're shutting some down as you make choices. I had a sales coach come in um one time and when I time and when I was agency side and he talked about you know, you and your team and your company now is here and where you want to be is here. Like, you have a goal of where you're trying to go and every single new deal you bring on brings you one step closer or one step farther away, and basically the thing is like, even if you aren't getting like actually farther, the fact that you've gotten one step closer on the horizontal axis, but not on the hypotenuse of that, means you are functionally farther away, and so every new deal is a chance to start to make a little change.
Speaker 2:That's right. So akin to that decision theory of McConaughey's. The point is is you have a million micro decisions every day that build into it. Right, it's the how you do anything is how you do everything. Concept.
Speaker 3:Right, exactly right, yeah, it goes back to what we were talking about earlier about shots on goal right. Like, if you look for an immediate feedback loop of success probably oh, I made a deal and I'm not the company that I want to be in a couple of years but if we say it's showing up or it's making you know it is that repetition you are closer.
Speaker 2:There's perfect practice. Right, there's 10,000 hours. To that point they say it's not any 10,000 hours, it's 10,000 hours of deliberate perfect practice is what gets me.
Speaker 1:I mean, there's also the recent I think it was the Dartmouth speech that Rafael Nadal gave, where he was like, yeah, everyone sees me as this gigantic winner, but I went back and I looked at the actual number of individual like points in a match that I won and it was only 50, 53 I think, like he only won 53 of the points that he had like 53 of like the parlays he ever served and like the. You know, the fact is, because it was more than 50, he's like he's won most of the matches, but within those matches, like you know, I don't.
Speaker 2:I don't know if it's ibarra that says this, but somebody I read recently said when you think about planning out your life, it might have been 4,000 weeks. You think about planning out your life, right. So almost the antithesis of this concept of being very pragmatic is to look at your life right now and go back in time, and how much of your current place you are is because of deliberate decisions you made versus random things that occurred. And that's also wild, because then you start to realize that, like Rafael Nadal, there might be somebody else out there with that same 53% that never had the same success because their 53% happened at the wrong time or the wrong place.
Speaker 3:Or is there? The lost 47% of points for the points that get you to the championship game.
Speaker 2:Yeah, exactly, exactly, and that's a wild one too, but that one's a great one to think about for people. On being resilient, adam Grant in Head Potential talks about resilience and the grass and where somebody stepped on the group before you. So much of life is not in our control, right, and so your job is also to be resilient, because even if you do have 10,000 hours of perfect practice and you do work the hypotenuse perfectly, you may still fail.
Speaker 3:Back when I was in the air, appearing for my first deployment, we went through this pilot program where UNC gave us like some performance coaches, uh, on mental stuff, uh, on physical stuff. It was this whole thing to see if it would reduce um casualties. It would reduce, you know, ptsd coming back, all that kind of stuff. And one of the things they said I remember very little of this, of the whole program, but one of the things that says the only thing you control is your breath. That's the only thing you can control. You can't control your random thoughts that are going all over the place. You can't control what happens around you. All you can control is how you breathe. And if you can control your breath, everything else.
Speaker 2:Yeah, yeah, you can't control the world, you can't control how you react to it, but these are lessons that need to be like in high school, right? I mean, so many Americans need to know this, because it's like people are out there lashing at the world because they're pissed about their situation and they just need to chill and take a deep breath and realize it's not that deep dude. Now, that's easy to say. I live in San Jose and I've had a successful career, so of course, I'm the guy.
Speaker 3:You and your ivory tower and all your success, talking about how you don't even worry about it.
Speaker 1:You said you recently launched as a career coach. Last thing to pub before you head out where can people find the Connor Group and where can people you know potentially get your career coaching services?
Speaker 2:So Connor Group's available. C-o-n-n-o-r-g-pcom, google it. I mean we're well known enough, we show up in the algorithms and we do a lot of work with a lot of good companies. And then coaching. I mean we're well known enough, we show up in the algorithms and we do a lot of work with a lot of good companies. And then coaching I mean people can ping me directly on LinkedIn. I do have a limited roster of folks because that's a side thing I do mostly for joy, and I've got a lot of hours I put into kind of group. But yeah, I'm happy to have that conversation with people and I also have a huge network of coaches. So if people reach out and don't have time I know a lot of people do I recommend it to anybody who's never tried it. Try it once, try it twice. If you don't like it, don't do it. But I bet you 50% 53%, to use Rafael Odell's number Like it.
Speaker 1:And thanks so much for coming on. Everyone enjoy the episode.