Episode Transcript
[00:00:00] Speaker A: Rob, welcome aboard to the show today. I'm so happy to talk to you. And I'd like to introduce my co host, Jordan.
You have an incredibly interesting company that you're working on, which I looked at this, and I'm like, we have to talk to Rob.
Bring our audience up to speed and tell us, what are you doing today?
[00:00:19] Speaker B: So thank you for having me. First of all, the company that I'm working on is called Intergendata. We are a life event prediction and insights company, and our number one focus, the main thing that we do is we sell data and solutions about when people are likely to have their most important life events happen and the impact that it has. Because there's always an emotional impact, there's a physical impact many times, but there's also a financial impact and a legal impact. And most of us don't know, and we don't plan for bad things to happen. But at some point in our life, things are going to happen, both good and bad. So the more that we can help you understand what's likely going to happen to people like you or based on people like you, here are the things that you should be considering in your life. Our goal is to help empower people to think ahead, to plan, to understand what they should be considering, and then to be able to move forward in a much less volatile situation, but also to be able to understand, hey, it's going to be okay. I can make it through. So our number one goal is to empower people with data.
[00:01:31] Speaker A: That's amazing. Break it down for us. What does this mean for an individual consumer? What are the type of events that you can predict, and how are these used? How do these generate value?
[00:01:42] Speaker B: Yeah, so I like the question in several fronts. Let's start with what we do. We take information from a all different types of sources. We'll take public information, we take private information.
We put that together, and what we've basically done is mapped out 338 million people in the US. And we've said, here are the common things that happen. So, I'm 55 years old instantly. You know, I'm at a life stage that is different from other people. So within that life stage, the real question is, what's going to happen to me? What should I be thinking of now? If I was 20 years younger, I'd be at a different life stage, and I should be thinking of other things. So, as an example, when we think of life, we think of health and wealth, different types of events. And that's what we try to predict. Two main categories, we can predict 95 different life events, and the first one is things like wealth. So in my younger days, it's who am I likely to marry? When am I likely to get married? How many children am I likely to have? How many will I likely get divorced? If so, when would I get remarried? Would I get remarried? But also the types of job, where I'm going to live. Am I going to have pets? How much money will I make? These are items that we call wealth related items. And on the other side, we also predict critical illnesses. So cancer, heart attacks, Alzheimer's, dementia. And in total, we can predict 95 different life events. We're working on more, but today we have 95 in production that we can tell you. Here's the year that they're likely to occur. Here's how much impact that it would have from a financial aspect. And then we provide that data to banks, insurance companies, to healthcare companies. We also provide it to financial services. So imagine if you had a family and you've got a daughter or a son in college and you taking care of elderly parents as they're getting older. You should be concerned with not only your expenses of your children, the expenses of your household, but what if something happened to my dad? What if something happened to my mom? What would I do with that? How would I be able to help? Can I? And do I have enough money to survive a long term event? What if. So we allow you to use that data to try and go through and think about the things, and they can be overwhelming at times, but we do it in a way that says, hey, people like you tend to go through this. If this is a concern, let's address it. Talk to somebody. Empower people to have the right conversations. That's kind of the overall scenario from how we look at the data and how you can use that as an individual.
[00:04:30] Speaker A: So now I get this information. There's a probability of a bunch of stuff happening to me. What do I do next? How do I plan, or how do I prepare, or how do I hedge myself against the risk?
[00:04:44] Speaker B: I have a daughter who's a sophomore in college.
I have another daughter who is a junior in high school.
I have my life and I have my wife's life.
We have family on both sides, and we're always concerned with what's going to happen next. Nobody wants to have a conversation. And, Ari, if you were my financial advisor, how comfortable would you feel if you picked up the phone and said, hey, Rob, can we talk about when you plan to have your wife get cancer? So I can put in how much it costs.
No. And if you actually had the guts to call me, I'd be like, no, dude, I'm done. I got to go play golf. I'm out. Right? It's not a conversation you want to have, but it's a conversation that's needed.
So I'm going to go a little personal for people in the audience.
Over the past six months, I have had my sister get cancer.
I have had the death of a grandmother. I've had the death of an aunt, a very loving aunt that we know.
I have had two other people get cancer, and I've had my wife have to go take care of her mom in a different state and live there for two months. I have to take care of both of my girls at the same time while all of this is going on, trying to run a company, trying to build a company, trying to work with others, and the funny part is, I'm trying to help people understand that these life events get in the way of life. You have to deal with them.
How do you deal with them if you have no clue what's going to happen and you just pick up, get in a car and drive, what's your destination? Where are you trying to go? At some point, you're going to have to stop for gas. You're going to have to stop to eat. You're going to have to stop to go to the restroom. If you have children with it, you're going to stop multiple times. And every time that delay means something.
If I don't have enough money to support the family, I can't continue the business. If I don't have enough money to support the family and I stop the business, well, then I can't take care of my family.
So these are the considerations. Like dominoes, they fall upon each other that we have to think about.
The hard part is actually starting to do it.
The easier part is getting the data. And as you said, eloquently enough, we're building data sets and data models that have and that are based on every birth in the country, every death in the country, every comorbidity, every car purchase, every home purchase, every pet, every disease, every heart attack.
And we don't want you to think about it. What we want to do is to be that data service that powers your apps, that powers your tools, that powers the ability for you to know, hey, I got an 80% chance this is likely going to happen. If you had an 80% chance that you were likely to run out of gas, would you stop to get gas?
The answer is yes. If you had an 80% chance that that five dollar well, maybe ten dollar piece of chicken today was undercooked, would you cook it a few more minutes?
The answer is yes. So why is it so easy to do that and make that decision like that?
But yet we won't think about our futures and the families and the friends and the loved ones.
That data is what we're trying to produce to help people. So you can think about that without having to do the work. That's what we're trying to do.
[00:08:32] Speaker C: How do your product go to the consumers? So it sounds like really it's going through the businesses, right. The insurance companies and then through that the customers are. Are getting, the consumers really are getting, the outputs are getting, you know, the. Here's what the data is telling us and what we would recommend based on that.
[00:08:52] Speaker B: Yeah. So we're built as a b, two b, two C company. Our job is to provide this data to the cetera's of the world, the LPL's, the Merrill lynches, to the Metlifes, to the UBS's. Our number one goal is to go where we can have the largest capability to provide our data set across many people. So not selling it individually to each different person, it would take a lot more money and it's a lot more expensive to do all of the marketing to do that. But what we're doing is using economies of scale and saying we built a massive data set that's on an enterprise solution such as Microsoft Azure within a snowflake environment that we can have globally around the world. People access that data very securely through an enterprise type of arrangement. So it keeps the data secure, so it keeps it available so that they can use it at any time for whatever purpose. Whether it's marketing, excuse me, whether it's for cross sell upsell, whether it's for helping them understand customers, making sure that they have the right products and services. So our goal and our business is to provide it to the actual service providers themselves. And then that way they would bring that to the individual.
[00:10:14] Speaker A: Jordan worked on a really interesting project where she was using data to actually give more affordable insurance through data. Is this a similar play?
[00:10:24] Speaker B: Let's say the actuarial table is on life.
Today's actuarial tables state that men die at 78, women die at 82.
[00:10:34] Speaker A: So let's just bring our audience with us. What's an actuarial table?
[00:10:40] Speaker B: So an actuarial table, being an actuary, is a person who's going to look into the numbers, try and understand all of the people, what is their life expectancy? What are the reasons for those life expectancies? And to use that table to say, if we're going to provide an insurance product, how much can we charge versus how much do we have to cover for the premium that we take in? So you buy insurance, you pay a premium, they take that money, they invest it in other things, they get a return, they have extra money that when somebody gets sick, somebody gets hurt, they can pay out those claims. And that we would pay well. That data on every individual that they do these calculations for is considered an actuary table. And then they use that data to say, here is the life expectancy of a person, and that's what we use, and that's what most of the industry uses does that.
[00:11:38] Speaker A: So going back to the value, we're doing this work anyway, right. The actuarial tables are our way to say, well, these are the predicted numbers.
And how is that then used?
[00:11:53] Speaker B: It is similar to providing insurance at a better price.
But if you only have $10,000 to purchase a car, you're very limited in the type of vehicle you can purchase. If you have $100,000, you can purchase a bigger car, you can purchase a more variety of type of vehicle.
$1 million gets you into a different echelon as well. So when you think of the types of insurance you would, you would need, and it could be, I want an individual life insurance policy for the remaining years of my life. Knowing that end of life scenario is a good thing because they're going to want to calculate how much premium they need to collect from me. If I have a $10 million home, then they're going to want to insure more. It's going to cost me more. If my home is $50,000, it's going to cost me less. So they use this information and they use these tables to help calculate that. The thing that's really interesting to me is they do it on these averages, and I'm going to go personal again. And I look at myself. So I'm Native American Indian. I'm indigenous to the US.
My tribe is here. My wife, though, is Korean, and she's basically first generation Korean. So knowing her and knowing me, I'm going to go back and look at myself and say, just because I'm native american, I have a higher propensity for type two diabetes. I have a prevalence that's built just for my genetics. My wife, who's Korean, doesn't have that same prevalence. She has a different prevalence. Hers is for cancer.
We have two people that are living together that are married. We have different prevalences, and technically, we'd have different lengths of life. Now, if most insurance companies say 78 and for men in 82, that's probably correct. But if I have a higher propensity for type two diabetes, and I live in an area where it's high sugar content, high fat content, and I don't get a lot of exercise, and I'm in a sedentary position, and that's all I do, I'm increasing my chances, and type two diabetes increases the chance of stroke by 50%. So now one thing has led to another, which would shorten my lifespan. So, within that, what's interesting is we use that to say, hey, it's not just 78 or 82. I would rather have personalized life insurance. I'd rather have personal. Why is it if I went and I was driving a 1972 Pinto and you were driving a Ferrari, you'd probably pay more for insurance. You have a nicer vehicle. But if we both drove Ferraris, we both made the same amount of money, and everything else was identical, but I had ten tickets and you had one, you should pay less, right? Then I would pay more. So the pricing models are unique in the sense that sometimes they do incorporate that, and other times they don't, whether it's a group mindset or not. But I think that we're in the point in time with technology that we can ask for individualized medicine, individualized financial planning, individualized experiences. When I listen to music and tea and watch tv, why can't I have individualized insurance? So we would say, use that to help better understand.
Rob is Native American. His wife is Korean. Ah, but wait, our children are a mix of both. They actually even need different insurance than what I have. That's cool. That's where we're trying to see this world get to. We want to see that happen. And that's where we think that the explosion of data and machine learning let the machine do what the machine's good at and calculating these things, and let the people do what they're good at, is help relaying the information, becoming that concierge, becoming that person, to help help make it easy to understand.
That's where we think that it goes to. But similar to understanding the pricing model that Jordan was doing earlier, you will need to increase and decrease prices. But I think that that's okay. I think that I want that. To me, it makes sense to some people. Maybe not.
[00:16:18] Speaker A: I think it does. Jordan, you were telling me a story about how personalized models can actually even help ensure the uninsured, uninsurable, maybe I should say. How does that work?
[00:16:32] Speaker C: Yeah. So actually what we're doing at kin is kind of similar, but on a homeowners insurance sort of basis. So the kind of hypothesis behind kin is that you can take a look at houses, right. And you can say, okay, a house on the Florida coastline is going to cost this much to insure. Right. Based on the value, based on the likelihood of hurricane hitting it at any given point. And so you can go kind of as high level as that.
[00:16:57] Speaker B: Right.
[00:16:57] Speaker C: And those two policies, maybe they're both $10,000, but if you get really into the details and as you mentioned, with the amount of data that's available out there today, you can get into how old is the roof? When was the last time the roof was replaced? What are all the different factors that really matter in terms of, okay, both of these houses have a similar chance of getting hit by a hurricane, but one of these houses has a lot better chance of surviving that hurricane, whereas the other one is likely to be a total loss or a larger loss. And so really being able to pinpoint, you know, kins focus on being able to pinpoint, what are those risks that we are willing to take where someone might be in Florida, they might be on the coastline, but they've got a great foundation. They've got all the things that they need in order for them to be less likely to have a significant claim if a hurricane comes through.
[00:17:45] Speaker A: What that means to me as an individual consumer is that instead of them, you know, the insurance companies, banks, just painting everyone with the same brush, you're actually looking at my risk. And Jordan, I think you mentioned to me that there's actually preventative measures where you can help people prevent themselves from a catastrophe if they have an issue. So you're not only saying, okay, I'm going to insure you where nobody else would, but, oh, by the way, you have a risk that we've identified similar to what you're doing, Rob, we know that because of the mix of genealogy that you have. Here's your risks. There's a preventative measure here. Now, I want you both to say something about this. So let's go one at a time. Jordan, how does the preventative measure come for home ownership?
[00:18:32] Speaker C: Yeah. So, I mean, the biggest one, and I think the biggest difference that you're going to see in a lot of states is the roof age. Right. So someone has a 20 year old roof. Most roofs have a much shorter lifespan than that. And so if you have a severe convective storm, which could be a hailstorm, it could be a lightning storm, or if you have a hurricane, again, depending on where you are, both of those, the homeowner is going to be much more likely to have a positive experience, and the roof has to be replaced eventually, if they have a new roof. And then talking through, okay, you're going to have a new roof. What is the best kind of roof that you possibly get? Obviously, you're thinking about homeowners associations and everything like that, but a tin roof, for example, is a very hardy roof, and it tends to last a lot longer. But you're thinking a little bit about the value prop versus cost trade off and sort of figuring out what's the right approach for you, what's your risk tolerance here, and how are you going to prioritize the short term versus the long term in terms of these costs?
[00:19:37] Speaker A: That's absolutely fascinating. Rob, what does that look like on your side?
[00:19:41] Speaker B: I want to say thank you, Jordan, because I'm going through that right now.
We recently downsized. We got a house, and I'm looking at the roof, and I'm going, maybe I should replace it. We've been through one hailstorm. Living in Texas, you get hailstorms. The other part is I'm seeing ads, and I'm like, ooh, I could get a metal roof with stone coating. Wow, do I want to. Do I want to spend that money? Right? And there comes that question, how much do I spend versus the length going forward?
I love that conversation, because that's. That hits home to me. That really hits home. Now, I will say, on a side note, if somebody said, hey, if your insurance will give you a bit more of a discount if you purchase this now, okay, that's the call to action for me. Hmm. 20% discount. Pay a little less. Better roof, longer roof, less claims. It's kind of win win for everybody.
Similarly, and from our perspective and what we do in the business, we look at it as in, there's the financial side. Right? So knowing that you have likely someone to go through cancer. My wife went through cancer. The first biopsy, by the way, $29,000.01 needle, $29,000. Our maximum out of pocket from the company I worked for, $16,000. I had to stroke in one check, gone. That's something that I was not prepared for.
Similarly, we also look at that because we were able to predict. Now, I say this, we ran my wife, through this system, it predicted her to get cancer at 45. She didn't get it at 45. She got it at 48. We were technically one, a little over one standard deviation away. But that was enough that if I had prepared ten years earlier, man, I would have had plenty of money saved up. I could have saved a dollar a day and had cancer insurance or some type of ride or something, right? But I didn't. So the other side to that is when we talk to people from the healthcare side, that type two diabetes, increasing your chances of stroke by 50% is a massive amount of money. But more importantly, it's, hey, eat a little less sugar today.
Don't go for the glazed doughnut. If you want to wean yourself off, go for the old fried doughnuts, and then start cutting down. Right?
Take the little steps today, the simple little steps today that compound over time to allow you to make a bigger impact. Don't go from eating sugar to zero and go from all carbs to no carbs. You got to wean yourself into this, and by doing so, you can actually change your mindset, change your habits. So, from a healthcare perspective, exercising a little bit more, walking, just walk, do something simple, will help longevity, help claims, help less costs, help mitigate risks. So all of these things work together with each other. So I love what Jordan said earlier, and I think we're similar in the approach in that sense.
[00:22:55] Speaker A: I just want to take a moment to send you a virtual hug and a lot of love. I think that in the professional world, we're all kind of doing our thing and delivering, but sometimes we forget that we're all humans. My dad is going through his own version of cancer now, and you're going through your own experiences. So I just wanted to say that be strong, and there is light at the end of the tunnel.
[00:23:21] Speaker B: Yeah. Thank you.
[00:23:23] Speaker C: Yeah, I think it's really interesting. These kinds of things can make a difference, not only personally, but socially and as a society.
What you were talking about, Robert, actually reminds me of Peter Attia, who's a doctor, and he kind of talks about medicine 1.02.03. .0 medicine 2.0 is where we are today, which is a lot of treating issues once you have them. So instead of the doctor saying, hey, you're pre diabetic, maybe you should make some changes to your diet. Now, we know that you're likely on the road to diabetes. We wait until someone has diabetes and then say, okay, now we need to get you on insulin or whatever it is.
And so he talks about medicine 3.0 being getting ahead of that. Right. We have the information. We know how these different actions and activities are going to impact you. And you can get a lot of information based on someone's genetics, based on some of the things that you're talking about, it sounds like on where are you likely to go, where are your issues likely to land? And there's a certain amount that's not controllable. Can't always control whether you're, whether you're going to have a heart attack, get cancer, that sort of thing. But there is a certain amount that you can do. And in society, as well as in individuals, everyone's going to be better off when they're thinking ahead and tackling these things proactively.
[00:24:38] Speaker B: Yeah, that's really key. I love everything that you just said and it reminds me of, so I can't say the name. We've got a new customer coming on board who's a hospital.
And one of the things that really resonated with all of the doctors and the data people, Washington, they want to move from, react and prescribe to, predict and prevent.
That's right. They realize that in effect, they're going to reduce their income. But we have in the US 75 million boomers that are going to hit 65 in the next six years.
We as a country aren't prepared for that.
What do we have? 30,000 long term care nursing facilities? You can't put 75 million people into 39,000 units. It does not work. So we're going to have to address it better today and start today and to do something, because otherwise you just, we will never be able to catch up and then we're going to be in a different and worse position, in my opinion. But just like we think about it, then we can think about it for ourselves in our own lives. So, yeah, thank you for that. Thank you, Jordan.
[00:25:51] Speaker A: This is, I mean, you know, we talk about AI and we talk about impact labor force to AI, but I think it's really easy to think about all the negative things that AI is going to bring with them. But once in a while, we need to remember that AI is just like a hammer or a knife. You can make a meal, you can build a house, or you can use it as a tool for violence. At the end of the day, the user determines the value that is driven.
Rob, I think about insurance.
Am I insured to the right things or am I going to have the insurance? And then, you know, I'm going to find out after something terrible happened that I'm not protected against what I really should be.
And, you know, an AI tool can come and look at so many different parameters and say, oh, by the way, you really need this because this is likely to happen to you. And then, you know, I'm not going to be in tears finding out that I've lost all my life savings. Or alternatively, as a Rotarian, one of the community that we were supporting was a doctor. You think doctors, they make a lot of money. This doctor was homeless, his wife got cancer. They lost all their money. They lost everything in supporting her. So this is. I mean, this is fundamentally something that can save you from becoming homeless, really, at its core. Essence and many other outcomes.
[00:27:17] Speaker B: Yeah. Unfortunately, that. That happens more often than not. You just don't hear the reports of it. You hear it when you talk to individuals and we have real conversations.
Right. As I said earlier, no one would have thought I went through all that I went through in the past six months unless you had a conversation.
No one would understand that you have to put all the assets of somebody in a trust five years before you put them into a long term care facility until you talk to somebody who's missed that date, our family missed that date. I want to help people not make the mistakes that we did. And yes, we'll do well as a company. We'll get to that point, but we've got it.
We have to use our experience and help. The wisdom we've gained can help others. And that's our. I think if I was to think altruistically, that would be the number one thing, is how can my experience help you and how can I help you? That's really what I got. I'm here to do.
[00:28:19] Speaker C: That's a great point. I think that's something similar to what the property insurance market is currently going through, is that we've treated a lot of these events like these huge black swan events, and there are black swan events that can come about. But like you said, there's a ton of data that you can use to predict. You may not predict to the day and to the year, exactly, but you're going to get a much better idea than if you just ignore all the data and sort of say, yeah, maybe one day I'll get cancer. Sometimes people do. Who knows? And they're not necessarily Blackstone events. Right. There are things that you can predict and there are actions that you can take.
[00:28:54] Speaker B: I like what you just said, jordan, because it also reminds me of something that's really unique.
Let's say insurance. Insurance is really? Protection.
I'm protecting my investments. My investments is my home, my house, my children, my income. I need to protect it. So one of the things people don't realize is how much insurance do they need? Right? We make the joke, and I make the joke when I go to conferences and speak to insurers, I say, ask ten insurance people how much insurance that I need. You get back 15 answers. Right? There's the rule of four. The rule of ten. Okay, well, the rule of four is four times your income or ten times your income over x years. Well, when I left corporate world, I was at the peak earnings. Now I make $100 a month. Right? Is it. You're telling me $1,200, $400? No, that's completely wrong. Right. And what if I lost a job and I had to take another job, or I'm working two jobs, part time jobs, then I have added more stress, increased the probability of diseases or critical illnesses or inflammation in my body. I've increased risk in multiple ways.
Maybe what we should do is look at it empirically and say, what does it cost you to live?
What about unplanned events? Maybe here's the total cost. And maybe it's not one x because one x is planning on perfection. Maybe I need 2.3 x. Maybe I need three x because I'm doing those two part time jobs. Right?
That's, that's. That's data. We can use that and that's what we try to help people do. So that's. I love what you said, Jordan, that really, that peaked my interest in terms of that.
[00:30:36] Speaker A: So thank you, Rob, with your permission, I want to change the topic a little bit and ask you, why did you even get into this business?
Why did you start the company? How did you start this journey?
[00:30:50] Speaker B: So, my story started when we moved my grandfather into a long term care facility.
And I dared to ask my mom the question, how much does that cost?
And her answer was, $14,400 a month.
And he stayed in that facility for four years.
If you total that up, that's nearly $700,000.
Now, my mom, who takes care of my dad, who's a quadriplegic from the Vietnam war, is used to working with the government, used to working through insurances, did a phenomenal job. While we were dealing with emotions, while we're dealing with. I don't know what's going to happen, what do we do? I don't know. While I saw us make some good decisions and quite a lot of bad ones, because we were basing it on emotion, not on real numbers.
So as this was occurring, I started to look, and at the time, I was chief information officer of about a top 40 broker dealer, first global here in Dallas.
And I said, wait a minute, I'm in this industry. I've been in this industry for x years.
How can I not help my own family?
What are the tools? So I looked at the tools I deployed. I looked at the tools we were building and we were using and integrating, and all of them said, well, we give you the ability to calculate those things, but we don't give you the data to know when they happen or how much they impact.
And I sat there and I said, wait a minute. So then your systems are all point in time. So unless an advisor or an insurance agent or CPA asked me what year do I plan to spend $12,000 or $14,400 to put my grandfather into a care facility, how do I know when to calculate that? And they said, you don't. You have to find out. And it went back to, well, nobody in their right mind is going to ask that question.
[00:33:00] Speaker C: What?
[00:33:01] Speaker B: Are you kidding me? And then I realized that it's a big lack of data.
So I searched and searched and started looking for, is there data, enough data to tell me when something typically happens? Can I find more data to tell me when it probably happens? Well, if I can probably look at things, can I get a little bit more data to make it statistically significant? Can I look at the data and break it down in certain ways? Well, the answer was yes. Yes, yes, yes. Well, now all of a sudden, it's a math problem. Okay, let me throw math at it. I can build prevalence and probability models and tell you statistically this is likely to happen. Now, does it mean it necessarily happens? No, but if I had bought enough protection and it didn't happen, are you happy for me because it didn't happen? Am I mad at you because you told me this would likely happen and it didn't? No, I've got extra money. Oh, my goodness, what a blessing. So I got on a soapbox. I apologize, but I started the business because of a life event, and I want to help people avoid the same.
[00:34:06] Speaker A: I love that.
[00:34:10] Speaker C: That makes me think about sort of the next step, right, which is influencing people's behavior, right? Saying, hey, we've given you this data, you know, what do you do with it, right? How do you decide to make that correct decision? You're talking about the diabetes, right? How do you start reducing your sugar little by little? How do you start moving. How do you take these positive steps, which, you know, any, any psychologist knows is kind of the hardest part? I'm very curious how you've seen, you know, some of your partners, how you've thought about implementing that behavioral piece.
[00:34:41] Speaker B: Yeah, that's a really good question. And that is.
That's where we sit today. I can tell you, I can't tell you the names of the companies, but I can tell you where they're heading towards. So, number one, start off with the fact that 50% of the people are going to hate your data. 50% are going to like, in reality, it's a third. A third, a third. One third says, I don't care what you show me. I don't believe you. So, okay, so you're never going to get perfection. You're never going to get 100%. One third will say, oh, my gosh, I want to see this, I want to know this, I want to do this, I want to plan, I want to use it. And that third that sits in the middle, that's really the group that you're looking for. So within that group of the. I'm not sure yet. What you need to do is find out other common commonalities amongst them as little groups of people and find out what really means a lot to them, what's contextually relevant, and then garner the data. So we're pulling in data from a couple of partners. One of them is DNA behavior. Great company. We pull in their data that talks about how people. How they are in terms of their emotionality, how do they receive data, how do they like to be spoken to? Because you and I both know a lot of people love to read.
That's how they take in data. Some people don't like to read. They want to hear, other people want to see. Some people like to do well. You have to provide the ability for them to access that data in those different ways. But if you've had customers for any length of time, you know, if they call, you know, if they email, you know what they're like, you understand their personalities. You use that data to help tailor a message to them. So in reality, you're right. It's the psychographic understanding. And then we append data that would say, oh, by the way, they happen to make $149,000, and that happens to be in the top 10% in their area. Ooh, guess what they're doing. Well, they're up and coming. They're moving up. And if they're 40 years old to 54, they're at this life stage with these two kids, here's what's going to happen next. Talk to them about the beauty of life going further. Or let's say they're in their sixties, and guess what? They're empty nesters. Well, now it's not about where you're going. It's about, ooh, spoil those grandchildren. So you get into their understanding of the psyche, of who they are. But that's what we do as people. I'm not doing anything different. We're just using data to actually facilitate the same thing. That's where they're going, is they're tagging the life event with the life stage. What they're looking forward to in the next one, three, five years, and then trying to use the data that they have about their people to actually help market to them. We call it hyper personalization. That's a term a lot of people use. I actually like the word tailor.
I'm getting a suit, not a massive suit that anyone can get. I want one that's specifically tailored for me. That's what they're doing, the tailoring, the.
[00:37:49] Speaker C: Personalization through kind of bringing all of this, just different kinds of data that, like you said, it's out there. Why not use it? Right? Why not use it to make people help, give people a better experience, help people make better decisions? I love how you sort of brought all that together.
[00:38:04] Speaker B: Oh, thank you. Well, I'm the recipient of it, right?
My music, my movies, they constantly tailor stuff to me every day. So why can't we do it?
[00:38:16] Speaker A: Let me ask you a little bit of the dark side question.
A lot of information is being collected about us. We're using sophisticated data, and models are always wrong, but they're many times useful.
How do companies like yours and others protect themselves and protect us, the communities, from bad players, abusing that data?
[00:38:43] Speaker B: So I will start with the simplest things that I can control.
We use tools, such as when we move data back and forth, such as encryption.
We use tools when we send. If we send emails, we use tools that, if I send you a file, I may use phalanx, which is a company I use for files that can be only used one time, that can't be forwarded. If we go enterprise wide and larger, in terms of infrastructure, we may take things off the open Internet and use Cleverdome, which puts everything into very specific type of security, a zero trust network that you have to be a part of and have to have certain keys to. That's the first way that I can do things that are in control. For moving the data. Another way is allowing you to get into our environment, where our infrastructure being on Azure or snowflake, you would have access to our data that we have set aside for you, or we can operate into behind your firewall on your azure or your snowflake environment. And for us, Snowflake is probably the best way to do that, because the customer then controls everything. So if we were providing data to a large bank or an insurance company or a hospital, that would be very beneficial to them. They're in complete control. So we try to tell them, you control it so that it meets your requirements, not ours.
That's first and foremost. When you speak about algorithms, you speak about the fact that, yes, algorithms, nothing is perfect, nothing will ever be perfect. You're never going to get 100%.
The only thing that I would say 100% on is that you pay taxes and you will die. That's it. Other than that, nothing is guaranteed. So until that time, I think that what we do is we have to realize that there has to be an iterative process where we learn where our algos aren't going to be right at first. But if we're doing this with a group of people to say, what can we do? How can we improve it? It's really easy to poke holes in everybody else's technology. It's actually hard to help people. That's what you want to do. So let's get together. That's wrong. Okay, how do we fix it? We can't. Okay, what do we do different? Oh, we can do this. Great move. That's the feeling. And when people say fail fast, that's what it is. It's accepting failure. And I've seen studies where a scientist will go up or mathematician will go up or professor on the chalkboard and write, one plus one equals two, one plus two equals three, one plus four. And then he gets to nine and goes, one plus nine equals eleven. And everyone starts laughing.
Why? He got 90% of it, right.
But they always pick on the 10%.
So when you start to think about people in your team, you start to think about building algos. You want to hire the right team. You want to make sure that from a management perspective, how are you intending to use this data? What are you doing with this data? I can influence and have an understanding. There's nothing says that I can't stop producing data for somebody because I don't like what they're doing.
Now, they may try to sue me for it, and I get it, but there's a point where I'm not, I can't cross that line. So I have to feel good about the data that we're providing, but I also have to feel good about how much data I'm providing them. So we don't provide everything. We provide enough to help them in their situations. And then internally, what I can control is who I hire, how I talk to them, how they look at the data, how can I control? They have access to the data. But ultimately, you're always going to find bad people do bad things. It's the question of how much management and oversight, and, you know, what's our approach? So there's no real answer. There's an approach, in my opinion, not fair.
[00:42:44] Speaker A: There's. In my opinion, there's a need to have these discussions educate the public, educate the lawmakers. Oftentimes with new innovation and I entrepreneurship, the government lags significantly. And by having these discussions explaining what are the dangerous, what is the difficult, what is the benefit, then we can really think about how we make laws to make sure that the environment remains ethical and valuable, and we're not going to get there if we don't have these discussions. So, you know, Rob, first of all, I want to just say thank you for you joining our show today and really taking the veil off of these incredibly complicated issues that oftentimes people just don't want to deal with them because they can also be painful. Oh, what are you gonna do when your mother dies? Excuse me? What?
[00:43:37] Speaker B: No. I ordered french fries. I didn't want to talk about that.
[00:43:40] Speaker A: No, I asked for you to insure me. Insure me? And don't ask me stupid questions.
So I think it's important. We need to have these conversations.
I would like to ask you a very simple question that we ask all our guests. What would you advise 20 year old rob?
[00:44:02] Speaker B: I've thought about that question so many times, and I think the biggest thing that I would advise myself would be to start failing sooner and don't listen to the herd.
That would be my biggest thing, because I played it safe for a number of years, and there were certain things that I could have done more effectively, more efficiently, and I'd be much better off and be able to help more people today.
And to me, it seems like a waste of time.
So for me, it would have been, just go for it. Do it. Fail. Get going. Move. Don't sit on your laurels.
[00:44:56] Speaker A: Rob, thank you for joining us today. Thank you for being vulnerable and opening your heart and sharing the incredibly interesting work that you're doing. We appreciate you joining the show.
[00:45:06] Speaker B: Thank you guys for having me. It's been an absolute pleasure. So I look forward to, to hearing this back. This has been a tremendous, tremendous time to visit with both of.
