Dave Stahoviak (3:45)

It's such a interesting analogy for psychological targeting because psychological targeting can do some amazing, incredible things as I think we're going to talk about in this conversation. It can also manipulate and exploit. Right. And a line I highlighted from you in the book was this one. And I think you were talking about in the context of growing up in a small town. Once I understood the game that was played and had a clear sense of what I wanted out of it, I learned to play it to my advantage. Suddenly I was winning more than I was losing. And there's a message there, I think, for all how much we learn about psychological targeting there too, isn't it?

Sandra Motz (4:25)

Yeah. So. So absolutely right. The. The way that. And it's funny because I give this analogy of the village and then I kind of very pretty rapidly shift to technology, right? So the, it's. It almost feels like a paradoxical comparison because the village was all about individual relationships. And now that we live in this, in this, what I think of as a digital village, it's not necessarily neighbors understanding exactly who we are and what we want, but it's all of these digital neighbors, like algorithms, who observe everything that we do from what you post on social media, credit card spending, the sensor data that gets captured by your smartphone, and you're absolutely right. It also has these two sides to some extent. Learning something about individuals offers amazing opportunities, and we probably are going to get to talk about this in a little bit. But it also has like this, this exploitative, manipulative angle to it. And for me, the same way that in the village I was trying to figure out, how do I amplify the positives and how do I mitigate some of these challenges and sides that I didn't appreciate about the village, the same way I'm thinking about this in the context of technology. So how do we make technology work for people rather than against them? And I think leadership really plays a critical role here.

Dave Stahoviak (5:37)

Yeah. And I think it's such a great opportunity for leaders to be knowledgeable about this. Like this. This is not going anywhere. Right. It's not disappearing. In fact, if anything it's going to. And more prominent in all of our work how organizations message us, how governments and individuals reach out to us. And so like, understanding how this works I think is key. And before we even get into some of the like, okay, what would we do as leaders? I'm just curious about some of the examples in the book because I think they're so powerful in just illustrating how this works in the identities that we craft online, whether we're conscious of it or not. And there's a really interesting example in the book of just, oh, there's so many examples. But one of them is how the differences between what high and low income people talk about online. Could you illustrate that a bit? Because I think it's just fascinating how that shows up.

Sandra Motz (6:34)

Yeah, so, and for me the interesting part is that data actually gives us an insight not into like just an individual psychology, but it also teaches us something about human behavior. So the example of high and low income individuals was actually relating that to what they talk about on social media and the things that they like on social media. And you can imagine some of these connections are actually obvious. So high income people, they talk about the fun vacations that they go on, they talk about the luxury brands that they buy, where that's not necessarily true for low income individuals. But there's also these more subtle nuances and these more subtle relationships that are not just interesting from a psychological point of view, but also show that there's like all of these, what I think of as like hidden relationships. It's not something that you intentionally put out there to signal your identity. It just kind of creeps into the language that you use. So this is, for example, low income individuals being a lot more focused on the, on the present than the future, for example. And again, in a way that makes sense because if you worried about running out of money, trying to figure out how to make ends meet, you're probably not going to think about the next summer, next year, 10 years from now. What you're trying to figure out is like, how am I going to get through this week? How am I going to get through this month? The same is true for references to the self. So low income individuals have a lot more References to the self. And again, it's much harder to worry about the problems of the world if you're struggling financially. So for me it's kind of both this interesting lens into human behavior and it also teaches us something about like just how hard life is when, when you have very little money available.

Dave Stahoviak (8:13)

Yeah, indeed. And it's. The book is, boy, it's so worth picking up the book just to look at the word clouds that you've surfaced in the research on. Like what are the kinds of words people use when they're in a low income situation? What are the kinds of words they use in a high income situation? What are the kinds of words extroverts and introverts use? It's really fascinating. And then of course the algorithms can be tuned to that to be able to target what kind of ad, see what kind of material shows up, what shows up in the feed.

Sandra Motz (8:47)

It.

Dave Stahoviak (8:47)

It is really, really fascinating.

Sandra Motz (8:50)

Yeah. And for me the interesting part in a way is that some of it is obvious, right? So if you look at the word clouds and you look at the word clouds for extroverts and introverts, like extroverts are out there talking about weekends and dating and so on. And then introverts talk about computers and anime and manga, all of the stuff that you do by yourself. And for me, that the obvious parts are actually nice because they show that it' rocket science. Right. The way that computers or algorithms translate your online behavior into these more holistic psychological profiles isn't really like this massive black box that AI is oftentimes made out to be. It's in a way, counting words and looking at these pretty obvious relationships. But then you also get the ones that are not as obvious and that we as humans might not have even been able to pick up on. So again, the references to the self for low income, for example, is also true for, for emotional distress. So if you're emotionally distressed and having a hard time, you just kind of think about yourself a lot more and talk about yourself a lot more because again, you're worried about like, why am I feeling so bad? Am I ever going to get better? And those are these relationships that show up in the word clouds that are not obvious but that computers can actually detect just because they have access to so much data of so many people at the same time.

Dave Stahoviak (10:03)

And speaking of the data and how much access we have to it now, like so many organizations, one of the really fascinating and also scary things that you talk about is just the prediction that some of the algorithms are doing through photography and the, and the photos of people online and how it's possible, not only possible, but really accurate in some cases, to predict personality and even sexual orientation through photographs. Could you share a bit about just like what the research is showing on that?

Sandra Motz (10:36)

Yeah, and I would say that this is some of the most controversial research in that space because it also has like, pretty important implications. Right. You could always argue, well, I don't have to post on social media if I don't want to be tracked. Or maybe I can leave my phone at home if I don't want the GPS records to figure out exactly where I go and who I meet. But the moment that we're talking about your physical appearance, that could be face, that could be anything related to grooming. There's almost no way to, to escape tracking. We have cameras on pretty much every corner in New York. Combined with facial recognition, it's very easy to pick up these signals. So what this research shows is essentially that depending on how you look at it. So some of it is grooming. Right. You can imagine one of my favorite examples in the book is extroverts and introverts. Like extrovert introverted women in this case. And just kind of seeing the differences between how they, how they present themselves on social media and what you see is like extroverts, they probably dye their hair a lot more often because their hair looks a lot blonder. They probably also wear contact lenses more often because their eyes look blue and there's no genetic reason for why that should be the case. They also seem to be much better at taking pictures because their pictures are. You don't see the nostrils, which suggests that they take pictures from above to make their faces look slimmer. So there's all of these traces that are somewhat curated and groomed. Now what the research suggests, and I think this is where it gets really creepy, also potentially interesting is that it also suggests that there might be some features of your face that actually predict personality. So independent of grooming, just the features of your face that are related to some of these psychological traits. And I think what creeps people out is that there was the signs for pseudo signs of physiognomy for a long time that suggested, well, maybe if your nose looks a certain way, maybe you have a certain character trait. And it was certainly abused in many different contexts. Now what we can do with computer science and AI is really look at the actual differences. And there's many ways in which that could be true. I remember when I first heard about this research, I was like, this is absolutely insane. I don't think that there's anything to this. But then there's many ways in which actually our physical appearance might be related to psychology. If you think about hormones, for example, we know that testosterone makes you more aggressive, makes you more assertive, but it's also related to, to your facial features. Or like one of my favorite examples or an argument that is oftentimes made is we just respond differently to our social environments. Right. Imagine you're like this beautiful baby, perfect symmetry of your face, everybody loves you, you constantly getting positive feedback from your environment. It's not so surprising to imagine that maybe you're also going to turn out a little bit more extroverted than other people. You're constantly getting this positive social feedback. So this I think is part of the signs that is the most, most controversial. So like your facial features really opening or like offering a window into your psychology, but also one that is the most creepy in a way, because there's no way that you can leave your face at home.

Dave Stahoviak (13:34)

Yeah, indeed. And like you said, even if you personally decide you're going to opt out, there's so many cameras, so many places, and I mean, it's really fascinating. Even some of the studies where they control for the grooming, like have everyone do their hair the same way, position the camera the same way. Even then, like the algorithms are pretty good. I mean it's. And, and you think about this and it's like really easy to go down a very dark tunnel very quickly of thinking, indeed, okay, how can organizations and governments manipulate this data? And we've seen examples of that already happening in, in media. Right. And there's a. Both and here, which is like, what are also the great things? Which is one of the reasons I wanted to talk to you, like, what are the great things that can possibly come out of this? And I love some of the examples you talk about in the book and some of them you've been involved with. I'm wondering if you could share the story of Savor Life and the work you did with them to highlight how this could actually be used for good.

Sandra Motz (14:31)

Yeah, it's one of my favorite examples. And in a way it comes back to the analogy of the village. Right. So in the village, the fact that someone truly understood me was a blessing in the way that they could really help me. They knew exactly what I wanted and they could offer the best advice. So Save a Life was one of the collab that I did with a fintech company that is Actually trying to help low income individuals save more. So if you look at the state of affairs in the U.S. the picture looks pretty grim. I think about 50% of people live paycheck to paycheck. 10% of people couldn't even go a week without being paid. And it's a terrible situation to be in because the only thing that has to happen to you is your car breaks down, you can't put it into the shop, you can't get it fixed, you can't get to work, you lose the job, and so on. So it's a very kind of easy slippery slope to, to losing everything. So what, what Save a Life was trying to do is to say, well, we know what these sneaky marketers do, right? So we know that psychological targeting allows marketers to sell you more stuff. If I know that you extroverted, I can advertise certain products, I can talk to you in a certain way. And we know from my own research that that allows marketers to essentially increase the likelihood that someone is going to buy a product. And the question that we had is, could we just flip this concept on its head? Could we, instead of using this understanding of your psychology to get you to spend more, could we also use it to get you to save more? So with users consent. So Save a Life users in the app, we surveyed their personality. So in this case, we actually did it using questionnaires with their consent. So it was very clear and transparent what we were trying to do. And we just said, okay, now that we understand that you might be more extroverted or more agreeable, which is like one of these personality traits that looks at how much people care about their social relationships, for example. And can we use these insights to help you save more with, with messages that we send you? So we kind of had this, we tapped into Save a Life. I think it's called race to 100. So this is a challenge that they set for their users where they encourage them to spend, to save at least $100 over the course of a month. And it might not sound too much to some of the listeners, but those were people who had less than $100 in savings. So this is like a massive undertaking which is really kind of trying to double your savings over the course of four weeks. And what we tried to do is we took Save Alive, the message that they had been trying to optimize for four years. So I think of it as like the gold standard that Save a Life was using at the time. And then we compared it to psychologically customized and psychologically tailored messages. So again, for agreeable people, for example, they're not necessarily going to be convinced by just having extra money in their bank account because what they care about is other people and their loved ones. So for them, messages would say something like, well, if you put some money to the side right now, this is an opportunity for you to make sure that your loved ones are safe now and in the future. And what we saw is that essentially in the number of people who managed to hit their saving goals of $100, we saw like a 60% increase in, in, in the group that got these psychologically customized messages compared to the gold standard. So this is like a pretty high bar to clear, right? This is save a life trying to figure out how do we best communicate with, with our users for, for quite some time. And still by just tapping into people's psychology and their motivation, we were able to, to increase that further. So for me, this is just a very nice what if scenario is like, we can use the same technology to either get people to spend more, reach deeper into their pockets, but we can also use it to help them save, which a lot of us aspire to do, but have a hard time with.

Dave Stahoviak (18:07)

It's an amazing example of how there's so much good that can come from this as well. And I think about some of the examples you cite in the book about just the chatbots and helping to support mental health for folks. And now there are some examples of how they fail spectacularly, of course. Right. But there's also some really amazing examples of like how by doing targeting can provide interventions and opportunities to connect with people before even their spouse or a partner or a friend or someone else would notice what's happening. Right?

Sandra Motz (18:48)

Yeah. So for me, like the ability and those systems are getting better and better. Right. If you look at the last five years, the development that we've seen with generative AI with these chatbots is just mind blowing. And what, what these chatbots can do is essentially two things in the context of mental health. One is tracking. And I think that's an important part because we know that still so many people go undiagnosed. Right. There's still like the, the number of suicides every year that could be prevented if someone was only diagnosed with something like depression are sky high. It's just like terrible numbers. And what happens typically is once you enter a full depressive episode, for example, it's really hard for you to reach out to people because one of the signs of depression is you're Essentially very much inward focused. You are having a hard time reaching out to the people who could support you. So what technology can do is essentially passively say, well, we kind of see that there's some deviation from your typical baseline. That could be anything from social media. Right. Again, how do you talk about your inner emotional life to something like GPS records embedded in your, in your smartphone? And that could be, well, we see that you're not leaving your house as much anymore. There's much less physical activity. Maybe you're not making, taking as many calls and it might be nothing, but maybe you're just on vacation and that's why there's much less physical activity. But why don't you look into this? So instead of saying we have to wait until someone is like deep in the valley, deep into, into a depression, we could intervene much earlier and say, okay, again, maybe it's nothing, it's not a diagnostic tool, but there seems to be some deviations. And maybe I'm going to try to point you towards the right resources, but maybe there's also a way in which, especially if you know that you have a history of mental health problems, why don't you nominate someone that you trust and love that could be, could be siblings, could be spouses who get alerted once we see that there's this deviation. And again, it's just like flagging it. It's like an early warning system. But they could then reach out and say, hey, everything okay? Is there anything that I could do to support so that the tracking part I think is already critical. And then as you mentioned, I think the treatment part with just having these chatbots and an alternative for people who otherwise can't access mental health care, which is most people. Right. According to the World Health Organization, I think for every 100,000 people looking for treatment, there's 13 professional therapists. So there's this huge gap in supply and demand. And even though those chatboards are maybe not perfect, they're certainly better than not having any care at all and available.

Dave Stahoviak (21:20)

At 3 in the morning when someone's in crisis too, that like a traditional therapist wouldn't be. And like you, you go to great lengths in the book to say like this is not a replacement for a traditional therapist necessarily. But boy, what the potential to be able to bring alongside the both and here of like to have a resource that's available and to, and to flag and to alert others. I mean it's, it's really powerful. And I, and this just brings me to maybe the obvious big question, which Is, wow, what do do with this? Right. As leaders? Because there is, of course, knowing that and many people listening have organizations that have done some version of this in recent years of doing psychological targeting and collecting data. And there's a lot of incentive for organizations to collect as much data about people as possible. Right. To be able to get them to do whatever they want, either good or evil. Right. And for someone who's listening, who's thinking, okay, maybe my organization is thinking about doing this. Maybe we've done a lot of this collection of data before and we're thinking about how we do this in the future. Where do we start? Like, how do we think about this differently in a way that really supports the good we want to create in the world with this?

Sandra Motz (22:30)

Yeah, I think there's, there's two main avenues that I see that the first one is that, and this is like a recommendation that I make whenever I work with companies is to the extent that you can, I would always involve customers and end users because. So first of all, like, when you use these predictive models, right? Translating data into psychological insights and then using them to try and shift behavior in a certain way, you're going to make mistakes. So those models are pretty damn accurate on average, but at the individual level, you're going to make mistakes. And that's not only annoying for your customer and user, but it's also like, very costly to you. Because if I think that you're someone who you're not, and I'm now optimizing my entire product offering, servicing the way that I communicate with you to that false profile, that's just a waste of money and it's annoying for you and I'm most likely going to lose you. And it's also a question of trust. Right? I think we've seen enough examples now where consumers figure out that companies collect all of this data in a way that they don't appreciate. So whenever you can make it part of the communication conversation that you have with users. My favorite example is actually a project that we did over 10 years ago now with Hilton. They were trying to figure out, how do you integrate some of these insights into the recommendations for victim. How do you create this, the best personalized experience for users and what they did is instead of saying, I'm just going to passively grab some data, predict your psychology, and then try to get you to spend more, they created an experience for their users and their customers to say, hey, why don't you try and help us understand who you are? We're going to create a traveler profile by you logging into your social media. It's all based on consent, and it's part of the value proposition. It's saying, well, by giving us access to your data, we're not only going to show you the predictions that we make, but we're also then directly creating value for you by tapping into these, into the psychological profile. So this is, number one, I think it's just to the extent that you can, I would always involve your customers and users. And number two is that this argument, and I really like the way that you described it, because I think it's a philosophy that a lot of companies have and that was pushed for a long time, and that the more data that you collect, the better. Because first of all, even if you don't need it right now, it's always good to have it because it's very easy and it's cheap to collect. So why wouldn't you? And I think that's no longer true. It's no longer true that you can only offer personalization, you can only offer the best service if you collect all of this data. And that's because we have new technologies that allow you to essentially extract intelligence from data without collecting it. So the technology that I, that I'm referring to is, it's often called federated learning. And it's the idea that traditionally, take take Netflix, for example. So traditionally what happened is Netflix is trying to create these recommendation algorithms that figure out which movies you might be interested in based on your past viewing history and what everybody else is into. And it used to be the case that you just have to send all of your data to Netflix. They process it on a central server, they create these models, and that's how you benefit. Now, the fact is that we do have these insane supercomputers in our pockets, right? Our phone is so much more powerful than the computers that were in the Challenger, for example, that we used to kind of launch into, launch into space. So we have these extremely powerful machines in our pockets. And what Netflix can do is they can, instead of grabbing our data, this data can stay on our phone and Netflix can just send the intelligence. So Netflix sends the model to my phone locally. It updates, it sees which movies I like, so it improves my recommendations locally on the phone. And then it sends back, instead of sending back the data, it just sends back the intelligence to Netflix and says, okay, now I've learned something about which movies go together, but I've never seen the actual data. And so this is. It's. It's an incredible technology where you can keep the data safe, but you can still generate all of these insights. And the question that I oftentimes get is like, why would companies do that? Right? Why would companies kind of not collect the data themselves, but instead go to technologies like federated learning? And I think there is a very good argument to be made. Like, unless you're in the, in the business of selling customer data, then you probably you're going to collect as much data as you want no matter what. But if that's not the case, you're much better off saying, well, I can provide the best service and with the best personalization, but I'm not responsible for safeguarding all of the user data, right? If you collect all of this user data and Netflix is a simple example, think about this in the medical space, in the medical space, medical histories, genetic data, the moment that you collect this centrally on your server, that's a huge responsibility to protect it. And we've seen data breaches kind of on the rise across the board. It's extremely expensive for companies to deal with these security risks and both financially and reputationally. So if you are again, not in the business of selling data, you're much better off saying, I can provide the same service, maybe even make it part of my value proposition to say, well, we are a company that gives you the same product without the risk of your data being out there instead of just hoarding it all in one place. And now you're responsible and now you have this huge risk of data breaches. So I think this is a trend that we already see playing out and there's some pretty powerful kind of players behind it. Apple and Google, they both develop some of these federated ecosystem open source, by the way. And I think this is going to be the future for many companies that have the best interest of consumers at heart and they also want to reduce the risk of some of these data breaches.

Dave Stahoviak (28:15)

It's such a critical way to think about this. And just in our own organization, we're a tiny, tiny organization, Sandra, and we have the principle of let's keep as little data as we possibly can about people just for that reason. And that's easy for us because it's my wife and I and we have a few contractors, like it's easy for us to have that policy and to implement it. One of the things you pointed out to me though is that but a larger organization that maybe a CEO or an executive director or someone's really aligned on good practices with data, is also thinking about the future in systems because sometimes someone else steps in, the next CEO, the next executive director, the next whoever, and they have a very different opinion about how data should be used. Right. And so like, think part of this is thinking through for the future. How do you design systems knowing that people are going to change those seats sometimes?

Sandra Motz (29:11)

Yeah, exactly. It's one of my favorite examples from industry and it's coming from Apple. So I have this friend at Apple at some point told me about the evil Steve test that apparently make their teams go through. And the idea of the Evil Steve test is that they know when they have teams working on products that everybody's excited. So you're putting out a new product and most of the people, they are really kind of, they see this is the data that we collect, but we're collecting it to make the product better and here's how we're using it. Now that means that oftentimes this like devil's advocate that says, well, but wait a second, what would happen if this data was actually being abused now that we've collected it? It's really hard to play the devil's advocate. So what they do with the evil Steve test, they essentially make all of their teams go through this thought experiment of saying, well, maybe you have the user's best interest at heart right now with the way that you're designing the product and the data that you're collecting. But what would happen if tomorrow we have a different CEO? So that's where the evil Steve comes from. We have a different CEO with completely different values. They're not trying to help our users, they're trying to exploit them to the best they can. Would you still feel comfortable collecting the data that you're collecting today and setting up the system in the way that you're setting it up right now? And if the answer is no, if the answer is well, there is a lot of potential for abuse and I wouldn't want anyone who has like intentions that are opposed to what we are trying to do here, get hold of that data. And then you go back to the drawing board and say, okay, maybe there's a way in which we can do this that doesn't put our users at risk of the data being exploited in the future. And I think it's just, it's such a nice thought experiment where you don't necessarily have to have just a single team member that says, but wait, what about all of the risks? Because usually that team member isn't hugely popular. So it's like a systemic, systemic intervention. That you can do, that you can implement as a leader that says, okay, we're collectively thinking through the risk and we're kind of bringing the future into the now to see if we can do better.

Dave Stahoviak (31:17)

Yeah, it's really, it's so critical. And just like so much of leadership is like answering the question of change. Right. And we are all called to have the responsibility to think of, okay, not just today, but like five years from now, 10 years from now, how do we put in systems with the best possible practices that will do this so well and fascinating, just fascinating. I hope folks will get the book Mindmasters and to just get into the details of thinking about this, understanding how organizations are making decisions just on a personal level. I consider myself a pretty savvy person on privacy and data and targeting and all that. And I still went in after reading the book and went into my smartphone and started changing a bunch of settings just like on a personal level. Because how you think about this like really does show up differently. And, and speaking of showing up differently, I'm curious in putting this book together and doing all the research you've done over the last couple of years, I mean, so much is changing on this, of course. What if anything, as you've been doing this, have you changed your mind on in the last year or two?

Sandra Motz (32:18)

Yeah, it's a great question. And one of the questions that I think is related is, and you mentioned it, right, you going on changing the permissions on maybe some of the applications. I think I've become a lot more pessimistic just observing my own behavior in terms of how much we can do as consumers. So if you look to some of the data protection regulations in Europe and California, they all kind of try to empower consumers by saying, well, we kind of first we advocate for transparency and control, so we explain to users and consumers what's happening with their data and then we give them the control to manage it. And it's a really, really nice idea in principle. And I do think that we, that we need control. But just looking at my own behavior, it's an impossible burden. But it's not really a right to say, oh, now I get to read all the terms and conditions of all of the products and services that I'm using and I have to keep up with all the latest technology to see how I might be exploited by companies. And by the way, it's also a full time job because if you really want to do it for everything that you use, you're no longer going to share a meal with your family. All you do is read through the terms and conditions and manage permissions and I don't do it. So I think about this topic almost every day for the last 1015 years and I constantly catch myself saying yes to stuff where I was like I just don't have the time, I don't have the energy to now manage all the cookies. I'm just going to mindlessly say yes. And I think that's it's to me it suggests that we just need different solutions. Right? That could be anything from privacy by design on a regulation level that makes it a lot easier for people to protect their data or something like federated learning where you don't have a trade off between I want the service and convenience, but I also want to maintain a certain level of privacy and self determination. So I think that's really something that's changed in my thinking of I don't think just putting the responsibility on users is going to do the trick. I really do think that we need regulators to step up and business leaders.

Dave Stahoviak (34:16)

Sandra Mats is the author of mindmasters the Data Driven Science of Predicting and Changing Human Behavior. Sandra, thank you so much for your work.

Sandra Motz (34:26)

Thank you so much Dave for having me.

Dave Stahoviak (34:33)

If this conversation was helpful to you, three related episodes I'd recommend and one of them is episode 381 serve others through Marketing. Seth Godin was my guest on that episode. He's been on the show a few times over the years. I've been following Seth's work for more than 20 years and it's where I learned first about the principle of permission marketing. Getting people's permission in order to engage with them. Have your organization engage with their messaging. You heard echoes of that in this conversation with Sandra and Seth's message so critical on that as well. Episode 381. A good compliment to the conversation today. Also recommended episode 521 the Way to Earn Attention Raja Raja Minar was my guest on that episode, Chief marketing officer at MasterCard. He talked about many principles of data and marketing in that conversation and one of the points that he made is him and his team at mastercard think about relationships with consumers as a affinity, not loyalty. At least not anymore. He talked about that distinction in that conversation plus a lot more and the importance of engaging people in the story. Episode 521 for that. And then finally I'd recommend the episode with Marcus Collins, episode 664 the reason people Make Buying decisions. People when they're making a buying decision or a decision to engage or donate or go affiliate with an organization, there's always a decision to connect with the brand. Whether that brand's a school, a nonprofit, or a for profit or even a government agency, there's a brand behind it. One of the points that Marcus makes in his work and in that conversation is it's not really about the brand. It's really not about you. It's about how people view themselves. Such an important principle to consider when thinking about it. How to engage and to earn people's attention. Episode 664 for that, all of those episodes you can find on the coaching4leaders.com website. And if you haven't already, I'm inviting you today to set up your free membership@coaching4leaders.com because that's going to give you access to the entire library of episodes that I've aired since 2011. And there are several areas that you can dive in and be able to find exactly what you're looking for. We're filing this episode under Marketing, also under Social Media, also under Data and Analytics. I mentioned that because there are a ton of other conversations in all of those categories inside of the episode library. It's hard to find that on the apps, but that's why we've built the website to really be able to support you. To help you find exactly what you're looking for right now, go over to coaching4leaders.com set up your free membership. When you do, you'll have full access to that, plus all of the other benefits of free membership that you'll see right there on the homepage. And if you're looking for a bit more, you may want to find out about Coaching for Leaders. Plus one thing I'm doing every single week is I am writing a journal entry. Short form, takes just about two minutes to read, but it is my thoughts specifically on a reflection from one of the guest experts who's been on the show or one of the situations one of our members has found themselves in recently. That's what happened this past week. One of our members said, I'm about to move into a new role. I'm the heir apparent for this new leadership position. But but because of the bureaucracy, it's going to take a couple of months for it all to play out. What do I do in the meantime? Do I start doing things? Do I start putting in initiatives? Or do I wait? Well, as we all know, there's a lot of things you can't do when you don't officially have the role. Even if you're the person who's going to have it eventually, but there's also a whole bunch you can do in the meantime. I talked about that in one of my recent journal entries to to find out more about that. Also all of the past entries that are databased on the website, also searchable by topic. Go over to Coaching4Leaders Plus. You'll find out more about the entries and all of the benefits inside of Coaching for Leaders. Plus Coaching for Leaders is edited by Andrew Kroger. Production support is provided by Sierra Priest. Thank you as always for the privilege to support you and I'll be back next Monday for our next conversation. Have a great week.