← The Brainy Business | Understanding the Psychology of Why People Buy | Behavioral Economics
The Brainy Business | Understanding the Psychology of Why People Buy | Behavioral Economics
510. When AI Meets Neuroscience
00:37:00
In this episode of The Brainy Business podcast, Melina Palmer welcomes Dr. Ingrid Nieuwenhaus, head of Science at alpha.one, to explore the fascinating intersection of machine learning and neuroscience. This insightful conversation, originally...
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- [00:00]
Melina Palmer
Welcome to episode 510 of the Brainy Business Understanding the Psychology of why People Buy. In today's episode, I'm excited to introduce you to Dr. Ingrid Nguyenhus. Ready? Let's get started.
- [00:18]
Ingrid Nguyenhus
You are listening to the Brainy Business Podcast where we dig into the psychology of why people buy and help you incorporate behavioral economics into your business, making it more brain friendly. Now, here's your host, Malene Melina Palmer.
- [00:34]
Melina Palmer
Hello. Hello everyone. My name is Melina Palmer and I want to welcome you to the Brainy Business Podcast. Did you know that AI has been used in advertising and brand testing for years? Long before ChatGPT and Midjourney made headlines, there were scientists blending machine learning and neuroscience to understand what really makes a great ad work and how to optimize it before it ever hits the the public. The conversation you're going to HEAR Today with Dr. Ingrid Nuanhouse is from a live podcast interview I did back in 2021 at Green Book's IEX Behavior Conference, and it's packed with insights that are even more relevant today than when we recorded it. As the head of Science at Alpha 1, Ingrid was, and I'm sure still is, doing cutting edge work using predictive eye tracking and neural networks to measure brand value, consumer attention and emotional impact in advertising. I chose to bring this episode back today because it's the perfect lead in to our next episode, a replay of the south by Southwest panel I moderated earlier this year called Enough with the Delving Staying Human in the Age of AI. With so much happening in the world of artificial intelligence, from generative models to neurotech, it's important to remember that these tools have been evolving for a long time and and when used thoughtfully, they can help us better understand people, create smarter brands, and keep the human at the center of every decision in life and in business. Really quickly, before we get into the conversation, I want to be sure you know that there are links in the show, notes for my top related past episodes and books, ways to get in touch, and more. It's all within the app you're listening to and@the brainybusiness.com 510. Now let's dig in on what happens when machine learning meets neuroscience.
- [02:30]
Malene Melina Palmer
Hello everyone. I'm Melina Palmer of the Brainy Business. I'm so excited to be here for this live podcast of When Machine Learning Meets Neuroscience with our amazing guest, Ingrid Nguyenhaus, who's head of Science at Alpha 1. We had a chat about pronunciation, so that's my best attempt. Apologies if I butchered it a little bit. So Ingrid, can. Can you tell everyone a little about you, your amazing background in neuroscience and what you do as head of science at Alpha 1?
- [03:04]
Ingrid Nguyenhus
Yes, definitely. Well, first of all, I'm Dutch and we love our vowels. To make everyone else very miserable in trying to pronounce my name. I'm Dutch. I've originally started in my career as a neuroscientist. I'm trained as a neuroscientist, but my PhD in neuroscience and I've always been really interested in where two domains meet. So I actually, you know, was very interested in behavior, but also how does it emerge from the brain. So that's how I got the neuroscience. And I did a whole PhD looking into memory and sleep and how, you know, the whole shebang. And moved to the US in 2010 to do a postdoc there. And at some point I became a little bit frustrated. I think is the best word about doing really good neuroscience in academia. But I think this is something we'll touch upon also in industry. To do it really right, you need a lot of different disciplinaries. You need to be really good at neuroscience. You need to be. But also at statistics programming. There are so many skills that are important and it's not always easy to actually work with a multidisciplinary team in academia. It's sort of you as the scientist, the supervisor, maybe some co authors. But it's not really a place where you can go deep on different disciplines. So after a while I thought I would try, you know, life outside of academia. And also because of course the timelines in academia from study until you have a finding is something that is also, you know, was a little bit too slow to my liking. So after, you know, after that whole academic career, I joined a company called Nielsen Consumer Neuroscience. At the time it was just acquired, it was a startup where these techniques that I used in my academic career like EEG and eye tracking were used to measure consumers response to all kinds of different marketing material, for instance TV commercials, packaging, but also concepts, animatic, a whole different broad range of different stimuli that we tested. And my role was really to help to translate the findings, the science findings to clients, really client facing, interacting, but also make sure the research was robust. After that I switched to moved to New York and worked at the marketing science department of Facebook and really dived more into digital marketing. And last year I moved back to the Netherlands after 10 years. It was supposed to be two or three years in the U.S. but that's how life works. After 10 years, I moved back to the Netherlands and I joined Alpha One, a company that I already got to know and was really interested about because it's a company where we have scientists from many different disciplines, like engineers, computer vision, but also people that have been in marketing their whole life, neuroscientists, all sort of coming together and working on different products. So that was a long introduction. Sorry about that. On the one hand, we also do similar things as I did at Nielsen, where we apply these lab based techniques to help clients find out how well their marketing material is working. But we're also doing, we're currently doing more and more was driven by Covid, of course, when the whole participant based lab based research was really tricky. We've been really starting to focus more and more on also applying machine learning techniques and actually, you know, sort of using the stimulus itself to extract all the learning she can from that using machine learning. So that's what we're currently working on. We have a platform where we can predict what people will look at. So it's like similar to eye tracking or the predictive eye tracking. And instead of having to show participants a stimulus like a video or an image, we can actually upload it to our platform and get a whole heat map of where people will look at just in a couple of seconds to minutes from a neural network.
- [07:27]
Malene Melina Palmer
Yeah, I think like you said, you had perhaps a long intro, but it's because you've earned it. You've done such amazing work over your career and I'm sure there are so many people that have questions for you as we're going. So everybody knows, definitely be putting your questions into the chat box. We'll be trying to address them as we go. And there may be some members of Ingrid's team maybe that can be chiming in and helping along the way. And so definitely use that chat box and we can be following up with you. So you talked just a little bit about your AI powered eye tracking platform and how this is doing predictive eye tracking, which I'm guessing for a lot of people we want to know how it works. And then there's the question of, well, if it's predictive, how close is it? How are you setting it up to where it does align with what a person would actually do? So can you talk a little bit about how that got even built and how you test and track to make sure that it is in line with what people would do? I know that we may think that the eye tracking of the participants would change quickly over time. Time. So you'd have to be constantly updating. I'm guessing that's not actually accurate. So tell a little bit about how Expose IO works.
- [08:54]
Ingrid Nguyenhus
Yes, definitely. So it is a technique based on deep learning. So deep learning is something that has become more and more popular. The older machine learning based solutions were really based on people trying to extract features from an image. Finding areas with high contrast by defining certain rules, finding faces by defining rules. But over the past, you know, 10, 20, 15, I don't know exactly how long, but it's pretty recent, there have been what we call deep neural networks that are based very loosely, I should say, on the brain. So these are networks that have many different layers and instead of finding, you know, having to tell the network what the rules are, the if you have a lot of data, the network can figure out these rules themselves. So what you need to train this network is a lot of labeled data. And in our case we use a lot of, you know, eye tracking or mouse tracking is very similar heat maps. So we have a lot of heat maps where we know where people were looking at. So you have these gold truth areas, these blobs that probably everyone who has worked with eye tracking is quite familiar with. So we know what people will look at. These have been acquired for over ten thousands of images, over thousands of participants. Of course, I'm saying we do it without measuring participants, but that is after the network is trained. Right? So to train a network like this, you have to first have a lot of data where people have actually looked at things, where, you know, in an image where people look at. And using that, you can actually train this deep neural net. It's a GAN network or convolutional neural network if people are interested. Maybe we can somewhere post that in the chat too. And then of course on the website you can read more about that as well. But when you have this big data set where you have all this labeled data where people actually looked at, you can actually get the network to predict, to just sort of create a. So that's also, it's a generative network to create a heat map that is very similar to a real heat map. And we've done a lot of validation of course, to make sure and to also measure how similar these are. And of course there are gazillion measures to quantify that. We're using this area under the curve measure. And using that an area under the curve just means it takes both the false positives and false. Again, I'm sorry, maybe I'm Boring or losing some people. But the people that are into machine learning will definitely want to know how we validate it. Because of course you can always come up with a high number depending on what type of validation you do. But as area under the curve it takes both false positives and false negatives into account. So it really measures if we where you predict that attention is going, if it really goes there. Right. You're not missing anything, but also you're not just creating blobs or areas that you think are grabbing attention while they aren't. And that's sort of close to. It's like 0.87. Currently we're continue to improve and train the network so this will likely go up over time. But already it's actually really close to real, to the real data, to the real heat maps.
- [12:25]
Malene Melina Palmer
I love all of it and I think it's good to get into like you said, the nitty gritty of what it does for and people that might have gotten bored. Maybe it helps them to know of stuff that they should be looking at. Maybe there's a reason to be hiring experts.
- [12:40]
Ingrid Nguyenhus
Yeah, exactly. And that's again getting back to the whole interdisciplinary thing. Right. So I work with my colleague who's actually built and trained this Network is a PhD in machine learning in computer vision. So yeah, I interact a lot with people that even can say this in much more complex terms. But yeah, I think this is sort of on the high level, what it is and how it works.
- [13:05]
Malene Melina Palmer
Yeah, I would be willing to bet with this audience you had very little that even got remotely bored. So it's really fascinating stuff with you coming on to Alpha 1 as head of science. I was reading a press release announcing your arrival that says you were charged with developing a platform that quantifies brand value generated by marketing to help determine the strengths of an ad and their weaknesses via machine learning, which you've talked about a little bit on the machine learning itself and how that works. Can you expand about how this can be quantified with the brand value that's developed by marketing of which I know there's a great study from what you've done with Heineken. Maybe it's somebody else that you would want to expand on, but can you talk a little bit about how this is important for marketing and branding?
- [13:56]
Ingrid Nguyenhus
Yeah, exactly. Now branding is of course very important. Also when we were doing this whole lab based research, one of the top things that we always give feedback to our clients to is how well a brand comes through, how well an ad is Branded. We know that these things, even in online advertising where people may think it's more about short term results, we know that in the long term to build value you have to build your brand. And therefore this is something that also as a memory researcher, that's how I started. As a little anecdote, I will never forget the first time I went shopping in the States after I just moved and I couldn't make a decision of what to choose because I had all these brands there and I had no knowledge about any of them. And I really realized how, you know, this whole mental representation of what a brand is and what it means and just helps you just to go about in your life and if you don't have that information, you're really lost. So building a brand and being able to communicate your brand in your, in your packaging, in your ads is so essential for successful marketing and for successful branding in general. So yeah, we are actually working on and that's also something that I really like. At Alpha One we're a startup so we're still, you know, we're working on a lot of different, a whole sort of different types of products to figure out what is resonating with clients. So on the one hand what we're doing on our machine learning experience flow space platform is really to automatically being able to extract all branding features in a stimulus. So what people already can do on the platform and our clients are doing often. And also in the blog I wrote on the Heineken example, I showed some examples there as well. Or you can actually draw an area of interest on, you know, on parts of the say, you know, frame in an ad or a still and you can quantify how much attention is actually going to the brand. Right. So say, say you are creating a banner ad and you want to, you know, make sure your brand is going through, coming through. You can do an A B test, have several different versions and then if you want to decide which one is really, you know, where is the attention going most of the brand you can actually draw a little area of interest around it in the different versions of the ad and just see before you have to do any A B testing already like purely based, this is actually something, maybe we should talk a little bit about this, what we are predicting, right, Because I said attention and of course eye tracking is both driven by the stimulus, what we call exogenous attention or bottom up attention and of course it also is dependent on what a consumer is actually interested in what we call more endogenous or top down attention. And says the psychologist in me, so what we are really predicting with Expose is really the stimulus driven attention. So differences in contrast, differences in color, but also things that we are just, our brain is wired to go to some type of stimulus like faces, right. We are social, I don't have to tell you this, of course, probably no one in the audience, but we are so wired for social interaction that our eyes are just unconsciously drawn to faces and are attracted to it. So these kind of things we can predict with the platform. So to go back to the example of the AB test, if you change something in the color contrast or if you change just the location, even of things, because it's also a very relative process where if you move something here can also affect how people are looking at something else, right? If, if you have a whole blue and then something pops out anyway, it's, it's a very contextual thing. So. And the reason why I'm saying that is that it's hard to, sometimes people think, oh, I can sort of guess where people will look at. Especially when, and of course when you are creative and have a lot of experience, you probably have a better feel for this. But we've seen very surprising examples where creatives were pre testing their ads and they were very surprised with what they saw. And also with small changes, you can really change how something is drawing attention. So this is something. And again to go back to the whole brand building using this, you can measure attention to brand, to brand name, to brand logos, etc. And then another thing which is more in a very early stage, but we're working on that and I'm super excited about it, is another method where we're actually quantifying brand value, which will be something also purely online, but where we will also use participant inputs. Because of course you cannot capture everything with an algorithm. Some things are, you know, just the example I already gave about top down attention, you know, what you as a consumer are interested in is something that is just hard to grab with an algorithm. And there are other examples as well. So as a company we're also, you know, not just focusing on purely machine learning, but also on getting online participants. And one of the products we're currently building is a product that is measuring brand value. So yeah, it's quite different than the exposed platform. But on both our, how you say it, on both our endeavors we are really focusing on being able to get measurements on brand because that is what our clients need and that is what we get asked for. And that is also, you know, what we're focusing on.
- [20:01]
Malene Melina Palmer
Just to take a step further, where you were talking about the small changes that can make such a big difference, which is one of my favorite things about the behavioral sciences, right? Just these little tiny things and that you think, you know, like you're saying. So I bet going to the Heineken example, anybody who was creating that ad probably thought, you know, hey, we put a can in the foreground. It's the only thing really on the table. It's of kind green. Everything else is kind of a neutral tone. People have to be seeing it. And you just assume that that's what's happening. But as you explain in your article, that just a tiny crop like zooming in because there was couch behind him and of the person who's sitting at the table and by just moving it a tiny bit, you know, like 10% that, that zoom making a difference. I think the lift went from, from, you know, 3% attention to 6% attention on the Heineken can. And like you said, changing just the color of what was around the can, making a difference. And you think you want to have the streaming, you know, the televisions in the background, and that's important, but there was just a tiny little red box, like you said. So contrast is going to draw our attention. And when a lot of the background is neutral and there's the tiny, you know, kind of little live, you know, live from the news, which you're used to seeing, right? So you don't even think about how that might be drawing attention. But removing this teeny, tiny little box that seems like it shouldn't do anything, it's just part of the TV also had a significant lift on the attention to the brand in a way that's not overbearing, right? Because I would be concerned if I was on the, you know, agency that's working for Heineken or with the brand. Because you also don't want to be beating people over the head with your brand because they're. They're going to tune out and go, ugh, stop showing me your logo. I get who this is for. So there's this balance of, you know, I don't know if you have an exact percentage to say, you know, you don't want to go over 25, 30% attention for however long to. Because it detracts from the story. I'm sure there's some. Whether you already know that or you're working on finding that out. But going from 3 to 6% when it's just part of the scene can make a really big difference for that familiarity bias, helping us to feel like we're smart when it ends up being about Heineken without feeling like we were forced into knowing who it was about.
- [22:42]
Melina Palmer
Right.
- [22:43]
Ingrid Nguyenhus
Yeah. No, and I mean, you're making really good points because there's always a balance between the story you want to tell and the emotion you want to create and how you want to draw people in. And on the other hand, you have to make the brand really a integral part of that. Right. Because. And it's very interesting. I've of course, sat a lot around the table with both the creative party and the brand managers. And sometimes there's a bit of. I mean, friction is maybe too big of a word, depending on the teams, but there is different priorities that all have to be met. And you want to have an engaging story, but you also want to make sure. And in the digital, you know, when things are digital, with so many ads being digital, I mean, people may not even see the whole story. Right. In reality. And even if we want this ad to go viral, most ads are just sort of seen very short, just a few seconds. And if your story has to build up and there's no brand in the first few seconds, it's really almost a wasted impression. Right. You're paying for it, but you don't get much in return. So being able to with small tweaks. And you already gave some examples, like the color of the table, which is something that doesn't detract from the story. Right. It's so subtle. But you can choose if you as a creative. No. Oh, let's choose a. And I don't remember what color. I know I tried several different colors just to sort of see how does that affect. But it was surprising. Right. I thought probably a red table for a green can is going to give me the most attention, but actually it wasn't. So these are also things that you cannot always predict. And also the logo that you were mentioning is something we talk a lot about with blinds as well. You have sometimes attention vampires. Something that you just don't realize it's grabbing attention actually is as someone running in the back of a screen, which was sort of a fun little thing that wasn't super important, sucking up all the attention or, you know, a logo that was sort of meant to make clear it was like a live tv. The example I gave in the blog, where it was a very strong contrast logo and it wasn't important for that logo to be so much grabbing attention. Right. So just sort of Making it smaller or different, you can really, you know, you can really increase the percentage that goes to the brand. And it may sound very small, right? Maybe from 3 to 6%. So what? But we know that, you know, if you go from, with a conversion just from, from 2 to 2.2%, right. That can already have a huge impact. So just increasing the chance that people will see your brand, it's a numbers game. So Even going from 3 to 6% can have a measurable effect on how successful your ad is. Even if people don't remember seeing it, it's still, you know, your brain is sort of absorbing all this information automatically. It's creating these whole associative networks and if your brand is in there, it just sort of, it gets a really consolidated impression. So I think that really good creative agencies often are able to integrate a brand into a story in a way that it isn't intrusive and the brand can really be the hero in the story. And we see that those ads are often also sales wise, the most effective. Right. Are really producing the most impact because people like watching it, they're engaged with the story and at the same time this brand is there all the time and really, you know, helping these friends being built and strengthened and become top of mind for the consumer when they see it.
- [26:37]
Malene Melina Palmer
I love that term attention vampires. It's got such a good visual to it that you totally get it. And it made me think, I feel like that was probably a moment where people are doing the chat like attention vampires.
- [26:49]
Melina Palmer
I love it.
- [26:49]
Malene Melina Palmer
Right. If I was as an attendee I would have done that. So as that reminder, if you have questions for Ingrid or the team or anything, definitely do put those into the chat box. As we're getting close to wrapping up here, I have at least one more question which you've talked about the benefit and really the reason you got into the applied side versus in academia is by being able to have these multifaceted people with different backgrounds coming together. It's one of my favorite things about the behavioral sciences as well. Just that diverse background. And so what sort of strategy do you either have of what you're doing at Alpha 1 or just recommendations for those that are listening and watching us right now about building interdisciplinary teams.
- [27:40]
Ingrid Nguyenhus
Yeah, that's a great question. I love it. It's because I'm so passionate about it. And it's also something that people don't always. I mean everyone is like, oh, interdisciplinary is so important. But then I look in practice and I've worked with many different companies. It's so often a group of people that know each other from some sort of background. So in a very organic way, you often get very similar, you know, similar people in a company, similar people on teams. So you have to really work on actually creating multidisciplinary teams. It's not something that accidentally always happens. Right. And it's actually also hard because sometimes depending on your background, you can speak totally different. It feels like you're speaking totally different languages. I've had the, you know, great experience at Facebook where I was in a team with all people with PhDs in statistics and they, you know, sometimes I was using my terms that we use for statistical testing in psychology and they were like, sort of looking at me and like, well, this is statistics, right? I thought you guys had a PhD in statistics. And you know, it's, it's also the difference in terminology and also being open to the fact that if you have people that have a very different background and a very different knowledge set, you can get this one on one is three phenomenon which, yeah, I, I'm super excited about. I've already, when I started, I was sort of interested in behavior, but I wanted to understand how it emerges from the brain. So I was sort of. I've always really liked this place between different expertise is right where psychology and biology come together. I think statistics is really an area that is so important and not always present in many companies. People with really deep expertise. And the reason why it's so important is that so much of what we're currently doing is this deep, you know, big data. And you, you see it in academia with the reproducibility crisis, as they call it, where people, you know, where it's very hard to reproduce findings because sometimes also statistical models that have been used or, you know, sort of all this things that you do to the data to get. And I get it right. I've been there too. You need to have a significant result. Otherwise you don't have, you know, also at journals it's hard to publish without this. So you sort of, you know, look into the data sometimes a bit for interesting results. But then of course you get these issues with reproducibility. So anyway, to get back to your question, how do you get these themes is to first be open to it, right. And realize that it helps and to realize, to be willing to put in the work, right. To understand each other and learn each other's jargon and learn each other's language. And when you take that time, actually often really cool things automatically emerges and it becomes really fulfilling and rewarding for everyone in it. I've seen that happen as well. Right. There's this little bit of, of work you have to put in there, but when you're at the same page, then you can. Yeah, I also really enjoy, you know, working with, as I was saying, the computer science where we're then talking about how you have different models to detect automatically all kinds of things and, you know, and how to implement this in a Google cloud to make it able to do it all in parallel. And that something may seem very simple to me. And they're actually, well, do you know that you do this, you have to actually spin up some extra GPUs and there's overhead. So therefore, anyway, I'm learning a lot. So you have to be willing to sometimes put in the work to get there. And as a tip, I think being willing as a company to invest in people with skills in statistics and programming and engineering and those people are often hard to get because, you know, there are so many fields where they really want it. But to have people contribute to the products that are built, I think it's essential. But also on the other hand, I've seen machine learning experts that are not often making use of all the things in neuroscience we know about the brain and how the brain is sort of solved solutions. And I think some really big breakthroughs in computer science or in computer, computer vision have actually been inspired by neuroscience. Right. So it's also there on the overlap between computer science and neuroscience, especially with this whole deep learning. There's so much that we can learn from each other if we're just, you know, open to it.
- [32:27]
Malene Melina Palmer
Yeah, such great points. I know when I talk to clients.
- [32:30]
Melina Palmer
About this, it's, I always will say.
- [32:32]
Malene Melina Palmer
Either, you know, for the one piece being just because you didn't think about it or acknowledge it doesn't mean it's not happening. So having a diverse team is really important and making sure you can think about as much as possible before you go launch? I think, you know, your software definitely helps with that as well. And then also, even if we're saying completely different things, we can both be right even if we're not saying the same thing. There's a lot going on at any given time.
- [32:59]
Ingrid Nguyenhus
Also, we're both wrong. And by talking to, to each other, we're actually figuring out something that neither of us. That's what I mean by the 1 and 1 is 3. Right. Because both often you need the input from both or, you know, all the expertises to actually get it right. So that's why it's not that you're both, you know who is right. I think it's, it's often the case, like how can we together find the real truth? Because often the real truth is really hidden where everything comes together.
- [33:31]
Malene Melina Palmer
Well, thank you again so much to Ingrid Nguyen hosts for joining me today for this live podcast talking about when machine learning meets neuroscience. I know, I love the conversation. I'm sure everyone else did as well. Such an important step for all the fields to be looking at. And just again, yeah, really enjoyed the conversation and I hope everyone else did as well. Enjoy the rest of the conference.
- [33:59]
Ingrid Nguyenhus
Thank you so much for having me. I really enjoyed it.
- [34:03]
Melina Palmer
So what got your brain buzzing as you learned about the intersection of machine learning and neuroscience today? For me, I always love examples that show how subtle changes like shifting the position of a product or adjusting background contrast can dramatically change where attention goes in an ad or other messaging. Ingrid's example with Heineken and how just a small shift doubled the visual attention to the brand is such a powerful reminder of how behavioral science and machine learning can work together to make marketing more effective and more human. I also loved our discussion around Attention vampires, those little unexpected elements that pull focus in a way you didn't intend, and how even the best creative teams can miss them if they don't test. The blend of deep science, real time AI and creative storytelling is such a rich area for exploration and it's amazing to see how far it's come and how far it can still go. And speaking of AI and keeping the human in the loop, that's exactly what we're diving into. Next time. In episode 511, my panel from south by Southwest called Enough with the Delving Staying Human in the Age of AI. I had the honor of moderating that panel and was joined by Ben Gutman, Joanna Lepore, and Usama Fayed. It is a lively, eye opening discussion about how brands and individuals can keep their humanity in our increasingly automated world. If you aren't already subscribed to the Brainy Business podcast, now is a great time to do so. To make sure you don't miss that or any other episode, it's going to be a great conversation for for sure. Until then, I'd love to know what role do you think AI should play in the future of branding and creativity? Come share it with me on social media. You'll find me as the brainy biz pretty much everywhere. And as Melina Palmer on LinkedIn, there are of course, links in the show, notes to make it easy, as well as links to my top related past episodes and books, ways to get in touch, and more. It's all waiting for you in the app you're listening to and@the brainybusiness.com 5100 and just like that, episode 510 with Dr. Ingrid Nieuwenhouse is done. Join me Tuesday for a very special replay of my panel at south by Southwest. It's going to be a lot of fun. You don't want to miss it. Until then, thanks again for listening and learning with me, and remember to be thoughtful.
- [36:42]
Ingrid Nguyenhus
Thank you for listening to the Brainy Business Podcast. Molina offers virtual strategy sessions, workshops, and other services to help businesses be more brain friendly. For more free resources, visit thebrainybusiness.com.