The Neuroscientist’s Guide To Scale DTC: How Daniel Brady Turns Brain Science Into eCommerce Revenue

Daniel Brady is the Co-CEO of Orita, a software company that improves email deliverability. As a neuroscientist turned data scientist, he has helped leading e-commerce brands organize their data sets. Daniel has also worked with CEOs, CTOs, and other leaders to build eCommerce companies.

Here’s a glimpse of what you’ll learn:

[2:05] Daniel Brady’s transition from neuroscientist to solving business problems with data
[4:28] Why Daniel pivoted from consulting to launching a machine learning platform
[12:27] How Orita leverages behavioral data to optimize customer segmentation
[16:04] Applying neuroscience techniques to model customer engagement
[19:33] The two ways brands use Orita to boost retention and email performance
[25:08] Common mistakes brands make after acquiring email addresses and how to segment lists effectively
[30:50] Daniel talks about the balance between prospecting and retention
[35:24] What brands can expect when using Orita and a case study of a brand doubling email revenue
[41:08] How Daniel’s neuroscience background influences his leadership style
[50:48] AI’s potential impact on cognitive development
[1:02:39] Daniel weighs in on the matter versus consciousness scientific debate
[1:05:31] Daniel’s surfing hobby and early interest in neuroscience

In this episode…

Email marketing promises big returns, but many brands unknowingly sabotage their success. They blast generic campaigns, over-segment based on recency, and trigger inbox fatigue that leads to declining engagement, retention, and revenue. How can e-commerce brands rebuild trust, retain overlooked buyers, and turn emails from a cost center into a compounding asset?

Neuroscientist turned machine learning strategist Daniel Brady has developed a behavior-driven approach to email marketing. By analyzing long-term customer behavior beyond recent clicks, brands can use intelligent segmentation to both reduce email volume and boost conversions. Daniel recommends recategorizing inactive subscribers, validating leads before sending campaigns, and treating email engagement as a dynamic two-way relationship. He also emphasizes the importance of rethinking post-signup flows, preventing deliverability spirals, and aligning marketing cadence with buyer behavior.

Join William Harris in today’s episode of the Up Arrow Podcast as he chats with Daniel Brady, Co-CEO of Orita, about turning email marketing into a scalable, intelligent revenue driver. He explains how neuroscience informs strategic audience segmentation, the impact of acquisition on retention, and how to adapt as you scale.

Resources mentioned in this episode

Quotable Moments

"You're just like, ‘oh, you mean this basic math... is like somehow revolutionizing things?’"
"Trust our machine learning. We spend so much time on our algorithms, not how to talk about us."
"Someone who wrote in 12 angry CRM tickets probably doesn't want to hear from you marketing-wise."
"You're not sending emails... they also would have bought if you didn't send them any emails."
"The most likely time for people to unsubscribe is within the first 30 days of signing up."

Action Steps

Reassess your email segmentation strategy: Moving beyond recency-based rules allows you to uncover high-value customers previously overlooked. Smarter segmentation increases engagement, improves deliverability, and drives more revenue with fewer sends.
Limit early email frequency post-signup: Sending too many emails in the first 30 days leads to higher unsubscribe rates and brand fatigue. Instead, tailor the flow based on actual buying patterns to build long-term trust.
Use customer behavior data holistically: Integrating CRM tickets, return history, and browsing activity creates a clear picture of true engagement. This data-rich view enables more effective and respectful communication with your audience.
Rescue dormant customers strategically: Many inactive subscribers are still likely to buy, they’re just on longer purchasing cycles. Identifying and reactivating them can generate surprising returns without new acquisition costs.
Balance acquisition with retention efforts: Over-investing in top-of-funnel growth while ignoring loyalty erodes long-term customer value. Building smarter retention systems pays compounding dividends over time.

Sponsor for this episode

This episode is brought to you by Elumynt. Elumynt is a performance-driven e-commerce marketing agency focused on finding the best opportunities for you to grow and scale your business.

Our paid search, social, and programmatic services have proven to increase traffic and ROAS, allowing you to make more money efficiently.

To learn more, visit www.elumynt.com.

Episode Transcript

Intro 0:00

William Welcome to the Up Arrow Podcast with William Harris, featuring top business leaders, sharing strategies and resources to get to the next level. Now let's get started with the show.

William Harris 0:16

Hey everyone. I'm William Harris. I'm the founder and CEO of Elumynt and the host of the Up Arrow Podcast, where I feature the best minds in e-commerce to help you scale from 10 million to 100 million and beyond as you up arrow your business and your personal life. Today's guest is part scientist, part strategist, and 100% the kind of founder you wish you had on speed dial when your email campaigns start tanking. Daniel Brady is the co founder of Orita, a machine learning life cycle marketing platform that helps e-commerce brands actually segment their audiences in a way that drives serious revenue, not just pretty reports. He's worked behind the scenes with some of the fastest growing DTC brands, turning what most people call email strategy into a full blown revenue engine. But here's the twist. Daniel started out studying neuroscience, yep, the literal inner workings of the human brain, which might explain why his approach to e-commerce is so refreshingly human. It's not just data driven. It's behaviorally intelligent. Whether you're at 5 million trying to breakthrough a plateau or you're deep in nine figure territory and trying to improve retention, this episode is a must listen masterclass on how to turn intelligence into income. DB, welcome to the Up Arrow Podcast.

Daniel Brady 1:22

Thank you so much for having everyone Great to be here.

William Harris 1:25

Yeah. I want to give a quick shout out also to the wonderful Diana Zhang for making this introduction so we could bring this knowledge to the world together. Thank you, Diana.

Daniel Brady 1:33

Thank you so much, and shout out to her. Yeah, last interruption.

William Harris 1:37

Then we'll get right into the good stuff. This episode is brought to you by Elumynt. Elumynt Elumynt is an award winning advertising agency optimizing e-commerce campaigns around profit. In fact, we've helped 13 our customers get acquired, with the largest one selling for nearly 800,000,001 that IPO. You can learn more on our website at elumynt.com, which is spelled elumynt.com that said on to the good stuff. Okay, you started your career in neuroscience? What made you trade brains for e-commerce software?

Daniel Brady 2:05

Yeah, I know it's, it's kind of, it's kind of a wild transition. That's, that's usually the first thing that people ask me. They're like, wait, you are neuroscientist, and now, now you do this. So the truth is that it was kind of by accident. So I after, you know, I got my PhD, and then I was doing my postdoc, and I was like, academia was a bit too slow. So like, the way that I talk about is that, like, I discovered what became my PhD thesis in my second year of grad school, and I spent another four years proving that that was true over and over and over again, because science is extremely careful, right? And so like, yeah. I mean, it was like, exciting. There's like, stuff along the way, but it was kind of like, no, yep, that original idea is true. Yeah, it's still true. Yeah, it's still true. And so, you know, I was like, I don't know if I really want to do that for like, my whole life. And so I moved from San Francisco to New York, and then I had a friend of mine who was the CTO of a same day delivery company, and he was just like, he was like, a while you're trying to figure out what to do. Why don't you, like, come to do. Why don't you, like, come to our office and and maybe we can scratch your brain a little bit. And I helped them on some routing problem. Like, I just used a little bit of probability theory and all this kind of stuff. Like, for me, it was like this, like, really, table sake sort of stuff to do as, like, a neuroscientist and scientist, and it, and it helped them figure out, like, how to schedule people a bit better. And they were like, okay, cool. Like, you're going to be one of our first employees now. And so, yeah. And so that was like, that was it there. Was just like, they're like, oh, this. And I was like, Oh, you mean this basic math that I've been doing for last six years is like, somehow revolutionary as a chain. They're like, Yep, you're in a plane now. And I'm like, oh. And then they raised around, and, like, every other company was the same to delivery company. So we eventually didn't exist after a year, because, like, eBay and Uber and Uber Eats and stuff existed, but that was really the start.

William Harris 3:45

I like that. You're like, a little bit of math, you know? It reminds me. It reminds me of the show numbers. You remember this show. It did not last very long, but I loved that show because it's getting math nerd or whatever.

Daniel Brady 3:57

I love that show too. Like, my father's actually a professor at Caltech. And oh, really, that number took place on the Caltech campus. And so like, yeah, everyone's a big fan of numbers.

William Harris 4:07

Oh, that's so great. Well, yeah, I it, unfortunately, admit its end too soon, in my opinion. But what I like about also talking about Orita a little bit, and if we could stick here for a second, is, this wasn't your original business plan. You pivoted from what I remember. It involved some hand taped posters at a conference. Yeah. What happened?

Daniel Brady 4:28

Yeah. So, you know, so, so there's three of us, co founders of Rita, but two of us are on the more machine learning, data science the end, Zak and I. So, you know, we both have machine learning and data science backgrounds, and we've worked at same day delivery companies, and mostly in the DTC and E commerce space, either focusing on marketing or on, like, operational stuff. And we were like, okay with our backgrounds, like, let's let's do some consulting to see what actually is a repeatable problem that a lot of brands and a lot of E commerce come. New space. So we were basically selling those services. And so we helped build customer data platforms for brands. We got them onto business intelligence tools, all the stuff. A lot of the stuff now exists as standalone companies. Like, there's daasity and there's, you know, triple Well, living like back in that, you know, back in the day, and like, those companies were just getting off the ground. And so, you know, we were going to this conference basically being like, hey, we do a lot of data engineering, a lot of data science, a lot of identity resolution to help you get your marketing. And we had booked it. We had bought, like a startup package for nothing with a booth. And then we decided, like, actually, on the advice of an advisor of ours, he was just kind of like, hey, you know, you've been circling around all these sorts of ideas, like, you know, this machine learning idea for marketing, I think that like, is a really good idea. And like, let's just, let's just, like, maybe go talk to a few people. And I was like, Yeah, that's a great idea. So I had a friend of mine who ran an agency, and it was a social media agency, but like, an agency none the list. And I was like, hey, Taylor, can you like, tell a handful of your brands if they're interested in machine learning for marketing like or Klaviyo customers, because we've read clears APIs, and it's really great to integrate with. And so he sent out an email to six of his brands, and five of them hopped on calls within 30 minutes for us, and they actually all became our customers. That's awesome. And this was like a week before this conference, when we had all these calls and we were just like, I was like, Zach, I think this is what we do now. Yeah. And so we had already bought this conference, and we had a reader on it, and it's like this, like, data engineer or something that was like, we don't do this anymore. We're not allowed to change our poster and the poster part of the conference. So we literally went to CBS the night before, and, like, with glue sticks and glitter. And was like, this is ml for marketing and stuff like that. We taped it all over. I can, like, look for the I think, I think they're on our website. Some are, like, some of the images, and it just like, looks really janky. So you have all these, like, very professional booths all around us, and then you have, like, what looks like children had, and they're like, made for their parents. So it's like, you know, save money for marketing, like, be more efficient, all that kind of stuff. Like, kind of stuff like that. And we're super high tech, yeah, exactly. Trust our machine learning. We spend so much on our algorithms, we don't spend our time on how to talk about it. And the craziest part about it is that, like, we had gotten, like, a lot of interest in in optimizing email, but we actually hadn't finished fully all the algorithms yet. Like, we had, we had a lot of the structure, and we had a lot of the ideas and, like, we added, like, a lot of the pseudo code done. But we were just like, everything is so inefficient right now. Like, and given our background, we know we can, we can make a big splash. But it was really crazy to be, like, literally, at the conference making posters. We updated our website in the middle of the conference to now, like, say, like, Oh, we don't do these consulting services anymore. This is what we do. And just full fledged in and like, we hadn't even, like, launched, launched yet. And we had even had like, hadn't even like, delivered the results. But we just knew that the interest was so strong that we would find our way here. And then eventually, what happened is, like, those first set of customers, we we started manipulating their customer files for them, and, you know, we thought it would be like, oh, we'll make things like, 5% 10% more efficient, and we'll just be really cheap, and we'll just be this thing that, like, kind of operates in the background that makes things slightly more efficient. And it turned out to be like, over the moon, like we for our first customer, we dropped their email sends by 40% but they actually had more clicks in that month because of this thing called inbox placement and deliverability, and they're sending to the right people now. So then we were like, Hey, can you tell someone about can you tell other people about us? He's like, I've already selected everyone I know about you exist and like that just kind of started this

William Harris 8:37

crazy process of where we're at today. That's amazing. I think you genuinely have now officially beat my trade show story. So my best trade show story was, I was, I was working for this like sales company. It was like sales and marketing collateral for real estate agents in car dealerships. And it was me, the National Sales Director, the CEO, and we're going through airport security, and I realized that the bag I forgot to check had, like, wires, wire cutters, like knots. We need to, like, put all this together. And I'm about to go through security, and I was like, Oh, I got too many bags. Hey, can you take this bag? And I hand it to the Sales Director, and he's like, I got too many also. So he and his CEO, I'm through and clear. He's through and clear. Next thing we know, we're already on the other side of security. And goes, it's not my bag, it's his bag, and he points at me, and I'm like, we're all going to jail. Now, thankfully, they just made me go check it right then. But I don't know. I think that the idea of hand drawn, you know, sprinkled, sparkled, colored stuff, like here, we could swipe your credit card right now if you want. That might Trump that.

Daniel Brady 9:40

I'm not, I'm not joking. We I went to a children's toy store and bought some monopoly games, and I, I put, I put my, my email address on the back as my business card. Do we mean, like we had nothing, and so like, I put, I put, I put db@orita.ai on Monopoly money to be like, Oh, do you ever card? And. And I'd be like, this is this? Because we're gonna make you money, you know? And they'd be like, This is insane. And so, and then, of course, all the people who, like, actually were interested in, like, you know, data engineering services like that, would come in and be like, oh, and you're like, No, we don't do that anymore. Like, like, this is now what we're here for, yeah. So

William Harris 10:15

it's bizarre to me, but the thing that I love about this story is that idea of the pivot, because a lot of people, if you're stuck right, like a big part of what I talk about is maybe they're stuck at 5 million, 10 million, and you're finding this place where you you can't, you can't find that pressure to move forward. Sometimes the pivot is the right thing, not always. But when I have done some investing in the investors that I talk to, they will always say that the you invest in the person, not the idea, because the idea is going to change, it will almost inevitably change over the next five years from whatever they're pitching you on today. You have to be able to recognize when that pivot needs to happen and when, when something like this takes place. I think that's really good proof that you're like, we're on to something by this. I

Daniel Brady 11:01

think, I think, you know, I think, like, I completely agree. And I the the one thing that I had a lot of confidence for in when we first started was that, like, if we started getting the seeds of product market fit, I would know that I would recognize that pull and it and that's why, even though so many things were in the works and it was so fast, we were just like, Nope, this is what we do. Because it was just like, it's like that, though, that sort of pull from that few number of interactions is such a strong signal, right, that you're just like, oh, like, you know, we didn't need to talk to 100 people to realize that 10 of them were interested. It was just like, Oh my gosh. Basically everyone is like, Oh, that's really interesting. And so we're just like, Okay, this is definitely where we should go. And to your point about pivots, like, it's not even pivots, like, refinements. It's just like, you know, when we first started, it was, like, it was really focused on who not to email. Now it's like, both sides of spectrum, the algorithms, stuff like that, are really similar, but how you use them? It's like, you know, you have these algorithms that can tell you who not to email, they can tell you who not to email, they can tell you who to email, and all that kind of stuff like that and all that can be packaged and differently. But like, you have to really figure out, like, to your point, like, evolve where the story makes sense to evolve to. And that could be a big pivot. It could be small things, but yeah, absolutely

William Harris 12:15

like that. Well, let's talk about the segmentation science a little bit. Yeah, in layman's terms, how is a reader using machine learning and AI differently than traditional e commerce tools are?

Daniel Brady 12:27

Yeah, so I think, I think there's like two, two things that are a little bit different. One is a bit more obvious and and actually less impactful. But I like to start here because, because a lot of market episode says that one is that, like we look at all the data that we can so for all of our customers are on Klaviyo, and there's this thing called the Klaviyo bench stream. And what's in there is not just marketing touchpoint data, like clicks and opens and received emails or text messages, but it's what's beautiful about Klaviyo and about Shopify and this ecosystem is that, you know, there's so many partnerships, and there's so many, like, really great integrations between them. So like you can connect to Shopify and acclavio, and you can see CRM tickets from Gorgias. You can see loop returns data. You can see on site quiz activity from octane.ai, like you see all this information. And so we strategically started on these platforms, compared to, say, like, you know, other ESPs, because we knew that that by connecting to them, like, we would actually have all of this information. And so that means that, like, when we built our algorithms, the first thing they do is, like, we don't know what's important. Let's look at all events. We understand that. Like, sure, a click is going to be indicative of good email behavior, but like, what else could there be, right? And so like, let's, like, look at all these things, like serum tickets and returns data. Because, like, I like to joke, like, you know, someone who wrote in 12 Angry CRM tickets probably doesn't want to hear from you marketing wise. Like, where's my stuff? He was like, how about this new thing? And so, like, that's one aspect of it. But the real, the real big thing that actually makes the biggest difference is just the ability to look at everything in an integrative manner, and that means, like, over a long period of time, because most marketing right now is really, really biased towards recency, and that means it's like, I'm making a decision whether to send you an SMS or send you an email because you just signed up, because you just bought, because you just clicked on an email within the last 90 days, and that is correlated with behavior like, what I say is like, you're basically making reactionary algorithms. Someone did this, therefore I should do that. Sure, but what that leaves out is is longer term behavior, if someone buys like clockwork every five years, there's no recency segment on the planet that is going to pick that out. But that is extremely easy for a machine learning algorithm to pick out. And then also, I think what's really important that the algorithms can can help pick out is like, what are you what are you as a brand doing? So, like, for example, a lot. People have Recent Feedback email, there's like, someone has clicked in the last 90 days or visited website. I'm like, Okay, well, if I've clicked three times versus one or 10 times versus one, that means something different. If I've clicked three times because I received five emails, or click three times and I received 500 emails, like, there's like, very different signals of engagement, intention, and so it's, it's like, what I think is unappreciated is that this is actually a two way conversation between the brands and the customers. Granted, the customers are mostly silent most of the time. So it's like, it's like trying to under the language that you're trying to understand is like silence punctuated by lots and lots of activity, of like visiting the website and buying stuff. But what you as a brand are doing is really, really critical in understanding those dynamics. And once again, that's easy to model or easier to model with machine learning and genuinely impossible to do with, like all or nothing segmentation rules that that most of the platforms have available to them right now.

William Harris 15:58

Yeah, that makes a lot of sense. How did your neuroscience background shape your product thinking like this?

Daniel Brady 16:04

Yeah, I mean, I think, I think it's really funny, because so when we first thought that this was something that we could really take, I actually had plotted. There's this thing called raster plots. It's a way to understand spiking behaviors for neurons. And I actually raster plotted a bunch of email profiles with their like, whether or not they were like, clicking or engaging in emails. And I just like, saw, like, all, they're like firing, they're like clicking, and also they stopped, and then they like, fired again. And so I just like, literally, data, visualize it the way that you do neurons, but instead of neurons as people, and they like interacting with stuff. And I just like, saw some patterns, and I was like, Okay, I think we can use some of the concepts from neuroscience of how we understand and like the TLG orbit was like, we basically modeled people like neurons and tried to predict if they're going to fire again, and their firing meant that they were going to do some sort of customer engagement. Our algorithms now are, like, much more sophisticated as we've learned all sorts of things about both email and both marketing. It's much more comprehensive than that. But, like, the nice thing about a lot of the neuroscience techniques is that they can work really fast and really efficiently. And so if you want to prototype something really quickly, it's like, let's port over some of these ways in which we understand neurons and then use that for marketing. So yeah, that's like, a pretty fun thing about it

William Harris 17:17

brilliant. I love, I love the idea of lateral thinking. I think we'll talk about that a little bit more. But just taking something taking something from one field, applying it to a different field, of just how much that can unlock significant insight. You once told me that adding 30,000 segmented users is way more powerful than better subject lines. It's a bold statement. I want to back it up. Yeah,

Daniel Brady 17:39

you know, I think, I think, you know the so I'll take a step back. So, you know, Zach and I are machine learning engineers, and so we're like, we can do stuff with llms. We can build our own like, we can do all this sort of stuff. And it's like, but if we thought about from first principles, like, not what everyone else is doing, but what would actually make the biggest difference, the biggest difference you can think about is like, what is your actual addressable audience at any given time? Sure, and and, like, our idea, because, like, if you're like, Oh, if I can reach 30,000 more people who might want to hear from me, that is a much better thing than saying, like, okay, of the 1000 who might convert this subject line will convert an extra one or two of them. And so what happens is, like, you'll see people have these numbers, they'll be like, oh, you know, with subject line testing, or best, like, all these sort of things. And these are all great. I'm not saying that you shouldn't, like, you should definitely do them, but these are, like, these downstream things that are closer towards the end of the funnel, they're optimizing for, like, handfuls of people, whereas, like, what can actually make a really big difference is like understanding how you can shape stuff earlier in their process. How can you get better people on your on your list? How can you then understand that right now you have a million people on your email list right now, but actually only 300,000 actually only 300,000 want to hear from you. And if you email more, it's it'll be in trouble. Like that is actually, like, the biggest lever, as opposed to, like, trying to, like, do I put an emoji in the subject line or not? Like, sure stuff,

William Harris 19:13

yeah, in this, this, let's just say, like, more intelligent segmentation. You alluded to this earlier. You know, it led towards a 40% growth in the one customer, or whatever. And like, you know, right away, like, in what ways? What are some of the unique ways that people are using Orita, or, like, the way that it's doing? How is it actually helping people get 40? I mean, like, that's a significant number,

Daniel Brady 19:33

yeah. I mean, it's so, like, there's, there's kind of, like, two, two canonical ways in which people, people use us. So the first way is that is like they just want a purely additive strategy. They have their existing segmentation strategy, but they're just like worried, once again, that, like they're emphasizing a little bit too much on recency. And so what they what they use us for, is ways to we rank everybody. We tell them, like, these are people that we really think you should contact, and they just add them along white side what they're doing. And maybe that adds 10,000 to 100,000 cent, or something like that. But they're the behavior of those people are much more on, like the medium to long term part of their customer journey, and so those signals are much harder to predict as a marketer by hand, and so that can drive a bunch of value like, the way that I think about is that, like, everyone knows what to do. If you just signed up for email send you right? But it's like, it's like, but if it's been 60 days since you've done something, or 90 or 180 it's like, how do you test and understand that sort of long term behavior? It's really hard to do unless you do it algorithmically. The other way that we really help brands, and this is, this is increasingly common, is that, like a lot of lot of brands are in trouble with email, and they're in trouble with email because, you know, you can get into these vicious cycles. And emails is really, really interesting channel that is kind of analogous, more like to SEO than to say, texting or ad placements, which is like history matters. Every single campaign that you send out affects the likelihood of people seeing your next campaign. Yeah. And so what that means is that a lot of brands can get into these vicious cycles where they go, they email everybody, and then they have a low click rate, they don't make that much revenue, and they go, Oh man, I can get more people on my list, and I'm gonna email even more people, and I'm not gonna make that much money, and I get more people on this, gonna email everybody. And then what happened is that, you know, I've talked to brands like, we can make more money. We literally email everybody like, that's the problem, sure. And so where segmentation can really happen is that, like, for for a couple of our brands, what we do is, like, we say, send to our segments. Instead, we're going to dramatically reduce the number of people that you're sending to, but they're going to be extremely high quality. And what's going to happen is, like, they're going to generate revenue because they want to hear from you, but it's also going to increase all that engagement, which is what the inbox providers really care about, right? And like, Gmail doesn't know willing that you eventually bought like if you mark someone as spam and then buy like the brand, can see that full customer journey Gmail. Does it? Gmail just sees the marking as spam, right? And so if you have that positive engagement, then what happens? Like the next time you send an even larger portion of that audience you send to are going to see that, and it's not like it's all or nothing. You're all in spam, or you're all in promotions, or you're all inbox. It's a probability distribution. There's some small number of people that are going to spam, some that are going to promotions, and they're going to primary inbox. And the idea is how to shift that more and more towards towards inbox, and that is how you can make so much more money from email.

William Harris 22:38

When I think the thing that you called out, that I really appreciate is that, like this spiral, because there's two versions of these spirals, right? Like one, if I understand what you're saying correctly, is, is the we're going to send it to everybody, and then the spiral is, now nobody's going to start seeing your information, right? Like nobody's seeing that's a bad place. But the other one is, okay, maybe you've got some basic RM segments, and so you've called that list from you've got a million people, but you're going to send it out to only 300,000 of them, because you 100,000 of them, because you've got that, but you're missing out on those other 40,000 that were also likely to have others, you know, signals in 10 other things that machine learning was able to pick up on that you're not sending them. And as a result of that, that list is now, let's just say every single time that 300,000 you know, because those 40,000 didn't receive it, maybe they didn't buy the 300,000 then the next time, now it's only 280,000 that are ready and primed and ready to buy. Now it's 260 and now it's 240 and it's just going to start shrinking itself.

Daniel Brady 23:29

That's right. And I think, and I think, and you also had on something on the head too, when you once you mentioned RFM stuff in it, I think our friend was a good way to analyze your customers. Is this, like, a lot of recency segmentation, a lot of RFM analysis. It's like, it's it's still reactionary. So what I mean by that is that, like the the machine learning algorithms, they always have some sort of objective function that they're trying to do. And ours is, is someone going to buy if an email sent? And that conditional sentence is the critical part. It's not just going to say William is generally going to buy, therefore email him. It's no we think that email will causally move him to buy. And so what's really interesting about that is that you get these really high performing lists, and sometimes they're correlated. People who are likely to buy are also likely to respond to emails to buy. But there are those interesting differences. And so this is really helpful in terms of attribution, because then what happens is that you go, like, it's not like, you're just, you're not sending emails, and it's just like, yeah, they all bought. But then you couldn't you also, they also would have bought if you didn't send them in emails, right? It's like, really important to, like, really tease that sort of part of like, when is this thing actually gonna be incrementally valuable as a channel?

William Harris 24:39

Yeah, and that's, that's the hard part. And I think, like you said, the only way that that can be done is through machine learning. Let's talk about then I want to switch to the post sign up, power move. We'll call it, right? So it's like, you actually got the email address. What do you do with it? And I think you talked about, when you and I were talking about this before, is the idea that you. Not just that you got the email address because of a purchase. They just maybe sign up for something. What are brands doing wrong after they get their email addresses?

Daniel Brady 25:08

Yeah, I think there. I think there's, there's a few things. I think this is very minor, but like one thing is, like any sort of light validation on the quality of the email before you send e commerce brands are particular. Can be particular targets for all sorts of malicious email activity because of the propensity, the desire to email people so quickly, where it's like, if you even, like, waited eight hours, did a little bit of validation steps, then, like, you wouldn't be in so much trouble. Um, so that that's, that's one thing and like, but the other thing to understand is to is to really take a take a step back and do analysis of like, how important is it and when is it important to send that first email? Like we've done, we've done work for a few of our brands where, you know, 90% of people were were 70% of people bought within an hour of signing up, and 90% bought within a day, and 99% bought within three three days if they were going to and the brand had a 60 day welcome, and it was like, you don't need to do that, right? Like, you do not need to do that whole thing. And it's just like, that's why the unsubscribe rate is so high, and, like, all this kind

William Harris 26:27

of stuff. Is that, like, were they, were they doing this to everybody even after they bought? Or it's like, were they at least taking those people out after they bought, or were they still keeping them in that world?

Daniel Brady 26:35

No, it should be fair. To be fair. They were taking the people out of okay, but it was just like, it was just like they were, they were really beating the dead horse on each of those that like that 1% Yeah, exactly. And, and, I think, but, but there are lots of brands that that that do do that, that are like, you know, they they jump in and they email very heavily at these early parts of the customer journey that can unfortunately leave a bad taste. And like As consumers, we feel this all the time, totally right? And I think that, I think that the idea is like, what are the short term and long term goals that you're trying to optimize? And if you look at the data, what you'll probably see is that, like, actually, yeah, there's this. There is this brief window where, like, a few emails make sense to, like, push over a handful of people to buy, but then I gotta stop and just like, either put them in my normal email program or reach out to them at some sort of other sort of cadence, in order to to actually, like, eventually get them into converting, convert over that first thing, because the most likely time for people to unsubscribe is, is within the First is within the first 30 days of sign up through email with, sure, sure. Yeah.

William Harris 27:44

That makes sense. How about what should brands do differently? I assume that there's a difference in the flow, the messaging, the way that they're going about it, the frequency, if you're 10 million, versus 1 million or $100 million brands. Like, how does

Daniel Brady 27:59

this change? Yeah. So there's a few things that are really, really important to think about. And like, I think it might make sense to take a step back and say, like, why do you need to segment to begin with? Sure, so, and I'm going to talk about from a data perspective, not for like, a brand marketing. The reason why you segment is because the people on your list become more and more variable. Sure, there's more data variability, that's why it's no longer one size fits all. You've been around for six months, you have a list size of 10,000 people. They can probably all hear the exact same message from you. They're early adopters, like they like what you're doing. But if you've been around for five years, you have a person who signed up yesterday, the person sent it five years ago. You know, you have someone who bought 10 times. You have someone who bought only once, someone who came in on a different set of collections of products you don't even make anymore. And like all of that sort of history means that, like, there isn't singular things that are like, clean ways to convey all those sorts of like, one product, one thing, whatever it is. And so that's kind of when segmentation really matters. And that is also coupled with the fact that, like, as you grow, you become under the microscope of the inbox providers more and more and more. So, like, if you have a point 5% click rate and you're sending to 5000 people, that is not a great cook rate, but you can probably keep doing that. If you try to do that with a million people, 10 million people, 100 million people, no one will see a message after one send. And so like literally, the rules are different, because the vast majority of emails are sent in this world around 80 85% of them are spam, and so as a bulk sender, what you actually have to do is continually prove to the inbox providers that you are not spam, and you can do that with authentication. But what besides authentication, what's really in your control is to make sure that the people who receive in your content interact with it, and like that is. Like the key, and so that, therefore, like, when you're at that $5 million brand, you know, you could, you might be able to get away with just like, emailing everyone who's who's done it, but, you know, if you're, if you're those big brands, if you're Nike, if you're whatever, like, they don't even have that concept, like, they're, they're, they're sending campaigns and stuff like that to, like, much smaller audiences, maybe the same size as the 5 million person brand, but like, they're doing it with like 1000 different campaigns at any given time.

William Harris 30:26

Yeah, no, that makes a lot of sense. I wanna talk about the healthy tension between prospecting and retention. It's something that we've talked about before in this show you, and I have talked about it as well. There's a healthy tension. I think there's a necessary tension, but it seems like a lot of times brands will chase acquisition at the expense of loyalty. How should brands think about it?

Daniel Brady 30:50

Yeah, I think, I think it's, I think this is it's really tough. It's really tough because there's so much difficult work involved in retention, whereas with prospecting, it's like, you know, with meta, with all this kind of stuff, like, there's so much help that they give you there. And like all this, you can, like, see these results immediately. The first few things that I'll say is that, like, when you change acquisition strategies, there are potentially positive or potentially negative effects that you will see in your CRM months later, right? And so, like, we've seen this for a lot of times where, like, a brand is like, hey, our click rates, all this kind of stuff, our repeat purchase rates have, like, fallen down the tubes, and we don't know what part of our messaging or prior segmentation try to use that, and we'd be like, well, then you start using these two social media platforms as your main advertisers, like eight months ago, and they're like, Yeah, like that is now being reflected in the quality of the people that are you're trying to reach out for. And so it's always really important in order to do that. And I think that the other side of it is that like, and this goes back to like, unsubscribes and like and stuff like that, is that remember that like people giving you their information to be contacted is like a really big step of trust, and you should balance how often and what type of way in which you reach out to them to make sure that you want to foster that, like, long term growth there, it is kind of Shocking that, like, we all accept the fact that like, as brands like the unsubscribe numbers make up 20, 3040, 50% of everyone who comes in our list is like, unsubscribe. Imagine if, if that was true for anything like, oh, you know, 50% of people who see my ad tell me that they can never see an ad for me again. Like, that'd be crazy, but that is something that, that is, that is, that is considered standard doing business as usual in these first party data channels. Like, oh yeah, my unsubscribe. But like, I'll just go get more people. That means that you're going to pay Tiktok payment even more money to make up for the fact that someone's gone. And, you know, there's been some studies about, like, what are the costs of unsubscribes and all this kind of stuff like that and and, like, you know, there's various ways to look at it, but it like, roughly translates to to to anywhere between one half or one full purchase. So, like, you're literally reducing the lifetime value by customer by about a purchase if you get to unsubscribe. And the number one reason why people describe this because they receive too many emails,

William Harris 33:21

yeah, it makes a lot of sense. I feel like, you know, we've I felt that personally, there's a lot of things that I have unsubscribed from and never see again, and the reality is I actually probably still liked the brand. Probably would buy again, and now I'm missing out it. It's not even at the forefront of my mind. Maybe I end up seeing an ad somewhere, maybe I see them out and about somewhere. Somebody tells me that brings me back into the brand. But it's never quite the same. If I was running a $10 million e commerce brand right now and I was coming to you, what are some of the signs or symptoms that I'm likely experiencing that you're like, Yes, this is clear that you have a retention problem, and we're gonna come in and we're gonna help. What are some of the what are some of those things that I should be looking for?

Daniel Brady 34:05

Yeah, I mean, I think, I think the things to look for, I mean, there's, like, the classic sort of things to look like, what are your repeat purchase rates and like, what proportion like, especially within certain windows, how many people buy again within a quarter or half a year, or something like that. I definitely look at things like, what proportion of your lists are unsubscribing or don't want to hear from you again, right? I also look at like, what is your actual engagement on all of your channels, right? And there's benchmarks you can go into cligo to look at what the benchmark are free industry, but it's like, Are you close to that? Are you a 10th of that, right? And, and that's also true for SMS. You can do all sorts of things. You can just for all these sorts of channels like that, will speak to the fact that, like when people have made the decision to give you their personal information to then start contacting them, that, like that aspect of of the engine that you're running can use a lot more love. It's like low engagement. You. As well as, like, not a lot of performance, in terms of, like, how many people are actually converting, especially coming back again and again. When you use these sorts of channels that seem to like, your attention, needs a lot of work. What

William Harris 35:13

are the what are the first 90 days look like? If, if I said, Okay, great, yeah, this makes sense. I'm going to move forward with doing something like a reader. What should I expect of the first 90 days, what? What's gonna happen?

Daniel Brady 35:24

Yeah. So there's, there's two, two main components, and so it depends upon what, what's the history that you they've had so far. So one way in which we drive a lot of value is that we look to see if you, as a brand, has list clean in the past. So traditionally, the way that a lot of these things work, and this is why it's so complicated with email. Is they, they go, okay, you know, William bought 365 days ago. We're gonna put him into a sun so wind back flow. Hey, here's 90% off my pair of jeans again. He didn't open it. He didn't open the email. Like, he never wants your summer again. He hates us. He's back to Levi's. You know what I mean? And like, they literally like, clean you and you're gone. And what's funny is that, like, if you pause and think about it from a consumer, because I was like, but sometimes, like, I just don't buy vans for two years, and then all sudden, I buy them every year again. And so, like, what we oftentimes do is we look at what is the activity on that list and think about when it doesn't make sense to bring sense to bring people back. We call it rescue. These are people that are basically being rescued. And so like, from that, we've literally found millions of dollars of literally gold in the couches. Because, like, you've put them on a do not just trip. Like these people never want to hear from us, but they're like, No, I just it's just because it took me three years to come back, as opposed to 90 days. And so what happens? Like, we can, like, find that so that can generate a lot of revenue for other brands. And the other one is, is to, is to reshape the the segments that they're using now. So, like, they'll still use the same segments in term of personas, if they're, like, oh, like, men and women, or prospects for buyers, like those sorts of things. Or, like, maybe have some idea of terms of like, these are for grandparents, friends, buying for each other, or these are likely leader products like that, or stuff maintains the same. But what we replace are like, the concepts of like engagement. So if someone might have a segmentation, which is like men who are prospects who recently signed up or visited site within last three days, that last part is what we replace, and then what you'll see is that you should see way higher, first of all engagement, so lot higher clicks, lot higher unique clicks, as well as more revenue coming in through all those campaigns. As the as what is decided to to who becomes in the segments is determined by machine learning, as opposed to, like, maybe well analyzed rules of thumb, but they're still

William Harris 37:42

take me through a brand where you've done this for that, maybe just shocked you or surprised you, and you're like, this was an impressive, an incredible turnaround.

Daniel Brady 37:50

Yeah. I mean, I it. I mean, it's really wild. So like, you know, we had one, one brand of ours, so there before period was actually in q4 so it was, it was, they're an apparel brand, and so like that has actually the big time. And if you look at the data, you can see the big spike, which was their Black Friday sale. And then they started working with us earlier this year, and they started sending to around 30 to 40% fewer people. And then within the course, within the first 30 days, they started making twice as much money per campaign. Wow, twice as much money per campaign while sending to 30 to 40% of your people. That's crazy. Yeah, it's and listen, your results may vary. Like, this is like, because what's happening with this brand is two components. One was that, like, with apparel, it's like, it's like, other things, it's like, sometimes the the life cycle is is longer than what we what us as marketers want it to be, right? And so we're giving up on people who like, are just like, No, just give me a little more time before I come back. So you're reshaping the list like you're finding those people that are like, on that medium to tail end of the list that actually have some gold in them, as well as dropping out those at like, you know, signed up 179 days ago, but have done nothing right? And like, actually, there's not much that signal there. And as a result of it, the composition of the list is much more valuable. And they started engaging with the emails more. And so then the gmails and the hotmails and the Microsoft offices of the world go like, Hey, this is actually better content. Then the next email that sent, once again, it's like, it's this virtuous cycle. And so literally, by doing that, you can start making more money. And the way that I frame it is that, like a lot of brands, don't realize that they're already in a revenue losing state, sure if they're having email issues. And so what we could do is kind of get them out there, and now that, now that the brand you know after 30 or 45 days or so, what happens is, like, now that they're in much better standing, you can start now opening up that list. Because what's really tough, what's really crazy about the engagement stuff, is that you go, okay, William didn't click on an email in the last 90 days. It's like, maybe just didn't see him. Maybe they went to spam for him. He actually isn't. Engaged customer, and you don't know that, you're just like, oh, he doesn't want to hear from

William Harris 40:03

Maybe he was moving, and he just had a kid, and he's just been busy, right?

Daniel Brady 40:07

100% yes, exactly. And so these are the sorts of things where it's like, it's both ml, which I which is obviously what we do an AI, but like, I think another part of it is also testing, and this is something that you can do programmatically, which is, and this is a large part of what we do, is we go, okay, even if this person doesn't have a lot of signal, let's just turn them on for like, a week and see, and then if they're not looking like, turn them back off and have them stop receiving emails. And that allows you to probe to see if someone's back in marketing, see if they're moving and having a kid

William Harris 40:38

or something like that. Yeah, I love that idea. I want to talk about some of the lessons of leadership from the actual builder seat. Because, you know, you sit in this seat as well, and there's a lot of things that you're doing, likely that are different from somebody who went to just like MBA school, right? What were some of the like? What are some of the things that you think that you're doing differently as a leader than somebody who went to business school specifically for business, versus coming in from neuroscience?

Daniel Brady 41:08

Yeah, I mean, there's, there's a lot so like we being a scientist who then is was a data science and machine learning person, and putting that seat is, like, a completely different way to think about things. So like, I think it probably goes without saying that, like, very product oriented as a result of this thing, right? Like, I don't know accounting, like, all that kind of like, I'm learning on the fly what a lot of these sorts of things mean. And so what this has practically made me really appreciate is this, like, really trying to find very, very good experts in all the other aspects of business. Like, I'm just like, new to, you know. So, you know, we have three co founders, and Aaron's our one. He's co CEO with me, and he's a go to market expert, and so good at all that kind of stuff. And like, you know, our first employee that we hired that wasn't one of the founders, uh, as always, our Director of Partnerships. I didn't know what partnerships was a year ago. Sure. I mean, a year before we hired her, I did not know what that was. And then all of a sudden, it was like, hey, you know? And what I didn't know is that, like, I had done it, which I reached out to an agency, that agency then reached out to their brands, and all of a sudden, that's how we had all these customers. I was like, there's a thing you called partnerships, and that's what their job is. And there's like, and this seems to be a really way, like, email agencies, love you guys, like that should be your primary way in order to customers, and so to be really open and flexible about how and to appreciate how much expertise there is outside of like, what it is that you do, and like, as a scientist, as that I deeply appreciate. Because, like, you know, at the end of your PhD, you realize how little you know about 99.99% of the world, because there's a handful of people that are as expert as you at this very specific thing. Like I, I for a while and no longer, was an expert in cross motor refinement by visual experience. Who knows? No one knows what that means. It's fine. Like, they're sure 10 people listen to, like, Oh yeah, you know that that's, that's the people. You were the guy, yeah, I was the guy. And then, and then, no longer, but like, that was, that was, that's like, one hit, wonder, like, that was like, that time, that time, the window. And to understand, like, very strong, and this is what I would think is like, there are people who are like that, for, for marketing, for engineering, for, for everything that makes what a business is. And to like, be very humble and like thoughtful, and listening to like, all those different voices. And then to like, combine that with with and what's the most part? It's like, what is the product? What is the vision for a company, for your product, for the experience you want to have your customers along

William Harris 43:48

that way? So you left, uh, let's just say the neuroscience, because it was a little bit slow, like you said, defending it again, what's been the harder, hardest thing about business and moving from that?

Daniel Brady 44:00

Yeah, I think, I think what's there's, there's a couple things that are interesting in there, that are difficult. One is that the different layers of of communication, especially of complicated and difficult ideas, that you have to do in business, is just like, way different. Once again, in neuroscience, like everyone's an expert. I could say one sentence and like, after them, right? And then all of a sudden you say that in the business context, and like, that machine learning engineer will understand. Our analysts might get 50% of it. Our market is like, I have no idea. And so like, you have to, like, constantly, like, code switch and context switch, because you're talking to customers, you're talking to investors, you're talking to other people on your team. And it's a really, really, really fun challenge, but that's like, not something that you have to worry about as much in as a scientist, like in the science, it's like, kind of like two things. It's like, who are like the other expert neuroscientists that you're with, and then who's a general audience who knows nothing about it, but. So in business aspect, you really see that full range that there is. And the other aspect, I think that's also really interesting too. Is that like, and this is really important for like, for AI tooling, is that like, how many people are both excited, but have also been burned by like ideas of like, the stuff in the past, like, everyone is trying to figure out what is working how to understand as this stuff is working. So, like, there's healthy skepticism for a lot of it, and a lot of over promising and under delivering. And so like to be able to speak to that and be like, Yeah, listen, this is why we do X, Y and Z and all this kind of stuff like that, in order to, like, get over those, those concerns.

William Harris 45:39

No, I completely agree with you. I think one of the things that I found interesting, as when we were doing EOS implementation at our business, you know, they talked about communication, and it's like, communication is very easy with two people. It's like one two, there's just two streams of communication, right? Like back and forth. You get to three people, and all of a sudden you've got six streams of types of communication. You dictate the four to five, and it becomes very, very, very complicated in the total amounts of conversations that can happen. And to your point, when one person explains it versus another person, and then you're explaining one concept to one person versus what you explain to another person. It can become very difficult. And then you're not the phrase that they use, that I like is you're not rowing together in the same direction, and so I'm rowing the boat this way, somebody else is rowing the boat this way. We're maybe still going in the general direction, but we're not going nearly as efficiently as we could be. Communication is really hard. The thing that I had Tim Webster on talking about that I really appreciated was, you hear people talk about, make sure you hire for culture fit, make sure you hire for you know, your vision values. And it sounds like, yeah, okay, like maybe that's not nearly as important as the talent or the skills. But what she said, that I think makes a lot of sense to me, and after reading her book and everything, is, if somebody fundamentally believes in solving a problem a different way than you do, because they have different values, then when it's time for some type of a change, there's a problem in the company. There's a pivot, small pivots, just little things you're dealing with, they will fundamentally want to solve that in a way that is different from what you are. And so no matter what you do, no matter what you say, no matter how clearly you communicate it, they're likely not going to be on board with the solution that you're presenting, and therefore you're not rowing in the right direction. And so when she said that, it made a lot of sense to me, and the way that you just explained it, I feel like really ties nicely into that.

Daniel Brady 47:36

I think so. And I think I think there's also, I think what's also really important, and this is, this is like, doubly important, I think in AI stuff, because it's like, it's like, a hot topic, and people want it to work, and it's but it's also confusing, and it's mathematically complicated. Is like to really try to peel back the onion and understand what are, what are your partners? What are, what are, what are your customers? What are they actually asking for? And stuff like that. Like, I and, you know, there's been great lessons, and there's been hard lessons about, like, you hear the language that people using, but it's like, that's not actually what they what they want, because it's a difficult thing to talk about. And then on the flip side, when you're thinking about hiring your team, is to, is to, is to understand that. And so, like, you know, I think if you talk to any, anyone at our company, like, I think what they would say is, it's extremely exciting to be in the space and to have the have all the projects that we've had, but, you know, especially on the go to market side, they're like, but this is also, like, the most difficult thing that I've ever tried to sell or try to market or try to do customer support for, because it's not like, yep, your package is here or your package is lost. You know what? I mean? It's like, this extremely complicated thing, and people have their own strategies for email, and they have their own strategies thing. And, like, they're trying to intersect it with, like this, this crazy, scary artificial brain that is also determining stuff, and, like, all of that sort of stuff means that it's just like, it can be a lot, um, Messier and like, like, what talks like? You really have to talk about strategy and really understand, like, what are understand, like, what are, what are the objectives that everyone is trying to achieve? I want

William Harris 49:07

to talk a little bit about neuroscience with you, because it's very rare that I have somebody that is a Harvard PhD in neuroscience on the show. I sent you an article that I wrote that talks a little bit about just the need for cognitive training in this AI world. My basically, the thesis that I have here is that the same way that now that we Okay. So let's just say, over the last generation or so, we have developed these issues with sitting down for work. And metabolically, we know that sitting down you burn significantly less calories. I believe I've read up to, like, 40% less calories. Like, there are metabolic issues that you have it shortens, like your psoas muscle. Like, there are issues that we have from literally just sitting down in a way that we weren't designed to, and we have to counteract that. And so we do training to try to counteract that. We do, you know, training to help with our posture. Again, training help with. And sitting and running and moving and all of these different things, mobility training with AI. But we can't avoid it like we have jobs that we require us to stem with AI. I think the same thing is happening. We have the need to use AI. And I think one of my buddies, David Herman, just talked about like, normalize. What did he say? Something like normalize thinking for yourself instead of using AI. Love the sentiment, but it's not reality like in reality. I think that we're going to have to continue to use AI in order to stay competitive. And so now the goal is you're going to use AI. How do you not atrophy certain parts of your brain? What are the types of cognitive training that we need to be doing in order to make sure that you know, you're still effective at thinking.

Daniel Brady 50:48

Well, I'm going to say, first off that ChatGPT and I are going to write the next great American novel together. And so that is no, I mean, I hear you. I think this is a very interesting topic, very interesting set of ideas. So here, here's and as a classic scientist, I don't know the answer, so I'm going to think about this out loud with you, in terms of, like, both pros and cons to some of what you wrote. So here, here's what I can say is something in favor of that. Is that, like, there are lots of studies that are like, about keeping your mind engaged. And what does that mean on a long term like, you know, doing crosswords to jokers for Alzheimer's, all that kind of stuff. Like, there's this idea, there is some sort of concept of, like, mental engagement has, and also physical engagement, actually, in fact, physical engagement tends to outweigh mental engagement in terms of, like, long term outputs, in terms of how your brain process information. So, like, I think it's really interesting. I think that the idea of, like, how AI is, is, is shaping our day to day life, and what is it's taking, what it's like, a huge unknown in terms of, like, what does that mean? Like, if no one is writing 10 page papers anymore by themselves, like, what is what does that mean as as a society? The other aspect that I think, the other side of it though, that I think is really interesting and to think about us, and this is now, from a biological perspective, is that what makes our species unique is the fact that when we're born, we're useless, because we, because we allow the environment to shape the way in which our brains process information. Now, the way in which it works is that you're you have like, a genetic layout with like, some like internal activity that kind of like, says, like, I think this is what is what is important. I think this part of your brain should process vision. I think this part of your brain should process sound like, yada yada yada. But there is this period for us, for a lot of mammals, but like, we're like, very strong in like, but now let's actually get that input from the environment to see if that like, genetic plan, layout, or whatever the patterning is like, makes sense. And if not, then let's rewire and like, this is why like early life, educational kind of stuff is so important, is that like early life experience is like, when all the stuff matters. And the reason why I bring that as the counterpoint is that like, what, what we don't what we don't know yet, is like, what is it that we do? That is, is singular. That means, like, if, if you don't have those experiences, then you lose it, versus like, Well, what else could your brain be doing? Because, like, once again, it's like, I think everyone should learn how to swim, but I don't, I don't think that, like you learning the motor coordination to not learn how to swim in your life is somehow means that, like, you're going to be dumber, right? It's just like, you're just, like, using, like, your that those part of their neurons, like, who would have encoded that sort of invasion, or, like, doing other things. And we don't really have a good idea of, like, what is the actual benefit? Like, are our brains meant to be reading all the time, all kinds of, I'm not saying don't read, but, like, well, like, you know, like, reading is a relatively recent invention in terms of human history, especially biological history, and so, like, we don't really know what those boundaries are. And so that's not to say that, like, I don't agree. I just think that, like, we always have to think, I always, I always, as a neuroscience, I always, like, turn to the biology. The biology is that, like, we have these huge processing systems that are incredibly plastic, which means they're incredibly adaptable. And we might not like what that adaptable state means when our attention is split in a million ways, like it is now, but it's, it's like, I don't think that we're gonna have, like, mass populations of neurons in our brain dying because, because now we are use these kind of AI tools, so

William Harris 54:42

not dying, but, but potentially incapable of doing deeper thinking of sorts, right?

Daniel Brady 54:49

Yeah, it's an interesting. It's, it's interesting. So, like now, yeah, so here, this is what's, this is what's really tough, is that, like, we don't, we don't know. So we don't know how to make things smarter, besides doing a lot of education. I mean, like, I think this generally make you smarter. Like, it's really, really interesting. Like, because, you know, there's lots of studies, and a lot of them are, like, funded by the government, something like that, which is kind of like, if we get you something like, how can it's called transfer warning, like, how can we get you good at something? And then it put then it ports over to something else? It's actually really hard to do, sure, and so, like, and so, but that being said, like, I think it's really interesting. It's like, what does it mean to be distracted, to not use a lot of your deep thinking processes? And I think, I think what I what I always struggle to understand, and we just literally don't have the answers to, is, like, are they long term or short term effects, right? So like, as someone who just studied developmental neuroscience, it was that's like, what the name of the game is, is to understand, is this going to be a long term issue or not? So here's a good example on Non, non cognitive stuff. If you have what's called amblyopia, if one of your con if one of your eyes is covered by a cataract, and you're one years old and or the first three years of life, you have that. So you don't have it for five years, from zero to five and they remove it, you won't be able to see another guy. And your your eye is perfectly fine, but the cortical part of your brain that was dedicated to the eye is going to be taken over by the other eye. And so you'll be you'll have a form of what's called cortical blindness. Do that E if tomorrow, I put an eye patch on you for five years and then we took it off, it'd be blurry for like, a week, but then you'd be able to see it. And so, like, that's a difference of, like, what's called a sensitive or critical period. It's like, is it actually going to have a long term effect once that stimulus is removed? And that is something that I genuinely don't know how to think about, interested in these sorts of things. It's just like, if social media were to disappear tomorrow, do we think that there'd be long term impacts on, say, teenagers, ability to pay attention, all that kind of stuff? It's an unknown, or, like, one year later, are we just all going to be how we were a generation ago? I think it's, I don't have the answer

William Harris 57:07

to that. Sure. I just nobody does yet, right? Like, the studies don't exist

Daniel Brady 57:11

yet. The studies don't exist. But I do think that. I do think that what is to your point, though, is that, like, instead of waiting to be like, Oh yeah, it definitely was that, like to keep your mind active. Like, I think we can agree that, like, definitely keeping your mind active and to push yourself, like, all these tools should be used in terms of enhancing what it is that you're doing. Like, our engineers use AI tooling all the time, and they're not worse at writing code. They're better at writing code and and I mean, it takes now a specific type of engineers, a specific quality, the specific experience, in order to get to that point where, like, it's not just doing it's like, Yes, I am now the power of two or three or four engineers, but I can, like, oversee all this kind of stuff. Like, that's how you should be thinking about using it, as opposed to being like, I'm gonna do this and watch a bunch of Rick and Morty episodes and then, like, later.

William Harris 57:59

So I can't help but when I think about where this goes, like you said, there isn't studies to support this one way or another. Do you remember the movie Idiocracy with Wilson? Yeah, that's the that's the future that I picture. And so to preemptively protect myself and my children against that, I do unique things, even right now, I was doing it before, but I'm even more intentional about trying to do things. A couple of examples, just for those who are listening, if they want to go down the weirdness path. With me, I've been trying to navigate to places without using GPS anymore, literally just looking at a map saying, here's the streets that I need to go and then going there. And it's wild, because you can sometimes go to some place, like four or five times, and you're like, I still feel like I need to use the GPS. I know how to get there, but I don't know how to get there, right? And so it's like just redoing that for my brain. I will intentionally try to memorize things every day I practice memorization just to make sure that I'm still capable of that. Because we don't remember people's phone numbers anymore. There's a lot of things that we don't do totally. And then there are things where I will try to do, you know, there's, I think that you would probably agree with this. I think I've read that there's no real, true multitasking, but I will try to do like, let's just say, really quick switches between things to be able to keep concentration on things. And so I haven't done this in a long time, but I was younger, and I didn't have kids. I would have somebody speak to me in Arabic in my left ear, and I would try to write sentences out to them with my left hand in Arabic, have somebody speak to me in English with my into my right ear and write out to them in English with my right hand, and have somebody speak to me German, and I would speak back to them in German. And I would try to do this at the same time, and I could never get more than maybe a one word or two word answer back and forth. But the goal is just like, how can I make sure that I can process multiple inputs all at once and be able to do things at different times with different times with different pieces of my hand, writing upwards, upside down, backwards, things like that, even writing with my toes, like, literally, like neuroplasticity, stuff of just, like, how do I make sure I'm engaging all of these things? I'm probably screwing up my brain. But my goal is, I don't want that future.

Daniel Brady 59:57

Yeah, I think, I think it's really interesting. I think. You know, I'll riff on on certain things that I do that are kind of similar, like, I think, I think what's really important that I try to do now is, is to work on things that require an enormous amount of concentration, that are very singular, because, like, we're so distracted, and it's so easy to to see a notification pop up your phone and to, like, kind of context switch a lot there, too. And so I think it's really interesting of what you're doing. I think also the opposite is also very interesting, which is kind of like, I'm going to sit down and try and read War and Peace for like, two hours straight, you know, and like, like, that sort of stuff. To your point, about, like, the deep thing in a deep analysis. And then I think, I think the other thing to think about, too, is that, like, is, um, is, is to do complicated, interesting, exciting things that are both using your mind and as well, using your body as well. Sure, because, like, that sort of, like, whole body, whole system, sort of way of doing it, like, I think server versus surfing is what I do, but I actually surfboards there, yeah. But, like, those are the sorts of things that I think actually, like, really, really big, because it's like, you know, one hour of serving to me feels both mentally interesting and physically stimulating, and I'm in nature and stuff like that, as opposed to, like, an hour browsing on my phone or something like that. Like, I think, like, all those sorts of things, can, can, can, can make a huge difference. That was a really

William Harris 1:01:16

good call. And I've got one more question along these lines, but I'm going to take you outside of your field, a hint, and so just for fun, but it's close enough, because I realized there's a difference between a biologist versus foundational physics. But what do you personally believe that matter or consciousness is foundational? Yeah,

Daniel Brady 1:01:37

I'm a biologist, so I think I believe, I believe in the matter aspect of it. And I think that consciousness kind of is like an emergent property from from that. I mean, I guess you could be all living in the matrix, but like that, that is, that is my thing, is definitely for matter first,

William Harris 1:01:53

yeah, and I'd say that that is the, that's still the, the mostly accepted thing. But I there's a really interesting video by Federico Fauci, or whatever. He's creator of the CPU, and he's talking about what I think he calls panpsychism. But him, as well as I've seen some scientists out of CERN that are starting to say they believe that consciousness might be actually, you know, more preeminent than that, biology follows that, and some of the, some of the things that they give are definitely on the fringe side of things, but I think it's an interesting discussion. Now with quantum field theory is kind of lending itself towards, oh, there's something interesting maybe happening here. How we could prove the difference between two? I have no idea, but I figured you were going to say matter first,

Daniel Brady 1:02:39

yeah. I mean, I think, I think it's a, it's like a really interesting idea. I think, you know, as in, I think what also kind of shocks people about neuroscience is like, how, how, because we are focused so much on the biology and the basic process of it, how we actually don't think about the philosophy as much as you would assume. But I think, but along these lines, I do think the idea of, like, these emergent properties as as to, like, how important interesting are, like, once again, like, as a as as someone who's a biologist, you know, I think a lot about about consciousness in terms of, like, well, what does it mean Between us to a bacterium. What does consciousness look like from that, like, from that sort of spectrum, like, what is an octopus, then what is like a sponge? And, like, I think, a really awesome animal that I love thinking about. It's called, it's called a tunicate, so in the kit, yep, the tunicate is like a little, a little sea creature, and it has a little brain, and it like moves around, kind of like a little squid. And in the second half of its life, it implants itself against a rock and becomes basically something like a see an enemy, seen enemy. And the first thing it does when it implants itself a rock is it eats its own brain delight. Yep.

William Harris 1:03:57

But why do we know why?

Daniel Brady 1:03:59

Well, so this is the thing about, like, what our brains for sure, right? And I And listen, I think our brains are awesome, and they've gotten completely out of hand. And more, not most of brains, like nervous systems are for is like, you know, it's to think about like, nervous systems are the primary use case for, when we saw nervous systems evolve, we're actually out of Nigerians, which are like, which are, like, jellyfish and things like that. Like, they have, like, prototype of these sorts of things. It's that, it's fast movement. That is the thing, right? Like, what's the difference between us and a tree? We run those guys can't run. They, like, slowly put their, their, like leaves and stuff like that, in their their, their limbs. And so what happens is that it's, it's really about fast fast reactions is, which is a lot of what the signals. White neurons use electric like a chemical electrical signal. And they also have captures, which is electrical signaling. Um, it's for fast invasive maneuvers. And then it became to plan those fast invasive maneuvers, and then to think and to plan different options, and like, add. More and more layers, but it's, like, really very movement based. And so that's why this little tunicate is, like, moving around. So it has this little brain, and this is, like, now I don't need to move anymore. Like, let's pop this thing. Let's like, it's this huge energy taxing thing. And let's, like, eat that. So let's see what the consciousness is of a tunicate that, at some point goes, You know what? I'm going to end this and my own little consciousness and just go ahead and eat

William Harris 1:05:22

my brain. That is a wild thought. I want to get into your consciousness. I want to learn a little bit more about Daniel Brady, aka DB surfing. You mentioned that you love surfing, but you also said you're in New York. I didn't realize that New Yorkers were known for surfing, but that's just my own naivety. Like, is there a big surfing community

Daniel Brady 1:05:41

there? I mean, there is, in terms of number of people, because New York has lots of everything, sure, but yeah, like, there's, there's lots of surfing along Long Island and New York and in New Jersey. And so, yeah, there is a quite, a, quite a big community most people serve in what's called the rockways, which is by JFK Airport. It's like, it's like, right by that in Queens, and so, yeah, that's where, that's where I go surfing all the time. Yeah,

William Harris 1:06:07

tell me about your childhood and how that has helped shape the direction and path that you've decided to take, both from, let's say, getting into neuroscience and then getting into, you know, now, e commerce software.

Daniel Brady 1:06:18

Yeah, I think so, you know, I I've known that I've been interested in in the brain and in behavior and in learning for a very long time. Like I read, there's a Michael cried in book called The terminal man. And is not it's not one of his best. That's why no one knows what it is. But but in it, there were these scenes where they were doing what's called electron micro stimulation, where they were stimulating different parts of the brain, sure, for human brains, and they'd be like, you know, it would make someone's lips tingle, or their motor cortex. Like, there's all these things have been done for a really long time. And I remember reading about this in seventh grade. I mean, like, what is this? This is crazy. Like, this is, this is who we are. Like, you can stimulate these parts of your brain. You literally start feeling things that are not really happening in the real world. And so it was from that point on, which was like, Okay, well, this is what I want to, want to do, and want to understand. And then when I went to college, it was like, Oh, you study medicine, you can do your PhD. And I was like, well, we don't understand the brain enough that I don't, I actually don't think the medicine part of it didn't really interest me that much. I like translational medicine, but not enough. I really want to understand, like, how do these neurons encode these sorts of behaviors? And so I decided to do my PhD. And so that's what I did for a really long time. Along the way of doing that, you learn how to program, you learn how to do experiments, learn a lot of math and science. And so then what happened is that, like, as I left academia, you have, like, all these great skills in terms of thinking like a scientist, like an engineer. Now you can pour that over into one in any other aspect of whether you have and like, the interesting thing about marketing is that, like, you know, it's basically mass social psychology, and that was actually my I studied neuroscience. I also studied psychology. Is in our grad and social psychology and developmental psychology are my favorite subjects. And so I oftentimes think about what, what those things mean. So, you know, we've talked about, like, a lot of, like, very practical things, if someone is mad at you on their CRM, don't market to them. Like, Sure, very, these are very like, these are these things about human behavior, and I think it's a really interesting thing to try and like, take artificial intelligence to like, have learning systems learn and adapt to what it is that people are doing and respond to that in order to send them content as well as like, what sort of interactions as us humans do we want to have behaviorally, not only with other people, but like, with these big Companies and these big forces that govern our lives. Like, what is my relationship like with this big company or this small company? And like, how is that bring me joy? Or, how does that annoy me? Like, being able to control that is super interesting. I

William Harris 1:08:50

like that you called out, just like the entity, like, how the relationship you have with a brand? I mean, we're starting to see relationship means something very different, because there is relationship with people, relationships with animals, let's say relationship with brands, relationships with machines, right? And it's like, this is adapting more than I think we've seen previously. What, what does relationship mean anymore now? Yeah,

Daniel Brady 1:09:14

I mean, I think, I think that's, I think that's like, a fundamental thing, and it's something that Zach and I used to think a lot about, especially right before, like, we made the transition, really was just like, we did a lot of like graph theory and all these sort of things for various projects. And like we got, we're very obsessed with idea of like relationships. And I mean by that, is that, like, is that? Is that there are, to your point, so many relationships that we have bonds with humans, Non Humans, like my dog, with companies that can last for a period of time, for lifetimes, and like, this is, you know, when we talk about customer relationship management, it's like, it's actually true, like, we live in like a relationship era, era, genuinely like, yeah, there was, there was humans, and then there was, like a social media. Era, and that was like, connection with other, with other, with other humans. But now we live in a much more complicated space where it's like, you can have relationships with companies. You can have relationships with AI agents at companies. And so like, we're to this point where like and to be what's interesting about humans is that, like, we're actually very adaptable in terms of who we can have relationships with. We have pets, right, right? Have we have favorite books that we carry with us every day. Like it's never been the case that we've only been with members of our own species. And so like that in this kind of, like, digital world means that we can have, like, really interesting, sometimes negative, hopefully more and more positive, like, relations with all sorts of different

William Harris 1:10:41

I like that. That's beautiful. You when you're not growing a really cool e commerce company and you're not surfing, what else do you like to do?

Daniel Brady 1:10:51

I mean, you know, I have, I have a three year old daughter, and as someone who was a former developmental neuroscientist, it's amazing, because all these like theories and stuff like

William Harris 1:11:02

that. I'm just like, are you experimenting on her? Like,

Daniel Brady 1:11:05

oh, I mean, I was, like, noticing all the time. It's like, no wonder what, like, you know, what PHA level of development she's at, like, right now. And like, I can like, and like, understanding, like, especially with, like, the language stuff and like, all that kind of stuff is, like, super fascinating. But like, Sure, um, I don't do experiments on her. I'm not that extreme. But I definitely, like, I definitely think about the context for how she's understanding and interacting with the world. And, like, what, what does that mean in terms of, like, what heuristics and precepts and categorizations is she able to do and not able to do right now is like, something that I constantly think about. And it's also, like, so interesting about, like, how much they passively pick up everything we used to study, like, what is the composition of neurotransmitter release in early parts of development versus later? And it's like, very clear how much they can pull from passive experience. That for you, and I know we had to, like, sit down and like, really, work really hard for her, she just, like, gets it right away. It's wild.

William Harris 1:11:58

Well, DB, it has been absolutely amazing talking to you, getting to learn from you, learn about you. If people wanted to work with you or follow you, what's the best way for them to do that?

Daniel Brady 1:12:08

Yeah, so, you know, our company is called Orita. We're at orita.ai and you can reach me out on LinkedIn. I'm not really that active on any other social media network besides that. But like, and you know, if you want to learn more about about marketing and ml and optimization, you know, company wise, we do, we make models, and we do free audits for every brand that wants to see so they could get a good understanding of if machine learning can help, it also helps us, because, like, there are lots of brands, there a handful of brands, we just can't help. They don't have enough history. They're doing things well, all right. They don't need ml at this point. So we like to always do that, but like, that's it. And if you just want to talk about earthline stupid

William Harris 1:12:48

enough to do that, well, again, I can't. Thank you enough for your time, knowledge, wisdom, and I've never said this on the podcast, but for letting me pick your brain. There's the only podcast I can say this on. Thank you very much. Thank you for having me. It's been a blast. Thanks everyone for listening. I hope you have a great rest of

Outro 1:13:05

your day. Thanks for listening to the Up Arrow Podcast with William Harris. We'll see you again next time, and be sure to click Subscribe to get future episodes.

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