On today’s pod I chat with Richard Harris, the Founder & CEO of Black Crow AI.
Black Crow is a no-code, real-time machine-learning based predictive software that helps companies understand likely customer behavior.
Richard’s a veteran entrepreneur and has been involved in tech since the 90s. He cut his teeth in the world of consulting, was involved with Travelocity during the dot com boom, then has continued to be a serial founder.
As you’ll hear him explain, the world is turning into a browser. Between mobile devices, computers, wearable tech, self driving cars - real time data will be streaming from every part of our lives. He’s using this insight to build a company to help startups and brands collect and understand their first party data so that they can increase revenue and margins.
In addition to discussing how Black Crow operates and the machine learning industry, we also talk about some strategies for how founders can navigate down market cycles - like the one we’re entering now. He’s been through three of these cycles and has some very helpful wisdom. If you started your company after 2010, then I definitely recommend that you give this one a listen.
Transcript (this is an automated transcript):
MPD: All right, let's jump in. Can you give us an overview of Black Crow?
Richard Harris: Sure. Black Crow is a real time machining learn machine learning platform. And the key thing we do is we ingest massive amounts of data, process it in real time and then produce predictions. And the key area where we work today is any e-commerce where in real time, as users are shopping in e-commerce environment, we predict their future value 15 milliseconds after anyone does anything inside of a brand's environment.
MPD: How does that work? That's that sounds too good to be true.
Richard Harris: It does. It does. And it almost is, except that it's not. So what we're doing, we've made, the core of what we do is a very sophisticated, real time auto ML platform. And it was designed to be able to as I mentioned, ingest massive amounts of streaming data.
So that's literally the event data that's kicked off by a user, interacting with a website, with a browser. And historically that has not been a widely solved problem, streaming data processing it and generating predictions on top of it has not been a widely solved problem. Massive companies like Google do stuff like this.
Like Amazon does stuff like this, but our particular mission is about making this accessible to the middle of the market. People who have historically not have. Not had access to fortune 500 grade machine learning. But the key thing is in order to get after that middle of the market, we needed to make it super simple.
So rather than being a multi-year multimillion dollar project, which is how it works at the fortune 500 level, it's literally one click to install. And what our machine does is it just listens to all of that user interaction, data, all of those real-time events and based on whatever the objective function is, meaning what is the key thing that this brand or store e-commerce company is trying to have happen?
Whether that's a subscription or a purchase, a repeat purchase, the machine starts finding the patterns in that without any work, frankly, on our side or on the customer side. And in about two weeks, we will have. Accurate predictive model built so that every time someone does something, so a hundred percent of visitors to a commerce environment of the machine can say, how likely is this person to do the thing you want them to do?
MPD: So where's the signal and all those. Cause I think about the stream of data flowing through, and I'm sure it varies by use case and by company and by customer. But I would just imagine a lot of it's noise. Is there an area where, Hey, people who click on a certain area or a certain amount of time, is there something that's usually like a higher probability prep, more probabilistic signal in the data stream?
Richard Harris: Yeah, it's interesting. So we obviously look at this all the time and the answer is nothing that is consistent. Now. In the smallest possible sense, meaning there's no, there's nothing I could narrate to you in a human understandable way, or I could narrate anything, not understandable by humans, but when you try to find the story and the predictions, it's often not there.
I can tell you though that, the way we work, the machine is listening to about 450 signals in real time. And it's trying to understand what, if any of those signals are predictive or I can create predictive knowledge and I'll say that. For every brand that we work with and we have our models running on about two hundred and forty two hundred fifty different commerce brands right now.
There's none of them are the same. So each model is built in a bespoke way for each brand. And what the machine is trying to do is figure out which of those 450 signals, they're after each user action is relevant for this brand and then the weighting of each variable and trying to predict what their future behavior and value will be.
But I'd say that the interesting thing is th there's, even though there's no human narrative story, what's so interesting is that this data that brands have that commerce companies have is data they own. And historically, because it's so hard to process time streaming. Historically, they haven't really thought of it as an asset that they can exploit.
And that's what we're trying to do is really using machine learning, which is the only way to understand when there's so much data using machine learning. How do we turn this thing that they own into a huge asset, right? When you can predict what users are going to do and how valuable they're going to be.
It changes the game on a whole bunch of elements of a
MPD: commerce business. So this could be as simple as someone hovers over a picture and then clicks it hovers over the buy button. And those two things in conjunction over a huge amount of data sets. You, and I won't see the pattern, but the machine will figure out that combine with two or three other things and a duration, boom.
This person's a high probability buyer.
Richard Harris: Yes. It's really as simple as that but I can tell you, but you're hitting on the right things, which is, what kind of event data is actually there. And if you think about it, Interacting with the brand that there's a few different buckets, right?
There's like, how did that user get to where they are with the brand? Where did they come from? Was it some sort of marketing campaign or did they just show up at the website or they come through social? And then all the normal stuff you get when you're just integrated into a browser, like, where are they, what time is it?
What kind of device? All that kind of stuff you get for free. So that's one bucket. We call it like the referring data, but then there's the rich in session data. How does how's this user behaving? Are they, as you said, lingering on images, are they looking at a lot of images? Are those images for the same product or different products?
Are they scrolling deeply on a page? Are they moving quickly through the funnel? So there's all that rich in session data, which can be extremely predictive. And then there's how that evolves over time. So are they coming back? Are they looking at the same products? Are they doing different things that deepen their search and all of their past interactions with the brand.
And then we also look at product economics of different categories have different values to the brand. And all of that gets crunched together in 15 milliseconds to say, boom, this user will rely is part of a population that will reliably convert at 65%. Whereas another user may be part of the population that will convert at 0.02%.
And when you just ask a brand like, Hey, if you knew this in advance, you'd, if you had 10 deciles, or low, medium high, if you knew that someone was part of a population that will convert at 60% versus something close to 0%, what would you want to do differently? And then we get back in extremely long list because.
Kind of want to do everything differently, right? Certainly how they market, maybe how they price or offers promotions, UX with their merchandising how they prioritize their customer service queue. So there's so many elements of a business, how they text or email them. There's so many elements of business that could be really optimized.
Once you have this future knowledge, which hasn't been available till now, that's
MPD: phenomenal. Can you, what can you extrapolate from that? Continue. Can you get customer profiles to the point where before someone even lands or right when they land, what bucket they're going to fit into or they've got to come self-identify through behavior and then once they're on.
Richard Harris: Yeah, no, it's a great question. And we because we use only first party data, so we're not merging profiles, stuff that like the ad tech business used to do, we're not involved in that in any way. We're literally just a data processor of that brand's own first party data. And so we have to be able to provide a predict prediction on the first landing, right from pixel zero, from the moment, a user lands on a page.
And we're able to do that. Obviously, the more data you have about a user's behavior, the more high fidelity that prediction gets, but we can users from anonymous users who had just cleared their cookies, et cetera, et cetera, based on that real-time data, we're able to D average them and provide a prediction better than just thinking about all your users on that.
Which is what most brands have been forced to do. Yeah.
MPD: The idea of segmenting the customers down this way is not the typical you talk about in marketing, right? Customer segmentation usually is based around some sort of high level conceptual demographic, like a geography or something else thinking about,
Richard Harris: or it's like some psychographic, these are empty-nesters or nomadic millennials or whatever. And it's okay, that's interesting, but what really do you want to do? You can make stuff up about what you'd want to do differently for a baby boomer or a nomadic millennial, but ultimately your brand, trying to curate a relationship with the user centered around this product, whatever value you're delivering to the user and how valuable they are to you as a brand, it's like the number one factor, right?
From which decisions should be made and now they can be made in real time. Now
MPD: the behavioral. Profiles, you're able to create those tend to correlate with any of the psychographic or demographic profiles that we're so used to thinking about, or do they correlate more with an intent or an interest or a need or some sort of state of mind?
Richard Harris: Yeah, again, we're super data nerds. So we haven't really connected those things, which is if we can tell you how likely a population of your user base is to buy or subscribe or whatever it is. That's the number one thing, eventually we'll get to stitching that together to wait, who are these people?
These people who convert at 60% versus 0%, is there anything common about them that differentiates them right now? We don't know. But it's also not the number one way to add value for our customers, which is what we're focused on is delivering the hardcore. How valuable will this person be in.
MPD: Cool. So what do companies do when they get this data? They find out customer a is a 65% probability of buying something. And customer B is a 2%, there's a whole litany of things you said they could throw at it. What do you typically see people
Richard Harris: Yeah, the number one use case. And because we work in e-commerce and we work with flooded direct to consumer brands, the number one use case, meaning the place people want to point these predictions, those future knowledge is into their marketing workflow.
So if you think about it, besides product costs and sometimes not even CAC customer acquisition cost is the number one line item on the P and L of most commerce brands. And so when that's the case, If you can add some efficiency to that process, if you can bring down your customer acquisition costs, it can have a really big impact on your P and L.
And so that's where it gets pointed first. And, just to give you a sense of how that works. So we're predicting the future value of every user. In real time, it's only, we only use first party data. So that prediction that we pushed back, like an API fire hose of predictions, that's also a unit of first party data that the brand owns.
And because basically all software, all tools, every part of the e-commerce stack is set up to ingest a brand's own first party data. We just push that right into those platforms. And so now Facebook, for example, which is the biggest place where most brands are, especially direct to consumer brands are spending their marketing dollars.
Our predictions to show up as audiences, right in the Facebook kind of ad manager. And now, Hey, there's this population. Expected 60% conversion rate reliably. And another with 2%, as you said, do you want to bid the same way or do you want to make sure you have the same share of voice for these two groups and spend your money peanut butter across them equally?
Definitely not. And so by just making more rational decisions about your marketing spend in line with the value of these different cohorts, these different segments, you can improve your return on ad spend by 25 to 50%. So it's pretty amazing. And you can also scale up. Yeah.
MPD: So you mentioned it's a lot of direct to consumer brands currently.
Where else do you see the supply?
Richard Harris: So really we think about it, for now we're, hyper-focused on, on commerce and TTC and ex you know, moving beyond paid marketing, which we're already doing to expanding the number of. Of use cases. So we already have customers plugging this into Klaviyo and postscript and Zendesk and gorgeous and dynamic yield.
So all these places where, as we were saying, if you knew this in advance, if you knew who was, who in advance, what would you want to do differently? So that's our roadmap for the next few quarters. But to get back to your question, really anyone that has a CAC LTV equation at the core of their business, meaning there's a product or a service or some value delivery at the center, but then the business lives or dies by how much does it cost to bring a consumer to that product and get them to interact or buy it.
And then what is the lifetime value? So how does that customer acquisition costs pay off over time? And so if you think about. There's so many verticals like consumer financial services, consumer software education that even healthcare, there are all these places where there's something unique at the center, but CAC LTV is how you live or die.
So those are the places where we think real-time machine learning and this sort of predictive power can have the biggest impact.
MPD: I love the way you frame that on the CAC LTV bit internally and interplay. We actually, I believe all companies live or die on the CAC LTV. The there's organizations that famously like direct to consumer pencil and track all of the data, they know exactly what they spend to buy a customer, the CAC, and they know exactly what the lifetime value is. The value of that customer over time. And there's organizations that don't do it historically, enterprise. But at the end of the day, it is quantifiable. And we actually look at this internally when we're evaluating investments we'll see a enterprise company come in, we'll say, great.
What is your cost of acquiring a customer? And they'll say we don't track that. And we'll say, great. What do you spend per salesperson? And they'll say X and they'll know it. Fully loaded, travel and entertainment. The whole thing. How many customers does that person acquire every year? And they know that number just a little division and suddenly you figure out, oh, it cost you 30 grand to get this customer.
And they usually know that they can back into some sense of LTV, longer cycles for those. But I think it's so fundamental to all companies. I think there's probably arguably a bigger trend here in sounds like you're well-placed for it. Where increasingly data-driven management teams are going to be looking at that ratio LTV.
Up and down the scale of like transactional sales to field sales, to relationship sales and beyond, I don't know if you think this has a place in the world of enterprise or not, but
Richard Harris: yeah. I certainly hope so. Like the question of, do we focus on B to C businesses or B to B businesses that was something we obviously thought about and grappled with at the beginning, you have to find a use case in a vertical where the market poll was just so strong.
But we think about it ourselves. We're a, we're a technology company, but I know when I go to raise my next round of fundraising, that, the first thing a VC is going to ask is what's your customer acquisition costs? What was your growth? What's your, those things are so fundamental to SAS, businesses, software, like so many B2B businesses and where there's a good data set.
So were a lot of the interactions are happening digitally. I think our predictions for.
MPD: These concepts were known when I started in VC. Oh 6 0 8 timeframe, but they have become mainstream expected KPI, vernacular. Yeah. I told you when raising money needs to be thinking about this on some level and you shouldn't just be doing it for your investors are looking at it because it's a key driver of the success and the health of the company, all management should be looking at this in their own accord.
If you want to spend a dollar, you want to make three. It's that simple. It's that simple. It's that simple. If you're not doing that calculus, who knows what you're spending. Yeah. Who knows if the test, it sounds long-term.
Richard Harris: Yeah, exactly. If the, if you can't either see now or a path to those sort of micro unit economics making sense, which is if I spend a certain unit on sales and marketing and I don't get a unit out the bottom of that whole.
That will over time exceed that by some meaningful margin, then it's hard to imagine what is this, right? Is this a business or is it something else? Is it just a, a product or a feature that belongs inside of somewhere else where you can leverage a customer acquisition? Pretty much yeah. I totally agree. You have to see that path,
MPD: Richard, how did you start this? What's your story that got you here?
Richard Harris: Yeah, so my, big picture story I've been working in software and digital. Startups from the first one I've founded or co-founded in 1999. But this one in particular it was, so it was a really interesting confluence of things.
So in my last company completely unrelated to what black Crow does, but we had a skunkworks that was helping to solve a very different problem, but that was working on predictions. And that team started cracking some very interesting problems. Again, those sort of not widely solved problems and the biggest ones being, how do you do hyper fast, 15 millisecond machine learning?
How do you do this in such a way that the prediction is available? Like the moment it's required the moment after some new event or piece of data has. And this is something that, you may hear about predictions in the in the CRM world or in other places, but those are usually predictions based on static databases, right?
Like your CRM file, right? If someone purchases eight times, I can reasonably assume that they're high value. But being able to do that, before someone is in your CRM file, when they're just some anonymous internet user that hadn't been solved. And so that was a key unlock that said to us, oh wow.
We actually figured out how to do the data pipeline, what tools to use. And we also started figuring out, where it had been done before this was done, like by one company internally. And the way they would usually work is, and this is the way sort of enterprise machine learning works is if you're like some think of a fortune 500 company, if you're Pfizer pharmaceuticals and you decide, I need predictions inside.
You'll go out and buy Databricks, which is like the, privately held, but worth tens of billions of dollars ML infrastructure company. So you'll go buy a contract on that. That'll be, $10 million over a few years to go hire an army of data scientists and data engineers. And you'll start working in building internally on top of these sort of developer tools and data science tools, those projects, by the way, depending on who you read Gartner wall street journal, they fail somewhere between 50 and 80% of the time, but eventually these guys get there and yeah, it's insane.
It's insane, but they get there and it starts driving these sort of predictive outputs. They're not as sexy as the AI articles you read about like robots and self-driving cars and computer vision, but they are the thing that is really driving meaningful sort of economic value inside of the very largest enterprise.
And so when we took a look at that market and said, wait a second, where is so knowing we cracked a bunch of these problems that would make it possible to do real-time analysis, knowing that the market worked on in this fortune 500 model, meaning it was just by definition confined to a very small set of pretty global enterprises.
We said wait, if we built something as good as that centrally two things. One, could we create bespoke instances of it and deliver it as a service so that you didn't need to do this build a million times? And then second, if we could do that, could we deliver it and our sort of mission. For less than the cost of one data scientist annually.
And so some of those problems we were working on in the skunkworks inside of my last companies, before starting black Crow, we knew they could be solved. And so we had an incredible head start getting black core up and running because we knew they could be solved, but it also helped us clarify the mission, which is the fortune 500 is going to do things one way.
But the rest of the market this thing needs to be democratized. Everyone means that the stuff I just described to you about real-time predictions and using it all these ways, Amazon's doing that because they built their own system. But the middle of the market, think direct to consumer brand, a Shopify store with 20 or $80 million of GMV.
Why shouldn't they have access to that? Why shouldn't they be served? And that's what we set out to do at
MPD: BlackRock. That's amazing. What are some of the companies using you guys now? Because you've brought this service downstream, so it's not going to be all the IBM's in the world. Who else is touching.
Richard Harris: Meaning our customers are in the, yeah, so we work with a lot of brands that you may know brands like farmer's dog Cotopaxi daily harvest. So a lot of folks who have a really important direct relationship with their consumers, they may be selling more one-off goods, or it may be more of a subscription service.
But there are people who are very much focused on acquiring customers efficiently, and then making sure that relationship that they start is as valuable as a candidate.
MPD: Your, you mentioned a lot of very forward-thinking marketing organizations, often venture backed. Are you finding that, more traditional marketing teams or, maybe more dormant brands are grabbing onto this concept too?
Or is it just the frontier, the.
Richard Harris: I'll be honest with you. So we're, we've started with the avant-garde or the, yeah. Those folks who are a little more digital native think get this immediately. Like they know that they're living in time by the calculate LTV equation, as you mentioned, not everyone is fully digested that, especially in more legacy businesses.
But that's a function of not whether we can add value or not, but really whether who's going to have a fast sales cycle, who's open to testing and learning. And the fact is, even for those brands, I mentioned, we work with lots of other ones, like Missouri or magic spoon that, companies who are very forward-thinking.
But the interesting thing about this is even though they're, forward-thinking, we're selling machine learning to someone who ultimately doesn't give a crap about machine learning, right? What they care about is the outcomes, right? Can you deliver value? And so that's why, we made it easy, I mentioned it's a one-click install. So this three-year $10 million Databricks project has turned it into one click and then the model builds in two weeks, no work required. And then we just let our potential customers use it for a month. No obligations, no money changes hands. So everything we build delivers value in 30 days or less.
That's one of our missions as well as being able to do it for less than the cost of a data scientist. And when those two things hold it's a pretty compelling, it's a pretty compelling proposition and certainly for the avant-garde, but I think we'll be elsewhere. I shouldn't say we have some very large and medium size multi-channel retailers.
Who've been around for a long time who are seeing the value in this as well. Do you think
MPD: this tool scales to be an enterprise solution or are there things you need an enterprise that are never going to make sense and a productized SAS.
Richard Harris: Yeah. Yeah. It's so it's interesting question. We, as you well know just finished fundraise, I don't know, seven, eight weeks ago.
And it was a question that almost every investor asked and here's, what's interesting. Certainly there is a set of fortune 500 who are doing this on their own, that's why Databricks is worth however many billions of dollars. But there are many large companies who are nowhere on this front.
And the question is can we deliver value there? I'm very confident that the answer is yes. There's not that many companies who are doing real-time machine learning. They're just aren't. Are really, the only question we have about it is the go to market at this stage for our business, going to be a sustainable one.
Sales cycles are longer. There's a lot of compliance and et cetera. So we'll get there eventually. But for right now, Like in the, just thinking like middle market e-commerce companies, 20 million to a billion of GMV. There's 40,000 of them in north America, which is often a surprising number. And so we're going to focus there, learn a lot, and then we'll figure out what's our approach to enterprise value delivery.
I'm confident. This
MPD: seems like such a no-brainer. And I know the sales cycle is very short for those, for the, as you said, avant-garde the forward-thinking tech folks. Yeah. What did naysayers say? Why do people not buy this? This seems if you're moving a product online, it's a must have.
Richard Harris: So our conversion rate, once you get someone into a trial, the people who say no are very small, right? It's, single digits, low double digits of people who get through a trial, see the value and no, it happens, but it's really. It's really getting people to take that first click and to run the one month trial.
And there's a whole range of things. The biggest one is this sounds too good to be true. Which is why we develop the whole, just use it for 30 days. See for yourself. There's a lot of resourcing constraints, meaning I know it's only one click to integrate, but I don't have the team right now that has the bandwidth to make sure that this test works.
But ultimately, and we've seen so much of our business comes from, word of mouth and people on, in the marketing channels of DTC companies and the more proof points we get out there, the more sort of logos we can put on our website. I think we're starting to maybe crack through a little bit where a lot of brands are thinking, ah, I think we should try this.
And I do have this sense that I'm sitting on a goldmine of first party data that I'm not leveraging. And especially in the face. There's been so much turbulence in the iOS landscape the way, people can use their own data outside of their own environments. That's prompting people to say, okay, I need some solutions here
MPD: before this.
I know you did intent media. You also did Travelocity. You were there for a while in a senior executive role. And that's obviously a famed tech company in the first lap here. What was like the big internet? Boom. Yes. Any stories or nuance from that experience that kind of informed your view of the world?
Richard Harris: Sure. Yeah. Trouble Aussie known by Expedia, and there's been a lot of consolidation and travel. But my path there was super interesting. I was part of a one of the co-founders of a startup called site 59. Probably haven't heard of it. It was a travel technology company and we were working on the problem of this was like 99, 2000 working the problem of distressed inventory in the travel industry.
So how can you let suppliers use pricing as a demand stimulant without completely crashing their their economics, wherever, and just wait to the last minute, buy us a cheap flight. So that business, I, 59 grew like a rocket ship and was we sold it to Travelocity. This would've been 2002, 2003, and we thought, we had some amazing tech.
We thought they would integrate our technology and fire us. We were a bunch of 20 somethings in New York city and it turned into quite the opposite. It was, it turned into a minnow swallowing the whale actually. So our team ended up taking over and running Travelocity. So we went from being part of the a hundred bruising company in New York city to running this multi thousand person publicly traded entity led by literally, the CEO, my co-founder Michelle Peluso of site 59 became the CEO of Travelocity.
I ran a $2 billion piece of the business, but our CTO, our head of Europe, our general counsel. So we were just running this thing and it was a real culture shock to go from a little startup to a big global company. But it was, it was an amazing experience. It was a bit of the wild west early on in the first.com.
And it was Texas, not New York. They're based in south lake Tesco, Texas. So I spent a lot of time there. But it was an amazing experience.
MPD: How does that work? How do you go, how do you deal with being an agile, small team operator to wading through mud? How does that work?
Richard Harris: Yeah, it is it's the question, isn't it.
It's how do you bring that sort of very lane fast pivoting had. Mindset into large organizations and there's an entire, multi-billion dollar consulting industry built around doing just that. And Travelocity was big, but it was also part owned by saber holdings, which was even bigger saber being the company when you go to the airport and the gate agent type in cryptic things into a green-screen that's saber behind the scenes is inventory management and booking solutions for that all over the airline industry.
MPD: any like real best practices on yeah, we showed up and it was mud. And here's how we got a shovel and made men at work or anything you learned.
Richard Harris: Yeah. Radical prioritization building trust. And this is, I used to be a consultant actually at BCG, and that was a similar case where it's young whippersnappers showing up all bright eyes with big ideas.
And it's very easy for the BCG client or for an acquirer to say, you know what, we've seen all this before all of it before, and you guys think you're, discovering America here, but here's all the reasons why this won't work. But I think if you just radically focus, build trust with where you're trying to create change and get people used to a test and learn mindset, which is okay, I don't need to change the whole company strategy, but what small thing can, is safe to try that we can test and then iterate on.
And once that starts becoming part of the company culture, that's the path to larger change.
MPD: So Travelocity like the PayPal mafia, where there's a group of you and you're still doing deals together and it's like a secret club. Is that happening Travelocity? I feel like all the old school, big names they've got that connective tissue flooding.
Richard Harris: For sure. It is the gang of treble lawsuits and incredibly talented group. They've now spread out all over the place. I was just having trainings with Sam Gilliland, who is first the CEO of Travelocity, then the CEO of Sabre and got onto all kinds of wonderful things. So we do keep in touch.
And a lot of folks are out doing really cool stuff. That's awesome.
MPD: So you mentioned consulting I think consulting lends itself naturally to what you're doing now with black Crow. What is, how would you describe the consultant mindset or worldview at its core? There's a lot of people I work with who have come from other great industries, banking, other corporate startups and find themselves in these really agile, complex business to dynamics.
I feel like there is something about the consulting, like Jedi mind. That is super useful in those worlds. What is that?
Richard Harris: It's a good question. And I was just interviewing an ex BCG person today for, we're doing a lot of hiring at black Crow. I think it's at its foundation. You don't do anything for very long when you're a consultant, at BCG you would have a case that would last from maybe three to nine months.
And then the next thing you do might be in a different industry, a different geography, completely different set of problems. And. This is changing a little bit in consulting, but back when I was a consultant, we were all kind of generalists. Like there were no super pigeonholed people until maybe you were a partner.
And that meant that you needed to be able to super quickly get up to speed on a whole industry and a whole company and the competitive environment. And you needed to start formulating and testing hypotheses very quickly. And so that mindset is not a fortune 500 mindset, right? Like how do we kill ourselves?
So before someone kills us, and that is the, what we were just talking about. We've seen all this before being a consultant is the opposite of that. You need to go in understand, assess, develop hypotheses and iterate. And I think that's a lot more like. A startup founder is doing it's all right.
What insight can I have about this industry or something that was broken in the world and how can I pretty quickly get up to speed on the environment? Not so much that it makes me cynical or unable to think laterally or creatively. And then how can I start iterating on solutions? How can I in a very low cost low-risk way start figuring out how to solve these problems.
So maybe that's why. Yeah.
MPD: When I hear you talking about it, what I hear undertones of the scientific method. Yeah. I feel like maybe there's some bridge from the scientific method was drove the Renaissance, everything else to what the consulting and mindset is, why maybe we're seeing so many entrepreneurs from consulting backgrounds.
They're trying to rethink test hypotheses, know that they know nothing, but try to frame and make decisions.
Richard Harris: I think that's, so it is the scientific method. And yeah, the Renaissance think of how much creativity and ferment came out of that period. And I also think, there's another interesting thing, which is, the bar is pretty high, McKinsey or BCG or wherever.
Like the bar is pretty high. They hire really good people. And then the people who self-select out of that into an entrepreneurial environment, have everything we were just talking about in terms of, agility and mindset. And then they're also like, but I don't want to be just being an advisor. I want to control shit.
I want to be in charge and build a culture and all that. Yeah, exactly. So that combo I think is pretty foul, powerful. Like you've got the toolbox and you're not satisfied being an advisor. Okay.
MPD: So it's interesting. I know earlier, before today's conversation, you had mentioned that you're a machine learning guy and I'd come in thinking you're a marketing guy.
I want to talk to you about marketing. Three years ago, maybe less when someone said they were going to do digital marketing, it was synonymous with ad buys on Facebook and Google. Apple probably most significantly has made some real privacy changes. Yep. How is the marketing game evolving?
Were dollars going? Are there places where there's fertile ground for yield now? Sure. What's the new landscape.
Richard Harris: Yeah, so you're right. Like the advent of iOS 14 five, which is a little over a year ago, that was a watershed, especially for brands and companies that had grown up with basically.
Infinite cheap supply of social media, specifically Facebook and Instagram advertising that if you crack the code on that, you could build a brand, right? It was the CAC LTV equation worked. It had been under siege and there was a lot more competition and customer acquisition costs started rising through the pandemic sort of 20, 20, 20, 19 and on but then I was 14 was a real, just off the cliff moment.
And, parts of this are a good thing for the industry. Meaning the parts of Apple's moves that really are about data privacy I think are great. Most of what apple is doing is not about data privacy. It's only about secure. Privileged access to that data for apple and not anyone else. But to the degree, there are incremental improvements in privacy.
It's good. Now, what's that what that has meant for brands is a few things as regards the Facebook, the Metta sort of ecosystem it's meant that what you do spend there needs to get much more intelligent and effective over time. A big part of that, which is, w what we do with brands is leveraging first party data, because a lot of these privacy changes were meant to attack this whole sort of ecosystem that existed of third-party data and data brokers and people in the, That's following you around the internet, that sort of but where did they get that data?
And people were buying and selling and cookies and blah, blah, blah. So that thing had been under attack through privacy legislation. It continues to be, but that is that ecosystem is what really has been under attack. A brand's ability to use its own first party data really will. I think always be there, meaning the ability to understand your users, your customers, and to build relationships with them.
And so in practice, what it's meant is you've got to get much more intelligent in how you spend inside of the meta ecosystem, much more focused and predictions are a great way to do that, but then also you need to diversify a ton, right? And that's happening first with social channels. So we see almost all of our customers saying what used to be a 99% Facebook.
Is now a Facebook and tick talk and Snapchat and Pinterest. And let me look at less costly search channels, like being, and let me look at Reddit and newsletters. And so all these things that you could be a little lazy about in the past when there was so much cheap Facebook inventory, you can't be lazy about anymore.
So it's putting a lot of pressure on marketers to find ways to work more efficiently.
MPD: What opportunities does that open? Are there new technologies that you think are now necessitated that no one needed when it was just a one channel game, two channels?
Richard Harris: Yeah, I think so. For sure. I think, I was just talking to a great company this morning called replay p.ai and they use computer vision to break down.
And help you understand as a brand, what, in your videos, whether they're like video ads or your organic Tik TOK feed, which elements are working versus not working. And so I think like for me, so much of this, and I fully acknowledged my, that. I see everything through a data lens, but a lot of this is going to be about brands, getting their data house in order, meaning their first party data and their zero per date, party data, the data they own, which customers willingly handover through their interactions or filling out forms or whatever.
And it's just not going to be an option to be on autopilot. You need to take all of the understanding you have about your customers and make sense. And activate it. And so those are the big, those are the big things where I see the industry focusing and activating. It could be anywhere from like creative optimization to better real-time decisions where we play to many other pieces of the organization.
But data is the key understanding what's actually going on. Now
MPD: your solutions focused on SMBs. What are the big companies doing? And like you mentioned apple one of the reasons they put some of these roadblocks up is to leverage the data for themselves. What angles do they have with that what's going on behind the scenes?
Cause it's definitely a build as a privacy protection maneuver. What's the underlying story here.
Richard Harris: Yeah. So there's a few different things, right? So apple, when you look at what they're doing many of the prohibitions on data use that apply to everyone. Who's not, apple are different from.
The access that apple has. And so if you think about, there's only a few companies like Facebook and Google and apple is probably a few other a few others that your digital life, which is becoming an increasingly large portion of your total life, your digital life is happening through them.
When I interact with the internet, it's almost always through Chrome, from on a desktop, I happen to be an Android guy, so another Google touchpoint, but for many people, it's through an apple device. And that means that you can't get into your digital life without passing through them.
And so apple sees all of this and they've stopped in some use cases for some data letting Anyone outside of that apple wall use all the data that they see, but they don't restrict themselves from using that data. And so for example, apple has a pretty giant ad business. They haven't ramped that at the moment to be as large as it could be, but they don't place the same restrictions on themselves that they're doing.
MPD: So there's a story here where apple is the next channel for your acquisition, your customer,
Richard Harris: a hundred percent. They will be
MPD: where those ads going to show up. What medium are they going to start putting ads in? Or is it more integrated? This is the app we recommend.
Richard Harris: Yeah. It's it's so many things like the app store is I think it's largely going to be about mobile.
It's largely going to be in the app. And the ways in which apple facilitates monetization inside of applications, that would be my bet. There are other, there are other contact points that apple has obviously like apple TV, they're creating their own content. They're developing cars and VR glasses.
And so there's going to be a lot of ways where it deploys where it deploys this understanding of apple users, some of which we'll be advertising, some of which will be all the various ways you can make money. Once you understand the intimate data patterns of the human.
MPD: So flipping over to the other side of your brain, the machine learning side, what does that industry need?
You're knee deep. Didn't you made the point that kind of real time aspects to machine learning is fairly new and certainly not widespread. Where is that technology going? What if, what should people be building to help, garner the movement, bolster the moon?
Richard Harris: Yeah. So there's there's two big themes that we operate on and that I think are broadly true.
And the first is democratization, right? So if you think about the various the waves of technological innovation that have happened, say over the last thousand years, or 2000 years, most technology tends to. Create differences between the rewards to capital versus the rewards to labor, meaning a farm implement or a tractor, right?
All of a sudden, if you invest in a tractor, the returns that you can get in agricultural output versus, a farmer, they start getting really, an individual farmer with a hole, they get incrementally out of whack. And I think AI is one of those tools where the returns are going to be so incredible.
They already are. And so we just have a belief that it's not a great thing for the economy or the world. If all of that value gets concentrated among a very small set of enterprises. Meaning if Google owns AI or apple owns AI or Facebook or whoever it is that won't be a great thing. And or Amazon.
A big focus of what we're doing here is democratizing AI, right? Making machine learning, accessible to people and companies where it wouldn't normally be accessible. And so that's what, as much as we're innovating, technically we're also democratizing technically, which is, we want to make sure this just doesn't all sit in a handful of global players.
So that's one. Oh yeah. Did you ever
Richard Harris: Great. Awesome. Okay. The other one, and this is a little more abstract, but it's what we're building for as a company. Which is, if you think about what we're doing today, right? We are, we work with commerce company and we're predicting the future value of all their users, 15 milliseconds, right?
That is the key source of data is that brand's website, it's the browser, right? It's the browser. And. So today browsers browser data app data is a perfectly good analogy that is this source of streaming real time data, but as sort of thing, where there's tons of value to be generated, but it feels like the world is becoming a browser.
And I don't just mean oh, we're all gonna live in the metaverse, but everything is starting to kick off real time data in the same way that our browser does and has for, 15, 20 years. Now if you think about the internet of things and wearables and RFID tags, sensors, and video monitors, like everything is becoming a source of real time digitized data.
And the thing about data is it's really cool to have it, but it needs to be made sense of, and that's all machine learning is it's making sense of vast quantities of data. So when we think about. What is black Crow in seven or 10 years? Cause that's our goal. We want to build a bit like, sustainable company.
It's really if the world's becoming a browser, it's going to generate all this data. The data needs to be made sense of. We want to be that company. That's making sense of all this streaming real time data.
MPD: I love that. You've got a lot of optimism especially going into this market cycle. And you've been through three of these downturns.
I'm not mistaken. You've been in the entrepreneur game for awhile. Any advice for founders that are right in their first wave here on how to navigate this moment in time?
Richard Harris: Yeah, I mean it's hard and I've had the luck or unluck I don't know what it is to be in the middle of founding a business during, the first.com September 11th crash and the global financial crisis.
COVID plus now the current financial market correction, hopefully. And yet there, there's one like super practical thing and one like intestinal thing, and one is understand that these are cycles. And if it's your first time in the game, and you've only been in the 10 year bull market that we've been experiencing, this is the normal part not the fact of a downturn, but the normal part is that shit changes on a dime, right?
Like what bullions turns to dread very quickly and fear, can rapidly follow greed. And so that's the number one thing is expect this cycle. And if you were lucky enough to launch a business in 2013 and exited in 2019. Great. But you haven't seen the whole story. And the second is be pretty opportunistic about capital.
So everyone is saying this right now, but if you, if your existing investors want to pony up a little bit more, so you have a bit of cushion so that you're not forced to make existential choices too early, I think that's great. And then the second is, and having been through this I've been in places where we had to make cuts and super difficult choices.
And I'd say, there's never a scenario I've been in through a downturn where anyone at any point for any, I know lots of different founders when anyone says we cut too much and that may sound brutal from a human perspective. And it is no one likes doing this stuff. It's always surprising when I've been or watched colleagues in those situations where they had to make cuts.
No one's ever regretted cutting too much. Cause it drives a certain hedgehog scrappy mindset. That's good.
MPD: How do you stay sane through all of this mentally? I know you've done work to find inner peace and Zen and all the other things that go out there. And how do you level out through the rollercoaster?
Richard Harris: Yeah I don't, as you can probably guess I'm a super hyper rational person with one asterisk, which is that. And I want to get all woo here, but yeah I meditate. I actually lived in a Buddhist monastery for a few months in Northern India, actually in Bodh Gaya, the town where the Buddha reached enlightenment.
And so what's super crazy about. Eh, forget whether Buddhism is your cup of tea or not. But I think everyone has, at some point encountered the wisdom of you need to appreciate, and now, the power of now living in the moment, being present in the present that sort of like folk advice at this point.
And it's actually true, right? There's something incredibly calming and focusing about being present in the present moment. And yet, if you're an entrepreneur, particularly a tech entrepreneur, you do not live in the present, you buy definitions of in the future, right? What have I been talking about?
The world's becoming a browser, like you're thinking about the future. You're thinking about what your P and L is going to look like in six months, next quarter, you're thinking about where the capital's going to come from next. Like you literally plunged yourself into the future as a career. And so those two things are super unaligned, misaligned I can't, I don't want to offer any particular advice except that I do know meditation and trying to be present even for relatively brief period periods of time helps it grounds you.
It's it's sorta like looking at the ocean, it adds perspective to everything. And for even just a few minutes, it takes you out of the worries of the future.
MPD: I think you really nailed the entrepreneur mindset pretty well. We're frantically running through for something that's like this never ending treadmill because we're always building something that is not now.
Yeah. Been a personal journey for me to be very present. Any particular. I'm not the guy getting interviewed here. Let's be honest. No, I'm kidding. No, I do. I do meditation is. I also try to integrate, I meditate a little bit through exercising different programs, but I try to do both of those for those who have an Oculus at home, I think the trip app, if you're not really a natural meditator is a great hack.
Eyes are open. Gets you in the mood, calms you down and brings you to the now and present. So I'd recommend that it's T R I P I think it's venture backed, which makes me want to support it more. Any tactical things you do is there a frequency, your meditation or any bigger
Richard Harris: It's as often as possible. I I there's an app that I use called waking up it's Sam Harris's. And there's an initial, I think 50 day class where it's just if you can commit to 10 minutes a day I think it's fantastic. Even for someone who had been doing this for awhile not consistently by any stretch, but trying to get back into it with consistency.
I thought that one was great. I've used that too.
MPD: That's a great app. Highly recommend.
Richard Harris: Yeah. Very
MPD: cool. Richard, thank you for being on today. Thanks for making time.
Richard Harris: It was my pleasure. I enjoyed the conversation
MPD: I loved about this conversation with Richard was talking about the data side of the world. You get a different lens of all the big business. We're all consuming in the news. I think about how data is being used. You're thinking differently about market trends, business decisions, and otherwise he had some really interesting topics and I hope it will.
Hopefully it was interesting to you as well. If you liked what you heard, please look us up with a like, or a five-star review and feel free to share with a friend. You can find me on Twitter at MPD. And to hear more of my conversations with innovators, subscribe on YouTube, Facebook, or any major podcast platform.
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**Please note, Interplay is an investor in Black Crow.