How AI Can Unlock the Value of Your Data, with Richard Harris

As Ecommerce companies, we have so much information – sometimes too much information. What can we do with all of this data from our customers to maximize their experience while maintaining their privacy?

Well today we’re talking about the challenges that Ecommerce companies face when implementing AI, and how AI can unlock the value of your data to increase profitability, lifetime customer value, and return on ad spend.

About Our Guest

Richard Harris is the Founder & CEO of Black Crow AI, a no-code, real-time machine-learning-based predictive software that helps companies understand likely customer behavior.

Richard is a veteran entrepreneur and has been involved in tech since the 90s. He cut his teeth in the world of consulting and was involved with a $10bn brand, Travelocity, during the dot com boom. Then, he has continued to be a serial founder with Black Crow AI being his 3rd VC-funded startup.

This episode of Deal Closers is hosted by Izach Porter, brought to you by WebsiteClosers.com, and is produced by Earfluence.

 

Transcript

Richard Harris – 00:00:00:

 

Business as usual in e-commerce is just unfortunately not going to cut it anymore. And the answer is data. And making sense of this huge asset that you’re probably under leveraging and may not even be aware of. Data is the new oil, and data is about people.

 

Izach Porter – 00:00:32:

 

All right, you’re listening to the Deal Closers Podcast brought to you by websiteclosers.com, a show about how to build your e-commerce business to be profitable, scalable, and one day even sellable. As e-commerce companies, we have access to so much information, sometimes maybe too much information, and the question becomes, what can we do with all of that information to maximize the experience for our customers, to maximize and optimize the conversion rate for our sites while maintaining the privacy of the information that is shared with us? Well, today we’re talking about the challenges that e-commerce companies face when implementing artificial intelligence and how AI can unlock the value of data in your organization and increase profitability, lifetime customer value and return on ad spend. Our guest today is Richard Harris. He is a tech veteran. He’s the Founder and CEO of Black Crow AI. Hey, Richard, how are you doing, man? Good to see you on the show.

 

Richard – 00:01:41:

 

I’m doing very well. It’s great to be here with you.

 

Izach – 00:01:43:

 

Yeah, thanks for being here. So I want to get into Black Crow, super interested in what you’re doing with data and artificial intelligence and how it will benefit a lot of our listeners. But this is a show about building your e-commerce company to get ready to sell it. I know I mentioned you’re a tech veteran. You’ve started, founded several VC backed technology businesses and then sold them. How is that process for you? And when you started those companies, did you always intend to sell them or was that something that you kind of figured out as you grew the businesses?

 

Richard – 00:02:21:

 

Yeah, I might be a rare well, I’m probably not a rare entrepreneur, but I’ll tell you that 100% from the moment I Founded or Co-Founded these companies, I knew I wanted to sell them. And you want to hit the right inflection point, meaning you don’t want to sell too early. You also don’t want to sell when your story is sort of stale in the market. But in general, selling them is the goal. And particularly once you take on venture capital money, which I’m sure a lot of your listeners will know. If they’re sort of a brand or an e-commerce company that has some outside investors, their goal, in 90 plus percent of the cases is not to be earning a revenue stream from your EBITDA over the next 20 years of your family run business, It is to exit. And the real payout is going to come from selling equity right, to a company buyer.

 

Izach – 00:03:18:

 

Yeah, absolutely. So how many exits have you had?

 

Richard – 00:03:21:

 

So I’ve had, I guess like two and a bit. So the very first company I was a Co-Founder of was in the Travel commerce space. And that was the very first company I had had a relatively traditional career as a management consultant and then left with a bunch I was at the Boston Consulting Group and left with a bunch of my colleagues from BCG, my office made at BCG the woman in the office beside us. And we started a company together. And that absolutely spoiled me because it was in the first sort of .com wave, heyday, we grew the thing. But it was an actual business. It wasn’t like we had Super Bowl ads with hand puppets and then managed to sell people on hype. It was a real business that grew to over 100 million in revenue in less than three years. We sold it to our biggest customer at the time, which was Travelocity, which is now owned by Expedia. But it was a fascinating journey. Happy to get into it in as much detail as you’d like. 

 

Izach – 00:04:31:

 

Man, I think we’re going to have to have you back on the shot. Would absolutely love to hear about the details of that and kind of how it came to be in the structure. Okay, so one other question on the acquisitions, because the most common question I get from my clients, and one of the things that we really help to put some strategy behind is when is the right time to sell? And our guidance is typically to sell on the way up while the company is still in a strong growth mode because that’s where we see the best multiples. But how did you know as a founder, how did you know when it was the right time to sell? Or did you?

 

Richard – 00:05:06:

 

I mean, you never totally know. But I think for me it’s have you proven your core sort of value proposition sufficiently that an acquirer can dream big, and so there’s almost a fork in the road. And as you said, yeah, you get big multiples based on growth. The other path is more of, if you’re EBITDA positive relatively consistently. There are brands that get sold to private equity firms. They tend to be lower multiples, but bigger overall numbers, just given the sort of scale and profitability of those companies. But I think there’s a couple of factors when someone sees a synergy with what you do. So beyond just the independent metrics that your business is generating, whether it be revenue, profit, growth, the big multiples start to happen. When a strategic acquirer says, wow, I see one plus one equals three. And meaning you’ve got something, whether it’s a product or a capability or a unique angle on the market or community that they feel like they can’t do without. And as soon as you start hearing more than one strategic acquirer talking about what they might do when you’re together, then you kind of know it’s probably a good time because I’m sure all your listeners know this, but the difference between having one party interested in buying your company and two is night and day.

 

Izach – 00:06:51:

 

That’s a fantastic point.

 

Richard – 00:06:53:

 

Yes. If you have one, things will be done to you. Right. And ultimately, you’ll need to be making a decision about, am I willing to set to live with this one price that one party has set? Or am I willing to gamble and walk away? When you have two, then they are both. Again, if that strategic synergy story that they’re telling themselves is powerful, then they both need to decide, this isn’t about you having things done to you. They both need to decide, can I live without this? And even sometimes, more importantly, can I live without this? And knowing my competitor is going to have this? And that’s where you want to start seeing things climb.

 

Izach – 00:07:37:

 

That’s a great point. We’re e-commerce and technology business brokers, right? So one of the things I talk about a lot is that we create competition in our process. We bring in multiple buyers. We’re looking specifically for strategic buyers. And where we see the best deals getting done for the sellers is when we’ve got multiple LOIs that we’re negotiating exactly what you’re saying. We just sold a company, and I did seven buyer-seller calls. We got six LOIs, and we went through four rounds of LOI revisions and ended up significantly over our asking price because of that competition. And it empowers the seller or the founder so much to be able to dictate the terms of the deal, because it’s not just the total enterprise value that matters, it’s the structure.

 

Richard – 00:08:27:

 

That’s right.

 

Izach – 00:08:28:

 

How much cash you get in closing, what are the triggers for an earn out? All those things.

 

Richard – 00:08:32:

 

That’s right. Can I just add one more thing? Because your point is so good for founders out there who are starting businesses or thinking about raising venture capital, the exact same process is at play, and the exact same power dynamics are at play. Meaning when you’re raising investment money, you have to realize that you are selling equity. It’s just like the stock market. It’s just like exiting your business. And you need to create a market for your equity. And that means not one bidder, right? One bidder. Things done to you, two bidders sky’s the limit. And when you’re raising venture capital, the same thing is true. No matter how perfect a single investor feels, you need to have more than that one investor at the table. Multiple people that want to buy your equity as an investment in order to get the best terms. And sometimes it’s about the amount of money, sometimes it’s about the amount of dilution, but often it can be around structure because there are so many ways to structure investments. So exactly as you said, I think you nailed it.

 

Izach – 00:09:40:

 

Yeah, great points. Really interesting discussion. So, Black Crow, how did you get started with the Black Crow? Where did the idea come from, what was kind of the journey that got this seed planted and got you involved?

 

Richard – 00:09:54:

 

Yeah. So this had been something that was sort of germinating for quite a while. In fact, at my last company, we had a skunkworks inside of that company that began for completely different market, completely different use case, began working on some sort of AI machine learning applications. And in that skunkworks, we cracked a few very difficult machine learning problems, particularly around real time predictions. And there that’s about the ability to ingest massive amounts of streaming data in real time, literally as it’s happening, and then hyper, hyper fast, be able to deliver a prediction and we can talk about predictions and everything later. So the idea sort of germinated there where we were working for very large publicly traded fortune 500 kind of companies. And as we were cracking these problems and seeing how much value we could deliver, we sort of asked ourselves the question of like, okay, this makes sense, this mode of doing things makes sense if you’re a Fortune 500 company or a global 2000 company. But what about everyone else, right? What about the middle of the market? And so as soon as we started asking ourselves that question, then we realized, oh, this is actually a business in its own right that we should take to market. Do you want me to get into a little more detail on that?

 

Izach – 00:11:19:

 

Yeah, love it.

 

Richard – 00:11:23:

 

First of all, when I talk about AI machine learning, there’s a few different pieces or formats that AI can take. I think everyone’s very familiar with generative AI and the sort of images and text that ChatGPT and Dolly are generating. There’s another sort of older, and by old, I mean like last summer. 

 

Izach – 00:11:45:

 

Ancient. In the in the ancient world.

 

Richard – 00:11:47:

 

Ancient, good old fashioned AI.

 

Izach – 00:11:49:

 

That’s why I love AI so much right now. I feel like we’re at this cutting edge cusp of so many cool things going on, so many new companies coming out and limitless possibilities for how to apply this new technology space that it’s blue ocean in a lot of use cases. That’s really exciting to me.

 

Richard – 00:12:08:

 

Yeah, that is so true. And just the pace at which the market changes capabilities change, the stuff that we do for Shopify stores, they’re a good chunk of our customers today. The stuff that we’re doing with a one click integration is stuff that would have cost tens of millions of dollars and that only a handful of companies like Amazon or Facebook or Google were doing largely for themselves. And we’re able to deliver it to, say, Shopify stores in one click for far less than the cost of a single data scientist. And so, in fact, that’s our big mission is sort of bringing fortune 500 grade machine learning to companies of any size, particularly.

 

Izach – 00:12:55:

 

To the Fortune 500,000. Right. I think that’s on your website.

 

Richard – 00:12:58:

 

That’s exactly right.

 

Izach – 00:12:59:

 

Bringing the fortune 500 to the Fortune 500,000. I saw that and I immediately got what you were doing.

 

Richard – 00:13:05:

 

Yeah.

 

Izach – 00:13:05:

 

And it’s a cool tagline.

 

Richard – 00:13:07:

 

Yeah. Thank you. So, just getting back to AI, so aside from generative, predictive is one of the biggest areas of artificial intelligence, meaning how do you make sense of the massive amount of data that companies generate? And in many, many cases, the volume of data is so extreme, even if you’re a relatively small company, the volume of data is so extreme that just humans can’t make sense of it. And it’s a classic case of if you bring machines to work, particularly intelligent machines, all of a sudden there’s this insight available to you. And it’s often in the form of predictions. And really when I say that, what I mean is if you can see into the future of the key drivers of your business, the key KPIs that you monitor to understand what sort of business outcomes you’re going to have, if you could know those in advance, rather than only retrospectively, like, how are my sales yesterday? Well, how many people converted yesterday? If you could know that in advance, you can now then take action now to affect the outcome of those key KPIs. And so, just to give you a very concrete example, one of the things that we do today for customers is we predict in real time the future value of every user that visits their e-commerce site. And so what that means is with a one click install, you download a Shopify app, the machine will train itself on all of the streaming data that you’re probably not using today that you may not even realize is a natural.

 

Izach – 00:14:43:

 

What are some of those data sources that are out there available to a Shopify store owner that maybe I don’t know about?

 

Richard – 00:14:50:

 

Yeah, we focus on user data, meaning the customer is at the center of most of these businesses. And so if you ask a brand, what is the king metric? It’s probably gonna be something like CAC, what’s my customer acquisition cost? And LTV. Right. So I have a product at the center, and my business is about getting people in front of that product for a value that makes it worth having done. So what we’re doing is with that one click, we’re predicting essentially what that LTV is for the most part, to start, we start with, is this user going to buy? But to answer your question on, like, what are the data sources? Because we solve that tough technical problem in AI, meaning streaming AI, we’re able to take all of the event data that’s kicked off by a user interacting with the store, interacting with the brand. So how they got to the brand, what their path through the purchase funnel is, how deep are they scrolling? Are they looking at images, lingering on images? Are they coming back, refining their search, looking at the same products, different products, how that evolves over time. So all this very rich in session data is what we process in real time. And it turns out there’s plenty of data, really, almost no matter how big or small you are, there’s plenty of data to make very accurate predictions. And so what we’ll push back is this particular user, for example, Isaac is shopping on my store. Ten milliseconds after Isaac does anything, click, scroll, whatever it is, we’ll push back a prediction that says, here’s how likely Isaac is to buy over the next 2 seconds to two months. And we just push that back to you and then flow it into whatever activation platform, software tool. It would be valuable to have it. Let me stop there. Does that make sense? We were able to tell the brand, hey, listen, Isaac at this moment in time, he’s part of, say, your top decile of users, and that’s a population that will reliably buy 70% of the time. Or Isaac is part of the lowest decile and they’re going to buy 0.2% of the time. And that’s just empirically verifiable retroactively, but we’re telling you far in advance. Right?

 

Izach – 00:17:20:

 

Okay. And so in order to make that prediction about my value on that site, does it assume that I’ve been to the site previously and transacted and then do you have data about me before I get to the site from other sources? Or how do you I guess in Layman’s terms, how can you where does the magic happen? How does the magic happen, Richard?

 

Richard – 00:17:45:

 

Yeah, no, it’s a question I get all the time. So just to briefly answer, so it does not require you to have visited the site before. So from the moment an anonymous user arrives from that very first pixel interaction with the user, we deliver a prediction and we are first party and zero party data. Fundamentalists meaning the answer is no. We don’t track users elsewhere. We don’t bring information about your behavior on a competitor brand or another brand or on the internet more widely to bear on our prediction. That’s because brands have this untapped gold, which is their own first party data, the events, how users behave in real time as they’re interacting with their website, that if you can make sense of it fast enough, you don’t need anything else. I don’t need to know your demographics. I don’t need to know the last time you shopped.

 

Izach – 00:18:44:

 

If I click on the page and I immediately go to the sale icon or the shop icon and you know how other people who have done the same activities have transacted, then you can apply that to the outcome of my activities on your store page.

 

Richard – 00:19:01:

 

That’s exactly right. That’s exactly right.

 

Izach – 00:19:03:

 

That is awesome. 

 

Richard – 00:19:04:

 

Yeah, it’s really cool. What the machine does is. 

 

Izach – 00:19:07:

 

How right are you? Do you have a confidence interval around the accuracy of the predictions?

 

Richard – 00:19:15:

 

Yeah. So we measure something called AUC, which is like area under the curve, and at 80 ish percent, the machine will self publish its model. And that is, if you’re doing brain surgery, you need to be 99.9. If I can say with 80% accuracy that this is what’s going to happen to this individual user, that can enable brands to make much more sophisticated decisions than they normally would.

 

Izach – 00:19:44:

 

Absolutely. If I know the potential value of somebody with 80% accuracy, I can push promos to them. I can spend more money on an initial discount to get them to transact. 

 

Richard – 00:19:55:

 

That’s, right. 

 

Izach – 00:19:56:

 

I could see a bunch of stuff.  You could push out referral program information to them.

 

Richard – 00:20:00:

 

Yeah. You’re zeroing in on all of the use cases and applications that we have. Some are out of the market, some are in the works, but we always ask the question. Actually, if you ask a brand like, what’s your conversion rate? Right. They’ll usually have an answer for you. It’s 1%, it’s 6%, whatever it is. And the big insight once you start working with Black Crow and it’s very easy to do as I mentioned, it’s one click, and you can just use it for 30 days if you see the value in it. But what you realize is that 3% conversion rate or whatever yours is, doesn’t actually exist. If you just think of your website traffic in ten deciles, right? You’ll have a decile, your top decile with a conversion rate of 70%, and you’ll have another decile with a conversion rate of 0.02%. Now, normally you don’t know who’s who. What we’re enabling is for people to know who’s who well in advance, so they can take all sorts of actions. If you ask people, wow, now I know Zach is a 0% converter versus a 70% converter. If you ask them, okay, with that knowledge in advance, what would you want to do differently? The smart brands will tell you everything, right? I want to market differently. I want to spend in Facebook and Instagram and wherever else. I want my email and SMS campaigns to be different. I want my offers and discounts and promotions to be different. If there’s a call center element to my sales process, I want to send some of those people to my best agents, onshore others. I want to bury that 800 number, I don’t want them to call and waste my agent’s time. So there are so many things you can do differently in advance with this intelligence.

 

Izach – 00:21:45:

 

So let’s talk about some more of the use cases, because I think that is really fascinating. How are you seeing your customers using this data in different ways? We’ve talked a little bit of a high level about a few, but what are some of the other things that brands using Black Crow are doing and what are kind of like best practices that are out there and then new things that you think are just cool?

 

Richard – 00:22:07:

 

Yeah. So I’d say the key use cases right now are in the customer acquisition phase of things and we’re increasingly getting into the LTV side. If you think of brands as CAC LTV engines, there’s just been so much pain in the e-commerce market because of iOS 14 and now the new ITP changes that making sense of your marketing activities and how much they generate and just generally losing performance in the channels. That sort of got a lot of brands there, meaning Facebook, Instagram, et cetera. That’s been just a massive pain point, and in fact, like an existential pain point for many folks. So we’re pretty focused on making sure that the customer acquisition side of the house gets more efficient very fast. And there we have brands using our prediction, so we just flow them right into Facebook and TikTok, Instagram, Google. Wherever you’re spending money, you can now spend it more effectively because you know the future value of the user.

 

Izach – 00:23:11:

 

Yeah, so I just want to dig in a little bit more on the customer acquisition piece. So I’ve got this data from Black Crow that says that now I know who my top decile customers are likely to be in terms of their willingness to transact and convert and drive LTV. So how do I then go and target those exact people on Facebook? Or am I targeting like a lookalike audience of those people?

 

Richard – 00:23:35:

 

You can do that. So you can target these exact people and you can target lookalike audiences. It might make sense here to just I just want to talk about one other fundamental problem that we solve with our sort of data infrastructure. You may know, especially everyone’s aware of iOS 14 plus and the impacts that that’s had, but also Safari’s ITP, meaning their reduction in their sort of enforced timeline reduction in cookie expiration has impacted brands fundamental ability to recognize their own users. Meaning if someone has been to your site before, maybe they’ve purchased, maybe they’ve signed up for your SMS alerts or your email newsletter, the fact that. 

 

Izach – 00:24:23:

 

Tell me more about that because I’m super aware of iOS 14, 15, not aware of the cookie expiration dates.

 

Richard – 00:24:32:

 

Yeah, and this is an area where Apple has just been just been hammering and there’s been releases even over the last, I think, week that further impact brands ability to recognize their users. And essentially, I think everyone knows that cookies have historically been the thing that brands or really any domain owner uses to identify their users. And what Apple is doing is just consistently pushing back the amount of time that cookies last. So first attacking third party cookies, which I think largely everyone sort of thought was a great idea, then attacking first party cookies set by third parties. And that’s where brands like our companies, service providers like Clavio and others have seen some impact, but it’s really sort of reducing to somewhere between 24 hours and seven days the amount of time that cookies last. And what Apple? Apple is doing two things right. One is they’re saying that we don’t want identity, user recognition, to really be owned by anyone except the brand. And they don’t say this part Apple, right? And so they’re trying to just set fire to the entire other field of people who understand who Internet users are, right? Like Facebook, like Google and everyone else.

 

Izach – 00:25:58:

 

So what’s the upshot for Apple there? Is there a nefarious business plan that comes in and monetizes this data for their own benefit?

 

Richard – 00:26:06:

 

Yeah. Now, here, you may think I’m a black helicopters, tinfoil hack kind of guy, but listen, under the guise of privacy, what Apple is doing is really trying to put up walls around their own users, right? People who use their devices, people who use their apps, people who use Safari, and they like Google and like Facebook, used to have a very privileged access to identity. So this is all about identity, right? This is the world of data. Data is the new oil. And data is about people.

 

Izach – 00:26:42:

 

Data is the new oil. I love that. 

 

Richard – 00:26:44:

 

Exactly. But that data for the most part is data about people, right? And so in order for that data to have value, you need to know who it’s attached to. And there’s a few people on the planet that have a really unique understanding of who is who. Now, one of those is Apple, and they’ve developed their own sort of ecosystem around it. Now, if you imagine, okay, I know Richard has a Mac. I’m talking on it right now. So I kind of know who Richard is. I know his email address, his phone number, probably a lot of other things that I accepted in their terms of service without reading. And so they have this really privileged access now to the degree they can put walls up around that. And so no matter who it is, if I want access to Richard, if I want data about Richard, if I want to be relevant in Richard’s lives, if they have to go through Apple in order to do that, that’s a pretty powerful position to be the sort of gatekeeper and probably eventually toll taker in order for you, no matter who you are, to access richard. And so that is a very, very powerful sort of wall or moat, whatever you want to call it, that they are erecting. And the thing they’ve said, but they are not adhering to, is, listen, the thing we believe we want to just get rid of tracking and blah, blah, blah. The thing we do believe is that brands who have relationships with users, that is sort of a sacred thing. We never want to get in the way of that. And yet the stuff they’re doing is getting in the way of that, right? And so you hear a lot of brands saying, I don’t know who my users are. This person who signed up for my email, I can’t recognize them when they come back. And if they add something to their cart, normally I would want to send an email to them because they’re on my list and they signed up and they gave me their SMS number or whatever. But now I don’t even know who’s who. Anyway, there’s a long way of saying one of the elements of our platform is solving that problem. And actually, even though I don’t like why Apple is doing it, we do this in the way that Apple wants. So it’s a piece of infrastructure that we deliver to brands that let them, at the domain level, set their own sort of lifetime ID. So it’s an Identifier that lasts in perpetuity. And so now you know who’s who all the time, forever. And so when you have a legitimate relationship with a customer, you can put whatever tools you have to work to foster and grow that relationship.

 

Izach – 00:29:16:

 

So how do you do that? How does that work?

 

Richard – 00:29:20:

 

Yeah, so it’s a piece of infrastructure that let brands themselves set their own Identifier. So it’s the equivalent of a permanent first party cookie. But because it’s actually set by the brand on the server side versus the client side, I e in the browser, it’s set on the server side. It’s a piece of their domain infrastructure. And that’s the way Apple wants it done. We’ve just made it very, very easy for people to do it the way Apple wants to do. And technically, there’s some complexity there, which we’ve cracked. And so now you can just, as a brand, do this very easily. You don’t need to worry about it. And now those Identifiers last in perpetuity. Now that has a bunch of benefits, right? If you think about the use case of, okay, now I know who my customers are when they visit, and I also know what their future value is, that makes a lot of things much more possible. Right? So I can not only email Isaac when he left something in the cart, I’ll know that Isaac is X percent likely to buy. And so that may influence if there’s a promotion I’m putting out to him or not because he’s going to buy anyway. And so all of those use cases become available when you have that combination of user recognition, permanent Identifiers, plus the predictive power to understand their future value.

 

Izach – 00:30:44:

 

When you think about kind of the competitive landscape within kind of data enabled AI companies out there, is anybody doing anything that’s similar to what Black Crow is doing? And if so, what do you think, you guys do better? 

 

Richard – 00:31:01:

 

It’s a weird thing to say, but we really don’t have like we’re never in a competitive process. We work with hundreds of e-commerce brands. We’re never in a competitive process. There are certainly other people who help you make sense of your data. Not using machine learning, but there’s great companies out there. Like, if I think. Of people who gather zero party data through surveys who actually ask you your opinions about things like, yes, they’re generating zero party data. There are companies like Udacity and Triple Whale who are helping you sort of understand the impacts of things that you do, but in terms of really getting in there to help you recognize your users and understand their value in a way that can just be activated super fast, cheap and easy, there’s no one else really doing that. And again, we do have like a pretty great head start on the technical side in bringing the same stuff, the same AI that Amazon uses for itself. We can now empower a $10 million Shopify T-shirt store to have that same power in their back pocket, and we just make it cheap and fast and easy.

 

Izach – 00:32:20:

 

Okay, what are the outcomes? Like, how are your customers measuring the return on their investment into your platform? What are kind of the outcomes that you’re driving?

 

Richard – 00:32:33:

 

Yeah, listen, ultimately it’s about revenue and profit. And so that’s why we do I mentioned earlier. Once you sort of integrate with us, it’s very fast, but we sort of give you 30 days to just use it, and we walk you through how to use it, how to set it up properly, et cetera. But that puts pressure on us, because we need to be able and our product is built to be able to show value to the commerce company in less than 30 days. And usually it’s really only 7, 14, maybe 21 to demonstrate that. Because if we can’t show that value extremely fast, people won’t pay to continue their subscription. And that’s obviously the right thing to do, and we want them to. If there’s no value there, please don’t subscribe because you’ll just end up churning lighter. So we want to be able to show that value fast. And so in user recognition, that piece of infrastructure I was talking about, the value that shows up in email and SMS, it’s just more revenue because you’re able to send more communications to the folks who have opted into communications to remind them of things, to basically do everything you would normally do in your communication flows to get them to buy. And so you’re just able to do that with more people. On the advertising side, like, say, in Facebook, for example, when you understand who your users are, you feed much better data to the Facebook algorithm, right? So they now have better data to train their bid algorithms on. If you use Advantage Plus and also able to attribute bookings much better on the in platform data or whether if you use an attribution solution. So that is just like an immediate benefit. Then when you layer predictions on top of it, for example, you’re able to only spend money on high future value users. And for the people who are never going to buy, which you now know you probably want to stop spending, certainly marketing dollars, paid marketing dollars on them, maybe you still want to email them with amazing discounts to see if you can get them over the hump because it’s relatively low risk. But you can sort of de average the actions that you take once you de average the customers that you’re talking to.

 

Izach – 00:34:52:

 

That’s really interesting.

 

Richard – 00:34:54:

 

Yeah.

 

Izach – 00:34:54:

 

So you’re focusing all of your efforts and money on the people who are most likely to transact.

 

Richard – 00:35:03:

 

Exactly. Right. If you know the future value and you’ve got $1 to spend, spend it on the people who have transaction value there right. Versus those who are just like, not serious, never going to be serious customers of yours. 

 

Izach – 00:35:19:

 

Okay. And so what are some success stories? Do you see row ads, doubling? How do you kind of measure that success? Or what are your customers telling you that’s working really well in some kind of metrics?

 

Richard – 00:35:34:

 

Sure. So in email, we see a sort of 30% to 50% increase in revenue from the email flows that you’re already using today. And obviously, if you’re not using all the best email flows, we’ll help you out and get those set up for you. But yeah, now you’re just able to recognize your users and send more. 

 

Izach – 00:35:53:

 

30% to 50% increase in revenue on email marketing campaigns.

 

Richard – 00:35:58:

 

That’s right.

 

Izach – 00:35:59:

 

That’s huge.

 

Richard – 00:36:00:

 

It’s huge. And again, you’ll see the data if you’re a Clavio, if you’re a Clavio user, you see all this data in the Clavio platform. So it’s sort of pretty uncontroversial new value generation. And obviously we’re a subscription software business, and that value you see in your Clavio dashboard will outstrip the cost of our software by many times. In Facebook, for example, on retargeting campaigns where, you know, oh, now Black Crow is telling me this is a high value segment, this is a low value segment. Let me prioritize my spend accordingly. You can see sort of 20% to 40% increases in ROAS there. Wow. And in prospecting, there’s a little more variability, to be honest, in prospecting, because it’s not actually value. We’re not predicting the value of the people you end up targeting. We’re predicting what the value is. And then Facebook does its sort of hocus pocus to say, where are some other people like this? But when it is effective in, say, Facebook, there’s another sort of 20% to 30% audience size expansion at the same sort of CPA. 

 

Izach – 00:37:17:

 

Okay. And is there a similar application with Google AdWords or TikTok or other platforms that can leverage advertising data?

 

Richard – 00:37:30:

 

Absolutely, yeah. So when we predict the future value user, we will push that where you want. So we’ll push it into Google, Facebook, Instagram, TikTok, Pinterest, really wherever you are spending dollars. Because I should have mentioned this. So I mentioned that we’re like first party data fundamentalists. But these predictions, we’re really a machine learning data processor. So we use your first party data, we process it, we create these predictions and push them back to you and you own them. Right. These are not our property. We’re not doing any networking kind of learnings or anything like that. So these predictions are an asset that you own as a company. We’ve made some oil out of data for you and now you can deploy them wherever you want. We have sort of built out playbooks for a bunch of use cases that we walk you through, and we have a success team that helps you get through those. But these predictions are your own data.

 

Izach – 00:38:36:

 

Very cool. Yeah, I think the thing that always interests me when I learn about new technology and new companies is from the MNA perspective. How can a business owner apply this technology to optimize their business, to drive revenue, cash flow, the things that are going to ultimately translate to a higher enterprise value, a higher purchase price when they sell the company? How can they do that and then can that solution be transferred to the buyer smoothly? Right. And so what I think is interesting here, Shopify integration apps, super easy to transfer the buyer. Shopify store is very easy to transfer to a buyer. And this is a great example of a technology that a current store owner who’s six to twelve months away from selling their business can implement, drive some really positive growth right before the planned time of their sale.

 

Richard – 00:39:35:

 

Absolutely.

 

Izach – 00:39:35:

 

And then transfer that to a buyer who can continue to build on it.

 

Richard – 00:39:39:

 

Absolutely. Yeah. Because this literally in owned channels like email and SMS yes. There’s a cost to the software. It’s not massive, and revenue comes out the other end. So it’s going to be both a revenue growth and margin growth. Right. Because the cost of that revenue from owned marketing channels is relatively low. And for brands that spend a lot in paid marketing, in Facebook or anywhere else, either or often both, your ROAS gets better. And you’re also able to expand the scale of your prospecting efforts without sacrificing profitability or ROAS. And that’s I’m sure you see this, but most companies run out of ROAS before they run out of budget. Right. Like, as long as there are ROAS opportunities where I can do something that will have a positive ROI, I will do it. But in the current climate, people just aren’t finding a profitable place to put the next dollar.

 

Izach – 00:40:44:

 

Yeah, there’s a lot of break even kind of opportunities out there that just from a return perspective, aren’t worth investing in because there’s nothing to gain from it.

 

Richard – 00:40:52:

 

That’s exactly right. Is it worth the squeeze? Often not.

 

Izach – 00:40:56:

 

Yeah. So improving that is hugely valuable. I mean, we’ve seen across our client base just lower ROAS in particular on Facebook after the iOS update. It put some companies out of business who were overly reliant on Facebook and couldn’t iterate fast enough to get in front of the loss of revenue that they had 

 

Richard – 00:41:22:

 

Yeah. And we see that generally, I’m sure you see it from buyers in this market, but there’s been a huge swing in the last even call it nine months from growth at all costs to sustainability and profitability of the business. And so that’s meant that we help brands do exactly that, which is you want to keep growing as much as possible, but especially if you’re like a venture backed commerce company. The shine has come off like direct to consumer VC investment just because of all the turbulence in the market. And so brands that had this sort of reliable growth playbook saw that get blown up by the combination of what Apple’s doing, changes in the economy, venture capital drying up, and I think everyone sort of got the message that, yeah, data is the new oil, especially first party data. You need to take advantage of it. And that’s what we are. We just enable all of these high value applications on top of getting your first party data house in order right. With user recognition infrastructure and ML predictions. And we got pretty detailed and deep. But honestly, when we’re out in the market, you don’t as a brand need to have any, even engineering, much less machine learning expertise to be able to use these products. These are designed for business users to get the value out of them.

 

Izach – 00:42:43:

 

Yeah, that’s very cool. When I was kind of going through the website, that was one of the things that I liked, is without a big data background, I was able to understand what you’re doing and it seems like a very friendly user interface. So if there’s kind of one call to action that you would have for our listeners from this conversation, what would it be?

 

Richard – 00:43:10:

 

Yeah, it’s like business as usual in e-commerce is just unfortunately not going to cut it anymore. And the answer is data and making sense of this huge asset that you’re probably under leveraging and may not even be aware of, which is your own first party data. And so get on it. We make it simple and cheap and easy to try. And that, I really think, is going to be the path forward in an age of Apple and others trying to disintermediate you.

 

Izach – 00:43:44:

 

Got you. Yeah. So what’s the best way for our listeners to connect with you? Or Black Crow?

 

Richard – 00:43:52:

 

Yeah, so our website is blackcrow.ai. You can fill out a form and we’ll walk you through a demo of exactly what we do. If you want to reach out to me directly, I’m on LinkedIn. You can always email me at [email protected].

 

Izach – 00:44:15:

 

That was Richard Harris, who you can find at www.blackcrow.ai. Thanks everyone for listening to this episode of the Deal Closers Podcast brought to you by websiteclosers.com. If you like the show, be sure to rate us, write a review, press the follow button and share it with your network and of course, if you’re looking for help selling your e-commerce business, be sure to visit websiteclosers.com. This episode was edited and produced by Earfluence. I’m Izach Porter. Follow me on LinkedIn and we’ll see you next time on The Deal Closers Podcast.