← The Digital Executive
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- [00:00]
Narrator
No one goes to Hank's for his spreadsheets. They go for a darn good pizza. Lately, though, the shop's been quiet, so Hank decides to bring back the $1 slice. He asks Copilot in Microsoft Excel to look at his sales and costs and help him see if he can afford it. Copilot shows Hank where the money's going and which little extras make the dollar slice work. Now Hanks has a line out the door. Hank makes the pizza, Copilot handles the spreadsheets. Learn more@m365copilot.com work so good, so good, so good.
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- [01:06]
Brian (Host)
Welcome to Coruscant Technologies, home of the Digital Executive Podcast. Do you work in emerging tech? Working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.corazon.com brand welcome to the Digital Executive. Today's guest is Nir Weingarten. Nir Weingarten is the co founder and CEO of Econa, a startup using gen AI and reinforcement learning to transform lifecycle marketing. A published AI researcher with a Master's degree in Machine Learning from Reichman University, Nir spent over a decade leading multidisciplinary technical and product teams across the fields of AI, data and performance. Together with co founder and CTO Omer Hakon, near built Econa to disrupt an area of marketing that has barely evolved in 20 years. While social media feeds have been algorithmically personalized for over a decade, the emails, SMS and push notification brands used to retain customers still rely on slow manual AB testing. Well, good afternoon Nyer. Welcome to the show.
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Nir Weingarten (Guest)
Hi Brian, It's a pleasure being here.
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Brian (Host)
Absolutely my friend. I appreciate it. And I appreciate the fact that you're in the Tel Aviv, Israel area making time to traverse time zones and calendars to get to Kansas City today. So again, really appreciate it. And near we're going to jump into your first question. You're a published AI researcher with a machine learning master's degree who spent over a decade leading technical and product teams across AI and data and then chose to apply that background not only to the most hyped AI categories, but to email, SMS and push notifications. What did you see in life Cycle marketing that made it the right place to build a company.
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Nir Weingarten (Guest)
Right. So I. Can I ask that back with a question? Can I can answer that with a question?
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Brian (Host)
Absolutely.
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Nir Weingarten (Guest)
Okay. So Brian, in the last week, did you see on social media a video, one or two videos you think that were generated by AI?
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Brian (Host)
Absolutely, every day.
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Nir Weingarten (Guest)
And did they involve furry mammals?
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Brian (Host)
I think I saw one, yeah.
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Nir Weingarten (Guest)
Yep. All right. And these are the ones I get a lot. Okay, and now for the second question. Do you know a single person that bought a plane ticket with an agent? With an AI agent?
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Brian (Host)
Yes.
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Nir Weingarten (Guest)
Okay. Did they get to the place that they wanted to?
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Brian (Host)
Absolutely.
- [03:34]
Nir Weingarten (Guest)
All right, now that's amazing. I don't know anyone that bought a plane ticket, but I see videos like that every day. And the point I'm trying to make is that from my experience, from my point of view, I think AI today at least is inherently good at. It's inherently talented in content and in engaging people, hacking their dopamine systems and understanding what makes people pause and look at a video and pause and read something. And there's other things that it struggles with. I think it's much harder, at least from my point of view, to create a system that would do a series of actions, that would actually have a high stake purchase at the end. Much more easier for the technology to create engaging content. And when you think about what's the simplest form of content that a list technology can actually automate end to end, and that has a lot of market value to. That's how we got to lifecycle marketing and that's how we got to stuff like emails and SMS messages. Think of an email, an email, in many cases it's an image, some microcopy, a subject line. And now don't get me wrong, creating an email from scratch or a mid market or enterprise brand, then that that email would be good enough to actually be shipped to tens of millions of people is extremely difficult. But it's possible to automate today end to end with today's technology. If you look at a website, for example, to create a website or to change a website dynamically with AI, we're not breaking stuff there, that's a couple of orders of magnitude more complicated. Also videos, by the way. So lifecycle marketing is in my opinion, in our opinion, the lowest hanging fruit for AI automation in terms of what the tech can do and in terms where there's a lot of commercial value. So I would say that's the first and most important aspect. And also it's a very underserved market. I Think that it hasn't been really disrupted since the early 2000s with the first marketing automation platforms. More or less we're doing the same thing when it comes to marketing automation since right we have user customer journeys, we built state machines, we send out blast emails exactly the same, we have very GUI today and some more features but the concept is the same. So it's underserved market. And lastly, I would say as a data scientist, it's a place where you have a lot of data. So when you're Talking about your CRM, if you're a B2C company, a B2C enterprise, you have a lot of data about your clients. You know what they bought, you know when they bought it, and you know what device they're using. Is it an Android or an iPhone or a desktop? Are they yahoo.com, hotmail.com, gmail users, what's their zip code? And because they did a purchase and if you know their zip code and that's your data, you can also infer other information about them and you can use that rich, rich data to give them content that would work so much better and be so much, so much more intimate and engaging for them and give them such a better experience. And that data today is almost non utilized at all. And it allows us to build a great product, a great technology and then also a moat around that.
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Brian (Host)
Thank you. Appreciate that. Really do unpacking that for our audience today. And you're right, there's a lot of. And I appreciate the initial questions there but today AI can do a lot of things. It's very competent in most tasks but as you mentioned, there still are some gaps, but you've nailed it here in the lifecycle. Marketing automation is certainly a use case for a AI to really knock it out of the park, as I would say. And of course it maximizes underserved markets, so I appreciate that. And Nir Econa applies reinforcement learning from human feedback, the same technique used to fine tune large language models to continuously adapt marketing content based on how customers actually engage with it. How do you explain that technical approach to a chief marketing officer who doesn't have a machine learning background? And what does it feel like to use from the marketer seat?
- [08:02]
Nir Weingarten (Guest)
Thanks for the question, Brian. I think it's spot on. Well, I think reinforcement learning is something that's very intuitive to understand because it's very similar to how we as people go about the world. And I'll just give a very crude example. So imagine a baby is born and that Baby crawls around the world and starts to discover it, and they don't know anything about the world yet, so they explore their environment and they try out different stuff. And then the baby crawls around the floor and finds a piece of candy. And so maybe it's on a plate because he doesn't pick the candy off the floor, but he finds a piece of candy and the baby tastes it, and it's sweet and it's tasty. And the baby has been rewarded by this experience. And the next time they'll see a piece of candy, they'll try to eat it. And the next day, say, that baby roams about, and then they found a slice of lemon, and they see the lemon, they try it out, and the lemon is sour and they don't like it. It's not tasty. So the next time the baby sees a lemon, they're not going to try it. That is reinforcement learning. And as very crudely. And what do I mean by that? I mean, you have some sort of autonomous agent. In this case, it's the, it's the toddler. You let them explore their environment, which is try out different foods, and you let them learn by experience what rewards them and whatnot, what's tasty and what's not. And to complete the analogy, we can say that we can train an AI model to try out different creative concepts or different content or different messaging, different creative intents, and learn by how actually people click on that or buy from that, what's engaging for these audiences. So we are replacing the baby with a neural net that generates content, and we're replacing the taste buds with counting clicks and revenue. And so that's the concept. I think it's pretty, pretty intuitive. And I think that a lot of marketers, you know, the first thing we ask, and we talk with really hundreds of marketers and we ask them if they A B test. Did you ever A B test something, Brian?
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Brian (Host)
Absolutely.
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Nir Weingarten (Guest)
And how did that work for you?
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Brian (Host)
Great. Gave some great feedback on which direction should go based on the results. We got back on the testing.
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Nir Weingarten (Guest)
Great. And, and, and would you say you, you, a B test is enough or did you want to do more A B testing?
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Brian (Host)
Love to do a lot more. Sometimes based on deadlines, we didn't have the time, but. But yes, doing more. We wanted to really dial that in so we could maximize our results.
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Nir Weingarten (Guest)
That's the answer we keep hearing across the board. I never, never heard otherwise. And when you ask people why, they tell you, well, we just don't have the time it doesn't scale. And it was very effective when we did that. And you remember the sale two years ago. And we try, but we never do enough like we want to. And then we say, okay, so this is a very big problem. Now we're going to create scalable a B testing, automated A B testing. So it's called reinforcement learning, but it's actually the next iteration of AB testing. It's more or less the same thing. It's like a B testing that just scales. And so I think for a lot of marketers, this is a very intuitive concept both on how it works and the need for it, because people are really waiting a long time for that to happen. And on the marketer side, basically, we try to make the product integrate into the existing workflow that people have. So, and you want to make this as frictionless as possible. Nothing is 100% frictionless. But you don't change your tech stack. You remain with the same tech stack that you have. You go about your day the same way. The minute you hit send to that email campaign or flow, you're going to be prompted by the system. It's going to create a slack message. You're going to get that on slack saying, hey, here's 20 variations the system created and select the ones that you like the best. This is what the system recommends. These are the pieces of candy the baby would like to try out and you select the ones you like and then these go out to see how tasty they are. So it creates another step in your day when you don't just send out an email, you have to select variations each time.
- [12:38]
Brian (Host)
Thank you. Really appreciate your insights and really again unpacking that for our audience. But I liked how you talked about that reinforcement learning, it's intuitive to understand and your example was that baby, new baby, crawling around, discovering the candy or the lemon and being rewarded for the candy and not so much for the lemon, but you are training AI to learn from consumers and their engagements or their clicks. And having that automated scalable AB testing with your platform allows businesses to really dial in really what they want, the results that they want much faster and obviously much more accurately and have a better results, more return on investment. So I appreciate that. And Near Econa is deployed primarily in retail, but is expanding into telecom, finance, insurance, healthcare and travel. What do those sectors have in common that makes lifecycle marketing such a high value problem there? And where are you seeing the most dramatic early results?
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Nir Weingarten (Guest)
Right. Thanks for that, Brian. And basically we, we can serve any B2C business. And that's large enough to have enough data for the system to learn. And mostly it comes about around 100,000 people on the. On the list that gets the content, the emails that's in this. The bigger the list is and the faster we can learn and the more iterations we can make. We started off in retail because it's just a market that's very early, has a lot of early adopters, and it's easier to build a product there. But as we went, we expanded to other verticals which are in many cases bigger. And I talked to. So this is a quote from a CRM manager for one of the biggest banks in North America. And he told me this. He told me, you know, what we do at this bank, we do two things. We need to very quickly compute the interest we give you on a loan, and we need to keep you from going to the other bank across the road because they have exactly the same algorithms to compute your interest. So I think in many B2C services and B2C enterprise companies, retention is a very, very important aspect of business. And it can be very, very profitable for these businesses to improve their messaging to their clients. So basically, we would like to work with brands that are companies that are as large as possible and, and also that retention is a big channel for them. In many cases, you'd see revenue from retention reaching as, as high as 60%. And we generate approximately between 20 and 40% uplift on that. So that can be very, very impactful, especially for bigger companies.
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Brian (Host)
Thank you. And those are interesting statistics, by the way, that revenue from retention can be as high as 60%. I thought that was interesting. And then the uplift alone could be 20 to 40% there. But I like how you can serve really any B2C business, but you've got approximately 100,000 list of users that can be tested with, with some great sampling of data here. So again, really appreciate what you've shared there. And near last question of the day. If, as we look ahead in five years, you want Econa to become the market leader in a new category you call adaptive marketing, where every message that reaches a customer is personalized to be more intimate, more warm and more effective, what needs to be true technically, commercially and culturally for that category to become the standard? And what's standing between where lifecycle marketing is today and that future?
- [16:31]
Nir Weingarten (Guest)
Well, I love this question. I think to break it down like you did on the technical level, we're there. We weren't there a year ago, but we're there today. And today we create end to end emails and SMS to some of the biggest brands in the world. And we do the complete email, we do the creatives, we, we keep the brand, we keep compliance, we keep everything there and we do that with our proprietary tech stack. So technology wise we're there. And commercially, which maybe is the biggest thing. I think the best analogy here is, is Brian, is Jeffrey Moore's Crossing the Chasm. Do you ever get a chance to read that?
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Brian (Host)
Though I have not.
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Nir Weingarten (Guest)
I would really recommend it. One of my favorites. So. So Jeffrey tells us that in evolving markets there's always an early market and a late market. Early markets are targeted by early adopters of technology. These are people in organizations that are either visionary by their character or really sick of the opportunity to advance using technology. So right now, since the technology is pretty early on, despite that it's working, but it's still an early market technology, it's still for people that are that like to adopt new technology. It's still the people that say I'm going to get AI in the organization and really, really mean it. Okay. To cross over the other side to the mainstream, to the early majority where most of the market sits. You just need more time to amass masses of the early adopters. So that's at least the thesis in Crossing the Chasm which I really connect to. Think of iPhone users, 2008, right. So the iPhone was very, you know, it was a niche back then. Not everyone had an iPhone. I had a Motorola, some people had a Nokia. But then everyone had an iPhone. And it happened like really quickly. Or think of Salesforce. So imagine Salesforce coming out as a product and you have Marc Benioff telling people they need to put their CRM data in the cloud, in someone else's server. No one wanted to do that. That was crazy. I would put my precious CRM data, people had that on prem. People had a box in their office that had the data inside. And now when you think of it, how crazy does that sound for someone to have a server in their office with the CRM data in and they have like a proprietary software to use that. So it's just a matter of time until the market reaches there. And lastly, culturally, like you said, there's a great term I read about. I really liked it. It's called media panic. Media panic is basically the concept is it goes like this. So every time a new type of media is introduced, the the initial reaction is suspicious. And because we are what we consume, we are, we think our all cognitions around the information that we consume. And whenever that changes, it's. It's a bit frightening. So a recent example would be social media. So if you remember when social media just came out, do you remember the backlash on that? Yep.
- [19:53]
Brian (Host)
Yeah.
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Nir Weingarten (Guest)
You know, it was really frightening. It is frightening still, but, you know, it's still. But it's a standard. Everyone uses social media. So it was the same when video games came out, and it was the same when television came out and when movies came out. You know what, there's even a quote that I saw attributed to Plato where he blames writing for people being messy and forgetting stuff. And I think writing is pretty much mainstream today. So I think that culturally, AI is very similar. It started off where people really resented seeing AI content. And today we see it all around us and in a matter of a few years, it's going to be, in my opinion, as ordained as seeing a TV show.
- [20:43]
Brian (Host)
Thank you. Appreciate that. You know, we talked a few things. I always like to highlight some things here. And technically and commercially, we talked about that your platform is now capable and moving towards this adaptive market category. And you shared some examples. IPhone in 2008, Salesforce, CRM moving to the cloud. And you saw that these markets shifted very quickly, in fact, overnight. Really? And then you talked about that cultural AI. Right. You know, the example was that media panic when something new or controversial is released. People kind of freak out initially and then it just kind of slowly becomes part of our DNA. And you shared some. Some great examples there, and I really appreciate that. And Nir, it was such a pleasure having you on today and I look forward to speaking with you real soon.
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Nir Weingarten (Guest)
Thank you so much, Brian. Was a real pleasure being here.
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Brian (Host)
Bye for now.