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Chapter 1: What is the main topic discussed in this episode?
Welcome to Corozant 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.corozant.com forward slash 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 Reikman 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 Hakone, New Year 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 A-B testing. Well, good afternoon, New Year. Welcome to the show. Hi, Brian.
It's a pleasure being here. 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 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 lifecycle marketing that made it the right place to build a company?
Right. So can I ask that back with a question? Can I answer that with a question? Absolutely. 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? Absolutely. Every day.
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Chapter 2: What is lifecycle marketing and why is it important?
And did they involve furry mammals? I think I saw one. Yeah. 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? Yes. Okay. Did they get to the place that they wanted to? Absolutely. All right. 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 too.
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, it's a 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 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 and 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.
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Chapter 3: How is AI transforming lifecycle marketing?
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 and 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 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. 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, I can do a lot of things. It's very competent in most tasks. But as you mentioned, there still are some gaps there. But you've nailed it here in the lifecycle.
The marketing automation is certainly a use case for 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 capabilities.
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's seat?
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.
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