Yaniv Makover is the Co-founder & CEO at Anyword. He has done research in the fields of Machine Learning and Natural Language Processing. Yaniv also served as a lieutenant in the Israeli Defense Forces. Anyword's AI Copywriting Platform and also the world's first Language Optimization Platform that helps publishers and growth marketers deliver and optimize the messages they use to deliver business results across web, social, email, and ads. In today's episode, how Anyword leverages large language models like GPT-3 to personalize copy for different segments of a business's audience, providing insights and analytics on how well it will work for specific target audiences. He also highlights the importance of prompt engineering and the value of feedback loops to improve copy performance. He discusses the challenges of building an AI product, emphasizing the importance of staying focused on specific problems and being disciplined in product management to ensure the best user experience. Tune in to learn more about Anyword's approach to AI copywriting and the future of personalized copy for readers.

Where to find Yaniv Makover: 

• LinkedIn: https://www.linkedin.com/in/yaniv-makover-a8590b3/


Where to find Dhaval:


• Twitter: https://twitter.com/DhavalBhatt

• Instagram: https://www.instagram.com/dhaval.bhatt/ 

• LinkedIn: https://www.linkedin.com/in/dhavalbhatt 



Transcript:-

Dhaval:

This founder built an AI copywriting product and got over a hundred thousand users in two years. In this episode, we talk about his approach to deciding where to invest your time, money, and energy, as an early stage AI founder and I learn a lot about his approach towards prompt engineering. Yaniv is the CEO and co-founder of Anyword leading AI copywriting solution designed for marketing performance. He has a Master's in Computer Science and Information Systems, and he has conducted extensive research in the fields of machine learning and natural language processing. His work optimizing ad and content channels for some of the largest publishers like New York Times, Lead him to found Anyword generative AI platform. Yaniv oversees operations across Anyword's New York, Aviv and satellite offices across the world.

Dhaval:

Welcome to the show. Yaniv. Tell us about your product.

Yaniv Makover:

Hi. Thank you for having me. Yeah, I'm co-founder & CEO of Anyword. We are in the coparating AI space. Our product primarily focus design on making copy more effective and converting more and engaging more.

Dhaval:

Wow. Okay. So Anyword and. how is it different than the plethora of other copywriting tools, AI-enhanced copywriting tools that are out there, at this time, or they're probably gonna come out now that there's a lot of those capabilities? Yeah. Tell me about how is it different. What's the differentiation there?

Yaniv Makover:

So we started out from becoming performance mindset. And I think there's one. Big problem that generative models can solve, or language, large language models can solve is basically helping you just get more content out there and high quality content removing solving for writer's block. And I think that's an interesting, huge problem to solve. For us, it's kinda like not our DNA. Our DNA is more about in our products DNA is how to make your. Copy better. So you already are a marketer. You already know what you're doing. You have a strategy. Yes, you could get ideas from ai, but this is how Anyword will work better for you for a specific audience. Or you're selling, I don't know, a sweater to somebody in the US versus other countries or a different occupation or different age or different gender. Then how, what are the best words to use for every use case and I think we use large language models to actually empower those insights or actually leverage those insights, to create ROI for our customers. I also think that when you're just a click of a button away from creating hundreds of variations of copy we thought it was a really big problem to solve. Which one are you, you're gonna publish, you can't really A/B test 1000 tweets you have to send one. And so we thought that was like the biggest problem solved. So we, early on, we focused on that. And our product, we pretty much tell you if with every copy variation that the AI generates, how will it will work and for whom and why. And if you wanna make improvements, how to do them.

Dhaval:

Cool. Okay. So you're taking the existing. LLMs and you are not only generating content, that's an easy problem to solve, but what you're really doing is you are helping fine-tune that content to resonate with the specific audience that your customers may have, and then fine tune the copy to increase engagement or retention with that audience is what is the primary metric that you aim for with your customers to improve? What is their North Star metric that you're helping them?

Yaniv Makover:

So typically it depends on the use cases of the marketing, but they'll, they'll measure lift in conversion rate or lift in engagement, and then they'll measure just ROI so if they're running ads, they'll, they should be able to see a lift in their ROI or if they're, the conversion rate on the lending page or open rates and emails. And it's pretty easy to measure easily. Just copy and see if see if it works for you.

Dhaval:

So do you, how do you get the information on their audience, like to fine tune the output with that highly fine-tuned output? Yeah,

Yaniv Makover:

so Anyword collected its own data, and basically, we have our pretty large corpus of data, performance data. And also when we, we partner with our customers, our partners, basically they have their own data sets, and then they upload in them into Anyword, and then we have, we fine-tune what we call custom models to help them predict better how their copy will do. So, for instance, just based on our data, we have an accuracy measurement of how well our model predicts performance of like copy that we already knew how well work. Somewhere around 76% depending on, on what we're testing. But if you, bring in your own data and you've actually A/B tested or just ran a copy in the past, then it goes up to 85. And that's just because you have your own audience, kinda like your own topics. I think for me, one of the most interesting parts of the space of large language models, not only they can write really well, they also understand text really well. So like five years ago, if you train a. Just lots of text and tell it, this text is good, this text is bad. We'll probably figure out that one has an emoji or an exclamation mark. But now there's a deep understanding of why text works. Like are you using, if you're missing out is that even relevant for some audiences and for some products or industries? And I think that's super exciting. So I think it wasn't possible a few years ago. And it's possible now. And I see this as kind like a. Booster and performance for marketing.

Dhaval:

Hundred percent. There has been a plethora of content generators using chatGPT and tools like that to create content.

Dhaval:

But what is still missing is the ability to fine tune that output to the specific segment of your audience and then be able to create content based on that readily, readily, as in with a click off a button. I'm sure you can stitch together a few data pipelines. Do that with existing tool suites. But what I don't see happening is the ability to just readily click of a button, say, this is my audience, this is my content. Generate some copies. Is that, am I understanding your pro product correctly? You offer that?

Yaniv Makover:

Yes. For every, like, while you're typing, you can even not, you can use your own copy, not even generate with ai. You'll have insights, analytics about that. Copy how well we'll do for your target. The way you defined it, what talking points work better and maybe replace them with others? And you can use your talking points that work for you while you're running ads in your emails and the talking points that work in your emails or the insights you gain from that and your landing page. And I feel like that is kind the future where there's just so much content that's gonna come out. You really know you have to know what, what resonates with your audience.

Dhaval:

Yeah, I imagine a world where the specific copy will be highly personalized to the reader that is consuming that information. And it could be hundreds and millions of variations of that depending on who's the reader, right? So what I am curious about is when did you. Now that I have the context of your product, let's talk a little bit about your business. Tell me about when you founded the company, and, tell me a little bit about the number of users you have amount of your revenue, et cetera. Anything you can share to provide us context on your business?

Yaniv Makover:

So Anyword launched March 21. And basically, it was a spinoff of the first company we founded, which called QE QE it's a SaaS platform for pu