Building a Generative AI Venture from Portugal: João Aroso, CEO of Leadzai (Part 5)

Sramana Mitra: How do you do your own lead generation? Can you walk me through  a use case and use your own situation to explain how your technology works? How you do your own lead generation using your technology?

João Aroso: On our platform, we input our target audience and who we want to acquire. Let’s say for pest control companies, we provide our website, leadzai.com, and the platform scrapes all the content that I have on my website, processes it through what today are a multitude of different things around NLP that we do.

Sramana Mitra: Can you double click down on that. What we are trying to do a generative AI story, right? What is generative AI doing in this context? What can your platform do? What is inside the black box?

João Aroso: We understand exactly which products and services are being served by that business. Leadzai has a little bit of an atypical process because we only have one product, right? But we understand what our product is, what’s the value added from our product.

You could have automatic generation of ads. You don’t need to understand about ads in order to create your ads. You would have aggregation of multi-platforms like Google and Facebook in only one platform and have a pay-per-lead model. If you use Google or Facebook, you pay for the traffic.

We de-risk that for you. We pay for the traffic and you only pay for the leads. I assume the platform tries to identify the big groups when we scrape the website. It starts by doing that and it structures what we call the ad sets or the ad groups of your campaign. So, we understand the several ad pools that we need to create in order to convert your customers.

Then, it gets all the content from the websites, sees what fits what, generate ads from that, trying to focus on your differences compared to the average. For example, for a sushi restaurant, if we see that the tuna tataki is specially mentioned in that restaurant, we focus specifically on the tuna tataki. So, we try to identify what’s more frequent in your website compared to the industry average and we see that those might be your specialties, right? We take your specialties and we try to highlight them in the ads that we create.

On top of that, we use all the data that we already been processing for years now of running thousands of campaigns to know what converts best for each audience. For that, we have a multi-dimensional mapping in the sense that we cross the vertical with demographics. So, if I’m advertising a law firm in San Francisco, I learn something from the audience behavior in San Francisco. And while it’s most relevant for law firms in San Francisco, it might also provide me some insights for plumbers in San Francisco.

The same way, if I run a campaign for a plumber in Lisbon, it is more relevant for plumbers in Lisbon. It not only gives me some insights on the Lisbon audience as a whole, but it also gives me some insights on audiences in regards to how they react to plumbing services. So, we kind of cross all that knowledge that we have and use that to optimize every single piece of copy to all the ad pools that we’ve been creating.

After that, we do pretty much the same for ad copy and for keywords. Then on top of that, we map the desired audience, which would be pest control companies in this case. So, I map that to the audience type of targeting on Facebook, Bing, and Google. So, all that mapping magic happens in ten seconds, and you see your campaign built after you scrape the website. So, you get to review the ads and upon approving the ads that the platform automatically suggested, the platform will give you a quote on how much we’re going to charge per lead.

This segment is part 5 in the series : Building a Generative AI Venture from Portugal: João Aroso, CEO of Leadzai
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