You Ask, I Answer: Role of AI in B2B Influencer Marketing?

In today's episode, Caitlin asks In the realm of B2B influencer marketing, how do you envision artificial intelligence playing a significant role in the coming years? What specific applications or advancements do you believe AI will bring to this field? Okay, so uh there's a bunch of different ways. Here's the thing artificial intelligence, particularly generative artificial intelligence, is all about language. Language and images, uh, which are the two areas where, as of the time of this recording, um, the most capabilities exist.

Large language models like the ones that power tools like chat GPT, for example, um, can generate language. So there's a couple different ways to think about this. What is an influencer do that involves language? Pretty much everything, right? So you will see the advent of the synthetic influencers, uh, a synthetic influencer.

This is very popular in B2C, and it will probably become popular in B2B, where you have a personality that isn't real. There's no human being behind it. There's a team of humans that maintain the infrastructure, but that personality does not exist. There is no person there. And those synthetic influencers have thus far been very popular.

And they're especially appealing to brands because, well, your influencer is never going to show up drunk uh, you know, or have compromising photos put up on Instagram or something like that because they're not real. They they simply do as they're told. So that's a big part of ways that you might see generative AI put to use. But the bigger and more obscure and arcane way to think about influence when it comes to AI, is the language model itself. Language models are trained on a gigantic amount of text.

Uh they uh are processing uh among other things, things like Common Crawl, which is a huge repository of the public internet. As a result, they see more and have more data in them than any other tool available to us. And as long as they are prompted correctly, you can extract out information from that massive meta index. Or you can drill down and supplement these tools with specific data that you may have internally. So, for example, let's say you do a survey of all your customers, and you say, who's most influential in our space?

Maybe this industrial concrete space. And you go and you read a bunch of blogs and you get a bunch of papers and you have a bunch of news articles, and you cram those into what is known as a vector database, which is a special kind of database that large language models like ChatGPT can talk to. That's they speak the same language, specific kinds of numbers. They turn content into numbers, and then they do a whole bunch of math on those numbers. When you do that, if you do that, if you build that tool, and I would expect the leading influencer marketing software companies out there, tools like Analytica, for example, to build that system.

You will be able to ask large language models who is influential in a space and get an answer that is coherent that is relevant to that specific space, but also has the broad knowledge of the internet, of everything that's been published on the internet in public spaces. Again, these models take in the public web, they take in books, they take in all the academic papers on sites like archive.org. And as a result, what you end up with is a pretty good knowledge base of what people, what people are out there, what is influential out there, books, podcasts, etc., that you may not be able to get from traditional influencer marketing tools or even influencer marketing agencies, because let's face it, no one human being can keep that much data in their head, but a language model can. So what companies should be thinking about, what they should be looking at is working with the leading edge, the cutting edge language model folks and firms things to build influence models that can do this kind of work that can extract data from language models, which are really just statistical models of the public internet, among other things, and come up with influencer conclusions that way. Because language models inherently can take and filter much more precisely and much more granularly than traditional influencer marketing tools.

You could say, for example, show me the influencers in B2B uh industrial concrete, but I specifically want to focus on influencers who are BIPOC, uh, black indigenous people of color. Who in my space is is that? Um you can ask those questions of a language model and you get information you would then have to go corroborate the information. You want to make sure it's not a hallucination, you want to make sure it's not the language model is not making things up. But you can ask those questions in ways that traditional influencer marketing software doesn't even know is a question.

So that's what I expect AI to provide to the influencer marketing space in in the coming months, not years, months, because every release of these models that comes out, the models get smarter and smarter. And every release of the technology, particularly in the open source side of AI, gives more capabilities for all of us, if you're technically savvy, to be able to construct custom influencer marketing tools that fit the specific needs that you and I have. So that's the answer to that question. It's a really good question, and it's very difficult to predict the future. I mean, we can't even predict four weeks out, much less years, but where the tools are today, we can gather this information.

We can we can use what we know for today and with the types of applications we can build on what exists in market today. If something new comes out tomorrow, we can adapt to it. But conceptually, from a uh strategic infrastructure perspective, this is where influence marketing could go if the leaders in the space are willing to invest and embrace these generative AI technologies. Thanks for the question. If you enjoyed this video, please hit the like button.

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