INBOX INSIGHTS: Business To Machine (B2M) Marketing, AI Digital Clone Part 3 (2026-04-08)

INBOX INSIGHTS: Business To Machine (B2M) Marketing, AI Digital Clone Part 3 (2026-04-08) :: View in browser

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INBOX INSIGHTS: Business To Machine (B2M) Marketing, AI Digital Clone Part 3 (2026-04-08)

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The Most Important Audience You’re Completely Ignoring

About seven years ago, Chris and I sat down and tried to work through an idea that was bugging us. We’d been thinking about audience segmentation — B2B, B2C, the usual suspects — and we kept coming back to this thing that didn’t have a name yet. There was another audience in the mix that nobody was talking about. Not a person. Not a business. A machine.

We called it B2M. Business to machine.

The concept was simple: if you’re doing anything in the digital space, there is an algorithmic layer between you and your audience. Google’s algorithm decides if your content shows up in search. Facebook’s algorithm decides if your post gets seen. Your email provider’s spam filter decides if your message even lands. Before a human ever sees what you’ve made, a machine has already decided whether it’s worth showing.

That was 2019. We talked about it, wrote about it, and then watched the rest of the marketing world continue to ignore it entirely.

Here’s the thing. Seven years later, B2M isn’t just a concept anymore. It’s the whole game.

GEO Is B2M, Fully Grown

If you’ve been hearing about GEO — Generative Engine Optimization, or whatever we’re all going to agree to call it eventually — and you’re thinking, “Great, another thing I have to panic about,” I want you to take a breath.

You don’t need to panic. But you do need to pay attention.

So here’s what’s actually happening. When someone asks ChatGPT or Gemini or Claude a question — like “who’s a good marketing analytics consultant?” or “what tools should I use for AI readiness?” — the large language model does a few things. First, it checks what it already knows from its training data. Then, depending on the setup, it runs web searches to fill in gaps. Then it synthesizes all of that into an answer.

If the model doesn’t know you exist, it’s not going to search for you. And if it’s not searching for you, you’re not showing up in the answer. Period.

This is B2M. It’s the same concept Chris and I were talking about seven years ago, except now the machine isn’t just a gatekeeper deciding whether to show your blog post in a search result. The machine IS the search result. It’s answering the question directly, and if you’re not part of its knowledge, you’re invisible.

You Have a New Audience. Treat It Like One.

On this week’s episode of In-Ear Insights, Chris and I dug into this. And one of the things I keep coming back to is that GEO is not a brand new strategy. It’s SEO plus. The core tenets of good SEO still matter — technical, on-site, off-site. You still need a functioning website. You still need good content. You still need people linking to you and talking about you.

But now you also have this additional segment of your audience that you need to cater to. The machines.

And they need different things than humans do. They need machine-friendly content — structured descriptions of who you are, what you do, what problems you solve. Not polished marketing copy. Not clever taglines. Clear, direct, keyword-rich information that a model can consume and say, “Oh, this is for me. I’m going to take this in.”

Think about it this way: you already know how to segment your audience. You know B2B looks different from B2C. Different messaging, different channels, different content. B2M is no different. It’s another segment with its own needs, its own preferences, and its own decision-making process. The difference is that this segment decides whether all your other segments ever see you in the first place.

Where to Start (If You Can Only Do Two Things)

I know. It’s a lot. So let me give you the practical version, because I’m not going to leave you hanging.

If you have limited bandwidth — and who doesn’t — here are the two things I would start with.

First, YouTube. Google still owns the majority of search, and YouTube is part of that ecosystem. If you’re already creating video content, great. But here’s the part people miss: the transcript and description matter more than the video itself for B2M purposes. Include a machine-friendly version of your content in the description. Who you are, what you do, what this video is about, stuffed with your keywords and services. It doesn’t have to be beautifully written prose. It’s not for a human. It’s for the machine.

Second, your own website. Look at your About page, your services pages, your product descriptions. Is there enough clear, structured information for a machine to understand what you do and why you’re credible? Maybe it’s adding a section to each page — almost like a footer — that’s written specifically for machine consumption. Talk to your IT team and make sure you’re not blocking AI crawlers. (True story: we had a client whose IT team had been blocking AI crawlers for over a year because they were worried about AI training on their content. They had effectively made themselves invisible to Gemini. That’s really, really bad.)

If you can do those two things, you’re ahead of most companies right now. And that’s not me being dramatic — that’s me being honest about where the market is.

The Part Nobody Wants to Hear

Here’s what I know is going to come up: “But Katie, if AI just gives people the answer, they’ll never come to my website.”

There’s some truth to that. If Gemini says “Trust Insights is the best management consulting firm for AI implementation,” that person might not click through. We don’t see that click. But we will see it downstream — in direct traffic, in referral traffic, in someone who remembers our name because a machine told them about us. It’s basically digital word of mouth. I don’t see the conversation you have with your dog about us (although I’d like to think it’s a good one), but it’s happening, and it shows up later.

And if you still care about driving actual traffic? Make interactive content. Give people a reason to not be satisfied with the AI summary. The more you create things that can’t be easily summarized — tools, assessments, interactive experiences — the more reason people have to actually visit your site. New tech doesn’t solve old problems. You still have to give people a reason to show up.

So What Now?

If you’re sitting there thinking, “We’ve been doing SEO for years, we should be fine” — maybe. But when’s the last time you thought about whether a machine knows you exist? Not whether a machine can find your website. Whether it knows you. Whether it would recommend you. Whether it even has you in its training data.

That’s the question now. And it’s the same question Chris and I were asking seven years ago, just with higher stakes.

If you want to dig into this more, we’re doing a live GEO webinar on April 10th where Chris and I walk through exactly how this works — the three phases of GEO, what to do about each one, and how to stop being invisible to the machines that are now answering your customers’ questions. Come join us. And if you want to talk it through before then, come find us in our free Slack group at trustinsights.ai/analyticsformarketers. Over 4,500 marketers are in there asking and answering questions every day.

I could be wrong about a lot of things. But I don’t think I’m wrong about this one — and I’ve had seven years to think about it.

Are you catering to your B2M audience segment? Reply to this email or join the conversation in our Free Slack community, Analytics for Marketers!

– Katie Robbert, CEO

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In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the future of work in the agentic AI world. You will discover how artificial intelligence will impact your career. You will explore the hidden reasons behind the upcoming leadership crisis. You will learn actionable strategies to protect your job from automation. You will build essential skills to succeed in this new era.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we looked at managing AI usage limits. Catch the episode replay here!

This week on So What? we’ll be sharing how to move up your AI skills from prompts to skills. Are you following our YouTube channel? If not, click/tap here to follow us!

Here’s some of our content from recent days that you might have missed. If you read something and enjoy it, please share it with a friend or colleague!

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In this week’s Data Diaries, let’s move onto part 3 in our series of building a virtual version of yourself. Last time, we talked about cataloging thinking and I developed a catalog of over 400 different thinking techniques.

The third stage is gathering all the data. If I wanted the project to succeed, I couldn’t just pour all the data into one big pile and hope AI sorted it out. That’s a recipe for disaster in any scenario. Instead, I needed to do data cleaning and engineering itself.

I started with three types of content – newsletters, call transcripts, and YouTube videos. For each type of content, I have code that processes the content in several ways. First, it splits documents by sentence boundaries. Here’s why: AI tools, especially AI agents, use popular command line tools to find chunks of text with utilities like grep.

These utilities are designed to process content by the line as their base atomic text chunk, and return it. There are many, many transcription tools that tend to return a wall of text in the transcript with no line breaks, and that means when a tool like grep fishes up some content, it returns the entire wall instead of just a single line. That quickly blows up your agent’s context window, causing it to fail.

So to immediately provide better results, a Python script identifies common punctuation for sentence boundaries (periods, exclamation marks, question marks) and splits up paragraphs into single lines. You immediately get better performance and reduced memory usage.

The second step is to deduplicate by line, because five lines in a row of “Yep. Yep. Uh huh. Yep. Yep.” is unnecessary and doesn’t offer any useful context.

Once the text files are clean, they get loaded into a database, and then with a specially-formatted version of the 400+ thinking techniques, each piece of content is sent to an LLM (Nemotron 3 Super) to have the individual texts analyzed. The results come back and get fed into the database:

After all the data is collated, I end up with some nice data tables of what thinking techniques I use, by content type, in named ordered pairs:

This is a cognitive blueprint of how I think. In the next and final part in this series, we put these cognitive blueprints to work. Stay tuned!

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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