Almost Timely News: 🗞️ How To Improve Advertising with AI (2026-06-28)

Almost Timely News: 🗞️ How To Improve Advertising with AI (2026-06-28) :: View in Browser

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What’s On My Mind: How To Improve Advertising with AI

This week, I was working on a new piece for Trust Insights, on AI use cases for marketing, when Claude asked me a question as it was putting together a list of things it wanted answers for.

As a bit of background, I don’t have AI write for me. I have AI make up challenging questions for me to answer, and when I have large periods of downtime - like driving long distances - I wire up my voice recorder and spend time answering those questions. This way, I keep my own executive functions sharp, keep myself thinking and exploring, rather than deskilling and handing things over to the machine.

Claude’s question was an interesting one: “How would you advise a marketer to use AI to make advertising more automated, especially Google Ads?“

I don’t run a ton of ads, though after thinking this process through, I may give it a try as I’m fairly comfortable that my answers are on sure footing. So let’s walk through the approach, which is underpinned by the 5P Framework from Trust Insights™.

Part 1: Purpose

Why do we run ads? Early in my career, I thought ads were just about selling stuff, and they are, but we can do this in many different ways. We can buy ads to build brand awareness, to reach people early in consideration and make them aware that we have solutions to their problems, to catch people when they’re assembling their short list, to intercept people just before purchase, or to help keep customers loyal by reminding them we exist.

Advertising, when done well, reaches out and captures attention of different audiences to help us reach people we might not otherwise have the means to reach, or to deepen the relationship we have with people who know us. These days, it’s harder than ever to keep attention, so ads can help with that.

Which means we have to answer the question: why are we running ads? What problem are we trying to solve? I can say for sure what the wrong reason is: “because our competitors are doing it!”. That’s the wrong answer most of the time.

What’s the right answer? Well, start with your analytics data. Katie Robbert did a demo of this recently for our team quite brilliantly by using Claude Code to sew together a bunch of disparate data sets and visualize our marketing operations funnel.

You should do the same thing - open up the agentic coding tool of your choice like OpenAI Codex, Google Antigravity, Claude Code, Claude Cowork, etc. Grab all your data that you have available - Google Analytics, your newsletter, your CRM or Shopify cart data, whatever you’ve got. Make sure you’ve got Jesse Vincent’s superpowers installed in your environment (it’s free), and kick off by having it outline your marketing operations funnel with the brainstorming skill.

Once you’ve done that, have it identify the stage in your marketing operations funnel where your transitions are weakest. That’s the area where ads might be of some use, to help nudge people out of the stuck stage.

That’s our purpose, to unclog that part of our marketing operations funnel.

As a sidebar, I keep calling it that because while the customer journey itself may be nonlinear, our ability to measure and manage it still very much is, especially as our organizations get larger and more complex. Everyone can’t and shouldn’t do everything after a certain point, so having a funnel that mirrors people’s responsibilities and skills makes good sense.

I put my data through and about half an hour later, Claude Code identified that this newsletter was doing almost all the lifting, and my website definitely needs to be contributing more. We’ll put a pin in that. In terms of where I should be using ads, it identified a clear initiative based on my data and the limited budget I specified (USD 25 a day) - to get more, better newsletter subscribers.

We now have a clear purpose for our Google Ads: get more, better subscribers.

Part 2: People

The next step in our process is to identify our people - both the people doing our marketing, and most especially the people we want to serve ads to. Who are these people? After all, Claude Code identified that to make the most of a limited ads budget, we should get more, better subscribers. What does this mean?

To know this, we want to look at two groups of data - the people we currently have access to, and the people we’d like access to. Google Analytics, if your website has enough traffic, can give you some basic demographic data such as inferred age, gender, and most valuable, affinities and interests. This latter part is what tells us about the people in greater depth, the subjects they’re most interested in outside of our website.

All of Google’s data comes from their advertising network, Doubleclick, and it’s entirely made by machines based on the places people visit. It’s not carved in stone, so treat it with the same level of surety that you’d treat any AI inference data - directional, but not rock solid.

Remember that your website, if it’s big enough, will likely have different demographics by section. For me, I have a lot of marketing and business professionals who are looking for help with AI and analytics, but I also have an audience of event planners and conference organizers. These are not the same people, and they have different backgrounds. So I’d want to use Google Analytics to explore if there are significant differences in these populations based on the pages they visit on my site, like my speaking page versus this newsletter.

If you want to automate ads well, you absolutely need an ideal customer profile with enough behavioral richness that you can have an LLM impersonate that audience. You can’t skip this step - and even better, if you have past ad data from the platform of your choice about who did and didn’t respond to your ads, now is the time to integrate this.

After we know who we have, it’s time to dig into who we’d want in our universe. The question you have to ask yourself is, where does your ideal customer spend their time that you can get data about? For example, most B2B businesses will have their ideal customers in places like LinkedIn, but also in places like golf courses. Is there data out there that can inform your audience building qualitatively and quantitatively?

One of my favorite sources for this kind of qualitative data is Reddit, especially as they’ve been doing a good job recently (perhaps too good) of cracking down on automation and AI bots farming clout. If you have a Reddit developer’s API key, you can extract data from the service in compliance with their Terms of Service, and it’s a very generous allocation.

The key questi