The End of Checkbox Marketing with Brendan Hufford from Growth Sprints

One of the easiest things to do in marketing is to turn a good idea into a stale one. No one does that intentionally, but bad habits can creep in, especially with channels that have proven to be effective. 

Like: Publish four blog posts. Send the newsletter every Thursday. Create the comparison pages. Add original data. Post on LinkedIn.

None of those are bad on their own, but the problem starts when completing the activity becomes more important than understanding why you're doing it.

Growth and content marketing consultant Brendan Hufford calls this checkbox marketing. He runs GrowthSprints.co and has spent time in agencies, in-house marketing teams and now consulting. At one point, he worked inside a $100 million software company where the marketing machine had become highly systematized, checkbox marketing was the modus operandi, and nobody was really asking why.

“I didn’t understand why we're continuing to check these boxes when revenue is not going up,” Brendan said. 

There was no shortage of activity. People were working harder than ever, hoping that more volume would eventually fix the problem. But it didn’t.

But how do you known when consistency becomes a checkbox? After all, repeatable programs are often what allow marketing teams to build an audience in the first place.

The distinction Brendan made was useful: Just because your program has recurring elements, doesn’t automatically mean its checkbox marketing.

It becomes checkbox marketing when the process is no longer producing what you need, but you keep doing it because the process itself has become the goal.

And AI search is creating a whole new set of boxes for marketers to check.

When safe marketing becomes all of the marketing

Brendan uses the familiar 70-20-10 model to explain the problem.

• Roughly 70% of the work can go toward proven programs. 
• Another 20% can explore adjacent ideas with more risk
• 10% can go towards the bigger swings that might completely change the trajectory.

Checkbox marketing happens when the 70% becomes 100%. Everything may be useful, but there's no room left to ask whether something else could produce a much bigger result.

This is especially tempting when growth slows. The instinct is usually to add more, and that temptation is even stronger because adding volume has never been easier. 

Name the problem before creating more content

One way to break out of checkbox marketing is to creatively name the customer problem the marketing team is trying to address. 

Instead of starting with another asset or keyword, start by identifying a problem your audience already experiences but may not have good language for yet. Name what the audience is experiencing. 

Checkbox marketing itself is an example.

Brendan didn't invent the underlying problem, but he gave the problem a useful name. Once he started using it, people immediately recognized what he meant.

His advice is straightforward: “Figure out what problem we solve. And then, what do we call that problem?”

Brendan compares the better version of this to what journalism sometimes does with a "conceptual scoop." The Great Resignation is an obvious example. People were already quitting their jobs and naming the phenomenon gave people a shared way to talk about it.

A trend may already be happening everywhere, but the person who gives it language helps everyone else understand it. Brands can do the same thing.

Brendan pointed to UserEvidence, which identified what it calls the customer evidence gap: the disconnect between the polished, aging case studies companies tend to produce and the current, credible customer evidence sales teams actually need.

Another company Brendan has worked with saw how much manual end-of-month verification limited the number of clients an accountant could manage. They started calling that constraint the verification tax.

Neither idea depends on creating a brand-new software category.

B2B companies love category creation because it sounds strategic. But sometimes you're better off naming the painful thing your customer already knows exists.

The best content research may already exist inside the company

Naming those problems requires more customer research than only a keyword tool or a few Google Trend searches. In fact, it’s important to talk to sales, customer success, and support. Brendan calls that approach the 3S strategy and those teams see different parts of the customer experience than marketing does. 

• Sales knows what objections keep appearing and what makes a prospect finally move.
• Customer success knows what separates the customers who get value from the ones who struggle. They also see renewal risks, misconceptions and the questions that show up after the deal closes.
• Support knows where customers repeatedly get confused, even when the documentation technically answers the question.

Practically, what questions should marketing be asking those teams? How do you start engaging? Here are some of the questions that Brendan suggested to uncover the true customer problems: 

• What objections are prospects raising most often?

• What features or aha moments tend to close deals?
• What new challenges are appearing in conversations?
• Are customers using language today they weren't using six months ago?
• What do the happiest customers have in common?
• Why did a customer you expected to renew ultimately leave?
• Why did another customer you expected to lose end up staying?

Now, if none of that sounds particularly revolutionary, that’s partly the point.

Marketing teams can spend hours analyzing search volume, keyword difficulty, competitors and citation patterns while missing the conversations happening only a few feet (or Zoom calls) away.

Those conversations can surface both the language for bigger thought-leadership ideas and the much less glamorous content that influences revenue, like build-versus-buy questions, pricing concerns, or implementation details. 

Which brings us back to AI search.

Build loops to influence AI search

One of Brendan's strongest points during our conversation was that AI search isn't simply Google search with a different interface.

“AI search is not Google search, it is not one channel. It is a full brand channel.”

That creates an ownership problem.

• Call it "brand" and suddenly everyone owns it, which often means nobody does.
• Call it "search" and companies may hand the entire problem to SEO, which can create the opposite problem: every challenge starts looking like something that can be fixed with a website change, technical optimization, or schema.

But the information influencing an AI answer can come from much broader parts of the brand.

Think of it as a series of loops:

• Relationship loops are the recurring places where you build familiarity with people who aren't ready to buy yet. Newsletters, podcasts and other audience programs fit here.
• Discovery loops include traditional search and AI search, where people are actively trying to understand a problem, compare products or make a decision.
• Viral loops are places like LinkedIn and YouTube where ideas spread through feeds and networks.

The important part is that these aren't independent anymore.

A YouTube video created because YouTube is an important channel may also influence AI visibility. The same is true of LinkedIn discussions, third-party mentions, podcast appearances and other public sources.

But Brendan made a distinction I really liked:

“Do the thing to do the thing.”

If you're doing YouTube, make something that's good for YouTube. If you're investing in LinkedIn, make something people on LinkedIn actually want to read. Don't flood a channel with generic AI content because you've decided the real audience is an LLM crawler.

AI-search visibility can be a second-order benefit of doing those channels well. It shouldn't become the excuse for doing them badly.

AI search is also reputation management

This broader view changes what teams should monitor.

Most AI-search measurement today focuses heavily on visibility: Are we mentioned? Are we cited? Where do we rank in the answer?

But there's another question:

What is the model actually saying about us?

Brendan mentioned a client that leads its category but is repeatedly described by AI platforms as expensive.

The problem isn't necessarily that the characterization is completely wrong. The problem is that there's almost no context around it.

The company's pricing varies. The website doesn't clearly explain how pricing works. So the web provides plenty of evidence for "expensive" and very little evidence explaining why customers might still consider it worthwhile.

The response shouldn't be to hide the issue.

Create better information.

Explain how pricing is calculated. Pair the pricing discussion with customer evidence about ROI. Give customers and AI systems something more useful than an adjective.

That's good content for humans whether an LLM ever cites it or not.

And that may be one of the better rules for AI-search strategy in general.

Is there a new checkbox for optimizing AI search? 

The irony is that AI search is already creating its own version of checkbox marketing.

Such as if a study finds that LLMs frequently cite specific statistics, then suddenly the rule becomes that every article needs four statistics. 

And then another checkbox gets added.

It’s understandable why marketing teams do that because they want something concrete to control with so many variables (like search behavior and traffic patterns) constantly changing. 

A checklist feels safer than uncertainty. Brendan compared the current moment to SEO around 2012, when marketers discovered tactics that worked and proceeded to industrialize them until everyone was doing the same thing.

Tactics and best practices aren’t always wrong, but they’re not a strategy either. 

Simple doesn’t mean easy

Near the end of our conversation, Brendan made a key point: a lot of this work is simple to describe. That doesn’t make it easy to do well.

Take a competitor comparison page. The task sounds straightforward: explain the differences, publish the page, and help buyers make a decision.

An accurate, useful comparison takes more work. You need to know your own product deeply, understand the alternatives, and verify details that competitors may not explain clearly on their own sites.

The same is true of those 3 S’s: talking to sales, customer success, and support. Starting a conversation is simple. Building a regular habit, spotting meaningful patterns, and turning those insights into content that helps buyers takes discipline.

The principles are simple. Executing them consistently takes judgment, expertise, and sustained effort.

That’s where the work is. And it’s a better place to start than adding another checkbox.

‍

To learn more about AirOps, visit AirOps.com to work with us. Learn more about Brendan at GrowthSprints.co. 

‍