📎 How to Use AI in Brand | Part 1: Using LLMs to Understand Your Audience

Stop guessing what your customers want, use AI to find out

Good morning, readers. Today I’m launching a multi-part series I’ve been excited about for a while: how we should be using AI to support brand and marketing work. Most of the resources out there over-index on how to create content faster and cheaper—the result being a lot of generic dreck. 🚮

I think one of AI’s best use cases is helping us better understand the people we’re marketing to and pushing our creative thinking further…not being creative for us. How do we do that? Let’s start with audience research because everything else—your positioning, your messaging, your creative—falls apart without that foundation.

Perfect timing, because Typeform just launched the next installment of their Get Real series, and we’re teaming up to ask the exact questions I’ve been wrestling with. They’re digging into how generative AI is reshaping our industry—the stigma around using it, the fear of being replaced by it, and the pressure to prove we’re still human. They’re capturing actual marketer perspectives through writing, video, and audio responses to get the nuance behind the data, not just numbers. You’ll see yours truly 👋🏼 in the survey asking (and answering) the exact question I was getting at above—how do we make sure our use of AI actually makes our marketing better for our audience, not just easier for us as marketers?

👉🏼 Share your honest take by September 26—I predict a spirited final report.

If someone sent you this post and you’re not subscribed, join those people learning how to tactically advocate for brand at your company. 📬

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🧷 A disclaimer before we begin: I’m not one of those people who is obsessed with prompting optimization, and I’m still (always) learning. These aren’t intricate or complex prompts, and I guarantee each one I share below can be improved and refined, let alone customized to your use case—so take them as a starting point, see what works for you, and tell me what you learn as you go!

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If you’re using AI to help produce any type of customer-facing creative output, there’s critical context you need to feed your tool. And whether you’re a marketer, a founder, a CEO, a sales manager, whoever—in order to create effective marketing, you need to understand your audience deeply, from demographics to firmographics to psychographics. It’s the responsibility of any marketer, but especially of the brand leader, to get past “our customers have these titles and work at companies with $X in revenue and have these functional problems” to understanding the underlying motivations, emotional drivers, and cultural context surrounding the people with whom we aim to connect.

My goal in this post is to share how I’ve used AI to build (and maintain) a tidy foundation of context to quickly ramp the LLM assistants you (should) set up for each skill area or workstream. Using these methods will help you get to strategic plans, campaign budgets, and asset production readiness faster—and makes it less likely you’ll wind up with the same garbo as anyone else with a free ChatGPT account. 😛

This post is focused on foundational context inputs, with 5 separate prompts/techniques, plus my audience insight brief template. The following posts in this series will build on this foundation, building up to a strategy for using AI systems empowered with your unique context to build a robust, measurably successful brand strategy. Don’t miss 📎 How to Use AI in Brand | Part 2: Building Brand Foundations.

AI audience research tools

Both Claude (Anthropic) and ChatGPT (OpenAI) are great for analyzing qualitative data like interview transcripts, support tickets, and survey responses, or for processing structured data and creating research frameworks. Perplexity is where I tend to go first for competitive research, advanced sourcing, and trend analysis (but all the major LLMs are getting better at citing sources). There are tons more tools out there, and I’m exploring them in real time with you.

The benefit of these tools compounds when you layer context over time within the same thread, like with Claude Projects (my personal preference) or Cursor Agents. The first step is to set up a project environment with a purposeful goal, like for customer interview analysis and persona development.

Technique 1: Mine your existing customer data

Homework: Gather/export any and all data you have related to your goal—sales call transcripts, support tickets, customer survey responses, review site comments, social media mentions. CSV files are perfect (and you can have your LLM clean those up before analysis, if needed). This works because you’re not asking AI to guess what customers want—you’re asking it to organize and analyze what they’ve already told you.

The prompt:

I'm sharing [X, Y, Z types of data] from our customers. Please analyze this content and identify:

1. The top 5 pain points mentioned most frequently
2. The language customers use to describe these problems (exact phrases, note any interesting nuances)
3. The outcomes and/or ulterior benefits they're seeking
4. Any differences in priorities between different customer segments
5. Emotional undertones (frustration, excitement, confusion, etc.)

Format your response as a table with direct quotes as evidence for each insight.

📋 Pro tip: Analyze both positive and negative feedback. Pay attention to complaints and support tickets where customers got frustrated—this usually shows you an opportunity via a gap between what they expected and what they experienced, which often points to positioning or messaging issues. I’ve found some of my best brand insights in Zendesk tickets where customers said things like “I thought this would...” or “Why doesn’t it just...”

Technique 2: Competitive message analysis

Homework:

Collect homepage copy, ad creative, and marketing emails from your top 5-10 competitors. If you’re using an LLM with live search on, you can ask it to pull these for you, but it’ll be more limited to what it can find publicly online (like web pages, not an email series). IMO, it’s worth it to spend the extra time to gather this yourself—I keep a big Figma file of my “competitive landscape” on hand with all kinds of screenshots of various competitors’ marketing, and I add to it all the time.

Screenshot of one of the pulls I made at Later, compiling influencer marketing homepages

The prompt:

Review the attached marketing messages from these companies in or adjacent to the [industry] space. Analyze and identify:

1. What themes/benefits do most competitors emphasize?
2. What language patterns do they use repeatedly?
3. What pain points or customer needs are they NOT addressing? Order these by impact and importance.
4. What positioning territories seem unclaimed or underexplored?
5. How could we differentiate our messaging in this landscape? 

Create a competitive messaging map showing where competitors cluster and where white space exists.

Include notation around where you see contradicting information, are making assumptions, or may need more information to fully validate your analysis. 

📋 Pro tip: The output for the “white space” opportunities (bullet #3 above) will benefit the most from any context you gather on customer needs and opportunities, so you don’t just end up with a list of every possible applicable feature. Secondly, you don’t have to limit yourself to direct competitors in this exercise. Include companies your prospects might consider as alternatives, even if they’re in adjacent categories—whatever is ultimately competing for your audience’s time and attention.

As a follow-on prompt, you can also enter this snippet again so the LLM creates a visual map separate from the text artifact (and here’s a super rough example of the potential output):

Create a competitive messaging map showing where competitors cluster and where white space exists.

Technique 3: Social listening at scale

Homework: Gather Reddit threads, LinkedIn posts, Twitter/X conversations, industry forums (consumer Facebook groups, startup forums like Bookface or Hacker News, etc.), and review sites where your audience talks about their challenges. Perplexity, for example, can also help pull these for you, but do your own search and see how it differs from what the LLM finds.

The prompt: