đ 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 Iâm launching a multi-part series Iâve 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 cheaperâthe result being a lot of generic dreck. đź
I think one of AIâs best use cases is helping us better understand the people weâre marketing to and pushing our creative thinking furtherâŠnot being creative for us. How do we do that? Letâs start with audience research because everything elseâyour positioning, your messaging, your creativeâfalls apart without that foundation.
Perfect timing, because Typeform just launched the next installment of their Get Real series, and weâre teaming up to ask the exact questions Iâve been wrestling with. Theyâre digging into how generative AI is reshaping our industryâthe stigma around using it, the fear of being replaced by it, and the pressure to prove weâre still human. Theyâre capturing actual marketer perspectives through writing, video, and audio responses to get the nuance behind the data, not just numbers. Youâll see yours truly đđŒ in the survey asking (and answering) the exact question I was getting at aboveâhow do we make sure our use of AI actually makes our marketing better for our audience, not just easier for us as marketers?
đđŒ Share your honest take by September 26âI predict a spirited final report.
If someone sent you this post and youâre not subscribed, join those people learning how to tactically advocate for brand at your company. đŹ
đ§· A disclaimer before we begin: Iâm not one of those people who is obsessed with prompting optimization, and Iâm still (always) learning. These arenât intricate or complex prompts, and I guarantee each one I share below can be improved and refined, let alone customized to your use caseâso take them as a starting point, see what works for you, and tell me what you learn as you go!
If youâre using AI to help produce any type of customer-facing creative output, thereâs critical context you need to feed your tool. And whether youâre a marketer, a founder, a CEO, a sales manager, whoeverâin order to create effective marketing, you need to understand your audience deeply, from demographics to firmographics to psychographics. Itâs the responsibility of any marketer, but especially of the brand leader, to get past âour customers have these titles and work at companies with $X in revenue and have these functional problemsâ 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 Iâve 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 fasterâand makes it less likely youâll wind up with the same garbo as anyone else with a free ChatGPT account. đ
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. Donât miss đ 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 Iâm 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 goalâsales 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 youâre not asking AI to guess what customers wantâyouâre asking it to organize and analyze what theyâve 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.đ Pro tip: Analyze both positive and negative feedback. Pay attention to complaints and support tickets where customers got frustratedâthis usually shows you an opportunity via a gap between what they expected and what they experienced, which often points to positioning or messaging issues. Iâve found some of my best brand insights in Zendesk tickets where customers said things like âI thought this would...â or âWhy doesnât it just...â
Technique 2: Competitive message analysis
Homework:
Collect homepage copy, ad creative, and marketing emails from your top 5-10 competitors. If youâre using an LLM with live search on, you can ask it to pull these for you, but itâll be more limited to what it can find publicly online (like web pages, not an email series). IMO, itâs worth it to spend the extra time to gather this yourselfâI keep a big Figma file of my âcompetitive landscapeâ on hand with all kinds of screenshots of various competitorsâ marketing, and I add to it all the time.
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. đ Pro tip: The output for the âwhite spaceâ opportunities (bullet #3 above) will benefit the most from any context you gather on customer needs and opportunities, so you donât just end up with a list of every possible applicable feature. Secondly, you donât have to limit yourself to direct competitors in this exercise. Include companies your prospects might consider as alternatives, even if theyâre in adjacent categoriesâwhatever is ultimately competing for your audienceâs 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 hereâs 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:


