AI-Driven Market Research Accelerates Insight for Resonant Ad Creative

The most effective AI-driven market research isn't about the tool, but the process. This conversation reveals a hidden consequence: relying solely on AI without a structured, multi-step prompting strategy leads to generic outputs that fail to capture the nuanced voice of the customer. For marketers and business owners, mastering this process offers a significant advantage by unlocking deep, emotionally resonant insights that fuel high-converting creative, saving weeks of manual research and preventing budget-burning guesswork. Those who adopt this methodology will gain a competitive edge by speaking directly to their audience's deepest needs and desires, rather than broadcasting surface-level platitudes.

The Hidden Cost of "Garbage In, Garbage Out": Why AI Needs a Research Plan

The common refrain with AI, particularly Large Language Models (LLMs), is "garbage in, garbage out." While accurate, this statement often masks a deeper truth: the real challenge isn't the AI's capability, but the user's ability to craft inputs that elicit truly valuable outputs. Cole Turner, speaking on Perpetual Traffic, highlights that most marketers bypass the most critical, albeit difficult, part of the process: robust market research. This isn't a new problem; it's an age-old marketing challenge amplified by the speed and scale of AI. The immediate, visible problem is ineffective advertising creative. The hidden consequence, however, is a compounding waste of resources and a fundamental disconnect with the target audience, stemming from a failure to understand their deeper emotional drivers.

Turner emphasizes that effective advertising doesn't just address surface-level needs; it taps into profound emotional levers. He uses the analogy of selling a knee brace: a generic pitch about pain is far less effective than one that speaks to the loss of identity or the longing for past activities. This nuanced understanding, he argues, is the bedrock of direct response marketing.

"The deeper you can go down that iceberg, the more you're going to be able to elicit emotion from someone and from what we know about direct response of course eliciting powerful emotions is the way to get someone to take immediate action."

This principle, rooted in the work of advertising pioneers like Eugene Schwartz, dictates that understanding the "stage of awareness" of the audience is paramount. For cold traffic channels like Meta, targeting the "problem-aware" segment--those who know they have an issue but haven't yet found a solution--is often the most profitable. This is where AI, when properly guided, can compress weeks of manual research into minutes, providing a deep well of "voice of customer" data.

The conventional approach, as described by Turner, involves manual scraping of comments and forums, a process that is both time-consuming and prone to human bias. The breakthrough he presents is a multi-step prompting strategy that leverages AI to first generate a comprehensive research plan, and then execute that plan using AI models with internet browsing capabilities. This structured approach ensures that the AI is directed to find specific, emotionally charged insights rather than generic information.

The Multi-Prompt Advantage: Building a Research Moat

The core innovation presented is not a single, magical prompt, but a deliberate, sequential process. The first prompt instructs the AI to act as an expert copywriter, direct response specialist, and market researcher, drawing on the wisdom of greats like David Ogilvy and Eugene Schwartz. Crucially, it's tasked with creating a detailed research prompt for a subsequent AI step. This intermediary step is designed to overcome the user's potential deficit in research planning expertise.

"The first prompt we're going to put into chat gpt is actually going to give us back another prompt that we're later going to give the chat gpt again so the reason behind that is that i'm not a research scientist i'm a good marketer i'm not the best like researcher on the planet so with this first step we're telling chat gpt basically who the market is and then we're telling it also to create a research plan a huge outline on that market that we're then going to give to another version of chat gpt."

This generated prompt is then fed into an AI model capable of deep internet research. This second AI then dives into forums, blogs, and comment sections to extract verbatim "voice of customer" quotes, frustrations, and emotional triggers. The output is a rich dossier of customer language, including phrases like "my lower back is screaming" or "I've been popping a [pain reliever] like candy." These are not just descriptive; they are hooks. They are the raw material for ads that resonate because they are spoken in the customer's own voice, addressing their specific pain points and emotional states.

This system directly combats the "vanilla ice cream" approach to marketing, where generic messaging appeals to no one. Instead, it allows marketers to identify and speak to the "Rocky Road" or "Chocolate Raspberry Swirl" of customer needs, creating a much stronger connection. The advantage here is clear: by understanding the specific language and emotional landscape of the target audience, marketers can craft creative that cuts through the noise, leading to higher engagement and conversion rates. This process, which used to take weeks, can now be compressed into hours, creating a significant time and resource advantage.

From Insight to Action: Training AI for Hyper-Personalized Creative

The true power of this multi-step AI research process lies not just in the data extraction, but in its application. The output dossier can be used to train custom GPTs, essentially creating AI agents specialized in generating marketing copy and creative directly from the research. This moves beyond simply understanding the customer to having an AI assistant that can articulate those insights into persuasive advertising materials.

The example provided of training a custom GPT for a standing desk demonstrates this. By feeding the AI the market research and instructing it to act as a direct response copywriter, trained on customer language and using frameworks like "Pain, Agitate, Solution" (PAS), the output is dramatically more effective than generic AI-generated copy. The AI is instructed to avoid fluffy language and instead use the specific fears, desires, and phrases identified in the research.

"Always write in the language of the customer using their fears desires and phrases apply proven persuasion principles this would be like your copywriting principles like pain agitate solution etc deep the copy simple blah blah blah and then never write generic or fluffy copy we all hate fluffy ads use buzzwords and then never ignore the uploaded research."

This capability represents a significant competitive advantage. While many might use AI for basic content generation, those who leverage it for deep, structured market research and then use that research to train specialized AI copywriters can achieve a level of personalization and resonance that is incredibly difficult to replicate. It’s about building a "moat" around your marketing efforts, not through proprietary algorithms, but through a superior understanding of the customer, facilitated by intelligent AI prompting. The effort required to set up this multi-step process and train custom GPTs creates a barrier to entry, ensuring that the "competitive advantage from difficulty" is realized.

Key Action Items

• Immediate Action (Within 1 week):
  • Download the provided prompt: Access the prompt template from tier11.com/prompt to initiate your AI research process.
  • Identify your target market segment: Clearly define the specific audience for your initial AI research.
  • Run the first AI prompt: Use the downloaded prompt in an LLM (like ChatGPT) to generate your research plan prompt.
  • Execute the research prompt: Feed the generated prompt into an AI model with deep research capabilities (e.g., ChatGPT with browsing, Gemini, Claude).
• Short-Term Investment (Within 1-2 weeks):
  • Analyze the research dossier: Carefully review the output for key customer phrases, emotional triggers, and pain points.
  • Train a custom AI copywriter: Use the research findings to build and train a specialized GPT for generating ad copy, scripts, or landing page content.
  • Develop initial ad creative: Use the AI-generated copy as a foundation for your first set of ads, focusing on resonance and specific customer language.
• Mid-Term Investment (1-3 months):
  • Test AI-generated creative: Launch campaigns using the AI-informed creative and monitor performance closely.
  • Iterate on prompts: Refine your initial prompts based on the quality of the research and the performance of the creative.
  • Expand to new segments: Apply the multi-step prompting process to other target audiences or product lines.
• Long-Term Advantage (6-12 months):
  • Integrate AI research into workflow: Make this structured AI research process a standard part of your campaign planning and creative development.
  • Develop proprietary AI research models: Continue to refine and build custom GPTs that are highly specialized to your business and market, creating a unique competitive moat.