Prompt Market Analysis by Marketing Leaders: Understanding What Buyers Ask AI

Conducting Prompt Market Analysis: Methods and Workflow

Marketing leaders conduct prompt market analysis through a structured workflow.

Step 1. Define your prompt framework

Before collecting data, marketing leaders define the structure of the prompts you want to study. Identify the categories of questions buyers ask across your product category. Then, map the questions users ask before, during, and after the buying process. 

For Brand Z, the prompt framework might include,

• TOFU (Top of Funnel – Informational intent):

→ “What is an AI-driven digital marketing platform like Brand Z?”

→ “Why use AI in digital marketing?”

• MOFU (Middle of Funnel – Commercial investigation intent):

→ “Best AI-driven digital marketing platforms like Brand Z”

→ “How to choose a marketing automation platform”

• BOFU (Bottom of Funnel – Decision/transactional intent):

→ “Brand Z vs Brand X for marketing automation”

→ “Is Brand Z good for small businesses?”

Step 2. Analyze prompt patterns at scale  

With a defined framework, the next step is to look for patterns. Group prompts by themes and intent types. This step helps marketing leaders understand how users naturally frame their questions and where high-intent clusters exist.

Common patterns include:

• Informational intent:

→ “What is…”

→ “How does…”

→ “Why use…”

These patterns indicate learning-focused queries where users are trying to understand a concept or category.

• Navigational intent:

→ “[Brand Z] login”

→ “[Brand Z] pricing”

→ “[Brand Z] features”

These patterns show users trying to find a specific brand or resource they already know.

• Commercial investigation intent:

→ “Best [category] for…”

→ “Top tools for…”

→ “How to choose…”

These patterns reflect evaluation behavior, in which users compare options and seek solutions tailored to their needs.

• Transactional intent:

→ [Brand Z] free trial”

→ “Book a demo”

→ “Sign up for…”

These patterns indicate action-oriented queries with a clear intent to convert.

Step 3. Test Prompts Across AI Platforms 

Run representative prompts across AI platforms. Observe how answers are constructed, what formats are preferred, and which types of content get surfaced. Focus on patterns, not just individual outputs. E.g., AI responses often prioritize list-based or comparison formats.

Step 4. Assess competitive visibility

Certain brands appear consistently across high-intent queries. Identify which brands, products, and concepts appear most frequently across prompts. This reveals the competitive landscape within AI responses and highlights who dominates high-intent queries.

→ “Brand X: appears in 70% of tracked prompts

→ “Brand Y: appears in 15%

→ “Brand Z: appears in 15%

Step 5. Identify opportunity gaps

Compare prompt demand with existing content coverage. Look for areas where user intent is strong but content is weak, unstructured, or missing. These gaps represent the highest-impact opportunities for visibility.

• Use-case prompts:

→ “AI marketing for lead generation”

→ “AI tools for campaign optimization”

• Segment prompts:

→ “for startups”

→ “for small marketing teams”

• Comparison prompts:

→ “Brand Z vs Brand X”

Step 6. Translate insights into content strategy

Marketing leaders use findings to guide content creation and optimisation. Prioritise high-intent prompts and structure content in a way that AI systems can easily interpret and cite.

This process helps them move from isolated keyword targeting to a broader, intent-driven strategy built around real user questions.

• Use-case pages (lead generation, campaign optimization)

• Segment-specific pages (for startups, SMBs)

• Comparison content (Brand Z vs competitors)

Figure 3: Workflow to conduct prompt market analysis.

For a deeper look at how your brand specifically appears and is positioned in AI responses, check out the AI Brand Narrative Audit guide.