How can B2B SaaS companies identify the specific questions prospects ask LLMs?

Detailed Explanation

Here is a breakdown of how B2B SaaS companies can identify these specific prospect questions:

1. Reverse-Engineering Conversational Flow (Prompt Mapping)

The core strategy in GEO is Prompt Mapping, which involves understanding the user's journey beyond the initial query, especially because LLM queries are typically much longer (around 25 words on average) and conversational.

• Anticipate Query Fan-Out: Generative Engines, particularly Google AI Overviews and AI Mode, use query fan-out or semantic decomposition to break a user's initial prompt into multiple sub-queries aimed at extracting different latent intents. B2B companies must map content not just to the core search term but to the full set of variations buyers use.
• Create a Prompt Map: Develop a comprehensive map that includes the entire buyer research funnel, such as:
  • Core searches (e.g., "Generative Engine Optimization agencies").
  • Adjacent evaluation prompts (e.g., "comparing GEO vs SEO agencies").
  • Deep research queries (e.g., strategies, best practices, technical differences).
  • Topically adjacent follow-up questions and competitor comparisons (Query Fan-Out Pages).
• Focus on Niche and Complex Queries: B2B SaaS often involves incredibly niche and complex technical queries. The long tail of questions is much larger in chat environments compared to traditional search, presenting an opportunity to win queries that may have never been searched before.

2. Mining Internal and External Customer Data

Since LLMs encourage natural, conversational questions that address context and pain points, valuable query data often resides outside of traditional keyword tools.

• Analyze Customer Interactions: Mine internal data sources that capture genuine customer language and intent, such as:
  • Sales call transcripts.
  • Customer support tickets or live chat logs.
  • Customer feedback from surveys or reviews to identify pain points and desired outcomes.
• Address the "Long Tail" Gap: Many specific use cases—such as complex integration needs (e.g., "Which meeting transcription tool integrates with Looker via Zapier to BigQuery?")—may not have dedicated help center content. Identifying these unaddressed questions from internal logs helps target the conversational long tail where citation opportunities are high.
• Monitor Community Platforms: LLMs are known to frequently cite User-Generated Content (UGC) sources to establish credibility and real-world applicability. Companies should monitor and extract questions from:
  • Reddit threads (highly cited in LLMs).
  • Quora discussions.
  • Industry forums like G2.

3. Transforming Traditional Search Data

Traditional keyword data can be repurposed to generate LLM-ready questions:

• Convert Keywords to Questions: Take existing high-value search terms or competitor paid search data (the "money terms") and transform them into natural language questions that prospects would ask an AI.
• Utilize LLMs for Query Generation: You can feed keywords or topics into an LLM (like ChatGPT) and prompt it to generate multiple conversational questions corresponding to those terms.
• Leverage Search Features: Tools like "People Also Ask" sections and "Please Also Search For" features in traditional search results can reveal specific, question-based intents already popular with users.

4. Direct Measurement and Competitive Intelligence

Since Generative Engines operate as a "black-box optimization framework", continuous tracking and analysis of live AI responses are necessary to see which questions trigger brand mentions.

• Manual Query Audits: Run regular queries across multiple LLMs (ChatGPT, Claude, Perplexity, Gemini). Perform these searches in incognito mode to prevent personalization bias.
• Mimic Buyer Intent: Phrase prompts naturally and conversationally, matching high-intent queries (e.g., "Best [product category] for [target persona]").
• Analyze Citation Networks: Look for who is currently showing up as citations for your target questions. This competitive intelligence allows you to reverse-engineer the evidence base that the LLMs are prioritizing.
• Use Automated Tracking Tools: Specialized platforms offer LLM citation monitoring to track how often your brand or content is cited across popular AI platforms and compare it against competitors' share of voice. These tools identify potential content gaps and reveal the types of queries users ask about your brand and the intent behind them (e.g., educational, research-based, or transactional).

By focusing on these strategies, B2B SaaS companies move from optimizing content for keyword density to generating content that aligns with the semantic coverage and conversational complexity that LLMs demand for citation. This process is crucial because getting cited in an LLM answer is about becoming the authoritative source the AI chooses to reference.