How to Generate Customer Questions for AI Search: Question Development Framework

With a deep understanding of your B2B SaaS product, its core value, and your target audience segments (as detailed in B2B SaaS Product Analysis: How to Map Features to Customer Pain Points for AI Search), you’re ready for the pivotal phase of crafting the actual questions your prospects and customers will ask AI search interfaces. This isn’t about simple keyword lists; it’s about anticipating natural language queries at every stage of the buyer’s journey.

This playbook will guide you through a systematic process to generate a comprehensive set of questions that will serve as your primary tool for tracking and analyzing your AI Search Brand Visibility. These questions, when posed to AI platforms, will reveal how your brand is perceived, positioned, and whether it’s mentioned at critical decision-making moments.

(This article is part of our comprehensive series on AI Search Brand Visibility. For an overview, see our Pillar Page: How to Track Your B2B SaaS Brand in AI Search Results: Complete Guide)

---

How to Create Questions Based on Customer Pain Points and Product Features

The effectiveness of your AI Search Brand Visibility tracking hinges on the relevance and comprehensiveness of the questions you test. Our methodology combines your product knowledge with user-centric thinking across the awareness funnel.

1. The Power of Pain Point + Feature Pairs

Recall the detailed work you did mapping specific customer pain points to the product features that solve them. These “Pain Point -> Feature(s)” pairs are goldmines for question generation. Each pair represents a distinct problem-solution scenario that your target users are grappling with and seeking answers for.

• Action: Revisit your list of Pain Point -> Feature(s) pairings. For each pairing, consider it a core theme around which questions will be built.
  • Example (ProjectFlow – Project Management SaaS):
    • Pain Point: Difficulty effectively tracking task progress and accountability across multiple team members.
    • Relevant Features: Kanban boards, task assignment with due dates, real-time progress dashboards, individual to-do lists, automated reminders.
  • This theme of “improving task tracking and accountability” will be explored across different user segments and awareness stages.

2. Tailoring Questions to Specific User Segments

Different user segments (personas) within your Ideal Customer Profile (ICP) will articulate their needs and questions differently, even if they are experiencing similar underlying pain points. Their role, technical expertise, priorities, and daily vocabulary will heavily influence their query language.

• Action: For each Pain Point -> Feature(s) pair, and for each of your defined User Segments, ask yourself: “How would this specific individual phrase their question or describe their need to an AI assistant regarding this theme?”
  • Example (ProjectFlow – Theme: Improving Task Tracking):
    • Strategic Leader Segment Query Style: Might focus on outcomes and efficiency gains. E.g., “How can I improve overall project delivery speed by optimizing task management across teams?”
    • Tactical Manager Segment Query Style: Might focus on processes, tools, and specific functionalities. E.g., “What are the best ways to track remote team tasks and ensure everyone knows their responsibilities?”
    • Individual Contributor Segment Query Style: Might focus on personal productivity or ease of use. E.g., “How can I easily see all my assigned tasks and their deadlines in one place?”

3. Mapping Questions Across the 5 Stages of Awareness

This is the most critical and nuanced part of question generation. The same Pain Point -> Feature(s) pair, when considered for the same User Segment, will generate vastly different questions depending on the user’s stage in the awareness funnel. Understanding these stages is key to covering all bases.

• Stage 1: Unaware: The prospect isn’t consciously aware they have a problem that needs solving. They might be experiencing symptoms but haven’t diagnosed the root cause or labeled it as a specific “problem.”
  • Goal of Questions for this Stage: To identify how this user might describe the symptoms or early frustrations related to the pain point, even if they don’t realize it’s a solvable issue.
• Stage 2: Problem Aware: The prospect now recognizes they have a problem and is actively seeking to understand it better. They are researching their pain, its causes, and its impact.
  • Goal of Questions for this Stage: To capture queries exploring the nature of the problem, seeking validation, or trying to define its scope.
• Stage 3: Solution Aware: The prospect understands their problem and knows that solutions exist. They are now researching different types of solutions, methodologies, or approaches to address their problem.
  • Goal of Questions for this Stage: To generate queries comparing different solution categories, features to look for in a generic solution, or benefits of certain approaches.
• Stage 4: Product Aware: The prospect is now aware of your specific product (and likely your competitors) as a potential solution. They are actively comparing specific products, features, pricing models, and suitability for their needs.
  • Goal of Questions for this Stage: To craft queries that directly mention your product, compare it to competitors, or ask about its specific capabilities in relation to their known problem.
• Stage 5: Most Aware: The prospect is highly familiar with your product and is close to making a purchase decision. They are looking for final validation, specifics on implementation, pricing details, social proof, or a compelling reason to choose your solution now.
  • Goal of Questions for this Stage: To formulate queries seeking reassurance, detailed logistical information, or specific offers related to your product.

(Conceptual Cue: Imagine a funnel diagram here, with each stage clearly demarcated, showing example question types moving from broad symptom-based queries at the top (Unaware) to highly specific product-related queries at the bottom (Most Aware).)

---

Practical Application: Generating Questions for “ProjectFlow”

Let’s bring this all together with our ongoing B2B SaaS example, “ProjectFlow.” We will walk through generating questions for different user segments and pain points across the awareness stages.

Scenario 1: Tactical Manager & Task Tracking Inefficiencies

• User Segment: Tactical Manager (e.g., Project Manager, Scrum Master)
• Pain Point: Difficulty effectively tracking who is doing what, leading to missed deadlines and project delays.
• Relevant Features: Kanban boards, task assignment with due dates, progress dashboards, notifications, resource allocation views.

Example Questions Across Awareness Stages:

• Stage 1: Unaware (Tactical Manager – Task Tracking)
  • “Why do my team’s projects always seem to be running slightly behind schedule?”
  • “How can I reduce the amount of time I spend in status update meetings?”
  • “Is it normal for team members to sometimes be confused about their immediate priorities?”
  • “What are common hidden reasons for project delays in agile software teams?”
  • “Feeling like I’m constantly chasing people for updates, how to fix this?”
• Stage 2: Problem Aware (Tactical Manager – Task Tracking)
  • “How to effectively track tasks and progress for a remote software development team?”
  • “Best ways to improve visibility on who is doing what in a project?”
  • “What are the signs of inefficient task management processes in a growing team?”
  • “Challenges of using spreadsheets and email for task tracking in a multi-faceted project?”
  • “My current task tracking method isn’t scaling, what should I consider?”
• Stage 3: Solution Aware (Tactical Manager – Task Tracking)
  • “What kind of software tools help with project task management and team visibility?”
  • “Compare Kanban boards vs. Gantt charts vs. to-do lists for software development projects.”
  • “Benefits of using a dedicated project management platform over a collection of single-purpose apps.”
  • “What key features should I look for in a task management tool for an agile team?”
  • “How can visual task management methodologies improve team workflow and reduce bottlenecks?”
• Stage 4: Product Aware (Tactical Manager – Task Tracking – ProjectFlow as our brand)
  • “How does ProjectFlow’s Kanban board and task assignment feature compare to Asana’s or Jira’s?”
  • “What are the main task tracking and progress reporting features of ProjectFlow?”
  • “Is ProjectFlow suitable for agile software development teams needing detailed task tracking?”
  • “Reviews of ProjectFlow specifically for managing team tasks and project deadlines.”
  • “ProjectFlow vs. Monday.com for ease of use in task visibility and daily status updates.”
  • “How does ProjectFlow handle task dependencies, notifications, and recurring tasks?”
• Stage 5: Most Aware (Tactical Manager – Task Tracking – ProjectFlow as our brand)
  • “What is the pricing for ProjectFlow’s professional plan for a team of 15 project managers?”
  • “How easy is it to import existing tasks from Trello into ProjectFlow?”
  • “Does ProjectFlow integrate with Slack and Google Calendar for task notifications and scheduling?”
  • “Are there any ProjectFlow case studies from digital agencies that improved project delivery times?”
  • “What kind of onboarding support and training does ProjectFlow offer for setting up advanced task management workflows?”
  • “Is there a discount for ProjectFlow if we sign up for an annual subscription?”

Scenario 2: Strategic Leader & Portfolio Resource Planning

• User Segment: Strategic Leader (e.g., Head of Engineering, VP of Product)
• Pain Point: Lack of clear visibility into overall project portfolio health and difficulty in strategic resource planning across multiple concurrent projects.
• Relevant Features: Portfolio-level dashboards, resource capacity planning views, AI-driven risk assessment, cross-project financial reporting, skills inventory.

Example Questions Across Awareness Stages:

• Stage 1: Unaware (Strategic Leader – Resource Planning)
  • “Why are our best engineers always seeming over-committed?”
  • “Is it common for strategic projects to be delayed due to unexpected resource conflicts?”
  • “What are the hidden financial impacts of poor cross-project resource allocation?”
  • “How can I get a reliable gut feeling for our true capacity to take on new large-scale initiatives?”
• Stage 2: Problem Aware (Strategic Leader – Resource Planning)
  • “How to effectively manage and allocate skilled resources across a portfolio of software projects?”
  • “Best practices for enterprise project portfolio oversight in a dynamic tech company?”
  • “What are the key indicators of a poorly balanced project portfolio leading to resource burnout?”
  • “Challenges in strategic workforce and resource planning for engineering and product departments.”
• Stage 3: Solution Aware (Strategic Leader – Resource Planning)
  • “What types of software are best for enterprise project portfolio management (PPM) and resource optimization?”
  • “Compare dedicated resource management software vs. all-in-one PM platforms for strategic planning needs.”
  • “What are the benefits of using AI in project resource allocation and predictive risk forecasting at a portfolio level?”
  • “Key features to look for in a PPM solution tailored for technology companies with matrixed teams.”
• Stage 4: Product Aware (Strategic Leader – Resource Planning – ProjectFlow as our brand)
  • “How does ProjectFlow’s resource capacity planning module compare to [Competitor PPM Tool A]’s capabilities?”
  • “What are the AI-powered predictive analytics features within ProjectFlow for assessing portfolio risk and forecasting resource needs?”
  • “Is ProjectFlow robust enough for managing a portfolio of 50+ concurrent software development projects with shared resources?”
  • “Independent reviews of ProjectFlow for strategic project and multi-project resource management.”
  • “ProjectFlow vs. [Competitor PPM Tool B] for executive-level portfolio dashboards and financial oversight.”
• Stage 5: Most Aware (Strategic Leader – Resource Planning – ProjectFlow as our brand)
  • “What kind of ROI and efficiency gains can we realistically expect from implementing ProjectFlow’s AI-driven resource optimization features across our engineering department?”
  • “How does ProjectFlow integrate with our existing HRIS and financial systems for comprehensive portfolio reporting?”
  • “What is the typical implementation timeline and change management support for ProjectFlow in an enterprise IT environment?”
  • “Can ProjectFlow provide customized portfolio dashboards and reports for our executive leadership team and board?”
  • “Details on ProjectFlow’s enterprise licensing and volume discounts for over 500 users.”

---

Advanced Question Discovery: Data-Driven Methods for Systematic Question Generation

While the foundational methodology above provides the framework, truly effective AI Search Brand Visibility requires moving beyond intuition-based question generation to systematic, data-driven discovery methods. This advanced approach ensures you capture the actual language your prospects use, not just what you think they might ask.

Current Gap in Traditional Approaches:

Most B2B SaaS companies rely heavily on internal brainstorming and assumptions about customer language, missing the nuanced ways real prospects actually articulate their needs and questions.

The Solution: Multi-Source Question Discovery Framework

1. Customer Support Ticket Analysis: Mining Real Customer Language

Your support tickets are goldmines of authentic customer language and pain points. Customers contact support when they’re confused, frustrated, or seeking specific solutions—exactly the mindset that drives AI search queries.

Implementation Process:

• Data Collection: Export 6-12 months of support tickets, focusing on pre-sales questions and implementation challenges
• Categorization: Group tickets by pain point themes and user segments
• Language Extraction: Identify exact phrases customers use to describe problems
• Question Transformation: Convert support requests into AI search queries

Example Transformation (ProjectFlow):

• Support Ticket: “Hi, our team is struggling to see who’s working on what. We have people working remotely and it’s hard to track progress. Do you have something that shows task assignments?”
• AI Search Question: “How can I track task assignments and progress for a remote team?”

Tools & Techniques:

• Use text analysis tools to identify common phrases and terminology
• Create word clouds to visualize frequently used language
• Track seasonal patterns in support questions
• Segment analysis by customer size, industry, or user role

2. Sales Call Transcript Analysis: Capturing Prospect Research Behavior

Sales conversations reveal the exact questions prospects ask when evaluating solutions. These conversations happen after initial research, providing insight into what information gaps remain after AI search interactions.

Implementation Process:

• Recording & Transcription: Use tools like Gong, Chorus, or Rev to transcribe sales calls
• Question Identification: Extract direct questions prospects ask during calls
• Context Analysis: Understand the research journey that led to each question
• Gap Analysis: Identify what information prospects couldn’t find through their initial research

Example Analysis (ProjectFlow):

• Sales Call Question: “We looked at several project management tools, but we’re specifically concerned about how well they handle dependencies between tasks across different teams. How does ProjectFlow handle that?”
• AI Search Implication: Prospects are researching “project management tools task dependencies” but not finding satisfactory answers
• Generated AI Questions:
  • “How do project management tools handle task dependencies across teams?”
  • “Best practices for managing cross-team task dependencies in software projects”

Advanced Techniques:

• Sentiment analysis of prospect questions to understand emotional drivers
• Competitive mention analysis to understand evaluation criteria
• Objection pattern analysis to identify common concerns

3. Social Listening Integration: Identifying Emerging Questions

Social media, forums, and professional communities reveal emerging trends and questions before they become mainstream search queries.

Key Platforms for B2B SaaS Listening:

• LinkedIn: Professional discussions and industry groups
• Reddit: Subreddits related to your industry (r/projectmanagement, r/startups, etc.)
• Twitter/X: Real-time industry conversations and pain point expressions
• Industry Forums: Specialized communities (Stack Overflow, Product Hunt, etc.)
• Slack Communities: Professional groups and industry-specific channels

Implementation Framework:

• Keyword Monitoring: Track mentions of pain points, not just your brand
• Question Harvesting: Collect actual questions people ask in these communities
• Trend Identification: Spot emerging topics before they become competitive
• Language Evolution: Track how terminology and phrasing evolves

Example Discovery (ProjectFlow):

• LinkedIn Post: “Anyone else struggling with project visibility now that we’re fully remote? Traditional status meetings aren’t cutting it anymore.”
• Generated Questions:
  • “How to improve project visibility for remote teams?”
  • “Alternatives to status meetings for remote project management”
  • “Best practices for remote project transparency”

4. Search Query Analysis: Leveraging Traditional Search Data

Your existing SEO and search data provides insights into how people currently search for solutions, which can inform AI search question generation.

Data Sources:

• Google Search Console: Actual queries driving traffic to your site
• Google Analytics: Search terms used on your site
• Keyword Research Tools: Semrush, Ahrefs, etc. for broader market queries
• Competitor Analysis: Keywords competitors rank for

Analysis Techniques:

• Long-tail Query Analysis: Focus on conversational, question-based queries
• Voice Search Optimization: Identify natural language patterns
• Featured Snippet Analysis: Questions that trigger rich results
• People Also Ask Mining: Related questions Google suggests

Transformation Process:

• Convert keyword-based queries into natural language questions
• Expand short queries into full conversational questions
• Add context and specificity based on user segments

Example Transformation:

• Search Query: “project management software remote teams”
• AI Search Questions:
  • “What’s the best project management software for remote teams?”
  • “How do I choose project management tools for distributed teams?”
  • “Project management software comparison for remote work”

5. Question Performance Scoring: Data-Driven Prioritization

Not all questions are equally valuable. Implement a systematic scoring framework to prioritize your question generation and testing efforts.

Question Discovery & Scoring Framework:

Step 1: Extract questions from 5 data sources monthly

• Support tickets: 30-40% of questions
• Sales transcripts: 25-30% of questions
• Social listening: 20-25% of questions
• Search data: 15-20% of questions
• Internal brainstorming: 5-10% of questions

Step 2: Score each question on three dimensions (1-10 scale):

Business Impact Score (1-10):

• 9-10: Directly relates to primary value proposition and high-value customer segments
• 7-8: Relates to secondary benefits or medium-value segments
• 5-6: Tangentially related to product benefits
• 1-4: Low relevance to business outcomes

Competition Level Score (1-10):

• 9-10: Highly competitive, many established players mentioned
• 7-8: Moderately competitive, some established players
• 5-6: Mixed competitive landscape
• 1-4: Low competition, opportunity for visibility

Achievability Score (1-10):

• 9-10: High likelihood of achieving brand mention with current content/authority
• 7-8: Moderate likelihood with content optimization
• 5-6: Possible with significant content investment
• 1-4: Low likelihood without major authority building

Step 3: Calculate Composite Priority Score

Priority Score = (Business Impact × 0.5) + (Achievability × 0.3) + ((11 - Competition Level) × 0.2)

Step 4: Prioritize questions with highest composite scores

• Focus initial efforts on questions scoring 8.0+
• Build content pipeline for questions scoring 6.0-7.9
• Monitor questions scoring below 6.0 for future opportunities

Step 5: Track question performance and adjust scoring model quarterly

• Monitor actual brand mention rates for tested questions
• Adjust scoring weights based on performance correlation
• Refine scoring criteria based on results

Advanced Scoring Considerations:

• Seasonal Relevance: Weight questions higher during relevant business cycles
• User Segment Value: Apply multipliers based on customer lifetime value by segment
• Competitive Intelligence Value: Bonus points for questions that reveal competitor weaknesses
• Content Leverage: Higher scores for questions that can be answered with existing content assets

Implementation Roadmap for Advanced Question Discovery:

Month 1: Foundation Setup

• Set up data collection systems for all 5 sources
• Create question database and scoring framework
• Establish baseline question inventory

Month 2-3: Data Collection & Analysis

• Begin systematic question extraction from all sources
• Implement scoring methodology
• Create prioritized question pipeline

Month 4-6: Testing & Optimization

• Begin AI search testing with highest-priority questions
• Track performance and refine scoring model
• Expand question discovery based on initial results

Ongoing: Continuous Improvement

• Monthly question discovery sessions
• Quarterly scoring model refinement
• Annual methodology review and enhancement

---

Visualizing Your Question Matrix: Bringing It All Together

To effectively manage and scale your question generation efforts, creating a structured repository is essential. A Question Matrix, typically built in a spreadsheet (like Google Sheets or Excel), allows you to organize and visualize your questions systematically.

(Conceptual Cue: Imagine a spreadsheet here. Rows might be User Segments, columns the 5 Awareness Stages. Each cell would contain multiple questions relevant to that Segment/Stage combination, covering various pain points/features. Or, rows could be Pain Point/Feature themes, columns User Segments, and cells list questions per awareness stage.)

How to Structure Your Question Matrix:

• Option 1 (User Segment Focused):
  • Rows: Your identified User Segments.
  • Columns: The 5 Stages of Awareness (Unaware, Problem Aware, Solution Aware, Product Aware, Most Aware).
  • Cells: For each User Segment at each Stage, list numerous questions. These questions in the cell will be derived from the various Pain Point -> Feature(s) combinations relevant to that segment.
• Option 2 (Pain Point/Feature Focused):
  • Rows: Your key Pain Point -> Feature(s) pairings (themes).
  • Columns: Your identified User Segments.
  • Sub-Columns (within User Segments): The 5 Stages of Awareness.
  • Cells: List questions specific to that theme, for that user segment, at that awareness stage.

Benefits of Using a Question Matrix:

• Comprehensive Coverage: Ensures you consider all critical intersections of user, pain point, and awareness stage.
• Organization: Keeps a large volume of questions manageable and easy to reference.
• Collaboration: Allows multiple team members to contribute and review.
• Prioritization: Helps you identify the most critical questions to track initially.
• Ongoing Resource: Serves as a living document for your marketing, sales, and content teams, informing content creation strategies beyond just AI search tracking.

Key Considerations When Generating Questions for Your Matrix:

• Volume is Your Friend (Initially): Aim for a comprehensive list. It’s better to generate many questions and then refine or prioritize, than to miss critical queries. Strive for at least 5-10 questions per cell in your matrix if possible.
• Embrace Natural Language: Think about how real people actually talk and type when using AI assistants. Include variations, synonyms, long-tail questions, and even questions with slight grammatical imperfections (as users might type them).
• Clarify User Intent: For each question, be clear in your own mind what the user is really trying to achieve or find out. This helps ensure the question is well-phrased to elicit the desired type of information.
• Iterate and Evolve: Question generation is not a one-time task. As your product evolves, your market changes, competitors shift, and AI search capabilities advance, you’ll need to revisit and update your question set regularly (e.g., quarterly or alongside major product releases).

---

By systematically creating this detailed question matrix, you develop a powerful dataset that directly reflects the potential conversational interactions your target audience will have with AI search tools. This dataset is the indispensable foundation for the next steps: fetching responses from AI platforms and analyzing your brand’s visibility within them.

Next Step: Learn how to take these questions and use them to uncover how your brand is seen by AI, and what tools can help.
➡️ Continue to:  Beyond the Buzz: A Practical Guide to Tracking, Analyzing & Benchmarking Your B2B SaaS Brand in AI Search