The Ultimate List of Brand Perception Survey Questions for 2026

Your Brand Has Two Audiences: Humans and AI. Are You Surveying Both?

For decades, brand perception surveys have been the gold standard for understanding what customers think about your brand. You run surveys, analyze the data, and adjust your strategy accordingly. This classic approach remains essential, but relying on it alone means you are only getting half the picture. A new, influential audience has emerged that shapes the opinions of millions: the large language model (LLM).

What AI models like ChatGPT, Gemini, and Claude "think" about your brand now directly influences what potential customers see and believe. When a user asks an AI for a product recommendation or a company comparison, the model's output is based on its trained understanding of your brand's sentiment, authority, and positioning in the market. This creates a new imperative for brand managers and marketers. You must now track not only human perception through traditional surveys but also machine perception by analyzing how AI presents your brand.

This article provides a complete collection of brand perception survey questions, updated for this dual-audience reality. We will cover the essential categories needed to gauge human opinion, from awareness and loyalty to trust and sentiment. Crucially, we will also detail how to "survey" AI models by running strategic prompts through tools like PromptPosition. This method allows you to see how AI ranks, describes, and compares your brand against competitors. By pairing these two approaches, you gain a full, actionable view of your brand's health, ensuring your strategy is effective for both human and machine audiences.

1. Brand Awareness and Recall Questions

The foundation of any brand perception survey is understanding whether your audience knows you exist. Brand awareness and recall questions measure exactly that. They gauge how easily and accurately consumers can identify your brand, both with and without direct prompts. This is a crucial starting point for any brand perception survey because if awareness is low, other metrics like brand association or loyalty are unlikely to be strong.

These questions help you determine your brand's "share of mind" within your industry. For example, a SaaS company might ask about business tool recommendations to see if they are top-of-mind, while an e-commerce brand could monitor product visibility in shopping queries. This data directly reflects the effectiveness of your marketing and advertising efforts.

How to Structure Awareness and Recall Questions

There are two primary forms of recall to measure:

• Unaided Recall (Top-of-Mind Awareness): This assesses spontaneous brand mentions. The goal is to see which brands customers think of first when considering a specific category.
  • Sample Question: "When you think of [product/service category, e.g., 'project management software'], which brands come to mind first?" (Use an open-text field).
• Aided Recall (Brand Recognition): This measures whether customers recognize your brand from a list. It helps you understand your brand's reach, even if it's not the first one people think of.
  • Sample Question: "Which of the following [product/service category] brands have you heard of?" (Provide a multiple-choice list including your brand, key competitors, and even a fake brand to filter out random guessing).

Modern Twist: The modern challenge is measuring recall not just in human memory, but in AI-generated responses. You can adapt aided recall questions by showing respondents an actual LLM output, such as a list of recommended tools from ChatGPT, and asking, "Before seeing this list, were you aware of [Your Brand Name]?" This links survey data directly to AI visibility.

To benchmark your position, always include competitor names in your questions. You can also correlate these survey results with data from AI visibility tracking tools like PromptPosition, which shows how often and where your brand appears in LLM outputs. This gives you a complete picture: human awareness from surveys and "machine awareness" from AI.

2. Brand Sentiment and Perception Questions

Beyond simple awareness, it's critical to understand the emotional context surrounding your brand. Brand sentiment and perception questions measure the feelings, attitudes, and trustworthiness consumers associate with you. This is especially important in the age of AI, where a single mention in a ChatGPT or Perplexity response can shape opinions, establishing you as either a trusted authority or a risky option.

These questions help you gauge whether your brand's appearances in AI-generated results are creating positive, neutral, or negative perceptions. For example, a tech company needs to know if being listed in a ChatGPT security recommendation builds trust. Likewise, a financial services firm must track whether mentions in AI-driven financial advice foster favorability. The answers reveal the direct impact of machine-generated content on human feelings.

How to Structure Sentiment and Perception Questions

Focus on capturing the emotional and rational judgments people make after encountering your brand. This requires moving from "Have you heard of us?" to "How do you feel about us?".

• Direct Sentiment Measurement: Ask about the overall feeling your brand evokes, often on a scale.
  • Sample Question: "After seeing [Your Brand] mentioned in an AI tool like ChatGPT, how would you describe your overall feeling toward the brand?" (Use a Likert scale from 'Very Negative' to 'Very Positive').
• Attribute Association: Measure specific qualities like trustworthiness, credibility, or innovation. This is key for understanding why sentiment is positive or negative.
  • Sample Question: "Based on information you may have seen in an AI-generated response, how much do you agree or disagree with the following statement: '[Your Brand] is a trustworthy name in [your industry].'" (Use a 'Strongly Disagree' to 'Strongly Agree' scale).

Modern Twist: Combine human survey sentiment with machine-measured sentiment for a complete view. Use an AI visibility tool to get a baseline sentiment score for your brand's mentions in LLM outputs. Then, use your survey to ask respondents if they recall specific AI-generated statements about your brand before rating their own sentiment. This connects the dots between what the AI says and what your audience feels.

For a deeper analysis, include comparative sentiment questions. For instance, "Thinking about the brands mentioned in the AI response, which one do you feel most positive about?". Segmenting responses by the LLM platform where a user saw the mention (e.g., ChatGPT, Perplexity, Claude) will also provide more granular, actionable data.

3. Brand Positioning and Differentiation Questions

Once you know people are aware of your brand, the next step is to understand how they see you relative to competitors. Brand positioning and differentiation questions assess whether your unique value proposition is cutting through the noise. They measure if your target audience perceives your brand as distinct and if your key differentiators are resonating, especially when discovered through AI-powered search.

These types of brand perception survey questions are essential for checking if your marketing messages land correctly. For a SaaS platform, it’s about confirming if AI search results position them as the "most user-friendly" choice. For a D2C brand, it’s about seeing if their sustainability claims appear in product recommendations from models like Gemini. This data validates whether your intended position matches public and machine perception.

How to Structure Positioning and Differentiation Questions

Focus on what makes your brand stand out. You want to gauge both perceived attributes and direct comparisons against competitors.

• Attribute Association: Ask respondents to connect specific traits with brands. This reveals if your core messaging is sticking.
  • Sample Question: "Which of the following brands do you most associate with 'enterprise-grade security'?" (Provide a multiple-choice list including your brand and key competitors).
• Perceptual Mapping: Use a matrix or scale to have users place your brand and competitors based on two key axes, such as "Price" and "Quality."
  • Sample Question: "On a scale of 1 (Basic) to 10 (Luxury), how would you rate the following brands?" (Repeat for another axis like 'Innovative').

Modern Twist: To connect human perception with AI's influence, directly integrate AI-generated content into your survey. Use a tool like PromptPosition to capture verbatim quotes from LLM outputs that describe your brand. You can then ask, "After reading this AI-generated description of [Your Brand], would you say it is positioned as a premium or a budget-friendly option?" This tests message clarity through the AI filter.

You can also ask respondents about their sources of information. A question like, "Where did you learn that [Your Brand] is a leader in [Your Differentiator]?" with options like "AI Assistant (e.g., ChatGPT)," "Social Media," and "Website," helps you trace the origin of your perceived positioning.

4. Purchase Intent and Recommendation Questions (NPS)

Ultimately, brand perception must connect to business outcomes. Purchase intent and recommendation questions directly measure this link, gauging the likelihood a consumer will buy from you, subscribe to your service, or recommend you to others. These behavioral intent questions are critical for calculating the ROI of your marketing efforts, especially as brand discovery shifts to AI-driven search and recommendation engines.

These questions help you quantify how brand perception translates into revenue. For instance, an e-commerce brand can track purchase likelihood after a user sees their product in a Gemini shopping recommendation. Similarly, a SaaS company can measure trial signup intent for users who encountered their tool in a ChatGPT response, connecting AI visibility directly to lead generation. This data moves beyond awareness to forecast actual business impact.

How to Structure Purchase Intent and Recommendation Questions

Your goal is to capture a respondent's next likely action. The wording should be direct and tied to a specific call to action.

• Likelihood to Purchase/Act: This measures the immediate probability of conversion. The key is to make the scenario specific, especially if you're evaluating the impact of an AI mention.
  • Sample Question: "Based on the information you've seen or heard about [Your Brand], how likely are you to purchase one of our products in the next three months?" (Use a 5-point Likert scale from "Very Unlikely" to "Very Likely").
• Likelihood to Recommend (NPS): The Net Promoter Score (NPS) is a standard for measuring customer loyalty and advocacy. It gauges how willing someone is to put their own reputation on the line by recommending you.
  • Sample Question: "On a scale of 0 to 10, how likely are you to recommend [Your Brand] to a friend or colleague?" (Use an 11-point scale from 0 "Not at all likely" to 10 "Extremely likely").

Modern Twist: Combine these brand perception survey questions with source tracking. Before asking about intent, ask respondents where they have recently seen or heard about your brand (e.g., "Google Search," "ChatGPT," "Social Media," "Friend/Colleague"). This allows you to segment your intent data by channel and identify which platforms, including specific LLMs, drive the highest conversion intent.

To get a clearer picture of AI's influence, you can use data from a tool like PromptPosition to understand your brand’s visibility and sentiment in AI outputs. Correlating this with your survey results can help predict which respondents likely encountered an AI mention and how it affected their purchase intent. This provides a direct line between your AI search visibility and bottom-line metrics.

5. Trust and Credibility Attribution Questions

Beyond simple awareness, it is vital to know if your audience trusts what they hear about you, especially from AI-driven sources. Trust and credibility attribution questions measure whether consumers believe the information presented about your brand in AI search results and if they see you as an authority based on LLM-generated content. This is critical for evaluating whether your positioning in AI enhances or damages your brand's authority.

These types of brand perception survey questions are essential in high-stakes industries. A financial brand, for instance, must know if users trust an investment recommendation that Claude attributes to them. Likewise, a healthcare company needs to assess the credibility of medical information about its products appearing in a Perplexity query. For brands, establishing a robust modern Trust & Safety strategy is paramount, as it directly influences how audiences attribute credibility and trust.

How to Structure Trust and Credibility Questions

The goal is to isolate the AI's influence on your brand's perceived trustworthiness. This requires careful question design that often involves a control group or before-and-after scenarios.

• AI Attribution Testing: Present a claim or recommendation and ask about trust levels, but show it to different audience segments with and without AI attribution.
  • Sample Question (with AI attribution): "An AI assistant recommended [Your Brand's Product] for [use case]. On a scale of 1 (Not at all trustworthy) to 7 (Completely trustworthy), how much do you trust this recommendation?"
  • Sample Question (control): "[Your Brand's Product] is a recommended solution for [use case]. On a scale of 1 to 7, how much do you trust this statement?"
• Source Verification and Competitor Comparison: Understand if users are cross-referencing AI-generated information and how your credibility stacks up against others mentioned in the same output.
  • Sample Question: "After seeing [Your Brand] mentioned by the AI, did you take any steps to verify the information, such as visiting the official website?" (Yes/No/I planned to, but haven't yet).

Modern Twist: Correlate survey responses with source tracking data from AI visibility tools. For example, PromptPosition can show you if an LLM cites your official blog or documentation when recommending your brand. If your survey shows low trust scores, and your source tracking shows the AI is citing third-party or inaccurate sources, you have a clear action item: improve your content's machine readability so the AI cites you directly.

6. Machine Perception: "Surveying" AI Models

Traditional surveys tell you what humans think. But to get a complete picture today, you also need to understand what machines think. "Surveying" an AI model involves systematically running prompts through it to analyze how it perceives and positions your brand. This isn't about human opinion; it's a technical audit of your brand's representation in the AI's knowledge base. Tools like PromptPosition are designed specifically for this task, giving you a new lens on brand perception.

By running queries like "best software for X" or "compare Brand A and Brand B," you can measure key machine perception metrics:

• Visibility & Rank: Does your brand appear? If so, where does it rank in lists?
• Sentiment: Is the description of your brand positive, negative, or neutral?
• Positioning: Is your brand framed as premium, budget-friendly, innovative, or reliable?
• Source Attribution: What sources is the AI citing when it talks about you?

How to "Survey" AI Models

Instead of asking questions to people, you're running prompts through an API or a tool and analyzing the output.

• Competitive Comparison Prompts: Run prompts that force the AI to compare you against rivals.
  • Sample Prompt: "Compare [Your Brand] and [Competitor Brand] on price, features, and customer support."
• Use Case & Solution Prompts: Test your visibility for high-intent queries related to problems you solve.
  • Sample Prompt: "What's the best tool for a small business to manage inventory?"
• Attribute Association Prompts: See which brands the AI associates with key qualities.
  • Sample Prompt: "Which CRM is considered the most secure for enterprise companies?"

Modern Twist: The entire concept of surveying AI is the modern twist. This process gives you a real-time, data-driven look at how your brand is being portrayed to millions of users. You can use platforms like PromptPosition to track these metrics over time, benchmark against competitors, and see the direct impact of your SEO and content efforts on your brand's AI perception. This is no longer optional; it's a core component of modern brand management.

7. Competitive Comparison and Benchmarking Questions

Understanding your brand in a vacuum is not enough; you must know how it stacks up against the competition. Competitive comparison questions measure your brand’s perceived strengths and weaknesses relative to key rivals. This is essential for identifying differentiation opportunities and understanding your specific market position, especially as AI-driven search and recommendation engines reshape how users discover and compare options.

These types of brand perception survey questions are designed to reveal how your audience rates you on specific attributes versus other players. For example, a CRM platform could ask users to compare its ease of use against Salesforce, or a cloud provider might benchmark its perceived cost-effectiveness against AWS and Azure. The goal is to pinpoint where you win and where you lag in the customer's mind.

How to Structure Competitive Comparison Questions

Focus on direct, attribute-based comparisons that force respondents to make a choice. This provides clear, actionable data about your competitive standing.

• Forced-Choice Comparison: This format asks respondents to select the best brand for a specific attribute or use case from a list.
  • Sample Question: "Which of the following project management tools do you believe offers the best features for remote teams?" (Provide a multiple-choice list including your brand, Asana, Monday.com, and Jira).
• Likert Scale Matrix: This allows for a more detailed comparison across several attributes at once. It shows not just who is "best" but by how much they lead.
  • Sample Question: "Please rate the following cloud providers on their 'developer-friendliness'." (Provide a 1-5 scale for your brand, AWS, Google Cloud, and Azure).

Modern Twist: Competitive perception is increasingly shaped by AI. You can build survey questions around actual AI-generated comparisons. Use a tool like PromptPosition to track when and how you are mentioned alongside competitors in LLM outputs. Then, present a screenshot of a ChatGPT response and ask, "The AI model above recommends [Your Brand] and [Competitor Brand] for [use case]. Based on your experience, which one would you choose and why?"

This approach connects human perception directly to your brand’s visibility and positioning within AI systems. Segmenting responses by use case is also critical, as your competitive strength may vary significantly between different applications or industries. This helps identify where to double down on your wins and where to address competitive losses in AI-mediated discovery.

8. Brand Values and Corporate Responsibility Questions

A brand's ethical stance and social impact are increasingly influential factors in consumer choice. Brand values and corporate responsibility questions in a survey help you measure how audiences perceive your company's character, ethics, and commitment to social good. This is especially important as AI models synthesize information from news articles, reports, and your own website to form a narrative about your corporate culture.

These questions reveal whether your intended values, like a commitment to sustainability or diversity, are actually reaching and resonating with your audience. For instance, a tech company can use these questions to gauge the perceived authenticity of its DEI initiatives, while an e-commerce brand can assess whether its sustainability claims are seen as genuine or just marketing-speak. This feedback is critical for building trust and a brand identity that goes beyond products and services.

How to Structure Values and Responsibility Questions

Combine direct questions about your initiatives with open-ended queries to capture authentic sentiment. The goal is to see if the values you project are the ones customers receive.

• Awareness of Initiatives: Start by checking if your audience is even aware of your efforts.
  • Sample Question: "How familiar are you with [Your Brand Name]'s commitment to [specific value, e.g., 'sustainable sourcing' or 'employee well-being']?" (Use a Likert scale from 'Not at all familiar' to 'Very familiar').
• Perceived Authenticity: Measure how genuine your corporate social responsibility (CSR) efforts appear to be.
  • Sample Question: "Based on what you've seen or heard, how would you describe [Your Brand Name]'s stance on ethical practices?" (Provide a multiple-choice list with options like 'A leader in ethical practices,' 'Meets basic ethical standards,' 'Has questionable practices,' 'I don't know').

Modern Twist: To understand how AI shapes this perception, show respondents an AI-generated company overview from a model like Claude or Perplexity. Then ask, "After reading this summary, what do you believe [Your Brand Name] stands for?" This open-ended question reveals which values AI is highlighting and how that influences human interpretation.

Use an AI visibility tool like PromptPosition to track whether your blog posts, press releases, and dedicated CSR content appear in LLM responses to values-related queries. If your sustainability report isn't being referenced by AI, it’s not informing perception. By aligning your content strategy with these insights, you ensure your corporate values are visible to both human and machine audiences.

9. Information Source Trust and Attribution Questions

In an AI-driven information ecosystem, where an answer comes from is as important as the answer itself. Information source trust and attribution questions measure whether your audience trusts the sources that LLMs cite when discussing your brand. These brand perception survey questions address the critical issue of information provenance, helping you understand if citations from your official website, a third-party review site, or a news article carry more weight with your audience.

Understanding source credibility is vital because not all mentions are created equal. A B2B company might find that a citation from a Gartner report drives significantly more trust than a mention in a company-authored case study. Conversely, an e-commerce brand may discover that a link to a popular review blog in an AI response hurts brand perception more than it helps. These insights are essential for shaping your content and digital PR strategy.

How to Structure Source Trust and Attribution Questions

The goal is to pinpoint which information sources bolster your brand's authority and which undermine it. You can structure these questions to directly compare audience preferences and measure the impact of different citations.

• Source Preference Questions: This directly asks users which type of source they prefer for learning about a brand or product.
  • Sample Question: "If you were researching [product/service], which source would you trust most for accurate information?" (Provide a multiple-choice list: The company's official website, Independent product reviews, News articles, Industry analyst reports, Customer testimonials on a third-party site).
• Source Impact Testing: This method isolates the effect of the source by presenting the same information but attributing it to different origins.
  • Sample Question: "After reading the following statement attributed to a news outlet, how has your perception of [Your Brand] changed? '[Your Brand] was recently named a leader in innovation by Tech Today.'" (Use a Likert scale from 'Much more negative' to 'Much more positive'). You can then show the same statement attributed to your own press release and compare the results.

Modern Twist: Combine survey data with AI visibility tracking to create a powerful feedback loop. Use a tool like PromptPosition to identify which sources LLMs are actually citing for queries related to your brand. Present these real-world citations to your survey respondents to get direct feedback on their trustworthiness. For instance: "ChatGPT cited [Source Name] when describing our services. How much do you trust information from this source?" This directly connects your source strategy to its real-world impact in AI.

By monitoring which sources appear most often in LLM responses, you can develop a targeted strategy to increase the inclusion of your most trusted and authoritative content. This ensures that when AI models talk about your brand, they do so using information that resonates most effectively with your audience.

10. Prompt-Specific and Query-Based Perception Questions

Generic brand questions provide a baseline, but customers often interact with brands in very specific contexts, especially within AI-powered search. Prompt-specific and query-based questions focus on how brand perception changes depending on the exact query a user enters into an LLM. This approach acknowledges that a brand's reputation isn't monolithic; it shifts based on the user's specific need or use case.

These questions are designed to measure perception at the moment of discovery. For instance, a SaaS company's brand might be perceived differently in the context of "best CRM for startups" versus "CRM for enterprise." Similarly, an e-commerce brand's image could vary between queries like "sustainable clothing" and "budget fashion." By tailoring brand perception survey questions to these specific prompts, you get a much more granular and actionable view of your brand's standing.

How to Structure Prompt-Specific Questions

The goal is to mirror the user's journey in AI search and then gauge their reaction. This involves presenting respondents with real or simulated AI outputs.

• Context-Based Perception: Show respondents the output for a specific, high-value query and ask how they perceive your brand within that result.
  • Sample Question: "Here is the result from an AI assistant for the prompt 'digital transformation consulting'. Based on this, how would you describe [Your Brand Name]?" (Use multiple-choice options like: 'A market leader', 'An affordable option', 'A specialized provider', 'Not a good fit for this need').
• Visibility Gap Analysis: Ask about prompts where your brand should appear but doesn't. This identifies missed opportunities directly from your target audience's perspective.
  • Sample Question: "When looking for solutions related to 'cost reduction strategies,' which of these brands would you expect to see recommended? Please select all that apply." (Provide a list of your brand and competitors).

Modern Twist: To create truly effective prompt-specific questions, you need to know which prompts matter most. Tools like PromptPosition can identify high-impact queries in your industry. Build your survey around these tracked prompts to connect human perception data directly with your AI visibility performance, ensuring your survey efforts are focused on the most critical customer touchpoints.

As you develop prompt-specific questions to gauge AI's output and its alignment with brand values, understanding the intricacies of mastering prompt engineering becomes crucial. By segmenting your survey audience and showing different groups the outputs for different queries, you can build a detailed map of how your brand's positioning holds up across various customer intents.

From Data to Dominance: Turning Perception into Performance

You have now explored a complete arsenal of brand perception survey questions, from foundational awareness queries to the nuanced questions needed to assess brand values and competitive standing. We have moved beyond the traditional survey framework, demonstrating that a modern brand perception strategy requires a dual-lens approach. The true power lies not just in asking the right questions, but in asking them across two distinct, yet interconnected, audiences: your human customers and the AI models shaping their discovery process.

Mastering this requires a shift in thinking. Your human-centric surveys, rich with NPS data and sentiment analysis, provide the "what." They are the direct voice of your market, revealing their feelings, associations, and loyalties. This is the bedrock of brand management, telling you where you stand in the hearts and minds of people.

Simultaneously, "surveying" AI models through prompt analysis and tools like PromptPosition provides the "why" and the "how." It uncovers the digital DNA behind your brand's machine-generated reputation. By analyzing how LLMs portray your brand in search results, summaries, and conversational responses, you gain direct insight into the source material that is actively shaping public opinion at a massive scale.

Synthesizing Human and Machine Insights

The most critical takeaway is the need for integration. These two data streams should not exist in separate silos; they must inform and enrich one another in a continuous feedback loop.

• AI Informs Surveys: Use the specific language, verbatim quotes, and source URLs surfaced by AI tracking to craft more pointed and relevant brand perception survey questions. If an AI model consistently misrepresents a product feature, you can design a survey question to measure how widespread that misconception is among your human audience.
• Surveys Guide AI Strategy: Use the insights from your human surveys to prioritize your AI optimization efforts. If survey data reveals that customers value your commitment to sustainability but AI models never mention it, you know exactly where to focus your content and digital PR strategy. The goal is to close the gap between how people perceive you and how machines represent you.

Key Insight: A discrepancy between human perception (from surveys) and machine perception (from AI tracking) is not a problem; it is your most valuable strategic opportunity. It points directly to the area where your content and SEO efforts will have the greatest impact on brand reputation.

By continuously monitoring both channels, you stop being a passive observer of your brand's perception and become an active architect. You can preemptively address negative sentiment before it becomes widespread, reinforce positive associations that resonate with your core audience, and ensure your key differentiators are present in every conversation, whether human or artificial. This integrated approach is the new standard for building a resilient, dominant brand. You are no longer just measuring perception; you are actively engineering it for performance across every modern interface. This is how you turn raw data into market dominance.

Ready to see how AI perceives your brand? The promptposition platform allows you to "survey" leading AI models to see how they position your brand against competitors, analyze the sentiment of their responses, and trace their answers back to the source. Stop guessing and start measuring your brand's AI-driven reputation today with a free analysis at promptposition.