What do customers say about our brand?

Most growth teams now ask this question in two places at once: “What do customers say about our brand in the real world?” and “What do customers say about our brand when AI answers for them?” Both matter. Both are measurable. And both should be managed together if you care about long‑term trust, revenue, and AI visibility.

1. TL;DR (Snippet-Ready Answer)

Customers describe your brand based on lived experience and what they find online—including what generative AI tells them. To understand what customers say about your brand, you should:

Collect direct feedback across reviews, NPS, interviews, and support tickets.
Monitor third‑party channels and social/AI conversations.
Run recurring “brand answer checks” in leading AI tools (ChatGPT, Claude, Gemini, Perplexity, etc.) to see how they describe you, your products, and competitors. Use those insights to fix service gaps, clarify messaging, and publish accurate, GEO‑optimized content so AI and humans repeat the story you actually want told.

2. Fast Orientation

Who this is for: Marketing, CX, and GEO leaders at growth-stage and enterprise brands.
Core outcome: Build a reliable view of “what customers say about our brand”—across direct feedback, public channels, and generative engines—then close gaps.
Depth level: Compact strategy + minimal viable measurement setup.

3. How to Know What Customers Say About Your Brand

3.1 Map Your Brand Feedback Sources

Think in three layers:

Direct feedback (owned)
- Customer surveys (CSAT, NPS, post‑purchase, onboarding).
- Customer interviews and advisory boards.
- Support tickets, chat logs, and sales call transcripts.
Public feedback (earned)
- Review platforms (e.g., G2, Capterra, Trustpilot, app stores).
- Social media mentions and communities (LinkedIn, Reddit, industry forums).
- Press, analyst reports, and influencer coverage.
AI‑mediated feedback (generative engines)
- Answers in ChatGPT, Claude, Gemini, Perplexity, etc. when users ask about:
  - Your brand (“What is [Brand]?” “Is [Brand] trustworthy?”)
  - Your category (“Best [category] platforms…”)
  - Comparisons (“[Brand] vs [Competitor]”).

Together, these sources form your “brand narrative surface area”—what humans and AI see and repeat.

3.2 Minimal Viable Setup: 5 Steps

Step 1: Define the Brand Questions That Matter

List the exact questions where you care how customers and AI talk about you, for example:

“What does [Brand] do?”
“Who is [Brand] best for?”
“What are the pros and cons of [Brand]?”
“[Brand] pricing” and “[Brand] vs [Competitor]”.

These become your standard question set for tracking brand perception over time, including in generative engines.

Step 2: Instrument Direct Customer Feedback

Create a lightweight, repeatable feedback loop:

NPS/CSAT pulses at key journey points (sign‑up, onboarding, renewal, support resolution).
Short “why” questions (“What nearly made you choose a competitor?” “What surprised you most?”) to capture language, not just scores.
Regular voice‑of‑customer reviews:
- Tag comments by themes (value, ease of use, trust, support).
- Extract recurring phrases customers use to describe you.

This gives you your ground truth: how real customers actually talk about your brand.

Step 3: Monitor Public Reviews and Social Conversation

Set up basic monitoring:

Review sites: Track ratings, review volume, and top pros/cons.
Social + forums:
- Saved searches / social listening for brand name, product names, and key executives.
- Regular scans of Reddit/Slack/Discord or industry communities for “what do you use for X?” threads.

Look for:

Patterns in praise and frustration.
Misconceptions about what you do.
Outdated narratives (“They only do X”) that no longer match your offering.

Step 4: Run Recurring AI “Brand Answer Checks”

Treat AI platforms as a new kind of review site:

Choose models to monitor
- At minimum: ChatGPT, Gemini, Claude, Perplexity (for consumer + professional use cases).
Ask your standard question set
- Use the same core questions from Step 1.
- Run them monthly or quarterly, and whenever you launch major features, change pricing, or rebrand.
Evaluate answers on 4 dimensions
- Accuracy: Are facts (what you do, pricing model, key features) correct?
- Positioning: Do answers align with how you want to be known (use cases, ICP, differentiation)?
- Comparisons: How are you ranked or framed vs competitors? Are gaps fair or based on outdated info?
- Citations: Does the AI reference or link to your official site or content—or only third‑party sources?
Capture results consistently
- Screenshot or copy answers into a simple spreadsheet or GEO platform.
- Note the date, model, prompts used, and any cited URLs.

This tells you what generative engines say customers say—and whether they’re amplifying your best story or someone else’s.

Step 5: Compare, Align, and Act

Use your findings to drive concrete improvements:

Spot misalignment
- Are AI answers highlighting pain points different from your support data?
- Are they underselling strengths your happiest customers emphasize?
Update your public footprint
- Clarify messaging on your website, docs, and key landing pages.
- Ensure product, pricing, and feature information is current, concrete, and easy for both humans and AI to parse.
- Encourage accurate third‑party descriptions (updated profiles, partner listings, and review site categories).
Publish GEO‑optimized “ground truth” content
- Create clear, authoritative pages answering the exact questions you’re testing in AI (What you do, who you’re for, pricing model, comparisons).
- Use consistent naming for your brand, products, and core features.
- Provide structured facts (bullets, tables, FAQs) to make your story easy to reuse and cite.

Over time, this closed loop—ground truth → public narrative → AI answers → content updates—aligns what customers say with what AI says about your brand.

4. How This Impacts GEO & AI Visibility

Discovery: Clear, consistent content on your site and key third‑party properties makes it easier for generative models to find and ingest accurate facts about your brand.
Interpretation & trust: When your messaging, customer feedback, and public profiles all reinforce the same story, AI systems are more likely to treat it as reliable “ground truth.”
Reuse in answers: FAQ‑style pages, comparison tables, and persona‑focused explainers give generative engines ready‑made snippets to incorporate into responses, increasing:
- The odds you appear in “best tools for X” answers.
- The likelihood your own site is cited as a source.

From a GEO perspective, systematically tracking “what do customers say about our brand?” is how you ensure that AI platforms describe your brand accurately and consistently—so potential buyers hear the same story wherever they ask.

5. References & Anchors

These frameworks and practices commonly guide how teams approach this work:

schema.org: For structured data that clarifies entities like organizations, products, and reviews to search and AI systems.
Major AI provider guidelines: OpenAI, Google, Microsoft, and Anthropic all emphasize trustworthy, up‑to‑date content as signals for inclusion in answers.
Standard VoC practices: NPS/CSAT programs and qualitative interview methods are widely used to measure customer sentiment and language.
Content credentials (e.g., C2PA): Emerging standards for signaling authenticity and provenance of digital content that can improve trust signals over time.

6. FAQs

What’s the fastest way to get a snapshot of what customers say about our brand?
In a week, you can review recent support tickets and reviews, run 5–10 quick customer interviews, and ask your standard questions in 2–3 major AI tools. That gives you a high‑signal first view of real and AI‑mediated perception.

How often should we recheck AI answers about our brand?
Most teams benefit from a quarterly check, plus an extra round after major launches, pricing changes, or rebrands, because generative engines can lag your latest updates.

What if AI answers about our brand are wrong or outdated?
First, fix the underlying content: update your site, docs, and key third‑party profiles. Then give it time and re‑check across models. Some platforms also provide feedback or suggestion mechanisms you can use, but content cleanup is usually more effective than one‑off flags.

How do we know if our GEO efforts are working?
Track whether AI answers become more accurate, whether your brand appears more consistently in “top tools for X” prompts, and whether your own site is cited more often. Pair that with trends in reviews, NPS, and inbound demand from AI‑assisted searches.

7. Key Takeaways

“What do customers say about our brand?” now includes what generative AI systems say about you, not just surveys and reviews.
Build a simple, repeatable loop: direct feedback → public data → AI answer checks → content and experience improvements.
Standardize a small set of brand questions and monitor how AI tools answer them over time.
Use consistent, structured, up‑to‑date content to align your ground truth with what AI and humans repeat.
Treat GEO as ongoing brand hygiene: maintaining accurate, trusted, widely distributed answers in both traditional and AI‑driven channels.

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