Analyzing Responses: A Complete Guide

The Responses page (Dashboard → Monitoring → Responses) is where you read what AI search engines are actually saying about your brand, your competitors, and your category. Every time one of your prompts runs against an AI model, the full response is captured here — the text, the cited sources, the brands mentioned, and the sentiment.

If the Prompts page tells you what to ask, the Responses page tells you what AI engines answered. This is the layer where most actionable insight lives — and it's the page you should spend the most time in.

Examples throughout use B2B SaaS and cybersecurity scenarios.

What the Responses Page Shows You

Each row in the Responses table is a single AI-generated response — one prompt, one model, one moment in time. The table is built for fast scanning across hundreds or thousands of responses.

Table Columns

How Responses Are Generated

Every response in this list came from a real AI engine answering a real prompt — not a simulation. When a monitor runs, GrackerAI sends each prompt to each selected AI model and stores the full response, the cited sources, and the metadata. You're seeing the same answer your buyer would see if they typed that question into ChatGPT, Perplexity, or Gemini at that moment.

This is why the Date column matters: AI responses change over time as models are updated and as the underlying web changes. The same prompt can produce different answers in March, April, and May. The Responses page captures that movement.

The Visibility Score on a Response

The Visibility percentage on each response tells you whether your brand was named in that specific AI answer.

• 0% — Your brand was not mentioned at all in this response. The AI engine answered the question without referencing you.
• Partial percentage — Your brand was mentioned, but other brands shared the response. A response that lists 5 alternatives where you're one of them will score lower than a response that names you as the recommended choice.
• 100% — Your brand was the dominant or sole brand mentioned in the response.

A row showing 0% on a prompt where competitors are showing up is the highest-leverage signal in GrackerAI. It means the AI engine considers this prompt answerable, but considers you irrelevant to the answer. This is where content investment pays back fastest.

Example: If a response for "Best EDR for healthcare SaaS" shows 0% visibility for your brand but the Brands column shows CrowdStrike, SentinelOne, and Microsoft Defender icons, you have a clear content gap to close. That prompt is being answered — just not with you in the answer.

Reading a Full Response

The Response column shows a truncated snippet — usually the first sentence or two. Click more to expand the full response in a side panel.

When you expand a response, you'll typically see:

• The full AI-generated answer (often several paragraphs)
• The list of every source the AI cited, with clickable links
• The exact text where each brand was mentioned
• The sentiment breakdown
• The model and timestamp

This is the most important view in the entire product. Reading the full response answers questions that no dashboard can:

• Why is the AI engine recommending a competitor? (You'll see the reasoning in plain text.)
• Which content is the AI engine citing as authoritative?
• What language is the AI using to describe you (or fail to describe you)?
• What features does the AI think are differentiators in your category?

A common workflow is to expand 5–10 responses on a prompt where you're losing, and look for patterns in the cited sources. If the same domains keep appearing in the citations — say, a specific G2 page, a specific blog, a specific subreddit — you've identified the content surfaces you need to compete on.

Understanding the Model Column

Each response is tied to one AI model. The icon in the Model column tells you which engine produced this answer. AI engines have meaningfully different behaviors:

• ChatGPT — Often the broadest answer, willing to recommend specific brands, sometimes pulls from older training data.
• Perplexity — Cites sources aggressively. Strong for fact-heavy and research-heavy queries. Heavily used by technical and security buyers.
• Claude — Tends to give nuanced, balanced answers. Over-indexes for developer and security audiences. Frequently surfaces lesser-known brands if they have authoritative content.
• Gemini — Strong on Google ecosystem topics. Often pulls heavily from YouTube and Reddit alongside web pages.
• AI Overview — Google's in-search summaries. Surface in roughly 16% of all searches. Critical for category and listicle queries.
• Microsoft Copilot — Dominant in enterprise and Microsoft-heavy organizations. Disproportionately important for cybersecurity buyers.
• Grok / AI Mode — Lower B2B share of voice today, but worth tracking for awareness.

The same prompt can produce dramatically different responses across models. A competitor might dominate ChatGPT for "best SIEM tools" while you dominate Claude. Reviewing responses by model — using the All models filter — lets you see exactly which engines you're winning and losing on.

Common pattern: If your brand is mentioned in Claude and Perplexity but absent from ChatGPT and Gemini, the gap is usually in mainstream content surfaces (G2, mainstream blogs, traditional media). If you're winning ChatGPT but losing Claude, the gap is usually in technical depth (developer docs, technical blog posts, security research).

The Sentiment Column

Each response is scored for sentiment — the overall tone of how your brand and the category are described. Sentiment is shown as a bar; longer/more-orange bars indicate more positive sentiment, shorter/lighter bars indicate neutral or negative.

Sentiment matters in three ways:

1. Brand sentiment over time — If your sentiment trend is dropping, AI engines are starting to describe you in less favorable terms. Investigate immediately. This often happens after a high-profile customer issue, a negative review wave, or outdated training data catching up.
2. Category sentiment — Sometimes the entire category is described negatively (e.g., "most observability tools are overpriced"). This is industry-level signal that affects all vendors.
3. Comparative sentiment — A response that mentions you and a competitor but describes the competitor more positively is a reputation problem, even if your visibility score is high.

To analyze sentiment systematically, use the All Sentiments filter to slice the response list by positive, neutral, or negative. Reviewing every negative-sentiment response in your library is a 30-minute exercise that consistently surfaces real issues.

The Brands Column: Your Competitive Map

The Brands column shows every brand named in each response. This is the single fastest way to map your competitive landscape as AI engines see it.

A few patterns worth recognizing:

• You + 1 brand — A direct comparison response. Read the full text to see how you're positioned against that one competitor.
• You + 5–10 brands — A category listicle response. The order brands appear in the response usually correlates with how the AI ranks them. If you're listed 7th, that's the market position the AI assigns you.
• No "you" + 5+ brands — A category response where you're absent. Highest-priority gap.
• One unfamiliar brand appearing repeatedly — A new entrant or a brand you weren't tracking. Add it to your Competitor monitors.

Example for a B2B SaaS observability vendor: If responses for "best observability platforms" consistently show Datadog, New Relic, and Grafana in the top three, with you appearing only in 1 of 10 responses, you have an authority problem in mainstream content — not a product problem.

Example for a cybersecurity EDR vendor: If responses for "best EDR for healthcare" show CrowdStrike and SentinelOne in every result but you appear in only 2 of 10, with sentiment for CrowdStrike trending higher, you have both a citation gap and a sentiment gap. The citation gap is solved with healthcare-specific content; the sentiment gap is solved with customer evidence.

The Domain Column: Where AI Pulls From

The Domain column shows how many unique source domains the AI engine cited to generate its response. A "13" means 13 different websites contributed to the answer.

Click into the response to see which specific domains were cited. Patterns to look for:

• Same 3–5 domains across most responses — These are the citation sources for your category. Earning placements on these domains has the highest ROI for visibility improvement. For B2B SaaS, this is often G2, Capterra, specific industry blogs, and a handful of high-authority publications. For cybersecurity, it's often SANS, Dark Reading, KrebsOnSecurity, BleepingComputer, and vendor-neutral analyst sites.
• Reddit and forum citations — AI engines pull heavily from Reddit, Hacker News, and Stack Overflow. If your category is being discussed there, those threads are influencing AI answers. Engaging authentically (not promotionally) with those communities is often necessary.
• Your own domain in the citations — If your domain appears in the cited sources but your brand doesn't appear in the response text, you have a structural problem. Your content is being read by the AI but not cited as a recommendation. This is usually a content-format problem (the page lacks clear product positioning) rather than a content-quality problem.
• Competitor's domain in the citations for prompts you should win — Their content is more authoritative than yours on this topic. Read the cited page, identify the structural elements (headers, lists, schema, depth), and rebuild against that benchmark.

Filtering and Searching Responses

The Responses page is built for libraries of thousands of responses. Use the filter controls heavily.

Search Responses

The text search box does a full-text match against the response content itself. This is far more powerful than it looks. Examples:

• Search "expensive" or "pricing" to find every response that discusses cost in your category.
• Search a competitor's name to find every response where they were mentioned, including ones where you weren't tracking the competitor explicitly.
• Search a specific feature name (e.g., "SAML" or "MITRE ATT&CK") to find every response where that feature came up.
• Search a customer name or industry term to find responses that mention your customer base or vertical.

All Models

Filter by AI engine. The most common uses:

• Audit a single model's behavior — "How is ChatGPT specifically describing my category?"
• Compare your visibility on one model against another.
• Investigate a sudden shift — if Gemini visibility drops 30% in a week, filter to Gemini and read the recent responses to see what changed.

All Sentiments

Filter to positive, neutral, or negative responses. Reviewing all negative-sentiment responses is the highest-ROI sentiment exercise. Read them, identify the pattern, and address the underlying content gap.

All Groups

Filter by the prompt group from the Prompts page. If you've adopted a consistent group naming convention (Comp-CrowdStrike, Vert-Healthcare, Prob-Ransomware), this filter lets you analyze all responses for one strategic theme at a time.

All Time

The date filter. Common uses:

• Last 7 days — Detect recent regressions or improvements.
• Last 30 days — Standard reporting window.
• Last 90 days — Quarterly trend analysis.
• Custom date range — Compare before/after a content launch, a competitor's funding round, or a major product release. If you launched a new content portal on March 1, comparing Feb 1–28 responses against Mar 1–31 responses shows whether the content moved the needle.

The All time default is fine for browsing. For analysis, almost always set a specific window.

20 Per Page

Bump this to 50 or 100 when scanning for patterns. Default of 20 is fine for spot-checking.

Exporting Responses

Click Export to CSV to download the currently filtered response list. The export includes the response text, model, sentiment, brands mentioned, cited domains, and date for every visible row.

A few high-value uses for the export:

• Quarterly reporting — Pull a 90-day export, calculate average visibility by model, and chart trends for leadership.
• Sentiment audits — Export all negative-sentiment responses, share with the customer success or product team to investigate root causes.
• Content gap analysis — Export all responses where you scored 0% on commercial-intent prompts. Hand to your content team as a prioritized backlog.
• Competitor intelligence — Export all responses where a specific competitor was mentioned. Read for patterns in how they're being positioned.
• Sales enablement — Export branded responses (responses to prompts naming you) and share with sales as a "what AI says about us" briefing for objection handling.

If you need a specific subset, filter first, then export. The CSV will only include rows currently visible after your filters are applied.

Common Workflows

These are the response-analysis workflows that pay back the most. None of them takes more than 30 minutes.

Workflow 1: Weekly Gap Analysis (15 minutes)

1. Set the date filter to Last 7 days.
2. Sort or filter to responses where your visibility is 0%.
3. Filter by All groups → Cat- prefixes (your category prompts).
4. Read 5–10 of the highest-traffic-potential ones.
5. For each, expand the response and note which brands AND which domains are being cited.
6. Hand the patterns to your content team as the week's content priorities.

Workflow 2: Competitor Intelligence Sweep (30 minutes)

1. Filter by All groups → Comp-[CompetitorName].
2. Set date range to Last 30 days.
3. Read the 20 most recent responses where that competitor is mentioned.
4. Note: how the AI describes their differentiators, what sources are cited, what use cases they're winning.
5. Specifically search for responses where they appear and you don't.
6. Share findings with sales and product teams.

Workflow 3: Sentiment Regression Hunt (15 minutes, monthly)

1. Filter to All Sentiments → Negative.
2. Set date range to Last 30 days.
3. Read every negative-sentiment response (usually 5–20).
4. Look for patterns — repeated criticisms, outdated information, customer complaints surfaced from old reviews.
5. For each pattern, identify the root content asset (a specific G2 review, a blog post, a forum thread) that's feeding the negative framing.
6. Either request a correction (when factually wrong) or counter with new authoritative content.

Workflow 4: Model Drift Detection (15 minutes, monthly)

1. Filter by one model at a time (start with ChatGPT).
2. Compare your average visibility this month against last month.
3. If there's a meaningful drop, investigate by reading the recent responses.
4. AI models update frequently; sudden shifts often correlate with model upgrades that change citation patterns.

Workflow 5: Cybersecurity-Specific — CVE Response Audit

For cybersecurity vendors, after a major CVE drops:

1. Search the Response text for the CVE number (e.g., "CVE-2026-").
2. Filter to Last 14 days.
3. Read every response that mentions the CVE.
4. Identify: which vendors are being recommended as the response, which advisories are being cited, whether your CVE coverage is being referenced.
5. Update or publish CVE-specific content within 48 hours of the disclosure to capture early-cycle visibility.

Connecting Responses to the Rest of GrackerAI

The Responses page is the diagnostic layer. It explains why your visibility scores look the way they do. The analysis you do here feeds three other parts of the product:

• Citations (Visibility → Citations) — Aggregates the Domain column across all responses. Use Citations to find the most-cited sources in your category, then use Responses to read the specific quotes from those sources.
• Recommendations (Actions → Recommendations) — GrackerAI's automated suggestions for closing visibility gaps. The Responses page is where you validate whether a recommendation is actually addressing a real pattern.
• Content (Actions → Content) — When you spot a gap in Responses, the Content tool helps you generate the content needed to close it. Reading the response that exposed the gap before generating content makes the output far more targeted.

A useful mental model: Prompts define your hypothesis space, Responses show what's actually happening, Citations tell you where authority lives, and Content/Recommendations tell you what to do next.

Common Mistakes to Avoid

Only looking at the Visibility score, not the response text. The score is a summary; the response text is the truth. Two prompts can both show "20% visibility" for very different reasons — one might mean "you're listed but ranked last," another might mean "you're mentioned in passing as a niche option." Reading the actual responses prevents you from chasing the wrong fix.

Filtering by your brand only. It's tempting to focus on responses that mention you. The bigger insight is in responses that don't mention you but should. Sort by 0% visibility on commercial-intent prompts to find them.

Ignoring the Domain count. A response with 50 cited domains is a high-research, high-authority answer where AI synthesized many sources. A response with 3 cited domains is a thin answer driven by a small set of authoritative pages. The latter is often easier to influence — fewer surfaces to compete on.

Reading responses without a hypothesis. "Browsing" the Responses page eats time without producing insight. Always start with a question: "Why am I losing CrowdStrike-comparison prompts on Perplexity?" Then filter to that subset and read with that question in mind.

Treating sentiment as decoration. Sentiment is a leading indicator. Negative sentiment trends usually predict visibility drops by 4–8 weeks. Catch them early.

Not exporting for cross-functional sharing. Responses are some of the highest-leverage qualitative data your sales, product, and customer success teams will ever see. Export and share regularly. AI-generated descriptions of your competitors often surface positioning insights the product team didn't know existed.

Reviewing responses once and then forgetting. AI responses shift week to week. A monthly review cadence is the floor; for fast-moving categories (cybersecurity especially), weekly is better.

Need Help?

If you'd like a custom response-analysis workflow tailored to your team and reporting cadence, book a call with our team (opens in a new tab) or reach out via the in-app chat. We work with B2B SaaS, cybersecurity, and fintech teams to turn raw response data into prioritized content and competitive action.

For more guides, visit gracker.ai/docs (opens in a new tab).