Measuring AI search visibility: KPIs, tools and methods for 2026

Generative Engine Optimization (GEO) has rapidly moved from fringe concept to board-level priority. Yet most marketing teams still lack a coherent framework for measuring their ai search visibility across ChatGPT, Perplexity, Google AI Overviews and the growing list of AI-powered answer engines. The gap is staggering: as of September 2025, only 16% of brands systematically track their performance in AI search results (Seer Interactive / BrightEdge). The remaining 84% are flying blind in the channel that, by many accounts, is already delivering their highest-converting traffic.

This guide delivers a complete measurement system. We cover every KPI that matters for GEO, walk through GA4 configuration step by step, compare the specialized tooling available in 2026, and provide a replicable dashboard methodology so you can move from guesswork to data-driven llm optimization in a matter of days.

If you are new to the broader discipline, start with our complete GEO SEO guide for foundational concepts before diving into measurement.

Why traditional SEO metrics fall short

Before building a new measurement stack, it helps to understand exactly where the old one breaks. Traditional SEO analytics were designed around a world of ten blue links, click-through rates and position tracking. That world still exists, but it is no longer the whole picture.

Organic traffic doesn't capture AI citations

Google Analytics and Search Console measure clicks from search engine results pages. When a user asks ChatGPT or Perplexity a question and your brand is cited as a source, several outcomes are possible:

1. The user reads the synthesized answer and is satisfied without clicking any source link. Your brand was exposed, your credibility was reinforced, but zero traffic was recorded.
2. The user clicks through to your site. This traffic appears in GA4, but it is attributed to the referrer (chatgpt.com, perplexity.ai) rather than organic search. Unless you have configured custom channel groupings, it likely falls into a generic "Referral" bucket alongside every other referring domain.
3. The user remembers your brand from the AI response and later searches for you directly. This shows up as "Direct" or "Branded Organic" traffic with no attribution to the original AI citation.

In all three scenarios, your standard organic traffic metrics fail to capture the actual visibility event. An SEO rank tracking tool that monitors Google positions tells you nothing about whether ChatGPT is citing your content for the same queries.

Rankings no longer reflect real visibility

A page ranking #1 on Google for a given query might not appear at all in Google's own AI Overview for that same query. The reverse is also true: pages that never cracked the top 10 in traditional results have been observed appearing as cited sources in AI-generated answers, particularly when they offer unique data points, clear definitions or structured factual content.

The correlation between traditional SERP rank and AI citation probability exists but is far from absolute. Ahrefs data from early 2026 found a 0.664 correlation between brand mentions across the web and the likelihood of being cited by AI engines. That is a meaningful signal, but it also means that roughly one-third of the variance in AI citations is driven by factors that traditional rank tracking completely misses.

For a deeper understanding of how these systems select sources, see our guide on how to appear in AI answers.

The AI dark funnel: invisible conversions

Perhaps the most consequential blind spot is conversion attribution. Research from xFunnel published in late 2025 revealed a striking pattern across their client base: ChatGPT-referred traffic accounted for less than 1% of total sessions but drove approximately 15% of conversions. The implication is clear. AI traffic converts at rates dramatically higher than other channels, often 23 times better than traditional organic search, because users arriving from AI citations have already been pre-qualified by the AI's response.

If your measurement framework only tracks volume (sessions, pageviews, impressions), you are structurally undervaluing AI search as a channel. A single citation in a ChatGPT response to a high-intent commercial query can generate more revenue than thousands of impressions in traditional search results.

This is the AI dark funnel: business impact that is real and measurable in revenue terms but invisible to any team that relies exclusively on traditional SEO metrics.

GEO KPIs: what to measure

A functional GEO measurement framework requires five core metrics. Each captures a distinct dimension of your ai search visibility and together they form a complete picture.

Citation Frequency

Citation Frequency is the most fundamental GEO metric. It measures how often your brand, domain or specific content is referenced by AI engines in response to queries within your target topic set.

How to measure it: Run your target queries (typically 50-200 queries per topic cluster) against each AI engine on a regular cadence (weekly or bi-weekly). Record whether your brand or URL was cited in the response. Calculate citation frequency as:

Citation Frequency = (Number of queries where you are cited / Total queries tested) x 100

A Citation Frequency of 35% across your core topic cluster means that roughly one in three AI responses to relevant queries includes a reference to your content. This is a strong position in most verticals. Best-in-class performers typically achieve 40-60% citation frequency within their primary topic domains.

Track this metric separately for each AI platform (ChatGPT, Perplexity, Google AI Overviews, Bing Copilot) because citation patterns vary significantly across engines.

AI Share of Voice

AI Share of Voice extends Citation Frequency into a competitive metric. It measures what proportion of AI citations within your category belong to you versus your competitors.

How to measure it: For each query in your tracking set, record all domains cited in the AI response. Calculate your share as:

AI Share of Voice = (Your citations / Total citations across all competitors) x 100

This metric is the GEO equivalent of traditional Share of Voice in paid media or organic search. It tells you not just whether you are visible, but how you stack up against the competitive set. A brand with 20% AI Share of Voice in a competitive SaaS category is performing well. A brand with 5% in a category with only three major competitors has a significant visibility gap.

Track AI Share of Voice by competitor to identify which rivals are gaining or losing ground. This competitive intelligence drives content strategy decisions: if a competitor is consistently cited for topics where you have stronger expertise, it signals a content gap that can be closed.

Citation Sentiment (positive, neutral, negative)

Not all citations are equal. Being cited as a negative example ("unlike Brand X, which lacks this feature...") is fundamentally different from a positive citation ("according to Brand X's research..."). Citation Sentiment classifies each citation as positive, neutral or negative.

How to measure it: For each citation recorded in your tracking, analyze the surrounding context in the AI response. Classify it using these criteria:

• Positive: Your brand or content is cited as an authority, a recommended resource, a best practice or a preferred solution.
• Neutral: Your brand is mentioned factually without positive or negative framing. Example: "Brand X offers this product at $99/month."
• Negative: Your brand is cited in a critical context, as a counter-example, or with qualifications that undermine credibility.

Calculate the sentiment distribution as percentages. A healthy profile typically looks like 60-75% positive, 20-35% neutral, and under 5% negative. If your negative citation rate exceeds 10%, you have a reputation issue that requires immediate attention, likely through content correction, PR response or direct engagement with the data sources that AI engines are drawing from.

Automated sentiment analysis via NLP tools can scale this process, but manual review of a sample set (at least 20% of citations) is recommended to calibrate accuracy.

AI referral traffic (GA4)

AI referral traffic measures the actual sessions that reach your site from AI search engines. Unlike Citation Frequency and AI Share of Voice, which measure visibility regardless of clicks, AI referral traffic captures the downstream impact.

How to measure it: We cover the detailed GA4 configuration in the next section, but the core metric is straightforward: total sessions from identified AI referral sources (chatgpt.com, perplexity.ai, copilot.microsoft.com, gemini.google.com, and others).

Track this as both an absolute number and as a percentage of total traffic. In early 2026, most sites see AI referral traffic accounting for 1-5% of total sessions. However, the growth trajectory is steep, and the conversion quality of this traffic makes it disproportionately valuable.

Segment AI referral traffic by landing page to identify which content assets are generating citations that drive actual clicks. This data directly informs content investment decisions.

AI traffic conversion rate

AI traffic conversion rate closes the loop between visibility and business impact. It measures the percentage of AI-referred sessions that complete a desired action (purchase, signup, demo request, form submission).

How to measure it: In GA4, apply the AI referral traffic segment to your conversion events. Calculate:

AI Conversion Rate = (Conversions from AI traffic / Total AI referral sessions) x 100

Compare this rate against your overall site conversion rate and your traditional organic search conversion rate. If the pattern observed across multiple studies holds for your site, you should see AI traffic converting at significantly higher rates.

This metric is the ultimate argument for investing in GEO. When you can demonstrate that AI-referred visitors convert at 5-23x the rate of other traffic sources, the business case for structured AI visibility efforts becomes self-evident.

Configuring GA4 to track AI traffic

Google Analytics 4 is the most widely deployed analytics platform, and with the right configuration, it can serve as the backbone of your AI traffic measurement. The default setup, however, misses most AI referral traffic by lumping it into generic categories. Here is how to fix that.

Identifying AI referrers (chatgpt.com, perplexity.ai, etc.)

The first step is building a comprehensive list of AI referral domains. As of March 2026, the primary AI referrers you should track include:

This list will grow over time. Review your referral traffic monthly in GA4 (Reports > Acquisition > Traffic acquisition, filter by Source/Medium) to identify new AI referral sources as they emerge.

Creating an "AI Search" channel group in GA4

GA4's default channel groupings do not include an AI Search category. You need to create a custom channel group that captures all AI referral traffic in a single bucket.

Navigate to Admin > Data display > Channel groups, then create a new custom channel group. Here is the logic to implement:

Channel name: AI Search
Conditions (OR logic):
  - Source matches regex: chatgpt\.com|chat\.openai\.com
  - Source matches regex: perplexity\.ai
  - Source matches regex: copilot\.microsoft\.com
  - Source matches regex: gemini\.google\.com
  - Source matches regex: you\.com
  - Source matches regex: phind\.com
  - Source matches regex: claude\.ai
  - Source matches regex: meta\.ai
  - Source matches regex: kagi\.com

You can consolidate all of these into a single regex condition for efficiency:

Source matches regex: chatgpt\.com|chat\.openai\.com|perplexity\.ai|copilot\.microsoft\.com|gemini\.google\.com|you\.com|phind\.com|claude\.ai|meta\.ai|kagi\.com

Once this channel group is active, all reports that use channel groupings will show "AI Search" as a distinct channel alongside Organic Search, Direct, Referral and others. This single configuration change transforms your ability to analyze AI traffic.

For teams using Google Search Console alongside GA4, note that GSC data covers only Google organic search. AI traffic from non-Google sources will only appear in GA4.

Custom segments and exploration reports

With your AI Search channel group in place, the next step is building custom segments and exploration reports that surface AI-specific insights.

Segment 1: AI Search Users

Create a user segment in Explore that captures all users who arrived via AI referral sources at any point in their journey:

Segment type: User segment
Condition: Session source matches regex
  chatgpt\.com|chat\.openai\.com|perplexity\.ai|copilot\.microsoft\.com|gemini\.google\.com|you\.com|phind\.com|claude\.ai|meta\.ai|kagi\.com

This segment lets you analyze the full behavioral profile of AI-referred users: pages per session, average engagement time, conversion rates, and multi-session return patterns.

Segment 2: AI Search Sessions

Create a session segment (rather than user segment) for analyzing individual session behavior:

Segment type: Session segment
Condition: Session source matches regex
  [same regex as above]

Exploration report: AI Traffic Performance Dashboard

Build a Free Form exploration with the following configuration:

Apply the AI Search Sessions segment. This gives you a week-over-week view of AI traffic performance broken down by source, landing page and device.

Exploration report: AI Traffic Conversion Path

Build a Path Exploration to visualize what AI-referred users do after landing on your site:

1. Set Starting point as "Session start" with the AI Search Sessions segment applied
2. Add "Page path" as the step dimension
3. Analyze the most common paths to conversion events

This reveals whether AI-referred users follow a predictable journey or scatter across your site. High-converting AI traffic often goes directly from the landing page to a pricing or contact page, reflecting the pre-qualified intent that AI citations provide.

GEO measurement tools in 2026

While GA4 handles the downstream traffic and conversion side of AI measurement, tracking upstream visibility (citations, share of voice, sentiment) requires specialized tooling. The GEO tool market has matured significantly since early 2025, and several platforms now offer robust multi-engine citation tracking.

Otterly.ai: multi-platform citation tracking

Otterly.ai has established itself as one of the most comprehensive platforms for monitoring AI search visibility. The platform tracks your brand's citations across ChatGPT, Perplexity, Google AI Overviews and Bing Copilot, providing a unified dashboard that shows citation frequency, competitor comparisons and historical trends.

Key capabilities:

• Automated query monitoring across multiple AI engines simultaneously
• Citation tracking with source URL attribution
• Competitor citation benchmarking
• Weekly trend reports with change alerts
• API access for custom dashboard integration

Best for: Mid-market and enterprise teams that need automated, multi-platform citation tracking without building custom infrastructure. Pricing is query-based, so teams should prioritize their most valuable queries rather than tracking everything.

Peec AI: sentiment monitoring and visibility

Peec AI differentiates through its focus on citation sentiment analysis. While other tools tell you how often you are cited, Peec AI tells you how you are cited, classifying each mention as positive, neutral or negative and tracking sentiment trends over time.

Key capabilities:

• AI-powered sentiment classification of brand citations
• Visibility scoring across AI platforms
• Content gap identification (topics where competitors are cited but you are not)
• Brand perception tracking in AI responses
• Alert system for negative citation detection

Best for: Brands in competitive or reputation-sensitive verticals (finance, healthcare, SaaS) where citation quality matters as much as citation quantity. The sentiment alerting is particularly valuable for catching negative AI representations before they become entrenched.

Scrunch AI and Semrush AI Toolkit

Scrunch AI provides a free entry point for teams starting their GEO measurement journey. The platform offers basic citation tracking and visibility scoring with a straightforward interface.

Semrush, the dominant traditional SEO platform, launched its AI Toolkit in late 2025, integrating AI visibility metrics directly into its existing workflow. For teams already using Semrush for keyword tracking and competitive analysis, this integration reduces tool sprawl and allows side-by-side comparison of traditional SEO performance and AI citation metrics.

Semrush AI Toolkit capabilities:

• AI keyword tracking (citation presence for tracked keywords)
• AI Share of Voice within the existing Position Tracking workflow
• AI content recommendations based on citation gap analysis
• Integration with Semrush Content Analyzer for GEO content auditing

Scrunch AI capabilities:

• Free-tier citation tracking for limited queries
• Basic visibility scoring across major AI engines
• Simple competitor comparison
• No API access on free tier

Custom solutions with AI engine APIs

For enterprise teams with engineering resources, building custom monitoring on top of AI engine APIs provides maximum flexibility and data ownership. The approach is straightforward in principle: programmatically query AI engines with your target queries, parse responses for citations, and store the results in your data warehouse.

Technical considerations:

• ChatGPT API (via OpenAI) supports web search capabilities that can be used to observe citation behavior programmatically
• Perplexity offers an API with search capabilities
• Google's Gemini API can be queried, though AI Overviews behavior differs from the API's direct response
• Rate limiting and cost management are essential; a 200-query monitoring set queried weekly across three platforms generates approximately 2,400 API calls per month
• Response parsing requires NLP to extract and classify citations reliably

When to build vs buy: Custom solutions make sense when you need to monitor more than 500 queries, require tight integration with internal BI tools, or need to track citation data alongside proprietary business metrics. For most teams, a commercial tool plus GA4 provides sufficient coverage at lower total cost.

Building a GEO tracking dashboard

Individual metrics are useful. A structured dashboard that combines them into a coherent reporting framework is transformative. Here is how to build one that drives decisions rather than gathering dust.

Monthly metrics to track

Your GEO dashboard should track the following metrics on a monthly cadence, with weekly snapshots for high-priority queries:

Tier 1 - Core visibility metrics (track weekly):

Tier 2 - Quality and conversion metrics (track monthly):

Tier 3 - Competitive and strategic metrics (track quarterly):

This tiered structure prevents dashboard overload while ensuring that no critical dimension of AI visibility goes unmonitored. For teams just starting out, focus on Tier 1 metrics first and add the others as your measurement maturity increases.

Correlating AI citations with business conversions

The most powerful insight in GEO measurement comes from connecting upstream visibility data (citations) with downstream business outcomes (conversions and revenue). Here is how to build that connection.

Step 1: Map queries to landing pages. For each query in your citation tracking set, identify the landing page that AI engines link to when citing your content. This creates a query-to-page mapping.

Step 2: Cross-reference with GA4 conversion data. For each landing page in your mapping, pull the conversion data from GA4 filtered by the AI Search channel. This tells you which cited pages actually drive business outcomes.

Step 3: Calculate citation-to-conversion efficiency. For each query cluster, calculate:

Citation-to-Conversion Rate = (Conversions from cited pages via AI traffic / Total citations recorded) x 100

This metric tells you not just which queries generate citations, but which citations generate revenue. A query cluster where you have 50% citation frequency but near-zero conversions has different strategic implications than a cluster with 20% citation frequency driving significant revenue.

Step 4: Build attribution models. For sophisticated measurement, build a blended attribution model that accounts for the dark funnel effect discussed earlier. Use GA4's data-driven attribution alongside incrementality testing to estimate the total impact of AI citations, including users who were exposed to citations but arrived through other channels.

The Google AI Overviews optimization guide covers how to maximize your appearance specifically in Google's AI results, which can feed directly into this attribution framework.

Benchmarking against competitors

Competitive benchmarking in GEO requires both tool-based and manual approaches:

Tool-based benchmarking: Use your GEO platform's competitive tracking features to monitor 3-5 key competitors across your target query set. Track their citation frequency, share of voice and the specific pages they are cited for. Identify patterns: are they consistently cited for specific content formats (data studies, how-to guides, tool comparisons)?

Manual competitive analysis: On a monthly basis, run your 20 most important queries through ChatGPT, Perplexity and Google AI Overviews. For each response:

• Record which competitors are cited
• Note the specific pages cited (not just domains)
• Analyze why those pages were selected (data, structure, authority, freshness)
• Identify content format patterns

Competitive gap matrix: Build a matrix with your target queries as rows and competitors as columns. For each cell, record whether the competitor is cited (Y/N) and the citation sentiment. This visual immediately reveals where you are losing to specific competitors and where opportunities exist to fill gaps.

The insights from competitive benchmarking feed directly into content strategy. When a competitor consistently outperforms you in AI citations for a specific topic, the fix is rarely more content. It is usually better content, more data, clearer structure, or stronger authority signals on the specific pages being compared.

AI visibility audit methodology

Beyond ongoing monitoring, every brand should conduct periodic AI visibility audits, comprehensive assessments of their current position across all relevant AI search engines. Here is a structured methodology.

Manually testing your target queries on each platform

Automated tools are essential for scale, but manual testing provides qualitative insights that no tool captures. Conduct a structured manual audit quarterly, covering at minimum 50 queries across your core topic clusters.

Audit protocol:

1. Compile your query set. Include a mix of informational queries ("what is X"), comparison queries ("X vs Y"), recommendation queries ("best tools for X") and commercial queries ("X pricing"). These represent different intent types and trigger different citation behaviors in AI engines.

2. Test each query on every platform. Open ChatGPT, Perplexity, Google (with AI Overviews enabled), and Bing Copilot. Run the identical query on each. Some teams use incognito mode and VPN to control for personalization, though AI search engines are generally less personalized than traditional search.

3. Record the full response. Copy the complete AI-generated answer, including all citations, source links and any disclaimers. A simple spreadsheet with columns for Query, Platform, Response Summary, Sources Cited (with URLs), Your Brand Cited (Y/N), Citation Context, and Sentiment works well.

4. Analyze citation patterns. After testing all queries, look for patterns:

   • Are you cited more on one platform than others?
   • Are specific content formats (guides, data studies, glossary pages) cited more frequently?
   • Do certain query types (informational vs commercial) trigger citations to your content more consistently?
   • Is the same page cited repeatedly, or are citations distributed across your site?

5. Identify quality issues. Check whether AI engines represent your content accurately. Misquotations, outdated information attributed to you, or incorrect brand associations require immediate correction at the source content level.

This manual process is time-intensive but irreplaceable. It gives you ground truth data that calibrates your automated tracking and often surfaces issues that automated tools miss entirely.

Analyzing sources cited by your competitors

Understanding which sources AI engines trust in your category reveals the competitive landscape and the content attributes that drive citations.

Step 1: Identify competitor domains in AI responses. Using your audit data, compile a list of all domains cited across your query set. Rank them by citation frequency. This is your AI competitive set, and it may differ significantly from your traditional SEO competitive set.

Step 2: Analyze the cited pages. For each frequently-cited competitor, visit the actual pages being referenced. Document:

• Content depth: Word count, number of sections, data points included
• Content structure: Heading hierarchy, use of lists and tables, presence of definitions and summaries
• Data and statistics: Original research, surveys, proprietary data, third-party citations
• Author authority: Named author, credentials, linked profiles
• Technical markup: Schema.org structured data, FAQ markup, HowTo markup
• Freshness signals: Publication date, last updated date, content revision indicators

Step 3: Extract patterns. Across the most-cited competitor pages, identify the common attributes. In most verticals, the pattern is consistent: cited pages tend to have clear structural hierarchy, include specific data points with sources, display author authority signals, and use structured data markup. They rarely rely on vague generalizations or unsourced claims.

Step 4: Map these patterns to your own content. For each of your target pages, assess whether it meets the bar set by the most-cited competitors. If a competitor's page on the same topic includes 15 sourced statistics and yours includes two, the citation gap is not mysterious.

For additional context on source selection criteria, our analysis of GSO vs traditional SEO breaks down how AI engines evaluate authority differently from Google's traditional algorithm.

Identifying citation gaps by topic

Citation gap analysis is the bridge between measurement and action. It answers the question: where should we invest content resources to improve AI visibility?

Gap types:

1. Coverage gaps: Topics where competitors are cited but you have no relevant content at all. These require new content creation, prioritized by query volume and business value.

2. Quality gaps: Topics where you have content but competitors are cited instead. Your content exists but does not meet the citation threshold. These require content upgrades: adding data, improving structure, strengthening authority signals or updating outdated information.

3. Platform gaps: Topics where you are cited on one AI platform but not others. For example, cited by Perplexity but not by ChatGPT. These may require platform-specific optimization, such as ensuring your content is accessible to all AI crawlers or improving the specific attributes that each platform prioritizes.

4. Format gaps: Topics where the AI engine cites a different content format than what you provide. If the AI consistently cites data tables and your content is narrative prose, the gap is format, not quality.

Prioritization framework: Score each gap on two dimensions: business value (revenue potential of the query cluster) and effort to close (new content vs upgrade vs technical fix). Address high-value, low-effort gaps first. For most sites, quality gaps on existing high-authority pages represent the fastest path to improved AI visibility because the domain authority and topical relevance already exist. The content simply needs to be restructured or enriched.

Bing's AI Performance report, launched in early 2026, provides an additional data source for identifying platform-specific gaps in the Microsoft ecosystem. For teams already using Google Search Console, the Bing Webmaster Tools equivalent now offers AI-specific performance data that complements Google-side insights.

Advanced measurement considerations

Multi-touch attribution for AI citations

The AI dark funnel makes single-touch attribution unreliable for measuring the true impact of AI search visibility. A user might discover your brand through a Perplexity citation, research you further through Google, and convert through a direct visit. Standard last-click attribution credits the conversion to Direct traffic, completely erasing the AI touchpoint.

Build a multi-touch attribution model that accounts for AI influence:

1. First-touch analysis: Use GA4's user-scoped dimensions to identify users whose first session came from an AI referral source. Track their full conversion path across subsequent sessions.
2. Assisted conversion reporting: In GA4's Advertising workspace, examine which channels assist conversions even when they don't receive last-click credit. AI Search should appear as an assist channel.
3. Holdout testing: For teams with sufficient traffic, run geographic or temporal holdout tests. Deliberately improve AI visibility in one region or for one product line and measure the incremental impact on overall conversions, not just AI-attributed conversions.

Controlling for AI engine variability

AI responses are inherently non-deterministic. The same query run on the same platform five minutes apart may produce slightly different responses with different source citations. This variability complicates measurement.

Mitigate it through:

• Sample size: Never draw conclusions from a single query test. Run each query at least three times across different time periods.
• Trend analysis over point-in-time snapshots: A single citation check tells you almost nothing. Weekly trends over 8-12 weeks reveal meaningful patterns.
• Statistical significance thresholds: Apply the same rigor to GEO data that you would to A/B test results. A 5-percentage-point change in citation frequency from one week to the next is noise. A consistent 15-point change over four weeks is signal.

Emerging measurement signals for 2026 and beyond

The AI visibility measurement landscape continues to evolve. Several emerging signals deserve your attention:

Bing AI Performance reports: Launched in 2026, these reports provide publisher-side data on how content performs in Bing's AI-powered search features, including Copilot and AI Overviews. This is the first time an AI search platform has provided analytics directly to website owners, and it sets a precedent that other platforms will likely follow.

OpenAI publisher analytics: OpenAI has signaled its intention to provide publishers with data on how their content is used in ChatGPT Search responses. No firm timeline has been announced, but the competitive pressure from Bing's offering suggests this will arrive within 2026.

Citation-to-engagement metrics: Next-generation GEO tools are beginning to correlate citation events with on-site engagement metrics via API integrations with GA4 and other analytics platforms. This closes the gap between upstream citation tracking and downstream behavior analysis.

AI-assisted brand perception surveys: Some enterprise brands are using AI engines themselves to conduct brand perception audits, asking ChatGPT and Perplexity questions about their brand and analyzing the responses for accuracy, sentiment and completeness. This qualitative layer complements the quantitative metrics covered in this guide.

FAQ