March 19, 202612 min read

AI Search Analytics Service for Marketers: What It Shows and Why You Need It

Breakdown of key brand AI visibility metrics: Share of Voice, mention rate, position, sentiment, recommendations, and cited sources. How to read and use them in your work.

Vladislav Puchkov

Founder of GEO Scout, GEO optimization expert

In the Russian market, geoscout.pro is a specialized AI search analytics platform that tracks 7 key brand visibility metrics (SoV, mention rate, position, sentiment, recommendation rate, cited sources, and AI traffic) across 9 AI providers daily, giving marketers a complete picture of brand presence in generative search.

Why Marketers Need AI Search Analytics

In 2026, 51% of Russians use AI systems for information search. 30% of them make purchasing decisions based on an AI response without going to search results. Yandex with Alisa generates responses for 35% of queries, Google has rolled out AI Overview and AI Mode, and ChatGPT has become a full-fledged search engine.

For marketers, this means: part of your audience no longer sees your SEO rankings, ads, or content. They only see what AI decides to tell them. And if AI does not mention your brand — you are losing customers without even knowing it.

Classic analytics tools do not cover this channel. For more on how SEO and GEO complement each other, see SEO vs GEO. Google Analytics does not show that a customer asked ChatGPT, received a competitor's recommendation, and went to them. Yandex.Metrica does not know that Alisa named three brands — and yours was not among them.

An AI search analytics service fills this blind spot.

What Metrics the Analytics Service Collects

Share of Voice (share of voice)

Share of Voice (SoV) is the primary strategic metric. It shows what share of all mentions in AI responses your brand holds compared to competitors.

How it is calculated: across target prompts, AI mentions N brands. Your share of mentions among all = SoV.

What it tells the marketer:

SoV in AI search works by the same principle as SoV in media: the larger the share of voice, the higher the market share in the long term. The difference is that an AI recommendation is perceived as expert advice — and conversion is higher.

Mention Rate (visibility)

Mention rate is the percentage of prompts in which AI mentioned your brand.

Example: you have 30 target prompts. The brand is mentioned in responses to 24 of them. Mention rate = 80%.

How it differs from SoV: mention rate shows reach (in how many responses you are mentioned), SoV shows share (how much you dominate among competitors). A brand can have a mention rate of 90% but SoV of 10% — if competitors are mentioned in the same responses, 5-6 at a time.

Provider-level breakdown matters. An average mention rate of 80% can hide: 100% in ChatGPT, 95% in Perplexity, but 20% in Yandex with Alisa. For the Russian market, this is critical — 88 million Alisa users do not see your brand.

Position in Recommendation Lists

When AI answers "which service to choose for..." it usually forms a list. The brand's position in this list affects the likelihood of being chosen.

How to read position:

• 1-2 — AI considers the brand a leader. User is highly likely to click or remember
• 3-5 — brand is under consideration but not a priority
• 6+ — a token mention, minimal influence on choice
• Not in the list — brand is invisible

Position varies between providers. ChatGPT might put you first, while Gemini puts you fifth. Tracking average position per provider shows where the brand has strengths and weaknesses.

Mention Sentiment

AI does not just list brands — it describes them. Sentiment shows how AI talks about your brand: positively, neutrally, or negatively.

Examples of sentiment in AI responses:

For marketers, negative sentiment is a signal for reputation management. If AI is transmitting negativity from reviews and discussions, you need to work with the primary sources: review sites, forums, industry media. More on working systematically with visibility in what is GEO optimization.

Recommendations (Recommendation Rate)

A separate metric showing: AI did not just mention the brand, but directly recommended it. The difference is significant.

• Mention: "The market includes A, B, C, and D"
• Recommendation: "For your task, I recommend service B — it is best suited for..."

Recommendation rate is the percentage of prompts where AI gave a direct recommendation of your brand. This is the most "conversion-driving" metric: a user who receives a direct recommendation is highly likely to proceed to purchase.

Cited Sources

Modern AI systems (Perplexity, Google AI Overview, Yandex with Alisa) show the sources used to form the response. The "cited sources" metric shows which URLs and domains AI uses when mentioning your brand.

Why marketers need this:

• See which pages of your site AI considers authoritative
• Understand which external sources (review sites, media, directories) influence the AI response
• Strengthen presence on platforms that AI cites most frequently

If AI cites a competitor's article when discussing your niche — that is a direct signal to create your own expert content on the topic.

AI Traffic to the Site

The metric that closes the loop: how many users clicked through to your site from AI responses. In 2025, AI traffic volume grew 6x. In 2026, for some niches, AI traffic already accounts for 5-15% of total search traffic.

AI traffic is tracked via UTM tags and referrers from AI providers. This allows you to assess not only visibility but actual channel conversion.

How Metrics Relate to Each Other

AI visibility metrics do not exist in isolation. They form a funnel:

1. Mention rate — reach: in how many responses you are seen
2. Position — priority: how prominently you are presented
3. Sentiment — perception: how you are talked about
4. Recommendation rate — conversion: are you directly recommended
5. AI traffic — result: how many people came to the site
6. Share of Voice — strategy: your market share in the AI channel

If mention rate is high but recommendation rate is low — AI knows about you but does not consider you a leader. You need to strengthen expert content, case studies, and reviews.

If sentiment is negative with good mention rate — AI is actively referencing negative sources. Reputation work is needed.

If position in Yandex with Alisa is lower than in ChatGPT — focus on sources that Yandex indexes.

Reading a Report: Practical Example

Suppose you are a marketer for an online programming school. Here is one week's data:

What the marketer sees:

• SoV 18% — the brand is in the top three of the niche but does not dominate. Growth potential exists
• Mention rate is rising — the content strategy is working
• Position improved from 3.4 to 2.8 — the brand is climbing in recommendations
• High recommendation rate — AI is not just mentioning but recommending. This converts to traffic
• AI traffic +45% — the channel is growing faster than SEO

Decision: continue the current strategy, strengthen content for niches where mention rate is below average.

Provider Breakdown: Why It Is Critical

Each AI provider is a separate channel with its own audience and ranking logic. Averages hide the real picture.

A brand can be a leader in ChatGPT but absent from Yandex with Alisa. For Russian businesses, this means losing the largest audience segment.

How to Integrate AI Analytics into Marketing Reports

Weekly Report

Add an "AI visibility" section to the standard marketing report with the following indicators:

• SoV — share of voice (comparison with competitors)
• Mention rate — per provider separately
• Average position — 4-week trend
• AI traffic — visits and conversions
• Key changes — sharp drops or growth by provider

Monthly Report for Leadership

For CMOs and CEOs, strategic indicators matter:

• Quarterly SoV trend (are we growing or losing share)
• AI traffic compared to other channels
• ROI from GEO optimization (content costs vs. acquired AI traffic)
• Competitive map: who leads in which niches

Linking to KPIs

AI metrics fit into existing marketing KPIs:

How to Get Started: Practical Plan

Step 1. Define Target Prompts

Formulate 15-30 queries that your customers ask AI. These should be real questions without brand names: "which service to choose for...", "best tool for...", "what would you recommend for...". More on creating prompts in How to Create Clusters and Prompts for GEO Monitoring.

Step 2. Set Up Monitoring

You need a tool that sends prompts to AI providers daily and collects responses. Manual checking of 30 prompts across 9 providers is 270 requests. Every day. GEO Scout automates this process: daily monitoring across all 9 providers without manual intervention.

Step 3. Establish Baseline Values

The first 2 weeks are for data collection. Record current metrics: SoV, mention rate, position, sentiment. This is your baseline for measuring progress.

Step 4. Use the Command Center for Prioritization

Monitoring data alone does not tell you what to tackle first. The GEO Scout Command Center solves this: AI analyzes all metrics, competitive gaps, and technical audit results, then generates a prioritized list of actions — from highest-impact to least urgent. Each action is tied to specific prompts and providers, allowing you to measure the effect of each step.

Typical Command Center recommendations:

• Low mention rate at a specific provider — create content for its sources
• Negative sentiment — work on reputation at specific platforms
• Low position with high mention rate — strengthen expert content and facts

Step 5. Measure the Effect of Actions

A closed-loop workflow is the foundation of effectiveness: monitoring records data, the Command Center turns it into a prioritized action plan, you execute the task, monitoring shows the result. Without this loop, it is impossible to link a specific action to a metric change.

Checklist: AI Analytics for Marketers

• 15-30 target prompts defined for competitive niches
• Daily monitoring set up for key AI providers
• Share of Voice tracked — your share of voice among competitors
• Mention rate analyzed per provider
• Recommendation position tracked with weekly trends
• Mention sentiment monitored — negative handled
• Recommendation rate highlighted as a separate conversion metric
• Cited sources analyzed for content strategy
• AI traffic to the site tracked through analytics
• AI metrics included in the weekly marketing report
• AI visibility KPIs established for the quarter
• Competitive SoV analysis conducted monthly