How to Measure Share of Voice in AI Chat: A Complete Guide

I notice that many businesses in Saudi Arabia are observing a shift in their traditional SEO traffic, with AI-generated answers increasingly capturing user attention at the top of search results. This transition to ‘Answer Engines’ can feel unsettling, leaving marketing teams unprepared and questioning how to maintain visibility. If you’re struggling to understand your brand’s presence in this new landscape, you’re not alone. The key lies in a new, critical performance indicator, and learning how to measure share of voice in AI chat is the first essential step toward adapting and thriving.

This complete guide is designed to provide you with a clear, practical framework. We will walk you through exactly what AI Share of Voice is, how you can start tracking it today-both manually and with automated tools-and most importantly, how to implement strategies that increase your brand’s mentions in ChatGPT and other AI responses. By the end, you’ll have the knowledge to not only measure this vital new metric but also to demonstrate its value and secure your brand’s future in an AI-driven world.

What is AI Share of Voice? (And Why It’s Replacing Rankings)

For years, the goal of SEO in Saudi Arabia and beyond was clear: secure the #1 spot on a list of ten blue links. This top position was a digital billboard, promising visibility and traffic. However, the landscape is undergoing a monumental shift. Users are no longer just scanning lists; they are asking AI chatbots for direct, synthesized answers. In this new world, success isn’t about being at the top of a list-it’s about being a trusted source within the answer itself.

This brings us to a critical new metric: AI Share of Voice. Put simply, AI SoV is the measure of your brand’s visibility and citation frequency within AI-generated responses compared to your competitors. It moves beyond the traditional marketing definition of Share of Voice, which was often linked to advertising spend. Instead, it quantifies your brand’s authority and relevance in the moments that matter most to a potential customer. Understanding how to measure share of voice in AI chat is no longer optional; it’s essential for future growth.

Think of it this way: a number one ranking was like having your business card at the top of a pile. An AI citation, however, is like being introduced as the go-to expert in the room. Which would you prefer?

Traditional SoV vs. AI Chat SoV

The distinction between the old and new models is crucial. Traditional SoV was often a direct result of budget, where brands with marketing campaigns costing thousands of Saudi Riyals could dominate the conversation. AI Chat SoV operates differently:

• Traditional SoV: Primarily an output metric tied to paid media spend, press coverage, or social media mentions. It measured reach but not always influence.
• AI Chat SoV: An earned metric based on the depth, authority, and quality of your content. It directly measures your influence at the critical point of customer consideration, when they are actively seeking a solution.

The Business Impact: From Invisibility to Influence

The stakes in this new era are incredibly high. If your brand is not cited in an AI answer, you are effectively invisible to a rapidly growing segment of users. You don’t get a chance to make your case because, in the AI’s view, you are not a relevant part of the solution. Conversely, earning a citation is a powerful form of validation.

When an AI model like ChatGPT or Gemini references your content, it acts as a strong, impartial third-party endorsement. The traffic that clicks through from these citations is often highly qualified. These users have already received a trusted summary and are now seeking deeper information, putting them further along the conversion funnel and signaling a strong intent to engage.

How to Manually Measure Your AI Share of Voice (A Step-by-Step Guide)

You don’t need expensive tools or a data science degree to get started. You can begin to measure share of voice in AI chat today with a simple spreadsheet. This manual process is an invaluable first step, helping you understand the competitive landscape and gain crucial qualitative insights before investing in automated solutions that can cost thousands of Riyals. Before diving in, it’s helpful to understand the foundational marketing metric of what is share of voice, as we are adapting that core principle for the world of generative AI. This guide will walk you through defining competitors, building a relevant prompt list, and calculating your initial score.

Step 1: Define Your Competitive Set

First, identify who you are competing against for attention. List your top 3-5 direct competitors operating within the Saudi market. Then, consider including indirect competitors or major industry publications that are frequently cited by AI models in your niche. For example, a local fashion e-commerce brand might track not only other online stores but also major lifestyle blogs in the Gulf region. This list defines the “total voice” in the conversation, which is the denominator in your SoV calculation.

Step 2: Build Your Master Prompt Bank

Gather a list of 20-30 high-value questions and queries that your target customers in Saudi Arabia are likely to ask AI chatbots. Use your existing keyword research or tools like AlsoAsked for inspiration. A strong prompt bank should include a mix of categories:

• Informational Prompts: E.g., “how does VAT apply to online shopping in KSA?”
• Comparison Prompts: E.g., “compare HungerStation versus Jahez for food delivery”
• Buying-Intent Prompts: E.g., “best oud perfume for men in Riyadh”

Step 3: Execute Queries and Collect Data

Now it’s time to collect the raw data. For unbiased results, always use a clean or incognito browser session for each AI platform you test. Track your findings meticulously in a spreadsheet with columns for: Prompt, AI Model Used, Your Mentions, Competitor A Mentions, Competitor B Mentions, and so on. Crucially, also note the position of the mention (e.g., mentioned first, middle, or last in the response), as this provides important context beyond a simple count.

Step 4: Calculate Your Basic AI SoV Score

With your data collected, you can calculate your baseline score using a straightforward formula. This score provides a clear benchmark for your brand’s visibility within AI-generated answers.

Formula: (Your Total Mentions / Total Mentions Across All Competitors) * 100 = Your AI SoV %

Calculate this score for your entire prompt list and also for each prompt category (informational, comparison, etc.). This will reveal your current strengths and weaknesses, giving you a starting point to improve and track your efforts over time.

Scaling Up: When to Switch to an Automated AI SoV Tracker

Manually checking a few prompts in ChatGPT is a great starting point. It’s free and gives you an initial snapshot of your brand’s visibility. However, as your brand grows and you begin to seriously measure share of voice in AI chat, this manual approach quickly reveals its limitations. Relying on it long-term is like trying to monitor nationwide traffic by looking out a single window-you only see a tiny, often misleading, fraction of the full picture.

The Limits of Manual Tracking

The core challenge with manual tracking is its lack of scalability and objectivity. AI responses are not static; they are influenced by factors like your location, previous chat history, and the time of day. A search performed in Riyadh can yield different brand recommendations than one in Jeddah. This makes one-off manual checks unreliable. The primary constraints include:

• It is extremely time-consuming. Manually entering, capturing, and logging results for even 30 prompts can consume hours that your team could spend on strategy.
• Results are prone to personalization bias. Your browser’s data and previous interactions with an LLM can skew the results, giving you a false impression of what a neutral customer sees.
• Scaling is impossible. Tracking hundreds of keywords across multiple models like ChatGPT-4, Claude 3, and Perplexity on a daily basis is simply not feasible for any human team.

Key Features of an Automated Tracking Platform

When you’re ready to move beyond inconsistent manual checks, a dedicated AI SoV tracking platform becomes essential. These tools are designed for consistency, scale, and deep analysis. Look for a solution that offers:

• Multi-LLM Support: The ability to track your brand’s presence across all major models (e.g., ChatGPT, Claude, Perplexity, Gemini) that your customers use.
• Scheduled Prompting: Automated, regular execution of your entire prompt bank to gather consistent data without any manual intervention.
• Historical Data Dashboards: Visualizations that show your SoV trends over time, allowing you to connect performance changes to specific marketing campaigns.
• Sentiment and Contextual Analysis: Tools that go beyond simple mention counts to analyze how your brand is being described, flagging positive, negative, or neutral contexts.

Making the Business Case for Automation

Investing in an automated tool is not just a matter of convenience; it’s a strategic business decision. By automating data collection, you free up your marketing team’s time, saving what could amount to thousands of Saudi Riyals in monthly labor costs. Instead of performing repetitive data entry, your team can focus on analyzing trends and developing strategies to improve your brand’s AI visibility.

Furthermore, automation provides the reliable, objective data needed for reporting to leadership. It replaces subjective anecdotes with concrete metrics, building confidence and justifying marketing spend. If you’re ready to get serious about tracking your AI share of voice, it’s time to let technology handle the heavy lifting. See how TrackMyBusiness automates ChatGPT mention tracking and provides the insights you need to win.

A 4-Week Action Plan to Improve Your AI Share of Voice

I notice that simply measuring your Share of Voice (SoV) is only the first step; the ultimate goal is to improve it. This four-week action plan breaks down the process into manageable sprints, focusing on transforming your brand into the most citable, authoritative source in your niche. The objective is to optimize your content for machine readability and extraction, making it the preferred choice for AI chat models.

Week 1: Audit and Opportunity Analysis

Begin by analyzing your baseline SoV report. Identify the specific prompts where competitors dominate and your brand is absent. For instance, if you’re in e-commerce in Saudi Arabia, are competitors cited for “best payment gateways in KSA” while you are not? Review the exact pages AI models are citing. Note their structure, clarity, and use of data. This analysis reveals the blueprint for the content you need to create or improve.

Week 2: Optimize Content for ‘Extractability’

Now, focus on making your existing content easier for AI to digest and quote. This is not a complete rewrite but a strategic enhancement.

• Update key articles with concise, bolded definitions at the top of the page.
• Incorporate quotable statistics, such as, “The Saudi Arabian digital services market is expected to grow by over 15% annually, reaching 150 billion SAR.”
• Implement structured data like FAQ and How-To schema to explicitly label questions and answers for machines.

Week 3: Create New, Authoritative Content

Using the insights from Week 1, create new content to fill your identified gaps. Don’t just replicate what competitors have done; build something better. Develop a comprehensive guide, publish original research on the Riyadh market, or create a data-driven study that becomes the definitive resource on the topic. Strengthen this new asset by building internal links to it from your other relevant service pages and blog posts.

Week 4: Measure, Amplify, and Refine

It’s time to close the loop. The only way to know if your strategy is working is to measure share of voice in AI chat again. Compare your new report to your baseline from Week 1. Next, amplify your new and updated content through digital PR and outreach to earn backlinks, signaling its authority. This process is cyclical; identify the next set of low-performing prompts and repeat the process for continuous improvement. For businesses looking to automate this tracking, a platform like trackmybusiness.ai can provide the necessary data.

Master Your AI Visibility: The Future is Now

The digital landscape is shifting. Traditional rankings are giving way to conversational AI, making Share of Voice the new benchmark for brand visibility. As we’ve covered, you can begin with manual spot-checks, but to truly compete and scale in a dynamic market like Saudi Arabia, a strategic approach is essential. Learning how to accurately measure share of voice in ai chat is no longer optional-it’s the foundation of your future digital strategy and a critical step towards dominating your niche.

Instead of spending valuable hours on guesswork, let automation provide a clear, competitive advantage. TrackMyBusiness allows you to monitor mentions across all major LLMs, get automated daily reports, and uncover competitor strategies hidden within AI answers. Ready to move from theory to action?

Start tracking your brand mentions in ChatGPT automatically. Request a demo of TrackMyBusiness today!

The AI chat landscape is your new frontier. Don’t just participate-start leading the conversation.

Frequently Asked Questions

How is AI Share of Voice different from visibility in Google’s AI Overviews?

AI Share of Voice (SoV) is a broader metric that measures your brand’s presence across multiple AI chat platforms like ChatGPT and Gemini. In contrast, visibility in Google’s AI Overviews is specific only to Google’s generative results. While AI Overviews are a key component, a true AI SoV analysis provides a more holistic view of your brand’s visibility across all the answer engines your Saudi audience uses to find information.

What is a ‘good’ AI Share of Voice score to aim for?

A “good” score is relative to your competition in the Saudi market. Instead of a single number, aim to surpass your top 3-5 direct competitors. A realistic initial goal is achieving 15-20% visibility for your core commercial topics. The most important metric is consistent growth. Improving from 5% to 10% in a quarter is a strong indicator that your Answer Engine Optimization (AEO) strategy is working effectively.

How often should I measure my AI SoV?

For most businesses in Saudi Arabia, we recommend you measure share of voice in ai chat on a monthly basis. This frequency is ideal for tracking trends and the impact of your AEO efforts without overreacting to small daily fluctuations. For highly competitive industries or during major marketing campaigns, a bi-weekly analysis can provide more immediate and actionable insights to help you adapt your strategy quickly.

Which AI chat platforms are the most important to track?

In Saudi Arabia, your priority should be Google’s AI Overviews, given Google’s market dominance. Beyond that, it is crucial to track your brand’s mentions and citations on OpenAI’s ChatGPT and Google’s Gemini, as they have significant user bases in the region. Starting with these three platforms will provide a strong, comprehensive foundation for understanding your visibility in the most impactful answer engines.

Can I improve my AI SoV without creating new content?

Yes, absolutely. You can significantly improve your AI SoV by optimizing your existing content. Focus on refreshing your most valuable pages by updating statistics, clarifying information, and adding new, relevant details. Enhancing E-E-A-T signals through improved author bios and citing credible sources is also critical. This process of refining current assets helps AI models better understand and trust your content, boosting its visibility.

Does traditional on-page SEO still matter for Answer Engine Optimization (AEO)?

Yes, traditional on-page SEO is the fundamental bedrock of AEO. AI models rely on well-structured, high-quality content to generate accurate answers. Core elements like clear headings (H1, H2), optimized meta titles, structured data (Schema markup), and logical internal linking are crucial signals. These practices help answer engines parse and trust your content, making it far more likely to be used as a source in AI-generated responses.