{"id":2913,"date":"2026-10-03T03:17:48","date_gmt":"2026-10-03T03:17:48","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/how-to-calculate-share-of-model\/"},"modified":"2026-10-03T03:17:48","modified_gmt":"2026-10-03T03:17:48","slug":"how-to-calculate-share-of-model","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/how-to-calculate-share-of-model\/","title":{"rendered":"How to Calculate Share of Model: Formula, Sample Design, and Tracking"},"content":{"rendered":"<p><em>By maxaeo.ai \uff5c Published 2026-10-03 \uff5c Updated 2026-10-03<\/em><\/p>\n<p>If you need to know <strong>how to calculate share of model<\/strong>, divide your brand\u2019s eligible mentions\u2014or weighted visibility points\u2014by the equivalent total for all brands in a controlled sample, then multiply by 100. The arithmetic is easy. The difficult part is defining prompts, competitors, AI engines, and counting rules consistently.<\/p>\n<h2>What Is Share of Model?<\/h2>\n<p>Share of Model, or SoM, is a brand\u2019s portion of competitive visibility within AI-generated answers. It measures how much of the category conversation your brand receives when buyers ask ChatGPT, Gemini, Perplexity, and other answer engines relevant questions.<\/p>\n<p>Current definitions commonly use one of two denominators: total AI responses or total category-brand mentions. Those methods answer different questions and should not be reported as interchangeable. Published methodologies also differ on prompt selection, deduplication, and position weighting. (<a href=\"https:\/\/getmentioned.com\/en\/blog\/share-of-model\" target=\"_blank\" rel=\"noopener\">getmentioned.com<\/a>)<\/p>\n<ul>\n<li><strong>Mention rate<\/strong> asks: In what percentage of eligible answers did the brand appear?<\/li>\n<li><strong>Competitive Share of Model<\/strong> asks: What percentage of all measured brand visibility belonged to the brand?<\/li>\n<li><strong>Citation share<\/strong> asks: What percentage of relevant source citations pointed to the brand\u2019s domain?<\/li>\n<li><strong>Recommendation rate<\/strong> asks: How often was the brand explicitly suggested as an option?<\/li>\n<\/ul>\n<p>A reliable dashboard should show these separately rather than compressing them into one opaque score.<\/p>\n<h2>How to Calculate Share of Model With the Right Formula<\/h2>\n<p>The most defensible competitive formula is:<\/p>\n<pre><code class=\"language-text\">Share of Model (%) =\nYour brand\u2019s visibility points\n\u00f7\nVisibility points for all measured brands\n\u00d7 100\n<\/code><\/pre>\n<p>For an unweighted calculation, one qualifying brand appearance equals one point:<\/p>\n<pre><code class=\"language-text\">Unweighted SoM (%) =\nYour brand mentions\n\u00f7\nTotal mentions of all category brands\n\u00d7 100\n<\/code><\/pre>\n<p>You can also calculate response-level presence:<\/p>\n<pre><code class=\"language-text\">Mention rate (%) =\nEligible responses mentioning your brand\n\u00f7\nAll eligible responses\n\u00d7 100\n<\/code><\/pre>\n<p>The distinction matters. If 40 of 100 answers mention your company, your mention rate is 40%. But if those answers contain 200 total competitor mentions and your company earns 40, its competitive Share of Model is only 20%.<\/p>\n<p>For a broader cross-engine scoring method, see the <a href=\"https:\/\/maxaeo.ai\/blog\/calculate-llm-share-of-voice\/\">LLM share-of-voice formula and position-weighting framework<\/a>.<\/p>\n<figure class=\"wp-block-image size-large\" style=\"margin:1.5em 0;\"><img decoding=\"async\" src=\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/10\/backend-4930-1.jpg\" alt=\"Diagram explaining how to calculate share of model from brand visibility points\" style=\"max-width:100%;height:auto;\"><\/figure>\n<h2>Which Sampling Framework Produces Comparable Results?<\/h2>\n<p>A valid sample holds the measurement environment stable. Before collecting answers, document a five-field <strong>SoM Measurement Contract<\/strong>: prompt universe, engine set, market, counting rule, and reporting window. This original framework prevents teams from treating a changed sample as genuine performance growth.<\/p>\n<ol>\n<li>\n<p><strong>Define the decision category.<\/strong> Specify the product category, audience, country, and language. \u201cCRM software\u201d and \u201cCRM for US healthcare startups\u201d represent different markets.<\/p>\n<\/li>\n<li>\n<p><strong>Build prompt strata.<\/strong> Include discovery, comparison, use-case, alternative, integration, and purchase-validation prompts. Use <a href=\"https:\/\/maxaeo.ai\/blog\/ai-search-intent-mapping-for-saas\/\">AI search intent mapping<\/a> to avoid overloading the panel with \u201cbest tools\u201d questions.<\/p>\n<\/li>\n<li>\n<p><strong>Freeze the competitive set.<\/strong> Decide whether the denominator includes named competitors only or every relevant brand detected. Record additions separately.<\/p>\n<\/li>\n<li>\n<p><strong>Select engines and runs.<\/strong> Test the same prompts across each chosen platform. If one engine receives more observations, normalize its weight so it does not dominate the composite score.<\/p>\n<\/li>\n<li>\n<p><strong>Set counting rules.<\/strong> Define aliases, misspellings, parent-company names, duplicate mentions, negative mentions, citations, and non-recommendation references before analysis.<\/p>\n<\/li>\n<li>\n<p><strong>Preserve a baseline panel.<\/strong> Keep a fixed core set for trend reporting. Test new prompts in an experimental panel until they can be incorporated without rewriting historical comparisons.<\/p>\n<\/li>\n<\/ol>\n<h2>Worked Example: A 120-Answer SaaS Panel<\/h2>\n<p>Consider an illustrative SaaS measurement panel containing 20 prompts, three AI engines, and two runs per prompt. That produces 120 answers. Eight answers name no relevant vendor, leaving 112 eligible responses.<\/p>\n<div style=\"overflow-x:auto;\">\n<table style=\"width:100%;border-collapse:collapse;margin:1.5em 0;font-size:0.95em;\">\n<thead>\n<tr>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">Metric<\/th>\n<th style=\"text-align:right\">Illustrative result<\/th>\n<th style=\"border:1px solid #e3e6ea;padding:8px 12px;background:#f6f8fa;text-align:left;font-weight:600;\">Calculation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Brand mention rate<\/td>\n<td style=\"text-align:right\">34.8%<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">39 \u00f7 112<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Explicit recommendation rate<\/td>\n<td style=\"text-align:right\">18.8%<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">21 \u00f7 112<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Competitive Share of Model<\/td>\n<td style=\"text-align:right\">27.1%<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">31.4 \u00f7 116 weighted points<\/td>\n<\/tr>\n<tr>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">Owned-domain citation share<\/td>\n<td style=\"text-align:right\">16.7%<\/td>\n<td style=\"border:1px solid #e3e6ea;padding:8px 12px;vertical-align:top;\">9 \u00f7 54 relevant citations<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The 27.1% score uses a disclosed prominence model: 1.0 point for the first brand presented, 0.7 for second, 0.5 for third, and 0.3 for later positions. These weights are analytical choices, not universal standards. Changing them creates a new metric version.<\/p>\n<p>This example reveals a useful diagnostic: the brand appears in 34.8% of eligible answers but captures only 16.7% of citations. The visibility gap is therefore not simply \u201cget mentioned more.\u201d The brand also needs stronger sources that AI systems can retrieve and cite.<\/p>\n<h2>How Should Share of Model Be Automated?<\/h2>\n<p>Automation should rerun a controlled prompt panel, retain the original answers, extract brand-level observations, and update trends without silently changing the methodology. Every record should include the prompt, engine, run date, market, answer text, detected brands, recommendation position, sentiment, and cited sources.<\/p>\n<figure class=\"wp-block-image size-large\" style=\"margin:1.5em 0;\"><img decoding=\"async\" src=\"https:\/\/maxaeo.ai\/blog\/wp-content\/uploads\/2026\/10\/backend-4930-2.jpg\" alt=\"Share of Model monitoring workflow across prompts, engines, answers, and brand metrics\" style=\"max-width:100%;height:auto;\"><\/figure>\n<p>Use version labels whenever you change:<\/p>\n<ul>\n<li>Prompt wording or intent mix<\/li>\n<li>Competitor inclusion rules<\/li>\n<li>AI engines or model configurations<\/li>\n<li>Geographic or language settings<\/li>\n<li>Position weights and eligibility filters<\/li>\n<li>Brand aliases or entity-matching logic<\/li>\n<\/ul>\n<p>MaxAEO monitors brand mentions, recommendations, sentiment, competitive position, and citations across eight AI engines on a daily cadence. It also stores raw AI answers for traceability and compares brand performance with competitors by mention frequency, ranking position, and citation source.<\/p>\n<p>Teams can start with a <a href=\"https:\/\/maxaeo.ai\/\">free AI visibility diagnostic<\/a> before establishing ongoing monitoring. No internal revenue data, customer lists, or technical installation are required for the basic diagnostic.<\/p>\n<h2>How Should Teams Interpret the Score?<\/h2>\n<p>Share of Model is a visibility metric, not market share, revenue attribution, or proof that an optimization caused a sale. Interpret it alongside recommendation rate, sentiment, factual accuracy, citation share, and downstream traffic or conversions.<\/p>\n<p>Use the following decision sequence:<\/p>\n<ul>\n<li><strong>Low mention rate:<\/strong> Improve topic and prompt coverage.<\/li>\n<li><strong>High mentions but low recommendations:<\/strong> Clarify positioning, use cases, and evidence.<\/li>\n<li><strong>High recommendations but low citation share:<\/strong> Strengthen citable product pages, documentation, research, and third-party coverage.<\/li>\n<li><strong>Strong overall score but weak priority prompts:<\/strong> Segment performance by buyer intent rather than relying on the average.<\/li>\n<li><strong>Sudden score movement:<\/strong> Check prompt, competitor, and engine versions before attributing the change to marketing.<\/li>\n<\/ul>\n<p>A broader <a href=\"https:\/\/maxaeo.ai\/blog\/measuring-brand-visibility-in-llms\/\">framework for measuring brand visibility in LLMs<\/a> can connect these diagnostic metrics, while an <a href=\"https:\/\/maxaeo.ai\/blog\/how-to-report-ai-search-performance-cmo\/\">AI search performance reporting framework<\/a> helps translate them into executive reporting.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the simplest way to calculate Share of Model?<\/h3>\n<p>Divide your brand\u2019s mentions by all relevant brand mentions in the same prompt-and-engine sample, then multiply by 100. Publish the denominator and counting rules with the score.<\/p>\n<h3>Should answers with no brands be included?<\/h3>\n<p>Include them when calculating mention or recommendation rate. Exclude them from a mention-share denominator because they contribute no brand points. Report the number excluded so readers can audit the calculation.<\/p>\n<h3>How many prompts are required?<\/h3>\n<p>There is no universal minimum. Start with enough prompts to represent major buyer intents, industries, use cases, and funnel stages. Coverage quality matters more than an arbitrary prompt count.<\/p>\n<h3>Can Share of Model scores from different tools be compared?<\/h3>\n<p>Usually not directly. Tools may use different prompts, engines, competitors, markets, run frequencies, and weighting systems. Compare trends within one documented methodology unless both datasets use identical rules.<\/p>\n<h3>How often should Share of Model be measured?<\/h3>\n<p>Daily or weekly monitoring can reveal direction, but decisions should use multi-period trends rather than one answer or screenshot. Preserve the same baseline panel so changes remain interpretable.<\/p>\n<p><script type=\"application\/ld+json\">\n{\"@context\":\"https:\/\/schema.org\",\"@type\":\"Article\",\"author\":{\"@type\":\"Organization\",\"name\":\"maxaeo.ai\"},\"dateModified\":\"2026-10-03\",\"datePublished\":\"2026-10-03\",\"description\":\"Learn how to calculate share of model with defensible formulas, a reproducible sampling plan, and a worked SaaS example. 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