{"id":187,"date":"2026-08-20T05:05:40","date_gmt":"2026-08-20T05:05:40","guid":{"rendered":"https:\/\/www.akarnik.com\/blog\/measuring-ai-share-of-voice"},"modified":"2026-08-20T05:05:40","modified_gmt":"2026-08-20T05:05:40","slug":"measuring-ai-share-of-voice","status":"publish","type":"post","link":"https:\/\/www.akarnik.com\/blog\/measuring-ai-share-of-voice","title":{"rendered":"Measuring AI Share of Voice: A 7-Step Playbook"},"content":{"rendered":"<p><em>Written by: Arjun Karnik, Growth Marketing Specialist<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Measuring AI Share of Voice<\/h2>\n<ul>\n<li>AI share of voice (AI SOV) tracks how often AI answers mention or cite your brand versus competitors, replacing rankings as the core visibility metric.<\/li>\n<li>Buyers now rely on AI assistants for vendor decisions, with 69% changing their choice based on AI responses and 33% buying from brands they had not heard of before.<\/li>\n<li>A seven-step playbook covers baseline audits, fan-out query mapping, multi-engine sampling, mention-rate vs competitive SOV calculations, position-weighted scoring, sentiment tracking, and impression-decay tripwires.<\/li>\n<li>Regular measurement and content refreshes are essential, because AI citations decay quickly and pages can lose most visibility within two months without updates.<\/li>\n<li><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\">See how AI Growth Agent tracks your AI SOV across ChatGPT, Google AI Overviews, Perplexity, and Gemini<\/a>.<\/li>\n<\/ul>\n<h2>How AI Share of Voice Works in Practice<\/h2>\n<p><strong>AI share of voice (AI SOV) is the percentage of AI-generated responses, across a defined prompt set and competitive field, that mention, cite, or recommend your brand relative to all brand mentions in those same responses.<\/strong> The core formula is:<\/p>\n<p><strong>AI SOV = (Your brand mentions \u00f7 Total brand mentions across all tracked brands) \u00d7 100<\/strong><\/p>\n<p>A brand mentioned in 25 out of 100 answers, while competitors collect 40, 20, and 15 mentions respectively, holds 25% AI SOV. That number only becomes useful when paired with a clear denominator, a documented competitor set, and a locked prompt library. Most teams lack that structure, which makes their AI visibility data noisy and hard to compare.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472409902-31baebe3e87f.png\" alt=\"Bar chart showing the share of B2B software buyers who start research with an AI chatbot more often than Google, rising from 29 percent in April 2025 to 51 percent in March 2026. Source: G2, 1,076 B2B software buyers and decision-makers.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>In under a year the starting point for B2B software research crossed over. More buyers now begin with a chatbot than with Google.<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>See this measurement framework in action across all four major AI platforms<\/strong><\/a>.<\/p>\n<h2>Step 1: Baseline Visibility Audit Across AI Engines<\/h2>\n<p>Start by capturing the current state across ChatGPT, Google AI Overviews, Perplexity, and Gemini. The audit shows where your brand appears, where competitors appear instead, and where no brand appears at all.<\/p>\n<p>Run a seed set of 20\u201330 category prompts across each engine, logging every brand mention, every citation (domain sourced directly), and every instance of competitor presence. This combined dataset becomes your control group, the baseline against which you compare every later run to detect changes in visibility.<\/p>\n<p>The baseline usually exposes a gap most founders do not expect. <a href=\"https:\/\/indexly.ai\/blog\/ai-share-of-voice-benchmarks-by-industry-2026-guide\" target=\"_blank\" rel=\"noindex nofollow\">Over 73% of brands have zero mentions in AI-generated responses despite ranking on Google page one.<\/a> <a href=\"https:\/\/aigrowthagent.co\/articles\/ai-search-visibility-strategy\/\" target=\"_blank\">80% of LLM citations do not rank in Google&#039;s top 100 for the original query.<\/a> The audit turns that gap into something visible and measurable instead of an invisible assumption.<\/p>\n<p>Log results in a spreadsheet with columns for engine, prompt, brand mentioned, mention position, citation (yes or no), and sentiment. That structure feeds every downstream step in the playbook.<\/p>\n<h2>Step 2: Fan-Out Query Mapping From Buyer Prompts<\/h2>\n<p>Each buyer prompt triggers many hidden retrieval queries rather than a single lookup. The AI system assembles the final answer from those underlying queries, so content needs to match the fan-out layer, not just the visible prompt.<\/p>\n<p>Extract fan-out queries directly from ChatGPT instead of inferring them from keyword tools. The target is the machine&#039;s questions, not the human&#039;s. Use this prompt structure:<\/p>\n<blockquote>\n<p>&quot;If a B2B buyer asked you &#039;[your category question]&#039;, what sub-questions would you need to answer to give a complete response? List them.&quot;<\/p>\n<p>Run that extraction across 10\u201315 seed prompts. The output becomes your production queue and determines which pages you create or rewrite, along with the exact language you use. In Arjun&#039;s tests on his own site, pages rewritten to match extracted fan-out queries earned citations while control pages did not.<\/p>\n<h2>Step 3: Multi-Engine Sampling Design for Stable Data<\/h2>\n<p>Once you have fan-out queries and a structured prompt set, test how your content performs against those prompts across all major AI platforms. Run the full prompt set across ChatGPT, Google AI Overviews, Perplexity, and Gemini.<\/p>\n<p>Single-run results behave like noise rather than signal. <a href=\"https:\/\/skulift.com\/share-of-voice\" target=\"_blank\" rel=\"noindex nofollow\">A single generative answer is an anecdote, and only repeated sampling over a fixed query set, engines, and time window converts stochastic output into a stable, attributable, and trendable SOV metric.<\/a> Run at least five samples per prompt per engine in fresh sessions to smooth out randomness.<\/p>\n<p>Measure each engine separately, because they behave differently. <a href=\"https:\/\/aigrowthagent.co\/articles\/best-perplexity-ai-seo-tools\/\" target=\"_blank\">A Visiby June 2026 benchmark based on 2,443 prompt-runs across 172 real buyer prompts found that the same brand&#039;s citation rate diverged by up to 24 percentage points depending on the engine measured.<\/a> <a href=\"https:\/\/aigrowthagent.co\/articles\/best-perplexity-ai-seo-tools\/\" target=\"_blank\">Only 11% of cited domains overlap between ChatGPT and Perplexity.<\/a> A combined figure is valid only when you use the same prompt set, market, and run windows and document the included platforms explicitly.<\/p>\n<p>Lock four variables before any run: the prompt set, the competitor list (five to seven names), the engines, and the weekly cadence. These variables define your measurement instrument, so changing any one of them mid-cycle means you are no longer measuring the same thing and week-over-week comparability breaks.<\/p>\n<h2>Step 4: Mention-Rate vs Competitive-SOV Calculation<\/h2>\n<p>Mention rate and AI SOV measure different things, and treating them as interchangeable leads to bad decisions.<\/p>\n<table>\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>Formula<\/th>\n<th>What it measures<\/th>\n<th>Denominator<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Mention Rate<\/td>\n<td>Answers mentioning brand \u00f7 Total valid answers \u00d7 100<\/td>\n<td>Absolute appearance frequency<\/td>\n<td>Total answers in the prompt set<\/td>\n<\/tr>\n<tr>\n<td>AI Share of Voice<\/td>\n<td>Your brand mentions \u00f7 Total brand mentions across all tracked brands \u00d7 100<\/td>\n<td>Relative competitive presence<\/td>\n<td>All brand mentions in the same answers<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The two-denominator problem is where most teams go wrong. <a href=\"https:\/\/frictionai.co\/blog\/competitor-share-of-voice-ai\" target=\"_blank\" rel=\"noindex nofollow\">A brand can record a high mention rate yet a lower share of voice when competitors are co-mentioned in most of the same answers.<\/a> A brand mentioned in 40 out of 100 answers holds a 40% mention rate. If competitors collect 80, 60, and 40 mentions in those same answers, the brand&#039;s AI SOV is 40 \u00f7 220 = 18.2%. The absolute number looks strong, but the competitive position is third place.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472358178-4b7e7b747cdd.png\" alt=\"AI visibility screen filtered to Google AI Overviews, showing a mention rate trend chart climbing over time and crossing above a dashed competitor benchmark line, with range controls and tabs for overview, wins, position trends and top URLs.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Mention rate over time against a competitor benchmark. This is the number that replaces rank position. The figures shown are a product view, not a client result.<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/mersel.ai\/blog\/how-to-measure-share-of-voice-in-chatgpt\" target=\"_blank\" rel=\"noindex nofollow\">The closed-pool error compounds this problem when tools calculate SOV only against a fixed list of three to four competitors and ignore additional brands surfaced by the LLM.<\/a> The correct denominator must include every brand the model naturally mentions. Track raw mention counts alongside the SOV ratio to see whether changes reflect category growth or shifts in your relative presence.<\/p>\n<h2>Step 5: Position-Weighted Scoring Table for Influence<\/h2>\n<p>Not all mentions carry the same weight. A brand named first in an AI answer influences buyers more than one listed fourth, so position-weighted SOV applies harmonic decay to reflect that reality.<\/p>\n<p>The standard harmonic decay model assigns weights using the formula Weight = 1 \u00f7 Position:<\/p>\n<table>\n<thead>\n<tr>\n<th>Mention Position<\/th>\n<th>Harmonic Weight (1\/n)<\/th>\n<th>Example: 10 mentions at this position<\/th>\n<th>Weighted Score<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1st<\/td>\n<td>1.00<\/td>\n<td>10 mentions<\/td>\n<td>10.00<\/td>\n<\/tr>\n<tr>\n<td>2nd<\/td>\n<td>0.50<\/td>\n<td>10 mentions<\/td>\n<td>5.00<\/td>\n<\/tr>\n<tr>\n<td>3rd<\/td>\n<td>0.33<\/td>\n<td>10 mentions<\/td>\n<td>3.30<\/td>\n<\/tr>\n<tr>\n<td>4th<\/td>\n<td>0.25<\/td>\n<td>10 mentions<\/td>\n<td>2.50<\/td>\n<\/tr>\n<tr>\n<td>5th<\/td>\n<td>0.20<\/td>\n<td>10 mentions<\/td>\n<td>2.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><a href=\"https:\/\/llmpulse.ai\/blog\/measure-ai-share-of-voice\" target=\"_blank\" rel=\"noindex nofollow\">Position-weighted SOV is calculated as: (sum of your brand&#039;s position weights) \u00f7 (sum of all brand position weights) \u00d7 100.<\/a> In a worked example with 100 prompts and five project-management brands, this method produced 16.8% for the tracked brand versus 31.6% for the category leader, which painted a very different picture than raw mention counts alone.<\/p>\n<p><a href=\"https:\/\/ranqo.ai\/blog\/how-to-measure-ai-share-of-voice\" target=\"_blank\" rel=\"noindex nofollow\">Position-weighted SOV needs at least five runs per prompt to stabilize because LLM outputs are probabilistic and brand ordering often changes between independent runs of the same prompt.<\/a> Apply weights only after averaging positions across those repeated runs, not from any single answer.<\/p>\n<h2>Step 6: Sentiment and Citation Tracking for Quality of Presence<\/h2>\n<p>Mention rate and SOV measure whether you show up, not whether that presence helps you. Citation rate and sentiment fill that gap.<\/p>\n<p><strong>Citation rate<\/strong> is the percentage of prompts where an AI response sources your domain directly: (Responses citing your domain \u00f7 Total AI evaluations) \u00d7 100. <a href=\"https:\/\/siteimprove.com\/blog\/answer-engine-optimization-metrics\" target=\"_blank\" rel=\"noindex nofollow\">A mention means your brand name appears somewhere in the response, while a citation means your domain was sourced directly as part of the answer.<\/a> The two can diverge, because an AI answer may name a brand without linking to its site or cite a URL without naming the brand in prose.<\/p>\n<p>Log sentiment modifiers alongside each mention. A positive modifier (+) means the AI framed the brand favorably. A neutral modifier (0) means the brand was listed without evaluation. A negative modifier (\u2212) means the AI introduced a caveat, limitation, or comparison that disadvantaged the brand. <a href=\"https:\/\/agentmention.ai\/blog\/ai-visibility-metrics-mention-citation-sov-sentiment\" target=\"_blank\" rel=\"noindex nofollow\">Weighted share of voice frameworks apply sentiment adjustments of +0.2 for positive framing and \u22120.4 for negative framing on top of position weights.<\/a> A wrong or negative AI answer hurts more than no answer, so sentiment tracking is mandatory rather than optional.<\/p>\n<h2>Step 7: Impression-Decay Tripwires and a Self-Healing Loop<\/h2>\n<p>Once you have baseline metrics for presence, position, and sentiment, the next challenge is keeping those gains over time. Content does not hold its position in AI answers without maintenance, and visibility can erode quickly.<\/p>\n<p>In Arjun&#039;s tests on his own site, pages lost the majority of their AI visibility within two months when left unmaintained, which created the decay pattern behind the tripwire thresholds described below. That decay stays invisible unless you instrument for it, and by the time it appears in a monthly report the position is usually gone.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786471826043-e712a9527e2b.png\" alt=\"Agent Actions board set to autopilot, showing day columns of task cards at stages from write and writing through draft in review, scheduled, published and refreshed. Decay cards flag pages down 41 to 62 percent on impressions and queue them for an update.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>The publishing cadence, running. New articles and refreshes sit in one queue, and pages that have started to slide get flagged and rewritten without anyone auditing a spreadsheet.<\/em><\/figcaption><\/figure>\n<p>Independent research confirms the same pattern from other angles. Seer Interactive analyzed 47,097 AI citations across 7,683 pages in ChatGPT, Gemini, and Perplexity between March and June 2026 and found that 75% of cited pages had been updated within the last year, with pages cited consistently across all four months averaging under six months since their last update. <a href=\"https:\/\/trakkr.ai\/trakkr-research\/citation-decay\" target=\"_blank\" rel=\"noindex nofollow\">Trakkr Research tracked 857,138 daily reports across 10,991 brands over 10 months and found the median brand drops to half its peak citation count in 31 days, with 73.5% of cited URLs appearing only once and never returning.<\/a><\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472438102-dfd59b5fabac.png\" alt=\"Bar chart showing 75 percent of pages cited by AI assistants were updated within the last year and 25 percent were older. Source: Seer Interactive, July 2026, 7,683 pages and 47,097 citations across ChatGPT, Gemini and Perplexity.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Three quarters of cited pages were updated inside a year, and the consistently cited ones averaged under six months. The page you refresh beats the page you write.<\/em><\/figcaption><\/figure>\n<p>Set tripwires in Google Search Console against your baseline impression curve, configured to flag any page that drops more than 20% week-over-week for two consecutive weeks. This threshold is calibrated against the decay behavior Arjun measured on his own site and catches problems early enough that a minor refresh usually fixes them. A refresh does not need to be a full rewrite, because adding a current statistic, updating a date reference, or expanding an answer section is often enough to re-enter the freshness window. <a href=\"https:\/\/aigrowthagent.co\/articles\/ai-search-visibility-strategy\/\" target=\"_blank\">76.4% of pages cited by ChatGPT had been updated within the prior 30 days.<\/a> The refresh loop becomes a moat, because it is hard for competitors to sustain and easy for incumbents to neglect.<\/p>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>Watch how automated tripwires catch decay before you lose position<\/strong><\/a>.<\/p>\n<h2>Prompt-Library Construction for Reliable Measurement<\/h2>\n<p>The prompt library acts as the measurement instrument, and its quality determines the reliability of every SOV figure downstream. Build it in three layers:<\/p>\n<ol>\n<li><strong>Awareness prompts<\/strong> are category-level questions a buyer asks before they know vendor names. Example: &quot;What tools help B2B companies track AI search visibility?&quot;<\/li>\n<li><strong>Consideration prompts<\/strong> are comparison and evaluation questions. Example: &quot;Compare [your category] options for a $5M SaaS company with two marketers.&quot;<\/li>\n<li><strong>Decision prompts<\/strong> are high-intent, specific queries. Example: &quot;Which [category] platform is best for measuring share of voice in AI answers?&quot;<\/li>\n<\/ol>\n<p>For each awareness prompt in your seed set, you need to extract the underlying fan-out queries that AI systems actually use to construct answers. To extract fan-out queries from ChatGPT directly, use this prompt template:<\/p>\n<blockquote>\n<p>&quot;You are a B2B buyer researching [category]. List every sub-question you would need answered before recommending a vendor. Format as a numbered list.&quot;<\/p>\n<p>Run this extraction across each awareness prompt in your seed set so the sub-questions become individual prompts in the production library. Lock the library at 50\u2013100 prompts before beginning weekly measurement. <a href=\"https:\/\/layer3labs.io\/guides\/ai-search-visibility\" target=\"_blank\" rel=\"noindex nofollow\">Share of voice calculations require locking the prompt set, competitor list, engines, and weekly cadence to produce comparable week-over-week results.<\/a> Changing the prompt set mid-cycle invalidates trend data and makes it hard to see whether your visibility is improving.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is a good AI SOV percentage?<\/h3>\n<p>Benchmarks vary by category concentration and how many competitors the AI surfaces. As a working framework, below 15% indicates a significant citation gap in most B2B categories, 15% to 25% is a competitive range, above 25% signals strong visibility, and above 40% suggests category leadership. Category leaders rarely exceed 60% because AI systems naturally diversify their citation sources. For B2B SaaS specifically, the top quartile clears 35% or above. The more useful target is relative performance: your AI SOV compared to your current market share. Brands holding higher AI SOV than their market share tend to grow, while those with lower AI SOV tend to shrink, so track the gap between the two numbers rather than the absolute percentage alone.<\/p>\n<h3>How often should I re-measure AI SOV?<\/h3>\n<p>Plan for weekly spot-checks on 10\u201315 high-priority prompts and a full prompt-library run monthly. The weekly cadence catches decay before it compounds, while the monthly full run produces the trend data needed for strategic decisions. AI citations are volatile by nature, and the median brand&#039;s week-over-week citation count swings by more than 50%, so monthly-only measurement misses most of the signal.<\/p>\n<p>Set impression-decay tripwires in Google Search Console to flag individual pages between scheduled measurement runs. The tripwires catch page-level decay, the weekly spot-checks catch engine-level shifts, and the monthly full run tracks competitive position over time. All three layers matter because they measure different parts of the system.<\/p>\n<h3>How do I handle direct and branded-search attribution for AI-driven traffic?<\/h3>\n<p>AI-influenced demand often shows up in analytics as direct traffic or branded search rather than anything traceable to the answer that caused it. The buyer reads the AI answer, closes the tab, and types your name directly into a browser or Google, which produces no referral URL and no UTM parameter. Whatever you measure in standard attribution is a floor, not a ceiling.<\/p>\n<p>Three signals help confirm upstream AI influence: unexplained increases in direct visits from new users, rising branded search queries in Google Search Console that do not correlate with active brand campaigns, and traffic arriving via chatgpt.com or equivalent AI referrer domains. Segment AI referrers as a distinct traffic class in analytics, because they convert differently from cold organic traffic and usually arrive pre-educated. Track all three signals together rather than relying on any single attribution model, and add self-reported source fields on high-intent forms as a fourth data point that no analytics tool can capture automatically.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1786472616931-9a13bc7984c5.png\" alt=\"Bar chart showing 2.5 percent of downstream brand visits after an AI mention carry a trackable referral parameter while 97.5 percent arrive untraceable. Source: Profound, analysis of more than 2 million AI conversations, January to June 2026.\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Buyers read an answer, then type your name into a browser. That visit lands as direct or branded search, so whatever you measure here is a floor and never a ceiling.<\/em><\/figcaption><\/figure>\n<h3>What is the difference between a citation and a mention in AI answers?<\/h3>\n<p>A mention means your brand name appears somewhere in the AI response text, while a citation means your domain was sourced directly as part of the answer, typically with a link or explicit attribution. The two metrics can diverge sharply and measure different things. An AI answer may name your brand without linking to your site, which is common in ChatGPT responses that synthesize from training data. An answer may also cite your URL without naming your brand in prose, which is common in Perplexity, where external links appear in over 77% of responses.<\/p>\n<p>Track both metrics separately. Mention rate tells you how often you are part of the conversation, and citation rate tells you how often your content acts as the evidence behind the answer. Citation rate is the stronger signal for content strategy because it confirms that the retrieval layer is reading and using your pages, not just that your brand name exists in the model&#039;s training data.<\/p>\n<h2>Conclusion: Run the Loop and Own the Answer Layer<\/h2>\n<p>The seven steps above form a closed loop rather than a one-time audit. The baseline audit establishes the starting line. Fan-out query mapping builds the production queue. Multi-engine sampling produces stable data. The two-denominator calculation separates absolute visibility from competitive position. Position-weighted scoring reveals whether mentions are leading or trailing. Sentiment and citation tracking separate presence from influence. Impression-decay tripwires keep the whole system self-healing instead of silently decaying.<\/p>\n<p>In Arjun&#039;s tests on his own site, the GEO subfolder went from zero to the only source of new impressions on the entire domain in 60 days, powered by AI Growth Agent at five to eight autonomous actions per day. The fan-out query method described in Step 2 proved decisive, and pages optimized this way consistently earned citations in Arjun&#039;s testing. The automated refresh loop described in Step 7 produced the GEO subfolder results mentioned earlier and showed how measurement and maintenance work together as a system.<\/p>\n<p><a href=\"https:\/\/openai.com\/blog\/chatgpt\" target=\"_blank\" rel=\"noindex nofollow\">OpenAI reported 900 million weekly active ChatGPT users in February 2026.<\/a> Google AI Overviews serves over 2.5 billion monthly active users. The answer layer now shapes buyer shortlists long before sales conversations begin, and it is where your buyers already make early decisions. The window for outsized gains is open now for the same reason it was open in the early SEO era: businesses that decode the new answer layer first lock in the settled answers that later competitors must displace.<\/p>\n<p>The measurement framework in this playbook is fully replicable without proprietary tooling. Run it manually on a 50-prompt set across four engines and you will see more signal than any rank report your current retainer produces. Add AI Growth Agent&#039;s automation and the loop runs at machine cadence without founder time.<\/p>\n<p><a href=\"https:\/\/www.akarnik.com\/demo\" target=\"_blank\"><strong>See the complete measurement loop running live<\/strong><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>See how often AI engines mention your brand vs. rivals. Arjun Karnik&#8217;s 7-step AI SOV framework covers ChatGPT, Perplexity &amp; more. Start measuring now.<\/p>\n","protected":false},"author":118,"featured_media":186,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-187","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts\/187","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/comments?post=187"}],"version-history":[{"count":0,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/posts\/187\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/media\/186"}],"wp:attachment":[{"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/media?parent=187"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/categories?post=187"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.akarnik.com\/blog\/wp-json\/wp\/v2\/tags?post=187"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}