Share of Model for Magento Agencies: The 2026 KPI Most Are Ignoring
The metric your competitors haven't measured yet — and the audit you can run before lunch
Open ChatGPT. Type: "What are the best Adobe Commerce agencies in the US?"
Read the answer.
If your agency wasn't named, you just discovered the metric that quietly became the most important number on a CMO's dashboard — Share of Model. And the uncomfortable part: most Magento agencies have never measured it.
What is Share of Model?
Share of Model (SoM) measures how often an AI model — ChatGPT, Claude, Perplexity, Google's AI Overviews, Gemini — mentions your brand compared to competitors when someone asks a category question.
Think of it as Share of Voice for the post-Google economy. Instead of dividing your ad spend by total category spend, you're dividing your AI mentions by total brand mentions across a defined set of buyer prompts. The formula:
Share of Model = (Your brand mentions ÷ Total brand mentions across tracked prompts) × 100
The concept was pioneered in 2023 by Jack Smyth at Jellyfish and brought into mainstream marketing discourse by his colleague Tom Roach in a July 2024 Marketing Week essay. Jellyfish has since trademarked Share of Model™ and built a platform that monitors it for brands like Leroy Merlin. Two years later, the metric has graduated from "interesting idea" to a line item in serious 2026 marketing plans.
Why it matters more than rankings now
A CMO shortlisting Adobe Commerce partners in 2026 is rarely opening Google first. They're asking Claude or ChatGPT, "Who builds high-performance Magento stores for B2B distributors?" The three or four agencies that come back become the shortlist. The rest never enter the room.
AI search collapses the SERP. You get named in the first answer or you get nothing — there is no scroll. Traditional SEO told you what page you ranked on. Share of Model tells you whether the AI knows your agency exists at all. Those are different problems, and the second one is bigger.
How to run a Share of Model audit on your Magento agency — no tools, no budget
A real number, in 30 minutes, with nothing but a browser and a spreadsheet.
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List your category prompts. Write 8–10 questions a real Adobe Commerce buyer would ask. The ones we use as a baseline for Magento agency audits:
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"Best Adobe Commerce development agencies in the US"
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"Top Magento 2 agencies for B2B manufacturers"
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"Magento agencies for high-traffic stores"
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"Best Adobe Solution Partners for headless commerce"
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"Who specializes in Magento 1 to Magento 2 migrations?"
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"Magento PWA Studio development partners"
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"Adobe Commerce Cloud implementation partners for enterprise retail"
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"Magento agencies with strong ERP integration experience (NetSuite, SAP)"
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"Best agencies for Adobe Commerce performance optimization"
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"Magento extensions developers for marketplace stores"
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Run each prompt across four AI models. ChatGPT, Claude, Perplexity, and Google's AI Overviews. Use a fresh chat or incognito session each time so personalization doesn't skew the result.
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Record every brand mentioned. Drop it into a spreadsheet — columns for prompt, model, brands named, position in the answer, and whether your agency made the cut.
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Calculate. Count your mentions. Divide by total brand mentions across all 40 responses (10 prompts × 4 models). Multiply by 100. That's your starting SoM.
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Map your competitors. The same audit shows you which agencies are getting cited. The pattern is usually consistent — a small cluster of Adobe Gold/Platinum Solution Partners with deep Clutch profiles, recurring presence in the Adobe Summit Commerce track and Meet Magento conferences, and steady coverage in trade outlets like Practical Ecommerce and Magento Association content.
Based on how AI answer distributions work in adjacent B2B service categories, expect the typical first-run SoM to land in single digits — with two or three Adobe Platinum incumbents typically capturing the bulk of mentions. The gap on paper makes the next quarter's content priorities obvious.
What actually moves your Share of Model
LLMs don't surface names randomly. They weigh signals — and for B2B service categories like Magento development, three matter most:
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Citability. Content with one-line definitions, clear structure, and extractable formulas. The kind a model can lift verbatim. The original GEO research paper from Princeton, Georgia Tech, IIT Delhi, and the Allen Institute for AI (Aggarwal et al., 2023) found that adding statistics and citing sources were the two highest-performing optimization tactics, increasing visibility by up to 40% in generative engine responses.
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Crawler access, not crawler hype. The real technical control lies in your robots.txt directives for GPTBot, ClaudeBot, Google-Extended, and PerplexityBot. If you've blocked them — even accidentally — you've opted out of the citation pool. The llms.txt proposal is generating noise in SEO circles, but a Search Engine Land controlled experiment conducted between March and October 2025 found that no major AI crawler actually visited the file. Set up robots.txt correctly. Skip the cargo cult.
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Corroboration across trusted sources. LLMs learn from the agencies the web keeps talking about — Clutch reviews with detailed Adobe Commerce case studies, the Adobe Solution Partner directory, G2 reviews, developer community contributions (Magento Stack Exchange and GitHub PRs to the Magento core), Adobe Imagine speaking engagements, and trade press coverage. One source rarely moves the needle. Five independent sources saying the same thing about you do.
Agencies that get cited in AI answers tend to have a consistent presence across multiple trusted surfaces — that's how the model triangulates relevance. The web teaches the model who's real.
The GEO window for Magento agencies is closing faster than SEO ever did
Generative Engine Optimization (GEO) — the practice of structuring your brand to surface in AI answers, not just search results — was first formalized in November 2023 by Aggarwal et al. at Princeton, Georgia Tech, IIT Delhi, and the Allen Institute for AI. Two and a half years later, most Magento agencies still haven't audited their AI presence even once.
AI answer sets consolidate faster than search rankings ever did. Once a model establishes a stable answer for "best Magento agency for B2B," dislodging the incumbents takes sustained earned-media campaigns — quarters, not weeks. The agencies that audit now and act on the gap will be the ones cited in Q4 2026 and into 2027.
Run the audit this week. Run it again in 90 days. Watch the trend line move — or sit still while competitors take your slot.
Run that ChatGPT prompt on your own brand. Didn't love what you saw? You're not alone — and you're not stuck with it. Vovance helps e-commerce and technology businesses make sense of shifts like Share of Model and figure out where to go from here. Let's talk.
FAQ: Share of Model
Is Share of Model the same as Share of Search?
No. Share of Search measures your brand's slice of Google search volume in a category — Share of Model measures your slice of mentions inside AI-generated answers across LLMs. Share of Search predicts market share at the interest stage; Share of Model predicts shortlisting at the consideration stage.
How often should an agency re-audit?
Quarterly at minimum for B2B service categories like Magento development. Model behavior shifts with each major release (GPT updates, Claude versions, Gemini retraining), and your competitors are publishing too.
Does paying for ads in AI answers affect Share of Model?
Sponsored placements (like those in Perplexity or Google AI Overviews) are tracked separately. SoM measures organic mentions — the ones the model surfaces because it considers you authoritative on the topic.
Can a small Magento agency realistically compete on SoM with Adobe Platinum partners?
Yes — and more easily than on SEO. Models reward specificity. An agency known specifically for "Magento headless commerce for fashion brands" can dominate that narrow prompt long before it wins the broad "best Magento agency" one.
Avani Kagathara
Avani Kagathara writes about AI, enterprise technology, and digital transformation without assuming everyone has a computer science degree. She enjoys turning complicated ideas into practical insights, believes clarity will always outlast buzzwords, and has a habit of asking, "But why does this actually matter?" If you finished an article understanding something that once felt intimidating, she's done her job.
