Every discipline that depends on measurement eventually develops its canonical metric. In traditional SEO, that metric is keyword ranking — position 1 to 10 on Google’s results page. It’s imperfect, but it’s legible and actionable. In generative search, there is no results page. There is no position 1. There is only the answer — and either your brand is in it, or it isn’t. This is the measurement problem that Share of Model solves. It’s the foundational KPI of Generative Engine Optimization, and for Australian businesses investing in AI search visibility, it’s the number that matters most in 2026.
What Is Share of Model?
Share of Model (SoM) measures how frequently your brand is cited, mentioned, or recommended when AI engines — ChatGPT, Google Gemini, Perplexity, Claude — respond to queries relevant to your industry, service category, or geographic market.
It is expressed as a proportion: of all the AI-generated responses to queries in your category, what percentage include a mention or recommendation of your brand?
Example: A Melbourne-based financial planning firm runs 50 relevant prompts across four AI engines — questions like “who are the best financial advisers in Melbourne?”, “which financial planning firm should I use for SMSF advice in Victoria?”, “recommend a financial planner for small business owners in Melbourne.” If the firm appears in 18 of those 50 responses, its Share of Model in that query set is 36%.
That 36% is a baseline. The goal of GEO is to increase it — and to ensure that when the brand does appear, it appears favourably.
Why Share of Model Matters More Than Keyword Rankings in 2026
Keyword rankings measure your position in a list. Share of Model measures your presence in a recommendation.
These are not equivalent. Consider two scenarios:
Scenario A: Your law firm ranks #3 on Google for “commercial lawyer Melbourne.” Organic CTR at position 3 averages around 5-8%. Of users who search that phrase, roughly 6 in 100 click your result.
Scenario B: When a user asks ChatGPT “which commercial law firm should I use in Melbourne?”, your firm is recommended in the AI’s answer. The user reads one recommendation, not a list. Conversion rates from AI-referred traffic are consistently higher than from organic search because the user arrives with a pre-formed recommendation rather than a choice to make.
As 65% of Australian searches end without a click and AI Overviews intercept an increasing share of high-intent queries, the value of a keyword ranking diminishes. The value of being the brand an AI recommends increases.
Share of Model captures the metric that actually reflects competitive position in AI search.
The Three Dimensions of Share of Model
A complete Share of Model analysis tracks three dimensions, not just citation frequency.
1. Citation Rate
How often does your brand appear in AI responses to relevant queries?
This is the headline number — the most direct measure of AI search visibility. It’s calculated by running a representative set of prompts across target AI engines and recording what percentage of responses include your brand.
Citation rate varies significantly by:
- Query type (informational vs. transactional vs. local)
- AI engine (ChatGPT vs. Perplexity vs. Gemini behave differently)
- Geographic modifier (queries with “Melbourne” or “Australia” may produce different results than unqualified queries)
2. Sentiment Score
When your brand appears, how does the AI characterise it?
An AI that says “You might consider [Brand X], though some users have noted inconsistent response times” is not an endorsement. Share of Model analysis must account for the quality of the mention, not just its presence.
Sentiment scoring involves analysing the language used when your brand appears: whether it leads the recommendation or is mentioned as an afterthought, whether it’s associated with positive attributes, and whether negative qualifiers appear.
3. Competitive Share
What is your citation rate relative to your direct competitors?
Share of Model becomes most useful as a relative measure. If your firm has a 36% citation rate but your closest competitor has 65%, the gap tells you more about your strategic position than the absolute number alone.
Tracking competitive share over time reveals whether your GEO investments are gaining ground, holding position, or losing ground to competitors who are also investing in AI visibility.
How to Measure Share of Model: A Practical Framework
Step 1: Define Your Query Set
Build a representative set of 30-100 prompts that mirror how your potential clients actually query AI systems. Include:
- Category queries (“best [service] in [city]”)
- Problem-based queries (“I need help with [problem] in [city]”)
- Comparison queries (“who is better for [specific need]”)
- Specific attribute queries (“most experienced [role] for [industry] in Australia”)
Avoid overly branded queries. The goal is to measure organic AI recommendation behaviour, not responses to prompts that mention your brand by name.
Step 2: Select Target Engines
At minimum, measure across:
- ChatGPT (GPT-4o or latest available model)
- Google Gemini
- Perplexity
- Claude
Each engine has different retrieval logic and will produce different Share of Model results for the same brand. Tracking all four gives you a full picture of your AI search landscape.
Step 3: Run Prompts Systematically
Run each prompt through each engine and record:
- Whether your brand appears (yes/no)
- Where in the response it appears (first mention vs. secondary)
- The sentiment of the mention (positive/neutral/negative)
- Which competitors appear in the same response
Consistency matters. Use the same prompts over time to track movement rather than changing the query set between measurement cycles.
Step 4: Calculate and Baseline
Calculate your citation rate per engine and overall. Document which query types produce citations and which don’t — the gaps reveal where your GEO strategy should focus.
Establish this as a baseline before any GEO interventions begin. Without a baseline, you cannot measure the impact of your strategy.
Step 5: Track Monthly
Share of Model is a lagging indicator for some AI engines (ChatGPT and Claude update their retrieval layers on longer cycles) and a leading indicator for others (Perplexity indexes continuously). Monthly tracking provides enough granularity to detect movement without the noise of daily variance.
What Drives Share of Model? The Key Levers
Based on our analysis of Australian brands across multiple industries, the factors most strongly correlated with high Share of Model are:
Topical authority — Brands that comprehensively cover their subject domain across multiple pieces of structured content consistently outperform single-page optimisers.
Third-party mentions — Appearances in industry publications, Australian business media, professional directories, and educational resources (particularly .edu.au domains) significantly increase the probability of AI citation.
Content structure — Pages with clear Q&A formatting, defined entities (organisation names, locations, services), and direct factual statements are extracted more frequently than narrative-heavy content.
Recency — For engines with live indexing (primarily Perplexity), recently published content performs better. For training-data-dependent engines, consistent long-term publishing history matters more.
Sentiment on the open web — AI engines pick up on aggregate sentiment from reviews, forum discussions, and media coverage. Brands with strong positive web presence tend to be characterised more favourably in AI responses.
Share of Model Benchmarks: Australian Market Context
While every category is different, here are approximate SoM baseline ranges we observe across Australian business categories:
| Category | Typical leader SoM | Typical mid-market SoM |
|---|---|---|
| Professional services (Melbourne) | 45-65% | 10-25% |
| E-commerce (national) | 30-55% | 5-15% |
| Local health services | 40-70% | 8-20% |
| B2B technology | 25-45% | 3-12% |
| Financial services (Australia) | 35-60% | 8-18% |
These figures reflect citation rates across a representative query set of 50 prompts per category. Individual results vary based on the competitiveness of the category, the brand’s existing authority, and the quality of existing GEO infrastructure.
From Metric to Strategy: Using Share of Model to Prioritise GEO Work
Share of Model is most valuable as a diagnostic tool, not just a reporting metric. The breakdown of your SoM data should directly inform where you invest GEO resources.
Low citation rate on ChatGPT but reasonable on Perplexity? Your content may be technically accessible but lacks the deep semantic authority that training-data-dependent engines reward. Priority: topical authority content and third-party mentions.
High citation rate but poor sentiment scores? Your brand appears but is associated with qualifiers or weaknesses. Priority: reputation management, review acquisition, and content that surfaces specific strengths.
Strong citation rate for generic queries but absent on local queries? Your GEO strategy lacks geographic specificity. Priority: locally-contextualised content and local authority seeding.
Cited frequently but always as secondary recommendation? Competitors are outranking you in the model’s assessment. Priority: competitive Share of Model analysis to identify their authority advantage.
How Latent Analytics Measures and Improves Share of Model
Our Latent Space Audit delivers a full Share of Model baseline for your brand: citation rates across four AI engines, sentiment analysis, competitive share against up to three nominated competitors, and a prioritised gap analysis.
For ongoing GEO engagements, we track Share of Model monthly and report on movement relative to both baseline and competitors — giving you the clearest possible picture of what your investment is achieving.
Get your baseline Share of Model — Latent Audit (A$299) →
Frequently Asked Questions
Is Share of Model an industry-standard term? The concept is widely used in GEO practice; the terminology varies. You may also see it referred to as “AI Share of Voice,” “LLM presence score,” or “model citation rate.” At Latent Analytics, we use Share of Model as the primary framing because it most accurately reflects what is being measured: your brand’s representation within the model’s learned knowledge.
How many prompts should I run to get a reliable SoM measurement? A minimum of 30 prompts per measurement cycle is required for statistical meaningfulness. 50-100 prompts produces a more reliable baseline, particularly in competitive categories where citation behaviour is variable.
Can I measure Share of Model without an agency? Yes, manually. It requires building a prompt set, running it systematically across engines, and recording results in a structured way. For ongoing monthly tracking at scale, most businesses find the time investment significant enough to warrant tooling or external support.
How quickly can Share of Model improve? For Perplexity, meaningful movement can appear within 4-8 weeks of targeted GEO work. For ChatGPT and Claude, the cycle is longer — typically 3-6 months before content and authority investments are reflected in retrieval. Google Gemini sits in between given its access to fresh web data.
Latent Analytics is a Melbourne-based generative engine optimization agency. We help Australian businesses measure and improve their Share of Model across ChatGPT, Gemini, Perplexity and Claude.
