Share of voice has been a marketing staple for decades. It started as a simple ratio: your advertising spend divided by total category spend. Over time it expanded to include organic search visibility, social mentions, and media coverage. The underlying logic stayed the same: the more space your brand occupied in the conversation, the stronger your market position.

That logic breaks down in AI search. When a buyer asks ChatGPT for a recommendation, the platform doesn't show ten results and let the user decide. It synthesizes a direct answer, names three to four brands, and frames each with specific positioning language. The brands that appear are being recommended. Everyone else is excluded from the consideration set entirely. This isn't share of voice. It's share of recommendation, and it operates on fundamentally different mechanics.

Why the distinction matters

Traditional share of voice measures exposure. How often your brand is seen, mentioned, or discussed relative to competitors. It counts impressions, mentions, and reach without differentiating between a passing reference and an active endorsement.

Share of recommendation measures influence. It quantifies how often AI platforms actively suggest your brand when a buyer asks for solutions. The difference is significant. A brand mentioned in a news article contributes to share of voice. A brand named as the top recommendation when someone asks "What's the best CRM for mid-size companies?" is capturing share of recommendation. One creates awareness. The other shapes purchase decisions.

Superlines' analysis frames this precisely: traditional metrics like impressions and click-through rates describe where you appear, while AI share of voice shows how often AI assistants actually recommend your brand when buyers ask for help. The metric is strongly predictive of future market position because brands that build high share of recommendation become the default answers AI repeats, compounding into lower acquisition costs and higher conversion rates over time.

How the metric works

The basic calculation is straightforward: the number of AI responses mentioning your brand divided by the total number of relevant prompts tested, expressed as a percentage. If you test 50 category-relevant prompts and your brand appears in 15 AI responses, your share of recommendation is 30%.

But basic mention frequency undersells the metric's value. Conductor distinguishes between mention-based share of voice (brand presence in conversations) and citation-based share of voice (authoritative sources driving AI traffic). Citation-based share reveals whether AI trusts your content enough to reference it as a source, not just name your brand in passing.

Semrush's Enterprise AIO weights mentions based on position within the response and, for ChatGPT, incorporates search volume. Being mentioned first in a high-demand query is worth substantially more than being fourth in a low-volume response. Position and demand context transform raw counts into a metric that correlates with business impact.

Single Grain's framework adds another dimension. An effective AI share of recommendation metric is built from three elements: the query set you care about, the answer engines you monitor, and the scoring rules you apply to each answer. The query set should prioritize bottom-of-funnel, purchase-intent prompts where recommendations directly influence buying decisions.

What drives the metric

Share of recommendation is shaped by signals that differ substantially from what drives traditional share of voice. Advertising spend, the original foundation of share of voice, has minimal impact on AI recommendations. Instead, the inputs that move share of recommendation include third-party mentions on authoritative sites, structured data that helps AI understand your brand, review volume and sentiment on platforms like G2 and Capterra, and consistent entity information across the web.

Birdeye's research emphasizes that counting mentions alone is a vanity metric. True measurement requires understanding quality, context, and intent. A recommendation framed as "industry-leading platform" carries different weight than "an option worth considering for smaller teams."

This is where share of recommendation diverges most sharply from share of voice. Share of voice increases with volume: more mentions, more impressions, more coverage. Share of recommendation increases with authority: stronger signals from trusted sources, more consistent entity information, better-structured content that AI can confidently cite.

Making it a core KPI

The brands treating share of recommendation as a primary KPI are integrating it into monthly reporting alongside traditional metrics. The measurement cadence matters. LLM Pulse recommends monthly benchmarks at minimum to track shifts, with weekly spot-checks in dynamic industries to catch fluctuations from model updates or competitor movements.

The strategic application goes beyond monitoring. Where you already dominate, invest in maintaining leadership. Where you lag slightly behind, focus on closing winnable gaps. Where a competitor has overwhelming advantage, consider redirecting investment elsewhere.

What makes this metric distinct from everything that came before it is the directness of the relationship between measurement and outcome. Share of voice was always a proxy. Higher visibility correlated loosely with market share, but the path from mention to purchase had many steps. Share of recommendation compresses that path. When AI names your brand as the answer to a buyer's question, you've moved from visibility to influence in a single interaction.