Share of Voice vs. Share of Model: Rethinking How You Benchmark Competitors

Share of voice has been a cornerstone of competitive PR analysis for decades. The concept is straightforward: measure how much of the total media conversation in your category your brand owns, relative to competitors. More coverage means more visibility. More visibility means more mindshare. More mindshare drives purchase intent.

That logic held up reasonably well in an era when the path to a buying decision ran through editorial coverage, search results, and word of mouth. But as AI-generated answers increasingly mediate the research process — intercepting buyers before they ever reach a search results page — share of voice alone no longer captures where the real competitive battle is happening.

The metric that does? Share of model.

What Share of Voice Actually Measures

Traditional share of voice analysis aggregates media mentions across a defined set of outlets over a given time period and calculates each brand’s percentage of the total conversation. More sophisticated versions weight mentions by outlet authority, sentiment, or audience size.

It’s a useful benchmark. Consistent share of voice data tells you whether your brand is growing its presence in the editorial conversation, whether competitors are accelerating, and whether category-wide events are reshaping the landscape.

What it cannot tell you is what happens after a buyer types a question into ChatGPT or Perplexity. It doesn’t capture which brand the AI recommends when someone asks for the best solution in your category. It doesn’t reflect how authoritative the AI considers your brand to be on specific topics. And it doesn’t reveal whether competitors are systematically outperforming you inside the AI systems that now sit at the front of the buyer research journey.

That’s the gap share of model fills.

Share of Model: The AI-Era Competitive Benchmark

Share of model measures how frequently your brand is mentioned, recommended, or cited in AI-generated responses compared to competitors — across a defined set of queries relevant to your category.

Think of it as share of voice, but measured inside the AI rather than across the media landscape. Instead of counting mentions in publications, you’re tracking presence in the outputs of ChatGPT, Gemini, Perplexity, and Google’s AI Overviews when buyers ask the questions your brand should be answering.

The competitive implications are significant. According to research from Position Digital, brands are 6.5 times more likely to be cited by AI through third-party sources than through their own websites. That means the competitive gap in share of model isn’t determined by who has the best website or the most content — it’s determined by who has earned the most authoritative third-party validation across the sources AI trusts.

A competitor with a smaller marketing budget but a more disciplined earned media strategy may be winning the share of model battle while your brand holds a stronger share of voice position. Without measuring both, that gap is invisible.

How to Measure Both

Share of voice measurement relies on media monitoring platforms — Muck Rack, Cision, Mention, and similar tools — that track brand mentions across publications, broadcast, and online sources. Most PR teams already have this infrastructure in place. The key is ensuring your competitive set is properly defined and that you’re weighting mentions by source quality rather than treating all coverage as equal.

Share of model measurement requires a different approach. Platforms like Otterly, Siftly, AthenaHQ, and Profound are purpose-built to track how brands appear in AI-generated responses. These tools let you define a library of queries relevant to your category — the questions buyers ask when researching solutions like yours — and then monitor which brands appear in the answers across multiple AI platforms over time.

Running both measurement systems in parallel gives you a complete competitive picture: where you stand in the editorial conversation and where you stand in the AI-mediated research journey. Gaps between the two are strategic signals. A brand with strong share of voice but weak share of model has earned media coverage that isn’t translating into AI authority — often because placements are concentrated in lower-authority outlets or because brand messaging lacks the consistency AI models need to build a coherent entity profile.

Using the Data to Close Competitive Gaps

The most actionable output of a combined share of voice and share of model analysis is a competitive gap map: the specific topics, query types, and AI platforms where competitors are outperforming your brand.

If a competitor is consistently cited by Perplexity when buyers ask about a specific use case your brand serves equally well, that gap is addressable. According to research from Try Profound, Perplexity cites sources in 97% of its responses — the highest citation rate of any major AI platform. That means well-sourced, authoritative content on the right topics has a clear path to citation. The question is whether your earned media strategy is generating the placements in the outlets Perplexity trusts.

Share of model data also helps prioritize content investment. When you can see which competitor-owned topics are driving AI citations, you can develop a targeted strategy — earned media placements, expert bylines, data-driven original research — aimed at building authority in exactly those areas.

The brands that will lead their categories in AI-driven search are the ones that close competitive gaps systematically, not reactively. Measuring share of model alongside share of voice is how that work begins.