# Can PR Go beyond Vanity Metrics? ## The Ancient History of SOV & ESOV In his 1990 article, John Philip Jones analyzed 1,096 advertised brands and demonstrated a correlation between a brand's share of market and its share of voice (the brand's portion of total media spend in the category). He established the idea that brands tend to maintain market share in proportion to their share of voice (Source: [Harvard Business Review](https://hbr.org/1990/01/ad-spending-maintaining-market-share)). Les Binet and Peter Field's research, including their 2013 paper "The Long and the Short of It," built on Jones's work. They found that, all other factors being equal, brands with a share of voice (SOV) lower than their share of market (SOM) tended to lose sales, while those with a share of voice exceeding market share gained market share. The rate at which a brand grows or shrinks tends to be proportional to its “excess” share of voice (ESOV), defined as the difference between SOV and SOM.  ![[Pasted image 20241219151438.png]] On average, an ESOV of 10 percentage points correlates with an annual market share increase of approximately 0.6 percentage points. This highlights how even small SOV gains can lead to meaningful revenue growth over time. However, the ESOV Efficiency—the rate of market share growth per mention or impression above equilibrium SOV—varies by sector and business model. For consumer brands, a 10% ESOV typically results in a 0.6 percentage point annual market share increase, aligning with the overall average. B2B brands often see slightly higher efficiency, with a growth rate of 0.7 percentage points for the same ESOV. Financial services, however, demonstrate exceptional ESOV Efficiency, with a 10% ESOV driving an estimated 1.5 percentage point increase in market share annually (Source: [Binet and Field](https://business.linkedin.com/content/dam/me/business/en-us/amp/marketing-solutions/images/lms-b2b-institute/pdf/LIN_B2B-Marketing-Report-Digital.pdf)). ## Is SOV & ESOV Actually Used Today? Increasing Share of Voice (SOV) is a proven, long-term strategy for market share growth. Technically, SOV originated as a measure of input (ad spend). The rise of digital has put SOV through a bit of a crisis. This shift is partly why some researchers (notably Binet himself) have explored modern alternatives. Among many PR practitioners, it's broadened over time to a measure of output (mentions). As a PR agency, we help companies sustainably increase SOV via what we call a *digital content engine*. This engine turns your data and expertise into blog content, organic media mentions, corporate announcements, and more. In essence, it delivers high-value content where relevant audiences spend time online. We use SOV as one measure when considering our campaign's effectiveness. SOV alone is not enough. You should also weigh mention quality and sentiment. Additionally, the aforementioned output-based spin on SOV captures movements that are not necessarily controlled by the brand. Some argue, therefore, that it may not be the best proxy for your content marketing choices. I argue that it at least this gives you an indication of how present your brand actually is. ## Calculating SOA in AI-powered Search SOV has 35 years of empirical backing. Perhaps the next logical hypothesis is Share of Answer (SOA). These days, AI assistants can drive tons of highly qualified traffic to your website or blog through *citations*, which link to your cite. Share of Answer (SOA) is how we measure your slice of citations vs competitors. You might calculate Excess Share of Answer (ESOA) as SOA minus market share. Perhaps brands maintaining SOA above their market share will grow, while those below will shrink. If your content isn't getting cited, you risk missing out on new business. How much? It depends on your business and to what extent AI-powered search drives your category's traffic (across all players). For example, research-intensive B2B industries will likely see more AI-referred traffic than consumable goods. If that's you, and you're invisible, you're potentially missing a meaningful slice of high-intent buyers at a critical moment. That said, the data infrastructure to test this hypothesis is only now being built. Profound (backed by Sequoia Capital and NVIDIA's venture capital arm, NVentures) and AthenaHQ (backed by Y Combinator) are the largest dedicated players in the "AI Visibility" space. At the same time, major marketing and SEO players like Semrush and Ahrefs have also recently launched competing products. As more brands measure SOA alongside revenue data, we may be able to estimate more precisely what that visibility gap actually costs. You might be thinking, "This sounds like AI hype." But Google's AI Overview (AIO) and ChatGPT answer billions of questions every day. And Google is only increasing the rate at which AIO is presented to users. But it's deeper than that. Search experiences are shifting from the best *average* answer to the best *individual* answer, powered by AI. These highly specific answers are so helpful that B2B buyer adoption, at any stage (but mainly evaluation and decision), is basically 100% in 2026. It seems reasonable, then, that a business would want to plan a little bit for these interactions. What can you do about it today? If you haven't already, map your customer profiles and what each stage in their buying journey looks like (e.g., discovery → evaluation → decision). Take stalk of your owned and earned content out there in the universe. Do you actually have helpful content for each stage? If not, you have some work to do.