Share of voice (SOV)
Investment-relative-to-size; the classic brand growth lever.
- Formula
- Brand ad spend (or brand mentions) / total category ad spend (or mentions)
- Unit
- %
- Models
- All models
| All | Excess SOV (SOV minus market share) predicts share growth — directional rule of thumb ~0.5 percentage point of market share per year per 10 points of ESOV (Binet & Field, IPA 'Media in Focus'). Mention-based SOV via PR/social listening is the modern substitute where ad-spend data is unreliable. | Binet & Field, IPA 'Media in Focus' |
What it is
Share of Voice (SOV) is the brand's advertising spend — or brand mentions — divided by the total category advertising spend or mentions. It captures how loud a brand is relative to the competitive set, either in paid media or in organic conversation.
How to calculate it
For paid SOV: divide the brand's tracked ad spend by the sum of all competitors' tracked ad spend plus the brand's own. For mention-based SOV: divide the brand's media mentions or social mentions by the total category mentions over the same period. Both versions produce a percentage.
Why it matters
Binet & Field's IPA 'Media in Focus' research established that Excess SOV (ESOV = SOV minus market share) is a directional predictor of market share growth. Brands that maintain SOV above their market share tend to grow; those below tend to shrink. Mention-based SOV via PR or social listening has become the practical substitute precisely because reliable ad-spend data across competitors is rarely available.
Benchmarks & pitfalls
This is a directional rule of thumb rather than a rigorously validated study benchmark: the relationship is approximately 0.5 percentage points of market share per year for every 10 points of ESOV. No absolute SOV figure constitutes "good" in isolation — only the gap between SOV and current market share is actionable. Paid ad-spend data is notoriously incomplete for private competitors, which is a core reason Binet himself advocates Share of Search as a more reliable substitute. If using mention-based SOV, define the listening query consistently across time periods before drawing trend conclusions.