Native Ad Share of Voice: Measuring Who Dominates a Vertical
You can't see a competitor's native spend, but you can count their live creatives and measure how long each one survives, and that gives you a defensible way to rank advertisers by share of voice in any vertical.
Spend data for native advertising is locked up. No network publishes what Taboola, Outbrain, MGID, or Revcontent advertisers pour into a vertical, and there is no impression-share API the way paid search has one. So when a media buyer asks "who actually owns the finance space on native right now?", the honest old answer was a shrug.
That shrug is no longer good enough. You can rebuild a defensible share of voice from signals you can actually see: how many distinct creatives an advertiser is running, and how long each one has stayed live. This study lays out the method. It is a presence-based SOV proxy you can run yourself, what each signal really means, where it breaks, and how to keep it honest.
To anchor the scale we are talking about: as of June 2026, OpenAdLibrary has captured 589,036 distinct creatives from 25,933 advertisers across 42 networks, with 5.4 million ad observations behind them. That is the raw material a vertical SOV pass draws from.
What native ad share of voice measures#
Native ad share of voice is one advertiser's slice of the total native ad presence in a vertical, measured against its competitors. Real spend is never disclosed, so the metric leans on two observable proxies: the count of an advertiser's distinct active creatives, and the total run-days those creatives accumulate. Together they approximate sustained commitment far better than any spend guess.
Classic share of voice has two lineages. The original, from TV and print, is spend-based: your category ad spend divided by total category spend. The digital era added impression-based SOV, where platforms hand you impression share directly. Native gives you neither. Spend is hidden and there is no impression report. What you can see is the surface area an advertiser occupies across publisher widgets, captured by a native ad spy tool. That surface area is the raw material for the proxy.
The logic holds because of how native buying actually works. Native advertisers, especially the affiliate and direct-response operators who dominate Taboola and Outbrain, kill unprofitable creatives fast. A creative that survives weeks survived because it pays. So presence over time is not just visibility. It is a noisy but real signal of where money is moving. Take this live finance ad as an example of the genre: a deadline-driven tax-relief angle, exactly the kind of evergreen direct-response play that sticks around when it converts.
Finance is not a random example. It is the single largest native vertical in our index: 17,232 creatives, ahead of insurance (15,629) and health (14,895). If you are picking a vertical to run this analysis on, those three are where the competition, and the budgets, are thickest.
The two signals, and why both matter#
Active-creative count#
This is the number of distinct live creatives an advertiser is running across the networks you track. It captures breadth: how aggressively a brand is testing and scaling. An advertiser with 80 live ad creatives in a vertical is operating at a different altitude than one with three.
Count alone is gameable, though. Some advertisers spray dozens of throwaway variants that die within days. Raw count rewards that churn and overstates their real footprint. You can see the testing instinct in ads like this one, a fresh consumer-product angle that may or may not survive its first week.
Run-days (longevity-weighted presence)#
Run-days is the number of days a creative has been observed live. Summed across an advertiser's creatives, it weights presence by durability. This is the signal that separates winners from noise:
A handful of creatives that have each run for months almost always represents more real, profitable spend than a flood of variants that vanish in a week. Longevity is the closest free proxy we have for "this is working."
We treat the longest-running native ads as the spine of a vertical: the proven workhorses competitors keep paying to serve. One honest caveat about our own numbers, because the rule of this study is no over-claiming. Our continuous-observation window currently tops out around 28 days per creative, so the longest-running ads in our index (a SmartAsset IRA-tax piece on Outbrain, Hidden Hearing's hearing-aid ad on Microsoft Audience Network, the relentless "My IQ" quiz creatives) sit at roughly 28 observed days. That is what we have actually measured. The "creatives that run 90-plus days" idea is general industry lore about native winners, useful as intuition, but do not read it as our finding. Run-day weighting bakes the longevity intuition into the calculation so a churn-heavy tester cannot out-rank a disciplined operator running evergreen winners.
A defensible SOV formula#
Combine the two into a single, transparent score. The simplest version that survives scrutiny:
Advertiser SOV % = (advertiser's longevity-weighted creative presence)
/ (sum of all advertisers' longevity-weighted presence in the vertical)
x 100
where presence = sum of (run-days of each active creative)
Optionally cap any single creative's run-day contribution so one ancient evergreen ad does not swamp an otherwise dynamic advertiser. Report both the raw creative count and the run-day-weighted score side by side. The gap between them is itself diagnostic.
Here is how the same five (illustrative, not measured) advertisers can re-rank depending on which signal you trust:
| Advertiser (illustrative) | Active creatives | Total run-days | Run-day SOV % | Read |
|---|---|---|---|---|
| Advertiser A | 14 | 1,820 | 31% | Fewer ads, deep winners. Efficient dominance |
| Advertiser B | 62 | 1,240 | 21% | Volume tester; breadth without durability |
| Advertiser C | 9 | 1,050 | 18% | Lean, evergreen-led |
| Advertiser D | 38 | 980 | 17% | Mid-funnel churn |
| Advertiser E | 21 | 760 | 13% | Steady challenger |
The numbers above are illustrative, built to demonstrate the proxy, not measured findings. Notice Advertiser B leads on raw count but trails A on run-day SOV. Ranking by count alone would have crowned the wrong leader. That divergence is the whole point of weighting by longevity.
Defining the vertical (this is where most analyses break)#
SOV is only as good as the denominator. Get the vertical boundary wrong and every percentage is fiction. Three ways to scope a vertical, in increasing order of rigor:
- By category or IAB classification. Fast, coarse. Good for "finance" or "health" at a high level, weak when verticals overlap (is a supplement "health" or "nutra"?). For perspective on the coarse-classification problem: across our index, Taboola alone shows 6,048 health creatives and 5,558 finance creatives, while Outbrain leans finance-first (2,640 finance, 2,016 health). The same advertiser can land in different buckets depending on the network and the classifier, which is exactly why category-only scoping is the weakest method.
- By keyword and creative angle. Filter on headline language and the creative angle in play. Tighter, but creative copy is messy. Two ads can sell the same offer with wildly different hooks.
- By landing-page destination. The strongest signal. Two ads with different copy that resolve to the same offer page (or the same pre-lander) belong to the same competitive cluster.
That third method is where click-tracing earns its keep. OpenAdLibrary follows each ad's click through to the advertiser's landing page without clicking the live ad, so you can group competitors by where the traffic actually goes, not by what the thumbnail says. It also resolves the real advertiser behind each placement, which matters because native is thick with programmatic native advertising where one buyer hides behind multiple ad-account aliases and arbitrage brands. Counting those as separate competitors inflates the denominator and deflates everyone's SOV. Look at the health vertical and you see why de-duplication matters: ads like the one below crowd the same "ditch your hearing aids" angle under different brand names.
How to run this yourself in OpenAdLibrary#
The whole point of a proxy is that it has to be reproducible. Here is the workflow end to end:
- Scope the vertical. Filter the ad index by category, keyword, or, best, landing-page pattern. Decide which networks count (Taboola plus Outbrain only? Add MGID, Revcontent, Teads, MSN, Yahoo?). Document the choice. It is part of the methodology.
- Set the observation window. SOV is a snapshot. "Active in the last 30 days" is a sensible default for a fast-moving vertical; widen it for slow seasonal ones.
- Group by advertiser. Use the resolved advertiser identity, then fold known aliases together by hand. This de-duplication step is the difference between a real ranking and noise.
- Pull the two signals. Active-creative count and total run-days per advertiser.
- Compute and chart. Apply the formula, optionally cap run-days per creative, and rank. Plot count against run-day SOV so the divergences pop.
- Sanity-check the leaders. Open the top advertisers' creatives. Are they really in your vertical? Are the long-runners genuinely live or stale captures? Trust, then verify.
Because the underlying index is captured live and the real creative image is stored at full quality, you can audit every data point behind a ranking. That is what makes a chart citable rather than hand-wavy. For network-level context on who shows up where, the breakdown of top native advertisers by network pairs naturally with a vertical SOV pass, and the vertical sizing in our 2026 numbers tells you which verticals are even worth running this analysis on.
What the SOV proxy does and does not tell you#
Keep the limits in view so you do not over-claim:
- It is presence, not spend. A high SOV means more durable surface area, not a confirmed bigger budget. Bid prices swing wildly by geo and publisher.
- Capture coverage is finite. No third party sees every impression on every widget. The proxy measures observed presence; treat it as a representative sample, not a census. State your coverage.
- Geo and device skew it. An advertiser huge in US desktop may be invisible in your tracked feeds if you sample AU mobile. Hold geo and device constant across competitors. A localized ad like the one below only surfaces if you are sampling the right market.
- Run-days can lag reality. A creative paused yesterday may still register as recently active for a day or two. Use a recency window to bound this.
None of these break the method. They define its honest confidence interval. A proxy you can explain and reproduce beats a precise-looking spend number someone fabricated.
Why this matters for your next move#
A vertical SOV chart is not a vanity ranking. It tells a media buyer which competitors to reverse-engineer, which evergreen native ad widget placements to chase, and whether a vertical is a two-horse race or wide open. Pair the leaders' SOV with their actual winning creatives (the angles, the pre-landers, the run-days) and you have a starting playbook instead of a blank page. For the wider context on how native is consolidating in 2026 (Outbrain's $900M Teads acquisition pushing the network toward video and CTV, the post-cookie shift to contextual signals), see The State of Native Advertising 2026.
Want to build your own vertical SOV chart from live, citable ad capture, with real advertisers and real creatives traced to the landing page? Start free: browse 200 ads, no card required, then go deeper from $29.99/mo.
Methodology note: the figures in the re-ranking table are illustrative, constructed to demonstrate the SOV proxy, not measured findings. Index totals and vertical counts are real (OpenAdLibrary index, June 2026). Run the workflow on your own vertical to generate real numbers, and always report your network scope, geo and device, observation window, and capture-coverage caveats alongside any SOV chart.
Sources: Brandwatch, Share of Voice formula and examples, Search Engine Land, Share of Voice guide, Taboola, Native advertising platforms 2026.
