When a brand distributes content through a third-party publisher network and sees its AI citation rates rise, a question arises - is that lift genuinely additive, or is the network simply capturing citations the brand domain would have earned on its own?
We won't bury the lede - distribution creates AI citations that wouldn't exist otherwise. The following analysis shows that distribution lift is entirely independent of brand & content factors, and that it is purely additive.
Network lift is truly additive
A two-predictor log-linear model was estimated across all 215 stories, with citation lift as the outcome and two predictors: brand citation volume and network citation volume.
By including brand citation volume as a control variable, it holds constant how strong the brand domain already is when measuring the network's independent contribution.
If high-DR publishers were simply selecting content that was already going to perform well (meaning content that the brand domain would have captured AI search presence for anyway) then the network coefficient should shrink toward zero once brand volume is held constant.
Our R² for the full two-predictor model was 0.61 - meaning the distribution model explains 61% of variance in lift, though other factors still impact real-world performance.
We saw a positive correlation between brand and network pickups (=0.+324+), which shows that stories with more network citations also tend to have more brand citations. This is strong evidence of independent and meaningful lift from distribution.
If there was a negative correlation here, it could suggest that citations from the Stacker network were appearing over brand citations. However, that is the opposite of what we’re seeing. We’re seeing that network citations support and strengthen brand citations, acting as a compounding mechanism.
So, more distribution = more total citations
In the simplest terms; the number of brand citations does not decrease as the number of network citations increases. Instead, more distribution = more total citations. The brand is not displaced to ‘make room’ for network citations - the two stack on top of one another.
Success doesn’t rely on pre-existing brand authority
The network coefficient (β = +0.981, t = 16.58) is large, positive, and independent of brand citation volume.
This means that we are not relying on pre-existing brand visibility for success - we can create it ourselves via distribution.
A 100% increase in network citation volume is associated with a 96% increase in citation lift, after holding brand performance constant.
In the simplest terms, the more placements you get on the network, the more AI presence you get - and this relationship holds regardless of how strong the brand already is.
The partial correlation between network citations and lift, while controlling for brand citations, is 0.751.
The zero-order correlation before that control is 0.564.
The network effect is stronger, not weaker, once brand strength is accounted for.
In other words, stronger brands add their strength to the strength of distribution; equalling a higher total than either of us could achieve alone.
But, pre-existing brand authority is not a prerequisite for success. The math shows that distribution acts independently of your brand authority, making it an additive solution for any brand’s strategy.
The network does not compete with the brand
If the network were cannibalising brand citations, you'd expect stories with high network volume to show lower brand rates. Instead, the two are essentially independent - meaning that the distributed versions of a piece of content compliment the original piece rather than competing with it.
Across all 215 stories, brand citation volume and network citation volume are moderately positively correlated (r = +0.32), meaning that stories where the network performs strongly also tend to be stories where the brand performs strongly. This further confirms that the network is not taking visibility from the brand, it is adding to it.
In sum, the network is not competing with the brand, it fills in any gaps the brand leaves.
Robustness
We ran three additional checks to confirm the result is not an artifact of model specification, or just a quirk of this dataset.
- Distribution performs consistently across all content types.
- Content factors do not predict network citations - distribution is an entirely independent lift vector.
- Topic doesn't explain the network effect. Whether a story is about career advice or auto insurance, more network citations still means more lift at roughly the same rate.
- Substituting response lift for citation lift as the outcome variable produces similar results.
- We verified this at two different levels of granularity, citations and responses - both showed what we would expect.
- We use the more granular data (citations) primarily in this report.
- Leave-one-out cross-validation confirms that this data is robust & generalizable.
- This helps ensure we didn’t over-fit the model to the data, and that no single datapoint skews this data.
- Essentially, the model learned something real about the relationship between distribution and citation lift, not something that only happens to be true in this particular dataset.
Enough math - what does this mean?
The lift you see in AI search due to distribution is additive.
These citations would not exist without distribution, making distribution a tried and true method for expanding your brand’s overall share of voice across LLMs.
Distribution through high-DR publishers adds AI citation volume that the brand domain cannot produce on its own, independently of how strong that brand domain is, and independently of topic category.
This is the power of a smart content distribution strategy.
Find our full methodology below.
Methodology
Data covers 215 stories published by 75 brands across Jan–Mar 2026, measured across 8 AI platforms (Google AI Mode, Google Gemini, Google AI Overviews, ChatGPT, Perplexity, Copilot, Claude, Meta).
Each story was measured against a standardized query set of 30 prompts per citing platform (6 platforms that surface URLs).
Publisher cluster analysis uses K-means clustering across 64 publishers with ≥1 story pickup. Domain rating data sourced from Ahrefs.
Tier-level correlations (r = 0.99, r = 0.97) are Pearson correlations computed across 4 cluster means. Citation lift is calculated as (network citation rate ÷ brand citation rate) × 100%. Stories where brand citation rate = 0 are excluded from lift calculations but included in hit rate analysis.
Note on uncited rate: 70.8% reflects responses across the 6 citing platforms only. The ~89% figure includes Claude and Meta, which return 0% by architecture — a URL-surfacing design decision, not a content quality signal.
Regression:
Four OLS log-linear models were estimated across 215 stories.
Model 1 regressed log(CPP) on domain rating at the individual publisher level (n=43), producing R²=0.82 and t(DR)=13.65. Models 2–4 were estimated at the story level (n=215), regressing log(citation lift) on log(brand citation volume) and log(network citation volume).
Model 2 produced R²=0.61, β(network)=+0.981 (t=16.58), β(brand)=−0.638 (t=−12.69), and a partial correlation of 0.751 between network volume and lift after controlling for brand volume.
Model 3 added topic category dummies; β(network) shifted by less than 1%, confirming topic is not a material confound.
Model 4 substituted response lift as the outcome variable; the network coefficient remained positive and significant (β=+0.676). Leave-one-out cross-validation on Model 1 produced an RMSE ratio of 1.047, confirming the model generalises with minimal overfitting.
Moderate heteroskedasticity was detected (r=0.37 between fitted values and absolute residuals); coefficient estimates are unbiased under heteroskedasticity and all t-statistics are sufficiently large that significance conclusions are unaffected.
All variables log-transformed prior to estimation.
Data source: Stacker GEO Pipeline study, Jan–Mar 2026.
Kevin Fowler is Head of SEO at Stacker, where he leads SEO, GEO and data strategy. With over a decade of experience, he has built and executed search strategies for brands in finance, e-commerce, media and tech. He holds an M.S. in Industrial and Organizational Psychology from Angelo State University and has worked in SEO roles at CoPilot, Volusion, Wunderman, and CreditCards.com.
Photo Illustration by Stacker // Shutterstock // Canva
