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How Earned Distribution Drives AI Search Citations: What the Data Shows

Summary

  • AI platforms tend to cite content that demonstrates high editorial standards and domain authority, which are two signals that earned distribution provides at scale.
  • Stacker's research with Scrunch found that earned distribution can increase a brand's AI citations by up to 325%.
  • Brands using earned distribution as a strategy to increase visibility in AI search results should prioritize building out a content program and measure how syndication impacts citations.


When brands consider strategies to increase their likelihood of being cited in AI-generated search results, they often look at optimizing their content so that it is easy for LLMs to parse. And while research has shown that well-structured content is indeed more likely to be cited by AI platforms, content structure is only one part of the equation.


Recent research has pointed to several factors that play a role in driving AI citations: how well-known a brand is across the web (i.e. measuring branded mentions on third-party sites), how authoritative its domain is, and how effectively its content is structured. Each of these involves its own strategy for improving AI search visibility.


But the factor that drives the most measurable lift is where that content lives, and how many other authoritative sites reference it. That's because LLMs consider credibility when deciding what to cite, and in many cases, that credibility is built through third-party validation — like an executive being quoted by a journalist writing for a well-known publication, or a news editor choosing to republish a piece of brand content alongside staff-written stories on their homepage.


In essence, it doesn't matter how well-optimized your content is if it only lives on one domain with no authority signals. AI platforms value editorial trust, source authority, and domain diversity, and earned distribution delivers all three. The following guide explains how.

What AI Platforms Look for When Evaluating Sources

AI platforms evaluate the source of a piece of content as much as what the content itself says, so the perceived quality and authority of a site is important.


Ahrefs' Domain Rating (DR) metric is a helpful proxy for understanding a domain’s quality. DR grades a website's authority on a logarithmic scale of 1-100, with higher numbers going to the more authoritative sites. This allows users to estimate the "strength" of a backlink from a domain.


Recent research has highlighted three important signals that LLMs consider when choosing which sources are credible enough to cite.

  • Domain authority: A Stacker analysis from April 2026 found that as a publisher’s DR rises, so does the probability of an AI citation occurring and the volume of citations when it does.
  • Source diversity: AI platforms see information as more reliable when it appears across multiple authoritative domains, Semrush has reported.
  • Editorial context: AI platforms consider editorial environments more trustworthy than brand-owned environments like blogs, press releases, or sponsored pages, according to Muck Rack’s Generative Pulse research.

Earned distribution — when content is syndicated onto a newswire for editors to place on their websites — combines all three of these elements.


News publications tend to have higher DRs than brand sites alone, and a newswire like Stacker's gives a brand the opportunity to surface its content on hundreds of these sites, providing domain diversity. An editor choosing to republish content alongside staff-produced articles is what makes this model earned and gives it the editorial context that AI platforms value.

What the Data Shows about Earned Distribution as a Strategy to Increase AI Visibility

Stacker's research with AI visibility and measurement company Scrunch has shown that earned distribution can increase a brand's AI citations by up to 325%. Stacker calls this metric "Citation Lift," and it's measured by comparing the total number of AI citations a brand has against the citations it earned from placements across Stacker's network of publishers. A Citation Lift above 100% means Stacker's publisher network is generating more citations than the brand earned on its own.


Stacker and Scrunch were able to measure this effect in two separate studies: one that analyzed eight stories and a second that analyzed 87 stories created by 30 brands. The research team queried 8 AI platforms with roughly 30 prompts per story, measuring citations to the brand's owned domain and to publishers in Stacker's network. Their analysis found that distribution led to a Citation Lift of 239% at the median.


The Stacker and Scrunch research also found that distribution nearly tripled how consistently brands surfaced across different AI platforms. Appearing across a wider set of prompts and platforms is important to overall AI visibility because LLMs make probabilistic decisions — the broader the coverage, the more surface areas where AI platforms can encounter your brand and deem it an authority in a particular topic area.


Earned distribution allows brands to borrow the authority profile of publications that have earned credibility over time through journalistic reporting and being cited by other sources.

An Example of How Earned Distribution Affects AI Citations

To understand what Citation Lift looks like in practice, take as an example a healthcare tech company that distributes its brand content with Stacker. Since beginning its partnership with Stacker at the end of 2025, the company has distributed nine stories and seen a Citation Lift of 192%.


The healthcare tech company's top-performing story was a state-by-state breakdown of how long it takes new medications to reach people across the US. The team analyzed publicly available data to offer a unique look at what happens when a new drug is approved. LLMs have a preference for this kind of proprietary, localized research, especially when the data is formatted in a way that is easy for them to reference.


The article ultimately saw a Citation Lift of 225% after earning 275 publisher pickups and reaching more than 76,000 estimated readers. For every citation the brand's domain earned in AI search results, a domain in Stacker's network earned two more.

How to Build Distribution into Your AI Visibility Strategy

Brands can improve their AI citation potential through strategies like increasing brand mentions through traditional PR, reworking content structure to be more readable to LLMs, and strengthening domain authority over time. But for brands looking to see direct, quantifiable Citation Lift, earned distribution is the most straightforward to scale and measure.

The strategy begins with a few steps:

  • Build content worth distributing — Create articles that could easily be published by a news outlet. This means stories that are editorial in nature, nonpromotional, and data-driven. Proprietary research tends to be popular with both news publishers and AI platforms.
  • Distribute through an editorial syndication platform — Syndicating articles through an earned distribution platform like Stacker places your content on high-DR sites and creates the authority signals that AI platforms are looking for.
  • Measure the impact on citations — Track how distribution affects your brand's response presence rate and citation rate. Pay special attention to Citation Lift, meaning: are publishers' versions of your content being cited alongside or instead of your brand's domain?

Distribution multiplies the ability of your brand content to create new citations. Each pickup earned creates another pathway to growing AI visibility. The brands whose content exists across the most credible sources are the ones LLMs increasingly treat as authoritative voices in their category.