For decades, the media plan has been the definitive artifact of marketing strategy. When the rubber meets the road – one page. Channels on the left, budget on the right, allocations mapped across the year. When the CEO asks where the money is going, you hand them the media plan. The media plan forces rigor. It makes tradeoffs explicit. It aligns teams.
Ask organic teams for something similar and you’ll get crickets.
That’s not a dig at SEO teams. For most of search’s history, SEO was a single-asset discipline. You optimized one asset, your website, for Google. The channel was narrow, the surface was clear, and the strategy could live inside one team.
That era is over.
The sources shaping how AI models represent your brand now extend far beyond your website. Reddit, YouTube, review platforms, third-party editorials, influencer coverage; they’re all contributing to the picture AI systems build around your brand. They also have different dynamics, owners and relationships with your brand. Their relative importance also vary wildly by brand, vertical and LLM. And right now, most brands are managing that portfolio with no plan at all.
The problem is that most organic teams still aren’t structured around that reality. Despite the shift, most organic programs still operate through disconnected activities: a content calendar here, a keyword roadmap there, maybe some digital PR running separately.
Enter the Organic Media Mix.
What is the Organic Media Mix?
The Organic Media Mix (OMM) is a strategic framework for allocating effort, resources, and budget across organic channels based on where AI systems are actually citing your category, and where you have a realistic chance of influencing those citations.
The output is a one-page strategic view: the document that tells you which organic channels you’re prioritizing this quarter, makes tradeoffs explicit, aligns cross-functional teams, and gives leadership a clear picture of where organic investment is going and what it’s expected to deliver.
The critical thing to understand before building one: no two OMMs look alike, and the differences between them aren’t small. The mix for a CPG brand is fundamentally different from a B2B technology company. And within the same brand, the picture changes depending on which AI model you’re looking at.
Step 1: Pull your citations report
A citations report shows you where AI models are sourcing their answers in your category. When a user asks a question relevant to your brand, a product question, a comparison, a recommendation, what sources does the AI cite? Your own site? A Reddit thread? A TechCrunch article? A YouTube review?
Most AI visibility platforms (Profound, seoClarity, AirOps and others) can generate this data. The process is:
- Define your prompt set, the questions your prospective customers are actually asking AI assistants, from informational (“what is X?”) to high-intent commercial (“what’s the best X for a mid-market SaaS team?”).
- Run those prompts against the AI models you care about: ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews.
- Capture which sources are cited in the responses.
- Categorize those sources: owned site, YouTube, 3P editorial sites, Reddit, review platforms, Wikipedia, influencer content, retail listings, other.
What you get is a map of where authority currently sits in your categor, not where you wish it was, but where AI models actually go to answer questions about you and your competitors.
The citations report will surprise you and the surprises are different for every brand
In one category we analyzed, Reddit didn’t appear in the top 100 citation sources. In another, it represented 21% of all citations and was 3x larger than the second most cited individual domain. Whether Reddit matters for your brand is not a general question, it’s an empirical one, and you can’t answer it without the data.
The variation across AI models adds another layer. For one brand we worked with, Reddit accounted for 23% of citations in ChatGPT responses, but only 3% on AI Overviews. The model you’re looking at changes the picture entirely. A strategy built on one platform’s data alone will miss how a significant portion of your customers are actually searching.
This is why the OMM can’t be a static, one-size-fits-all document. It has to be grounded in your specific citations data, across the models your customers are actually using.
Step 2: Score each channel across four dimensions
Raw citation volume tells you what’s happening. It doesn’t tell you where to invest. For that, you weight each channel across four dimensions.
Degree of influence. How much control do you have over what appears on this surface? Your owned website sits at one end, you write it, publish it, and update it without asking anyone. Wikipedia sits at the other extreme. Reddit, review sites, and digital PR all fall somewhere in the middle. High-influence channels aren’t always the highest priority, but all else equal, you can move them faster.
Difficulty of implementation. Some channels require a single team and a content calendar. Others require developer resources, agency relationships, legal sign-off, or long editorial cycles. Scoring implementation difficulty helps you sequence the roadmap realistically.
Prompt commercial proximity. Not all citations carry equal weight. A citation earned from a “what is protein powder?” prompt matters significantly less than one earned from “what’s the best high-protein supplement for endurance athletes over 40?” The closer a prompt is to a buying decision, the more valuable the citation. Score your prompt set accordingly, and weight citations from high purchase proximity prompts more heavily in your analysis.
Sentiment. A mention isn’t a win if it’s negative. Favorable citations, especially from trusted third-party sources, carry significantly more weight than a neutral or critical mention. Score each citation source for the sentiment it consistently delivers, and factor that into where you invest. A forum thread that cites your brand unfavorably in a heavily cited context is an active liability, not a neutral data point.
Step 3: Build the mix
With your citations report categorized and your channel scores in hand, you can now build the Organic Media Mix.
Here’s a hypothetical for a CPG brand. Before running the citations analysis, the team assumed owned content was doing most of the work and had allocated accordingly. The data told a different story.
Anonymized example: Consumer health brand (illustrative)
The finding that shifted strategy: third-party editorial was the single largest citation driver, with high commercial proximity and consistently positive sentiment, but the brand had almost no structured investment in it. The team had been treating digital PR as a brand awareness play, not an AI visibility play. The OMM reframed that conversation and unlocked budget reallocation.
Step 4: Assign resources, teams, and owners
The OMM only has value if it drives decisions. Concretely, it should answer three questions.
Who does what? AI Visibility is not an SEO-only discipline, it’s a marketing team sport. Executing an OMM requires SEO practitioners for owned content and technical infrastructure, PR and comms teams for digital PR and editorial coverage, social teams for organic social and community, and potentially influencer or affiliate leads for video and third-party advocacy. The OMM is the document that forces a cross-functional conversation about ownership.
What’s the budget? Not necessarily in ad dollars, but organic isn’t free. Content production, outreach, PR agency retainers, community management tooling, and influencer partnerships all have costs, not to mention the invaluable time and focus of your marketing team. The OMM makes those costs explicit and ties them to expected citation outcomes, the same way a paid media plan ties spend to impression and conversion targets.
What’s the sequencing? Not everything can happen at once. Use difficulty scores and citation gap analysis to sequence the roadmap: high-priority, low-difficulty items first, with long-lead infrastructure investments planned in parallel.
Bridging organic and paid
One of the most underused applications of the OMM: using it to connect organic intelligence to paid investment decisions.
If your citations report surfaces a Reddit thread that’s heavily cited and consistently favorable to your brand, that’s not just an organic signal, it’s a paid opportunity. Can your paid social team put a targeted placement in that thread or adjacent community to amplify the content?
The same logic runs in reverse. If a paid campaign is driving awareness around a specific use case, your organic team should be building citation coverage for the same queries, so your brand appears across multiple trusted sources, not just your own site.
This is the kind of cross-functional play that’s invisible without the data in front of the whole team. Paid and organic have historically been managed in silos. The OMM creates a shared language that makes bridging them practical.
The document your CMO is missing
Too many AI Visibility strategies are driven by vertical agnostic assertions, what worked for traditional SEO and vibes. The Organic Media Mix is the document that solves that. It starts with data (your citations report across the AI models that matter), weights channels against criteria that drive real outcomes (influence, difficulty, commercial proximity, sentiment), and ends with a structured framework for allocation of resources, owners, and priorities.
Your OMM won’t look like anyone else’s. The channel mix, the model variation and the sentiment picture is all specific to your brand and category. That’s the point. Generic organic strategy is why most brands are invisible in AI-mediated search. Specificity is the advantage.
If your CMO can hand a board a media plan showing where every paid dollar is going, they should be able to hand them an OMM showing where every organic dollar is going too.
Brainlabs builds Organic Media Mix strategies for brands navigating AI visibility. If you want to understand where your category citations are coming from and where you should be investing get in touch.
