The generic, cliff notes version of every topic has already been absorbed by AI. In a sea of endless AI slop, simply being accurate or comprehensive is no longer enough. The brands getting rewarded with higher mention and citation rates are the ones bringing something new to the conversation.
So how do you actually pull that off? LLMs place a premium on content that’s distinct, but also validated by data. That’s why first-party data (1PD) is getting a well-deserved second spin in the spotlight, this time for AI SEO.
Data privacy legislation (CCPA and GDPR) and tech company positioning… err, policy (Apple’s App Tracking Transparency) sparked a 1PD frenzy that ran hot from 2021 through 2023. “Data is the new oil” became the marketing conference talk track, matched by a real push to refine targeting, invest in CDPs, and stand up data clean rooms.
That hype cycle has largely died down since. Meta, TikTok, and other social platforms found better ways to target ads without cookies. Google reversed its own Chrome cookie phaseout plan. And CDPs and data clean rooms turned out to be exactly what they looked like: expensive, slow-moving projects. Lost in all of that noise was a simpler truth. Getting your brand’s 1PD organized and finding real use cases for it was always worth doing. It just took AI to make that payoff obvious.
Unique data wins citations, and now there are real numbers behind it. Kevin Indig’s research on AI-cited pages found that content built on primary research draws roughly 3.3x more citations than everything else in the mix. Data-led content was cited as the most common tactic in digital PR in a survey (95% of respondents). As earned mentions also feed LLMs, this creates further momentum. That’s good news for users too, not just brands: it means LLMs are weighted toward surfacing content with an actual point of view, not the tenth rehash of the same listicle. One more wrinkle worth remembering: data framed to answer a comparison, not just presented as a standalone stat, is what performs best of all.
So what kind of 1PD are we talking about? It’s helpful to work backward from the primary use cases:
- Deciding which content to produce
- Determining which prompts to track and prioritize for visibility
- Differentiating the content you do produce
The first tells you what to build. The third tells you how to make what you were already building impossible to ignore. The second is how you keep score on both.
Deciding Which Content to Produce
Mining sales, support, and call transcripts is a no-brainer starting point, and the data doesn’t need to be perfectly structured or sitting in a CDP to be useful. Look for patterns in customer misconceptions, complaints, and favorite product features. It almost always turns up low-hanging fruit. A few hypotheticals:
- A running shoe retailer’s on-page reviews praise how well a specific model holds up in wet conditions → add “water resistant” language to PDPs and product feed descriptions, and engage influencers, especially in wetter climates like the Pacific Northwest, to put the shoe to the test.
- A retail bank finds frequent confusion about what happens to a teen checking account when the account holder turns 18 → build out clearer guidance on the site to address.
- A payroll software’s users frequently complain about the difficulty of setting up payroll for contractors outside of the US → build out YouTube content walking through setup.
Remember Indig’s point about comparisons? This is where it pays off. If any of these themes double as a real competitive advantage, like the shoe holding up better than competitors’ in wet conditions, don’t stop at fixing the content gap. Build the head-to-head comparison LLMs can cite when someone asks “X vs. Y.”
For bonus points, try to map some of these themes to the journey stage and weight them based on commercial proximity (ie – which themes are most closely tied to SQLs in your CRM).
Determining which prompts to track and prioritize for visibility
Too many brands are still copy-pasting their SEO keyword lists over to whichever third-party measurement solution they’re using for AI SEO. These keywords, which almost always overindex toward 1-3 word high volume queries, don’t represent how consumers prompt LLMs. Picking prompts that are realistic and strategically important, not just easy to import, is what keeps your strategy pointed at the right target.
To build this prompt universe, start with the content themes from the previous section and cross-reference with Google Search Console, GA4 and site search data. Using the examples above, that might look like:
- “Best waterproof running shoes for the Pacific Northwest.”
- “What happens to my teen checking account when I turn 18?”
- “How do I run payroll for a contractor outside the US.”
Then go a layer deeper. How are people finding the products they end up buying? Are there specific FAQs correlated with purchases?
Differentiating Content You Produce
Differentiation is where you get the most room to be creative, and the opportunities here are close to endless. For informational prompts especially, dropping in real, anonymized customer or usage data makes content inherently more citable. A few examples:
- Wearable fitness brands citing differences in step counts, sleep scores or heart rate spike times based on geography or demographics
- Ad agencies highlighting KPI trends by verticals or before-and-after benchmarks relative to new ad platform product releases
- Hotel chain releasing data on average stay length by city
As always, the differentiation that matters most still sits in the lower funnel: comparison and buying-signal prompts.
| Prompt Category | 1P Data Pull |
|---|---|
| “Best ___” (general) | Validate the strength of your offering via NPS or CSAT scores for overall |
| Quality related prompts | Highlight low product return rates, 1P reviews, etc. |
| Product performance prompts | Provide product performance data against industry benchmarks (speed, durability, downtime) |
| “How to use it” prompts | Usage data: adoption rate, breadth of teams or departments using it, depth of feature use |
| Contextual prompts (“Best ___ for situation/demographic”) | Segments where you overindex |
Eating Our Own Dog Food
At Brainlabs, we put these principles to work on our own marketing. The prompt universe behind our 35% AI Share of Voice growth didn’t come from a third-party keyword tool. It came from cross-referencing Search Console data with the questions we were already fielding in client briefs and calls, the exact first-party signal this piece is arguing for. We backed it up with specific before-and-after comparisons of our AI visibility metrics in the article covering the strategy.
That’s the final step: validation. Once your prompt list and content priorities are set, check whether you’re actually gaining share on those categories. To summarize:
- Identify 1PD sources
- Extract themes and corresponding data points
- Validate against real prompt behavior
- Publish in an extractable format
- Measure mention and citation rates to confirm it’s working
1PD is having a real moment in AI Search, and this time the old excuses don’t hold up. You don’t need a CDP. You don’t need a clean room. You need a process:mine the data you already have, and turn it into content and prompts before someone else does. Once process is running, you’ve got a durable moat. Positioning can be copied. Proprietary data can’t.
