Ask most marketing teams how they research their audience, and you'll hear the same three answers: personas, keyword volume, and whatever demographic data came bundled with the last platform they bought. That's not wrong, exactly. It's just incomplete.
We've written before about how audience understanding is the thing that gets you through the AI wave, and every wave after that. The point of that article was that empathy for your audience is the one competitive advantage that doesn't erode every time a search platform changes the rules. This one picks up where that left off. If audience understanding is the advantage, where does it actually come from? Here's the honest answer: it comes from a handful of sources most teams either don't know about or don't use consistently, and from a process for turning what those sources surface into decisions instead of decoration.
The sources we actually pull from
SparkToro
We'll start with the least surprising one. SparkToro has been a reliable part of our research stack for a while now, and it earns that spot honestly: it's one of the fastest ways to see where an audience already spends its attention — what they read, watch, follow, and listen to — without guessing. It's a good starting point for anyone doing this work. And we do mean starting point — it tells you where an audience is, but it doesn't tell you why they're there, and it won't tell you what to say once you show up.
Reddit (carefully)
This is where research used to get expensive. Understanding what an audience actually talks about — not what they say in a survey, but what they say to each other — meant manually diving into subreddits, reading threads, and pulling out patterns by hand. It worked, but it didn't scale, and it ate hours we'd rather spend acting on the insight than mining for it.
AI changed all that. Now we can point a tool — sometimes something we've built ourselves, sometimes an assistant like Claude — at the relevant corners of Reddit and get it to crawl, cluster, and summarize at a scale a person never could. What used to take a day of manual reading now takes an afternoon.
We'd be doing you a disservice if we didn't add the caveat: Reddit is a messy, unpredictable place, and AI summarization has a real hallucination risk. We don't treat what comes back as gospel. We treat it as a strong signal worth validating — which, as you'll see below, is exactly the position we take with every alternative data source on this list.
Brand sentiment, as seen by AI
This one didn't exist as a category a couple of years ago, and now it might be the most important addition to the stack. As more of your audience's information diet runs through AI tools instead of a search results page, how those models describe, summarize, and position your brand matters as much as how a person would. We use a dedicated tool to track exactly that — what AI systems are saying about a brand's reputation, tone, and standing — because that perception is quietly becoming part of the audience's first impression before they ever land on a website.
Competitive intelligence
We'll be honest: whether this counts as "audience research" is a fair question, and it depends on the angle you take. It's not audience data in the direct sense — it's not telling you what your audience wants. But it is telling you where everyone else is already standing, which is exactly the information you need to identify potential gaps where you can carve out a niche. We use competitive tools to see the landscape a given audience is already being pitched by, so that positioning and messaging can be built to be distinct rather than redundant. It's audience-adjacent, and it earns its place in the process because of what it protects against.
Performance data — the biggest tool in the arsenal
Here's the one that doesn't get called "research" often enough, even though it's the most honest data source on this entire list. Every piece of content and every ad we run generates real behavioral evidence: what people actually clicked, read, scrolled past, converted on, or ignored. That's not a proxy for audience understanding. That is audience understanding, collected at the moment it matters most.
We treat performance data as a continuous research loop rather than a report card. The metrics from what we published and ran last month directly shape what we test next, on both the organic and advertising side. It's the same discipline in both places: lay out a deliberate testing strategy, learn from what the data says, act on it, and repeat. It's the least glamorous data source on this list and the one we lean on hardest.
How we actually use it
Collecting all of this only matters if it changes what we do next, so here's where it actually shows up.
The biggest shift these sources make is where we start. Instead of opening a project with a blank page and a guess, we open it already knowing where an audience spends attention, what they're actually saying in their own words, how they're being perceived, and what has already worked in market. That means the first bets we make aren't really guesses anymore — they're informed decisions, and they're usually good ones.
It also makes your testing more impactful. When you skip the upfront research, what you might call “testing” is often just throwing several ideas at the wall and hoping one sticks. When you start from real audience signal, testing becomes validation and continuous improvement. You're not asking "did anything work?" You're confirming that the smart bet you already had reason to believe in actually performed the way the data suggested it would, and refining through your follow-up tests. That's a faster, cheaper, and considerably less stressful way to run a testing program.
On the ad side specifically, this research is what tells us which platforms to prioritize first and which messaging angles are worth leading with, before a dollar of media spend goes out the door. That's the difference between testing your way to an audience and starting with one.
And it's woven directly into how we approach GEO. Every part of that process — the topics we choose, the way we structure content, the actual prompts and structures we build around — starts with audience motivators and pain points before anything else. That's the same audience-first thinking behind our take on navigating AI search — the tools change, but starting with the actual human on the other end of the query doesn't.
None of these sources is a silver bullet on its own, and that's sort of the point. SparkToro tells you where they are. Reddit tells you how they talk. Sentiment tools tell you how they're perceived. Competitive intelligence tells you what to avoid repeating. And performance data tells you, with total honesty, whether any of it actually worked. Put together, they don't just describe an audience — they make every bet after that one a smarter one.
Want a second set of hands on this?
This might sound like a lot when reading it, but for us it’s a process we have down — and it’s remarkably efficient. It’s the process we run for clients every day: pulling the alternative signal, validating it instead of guessing, and building content, ad, and GEO strategy on top of it from day one. If you'd rather have a team already running this playbook than build the muscle in-house from scratch, let's talk about what that could look like for your brand.
