Playbooks
Find Micro-KOLs Whose Audience Actually Overlaps With Yours
Stop chasing the same five big names
Every project in your category is already in the DMs of the same handful of large accounts. Those creators are expensive, oversubscribed, and their audiences have seen the same kind of post a dozen times this month. The reach looks impressive on paper, but a lot of it doesn't overlap with the people you actually want to reach.
There's a better starting point. Instead of sorting creators by follower count, start from the audience you care about and work backward to the smaller creators whose followers genuinely match it. That's the whole idea behind learning to find micro-KOLs in crypto by overlap rather than by raw size — and it's a workflow you can run end to end inside Dopamyn.
Why follower count is the wrong filter
Follower count tells you how big a creator is. It tells you almost nothing about who follows them. As an illustration: a 12,000-follower creator whose audience is packed with the exact wallets, traders, or builders you're targeting can be worth far more to a campaign than a 500,000-follower generalist whose audience barely intersects with yours.
The question that actually matters is: whose smaller follower base overlaps most with the audience I care about? Micro-influencers are smaller in absolute size, but the right ones have followers that disproportionately include members of your target audience. Surfacing those creators is an overlap problem, not a ranking-by-size problem.
Step 1: Start from a watchlist
Everything begins with a defined audience. The simplest way to create one is a watchlist — a set of X accounts you care about. That might be your competitors' accounts, a cluster of projects in your niche, or a curated group of accounts whose followers represent your ideal customer.
From there, Common Followers answers a foundational question: who follows the accounts in my watchlist? It exposes the shared follower graph of that set of accounts, filtered and ranked by profile data like tags, follower count, engagement score, and smart-follower counts. A brand studying its competitors' followers can use this to identify high-quality accounts to target — and to define the audience that the rest of this workflow runs against.
If you've already built a reusable, named list, you can use that instead. Audiences are the targeting primitive across the platform: static lists you curate by hand, or dynamic lists driven by a filter config that re-evaluates membership automatically. Either kind works as the input for the next step.
Step 2: Run overlap analysis to surface micro-KOLs
This is the core move. Once you have an audience — a watchlist, a saved audience, or a filtered slice of the directory — you open the Find micro-KOLs dialog and run the overlap query against it.
What comes back is a ranked table of micro-influencer candidates: accounts that are smaller in absolute size but whose followers disproportionately include members of your target audience. Each row carries per-row overlap and match-quality metrics, so you're not guessing about fit. When a candidate's overlap coverage is weak, the row is flagged with a match-quality indicator rather than presented as a confident match — so you can quickly separate the strong fits from the marginal ones.
A few practical notes from how the surface works:
It takes three kinds of input. You can run overlap from a watchlist, from a saved audience (static or dynamic), or from a directory filter.
A directory source needs filters applied. If you open Find micro-KOLs against the directory with no filters set, you'll see a "needs filters" prompt instead of a result — an unfiltered query would pull in too large an input set to be useful.
Suspended accounts are excluded. Suspended accounts are filtered out before the analysis runs, so the candidate list isn't padded with dead accounts.
The output is the opposite of the big-names list everyone else is working from: smaller creators, ranked by how well their audience matches yours.
Step 3: Launch a campaign to the creators you found
Finding the right micro-KOLs is only useful if acting on them is fast. From the results table, you select the candidates you want and use the Launch campaign flow — which hands your selection straight into the standard campaign and DM publishing pipeline. There's no exporting a list, re-importing it somewhere else, or rebuilding your targeting by hand. The creators you surfaced become the audience for your outreach in one continuous flow.
Because audiences are reusable, you can also save a strong set of candidates as a named list and fold it into future campaigns, rather than rediscovering it every time.
Put it together
The workflow is three steps:
Define the audience — build a watchlist (or use Common Followers / a saved audience) that represents who you actually want to reach.
Run overlap — open Find micro-KOLs, run the analysis, and read the ranked candidates with their match-quality signals.
Launch — select the creators whose audience overlaps yours and push them straight into a campaign.
This is how you stop competing for the same oversubscribed accounts and start reaching smaller creators whose followers are the people you're after. On Dopamyn, humans approve and agents execute, so brands run automated, performance-based creator campaigns where creators earn USDC on-chain for real, verified engagement.
Ready to run it on your own audience? Explore Dopamyn for projects, or read more on how to find crypto KOLs and how to run a Web3 KOL campaign.