How to find the LinkedIn voices your buyers actually follow
Work backwards from your buyers, not forwards from follower counts. Seed a list from where your ideal customers already show up (whose posts your best customers comment on, who gets named in your sales calls, who speaks at their events). Then let engagement data correct you: watch who actually engages each voice, check those engagers against your ICP, and keep the voices whose audiences repeatedly contain your buyers. Reach is not relevance: a 20k-follower operator whose comments are full of your ICP beats a 500k-follower celebrity whose audience is everyone.
Last reviewed: July 2026
Why the voice list is the whole game
Signal-based selling on LinkedIn starts from one asset: the set of profiles, pages, and posts you watch. Get the list right and every downstream step (capture, scoring, outreach) inherits its quality; get it wrong and you are efficiently processing noise. About half of decision-makers spend an hour or more per week on expert content, and their feeds are built around a fairly small set of voices. Finding that set for YOUR buyers is the job.
Seed the list from your buyers, not from leaderboards
Start with evidence you already have. Whose posts do your current best customers comment on? (Open their activity and look.) Which names come up in sales calls, communities, and podcasts your buyers cite? Who runs the newsletters and events your ICP attends? Add your competitors' pages and founders, because their engagers are shopping your category. Ten to twenty seeds is plenty to start; you are building a portfolio, not a directory.
The trap to avoid at this stage is follower-count gravity. Reach is not relevance: a practitioner with 20,000 followers whose comment section is full of your buyers is worth more to you than a 500,000-follower generalist whose audience is everyone. With 95% of buyers out-of-market at any moment, you want the rooms where your specific future buyers keep showing up, not the biggest room.
Let the data correct you
The seed list is a hypothesis. The test is co-engagement: watch each voice for a few weeks and check the people engaging against your ideal customer profile. Three questions per voice: what share of its engagers fit your ICP? Do the same fitting people show up across its posts (a real audience, not passing traffic)? And did any become actual conversations? Keep the voices that yield, cut the ones that do not, and mine the winners for adjacent candidates: the interesting people in a good voice's comments are often worth watching directly. Done honestly, this converges fast, because audience overlap is measurable in a way "influence" never is.
Prune on a schedule
Voices go quiet, pivot topics, or drift audiences. A quarterly pass (is it still posting, still pulling your ICP, still yielding?) keeps the portfolio compounding instead of decaying. The list you run a year from now should share maybe half its names with the list you started with.
Slingapult's read: this loop is automated end to end. Starter listeners are seeded from your ICP, every voice's engagers are scored for fit so yield is measured rather than guessed, and Discover recommends new voices by exactly the co-engagement test above: whose audience keeps containing your buyers.