
If you run campaigns often, manually hunting YouTube influencers quickly turns into a part-time job: endless tabs, spreadsheets, and second-guessing metrics. A structured way to search keeps your brand safe and your budget focused on the right creators, not just the loudest ones. Delegating the legwork to an AI computer agent means it can scan channels, pull stats, and pre-filter fits in the background, so you stay focused on strategy, offers, and creative angles instead of copy‑pasting URLs all day.
When you’re just starting with YouTube influencer marketing, it’s natural to do everything yourself. You type in a few keywords, click through videos, open creator profiles, and drop promising channels into a spreadsheet. It works, until the campaign scales.
Manual search gives you intuition, but it doesn’t scale. Once you need 50, 200, or 1,000 qualified influencers, the workflow becomes brittle and exhausting.
Pros: Free, great for building a feel for your niche.
Cons: Slow, subjective, hard to repeat or hand off to a teammate.
Pros: Helps you spot proven promo partners.
Cons: Still highly manual; easy to miss mid‑tier creators.
Pros: Fast way to get an initial batch.
Cons: Lists can be outdated; data quality varies.
Now imagine you describe your ideal creator once: niche, country, language, view ranges, minimum engagement. An AI computer agent then opens YouTube in a browser, runs searches, clicks into channels, reads public stats, and logs everything into your Google Sheet.
Instead of you hopping between tabs, the agent:
Pros of AI‑driven search:
Cons:
Use manual exploration to define what a “great” YouTube partner looks like for your brand. Then encode that into an AI computer agent workflow. Let the agent mine the internet; you keep the final yes/no call and the human conversations.
Start with alignment, then numbers. First, check if their content, tone, and audience match your brand—watch several videos, skim comments, and note who they attract. Next, look at performance: average views vs. subscriber count, consistency of uploads, and engagement rate (likes, comments, shares). Finally, confirm authenticity by spotting sudden spikes, low-quality comments, or obviously bought subscribers before adding them to your shortlist.
Pick 3–5 core keywords your buyers would search on YouTube. For each, collect the top 20–30 relevant videos. Open the channels behind those videos and log their subscriber count, average views, and contact info in a sheet. Then expand via YouTube’s suggested channels and “related videos” for those creators. Once that skeleton list exists, hand it to an AI agent like Simular to automate deeper searches and fill in missing data fields at scale.
Subscribers are vanity if they don’t watch. Focus on average views per video, view velocity in the first 48 hours, and ratio of comments to views. Read the comments to see if the audience is your target persona and whether they trust the creator’s recommendations. Check past sponsored content: did those videos get similar or better performance than organic ones? Use an AI computer agent to log these metrics in one place so you can compare creators side by side.
Create a master spreadsheet with clear columns: channel URL, niche, country, language, subs, avg views, engagement estimate, email/handle, past sponsors, and status (new, contacted, active, paused). Manually define this structure once. Then let an AI agent like Simular Pro populate and update it by scraping YouTube and your inbox or CRM, so your team always works from a single, living source of truth.
Treat your list as a living asset, not a one-off project. For active campaigns, refresh every 2–4 weeks: add rising creators, remove inactive channels, and update performance metrics. You don’t need to do this by hand; schedule an AI computer agent to re-run your YouTube searches, revisit existing channels, and overwrite old stats in your sheet. That way, your outreach is always driven by fresh data, not last quarter’s snapshot.