How to Find YouTube Influencers by Niche with Engagement Data in 2026

Influencer marketing fails at the shortlist stage. You can find a hundred channels in a niche, but which ones are worth pitching? Subscriber count lies — a 500K-subscriber channel pulling 8K views per video is a worse buy than a 40K-subscriber creator landing 30K. The signal you need is engagement relative to size, plus a way to reach the creator, and YouTube exposes none of that in a usable form. This guide covers how to discover creators by niche in 2026, how to compute real engagement, and how to turn a keyword into a scored, contactable shortlist.

What’s worth extracting

This actor’s whole job is producing a decision-ready creator record, so it combines discovery, performance metrics and contact data per creator:

• Identity — channel name, @handle, channel URL.
• Size — subscriber count.
• Performance — recent average views, sampled across the creator’s latest uploads.
• The key metric: engagement rate — computed as average views ÷ subscribers, the single number that separates a real audience from a vanity count.
• Contact — business email and social profile links parsed from the channel description.
• Context — latest upload timing (is the creator active?), topic keywords/tags, and niche relevance.

That engagement rate is the differentiator versus a plain channel scraper. Anyone can list channels; this ranks them by whether their subscribers actually watch.

How the data is exposed (search + sampling, no API key)

The actor works two ways, both login-free and key-free:

1. Keyword discovery — it runs a YouTube channel search for your niche term and collects the matching creators, the same way the website’s search does.
2. List enrichment — or you hand it a list of channels directly and it skips discovery, going straight to enrichment.

Then for each creator it:

• Reads channel metadata (subscribers, country, description) from YouTube’s internal data layer.
• Samples recent videos to get a representative average-views figure — a snapshot of current performance, not a lifetime average skewed by one old viral hit.
• Computes engagement rate from that sample against the subscriber count.
• Parses the description for business email and social links, so the shortlist is immediately contactable.
• Refreshes its access key per run with retry logic, so large discovery sweeps complete reliably.

The honest framing: engagement rate here is a recent-sample estimate, not an audited analytics figure. It’s directionally accurate for shortlisting — exactly what you need to decide who’s worth a closer look — but it’s a sampling, not the creator’s private dashboard.

▶ Run the YouTube Influencer Scraper — search a niche by keyword and get creators ranked by engagement rate, with subscriber count, average views, business email and social links. No login, no API key.

Schema design for downstream use

For a campaign workflow, one scored, contactable row per creator:

{
  "channel_name": "Niche Creator",
  "handle": "@nichecreator",
  "url": "https://www.youtube.com/@nichecreator",
  "subscribers": 41200,
  "avg_views_recent": 29800,
  "engagement_rate_pct": 72.3,
  "latest_upload": "5 days ago",
  "topics": ["home barista", "espresso", "coffee gear"],
  "email": "collabs@nichecreator.com",
  "socials": {
    "instagram": "https://instagram.com/nichecreator"
  },
  "query": "espresso review",
  "scraped_at": "2026-05-27T13:00:00Z"
}

Schema choices worth making:

• Sort and filter on engagement_rate_pct, not subscribers. This is the whole point — it surfaces the micro-creators who outperform their size.
• Keep avg_views_recent alongside the rate. A 200% engagement rate on a 2K-subscriber channel reads differently than the same rate on a 200K channel; you want both numbers.
• Use latest_upload to filter dead channels. A great historical rate is worthless if the creator hasn’t posted in eight months.
• Tag rows with the query. When you sweep multiple niches, this keeps each creator attributable to the search that found them.

Typical use cases

What this drives:

• Influencer outreach lists — build contactable shortlists scored by engagement, ready to hand to the email finder or your CRM.
• Micro-influencer campaigns — target a specific subscriber tier to find affordable, high-engagement creators who convert better than mega-channels.
• Sponsorship vetting — compare engagement vs. subscriber count before booking a placement, so you don’t overpay for dead reach.
• Agency prospecting — discover creators to represent or pitch services to.
• Competitive creator mapping — see who’s active in a category and how their performance stacks up.

The common thread: this is the qualification layer of influencer marketing. It answers “who’s worth my time?” before you spend it.

How this differs from the other YouTube actors

Worth being precise, because the suite overlaps:

• Channel scraper dumps one channel’s full video catalog. This discovers many channels and scores them.
• Search scraper returns videos matching a keyword. This returns creators matching a niche, with engagement and contact data attached.
• Email finder is pure contact extraction across a known channel list. This does discovery + engagement scoring and light contact parsing — use the email finder when you need deeper, two-layer email extraction on a finalized list.

In a real workflow: discover and score here, then enrich the finalists through the email finder, then send.

Cost math for the managed approach

Pricing is pay-per-event — a small per-run start fee with no per-result charge. Because the actor samples recent videos per creator, compute scales with shortlist size, but a discovery sweep returning a few hundred scored creators lands in single-digit dollars. Against the alternative — a media buyer manually opening channels, eyeballing view counts and guessing at engagement — the ROI is immediate: one well-chosen micro-creator placement pays for years of runs.

Versus building it yourself, you skip:

• The discovery plumbing — channel search pagination and dedup.
• The sampling logic — fetching recent videos and computing a stable average.
• The engagement math and contact parsing — done per creator, automatically.

Common pitfalls

Before you trust the shortlist:

• Engagement rate is a recent sample, not gospel. Use it to rank and filter, then sanity-check your top picks manually.
• Watch out for view-bought channels. A suspiciously high subscriber count with thin engagement can signal inflated numbers — the rate helps flag these.
• Niche keywords matter. Broad terms (“fitness”) return huge, generic channels; specific terms (“kettlebell mobility”) surface the targetable micro-creators.
• Not every creator lists an email. When email is empty, fall back to socials or run the finalist list through the dedicated email finder.
• Recency cutoffs are yours to set. Decide what “active” means for your campaign and filter on latest_upload accordingly.

Wrapping up

If you’re booking one big placement, manual vetting is survivable. If you’re running real influencer campaigns — building shortlists across niches, ranked by engagement and ready to contact — let a maintained actor do the discovery, sampling and scoring, and hand you a list you can actually act on.

▶ Open the YouTube Influencer Scraper on Apify — keyword discovery, engagement scoring and contact extraction, exported to JSON, CSV or Excel. Start with Apify’s free monthly credit.