Drive awareness and sales with proven lesbian youtubers couples who connect authentically - but finding verified creators wastes time and risks fraud. We filtered by semantic content, audience quality, geo and engagement metrics so you can pick fast - start a 7-day free trial to analyze more.
We analyzed content, audiences and engagement to surface a compact lesbian youtubers couples channel roster you can review instantly - each creator ranked by reach, ER, fraud score and audience fit.
Metrics refresh on a rolling last-30-days window - profile stats and engagement scores reflect recent activity and update in seconds when you paste a profile, giving L30D averages for ER, views and cadence for fair comparisons.
Yes. After you sign in exports become available - download Influencer Profile Analysis (PDF/JSON), Favorites CSV, Media Plan Builder as a live Google Sheet, and Campaign Monitoring CSV/Excel. API endpoints also provide profile, audience and performance data for integrations.
We standardize metrics across platforms by using last-30-day aggregates and cadence context - ER% is likes+comments relative to followers averaged over L30D, views are min/avg/max from recent-post distributions, and posting cadence is applied so ER and view bands are normalized for fair apples-to-apples comparisons.
Yes. Build a live, shareable shortlisting sheet via the Media Plan Builder after sign-in - creators appear as apples-to-apples rows with L30D subscribers, ER%, min/avg/max views, posts/mo, audience breakdowns and outreach fields. Use it for approvals before you launch campaigns.
Audience demographics refresh on the same rolling last-30-days cadence. When a creator posts, profile stats and the L30D audience snapshot update in seconds after you paste or query the profile, so demographic shifts from recent activity are reflected within that rolling window.
We combine behavioral signals and audience analysis over the rolling L30D window: sudden subscriber spikes, inconsistent posting-to-reach ratios, low view-to-subscriber rates, abnormal engagement distributions, and audience geography/language mismatches. Those signals form a fraud score so you can reject likely fake reach.