Top Dsmp Youtubers For Fast Campaign Wins

Like finding a signal in static, our AI slices through noisy creator feeds so busy founders and CMOs can pinpoint DSMP YouTubers with clean audience metrics, fraud checks, engagement benchmarks and export-ready reports to launch campaigns faster and smarter.
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Explore Top DSMP YouTubers For High-Impact Collabs

Browse our AI-curated dsmp youtubers - ranked by engagement, audience quality, geo and language fit, fraud-scores and export-ready contact packs to launch faster.
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FAQs

How quickly do DSMP YouTuber profiles and stats refresh?
Profiles and stats refresh with rolling 30-day views; paste a profile and its stats refresh in seconds—so DSMP YouTuber data you load updates near real-time while overall metrics reflect the last 30 days for fair comparisons.
Can I export DSMP YouTuber contact details and analysis reports?
Yes. After signing in you can export YouTuber contact fields and analysis: Profile Analysis (PDF & JSON), Favorites CSV, and Media Plan rows to Google Sheets. Exports require account access; live profile/contact refreshes use the rolling 30-day stats shown in-platform.
Can I compare DSMP YouTubers side-by-side across platforms?
Yes. Our grid standardizes Instagram, YouTube and TikTok metrics - ER%, views, cadence - so you can view DSMP YouTubers side-by-side with creators on other platforms. Rolling 30-day normalization ensures apples-to-apples comparisons across reach, engagement and audience geography.
How does IQFluence detect fraud and verify DSMP YouTuber audiences?
We combine behavioral and audience signals: engagement patterns over 30/90 days, view distributions, follower growth vs. post cadence, audience geography/language consistency, percent-of-followers-likely-to-see, plus semantic content checks. Outliers raise fraud scores; profiles failing consistency or showing abnormal spikes are flagged for review.
Can I build a media plan shortlist and export it to Google Sheets?
Yes. After signing in you can shortlist creators in Discovery, push those rows into the Media Plan Builder and export a live Google Sheet. The sheet contains apples-to-apples L30D rows - followers, ER%, min/avg/max views, cadence, audience splits and outreach fields ready for approvals.
How do you normalize ER, views and cadence for fair cross-platform comparison?
We compute platform-agnostic metrics using the last 30 days: ER% = (likes+comments)/followers averaged with posting cadence context; views normalized by post type and follower band into min/avg/max view bands; cadence expressed as posts/month. Those L30D figures are scaled and bucketed so ER, views and cadence can be compared fairly across Instagram, YouTube and TikTok.