Audience-sourced data reveal the five creator trust-killers ranked by percentage

Opens with a credibility-building data collection claim (792 responses + AI analysis) to establish the findings as research, not opinion. Delivers ranked list from #5 to #1, saving the highest-percentage finding for last to create a payoff. Closes with a soft engagement prompt that mirrors the original research mechanic, inviting comments to extend the content loop.

Closes with an open question ('I'm curious what other reasons make you stop liking a creator') , invites comment engagement without a follow or link ask, using the audience's own curiosity as the CTA mechanism.

- Open with a specific response count in the first sentence ('I got 792 responses') , a precise number signals real data and filters out opinion-piece skepticism before the list begins. - Use AI-as-methodology framing ('I plugged those into Claude, asked it to organize') to add a process layer that makes crowd-sourced content feel analytical rather than anecdotal. - Structure ranked lists from lowest to highest percentage, never highest to lowest , this forces the viewer to stay through the end to reach the most significant finding. - Embed a direct audience quote mid-list ('when they start promoting brands I know their ass wasn't using') , one raw, unpolished quote breaks the data monotony and signals you are not sanitizing the findings. - Name the monetization trust-killer explicitly and first (28% , 'does nothing but sell') , for prop firm or affiliate creators, this is a direct structural warning: ad density and link volume are tracked by audiences as a credibility signal, not just an annoyance. - Frame the 'authenticity' finding with a specific behavioral description ('replaces curiosity with certainty, talks at me instead of bringing me along') , this gives creators a concrete behavioral checklist, not a vague instruction to 'be real'. - Mirror the original research mechanic in the CTA , asking the same question to your own audience generates a second data set, which becomes the source material for a follow-up video, creating a content loop with no external research cost.