Suppression Campaign Analysis Reveals Initial Ranking Position Predicts Search Cleanup Success
An analysis of 714 suppression campaigns by Erase.com found that the starting search position of negative content—not the volume of positive content published—was the strongest predictor of successfully clearing negative results from Page 1 search results. Campaigns beginning with the worst negative at position four or lower succeeded 3.5 times more often than those starting at number one, while content on Page 2 is increasingly insufficient given the visibility of AI-generated summaries. News articles and government pages comprised the majority of difficult-to-remove negative results tracked across the campaigns.
Erase.com's analysis tracked over 2,400 negative search results across nearly 750 campaigns, finding that initial placement proved far more consequential than the volume of competing positive content. When unwanted results appeared at position four or lower, suppression succeeded substantially more often than when top-ranked negatives dominated page one. The research distinguished between removable content—like court records and mugshots, which typically disappeared within weeks—and persistent material like news archives and official government pages that resisted deletion efforts.
The study also flagged a meaningful shift in suppression strategy driven by AI-powered search summaries. Traditional thinking held that pushing negative content to page two was sufficient, since most users stop searching after the first page. However, AI overview features that synthesize information across multiple results mean that page-two placement may no longer provide adequate visibility protection, requiring suppression campaigns to maintain stricter standards for what qualifies as success.
The findings could reshape how organizations approach online reputation management and the realistic timelines they should expect. Individuals and companies seeking to suppress negative information may need to adjust strategies based on starting conditions rather than assuming uniform difficulty levels. The research may also inform broader discussions about search result visibility in an AI-summarization era, potentially affecting how platforms balance content discovery with user experience and raising questions about whether traditional SEO suppression methods remain adequate in evolving search environments.