I started writing about search rankings in 2020 when I realized most advice online was either outdated or built on assumptions rather than testing. What began as documenting my own experiments has turned into something people actually reference when making decisions about their sites.
Behind the Analysis
My approach is simple: test assumptions, track what actually moves rankings, and write about patterns that hold up across different sites and industries. No grand theories, just what the data shows when you watch closely enough.
Why Rankings Matter
Most businesses live or die by visibility. After working with dozens of sites, I noticed the same mistakes repeating across industries. People obsess over keywords while ignoring technical signals. They chase backlinks without understanding content depth. Rankings improve when you fix fundamentals, not when you follow trends.
- Technical audits reveal patterns competitors miss
- Content structure affects crawl efficiency more than most realize
- Small signal improvements compound over time
- Most ranking drops trace back to ignored warnings
What I Actually Do
I run controlled tests on real sites, measuring how specific changes affect rankings over weeks and months. This means tracking dozens of variables simultaneously, isolating what matters from what everyone assumes matters. The results often contradict popular advice, which is exactly why testing beats theory.
- Monthly testing across 15+ active sites
- Tracking 200+ ranking factors simultaneously
- Documenting pattern changes across algorithm updates
- Publishing findings that challenge standard practices
How This Helps You
Every article here comes from actual testing data, not speculation. When I write about title tag optimization, those recommendations come from watching 50+ variations compete for the same queries. When I discuss site speed, those thresholds are based on measured ranking correlations, not arbitrary targets someone invented.
- Clear documentation of what changed and why
- Specific implementation steps with expected timeframes
- Honest assessment of effort versus probable impact
- Regular updates when patterns shift after algorithm changes