Machine Learning for SEO Before ChatGPT Was a Thing

The Early Days of ML in SEO

Back in 2020, when I was at Action, ChatGPT didn’t exist yet. But we saw the potential of machine learning to solve a critical SEO challenge: optimizing product descriptions at scale.

The Challenge

Action had thousands of products, each needing unique, SEO-optimized descriptions. Writing these manually was:

  • Time-consuming
  • Inconsistent
  • Difficult to scale across markets
  • Hard to optimize based on performance data

Our Solution

We initiated a machine learning pilot that would:

  1. Analyze high-performing content - Train models on our best-performing product pages
  2. Identify patterns - Understand what made certain descriptions rank better
  3. Generate suggestions - Create ML-powered recommendations for copywriters
  4. Learn and improve - Refine the model based on actual search performance

What We Learned

The Good

  • ML could identify patterns humans missed
  • Scalability improved dramatically
  • Consistency across product categories increased

The Challenges

  • Quality control was critical
  • Human oversight remained essential
  • Training data quality made or broke the results

Lessons for Today’s AI Era

Now that tools like ChatGPT are mainstream, the lessons from our early experiments are more relevant than ever:

  1. AI augments, not replaces - Human expertise remains critical
  2. Data quality matters - Garbage in, garbage out
  3. Test and iterate - Start small, measure everything
  4. Keep humans in the loop - Especially for quality control

The technology has evolved, but the principles remain the same. AI is a powerful tool for SEO, but it works best when combined with human expertise and data-driven decision making.