Search engine optimization, or SEO, has changed significantly in the past few years. Once upon a time, we did simple tricks to please search engines. We highlighted and bolded some text on page, we spammed Wikipedia, we sent out a few emails, and watched the search volume flood in.
Those days are long gone. Thanks to the power of machine learning and artificial intelligence, the only way to build a sustainable, long-term SEO strategy is to create content people want to share. Machine learning and deep learning render tactical, surface-level tricks meaningless today; even the concept of “search engine algorithm updates” is largely meaningless. Deep learning technology improves continuously, not in batches like software of old.
To combat these new trends, we need to employ our own machine learning technology to find what works and perform modern SEO at scale. In this new series, we’ll explore the 10-step machine learning process to build better SEO from top to bottom:
The AI-Powered SEO Process
- Step 1: Inventory – understand what we have and how it performs.
- Step 2: Enrich – building off what we have, see what else is out there
- Step 3: Validate and Select – check that we’re working towards high-volume topics, make choices from the topic buffet of what to pursue
- Step 4: Extract – determine what else makes the top performing pages work so well from a content and topic perspective
- Step 5: Predict – understand the seasonality of our chosen topic
- Step 6: Construct – improve or create new content for our chosen topic
- Step 7: Associate – explore Key Opinion Leaders (KOL) who are influential in our topic’s space
- Step 8: Distill & Reach Out – understand what sites our KOLs share and their search authority, pitch our new content to our KOLs and their media sites of choice
- Step 9: Wrap Up/Conclusion – review where we’ve been
We’ll explore each of these steps in this series with examples. Stay tuned!
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