Revolution in SEO Strategy: Navigating the Confluence of Machine Learning and User Data

Revolution in SEO Strategy: Navigating the Confluence of Machine Learning and User Data

Revolution in SEO Strategy: Navigating the Confluence of Machine Learning and User Data

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Pretty much everyone knows we’re using clicks in rankings. That’s the debate: ‘Why are you trying to obscure this issue if everyone knows?’” This compelling statement comes straight from Eric Lehman, a key figure in the ever-evolving landscape of Search Engine Optimization (SEO). Lehman, a former Google software engineer, recently made the disruptive decision to depart from the company, leaving a bit of a stir and speculation in his wake. His bold statement, and the context enveloping it, has since become a catalyst for much debate. It’s situated in the midst of the ongoing U.S. vs. Google antitrust trial, propelling this topic into the spotlight and creating anticipation amongst its observers.

Embarking on a journey into the new sphere of SEO, user data, and machine learning, Lehman’s insights guide us towards greener marketing pastures. He stresses the changing dynamics between user data and newer systems like Google’s BERT and MUM, arguing that these complex algorithms require less user data. As Lehman puts it, “In one direction, it’s better to have more user data, but new technology and later systems can use less user data. It’s changing pretty fast,” He further argued in a 2018 email, as reported by Fortune, that raw text could soon replace large amounts of user feedback, a statement that implies a paradigm shift.

An intriguing aspect of this discussion is the potential gray area between “user data” and “training data.” This topic surfaces when the inner workings of BERT take center stage, as revealed in the report titled “Big Tech on Trial.” The Department of Justice argues that Google’s prominent use of BERT may unfairly leverage its access to an avalanche of user data. However, Lehman counters with the point that Google’s true advantage derives from having invented BERT, rather than the surplus of user data at its fingertips.

The DOJ furnished an exhibit, ominously labeled “Bullet points for presentation to Sundar,” which ignited speculation that the phrase “training data” was being obfuscated as “user data,” setting a contentious stage for the further drama of this antitrust trial.

Perhaps one of the most impactful revelations lies in Lehman’s slide on “Sensitive Topics,” which instructed Googlers to “not discuss the use of clicks in search…” This controversial directive pushes us to examine Google’s stance on the use of user data in ranking results. According to the “Big Tech on Trial” report, Google’s reluctance to confirm the use of user data in ranking might be framed as a preventative measure against manipulative SEO practices.

As we pull back from this tumultuous landscape, the question arises: With these ongoing adjustments, what does the future of SEO look like? Ever-changing machine learning systems such as BERT and MUM are beginning to make vast strides, and the value of user data is in a state of flux. Businesses need to adapt swiftly to these seismic shifts. Will they manage to swim in these turbulent waters or risk being left behind? As we step into this new age of SEO, fueled by machine learning and user data, it’s an exciting – albeit challenging – future that awaits us.

 
 
 
 
 
 
 
Casey Jones Avatar
Casey Jones
7 months ago

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