SEO Split-Testing: How to A/B Test Changes for Google – Moz

At our recent SearchLove conferences, I’ve been talking about things we need to do differently as marketers amidst the big trends that are reshaping search. My colleague Tom Anthony, who heads up the R&D team at Distilled, spoke about 5 emerging trends: Implicit signals Compound queries Keywords vs intents Web search to data search Personal assistants All of these trends are powered by Google’s increasing reliance on machine learning and artificial intelligence, and mean that ranking factors are harder to understand, less predictable, and less uniform across keywords. It’s becoming such a complex system, we often can’t really know how a change will affect our own site until we roll it out. The lack of transparency and lack of confidence in results has two major impacts on marketers: It damages our ability to make business cases to justify targeted projects or initiatives (or even just to influence the order in which a technical backlog is addressed) It raises the ugly possibility of seemingly good ideas having unforeseen negative impacts You might have seen the recent news about RankBrain, Google’s name for the application of some of this machine learning technology. Before that announcement, I presented this deck which highlighted four strategies designed to succeed in this fast-changing world: Desktop is the poor relation to mobile Understand app search Optimize for what would happen if you ranked Test to figure out what Google wants from your site It’s this last point that I want to address in detail today — by looking at the benefits of testing, the structure of a test, and some of the methodology for assessing winning tests.

Source: SEO Split-Testing: How to A/B Test Changes for Google – Moz


About Bob Warfield

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