A major piece of the digital marketing puzzle is SEO, and no one knows the landscape better than Moz Senior SEO Scientist Britney Muller. She joined Site Strategics CEO Erin Sparks and Digital Media Director Tom Brodbeck on EDGE of the Web in Episode 309 for a deep dive into machine learning and its impact on SEO. Here’s what we learned:
Britney Muller: Her Background and Experience
Britney’s job at Moz entails researching SEO concepts, creating educational content, helping to educate people about SEO (internally and at conferences around the world), and communicating SEO trends and desires to product teams. Her big-picture goal is to help drive product initiatives through data-driven research and industry knowledge.
Here career path into the field was a bit “strange” as she calls it. After college, she couldn’t find a job. She moved to Breckenridge, Colorado to fulfill her dream of being a “snowboard bum,” but soon grew bored with waiting tables. Through Twitter, she connected with a local realtor who saw Britney had studied journalism and enlisted her help to write local listings. It was this realtor who introduced Britney to SEO, basic HTML and CSS. She was immediately fascinated by being able to easily see how many searches were being done in a month on any given topic. Soon she was developing test sites to compete with major brands, and her results were incredible. In fact, when Burton (snowboarding gear company) first brought the Burton US Open to Vail, Britney’s site about it outranked Burton’s own site, claiming the #1 position in the results.
Rich Staats at Secret Stache Media took note of her developing skill and gave her a job. After a year with Secret Stache, Britney founded Pryde Marketing to deliver strategic medical marketing services to private practices in the Denver area. After five years of that, Britney was approached by Moz and she took the opportunity. She’s had the opportunity to speak at many well-known conferences around the world, including SearchLove, MozCon, Learn Inbound, Retail Global and many others. Coming up is Search Leads in June, MozCon in July and then the unbounce CTAConf 2019 in September.
Machine Learning and SEO
The beauty of machine learning is how it can take huge amounts of input training data and output a model that automates key aspects of digital marketing and SEO. It can be used to write meta-descriptions for pages, optimize page titles, identify major SEO issues, and even to work with SEO topic groupings or categories as JR Oakes mentioned in episode 308. It’s a beautiful way to distill incredible amounts of data into easy-to-consume insights. The application to SEO is huge in terms of a better way to handle all those more mundane SEO tasks. This can then allow SEOs to focus on higher-level strategy.
The key here is understanding when you can and can’t use machine learning for. The distinguishing factor here is the training data that you have to feed into the process for machine learning to take place effectively. You must have huge amounts of clean, high-quality training data for it to work. The old computer adage of “garbage-in-garbage-out” (GIGO) is even more relevant today when it comes to machine learning. And here is where the ethical concerns come into play, because if your input data is biased or slanted, then your output is also going to reflect that bias. You have to be extremely careful about what you’re building and how you’re building it.
The Democratization of Machine Learning: Anyone Can Just Do It!
The vast computational power needed for machine learning has been a limiting factor, although this keeps getting better and better. The biggest bottleneck right now in applying machine learning is the lack of people who understand how it works and have the expertise to build and apply the needed models. But those people also have to bring deep domain knowledge as well in order to vertical adaptations of machine learning.
The kinds of innovation happening with machine learning in the medical field, such as around image recognition for cancer diagnostics is only possible with deep domain expertise. The good news is that companies like Google are making it so much easier these days for anyone to get more involved creating and applying machine learning models. As Britney puts it, “You wouldn’t believe what you can do if you know where to look.” One of her favorite resources is Google Codelabs, which can really walk you through the steps of a machine learning process. This is pure gold for visual learners. There are all kinds of YouTube videos that are also very helpful in this regard. Anyone can now get involved in machine learning. JR Oakes mentioned Jupyter Notebooks, and now Google has the same thing with its Google Colaboratory, and it gives you incredibly powerful GPU processing for free, and very accessible sharing with teams.
People in our field are doing incredible things. Take Paul Schapiro, for example. He was recently at the Traffic Think Tank conference in Philadelphia and talked about how a couple of years ago he built a system to automatically create 301 redirects to relevant pages. It involved a crawler to go through archive.org and find all the site’s pages that were currently 404, then used a machine learning model to detect a relevant current page to which it could be redirected and then automatically access the HT access file to implement the redirect. That is impressive!
Connect with Britney Muller, Senior SEO Scientist at Moz
Twitter: @BritneyMuller (https://twitter.com/britneymuller)
Britney’s articles on Moz: https://moz.com/community/users/514135
Updated Beginner’s Guide to SEO: https://moz.com/beginners-guide-to-seo
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