When Site Strategics CEO Erin Sparks spoke with special guest Ginny Marvin, Editor-in-Chief for Third Door Media in episode 314 of the award-winning EDGE of the Web podcast, the topic du jour was automation and machine learning in SEO. Here’s what we learned:
Ginny Marvin: Her Background and Experience
Ginny Marvin is Third Door Media’s Editor-in-Chief, managing day-to-day editorial operations across all of their publications. Ginny writes about paid online marketing topics including paid search, paid social, display and retargeting for Search Engine Land, Marketing Land, and MarTech Today. With more than 15 years of marketing experience, she has held both in-house and agency management positions.
Why Automation and Machine Learning Matter
For digital marketers and SEOs, automation and machine learning matter because they are literally affecting everything that happens in the industry. Machine learning is affecting how organic search results are shown, it’s affecting every single aspect of paid search and digital campaigns from the ways ads are created to the way campaigns are implemented and the way attributions are delivered as well. The reason everyone has to pay attention is that it’s an undercurrent affecting everything.
There are the Opportunity Tabs in Bing Ads and Google’s ad rotation, both of which are being guided by machine learning. Bing’s Opportunity Tabs are interesting. There’s a lot of value in the ability to get surface insights quickly without having to dig into massive Excel sheets. The caveat comes when the platform that you’re paying to serve ads is also the one that’s giving you the recommendations, so some would say take those recommendations with a grain of salt. But it’s also true over recent years that those recommendations from Bing (Microsoft) Advertising and from Google Ads have become more sophisticated and more robust. And automation can free up significant amounts of time for digital marketers from a tactical perspective. They no longer have to spend countless hours collecting and analyzing data, which gives them time to create no more experiments with the data.
It’s also important to differentiate machine learning from artificial intelligence (AI). AI is more of a sweeping concept of computers and software being able to mimic human intelligence. That’s still very far from reality. But machine learning is a here-and-now technology in which computers and software can “learn” to see things in vast quantities of data humans would take forever to see, or would miss entirely. It’s all about building models to analyze data. Building these models has become highly desirable, sought-after skills, which is why companies are all fiercely fighting for talent.
Can We Trust Automation and Machine Learning?
One issue that quickly comes up is to what extent can all this automation and machine learning really be trusted? All these different big platforms such as Bing, Google, Facebook, LinkedIn and so on are all saying trust us, trust our algorithms to guide your digital marketing campaigns. It’s a big leap of faith because there a lack of transparency for end-users. The advertisers are the guinea pigs in all this experimentation the big platforms are doing around automation and machine learning. The good news is that although some of these algorithms were pretty horrible when they first came out, they are getting better and better all the time. But there’s still this whole “black box” nature to machine learning creating a lot of tension between the platforms and the users (digital marketers). It would be nice to have a little more transparency, to pull back the curtain and see how these models work, but all of that is intellectual property (IP) the platforms don’t want to reveal because it’s how they differentiate themselves from their competitors. Advertisers have to just trust the platforms, and it’s a hard pill to swallow – especially if sometimes the results are subpar.
Because the intent of the machine learning is primarily about pattern recognition through the models, would it be a huge stretch for the platforms to open the doors to the advertisers to be able to help create the modeling? There could be sandboxes where they could actually contribute to an understanding of their marketing target, not just being beholden to how the platforms are interpreting data and modeling at a particular date. Surely finding some ways for the advertisers to be more involved would make a lot of people more comfortable. Of course, it’s not that simple. The sandbox idea is a good one, but most advertisers and digital marketers simply don’t have the skills that would be needed to get anything out of it.
And yet some way for the marketers to be more involved would be good, such as contributing contextual mapping. After all, the marketers should know more about their audiences they’re trying to target. There should be a mechanism for incorporating their insights into these platforms. The platforms are saying that’s what they’re giving when they’re showing you projections if you increase your budget or if you increase your bids and those projections are based on historical data from auction data. Then again, is Google really ever going to recommend decreasing your budget? Some do say the new recommendations are starting to be more nuanced, which is a positive sign. You might even see some recommendations about lowering your budget if it determines you can get the same results for less money.
Connect with Ginny Marvin, Editor-in-Chief for Search Engine Land, Marketing Land, and MarTech Today (note that all three offer daily newsletters to stay up-to-date)
Twitter: @GinnyMarvin (https://twitter.com/GinnyMarvin)
Search Engine Land website: https://searchengineland.com
SEL Twitter: @sengineland (https://twitter.com/sengineland)
Marketing Land website: https://marketingland.com
ML Twitter: @Marketingland (https://twitter.com/marketingland)
MarTech Today website: https://martechtoday.com
MTT Twitter: @martech_today (https://twitter.com/martech_today)
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