Bottom-line-minded CEOs and growth-focused heads of marketing are embracing search engine optimization (SEO) like never before. And for good reason.
With the end of Covid now in sight, search engine cost-per-click rates are increasing for most sectors, according to Merkle’s Digital Marketing Report. Retail CPCs were up 3% in Q4 2020, and B2B CPCs were up 13%. As the economy continues to reboot, one can be sure that these costs will continue to rise.
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At the same time, organic search visits are up a staggering amount, except for travel sites. Retail organic search visits were up 40% in Q4, as an example. Users are, it seems, getting tired of so many search ads and preferring to click on search results they know weren’t paid for.
So, with SEM costs rising, and organic search’s importance increasing, no wonder SEO is being adopted more and more by anyone doing business online.
What guides many (most?) SEO efforts today are “best practices.” You’ll find the same general techniques listed in many a blog, press article, and marketing conference speeches:
- Make your content align to your search terms
- Make the page title and URL and meta description of your page match the search terms
- Improve your user experience and page speed
- Link key pages on your web site to others, and fix broken links
- Get other sites to link to yours
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And so on.
These are not “best practices.” They are average practices.
Now, don’t get me wrong. These are good things to do to optimize your site for search engines. But are they best? To me, “best” means best for me (and, by extension, better than my competitors).
What’s made SEO a difficult sell to data-driven and ROI-minded CEOs and heads of marketing, even though they know the importance of ranking high on the search results, are these problems:
- SEO is a long game
- SEO is resource-intensive
- SEO has been wait-and-see — you can’t predict results
Fortunately, for those companies that want to increase their organic traffic, and lower their cost per customer acquisition, AI is now changing SEO in the same ways it’s changed conversion optimization, email copywriting, paid search marketing and other parts of the marketing stack.
The biggest change is in the area of search engine modeling. Using machine learning, one can now build models — accurate simulations — of the search engines and how they weight different factors on your site to determine rankings. These weights are different for each keyword, as well as different for each region, so this is no simple task.
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Previous human efforts to reverse engineer certain search engine algorithms haven’t succeeded. Some have approximated the general results, but these approaches can’t account for the fact that different keywords can be considered radically different in terms of what site and page factors matter.
And the search engines change all the time, both with major updates and the continuous evolution of their underlying neural networks.
AI-based search engine modeling solves these problems. By building models for each search engine/query combination that matters to you, you now can analyze web sites (yours and your competitors) and understand what factors drive rankings and where gaps exist — where you are ahead and where you are lagging, for each factor that the search engines consider.
This makes SEO less resource-intensive, or perhaps more resource-effective, as with this approach you can know exactly where to spend your time and what to leave for another day.
These search engine models also allow you to test the changes you want to make and predict the results within the time it takes to do a site crawl (an hour or two, normally), vs. wait 30-60 days to see. So you can predict in advance for those CEOs and marketing heads that want backup.
SEO is still a long game, but AI is changing that game. Just like Big Blue beats the chess grandmasters, and AlphaGo takes down the top human Go players, so are AI-based search engine models defeating the traditional “best practices” approach to SEO.
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