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How The Unique Shall Inherit the Web with Machine-Readable Entity IDs

Artificial intelligence and machine learning are producing radical changes in search, as discussed in our previous blog about search marketing automation. While AI has opened new possibilities to marketers with its capacity for instant optimization, it does have a few shortcomings. Notably, AI is still limited in its ability to understand language syntax and cultural perceptions.

For example, you’ve likely experienced some communication hiccups while talking to virtual assistants like Siri and Alexa. A machine can process millions of data signals within seconds, but it can’t quite think and interpret like a human being. This means for ambiguous terms or phrases, AI-powered search algorithms may struggle to decipher the user’s true intention. How do search engines deliver the most relevant results when conflicting meanings are an issue?

Removing Ambiguity from Search

It’s happened to all of us: You enter a search term or query into Google only to receive a stream of completely unrelated results. Chances are this happened because you were looking for something that could easily be mistaken for something else. Perhaps you’re a fan of British film director Steve McQueen and want information about his next project. You search for his name and are instead, are greeted with links pertaining to legendary American actor and “King of Cool” Steve McQueen.

That moment you search for Steve McQueen and get the actor, not the director.

Shared names – both among humans and brands – are hardly unusual, so this kind of misinterpretation happens frequently. How does a search engine determine what you’re seeking without becoming an actual mind reader? Enter machine-readable entity IDs.

Machine-Readable Entity IDs (or MREIDs) are alphanumeric strings that act like barcodes for the web. They’re uniquely assigned to distinct entities, making it easier for search engines to recognize different people, places, institutions, companies, and more. In keeping with our earlier example, Steve McQueen the actor and Steve McQueen the director each have their own MREID. When Google processes one code over the other, it’ll know which person is being referenced.

Finding your own MREID and implementing it in your site’s structured data is a must-do for retailers, especially if your brand name is prone to confusion with other terms. This will allow search engines to identify you properly and include you in relevant search results.

How Do I Get My MREID?

There are several ways to locate your brand’s MREID, through avenues like Google Trends, Google Knowledge Panels, and Wikidata. We highly recommend reading this article by Mike Arnesen of UpBuild for an explanation of each method. We’ll break down the most straightforward route by explaining how to get your MREID from Google Trends.

Finding Your MREID on Google Trends

  1. Begin by searching for your business name on Google Trends.
  2. Select the result that accurately describes your business entity. For instance, we searched for our company name “NetElixir” and chose “Marketing agency in South Brunswick Township, New Jersey.”
  3. On the following page, take a close look at the URL. You’ll see a short alphanumeric code near the end of the address. This is your MREID!
  4. Note that “%2F” is read as a forward slash, so the MREID for NetElixir reads as “/g/1tl0y8sm”. (See below.)
NetElixir’s MREID can be found in the URL of our page on Google Trends.

Establishing Patterns of Association

Once you have your MREID, add it to your current JSON-LD script tag as an attribute-value pair. Whenever Googlebot encounters your brand name on your site, it will now see it as the alphanumeric MREID code. It doesn’t need to understand precise cultural nuance or language syntax to know who you are – just the string of letters and numbers. (For assistance putting the code into your structured data, our resident SEO experts would be happy to help. Just contact us for more info.)

Why is finding and implementing your MREID an advantage in the current environment? You’ve probably heard of RankBrain and similar Google updates that use machine learning algorithms to filter search results based on user intent. In order to deliver the most relevant results, the algorithm must be able to distinguish between unique entities and understand the relationships between them.

MREIDs unlock these patterns of association for search engines, allowing them to display accurate results. When Google knows you’re looking for information about Steve McQueen the actor, it will provide results like Bullitt and The Great Escape when you search for “Steve McQueen’s best movies.”

How does a search engine determine your intent? Your individual search history provides a big clue. If a person searches for the film 12 Years a Slave followed by “Steve McQueen,” (see below) the search engine can infer that he or she is most likely looking for the director.

Steve McQueen, director of 12 Years a Slave.

Make MREIDs Work for Your Business

As search results grow more personalized and tailored to specific user intent, retailers want to ensure they’re as visible as possible on relevant SERPs. If you’re a vegan restaurant, you don’t want to appear in results for “best local BBQ joints.” Associating your site content with your unique MREID helps search engines classify you correctly so you can be shown to users who are actually interested in what you have to offer. This directs more qualified traffic your way, which will hopefully result in more customers and revenue.

What else do we see on the horizon of search marketing? Catch up on our blog series with previous post on contextual search and the rise of automation. Then, stay tuned for our next entry, where we’ll discuss the myth of the “ideal” customer.