Some New SEO Terms
SEO is an industry that’s well accustomed to rapid and interesting changes in both technologies and opportunities, and keeping up with the changing trends can be a challenge. To help you stay on top of things, we’ve put together a list of six new SEO-related terms that are good to know.
For a more extensive list of SEO terminology, please see our list of essential SEO terms and phrases.
People have begun using more conversational language in searches over the last few years according to Google. Conversational Search refers to the more intuitive ways people are interacting with search engines and how search engines are changing in response.
For example, where a user three years ago might have Googled “mortgage calculator” or “mortgage rates”, today they are more likely to search for “can I get approved for a mortgage?” People are speaking more naturally to their devices, and in response search engines have been developing technologies to generate more human-like responses along with machine learning algorithms that are better at processing natural language.
With respect to search strategy, conversational search is one more factor shifting the focus away from overly keyword-focused content and more towards content that supplies answers and provides value while tailoring it to the more intuitive and nuanced ways people are using technology. Consider bolstering your keyword strategy with additional phrases that you could imagine a user speaking into a mobile phone to find an answer related to your offerings, and incorporate that language into your content.
Influencer marketing is a type of marketing centered around the opinions and recommendations of trusted and well-known people in a given market. Influencers are not just defined by the size of their YouTube subscriber count or Twitter following, but by a combination of their popularity and reach within a specific market, their perceived expertise and trustworthiness, and their effectiveness at communicating.
According to the Forbes Communications Council, the simplest way to work with influencers is to pay them, similar to the time-honored tradition of using celebrity endorsements in advertising campaigns. Potential advantages to working with influencers could be much lower costs, higher perceived authenticity, and much larger and more targeted reach, especially online, than traditional celebrities.
In terms of SEO, inbound links from influencers can have significant authority, as well as a high potential for referral traffic. Affiliations with influencers—especially those who develop a reputation for being honest and genuine—can also have positive effects on perceptions of a website’s own authority.
Machine Learning (ML)
Machine Learning (ML) is a branch of Artificial Intelligence that refers to the idea of a program learning from data without the need for human intervention. Machine Learning algorithms use a variety of techniques, but ultimately the goal is to automatically identify patterns from data inputs and use them to make decisions.
Google has included machine learning in its search algorithms for many years, most notably incorporating RankBrain in 2015. RankBrain uses ML to interpret factors about the user and query to get a clearer picture of the user’s search intent and deliver more accurate results. Google also recently announced the introduction of BERT into its search products. BERT is an ML-based technique that improves and simplifies the process of “pre-training” algorithms to do a better job of understanding and processing natural language.
Machine Learning & SEO
As ML improves, semantics-based search engines are more likely to analyze queries based on an array of possible factors, like grammar, sentence structure, user location, user tracking data, keywords, and more. ML has and will continue to make search engines increasingly capable of taking into account a dizzying array of data points and improved processing capabilities able to deliver increasingly accurate results.
The rising role of machine learning in search underscores how important it is to leave no stone unturned or detail ignored in your SEO efforts. SEO strategies must deliver quality, useful content while taking into account the myriad of other variables machine learning algorithms may be evaluating when processing search queries and determining which websites to rank above others.
Mobile SEO is a mobile-first approach to SEO that takes into account the unique traits associated with mobile search.
Mobile search refers to search engine queries performed on a phone, tablet, smartwatch, or other mobile device. Mobile search differs from desktop search in several ways, and it’s important to take these differences into account when producing content for mobile.
For starters, mobile search queries are more likely than desktop queries to be spoken, and are more likely to include phrases like “should I” or “can I”. Mobile search is also taking a growing share of total searches, with mobile search making up 63% of Google searches in 2019. Lastly, mobile search is far more likely to be location-based.
Mobile SEO strategies in 2020 should take into account the differences of how users are conducting searches on different devices, and create content specifically tailored to the nuances of mobile search.
Search intent is the main purpose of a search and ultimate goal of a person using a search engine. Given the broad variety of ways and reasons people use search engines, it’s critical from an SEO and marketing perspective to understand search intent when creating web content.
For example, take a person who searches for “real estate agent” compared to someone who searches for “do I need a real estate agent”. In previous years, it may have been sufficient to focus on the common keywords and produce content designed to rank for both. While there will be overlap in content targeting both searches, in 2020 understanding and writing to the differences in each user’s search intent will lead you to produce completely different content.
Web content should be created with as clear an idea as possible of the likely intent of the people who will find it. It’s useful to think of your content as a product, and who would develop a product without a clear idea of the target consumer who will (or won’t) buy it? Incorporating a similar approach to your content strategy and creating highly specific versions to provide a selection of more targeted content will be essential moving foreward.
Voice search refers to voice-activated technology that enables searches by speaking, rather than typing. Various platforms have seen wide adoption over the last five years, and examples include Google Voice Search, Amazon’s Echo, Apple’s Siri and more.
Contributing greatly to the trend towards more conversational language in search, especially on mobile devices, voice-based searches are expected to account for over 50% of all searches by the end of 2020.
In addition to understanding the platforms people are using for voice search, understanding how to create content that is optimized for voice search is critical.
For example, one technique is to write and code multiple variations (but not duplicates) of related content, with simplified callouts or even entirely separate versions that would be more helpful to a user speaking a question into their phone. More in-depth versions can also be created, which focus on situations where a user might instead type queries into a search engine. Writing in a more conversational tone, placing less emphasis on traditional keyword strategies, and hyper-targeting answers within context (likely intent, type of device, location, etc.) are all keys to optimizing for voice search.
Structured data also plays an important role in optimizing for voice search, and it’s a good idea to become familiar with the speakable (beta) schema.org property, which indicates to audio playback text-to-speech technologies that your content is optimized for those platforms.