5 Changes to SERPs via Google’s Knowledge Graph in 2014

Throughout 2014, there have been several changes in the world of SEO — from the always looming algorithm updates that massively affect the way search engine results rank and display, to adjustments to the more dynamic and intelligent features of search engine results pages (SERPs) that we have all come to know and appreciate since the advent of the Hummingbird algorithm, there have been some significant updates in 2014. Penguin 3.0. Panda 4.1. Pigeon. Yet, there have been some significant updates to the way in which search results display that welcome discussion, and can yield insight into the way in which Google and the behavior of its users will continue to evolve in the coming year.

Loco Local Search Results

It’s no secret that Pigeon was a pretty significant–and at times, even unexpected–change in the way in which businesses were forced to compete for local search and visibility on SERPs. What we saw from Pigeon was a concerted effort from Google to really qualify what constitutes a need for a local result. In Moz’s piece detailing advice from experts about Pigeon recovery, it becomes clear that Pigeon did several things all at once, much of which leaned heavily on the implicit data passed to Google during the search query, like location information and the device on which you are searching. The location information is especially important, as we saw Google restrict the geographic radius of local-pack search results, and also use implicit data to deliver local listings that are physically closer to the searcher. For those using explicit geographic information as part of their search strategy, this was a bit of a rude awakening as Google also restricted the types of queries and associated vocabulary that generated local listings, and presumably leaned heavily on user data to adjust the types of industries that see local listings rather than strictly organic search results. For example, searching for a restaurant may yield a local pack, while searching for available local real estate may not.

Pigeon was far from a picnic, though generally didn’t disrupt things too much. Search Engine Land even suggested that the best thing to do about Pigeon was generally to stay the course. However, this ushered in a larger conversation about the way in which Google was looking to change local results as a whole. Leveraging the Knowledge Graph, Google also looked at shifting ways to display local results in SERPs. The carousel is now being replaced by answer boxes that feature the same information, providing a more intuitive user experience and illuminating Google’s shifting point of view on the carousel’s UX.

 

A third major shift in local results was the formal introduction of the now re-named Google My Business, which was no small feat; Google Local, and the host of other names for local business results are now populated by Google My Business, entities that tie a businesses local platform’s local, social, brand sentiment, and content all together via Maps and Google+. This was a significant launch for Google, and should provide some significant advantages to ensuring we optimize local search.

Farewell to Freebase

Google’s knowledge graph continues to help illustrate some of the most sought after information for searchers directly on the SERP itself. While earlier iterations of its Knowledge Graph caused dips in organic traffic, robbing traffic to individual sites by quickly answering the user’s question from the search result, new iterations of the Knowledge Graph encourage further exploration and more detail than ever before. From answer boxes to extended data and related searches, Google’s commitment to making their SERPs smarter and more user-friendly remains steadfast.

Much of Google’s understanding about how particular entities are related to one another comes from Freebase, an open-source database that was heavily moderated and one to which many users continue to contribute. However, Google is moving its source of information and the structured data used to establish relationships from one entity to the next to Wikimedia’s Wikidata, and eventually phasing out the Freebase API. It’s unclear to see how this will affect how SERPs display information within the Knowledge Graph, but any time there are major shifts in open-source platforms, users can expect to see at least some variance with the ways in which new users behave.

Smarter Search Results Using In-Depth Answer Boxes

As Google gets smarter, users naturally expect a smarter search, and their engineers seem to have been hard at work preparing to deliver just that. An example of this that’s certainly been around for a while is the way in which Google displays definitions. Consider the following:

Leaning more heavily on structured data and relationships, Google is able to create a box that succinctly defines the word, yet looks far beyond the word’s definition into the etymology, history, and root of the word. Similarly, these extended relationships are appearing more frequently in answer boxes, including showing song lyrics that link directly to Google Play.

Updating Display

Google’s commitment to improving user experience caused two small, albeit significant, changes to the way in which they display certain types of search results. The search results now displaying an Answer Box or Carousel now contain breadcrumb navigations for secondary questions.

In Search Engine Land’s piece outlining this new feature, they use an example of a search query like [michael jordans children]. The resulting SERP has photos of his children in the carousel, and a bread crumbs for searches specific to Michael Jordan. This helps provide a better user experience for users seeking to understand the relationship between, people, places, and things of interest.

Similarly, Google worked to deliver the highest quality content to users no matter what device they were using. There are now small clues indicating when a site may be appropriate for a user to visit on a mobile device, which is a fantastic way to pre-qualify a site’s ability to compete. As user experience will always remain a sustainable ranking factor, Attaining these visits on the right device is paramount to keeping them there!

In a seemingly insignificant switch, Google started labeling search results with “Mobile-friendly” or in some instances, included an icon representing a mobile phone for mobile sites. This clearly shows the importance of having a seamless UX no matter what device a user may be using to access your site.

 

More than a Meta Description

The final thing I wanted to look at that delves into how Google is using structured data, the Knowledge Graph, and relationships between entities to better present data to the world is looking at the evolving structured snippets that appear at the bottom of search results. SERPs now display more structured data underneath each meta description, highlighting the importance of structured data and rich snippets to help better identify your business and corresponding information.

Google’s changes to the way in which data is presented on SERPs are significant, and definitely illustrate a commitment to structured data all based on an improved UX. This is by no means a comprehensive list, but just a short look at how relationships and entities are better infiltrating Google’s ability to address topics rather than just keyword-based queries. We are eager to see how these changes continue to evolve in the coming year!

 

How to Optimize Keywords for Google: Or How I Learned to Stop Worrying and Love LSI

Recently, a post on the Moz blog seemed to ignite a particularly intriguing debate that centered around Google’s famed list of the 200+ factors that they use to rank results. Within the post, the author posited that Google has never relied on keyword density as a ranking factor. While this ignited a fiery debate within the comments section, it also ushers in an important conversation that search marketers should keep in mind–one that touches on the merits of looking at correlation vs causation, and one that looks at the complexities of language as a looming variable in the world of search.

To answer the initial question: No, it is very unlikely that Google uses keyword density as a ranking factor. However, to say that keywords in content won’t influence your position in search is naive, at best. Descriptive keywords not only dictate the way in which bots and search engines process and index your site, but also the way in which the public at large talks about your product or service, playing a major role in search. However, the early days of search still seem to guide the strategies and tactics; exact-match keywords strategically dot a page, rampantly reinforcing the keywords for which you are attempting to rank.

Yet Google’s come a long way; from the very public introduction of the Hummingbird algorithm, to the publicly announced, but less discussed addition of Ray Kurzweil to the Google Search team, and further explorations into AI, Google is becoming more fluid, adaptive, and intrinsically intelligent with how it understands and interacts with language. Today, we wanted to take a look at three complex ways in which Google processes queries and indexes information. Term frequency, semantic distance, the evolution of Google’s understanding of pronouns, synonyms and natural variants, and co-citation and co-occurrence, all govern how Google understands language on the Web.

While many may think that this is simply another word for keyword density, Google has made numerous references over the years to term frequency and inverse document frequency in applications for patents, as well as other documents. Term frequency and inverse document frequency focus less on keywords and how often they appear on a page, and more on the proportion of keywords to other lexical items within a document.

Expertly covered by Cyrus Sheperd on this Moz blog, TF-IDF is a ratio that helps Google compare the importance of particular keywords based on how often they appear in contrast to other documents on the page, as well as the greater corpus of documents as a whole. Supported by Hummingbird, this allows Google to have a more complex understanding of the way in which natural language can support overarching topics from a top level. Using language in a way that’s natural, and in a way that resonates within your niche or industry may be a better use of your time than trying to ensure your document includes your keywords a set number of times!

This goes without saying, but using synonyms and natural occurring variants of your target keyword help Google to identify a natural match for the searcher. In the previously referenced Moz blog, they use the example of “dog photos.” There’s a good chance that if someone is referring to dog photos, that other words on the page might exist, including “pictures of dogs”, “dog pictures”, “puppy pics” or “canine shots”. By ensuring that synonyms of your target keyword regularly appear, Google and other search engines are able to affirm the page’s intent and align it with that of the searcher by finding words with similar meanings that could potentially answer a user’s query.

Over 70% of searches rely on synonyms. According to Shepard, “To solve this problem, search engines possess vast corpuses of synonyms and close variants for billions of phrases, which allows them to match content to queries even when searchers use different words than your text.” Again, this is more incentive for marketers and webmasters alike to create copy that departs from a minimum requirement for keyword density, and instead rewards natural language that allows users to refer to their target keyword and other potential variations.

Related to the idea of synonyms and variants are the idea of co-citation and co-occurrence. First of all, Bill Slawski, of SEO by the Sea, has stated that co-citation and co-occurrence are part and parcel of the Hummingbird algorithm, which uses co-citation to identify words that may be synonyms. The search engines rely on corpus of linguistic rules and may even replace a query for a synonym where co-citation and co-occurrence have determined a better match or a heightened probability of a better search result.

This also helps determine and parse out different search queries for words that may have multiple meanings; in the example above, “dog picture” is a very different search than “dog motion picture”. However, in a more extreme scenario, a “plant” could refer to a tree, a shrub, or a factory, while a “bank” may refer to an institution that lends money, an index of thoughts or memories, or the land that dots either side of a river. A “trunk” may refer to an article of furniture, a part of a tree, a car, or an elephant. Contextual clues within the content help parse out the inferred meaning of the content on-site and ensure that Google serves a page that’s relevant to the searcher.

However, this is also playing a significant role in off-site optimization as well. While keyword-rich anchor text is still valuable, it is noticeably declining in importance due to concerns about spam. In a different piece, Rand Fishkin noted that queries for “cell phone ratings” regularly returned results on the first page that didn’t even contain the word “ratings” within the title, and instead used “reviews” or “reports”. This is a highly competitive query, yet Google used co-occurrence from both on-site and off-site content to determine that these sites are more relevant than those that contain the keyword.

One benefit of looking at co-occurrence from the search engines’ point of view, is that it is extremely hard to manipulate. This relies on a heavily updated corpus featuring an amalgamation of sources that are talking about the keyword in such a way to support the surrounding co-occurring words or phrases. It is an incredible testament to the algorithm’s ability to understand and naturally parse out how language intrinsically sounds. While Latent Semantic Indexing has been around long before Google or search engines, co-occurrence is a part of the algorithm that works much in the same way, identifying relationships between phrases and lexical items to extract and assign meaning.

The growing ability to detect and extract meaning to seemingly unrelated pieces of text illustrates Google’s growing ability to use artificial intelligence to understand language. From leaning on a user’s personal historical searches to understand pronouns, like a recent Google patent demonstrates, Google continues to lean on the information available to make the search process a more conversational and intuitive one.

Similarly, in appointing Ray Kurzweil and their acquisition of DeepMind, Google continues to leverage some of the sharpest minds in artificial intelligence to truly understand and engage with a user’s language.

Language is an incredibly dynamic and fundamental component of society, and Google and other search engines continue to expand their indices to ensure that they provide the best experience possible. As a result, marketers need to forget about manipulating Google’s search results, and instead engage with their community in their own voice. Worry less about keyword density, and instead look at how to present something in a way that is engaging and natural. Relevant, unique, and natural content both on-site and within the online community will help influence your position as an influencer and industry-leader.

Leaving SERP City

Google Rolls Out Increased Entity Search in 2014

It’s not news that things have come along way since the typical search engine results page (SERP) displayed ten simple blue links for a given search query. From maps to products, from news to authors, from contributors and carousels to answers, news, ratings, and more, the search engine continues to show more and more marked up data within its results, transforming the search engine into a decision engine. Below, I wanted to take a look at a few examples I’ve seen in the wild to shed light on how Google is now behaving, underscoring how rich snippets and structured data are now Hummingbird nectar for Google’s brand new engine. We’re leaving SERP City for entity search in 2014. And for the sake of consistency, and because I’m a sports fan, unless otherwise cited, all examples below will be sports-related.

Answer Boxes Everywhere

The knowledge graph already generally answered most questions by pulling text from trusted sources; from iMDB to Wikipedia, we saw queries answered with concise snippets displayed to the right of these major search results, complete with images, relevant data and related searches. However, when Google announced the rollout of the Hummingbird algorithm, they specified that it was designed to respond to an increasing need to answer more complex search queries that users were increasingly inclined to type. While many a naive webmaster may have rushed to hurriedly create an FAQ page, the result of Hummingbird looks more like this:

Recently, my girlfriend and I sat watching a hockey game when she asked me how big a hockey rink was. Having been an avid hockey fan and player for a number of years, I was shocked at my inability to answer the question; however, I wholly was unsurprised at Google’s efficiency. Using the answer box above, Google responded with a concise response that satisfied my query, complete with a citation!

What’s extraordinarily curious is the fact that the cited domain, nhl.com, was the third or fourth organic result in the search. Clearly, the algorithm is forced to reconcile a compromise between the authority on the topic, in this case, the NHL, and other factors like the Domain Authority and TLD of a top-ten search result candidate like Wikipedia. Similarly, the willingness to have a content-diverse SERP robs the user of getting to the website cited in the answer box by putting News results ahead of their preferred cited source. It’s a curious development that begs the question: If Google deemed this site worthy of answering my question, why is it losing out to other sites in ranking?

A corresponding discovery on the way in which News results are changing includes the gradual rollout of cards to display News results.

 

The resulting display showed news concisely displayed with something that appears to me like it would be more intuitive for users on a mobile device.

Decisions, Decisions: SERP Dominance

With other queries, Google’s propensity and confidence in satisfying a user’s query is getting so high that even paid channels are shut out. An example that caught my eye on Twitter was for another sports-related search for [NCAA tournament].

This screenshot is zoomed out to illustrate that the user has to actually go below the fold to even get to the first organic search result. From the marked-up data for two separate NCAA-level tournaments to the knowledge graph on the right with information about the organization itself, it’s incredible to see Google be so decisive with its results.

Prominent Personalization

The last element that really caught my eye was an increased personalization due to the rollout of hashtags in Google+. As a soccer obsessive and a huge proponent of the English Premier League, I recently wrote a blog about my club, Aston Villa, and shared it on Google+ with all the relevant hashtags. Shortly thereafter, when performing a search for the club, I was shocked to find that my blog had made it to position two in the SERPs!

This was, of course, a result of personalization. Forget that Google might now nofollow links within your Google+ profile; when signed into Chrome or Google+, hashtags that satisfy your query within your circles tend to shine through SERP pages and ascend right to the top. And why not? These links within these posts are from users that you’ve vetted by including them in your circles–the primary social circle you share with Google.

While this may improve user experience for those that have devoted time into currating their circles on Google+, it may be alarming to others looking for more qualified search results within their SERP. There’s a chance that these items may still be testing, but seeing these SERPs throughout the past week indicates not only a continued willingness to provide the best user experience through great content, but also a growing preference for marked up sites, and the increasing importance of structured data.