Optimizing for Hanukkah: Sometimes it’s still strings, not things

My wife came to me with a problem. She wanted festive, whimsical, and potentially matching Hanukkah pajamas. But there weren’t enough options coming up in Google under one spelling of the holiday’s name, so she told me she was systematically going through all spellings to compile her list of shopping items.

I was pretty surprised by this — I had expected Google to be smart enough to recognize that these were alternative spellings of the same thing, especially post-Hummingbird. Clearly, this was not the case.

Some background for those who don’t know: Hanukkah is actually a transliterated word from Hebrew. Since Hebrew has its own alphabet, there are numerous spellings that one can use to reference it: Hanukkah, Chanukah, and Channukah are all acceptable spellings of the same holiday.

So, when someone searches for “Hanukkah pajamas” or “Chanukah pajamas,” Google really should be smart enough to understand that they are different spellings of the same concept and provide nearly identical results. But Google does not! I imagine this happens for other holidays and names from other cultures, and I’d be curious to know if other readers experience the same problem with those.

Why am I surprised that Google is returning different results for different spellings? Well, with the introduction of the Knowledge Graph (and Hummingbird), Google signaled a change for SEO. More than ever before, we could start thinking about search queries not merely as keyword strings, but as interrelated real-world concepts.

What do I mean by this?

When someone searches for “Abraham Lincoln,” they’re more than likely searching for the entity representing the 16th president of the United States, rather than the appearance of the words “Abraham” and “Lincoln,” or their uncle, also named Abraham Lincoln. And if they search for “Lincoln party,” Google knows we’re likely discussing political parties, rather than parties in the town of Lincoln, Mass., because this is a concept in close association with the historical entity Abraham Lincoln.

Similarly, Google is certainly capable of understanding that when we use the keyword Hanukkah, it is in reference to the holiday entity and that the various spellings are also referring to the same entity. Despite different spellings, the different searches actually mean the same thing. But alas, as demonstrated by my wife’s need to run a different search for each spelling of the holiday in order to discover all of her Hanukkah pajama options, Google wasn’t doing the best job.

So, how widespread is the Chanukah/Hanukkah/Chanukkah search problem? Here are a couple of search results for Chanukah items:

As you can see from the first screen shot, some big box retailers like Target, Macy’s and JCPenney rank on page one of Google. In screen shot two, however, they are largely absent — and sites like PajamaGram and Etsy are dominating the different spelling’s SERP.

This means that stores targeting the already small demographic of Hanukkah shoppers are actually reducing the number of potential customers by only using one spelling on their page. (Indeed, according to my keyword tool of choice, although “Hanukkah” has the highest search volume of all variants at 301,100 global monthly searches, all other spellings combined still make up a sizeable 55,500 searches — meaning that retailers optimizing for both terms could be seeing 18 percent more traffic.)

Investigating spelling variations and observations

Since I’m an ever-curious person, I wanted to investigate this phenomenon a little further.

I built a small, simple tool to show how similar the search engine results pages (SERP) for two different queries are by examining which listings appear in both SERPs. If we look at five common spellings of Hanukkah, we see the following:

Keyword 1Keyword 2SERP SimilarityChannukahChanukah90.00%ChannukahHannukah20.00%ChannukahHannukkah20.00%ChannukahHanukkah30.00%ChanukahHannukah20.00%ChanukahHannukkah20.00%ChanukahHanukkah30.00%HannukahHannukkah90.00%HannukahHanukkah80.00%HannukkahHanukkah80.00%

The tool shows something quite interesting here: Not only are the results different, but depending on spelling, the results may only be 20 percent identical, meaning eight out of 10 of the listings on page one are completely different.

I then became curious about why the terms weren’t canonicalized to each other, so I looked at Wikidata, one of the primary data sources that Google uses for its Knowledge Graph. As it turns out, there is an entity with all of the variants accounted for:

I then checked the Google Knowledge Graph Search API, and it became very clear that Google may be confused:

KeywordresultScore@idnameDescription@typeChannukah8.081924kg:/m/0vpq52Channukah LoveSong by Ju-Tang[MusicRecording, Thing]Chanukah16.334606kg:/m/06xmqp_A Rugrats Chanukah?[Thing]Hannukah11.404715kg:/m/0zvjvwtHannukahSong by Lorna[MusicRecording, Thing]Hannukkah11.599854kg:/m/06vrjy9HannukkahBook by Jennifer Blizin Gillis[Book, Thing]Hanukkah21.56493kg:/m/02873zHanukkah HarryFictional character[Thing]

The resultScore values — which, according to the API documentation, indicate “how well the entity matched the request constraints” — are very low. In this case, the entity wasn’t very well matched. This would be consistent with the varying results if it weren’t for the fact that a Knowledge Graph is being returned for all of the spelling variants with the Freebase ID /m/022w4 — different from what is returned from the Knowledge Graph API. So, in this case, it seems that the API may not be a reliable means of assessing the problem. Let’s move on to some other observations.

It is interesting to note was that when searching for Channukah, Google pushed users to Chanukah results. When searching Hannukah and Hannukkah, Google pushed users to Hanukkah results. So, Google does seem to group Hanukkah spellings together based on whether they start with an “H” or a “Ch.”

Chanukah, Hannukah, and Hanukkah were also the only variations that received the special treatment of the Hanukkah menorah graphic:

What a retailer selling Hanukkah products should do

Clearly, if we want full coverage of terms (and my wife to find your Hanukkah pajamas), we cannot rely on just optimizing for the highest search volume variation of the keyword, as Google doesn’t seem to view all variants as entirely the same. Your best bet is to include the actual string for each spelling variant somewhere on the page, rather than relying on Google to understand them as variations of the same thing.

If you’re a smaller player, it may make sense to prioritize optimizations toward one of the less popular spelling variants, as the organic competition may not be as significant. (Of course, this does not bar you from using spelling variants in addition to that for the potential of winning for multiple spellings.)

At a bare minimum, you may opt to include a spelling beginning with H- and Ch- and hope that Google will direct users to the same SERP in most cases.

Future experiment

I started an experiment to see whether the inclusion of structured data with sameAs properties may be a potential avenue for getting Google to understand a single spelling as an entity, eliminating the need to include different spelling variations. As of now, it’s a little too early to know the results of the test, and they are inconclusive, but I look forward to sharing those results in the future.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

How machine learning levels the SERP playing field

We don’t ordinarily think of Google when we think about competition in the digital marketing world, since it seems to reliably dominate most areas in which it does business. A recent segment discussing corporate monopolies on John Oliver’s “Last Week Tonight hilariously referenced Bing as the dominant search engine with a graphic that stated, “Bing. The best place to Google something.”

For the most part, however, the digital marketing sphere has been a fairly competitive landscape, though there were exceptions to this maxim. Established brands frequently dominated top SERP positions because of long-standing trust, fresh domains had to wait their turn in line, and black-hat SEO allowed webmasters to game the system and deliver high rankings for thin content. A decade ago, SEO agencies and webmasters could apply simple heuristics and buzzworthy keywords to rank content regardless of its utility to user intent or actual quality.

The Hummingbird update and subsequent rollout of RankBrain changed all of these notions entirely.

They should also be changing SEOs’ ideas of how to achieve success. Though many SEO experts understand the importance of RankBrain, or at least how important it will be, they still employ conventional strategies we made a living off of a decade ago.

In this column, I’ll explain why you should remodel the way you look at search engine optimization. And I’ll also offer some advice on machine learning applications and SEO strategies you can employ to compete in the cutthroat SEO landscape.

How machine learning revolutionized search

Machine learning is a subset of artificial intelligence that allows computers to learn independently of human intervention, learning in iterations by grouping similar properties and determining values based on their shared properties.

Google’s RankBrain, which the company says is its third most important ranking factor, is applied to determine the context of new search queries that it has not received before. RankBrain distinguishes the context of unlearned searches by pulling semantically similar keywords/phrases and comparing them with similar past searches to deliver the most relevant results.

Google employs machine learning technology to find patterns and make sense of relevant data when it analyzes user engagement with web pages in its SERP listings. With this data, Google’s algorithm evaluates user intent. From Google’s perspective, this helps filter results more effectively and rewards users with a better experience.

Currently, conventional signals are still applied to rank the best results. With each subsequent, relevant search, machine learning can analyze which web pages are receiving the best user signals and provide the best results to meet user intent. It’s important to note that machine learning isn’t instantaneous but would result in slow ranking changes based on growing data from its SERPs.

This has two broad implications for keyword research and ranking:

    Keyword rank is no longer affected by dramatic shifts.Google’s algorithm is more dynamic; different algorithms are employed for each unique search.

In more competitive niches, content quality and increased user engagement will slowly take precedence over conventional signals, leveling the SERP playing field. In low-volume searches, conventional ranking signals will still be applied as the de facto standard until enough data is available to determine user intent.

This has also brought semantic search to the fore for SEO experts. Semantic search allows content to rank for multiple keywords and get increased traffic by meeting the intent of various related search queries. The clearest example of semantic search’s impact is the related search field at the bottom of Google SERPs and what “People Also Ask” below the featured snippet field.

As Google becomes capable of understanding human intent and linguistic intelligence, technical SEO and keyword usage will take a back seat to user signals. Considering different algorithms are applied to unique searches, links will be reduced in their role as the arbiters of content quality, and smaller domains will have a better fighting chance to compete against industry titans organically.

If searcher intent determines which algorithm will be pulled for SERP listings, how do we optimize and even track this? The answer involves using both conventional strategies and our own machine learning technology.

Give the people what they want

Here are a few methods SEOs should be using to keep current with the evolving environment:

1. Improve user experience

Searchmetrics’ 2016 report on ranking factors illustrated just how important user signals were to organic ranking. The company found that user signals were second only to content relevance in terms of importance.

One of the best ways that a search engine can determine user intent is by analyzing user signals, which it gathers through its Chrome browser, direct URLs, SERPs and so on. But Google’s most valued user signal remains CTR.

To ensure your web pages deliver good user signals, you must create a solid UX foundation. This means providing thematic continuity across your web pages, creating high-quality and relevant landing pages, using engaging images, offering interactive content, delivering fast page speed and developing an organized internal linking structure.

Metatags and rich snippets can also influence your click-through rate, so optimize for both. Google will obviously lower your rank if your website suffers from a low CTR in a high-ranking result.

Other considerations to keep in mind include:

employing 301 redirects for missing pages and rel=canonical tags for duplicate content.optimizing structured data and alternative tags to help search engines index content.resolving any broken links that could affect crawl structure.

Even though Google’s AI and RankBrain are incredibly advanced, Google still needs your help to crawl web pages and index them. It doesn’t hurt that these factors also improve your website’s navigation and user experience.

2. Embrace thematic continuity

Despite all of these advancements in search, I still commonly encounter clients who operate their websites with thin content and no keyword focus. My team begins client campaigns with research on keywords, competitors and some technical aspects.

Recently, though, we began focusing on creating more seamless hierarchical structures that leverage semantically linked keywords and topic clusters to promote an awesome UX. As opposed to simply creating content with a limited keyword focus, we focused on ranking our clients’ most important pages.

HubSpot refers to this exciting new practice as “topic clusters.” Topic clusters focus on pillar pages that represent your most important topics. These will be broad, overarching pages that rank high in your information hierarchy and attempt to discuss and answer the most important questions related to your main topic.

Subtopics are then discussed in greater detail on lower-hierarchy pages that contain internal links back to the pillar page. This strategy helps communicate your most important pages through a sophisticated interlinking structure, promotes seamless navigation and helps position your pillar page to rank for multiple keyword phrases.

These evergreen pieces are also supplemented by a consistent blogging strategy that discusses trending topics related to the website’s theme. Each piece of content produced is actionable and focuses on driving conversion or desired actions.

When modeling each piece of content, it’s important to ask yourself this question: What are the problems this piece of content is seeking to address, and how will it solve them? As more questions pop up, write content addressing these issues. Now you’ve created a website that satisfies user intent from almost every possible perspective. This helps you rank for a lot of keywords.

You can also employ machine learning technology to improve the workflow of your content marketing campaign. Applications, such as the Hemingway App and Grammarly, are excellent tools that can provide suggestions where improvements could be made in sentence structure, author voice and word usage.

3. Employ natural language

Perhaps the best way to optimize for an artificially intelligent search world is to optimize for voice search, as opposed to text search. This involves optimizing your website for mobile and your content to achieve featured snippets, given that answers to questions asked to a personal assistant device are pulled from the featured snippet field on a Google SERP.

In addition to following the strategies outlined thus far, this involves crafting cogent page copy that seeks to answer as many questions as possible and provide actionable solutions.

Research has also shown that people searching by voice, rather than text, are more likely to use search phrases from four to nine words in length. This means you need to optimize for long-tail keyword phrases — which are usually longer in length — and page copy that is more representative of natural language. For example, a text search for flights to Hawaii may be “cheap flights Hawaii,” while a voice search may say, “What are the cheapest flights to Hawaii?”

With the rise of machine learning, optimized content that appeals to natural language could satisfy user intent for both broad match searches over text and long-tail voice searches.

Consider how chatbot assistants incorporate Natural Language Understanding (NLU) to more readily understand linguistic syntax and meanings. With advancements in NLU applications, search engines will eventually be able to entirely assess the meaning and quality of content the same way a human does.

4. Personalize the buyer’s journey

With more big data being created this year than in the past 5,000 years, businesses will need to leverage machine learning technology to interpret vast amounts of user data at an unprecedented speed.

One way this is already being executed is by mining conversational text data from chatbots. As we move from a graphical interface world into a conversational interface, chatbots are being used to map inputs and data from customer journeys to help companies improve their user experience.

This technology is still in its infancy, but we can also apply machine learning technology and data mining to personalize touch points along the buyer’s journey. Customer journey mapping can be used to build out buyer personas and personalize marketing touch points to maximize conversions and sales.

Using customer journey mapping, businesses can personalize touch points to deliver content or advertisements when intent is highest. Real-time responses can be instituted to respond to customer service calls immediately, deliver calls to action to high-scoring leads and segment advertisement campaigns based on real-time data.

Predictive analytics can also be applied to deliver predictions of estimated campaign performances based on real-time data. This will greatly save time on A/B testing and improve campaign efficiency.

Fortunately, machine learning technology can be used by anyone. Given the sheer speed and scale of machine learning applications, relying on conventional SEO strategies to rank organically may eventually put you at an incredible competitive disadvantage.

The future is already passing

Don’t worry, automation won’t totally displace humans any time soon. Machine learning technologies can help augment marketing campaigns, but the creative and execution ultimately rely on the expertise of human intelligence. But we will probably reach a point soon enough that clients will actively seek out digital marketing firms that have expertise in customer journey mapping and AI-enabled applications.

In my opinion, these technologies have the potential to greatly improve the competition for SERPs and will also allow digital marketers to deliver a stronger product.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

How machine learning impacts the need for quality content

Back in August, I posited the concept of a two-factor ranking model for SEO. The idea was to greatly simplify SEO for most publishers and to remind them that the finer points of SEO don’t matter if you don’t get the basics right. This concept leads to a basic ranking model that looks like this:

To look at it a little differently, here is a way of assessing the importance of content quality:

The reason that machine learning is important to this picture is that search engines are investing heavily in improving their understanding of language. Hummingbird was the first algorithm publicly announced by Google that focused largely on addressing an understanding of natural language, and RankBrain was the next such algorithm.

I believe that these investments are focused on goals such as these:

    Better understanding user intentBetter evaluating content quality

We also know that Google (and other engines) are interested in leveraging user satisfaction/user engagement data as well. Though it’s less clear exactly what signals they will key in on, it seems likely that this is another place for machine learning to play a role.

Today, I’m going to explore the state of the state as it relates to content quality, and how I think machine learning is likely to drive the evolution of that.

Content quality improvement case studies

A large number of the sites that we see continue to under-invest in adding content to their pages. This is very common with e-commerce sites. Too many of them create their pages, add the products and product descriptions, and then think they are done. This is a mistake.

For example, adding unique user reviews specific to the products on the page is very effective. At Stone Temple, we worked on one site where adding user reviews led to a traffic increase of 45 percent on the pages included in the test.

We also did a test where we took existing text on category pages that had originally been crafted as “SEO text” and replaced it. The so-called SEO text was not written with users in mind and hence added little value to the page. We replaced the SEO text with a true mini-guide specific to the categories on which the content resided. We saw a gain of 68 percent to the traffic on those pages. We also had some control pages for which we made no changes, and traffic to those dropped 11 percent, so the net gain was just shy of 80 percent:

Note that our text was handcrafted and tuned with an explicit goal of adding value to the tested pages. So this wasn’t cheap or easy to implement, but it was still quite cost-effective, given that we did this on major category pages for the site.

These two examples show us that investing in improving content quality can offer significant benefits. Now let’s explore how machine learning may make this even more important.

Impact of machine learning

Let’s start by looking at our major ranking factors and see how machine learning might change them.

Content quality

Showing high-quality content in search results will remain critical to the search engines. Machine learning algorithms like RankBrain have improved their ability to understand human language. One example of this is the query that Gary Illyes shared with me: “can you get 100% score on Super Mario without walkthrough.”

Prior to RankBrain, the word “without” was ignored by the Google algorithm, causing it to return examples of walkthroughs, when what the user wanted was to be able to get a result telling them how to do it without a walkthrough. RankBrain was largely focused on long-tail search queries and represented a good step forward in understanding user intent for such queries.

But Google has a long way to go. For example, consider the following query:

In this query, Google appears unclear on how the word “best” is being used. The query is not about the best down comforters, but instead is about why down comforters are better than other types of comforters.

Let’s take a look at another example:

See how the article identifies that the coldest day in US history occurred in Alaska, but then doesn’t actually provide the detailed answer in the Featured Snippet? The interesting thing here is that the article Google pulled the answer from actually does tell you both the date and the temperature of the coldest day in the US — Google just missed it.

These things are not that complicated, when you look at them one at a time, for Google to fix. The current limitations arise because of the complexity of language and the scale of machine learning required to fix it. The approach to fixing it requires building larger and larger sets of examples like the two I shared above, then using them to help train better machine learning-derived algorithms.

RankBrain was one major step forward for Google, but the work is ongoing. The company is making massive investments in taking their understanding of language forward in dramatic ways. The following excerpt, from USA Today, starts with a quote from Google’s senior program manager, Linne Ha, who runs the Pygmalion team of linguists at the company:

“We’re coming up with rules and exceptions to train the computer,” Ha says. “Why do we say ‘the president of the United States?’ And why do we not say ‘the president of the France?’ There are all sorts of inconsistencies within our language and within every language. For humans it seems obvious and natural, but for machines it’s actually quite difficult.”

The Pygmalion team at Google is the one that is focused on improving Google’s understanding of natural language. Some of the things that will improve at the same time are their understanding of:

    what pages on the web best match the user’s intent as implied by the query.how comprehensive a page is in addressing the user’s needs.

As they do that, their capabilities for measuring the quality of content and how well it addresses the user intent will grow, and this will therefore become a larger and larger ranking factor over time.

User engagement/satisfaction

As already noted, we know that search engines use various methods for measuring user engagement. They’ve already publicly revealed that they use CTR as a quality control factor, and many believe that they use it as a direct ranking factor. Regardless, it’s reasonable to expect that search engines will continue to seek out more useful ways to have user signals play a bigger role in search ranking.

There is a type of machine learning called “reinforcement learning” that may come into play here. What if you could try different sets of search results, see how they perform, and then use that as input to directly refine and improve the search results in an automated way? In other words, could you simply collect user engagement signals and use them to dynamically try different types of search results for queries, and then keep tweaking them until you find the best set of results?

But it turns out that this is a very hard problem to solve. Jeff Dean, who many consider one of the leaders of the machine learning efforts at Google, had this to say about measuring user engagement in a recent interview he did with Fortune:

An example of a messier reinforcement learning problem is perhaps trying to use it in what search results should I show. There’s a much broader set of search results I can show in response to different queries, and the reward signal is a little noisy. Like if a user looks at a search result and likes it or doesn’t like it, that’s not that obvious.

Nonetheless, I expect that this is a continuing area of investment by Google. And, if you think about it, user engagement and satisfaction has an important interaction with content quality. In fact, it helps us think about what content quality really represents: web pages that meet the needs of a significant portion of the people who land on them. This means several things:

    The product/service/information they are looking for is present on the page.They can find it with relative ease on the page.Supporting products/services/information they want can also be easily found on the page.The page/website gives them confidence that you’re a reputable source to interact with.The overall design offers an engaging experience.

As Google’s machine learning capabilities advance, they will get better at measuring the page quality itself, or various types of user engagement signals that show what users think about the page quality. This means that you will need to invest in creating pages that fit the criteria laid out in the five points above. If you do, it will give you an edge in your digital marketing strategies — and if you don’t, you’ll end up suffering a a result.


There are huge changes in the wind, and they’re going to dramatically impact your approach to digital marketing. Your basic priorities won’t change, as you’ll still need to:

    create high-quality content.measure and continuously improve user satisfaction with your site.establish authority with links.

The big question is, are you really doing enough of these things today? In my experience, most companies under-invest in the continuous improvement of content quality and improving user satisfaction. It’s time to start putting more focus on these things. As Google and other search engines get better at determining content quality, the winners and losers in the search results will begin to shift in dramatic ways.

Google’s focus is on providing better and better results, as this leads to more market share for them and thus higher levels of revenue. Best to get on board the content quality train now — before it leaves the station and leaves you behind!

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

Search Update Impact On SEO & Content Strategies: Staying Ahead With A Focus On Quality

Since Google was first launched in 1998, the company has been continually refining its search algorithm to better match users with online content.

Over the years, many algorithm updates have targeted spammy and low-quality content in an effort to surface this content less frequently in search results. Other algorithm updates have been aimed at improving Google’s “understanding” of search queries and page content to better align search results with user intent.

The bottom line is that focusing on quality content and the user experience really is the best way to ensure your search engine optimization (SEO) and content marketing campaigns are update proactive rather than update reactive.

Many Google updates have impacted numerous reputable sites. Search marketers have had to learn how to better optimize their pages with each update to avoid losing rankings. Considering that 67.60 percent of clicks go to the top five slots on SERPs, a drop of just a few positions because of an algorithm update can have massive impact on traffic, revenue and conversions.

Over the coming weeks and months, as recent updates set in and impending updates come to pass, it will be interesting to see how SEO and content strategies evolve in response. In the meantime, here’s my overview of Google’s major algorithm updates (past, present and future) and their impact on the digital marketing landscape.


The Panda update was first launched in February 2011, though it has been updated several times since then. This update is designed to target sites with low-quality content and prevent them from ranking well in search engine results pages.

Sites that have pages of spammy content, too many ads or excessive duplicate content, for example, often experience Panda penalties.

It was recently announced that Panda was added to Google’s core ranking algorithm, which has caused considerable buzz in the industry.

While there are still some questions about what it means, there are some things we’re fairly certain about. Panda updates are expected to run more regularly, for example, which will be very helpful for brands who have seen their websites hit by Panda penalties.

However, contrary to early rumors, the update will not be run in real time.

When it comes to content production, since the initial Panda release, websites have needed to really focus on providing high-quality information. Websites that have pages of low-quality content, such as thin material with little insight, should improve the existing pages, rather than just deleting them.

Keep in mind that “quality” isn’t measured in content length, so you won’t improve your low-quality pages simply by adding more text. Content can be short or long — what matters is that it provides the information the user seeks. The quality of the content on a website matters more than the quantity.


The Penguin update was first released about a year after the Panda update, in April 2012. The two are often grouped together when discussing Google’s big push to raise the quality of content that appears in search engine results.

This update focused largely on targeting spammy links. Google looks at backlinks as a signal of a website’s authority and reputation, taking a site or page’s backlink profile into consideration when determining rankings.

Back when its core algorithm was less sophisticated, people figured out that they could effectively game search engine rankings simply by obtaining significant numbers of (often spammy and irrelevant) backlinks.

Penguin combatted this manipulative technique by targeting pages that depended upon poor-quality links, such as link farms, to artificially raise their rankings. Websites with spammy backlink profiles have been forced to remove or disavow bad links in order to avoid ranking penalties.

Quality links still have something of value to offer websites, although Google emphasizes that sites should focus on developing a quality backlink profile organically. This means creating informative pieces that people will want to source with a backlink.

To attract attention to your piece, you can leverage the search, social and content trifecta. By creating high-quality pieces and then distributing them on social media, you start to attract attention to your work.

This can increase your readership and (in theory) help you acquire more backlinks. You can also use techniques such as posting guest posts on other reputable blogs to leverage your content and build a strong backlink profile.


The Hummingbird update followed in the summer of 2013. This update was designed to improve Google’s semantic search capabilities. It was becoming increasingly common for people to use Google in a conversational way, to type their queries as though they were asking a friend.

This update was designed to help Google respond by understanding intent and context.

With this update, the development of content had to shift slightly again. With the emphasis on intent, Google was not simply playing a matching game where they connect the keywords in the query with the keywords in the content.

Content needed now to go beyond just the keyword. It needed to demonstrate an understanding of what users are interested in and what they would like to learn.

While keywords still are an important part of communicating with the search engine about the topic of the content, the way they were used shifted. Long-tail keywords became more important, and intent became crucial.

Content developers needed to direct their focus toward understanding why customers might be typing particular words into the search engine and producing content that addressed their needs.

Mobile Update

The year 2015 saw several major updates that impacted content development. The first, Google’s mobile-friendly update, occurred in April. This update was unique because Google actually warned website users in advance that it was coming.

With this update, Google recognized that mobile was beginning to dominate much of search and online customer behavior — in fact, just a couple months after the mobile-friendly update was announced, Google noted that mobile searches had officially surpassed desktop. The mobile-friendly update forced sites to become mobile-friendly or risk losing visibility to sites that were.

With this update, Google wanted sites to take into account what mobile users wanted to do online and how these needs were being addressed.

This meant that SEOs and content marketers had to start considering design factors such as:

Responsive design or a mobile page.Having site navigation front and center and easy for customers to use with their fingers.Avoiding frustrations caused by issues such as buttons too close together.Having all forms as efficient and as easy as possible to fill out on a smartphone screen.

This mobile update also brought to the forefront the importance of brands optimizing for mobile, even going beyond what was required by Google to avoid a penalty.

For example, customers on mobile are often very action-oriented. They want to be able to call you or find your address. They want to view the information on your screen easily, without excessive scrolling. While long-form content is commonly read on mobile devices, making it easier for people to get back to the top is very beneficial.

Mobile users also tend to be very local-oriented. Content developed for mobile devices should take local SEO into account to maximize the mobile opportunities that present themselves.

Quality Update

Not long after the mobile update went live, people began reporting evidence of another Google update, which has since been nicknamed the Quality Update. It happened so quietly that even Google did not acknowledge the change at first.

During this update, sites that focused on the user experience and distributing high-quality content were rewarded, while sites that had many ads and certain types of user-generated content were more likely to be penalized. This was even true for established sites like HubPages.

Interestingly, however, not all user-generated content was hit on all sites. Some pages, like Quora, actually received a boost from the update; it is suspected that this is because this site is very careful about the quality of the responses and content that are posted on the page.

The key to avoiding a penalty with this update seemed to be avoiding thin content or other material that did not place the needs of the user first.

Sites also need to make sure that their pages are working well, as error messages place a site at risk for a penalty from this quality update. Google knows how frustrating it is to try to find an answer to a question and instead get treated to an overly promotional article or a 404.


RankBrain was announced in the fall of 2015, and it was also a unique change to the Google algorithm. With this update, the search engine ventured into the world of AI (artificial intelligence) and machine learning.

This system was designed to learn and predict user behaviors, which helps Google interpret and respond to the hundreds of millions of completely unique, never-before-seen queries that it encounters each day.

It is also assumed that RankBrain helps Google to interpret content and intent in some way. Although Google has given little information about how their new AI works, they have said that it has become the third most important ranking signal. For site owners, this has placed an even greater emphasis on creating content that matches the user intent.

Since RankBrain has gone live, some marketers have spoken about the importance of making sure that the technical side of SEO, such as schema markup, is all up to date. It is likely that as search engines become more dependent upon AI, these little details will become significant.

The Buzz Over The Last Week: Panda & The Core Algorithm

Last week, some marketers were caught off guard by a new update that seemed to impact ratings for numerous sites. Although there were initially rumors circulating that this update might be the anticipated Penguin update or something to do with Panda, Google put those rumors to rest and officially confirmed that this was a core algorithm update that was not linked to other established updates.

Based upon the patterns established over the past few years, it is most likely that this adjustment, like the others, focused on better understanding user intent and identifying high-quality content.

As “updates on updates” change constantly (even from the seven days it takes a post go live), the best way to stay up to date on core changes is via Barry Schwartz on Search Engine Land.

We will know more in the coming weeks about what this update targeted and how brands can better respond. For right now, content developers need to continue to focus on creating high-quality content that responds to what their customers want to see.

Conclusion: Stay Ahead By Focusing Quality Content And User Experience

Google, quite rightly, is always looking for brands to provide a high-quality user experience from the web properties. Updates are designed to try to evaluate user experience. Marketers need to pay attention to the various algorithm updates and adapt as needed to maximize their exposure.

In order to stay ahead and minimize risk, being proactive is far better than being reactive.

In general, focus on the following;

Providing high quality content. Provide content that is unique and relevant to the user. Do not overstuff content with target keywords.Optimizing your site for the user. Ensure that users have a positive experience and get the information they need (searched for). Factor in page speed, load times and design. Follow best practices for mobile search.Earning links rather than buying links. Focus on high-quality, credible sources, and only link to relevant content.

Nearly every year, Google updates have far-reaching impacts across industries as brands find their content rising and falling overnight, depending upon how well their content is aligned with the new criteria.

Google wants content development to focus less on just rank and more on the user. Marketers that focus on quality content and digital strategies will reduce any risk associated with Google updates and take market share away from their competition (those that don’t).

Be proactive rather than reactive.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

How RankBrain Changes Entity Search

Earlier this week, news broke about Google’s RankBrain, a machine learning system that, along with other algorithm factors, helps to determine what the best results will be for a specific query set.

Specifically, RankBrain appears to be related to query processing and refinement, using pattern recognition to take complex and/or ambiguous search queries and connect them to specific topics.

This allows Google to serve better search results to users, especially in the case of the hundreds of millions of search queries per day that the search engine has never seen before.

Not to be taken lightly, Google has said that RankBrain is among the most important of the hundreds of ranking signals the algorithm takes into account.

RankBrain is one of the “hundreds” of signals that go into an algorithm that determines what results appear on a Google search page and where they are ranked, Corrado said. In the few months it has been deployed, RankBrain has become the third-most important signal contributing to the result of a search query, he said.

(Note: RankBrain is more likely a “query processor” than a true “ranking factor.” It is currently unclear how exactly RankBrain functions as a ranking signal, since those are typically tied to content in some way.)

This is not the only major change to search in recent memory, however. In the past few years, Google has made quite a few important changes to how search works, from algorithm updates to search results page layout. Google has grown and changed into a much different animal than it was pre-Penguin and pre-Panda.

These changes don’t stop at search, either. The company has changed how it is structured. With the new and separate “Alphabet” umbrella, Google is no longer one organism, or even the main one.

Even communication from Google to SEOs and Webmasters has largely gone the way of the dodo. Matt Cutts is no longer the “Google go-to,” and reliable information has become difficult to obtain. So many changes in such a short time. It seems that Google is pushing forward.

Yet, RankBrain is much different from previous changes. RankBrain is an effort to refine the query results of Google’s Knowledge Graph-based entity search. While entity search is not new, the addition of a fully rolled-out machine learning algorithm to these results is only about three months old.

So what is entity search? How does this work with RankBrain? Where is Google going?

To understand the context, we need to go back a few years.


The launch of the Hummingbird algorithm was a radical change. It was the overhaul of the entire way Google processed organic queries. Overnight, search went from finding “strings” (i.e., strings of letters in a search query) to finding “things” (i.e., entities).

Where did Hummingbird come from? The new Hummingbird algorithm was born out of Google’s efforts to incorporate semantic search into its search engine.

This was supposed to be Google’s foray into not only machine learning, but the understanding and processing of natural language (or NLP). No more need for those pesky keywords — Google would just understand what you meant by what you typed in the search box.

Semantic search seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Major web search engines like Google and Bing incorporate some elements of semantic search.

Yet we’re two years on, and anyone who uses Google knows the dream of semantic search has not been realized. It’s not that Google meets none of the criteria, but Google falls far short of the full definition.

For instance, it does use databases to define and associate entities. However, a semantic engine would understand how context affects words and then be able to assess and interpret meaning.

Google does not have this understanding. In fact, according to some, Google is simply navigational search — and navigational search is not considered by definition to be semantic in nature.

So while Google can understand known entities and relationships via data definitions, distance and machine learning, it cannot yet understand natural (human) language. It also cannot easily interpret attribute association without additional clarification when those relationships in Google’s repository are weakly correlated or nonexistent. This clarification is often a result of additional user input.

Of course, Google can learn many of these definitions and relationships over time if enough people search for a set of terms. This is where machine learning (RankBrain) comes into the mix. Instead of the user refining query sets, the machine makes a best guess based on the user’s perceived intent.

However, even with RankBrain, Google is not able to interpret meaning as a human would, and that is the Natural Language portion of the semantic definition.

So by definition, Google is NOT a semantic search engine. Then what is it?

The Move From “Strings” to “Things”

[W]e’ve been working on an intelligent model — in geek-speak, a “graph” — that understands real-world entities and their relationships to one another: things, not strings.

Google Official Blog

As mentioned, Google is now very good at surfacing specific data. Need a weather report? Traffic conditions? Restaurant review? Google can provide this information without the need for you to even visit a website, displaying it right on the top of the search results page. Such placements are often based on the Knowledge Graph and are a result of Google’s move from “strings” to “things.”

The move from “strings” to “things” has been great for data-based searches, especially when it places those bits of data in the Knowledge Graph. These bits of data are the ones that typically answer the who, what, where, when, why, and how questions of Google’s self-defined “Micro-Moments.” Google can give users information they may not have even known they wanted at the moment they want it.

However, this push towards entities is not without a downside. While Google has excelled at surfacing straightforward, data-based information, what it hasn’t been doing as well anymore is returning highly relevant answers for complex query sets.

Here, I use “complex queries” to refer simply to queries that do not easily map to an entity, a piece of known data and/or a data attribute — thereby making such queries difficult for Google to “understand.”

As a result, when you search for a set of complex terms, there is a good chance you will get only a few relevant results and not necessarily highly relevant ones. The result is much more a kitchen sink of possibilities than a set of direct answers, but why?

Complex Queries And Their Effect On Search

RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities — called vectors — that the computer can understand. If RankBrain sees a word or phrase it isn’t familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries.

Bloomberg Business

Want to see complex queries in action? Go type a search into Google as you normally would. Now check the results. If you used an uncommon or unrelated set of terms, you will see Google throws up a kitchen sink of results for the unknown or unmapped items. Why is this?

Google is searching against items known to Google and using machine learning (RankBrain) to create/understand/infer relationships when they are not easily derived. Basically, when the entity or relationship is not known, Google is not able to infer context or meaning very well — so it guesses.

Even when the entity is known, an inability to determine relevance between the searched items decreases when relevance is not already known. Remember the searches where Google showed you the words it did not use in the search? It works like that, we just don’t see those removed search terms any more.

But don’t take my word for it.

We can see this in action if you type your query again — but as you type, look in the drop-down box and see what results appear. This time, instead of the query you originally searched for, pick one of the drop-down terms that most closely resembles your intent.

Notice how much more accurate the results are when you use Google’s words? Why? Google cannot understand language without knowing how the word is defined, and it cannot understand the relationship if not enough people have told it (or it does not previously know) the attributes are correlated.

These are how entities work in search in simplified terms.

Again, though, just what are entities?

Generally speaking, nouns — or Persons/Places/Ideas/Things — are what we call entities. Entities are known to Google, and their meaning is defined in the databases that Google references.

As we know, Google has become really excellent at telling you all about the weather, the movie, the restaurant and what the score of last night’s game happened to be. It can give you definitions and related terms and even act like a digital encyclopedia. It is great at pulling back data points based around entity understanding.

There in lies the rub. Things Google returns well are known and have known, mapped or inferred relationships. However, if the item is not easily mapped or the items are not mapped to each other, Google has difficulty in understanding the query. As mentioned previously, Google basically guesses what you meant.

Google now wants to transform words that appear on a page into entities that mean something and have related attributes. It’s what the human brain does naturally, but for computers, it’s known as Artificial Intelligence.

It’s a challenging task, but the work has already begun. Google is “building a huge, in-house understanding of what an entity is and a repository of what entities are in the world and what should you know about those entities,” said [Google software engineer Amit] Singhal.

So, How Does This Work?

To give an example, “Iced Tea,” “Lemons” and “Glass” are all entities (things), and these entities have a known relationship. This means that when you search for these items — [Iced Tea, Lemons, Glass] — Google can easily pull back many highly relevant results. Google “knows” what you want. The user intent is very clear.

What if, however, I change the query to…
Iced Tea, Rooibos, Glass
Google still mostly understands this search, but it is not as clear an understanding.
Why? Rooibos is not commonly used for Iced Tea, even though it is a tea.Now, what if we change this query to…
Iced Tea, Goji, Glass
Now, Google is starting to throw in the kitchen sink. Some items are dead on. Some items are only relevant to goji tea, not iced tea.
Google is confused.Now, if I make a final change to…
Iced tea, Dissolved Sugar, Glass
Google loses almost any understanding of what this query set means.  Although these are the ingredients in the recipe for sweet tea, you will see (amidst a few sweet tea recipes) some chemistry-related pages.
Why? Google does not know how to accurately map the relationship.
But what if I look at the drop-down for other terms that mean the same to me as a human when Google can no longer determine these entities and their relationship? What if I search the drop-down suggested result?
Glass of Sugary Iced Tea
The only meaningful words changed were “sugar” to “sugary,” and the word “dissolved” was dropped. Yet, this leads us to a perfect set of Sweet Tea results.

But why?

What Google can do is understand that the entity Iced Tea is, in fact, a thing known as Iced Tea. It can tell that a Glass is indeed a Glass.

However, in last example, it does not know what to do with the modifier Dissolved in relation to Iced Tea, Sugar and Glass.

Since this query could refer to the sugar in Iced Tea or (in Google’s “mind”) a sugar solution used in a lab, it gives you results that have Iced Tea. It then gives you results that do not have Iced Tea in them but do have Dissolved Sugar. Then, you have some results with both items, but they’re not clearly related to making Iced Tea.

What we see are pages that are most likely the result of RankBrain trying to decipher intent. It tries to determine the relationship but has to return a kitchen sink of probable results because it is not sure of your intent.

So what we have now is a set of query terms that Google must assess against known “things” (entities). Then, the relationship between these things is analyzed against known relationships, at which time it hopes to have a clear understanding of your intent.

When it has a poor understanding of this intent, however, it may utilize RankBrain to list you the probable result set for your query. Simply put, when they cannot match intent to a result, they use a machine to help refine that query to probabilities.

So where is Google going?

Google’s Future

While Google has been experimenting with RankBrain, they have lost market share — not a lot, but still, their US numbers are down. In fact, Google has lost approximately three percent of share since Hummingbird launched, so it seems these results were not received as more relevant or improved (and in some cases, you could say they are worse).

Google might have to decide whether it is an answer engine or a search engine, or maybe it will separate these and do both.

Unable to produce a semantic engine, Google built one based on facts. RankBrain has now been added to help refine search result because entity search requires not only understanding what the nouns in a search mean, but also how they are related.

Over time, RankBrain will get better. It will learn new entities and the likely relationships between them. It will present better results than it does today. However, they are running against a ticking clock known as user share.

Only time will tell, but that time is limited.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

Do You Hate “Not Provided”? Not So Fast… It May Be a Blessing In Disguise!

It’s no surprise to us SEOs that Google has been obscuring referring keywords from webmasters (a.k.a. “not provided”). Indeed, it has been the talk of SEO since before Google’s big switch to secure search, because it changes so much of how we approach keyword research and analysis.

One of my favorite tools for uncovering keywords in this brave new world of “not provided” is Searchmetrics, a search analytics platform that won “Best SEO software” in October at the U.S. Search Awards.

I recently had the opportunity to interview Searchmetrics’ founder, Marcus Tober, about the implications of “not provided,” potential workarounds and the future of keyword analysis and SEO in general.

Tober had a surprisingly user-centered mentality about the “not provided” issue in contrast to most discussion which has usually been all about the marketer’s woes. He encourages marketers not just to seek a direct solution to “not provided” by simply finding keyword data (although Searchmetrics does indeed supply it), but also to let it be a catalyst for reassessing our sites’ relationship to keywords.

Tober pointed out that, to the searcher, “not provided” isn’t an issue at all. There have been a lot of questions and “answers” circulating about why Google would enact secure search. Perhaps it is because, ultimately, targeting one specific keyword on a page is not resulting in the best possible user experience, and is, instead, just instances of ugly, unhelpful keyword stuffing.

Rather, getting rid of the ability to target one specific keyword from query to conversion may encourage sites to move toward a content strategy that provides the most helpful information possible on every page.

Instead of targeting one keyword, pursue a breadth of information that makes the page optimized for the searcher, not the search engine. Searchmetrics does, indeed, provide ways to get most of the data that became unavailable. However, that should not be the end-all be-all of your keyword action plan.

First (because you know you want it), let’s go over how you can indeed get your data “back.”  Then (because you need it), let’s go over how you should actually be using this data.

Keyword Data, Revealed!

Searchmetrics is capable of analyzing keyword reach by accessing its enormous library of keywords (over 600 million), which is the biggest in the biz.

When you begin a campaign, you can actually just input a URL, a domain or subdomain, and Searchmetrics gives you the option of putting in your own keywords for the domain you want to track, or it will scrape the page for keywords that are relevant and that it is ranking for. You then can monitor these keywords and create campaign goals.

‘Not provided’ took away the ability to evaluate the query to conversion calculations; however, it did not take away the ability to scrape the browser and take snapshots of the SERP. Through a series of parameters that evaluate the total of the SERP page, Searchmetrics comes up with a dynamic click calculation based on “there are x amount of users clicking on yourpage.com based on the site.”

This is also how SEO Visibility is calculated. SEO Visibility is the Searchmetrics metric that encompasses a large number of search ranking calculations to come up with one ‘visibility’ factor. SEO Visibility is now virtually accepted as industry standard for calculating relative SEO winners and losers.

You can also integrate your campaign with Google Analytics, Adobe Analytics, all major analytics companies, and even Google Webmaster Tools. This feature is totally free, and I absolutely recommend utilizing it, as any keyword conversion info is potential informational gold and allows you to integrate other information like social traffic into the keyword research process. Webmaster Tools will also give you a more in-depth look at data for your past 90 days.

Keyword Optimization 2.0

The old SEO strategy of optimizing one page for a specific keyword and then optimizing another page for another keyword is, frankly, out of style. “That paradigm is gone,” Tober insists. “The old methodology was to put one instance of the keyword in the title tag, once in the H1, a couple in the page copy. And you end up keyword stuffing.”

Tober agrees that this is no longer the methodology — not only because of “style,” but because it is no longer effective in SEO.

When Google released the Hummingbird algorithm, its engineers claimed that Google is now working to understand the meaning of a query, rather than simply matching strings of keywords. If Google is able to understand the meaning of a query, this also means it is able to understand the meaning of content.

This means that keyword stuffing will be unnecessary, as Google will be able to identify synonymous words and phrases. Searchmetrics had thousands of examples of similar keywords for which 9 of the top 10 results were identical after Hummingbird.

Google’s intention is to be able to understand well-built, useful, informative content such that it can determine the entire body of keywords a page should rank for, rather than just the keywords that a page is overtly “optimized” to rank for.

Because of this, Tober has crafted Searchmetrics’ capabilities to address “not provided” further — to give the user supplementary resources in creating related, holistic content pertaining to the site’s topic.

This can be especially effective for optimizing your landing pages. Let’s say your landing page is selling small business accounting software. You simply plug in the keyword at the top of the Searchmetrics interface, and the database searches its 600 million keyword inventory to come up with relevant, related keywords and concepts that you can add to the page to provide better, more user-friendly content.

Tober notes that his product is a vector database; consequently, the more specific you can get with your keyword target or topic, the more confidently the software can be that those keywords belong with that page.

However, if you are looking to expand the breadth of your site’s reach, it may be a great idea to put in a more general term to create ideas for new pages, features or blog posts.

What This Means For The Future Of Search

“Not provided” signals a future that may, indeed, already be here. Tober points out the clear winners and clear losers in the past couple of years, with trends that show an obvious shift in SEO from the outright technical to the user experience-oriented.

“Before it was more, ‘How can I create a campaign to get as many links as possible?’ or, ‘How can I cheat the system to get as many good links for cheaply as possible?’

However, now it is becoming more advanced,” Tober says. “Only the companies that get decent insights, with an algorithmic approach, will be those who are successful. Look at all these retailers — Amazon, Best Buy, Target, eBay, etc. — and you’ll notice they’re competing for the exact same keywords.”

Because there’s so much competition, the “not provided” situation guides us to instead reverse engineer our pages to achieve a better user experience than that of the competition. “The reason Amazon is so successful is that Amazon delivers diversity.”

Tober claims, “It’s not just a landing page that has just the product and nothing else, you have reviews, you have related products, you have really good content. Companies like Best Buy messed up because they didn’t understand how important the diversity of content via subtopics is. Amazon invested in that very early.”

So, if you’re not Amazon and aren’t incredible fortune-tellers of the future of search, or you don’t have the capability to time travel 10 years in the past to invest in user experience, Tober sums up his advice confidently as:

“Your page might have a lot of stuff going for it, but there is more that you should cover… Not just a couple of terms, but many terms that belong together.” 

Keyword research should be about discovering what is working for you, but then using that information further to extrapolate what could work better for you. Think more holistically about each page.

What other information could you provide on a page, or link to from a page, that would make it a more useful resource on the topic it’s focused on? How well is the search engine going to be able to determine exactly what your page is “about”? Is it focused on providing information on a specific topic or product? Does it have information that is “off-topic” and thus may confuse the engines as to the theme of the page?

Pages and sites that can answer these questions and provide true value to users are the ones which are more likely to rank well going into the future than pages that are very specifically optimized to try to rank for a single, specific keyword phrase.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

According To Google, Barack Obama Is King Of The United States

Ask Google who is the [King Of United States] and Google will inform you that it is Barack Obama, the current President of the United States.

The Google Answer is pulled from Breitbart, a story they posted five days ago named All Hail King Barack Obama, Emperor Of The United States Of America!

It appears that Google picked up that story and assigned it to their Google Answers database, potentially filled within their knowledge graph.

Why would Google show this answer when it is clearly wrong? Is it an Easter Egg? I strongly doubt it. But it does look to be a case where Google got it wrong when it comes to getting answers from third party sources.

This does remind me of the classic Google Bombs, where marketers were able to influence the search results to return specific articles based on specific queries. Google shut that down for the most part, but I guess this is an example of a Google Answers Bomb…

Via landt.co via Hacker News.

Postscript: Well, now it seems this site is powering the answer for this query:

Google Mobile Testing Search Results Snippet Descriptions

Google is testing showing site and search results description snippets in the mobile search results interface. Google launched site description overlays in the desktop results earlier this year and now it appears to be coming to mobile results.

I personally cannot replicate it but @northeasternext shared several screen shots with me to show this in action.

Here is a search result in the mobile interface that shows a snippet of data on Yelp:

When you click on that box with dots in it, the information will expand to show you knowledge graph like data below:

I cannot replicate this on any browser on my iPhone, nor within the Google search app.

Image credit to ShutterStock for top graphic

Google Quick Answers Adds Images

Google’s quick answer results have recently added images to some of the answers. Alex Chitu spotted this and I was able to replicate it for many queries.

Such as for [how old is barack obama] to [how many children does michael jordan have?] Here are both desktop views and mobile views of the images in the quick answers on Google.

The images do not come up for all searches, such as [how much does the iphone 5s weigh?] and []how many megapixels for iphone 5s] but it does work for iphone 5s focal length].

Some Of The Weird Issues When Google's Quick Answers Come From Random Sources

As Google’s Hummingbird algorithm continues to shape the answers within Google’s search results, webmasters, SEOs and searchers ask themselves, why is Google showing this knowledge graph or that quick answer. Often webmasters find themselves at a loss because Google takes their content and puts it at the top of the search results. While searchers ask, why is this the proper source for the answer – why not license content versus scrape content from webmasters?

I participated in a Google+ Hangout with Google’s John Mueller, who was presented some interesting cases of “branded” quick answers in the Google search results. John said that Google should avoid showing any branded details in the quick answers, Google says quick answers are technically not part of the knowledge graph. John said:

But I think, branded answers like that, probably not what we are trying to do here. I can definitely talk with the team here that works on that and see if we can improve that a little bit.

The two specific examples presented to John were these:

You can see here that Regus, a company that sells virtual office spaces, is being pulled for the quick answer in Google UK. But if you try the US search results, you get a definition:

The definition is being pulled from Oxford Dictionary but not citing it. Besides for the question on why Google isn’t citing the source of the answer here, why is Google showing the definition from Oxford in the US and a branded result from Regus in the UK?

Also, how about a search for [what is 411], we get AT&T, which is a huge brand for telecommunication. Why are we showing results from AT&T? Why not pull from Wikipedia or Oxford here, and why not Verizon or another telecom? You can see why this gets scary:

The last example, which I did not share previously, touches on a spammy area, the Payday Loan area:

Google is displaying a site that is a parked domain that is looking to sell. A year ago, they had content with a glossary page, see the wayback machine. Today, it is just a parked domain.

Maybe the solution for Google is simply to license content from non-branded third-parties and stop scraping answers from sources that may make searchers scratch their heads with confusion?