The Growth of Search Complexity

Search has been evolving rapidly over the past decade and a half. At the WSDM conference in February 2009, Google Fellow Jeff Dean provided some interesting metrics that tell part of the story:

• Google search volume had grown 1,000 times since 1999.

• Google has more than 1,000 times the machines it had in 1999.

• Latency dropped from less than 1,000 ms to less than 200 ms.

• Index update latency improved by about 10,000 times. Whereas updates took Google months in 1999, by 2009 Google was detecting and indexing changes on web pages in just a few minutes.

These are staggering changes in Google’s performance power, and these are from six years ago. And of course this is just part of the changing search environment. Some of the early commercial search engines, such as Web Crawler, InfoSeek, and Alta Vista, launched in the mid-1990s. At that time, web search engines’ relevancy and ranking algorithms were largely based on keyword analysis. This was a simple model to execute and initially provided pretty decent results.

However, there was (and is) too much money in search for such a simple model to stand. Spammers began abusing the weakness of the keyword algorithms by stuffing their pages with keywords, and using tactics to make them invisible to protect the user experience. This led to a situation in which the people who ranked first in search engines were not those who deserved it most, but were in fact those who understood (and could manipulate) the search algorithms the best.

By 1999, Google launched, and the next generation of search was born. Google was the search engine that most effectively implemented the concept of citation analysis (or link analysis). As we outlined earlier in the book, link analysis counted links to a website as a vote for its value. More votes represent more value, with some votes being worth more than others (pages with greater overall link authority have more “juice” to vote).

This created a situation that initially made the spammers’ job more difficult, but the spammers began to catch up with this advance by purchasing links. With millions of websites out there, many of them with little or no revenue, it was relatively easy for the spammer to approach a site and offer it a nominal amount of money to get a link. Additionally, spammers could implement bots that surfed the Web, finding guest books, blogs, and forums, and leaving behind comments with links in them back to the bot owner’s site.

The major search engines responded to this challenge as well. They took two major steps, one of which was to build teams of people who worked on ways to detect spamming and either discount it (by not attributing value) or penalize it. The other step was to implement an analysis of link quality that goes deeper than just the notion of PageRank. Factors such as anchor text, relevance, and trust became important as well. These factors also helped the search engines in their war against spam.

But the effort to improve search quality as well as fight spammers continued. Historical search result metrics, such as how many clicks a particular listing got and whether the user was apparently satisfied with the result she clicked on, have made their way into search algorithms. In 2008, then–Yahoo! Chief Scientist Jan O. Pederson wrote a position paper that advocated use of this type of data as follows:

Search engine query logs only reflect a small slice of user behavior—actions taken on the search results page. A more complete picture would include the entire click stream; search result page clicks as well as offsite follow-on actions.

This sort of data is available from a subset of toolbar users—those that opt into having their click stream tracked. Yahoo! has just begun to collect this sort of data, although competing search engines have collected it for some time.

We expect to derive much better indicators of user satisfaction by considering the actions post click. For example, if the user exits the clicked-through page rapidly [then] one can infer that the information need was not satisfied by that page.

Throughout the last few years, Google, Bing, and Facebook have invested in making use of social signals as well. As we discussed in Chapter 8, links and mentions on social media sites could potentially be used to corroborate to the search engines that your content is worthy of ranking, though this does not appear to be in use today (other than the impact of Google+ on personalized search).

Consider also the notion of author authority. While Google killed the rel="author" tag, it can still create associations between a website and an author. If the author begins to establish a high level of authority, this could become a strong signal to the benefit of the site, even if that authority is established elsewhere.

In May 2007, Google made a big splash with the announcement of Universal Search (delivering what we have referred to in this book as “blended” search results). This was the beginning of the integration of all types of web-based data into a single set of search results, with data from video, images, news, blogs, and shopping search engines all being integrated into a single search experience.

This was only the very beginning of improved indexing for multimedia content. Google already has the ability to recognize the content of images in many cases. As we discussed in Chapter 10, Google Images already allows you to drag an image into the search box and it will try to recognize that image’s content.

Search engines also can make use of other data sources, such as domain registry data to see who owns a particular website. In addition, they have access to analytics data, data from their browsers, data from their ad networks, data from their web search toolbars, and data from free WiFi and Internet access distribution to track actual web usage on various websites. Other data sources will come into play too, such as data from wearables (e.g., Google Glass), embedded devices, and even your thermostat.⁷ Although no one knows how, or how much, the search engines use this type of data, these are additional information sources at their disposal.

Search engines continue to look for more ways to improve the quality of search results, and of the overall user experience within search. Google’s efforts toward personalization allow it to look at a user’s search history to get a better idea of what results will best satisfy that user. In 2008, Danny Sullivan summarized this entire evolution into four phases:⁸

• Search 1.0: keywords and text

• Search 2.0: link analysis

• Search 3.0: integration of vertical results

• Search 4.0: personalization

So, what makes up Search 5.0? Increased use of social media data appears to be one major possibility. The “wisdom of the crowds” may be becoming a factor in ranking, as discussed in Chapter 8. Mike Grehan talks about this in his paper, “New Signals to Search Engines”. He summarizes the state of web search as follows:

We’re essentially trying to force elephants into browsers that do not want them. The browser that Sir Tim Berners-Lee invented, along with HTML and the HTTP protocol, was intended to render text and graphics on a page delivered to your computer via a dial-up modem, not to watch movies like we do today. Search engine crawlers were developed to capture text from HTML pages and analyze links between pages, but with so much information outside the crawl, is it the right method for an always-on, ever-demanding audience of self producers?

Universal search was a step that acknowledged part of this problem by making all types of data available through web search. But many of these data types do not provide the traditional text-based signals that search engines rely on. Here is more from Mike Grehan’s paper:

Signals from end users who previously couldn’t vote for content via links from web pages are now able to vote for content with their engagement with that content. You can expect that these types of signals will become a significant factor in the future.

User engagement and interaction with content provides the search engines with information about the value of content beyond links and on an array of data types (such as images and video), and this gives the engines more tools to improve search quality. This type of data was first confirmed as a factor in the rankings of videos on sites such as YouTube, which has become the third largest search engine for the United States (according to comScore, Hitwise, and Nielsen Online, and behind the combined Bing/Yahoo! results), and was confirmed as a factor in general web search in 2011. YouTube’s ascent in search volume is particularly interesting because it is not a general web search engine, but one that focuses on a specific vertical—videos. This speaks to demand shifts taking place among the consumers of search results.

At the end of the day, the best results are likely to be provided by the best sites (there are exceptions; for example, for some search queries, the best results may be “instant answers”). The technology that has driven today’s engines was based on two ranking signals: good keyword targeting and good links, but user experience and engagement are now significant factors too.

More data collection means more opportunity to win, even if your site doesn’t conform flawlessly to these signals, and a better chance that if there is only one indicator you’re winning, you could be in big trouble. Keywords and links will likely remain the primary ranking factors for the next few years, but the trend toward search engines using the new signals is steadily gaining momentum and strength.

Following these advances, what will be next? Artificial intelligence (AI) already plays a huge role in search. As Google’s director of research Peter Norvig indicated to Eric Enge in an October 2011 interview:

If you define AI as providing a course of action in the face of uncertainty and ambiguity, based on learning from examples, that’s what our search algorithm is all about.

Further evidence of Google’s fascination with AI can be seen in Google cofounder Larry Page’s March 2014 TED presentation, “Where’s Google Going Next?”. In that presentation, he discussed how Google was applying machine learning to allow a computer to do things like play video games or, on their own, extrapolate the concept and nature of an entity like a cat. Google’s goal is to apply that kind of learning to enable machines to understand the nature of information and how to present it to searchers.

One example of this is how Google Translate works. It examines and analyzes millions of real-world translations to learn how one language translates into another. It learns by example, rather than trying to learn an artificial set of rules. Learning a language based on grammar does not work, because language is far too dynamic and changing. But learning from real-world usage does work. Using this technology, Google can offer instant translation across 58 languages.

Voice search works much the same way. Historically, speech recognition solutions did not work very well and required users to train the system to their voice. Google Voice uses a different approach, as noted by Peter Norvig in that same interview:

[For] Voice Search, where you speak your search to Google, we train this model on around 230 billion words from real-world search queries.

Google’s Dominance

Thousands of posts, news articles, and analysis pieces have covered the central topic of battling Google’s dominance in web search, but few have discussed the most telling example of the search giant’s superiority. Many believe that the key to Google’s success, and more importantly, a key component in its corporate culture, is its willingness and desire to return information relevant to a searcher’s query as soon as possible.

Some also believe that Google’s biggest impact in the search engine market is its advertising platform, which is the world’s largest. By expanding its search capabilities, it is able to create a more enticing advertising platform through AdWords, AdSense, and its embeddable Google search box. Its incredible infrastructure allows for site speed and crawling depth that is unmatched by any other engine.

However, it goes a bit deeper than that. In late 2008, tests were performed in which users were asked which search engine’s results they preferred for a wide variety of queries—long-tail searches, top-of-mind searches, topics about which their emotions ranged from great passion to total agnosticism. They were shown two sets of search results and asked which they preferred (see Figure 15-1).

Numerous tests like this have been performed, with a wide variety of results. In some, the brands are removed so that users see only the links. Testers do this to get an idea of whether they can win from a pure “quality” standpoint. In others, the brands remain to get an unvarnished and more “real-world” view. And in one particular experiment—performed many times by many different organizations—the results are swapped across the brands to test whether brand loyalty and brand preference are stronger than qualitative analysis in consumers.

It is this last test that has the most potentially intriguing results. Because in virtually every instance where qualitative differences weren’t glaringly obvious, Google was picked as the best “search engine” without regard for the results themselves (see Figure 15-2).

Figure 15-1. Comparing Google and Bing results

Figure 15-2. Results indicating that users may have a strong emotional preference for Google

Fundamentally, testers find (again and again) that the brand preference for Google outweighs the logical consideration of the search results quality.

Search engines that plan to take market share from Google are going to have to think differently. If Microsoft or a start-up search engine wants to take market share, it’s going to have to think less like a technology company trying to build a better mousetrap and more like a brand trying to win mindshare from a beloved competitor. How did Pepsi take share away from Coke? Or Toyota from Ford? That is beyond the scope of this book, but it is a process that can take more than a great idea or great technology. It requires a massive psychological shift in the way people around the world perceive the Google brand relative to its competition.

One strategy that Bing has embarked on is to leverage Facebook data in the Bing results; this data is shown in the right rail of the results page as a separate set of results. On October 13, 2010, the two companies signed a deal that provides Bing with substantial access to Facebook’s data on shares, likes, and more.⁹

In addition, as noted earlier, Bing’s Stefan Weitz talked about Bing’s focus on embedding search technology into devices and apps:

The question is, where is search really going? It’s unlikely we’re going to take share in [the pure search] space, but in machine learning, natural language search...and how we can make search more part of living. For us, it’s less about Bing.com, though that’s still important. It’s really about how we can instead weave the tech into things you’re already doing.

Google’s social media network, Google+, isn’t the runaway success Google executives hoped it would be. Google purportedly continues to invest heavily in Google+¹⁰, yet in February 2014, the New York Times likened Google+ to a ghost town, citing Google stats of 540 million “monthly active users,” but noting that almost half don’t visit the site. A significant redirection with Google+ is likely inevitable

Also consider the official Google mission statement: “Google’s mission is to organize the world’s information and make it universally accessible and useful.” It is already moving beyond that mission. For example, Google and NASA are working on new networking protocols that can work with the long latency times and low bandwidth in space.

Google is also pursuing alternative energy initiatives, which clearly goes beyond its mission statement. One example is its investments in self-driving cars. In addition, Google has ventures in office productivity software with Google Docs. These two initiatives have little to do with SEO, but they do speak to how Google is trying to expand its reach.

Another recent, now cancelled, venture for Google was its exploration into wearable technology, primarily in the form of Google Glass. Though Google Glass fell out of favor with much of the technology community, Glass was likely intended only as a first foray into the realm of wearable tech.¹¹ Despite the fact that Glass itself may have been released too soon, and in too much of a “beta” format, it dominated the early wearable search technology market. As wearable technology becomes more pervasive, expect Google to reintroduce another iteration of Google Glass in a bid to take a leading role in that market just as it has in mobile technology with Android.

Another potential future involves Google becoming a more general-purpose pattern-matching and searching engine. The concept of performing pattern matching on text (e.g., the current Google on the current Web) is only the first stage of an evolving process. Imagine the impact if Google turns its attention to the human genome and creates a pattern-matching engine that revolutionizes the way in which new medicines are developed. And the plethora of potential applications of pattern matching in the “real world” (such as disaster preparedness and logistics) is astounding; Google is uniquely poised to capitalize on this global opportunity with its understanding of our physical world from the ground (e.g., by Google’s self-driving cars), from in our homes (e.g., Google’s Nest acquisition), and from the sky via satellites and drones (e.g., Google’s acquisitions of Skybox Imaging and Titan Aerospace).

Engines Will Make Crawling Improvements

The search engines are breaking down some of the traditional limitations on crawling. Content types that search engines could not previously crawl or interpret are being addressed.

In May 2014, Google announced that it had substantially improved the crawling and indexing of CSS and JavaScript content. Google can now render a large number of web pages with the JavaScript turned on so that its crawlers see it much more like the average user would. In October 2014, Google updated its Webmaster Guidelines to specifically advise that you do not block crawling of JavaScript and CSS files.

Despite these improvements, there are still many challenges to Google fully understanding all of the content within JavaScript or CSS, particularly if the crawlers are blocked from your JavaScript or CSS files, if your code is too complex for Google to understand, or if the code actually removes content from the page rather than adding it. Google still recommends that you build your site to “degrade gracefully”, which essentially means to build the site such that all of your content is available whether users have JavaScript turned on or off.

Another major historical limitation of search engines is dealing with forms. The classic example is a search query box on a publisher’s website. There is little point in the search engine punching in random search queries to see what results the site returns. However, there are other cases in which a much simpler form is in use, such as one that a user may fill out to get access to a downloadable article.

Search engines could potentially try to fill out such forms, perhaps according to a protocol where the rules are predefined, to gain access to this content in a form where they can index it and include it in their search results. A lot of valuable content is currently isolated behind such simple forms, and defining such a protocol is certainly within the realm of possibility (though it is no easy task, to be sure). This is an area addressed by Google in a November 2011 announcement. In more and more scenarios you can expect Google to fill out forms to see the content that exists behind them.

Engines Are Getting New Content Sources

As we noted earlier, Google’s stated mission is “to organize the world’s information and make it universally accessible and useful.” This is a powerful statement, particularly in light of the fact that so much information has not yet made its way online.

As part of its efforts to move more data to the Web, in 2004 Google launched an initiative to scan in books so that they could be incorporated into a Book Search search engine. This became the subject of a lawsuit by authors and libraries, but a settlement was reached in late 2008. In addition to books, other historical documents are worth scanning. To aid in that Google acquired reCAPTCHA (e.g., see http://www.google.com/recaptcha), and in December 2014, Google announced a major enhancement to how reCAPTCHA works, with the goal of making it much more user-friendly.

Similarly, content owners retain various other forms of proprietary information that is not generally available to the public. Some of this information is locked up behind logins for subscription-based content. To provide such content owners an incentive to make that content searchable, Google came up with its First Click Free program (discussed earlier in this book), which allows Google to crawl subscription-based content.

Another example of new sources is metadata, in the form of markup such as Schema.org, microformats, and RDFa. This type of data, which is discussed in “CSS and Semantic Markup”, is a way for search engines to collect data directly from the publisher of a website. Schema.org was launched as a joint initiative of Google, Bing, and Yahoo! to collect publisher-supplied data, and the number of formats supported can be expected to grow over time.¹²

Another approach to this would be allow media sites and bloggers to submit content to the search engines via RSS feeds. This could potentially speed indexing time and reduce crawl burden at the same time. One reason why search engines may not be too quick to do this, though, is that website publishers are prone to making mistakes, and having procedures in place to protect against those mistakes might obviate the benefits.

However, a lot of other content out there is not on the Web at all, and this is information that the search engines want to index. To access it, they can approach the content owners and work on proprietary content deals, and this is also an activity that the search engines all pursue.

Another direction they can go with this is to find more ways to collect information directly from the public. Google Image Labeler was a program designed to do just this. It allowed users to label images through a game where they would work in pairs and try to come up with the same tags for the image as the person they were paired with. Unfortunately, this particular program was discontinued, but other approaches like it may be attempted in the future.

Multimedia Is Becoming Indexable

Content in images, audio, and video is currently not easily indexed by the search engines, but its metadata (tags, captioning, descriptions, geotagging data) and the anchor text of inbound links and surrounding content make it visible in search results. Google has made some great strides in this area. In an interview with Eric Enge, Google’s director of research Peter Norvig discussed how Google allows searchers to drag an image from their desktop into the Google Images search box and Google can recognize the content of the image.

Or consider http://www.google.com/recaptcha. This site was originally used by Google to complete the digitization of books from the Internet Archive and old editions of the New York Times. These have been partially digitized using scanning and OCR software. OCR is not a perfect technology, and there are many cases where the software cannot determine a word with 100% confidence. However, reCAPTCHA is assisting by using humans to figure out what these words are and feeding them back into the database of digitized documents.

First, reCAPTCHA takes the unresolved words and puts them into a database. These words are then fed to blogs that use the site’s CAPTCHA solution for security purposes. These are the boxes you see on blogs and account sign-up screens where you need to enter the characters you see, such as the one shown in Figure 15-3.

Figure 15-3. ReCAPTCHA screen

In this example, the user is expected to type in morning. However, in this case, Recaptcha.net is using the human input in these CAPTCHA screens to translate text that wasn’t recognized by OCR. It makes use of this CAPTCHA information to improve the quality of this digitized book.

Google used this methodology for years, but has since changed reCAPTCHA to focus more on images instead.¹³ This new approach is image based, and is intended to help Google with its computer vision projects.

Similarly, speech-to-text solutions can be applied to audio and video files to extract more data from them. This is a relatively processing-intensive technology, and it has historically had trouble with many accents and dialects, so it has not yet been universally applied in search. Apple’s Siri and Google Voice are leading the charge in addressing this issue. In addition, if you upload a video to YouTube, you can provide a search-indexable caption file for it, or request that Google use its voice recognition technology to attempt to autocaption it.

The business problem the search engines face is that the demand for information and content in these challenging-to-index formats is increasing exponentially. Search results that do not accurately include this type of data will begin to be deemed irrelevant or wrong, resulting in lost market share and declining ad revenues.

The dominance of YouTube is a powerful signpost of user interest. Users want engaging, multimedia content—and they want a lot of it. For this reason, developing improved techniques for indexing such alternative content types is an urgent priority for the search engines.

Interactive content is also growing on the Web, with technologies such as AJAX. In spite of the indexing challenges these technologies bring to search engines, their use is continuing because of the experience they offer broadband users. The search engines are hard at work on solutions to better understand the content wrapped up in these technologies as well.

Over time, our view of what is “interactive” will likely change dramatically. Two- or three-dimensional first-person shooter games and movies will continue to morph and become increasingly interactive. Further in the future, these may become full immersion experiences, similar to the holodeck on Star Trek. You can also expect to see interactive movies where the audience influences the plot with both virtual and human actors performing live. These types of advances are not the immediate concern of today’s SEO practitioner, but staying in touch with where things are headed over time can provide a valuable perspective.

User Intent

As just mentioned, Google personalized results are tapping into user intent based on previous search history, and serving up a mix not just of personalized “blue links” but of many content types, including maps, blog posts, videos, and local results. The major search engines already provide maps for appropriate location searches and the ability to list blog results based on recency as well as relevancy. It is not just about presenting the results, but about presenting them in the format that maps to the searcher’s intent.

User Interactions

One area that will see great exploration is how users interact with search engines. As the sheer amount of information in its many formats expands, users will continue to look to search engines to be not just a search destination, but also a source of information aggregation whereby the search engine acts as a portal, pulling and updating news and other content based on the user’s preferences.

Marissa Mayer, then Google’s VP of Location and Local Services (now CEO of Yahoo!), made a particularly interesting comment that furthers the sense that search engines will continue their evolution beyond search:

I think that people will be annotating search results pages and web pages a lot. They’re going to be rating them, they’re going to be reviewing them. They’re going to be marking them up...

Indeed, Google already offers users the ability to block certain results. Mayer’s mention of “web pages” may be another reason why the release of Google Chrome was so important. Tapping into the web browser might lead to that ability to annotate and rate those pages and further help Google identify what content interests the user. As of February 2014, StatCounter showed that Chrome’s market share had risen to an impressive 44%.

Chris Sherman, executive editor of Search Engine Land, offered up an interesting approach that the search engines might pursue as a way to allow users to interact with them and help bring about better results:

[F]ind a way to let us search by example—submitting a page of content and analyzing the full text of that page and then tying that in conjunction with our past behavior...

New Search Patterns

This is all part of increasing the focus on the users, tying into their intent and interests at the time of search. Personalization will make site stickiness ever more important. Securing a position in users’ history and becoming an authoritative go-to source for information will be more critical than ever. Winning in the SERPs will require much more than just optimizing for position, moving toward an increased focus on engagement.

Over time, smart marketers will recognize that the attention of a potential customer is a scarce and limited quantity. As the quantity of information available to us grows, the amount of time we have available for each piece of information declines, creating an attention deficit. How people search, and how advertisers interact with them, may change dramatically as a result.

In 2008, The Atlantic published an article titled “Is Google Making Us Stupid?”. The thrust of this article was that Google was so powerful in its capabilities that humans need to do less (and less!). Google has made huge advances since this article, and this is a trend that will continue. After all, who needs memory when you have your “lifestream” captured 24/7 with instant retrieval via something akin to Google desktop search or when you have instant perfect recall of all of human history?

These types of changes, if and when they occur, could transform what today we call SEO into something else, where the SEO of tomorrow is responsible for helping publishers gain access to potential customers through a vast array of new mechanisms that currently do not exist.

Growing Reliance on the Cloud

Cloud computing is transforming how the Internet-connected population uses computers. Oracle founder Larry Ellison’s vision of thin-client computing may yet come to pass, but in the form of a pervasive Google operating system and its associated, extensive suite of applications. Widespread adoption by users of cloud-based (rather than desktop) software and seemingly limitless data storage, all supplied for free by Google, will usher in a new era of personalized advertising within these apps.

Google is actively advancing the mass migration of desktop computing to the cloud, with initiatives such as Google Docs, Gmail, Google Calendar, Google App Engine, and Google Drive. These types of services encourage users to entrust their valuable data to the Google cloud. This brings them many benefits (but also concerns around privacy, security, uptime, and data integrity). In May 2011 Apple also made a move in this direction when it announced iCloud, which is seamlessly integrated into Apple devices.

One simple example of a basic application for cloud computing in the notion of backing up all your data. Most users don’t do a good job of backing up their data, making them susceptible to data loss from hard drive crashes and virus infections. Companies investing in cloud computing will seek to get you to store the master copy of your data in the cloud, and keep backup copies locally on your devices (or not at all). With this approach you can more easily access that information from multiple computers (e.g., at work and home).

Google (and Apple) benefits by having a repository of user data available for analysis—which is very helpful in Google’s quest to deliver ever more relevant ads and search results. It also provides multiple additional platforms within which to serve advertising. Furthermore, regular users of a service such as Google Docs are more likely to be logged in a greater percentage of the time when they are on their computers.

The inevitable advance of cloud computing will offer more and more services with unrivaled convenience and cost benefits, compelling users to turn to the cloud for their data and their apps.

Local Search

Local search was an active business before the advent of the Internet, when Yellow Pages from your phone company were the tool of choice. As the World Wide Web gained prominence in our lives, the Yellow Pages migrated online. These types of local search sites have themselves evolved to leverage more of the unique nature of the Web. Some of the major players are CitySearch, Local.com, YellowPages.com, and Superpages.

As the major search engines evolved, they integrated sophisticated mapping technology into their systems, and then began to map in local business data as well. You can find these local search engines at http://maps.google.com/, http://local.yahoo.com, and http://www.bing.com/maps. These search engines map in some of their business data from the same types of data sources that drive the Yellow Pages websites, such as Acxiom, InfoUSA, and Localeze, but they also supplement that data by crawling the Web.

One of the big challenges facing the local search engines is map spam, the proliferation of spam results in local search. Here are two blog posts that provide examples:

• “Illusory Laptop Repair—A Most Elegant Google Places Hack”

• “Google not Good Enough in Controlling Local Map Spam”

Resolving these issues is obviously critical for the search engines and is a major area of investment for them. We believe that local search is an area whose importance will continue to grow. With a very large number of searches having local intent, this is potentially a major frontier for developments in search.

Over time, you can expect that these problems will be worked out. Local search is already extremely powerful, but you can anticipate that the search engines will control the flow of customers in cyberspace and the real world, because customers will be finding merchants through search. You can also expect that more and more of these local searches will take place on mobile devices. Success in local search will be what makes or breaks many businesses.

Consumers are becoming increasingly reliant on local search and its auxiliary services—street maps, directions, satellite views, street views, 3D visualizations (Google Earth), enhanced listings, user reviews, and ratings. Through Google Mobile’s image recognition (formerly Google Goggles), Google even has the capability of searching based on photos you take with your mobile device. Augmented reality, where we see metadata juxtaposed on top of the physical world via a “looking glass,” is around the corner, and Google Glass is just a hint of what is to come.

With the help of social apps such as Yelp, FourSquare, and Facebook, mobile users can locate nearby friends, and search for special offers and discounts when they “check in” at certain stores or restaurants. Such tools guide consumers’ movements and their decision-making processes; this means the search engines and other search services are in a powerful position to manage the flow of customers from the Web to brick-and-mortar businesses. So, in a way, the search engines are in the logistics business, building the connection between online and offline.

Mobile Search

According to the June 2015 Ericsson Mobility Report, there were 7.1 billion worldwide mobile subscribers in 2014, with a forecast of 9.2 billion subscribers by 2020. (http://www.ericsson.com/res/docs/2015/ericsson-mobility-report-june-2015.pdf).¹⁴ As a result, the opportunity for mobile search has begun to grow rapidly. A Google study showed that search engines were the most visited sites on mobile devices, with 77% of those surveyed reporting their use.¹⁵ The same study showed that 9 out of 10 searchers took action as a result of conducting a search.

In addition, as of October 2014, Google began experimenting with using the mobile friendliness of a site as a ranking factor, and in May 2015 Google made Mobile Friendliness a significant ranking factor impacting results for users searching from Smartphones.¹⁶ This provides strong incentive to have a mobile search strategy in place (see “The Mobile Landscape” for more on this)!

The convenience of being able to get the information you need while on the go is just way too compelling. Why be tied to a desk if you do not have to be? Further, in many countries, freedom from a desktop is a necessity because users have much less personal space than their U.S. counterparts.

The ever-expanding versatility and power of tablet and mobile devices—from indispensable utility apps, to immersive multimedia players, to massively multiplayer online games, to paradigm-shifting hardware advances such as the iPhone’s multitouch display, proximity sensor, GPS, and gyroscopes—will fuel this growth. The network effect, whereby the value of the network grows by the square of the size of the network (Metcalf’s Law), gives further incentive for users to migrate to their mobile devices as more and more apps allow them to interact with their peers in increasingly more interesting ways.

The small keyboard/typing surface is currently a limitation, but Apple’s and Google’s voice-based solutions have already made great strides in replacing the keyboard as the input device of choice. The advent of the Linguistic User Interface (LUI) will continue to revolutionize mobile search, and with the LUI comes a whole new set of skills that will be required of the SEO practitioner.

In addition, wearable devices such as the Apple Watch are gaining traction. Voice technology is improving to the point where the absence of a keyboard will not be a significant limitation.

Voice Recognition Search

When users are mobile they must deal with the limitations of their mobile device, specifically the small screen and small keyboard. These make web surfing and mobile searching more challenging than they are in the PC environment.

Voice search is a great way to improve the mobile search experience. It eliminates the need for the keyboard, and provides users with a simple and elegant interface. Speech recognition technology has been around for a long time, and the main challenge has always been that it requires a lot of computing power.

Several examples already exist:

Google Voice Search

This is a free service from Google that enables you to perform a Web search based on text-to-speech voice queries instead of traditional text-typed queries.

Google Mobile App for iPhone

Included in this application is voice searching capability. You can speak your query into the application and the results are displayed on-screen.

Apple’s Siri

What made Siri a significant step forward after its launch was its use of more natural human speech (Google offers similar capabilities now). This was the start of a movement toward the device learning the user, rather than vice versa.

Microsoft Cortana

In April 2014, Micorosft unveiled its personal assistant offering, known as Cortana. Cortana is being promoted as offering more capabilities for learning your personal preferences than the competing options. Currently, it is designed to work only on Windows Phones.

Processing power continues to increase, even on mobile devices, and the feasibility of this type of technology is growing. This should be another major area of change in the mobile search landscape.

In addition, it is reasonable to expect that voice recognition technology will be applied to the actual recorded content of audio and video files, to determine content and aid in ranking these media types within search results—something that will likely lead to video “script” optimization as an added component of video SEO.