Another Google Game-Changer: How Going Mobile Friendly Will Boost Your Search Visibility

From Panda to Penguin to Hummingbird, Google has rolled out a series of major changes to its ranking algorithms over the past three years that have sent major shockwaves echoing through world of SEO.

While its latest announcement hasn’t been met with the same level of fanfare as these previous updates, it heralds an important turning of the tides in the future of search and signals to any business that depends on web traffic that the time has arrived to pay heed and take action.

So what is this latest game-changer from Google? In an article released November 18 entitled “Helping Users Find Mobile-Friendly Pages”, the search giant announced that it is now adding an eye-catching “mobile-friendly” label in front of its mobile search results.

How does Google define mobile friendly? According to the article, a page is eligible for the “mobile-friendly” label if it meets the following criteria as detected by Googlebot:

• Avoids software that is not common on mobile devices, like Flash
• Uses text that is readable without zooming
• Sizes content to the screen so users don't have to scroll horizontally or zoom
• Places links far enough apart so that the correct one can be easily tapped

Okay – so what's the big deal about mobile friendly?

On the surface, this may seem like a relatively minor aesthetic enhancement to Google’s search results pages. However, it’s the reasoning driving this modification that portends even bigger changes ahead. In Google’s own words:

“Have you ever tapped on a Google Search result on your mobile phone, only to find yourself looking at a page where the text was too small, the links were tiny, and you had to scroll sideways to see all the content? This usually happens when the website has not been optimized to be viewed on a mobile phone…We see these labels as a first step in helping mobile users to have a better mobile web experience.”

It’s those last few words – “a first step in helping mobile users to have a better mobile web experience” – that should prompt smart marketers, SEOs and webmasters to sit up and pay attention. While Google has long recommended the use of responsive web design for mobile device support, they are now taking proactive steps to draw attention to those sites that deliver an optimal mobile browsing experience in order to ensure that they are providing the best search results for mobile users.

What this means for you is simple: if your site is not yet optimized for mobile devices, now is the time to change that!

Will “mobile-friendly” sites receive higher ranking on search results pages?

As you can see from the example above, the new “mobile-friendly” label will certainly capture the attention of users searching on mobile devices over results listings that are not given the same designation. In that way, mobile-friendly sites will automatically receive a boost in visibility in mobile search results.

But the question on everyone’s mind is whether having a mobile-friendly site will actually affect where their listing is ranked on the search results page. The answer: quite likely. In the article, Google states that the labels are a “first step” in creating a better mobile web experience, but they also conclude by saying that they “are also experimenting with using the mobile-friendly criteria as a ranking signal.”

It has long been speculated that Google does, in fact, give extra weight to sites that offer quality multi-device support by employing a responsive design framework, but this statement is the first official acknowledgment of this practice. What this means is that in the months ahead, you may see websites that create an experience optimized for smaller-screen, touch-based devices start to climb the rankings over others that do not offer the same. Who knows – maybe Google will even start exclusively displaying mobile-friendly sites for mobile searchers, a move that could really shift the balance in favor of those that provide an optimal experience for mobile users!

If your company is searching for ways to rise above the competition and increase your exposure in search engine rankings, this could truly be a game-changing development – one that should absolutely be capitalized on immediately. Not only will optimizing your site for mobile improve the experience for a growing percentage of your users, but a responsive site is also very likely to be your ticket to improved Google rankings in the future while also earning you their new “mobile-friendly” designation today.

How do I get started?

Google offers a number of tools to help you determine whether or not your site is mobile friendly, starting with their “Mobile-Friendly Test.” Simply plug the URL for any page of your website into this tool, and if it fails the test, Google will offer some suggestions and recommended links to more information about how you can improve your site’s support for mobile users.

You can also use the Mobile Usability Report in Google Webmaster Tools, which highlights major mobile usability issues across your entire site, not just one page.

These tools are a good start, but there is a difference between “mobile friendly” and “mobile optimized.” A site that scales down to better display on small-screen devices and features navigational links that are easily usable on those mobile devices is “friendly”, but there are many other considerations that go into creating a site that provides a truly optimized experience for users on those devices. If your site fails the Google tests, evaluate the suggestions they offer and also be sure to speak to your web design firm about how best to address mobile device support on your site.

The bad news

So optimizing your site for mobile is going be awesome, right? It will improve the user experience for many of your customers, and now that Google is taking a firm stand on this issue, mobile optimization can actually mean greater visibility in search and improved rankings. Those are great reasons to jump aboard the responsive bandwagon, but the bad news is that making a site responsive is no small task.

Responsive web design is not a feature you can simply tack on to an existing site, especially one that is quite old and outdated. Responsive design often requires rethinking how a site’s content is presented, and it almost always involves rebuilding your site from the framework up. This means that to make your site truly mobile ready, you may be looking at a complete redesign.

As 2014 nears its end and 2015 is on the horizon, your company is likely in the midst of budgeting for the coming year, including planning your marketing expenses. Mobile optimization for your website, even if it will require a full site redesign, should be on your agenda because as Google has so clearly stated in their recent announcement, support for mobile devices is no longer just a nice-to-have luxury. Rather, to compete successfully on the Web of today and tomorrow, optimization for mobile devices is an essential element.

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Intelligent Design: Transform Your Website into a Sales Engine with Machine Learning

Machine learning: The phrase evokes images of computers playing chess or IBM’s Watson destroying two legendary Jeopardy! champions in a three-day tournament. The truth is, though, machine learning is no longer a novelty; it’s now an integral part of our daily lives. Every time you receive a product recommendation from Amazon, your email server weeds out spam before it reaches your inbox or you enjoy a playlist on Pandora, you’re seeing machine learning in action. In a nutshell, machine learning is the science of training computers to recognize data patterns and make adjustments automatically when those patterns change. While on the surface this may not sound very exciting, nothing could be further from the truth. In fact, machine learning is the key to transforming your website into a lean, mean selling machine.

Understanding machine learning in 100 words or less

Machine learning uses algorithms to build models from data; as more data is collected, the algorithms are “trained” to adapt to changes. There are two ways in which machine learning can be implemented: supervised and unsupervised. Supervised learning algorithms are used to create models that establish relationships between types of data — the relationship between purchase data and user clickstream data, for example. Unsupervised learning uses algorithms to gain insights into customer behaviors and preferences by looking for patterns within the data. Both of these methodologies are designed to make marketing and e-commerce more exact, more personal and more profitable.

Putting machine learning to work

Netflix, Pandora and Amazon are all familiar examples of machine learning in action. All three use recommender systems powered by complex algorithms. These systems collect data about your browsing activities, past selections and any ratings or reviews you may have provided. Then they segment you into clusters with other customers who have demonstrated similar interests or behaviors and use this data to suggest items that might appeal to you based on the browsing and purchasing habits of these other customers. You see this on Netflix as the category titled “Because you watched...” and on Amazon as “Customers who viewed this also viewed...” To gain a deeper understanding of how these algorithms work, let’s take a closer look at Amazon. To Amazon, you are a very long row of numbers in a massive table of data. Your row represents everything you’ve looked at, clicked on, purchased (or, equally as important, not purchased) or reviewed on the site. The other rows in this gargantuan table encompass the same thing for the millions of other customers who shop on Amazon. With every click, visit and purchase, more data is added to your row, which allows Amazon to constantly mold and shape the products it recommends to you and the special offers you receive based on an ever-evolving stream of information about you that is being collected and stored. Another innovative example is True Fit, a retail software start-up that is on a mission to apply data analytics to increase customer confidence in online clothing purchases while decreasing the number of returns for e-railers. Well-known fashion retailers, including Nordstrom, Macy’s and Guess, have implemented True Fit’s algorithms on their e-commerce sites. When customers shop on these sites, they’re asked to create a profile that includes their height, weight and perhaps most importantly, the size and brand of their favorite piece of clothing. Using that data, True Fit is able to recommend the correct size for a specific brand and article of clothing. Even more importantly, as customers continue to use the True Fit system, it learns more about their personal style and preferences and steers them toward purchases they’ll be more likely to keep and enjoy rather than return.

How machine learning drives smarter marketing

You don’t need the resources of major e-commerce giants like Amazon or Netflix to take advantage of machine learning to to improve your e-commerce site and your online marketing efforts. By enhancing your existing site with systems that allow you to create a virtual marketing intelligence brain, you can create a more personalized – and therefore higher quality – shopping experience for your customers. By establishing this type of marketing intelligence ecosystem, you can mine the data provided by customers every time they visit your site to answer vital questions that will help you fine-tune your site and your online marketing strategy – questions like these:

• How likely is a given website visitor to convert?
• What behaviors characterize customers who are likely to buy?
• What behaviors characterize customers who are likely not to buy?
• How can new visitors be identified as high-potential long-term customers?
• Which type of web traffic has the most value?
• Which products or services appeal most to a given segment of customers?
• Given the contents of a particular customer’s shopping cart, which additional products are high-potential recommendations?
• How can website visits be optimized to provide the best possible experience for each individual customer?

Making it personal

The final question in the list above is one that deserves special notice because of the staggering potential for using machine learning to create a more personalized shopping experience – one of the key drivers for increasing online sales. Not only can the data collected via such marketing intelligence ecosystems be used to drive recommender systems, it can also be used to create personalized advertising based on market segments — or even individual profiles — that can be distributed across a variety of desktop, mobile and social platforms. This type of advertising can be tailored to any number of personal preferences and demographic information, including age, marital status, location, lifestyle choices, typical purchases, brand preferences and so on. Ads can be focused to such a granular level that they reflect specific colors a given customer prefers, and their individual purchase drivers, such as status or cost-effectiveness. Another exciting aspect of machine learning-based personalization is the development of individual customer profiles. You can even combine online and offline customer data to create a more complete picture of a given user. Types of data included in this profile might include online and in-store purchases, membership and activity in rewards programs, product ratings and clothing sizes. Just imagine how much more powerful your marketing efforts could be if you were armed with this level of information. One of the most important aspects of a successful marketing intelligence ecosystem is how data mined from customer activities is combined with sound business rules in order to make smart recommendations that are well received by customers and that do not compromise their trust in your brand. For example, most people who walk into a supermarket like bananas and will often buy some. So shouldn’t the recommender simply recommend bananas to every customer? No – because it wouldn’t help the customer, and it wouldn’t increase banana sales. So a smart supermarket recommender would always include a rule to exclude recommending bananas. At the other end of the spectrum, the recommender shouldn’t push high-margin items just because it’s beneficial to the seller’s bottom line. It’s like going to a restaurant where the server steers you toward a particular high-dollar entree. Is it really his favorite? Or did the chef urge the staff to push the dish because it comes with a side order of premium mark-up? To build trust, the best recommender systems strive for some degree of transparency by giving customers clues as to why a particular item was recommended and letting them adjust their profile if they don’t like the recommendations they’re receiving.

Science fact, not fiction

Machine learning can give your business a serious competitive edge by opening the door new opportunities in the marketplace. It can help you personalize and improve your customer experience dramatically and thereby drive sales and revenues. Creatives and developers alike are rapidly pioneering new and innovative ways for marketers to use machine learning — and the future of marketing built on these ideas has seemingly endless possibilities.

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