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It’s that time of year. Holiday music, sweet treats, classic movies — all the reasons to cozy up and spend time at home with the ones you love. And with Google Home, there are countless ways to spread the holiday cheer.  

Play Netflix on your TV with the magic words “Ok Google”

Serve up the holiday classics that make your season bright, hands-free, right to your TV — when Google Home is paired with a Chromecast device. Now, in addition to YouTube videos, you can stream any movie or TV series from Netflix to the TV, using just your voice*. Start with “Ok Google, play Stranger Things from Netflix on my TV” or “Ok Google, play Love Actually on Netflix on my TV”. You can even control your media with commands like “Pause this episode”, without lifting a finger. Here’s how to link your Netflix account to the Google Assistant from the Google Home app (make sure you’re running the latest version). Netflix linking in the Google Home app is available now for iOS and rolling out to Android this week.

Enjoy your Google Photos on the big screen, hands-free.

The holidays is the perfect time to gather around and relive memories from the year. Google Photos now works with Google Home, so you can share your photos on your Chromecast-connected TV, using just your voice. Simply say, “Ok Google, show my photos of Lake Tahoe on my TV” to see your beautiful pictures bigger. Even if you can’t remember the name of the album, you can search for pictures. Try “Ok Google, show my photos of selfies at Christmas on my TV” or “Ok Google, show my photos of Brazil sunsets on Chromecast”. You can even use your voice to search for your places, things and people.

Learn how to link your Photos account to the Google Assistant here.

Enjoy holiday music from your favorite apps

Fill your home with festive tunes from your favorite music apps like Google Play Music, Spotify, Pandora, or YouTube Music with just a simple voice command. Say, “Ok Google, play Hanukkah music” or “Ok Google, play Christmas music” and you’ll get into the spirit in no time.

Keep track of Santa

Whether he’s preparing his sleigh or already in flight, your Assistant keeps you informed of Santa’s latest whereabouts (tracked by Google Maps). Just say “Ok Google, where’s Santa right now?”

Get YouTube Red FREE for six months

Keep the holiday glow going well into the new year as you enjoy a premium music experience — more songs, more versions, no ads — with YouTube Red. Get six months free of YouTube Red when you buy a Google Home before December 31, 2016**.

Make your holidays truly magical with Google Home.

*Netflix streaming membership required

**Offer expires 12/31/16. Terms apply. New subscribers only.

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Posted by Dr-Pete

Summary: RankBrain represents a more advanced way of measuring relevance, built on teaching machines to discover the relationships between words. How should RankBrain change our approach to SEO and specifically to keyword research?

This story starts long before RankBrain, but the action really kicked in around May of 2013, when Google announced conversational search for desktop. At the time, voice search on desktop may have seemed like a gimmick, but in hindsight it was a signal that Google was taking natural language search seriously. Just a few months later the Hummingbird update rewrote Google’s core engine, and much of that rewrite was dedicated to dealing with natural language searches.

Why should you care about voice? For most sites, voice is still a relatively small percentage of searches, and you’ve got other priorities. Here’s the problem, illustrated by the most simplistic Google algorithm diagram I’ve ever created…

If there were two algorithms – one for text search and one for voice search – then, yes, maybe you could drag your feet. The reality, though, is that both text and voice search are powered by the same core algorithm. Every single change Google has made to adapt to natural language searches impacts every search, regardless of the source. Voice has already changed the search landscape irreversibly.

Natural language in action

You may be skeptical, and that’s understandable. So, let’s take a look at what Google is capable of, right now, in 2016. Let’s say you wanted to find the height of Seattle’s iconic Space Needle. As a seasoned searcher, you might try something short and sweet, like this…

“Space Needle height”

Google understands this question well enough to attach it to the corresponding Knowledge Graph entity and return the following:

The corresponding organic results appropriately match the informational query and are about what we’ve come to expect. Google serves this search reasonably well.

“What is the height of the Space Needle?”

Let’s try to shake off our short-form addiction and try a natural language version of the same search. I won’t repeat the screenshot, because it’s very similar, as are the organic results. In 2016, Google understands that these two searches are essentially the same.

“How tall is the Seattle Space Needle in meters?”

Let’s try another variant, switching the “What” question for a “How” question, adding a location, and giving it a metric twist. Here’s what we get back:

Google understands the question and returns the proper units. While the organic results vary a bit on this one, reflecting the form of the question, the matches remain solid. Natural language search has come a long way.

Build great concepts!

This all may be a bit alarming, from a keyword research perspective. Natural language searches represent potentially thousands of variants of even the simplest queries. How can we possibly operate on this scale as search marketers?

The popular notion is that we should stop targeting keywords and start targeting concepts. This approach has a certain logic. The searches above share a general notion of “tallness,” which might look something like this:

“Tall” and “height” are fairly synonymous, words like “size” and “big” are highly related, and units like “feet” and “meters” round out this concept. In theory, this makes perfect sense.

In practice, the advice to target concepts is a bit too much like saying “build great content.” It’s a good goal, in theory, but it’s simply not actionable. How do we build great concepts? We all intuitively understand what a concept is, but how does this translate into specific search marketing tactics?

There’s an even bigger problem, and I can illustrate it with one box:

Ok, one box, a logo, and two buttons. At the end of the day, you can’t type a concept. Search users, whether they’re typing or speaking, have to put words into that box. So, how do concepts, which we all agree exist and are useful, translate into keywords, which I hope we can all agree are still unavoidably necessary?

Language in action, part 2

We need to take a side path on this journey for a moment. Part of rethinking keyword research is understanding that we’re no longer bound by an exact-match world. This isn’t a bad situation to be in, just a complex one. I’d like to tell a story with examples, showing just how far Google has come in understanding the ways that different keywords relate to each other…

Plurals (“scarf” & “proxies”)

While we all know the dangers of keyword stuffing, it originated out of a certain necessity. Search engines simply weren’t capable of equating even simple terms, like plurals. Those days are long behind us. Google understands, for example, that a search for “scarf” should also return results for “scarves”:

In these examples, I’ll be using Google’s own highlighting (the bold text; I’ve added the green boxes) to show where Google seems to understand equivalence or related concepts. Of course, Google’s core relevance engine and highlighting engine are not exactly the same, but I think it’s safe to say that the latter is a useful window into the former.

Google is also fully capable of understanding the reverse. Let’s say, for example, that a “friend” of mine wants to buy proxy IPs. He might search for “proxies”:

Google can easily understand even irregular plurals in both directions.

Stemming (“ballroom dancer”)

Plurals are relatively easy. Let’s step it up a little. Another frequent problem in search is dealing with stemming, which relates to root words and the forms they can take, such as “run” vs. “running.” Here’s a sample search for “ballroom dancer”:

Google is perfectly capable of equating “dancer” to other forms of the word, including “dances,” “dance,” and “dancing.” Once again, keyword stuffing is at best outdated thinking.

Abbreviations (“Dr. Who”)

Can Google recognize common abbreviations? Let’s try a search for our second-favorite doctor (hint, hint, wink), “Dr. Who”:

Google easily makes the connection between “Dr.” and “Doctor.” Interestingly, none of the organic titles or snippets I see on page one contain the word “Dr.”

Acronyms (“SNL skits” & “TARDIS”)

How about acronyms? Here’s a search for “SNL skits”:

Google has no problem interpreting “SNL” as equivalent to “Saturday Night Live.” Interestingly, they also understand that “skits” is synonymous with “sketches.” What if we spell out an acronym that isn’t usually spelled out, such as “Time And Relative Dimension In Space”?

Here, Google is happy to tell us “Hey, nerd, just say ‘TARDIS’ like everyone else.” The six-letter acronym is interchangeable with even the much longer search string.

Acronyms+ (“NJ DMV”)

This is where things get interesting. Here’s a search for “NJ DMV.” Look closely:

Not surprisingly, Google understands that “NJ” equals “New Jersey.” There’s a problem with this search, though – New Jersey doesn’t call their motor vehicle office the DMV, they call it the MVC (Motor Vehicle Commission). Google understands not only how to expand an acronym, but that the acronyms DMV and MVC are conceptually equivalent.

Synonyms (“discount airfare”)

The flip-side of no longer being confined to exact-match keywords is that you might just be finding yourself faced with a lot more competition for any given keyword. Let’s look at a competitive, commercial query, such as “discount airfare”:

Here, “discount airfare” gets matched to “airfare deals,” “discount tickets,” and “cheapest flights,” with even more variations on the rest of page one.

Synonyms+ (“upscale department stores”)

Wait, it gets worse. Google can go beyond traditional synonyms. Consider this search for “upscale department stores” (run from my home-base in the Chicago suburbs):

Not only does Google recognize that “upscale” is synonymous with “luxury,” but they’ve matched on actual examples of luxury department stores, including Bergdorf Goodman, Saks Fifth Avenue, and more.Answers (“Doctor Who villains”)

We’ve moved from simply synonyms to a world of answers. Here’s another example, a search for “Doctor Who villains”:

It’s a parlor trick to tell you that “villains” is synonymous with “monsters” and “enemies.” What you really want to know is that Doctor Who’s rogue’s gallery includes Daleks, Cybermen, and Weeping Angels. Google can make this connection.

These aren’t just exceptions

It’s easy to cherry-pick examples, but are these edge cases or the new normal? I ran an analysis on 10,000 keywords (page one only) and found that only 57% of results had the search phrase in both the title and snippet. I used a pretty forgiving match (allowing for plurals, for example) and the keyword set in question is mostly shorter terms, not long-tail queries. I also allowed the terms to occur in any order. Keep in mind, too, that display snippets aren’t always META descriptions – they’re chosen by Google to be good matches.

All of this is to say that, even with a fairly forgiving methodology and a loose definition of a “match,” just over half of page-one results in my data set matched the search query. The examples above are not outliers – they are our immediate, unavoidable SEO future.

The Algorithm is learning

This deep into the article, you may be wondering what any of this has to do with RankBrain. There’s been a lot of speculation around RankBrain, and so I’m going to do my best to work from the facts as we understand them. You’re going to need some essential background information…

What, exactly, is deep learning?

First, the one thing we all seem to be able to agree on is that RankBrain uses machine learning, thus the “brain” part. Specifically, RankBrain uses “deep learning.” So, what is deep learning? According to Wikipedia:

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations.

Crystal clear, right? To understand deep learning and the state of modern machine learning, you have to understand neural networks. Let’s start with a simple neural network, the kind that were popular in the early 1990s:

Neural networks were built on a basic understanding of the human brain as a system of “nodes” (neurons) and connections between those nodes. At scale, the human brain is capable of learning incredibly complex ideas using this system of nodes and connections.

So, how do we put this model to work? Let’s start with what’s known as “supervised learning.” In a neural network like this, we have a known set of inputs and a desired set of outputs. Given a certain X, we want to teach the system to return Y. We use these inputs and outputs to train the system, gradually weighting the connections. The hidden layer adds computational complexity, giving the machine enough connections to encode interesting data.

Training itself uses methods that are cousins of linear regression (at the risk of oversimplification). Over a large set of inputs and output, we want to minimize the error of our model. In some cases, we work backward from the output(s) back to the input(s), in much the same way you might work a difficult paper maze from the finish back to the start.

Why go to all this trouble? If we know the inputs and outputs (sticking just to supervised learning, to keep this simple), why don’t we just have a lookup table? If X, then Y – simple. What happens when we get an input that isn’t in the table? The system fails. The magic of neural networks is that, if the system is properly trained, it can return outputs for completely new inputs.

To make a very long story only medium-long, these simple neural networks were interesting playthings, but weren’t capable of solving many complex problems. So, we put them aside. Then, the inevitable happened – computing power increased exponentially and got cheaper (thanks, Gordon Moore!). Specifically, we invented the GPU. You might think of the GPU as something built for gamers, but it is, in essence, a very powerful math machine.

At some point, simple neural networks scaled up massively, and I mean massively – on the order of 1,000,000X larger. These new machines were able to perform much more interesting tasks, and a new age of neural networks was born. These new machines required more complex methods, and thus, at the risk of oversimplifying a very complex topic, deep learning was born.

How does Google use deep learning?

Fortunately, we know a bit more about RankBrain. In Steven Levy’s excellent article about Google’s machine-learning ambitions, he quotes the following from Jeff Dean, head of the broader Google Brain group…

By early 2014, Google’s machine learning masters believed [Amit’s approach] should change. “We had a series of discussions with the ranking team,” says Dean. “We said we should at least try this and see, is there any gain to be had.” The experiment his team had in mind turned out to be central to search: how well a document in the ranking matches a query (as measured by whether the user clicks on it). “We sort of just said, let’s try to compute this extra score from the neural net and see if that’s a useful score.”

Amit Singhal, the head of Google’s Search team until early 2016, pioneered the heuristic approach – what we might call the “ranking factors.” Machine learning (ML) advocates at Google eventually were able to convince the team to test ML in a ranking context. By all accounts, that experiment went very well and the score was indeed useful.

It’s also worth noting that Amit, who was reported to be skeptical of using ML in organic search, left Google and was replaced by John Giannandrea, who was instrumental in many ML projects at Google. I won’t speculate on Amit’s motivations, but the shift in leadership to a strong ML advocate clearly implies that Google considered the RankBrain experiment a success.

Of course, it begs the question: How exactly are ML and deep learning in play in organic search? Google teaches a deep learning course on Udacity, and I was intrigued to find this screenshot in a quiz. The quiz asked how Google might use deep learning in rankings, and this was the answer:

When we train an ML model, the “classifier” is essentially the resulting decision machine. In this case, that classifier takes in a search term and web page as inputs and decides how relevant they are to each other.

Two things are worth noting in this deceptively simple screenshot. First, ML is being used as a relevance engine. I think it’s safe to say that the quiz is not entirely hypothetical. Second, notice the query and the matching page. The query is “Udacity deep learning”, but the matching result title contains the related phrases “machine learning” and “supervised learning.” This is starting to look like some of the examples we saw earlier.

Another resource we have is the original Bloomberg article about RankBrain, which is still one of the more comprehensive pieces on the subject. The article quotes senior Google research scientist Greg Corrado and makes the following very specific claim:

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.

Again, RankBrain is being called out as essentially a relevance engine, a machine for better understanding the similarities and relationships between words. What are these vectors the article mentions, though? In the general sense, vectors are a mathematical concept – a point in space with both direction and magnitude. Vectors are a way of encoding complex information.

Thankfully, we have another clue, from Google’s public ML project, TensorFlow. One of Google’s side projects is a library called Word2Vec that, as the name implies, uses ML to convert words into vectors. Traditional methods of encoding words for information retrieval can deal with simple problems like pluralization and stemming, but have little or no sense of relationships. Word2Vec and similar models are capable of learning relationships like the examples below (Source: Tensorflow.org, ©2016 Google):

Here, Word2Vec has learned that the relationship between man and woman is the same as the relationship between king and queen (encoded in the direction of the vector). Similarly, the relationship between the verb tense walking to walked is the same as the relationship between swimming and swam. More importantly, these rules didn’t need to be specified. The machine learned them by studying large collections of real words in context.

Google’s actual algorithms are almost certainly more complex than the publicly available Word2Vec library, and researchers have combined vector-based approaches with other approaches, such as the more familiar LDA (latent dirichlet allocation), but it seems very likely that an approach like this is in play in RankBrain.

RankBrain is NOT query translation

It’s easy to mistakenly jump to the conclusion that RankBrain simply translates unfamiliar queries into more familiar ones, or long queries into short queries. This is not the case. RankBrain seems to operate in real-time and can compare multiple versions of a search phrase at once.

If I mistakenly type a search like “Benedict Crumblebatch,” Google will tell me this:

In this case, Google has tried to interpret my intent and has replaced my query with what it thinks is a better version. This is query translation. In this case, all of the results match the translated query and it overrules my original search.

Revisiting an example from above, if I search for “scarf,” I can get back matches on both “scarf” and “scarves” (even in the same result):

Google is not translating “scarf” –> “scarves” and then returning matches on the new term. Google is applying a powerful relevance engine that recognizes these matches in real-time.

Are we sure it’s RankBrain?

Let me be clear on one thing – relevance is a very complex process, and it’s hard to know for sure where traditional information retrieval methods end and RankBrain begins. I can’t say with certainty that all of the examples I showed previously represent RankBrain in action.

However, there is one more piece of evidence. Remember the “NJ DMV” example? Google was able to understand that “DMV” (Department of Motor Vehicles) and “MVC” (Motor Vehicle Commission) are equivalent concepts in New Jersey.

Our data science team, led by Matt Peters, put together an ML prototype that uses a method similar to Word2Vec. If you input search terms into this tool, it looks at the corresponding Google results and calculates the similarity between those results and the original query:

This screenshot has been edited, but the data is real. What the tool is saying is that a page with the title “State of New Jersey – Motor Vehicle Commission” is a good match (93%, although the system is a little forgiving) for “NJ DMV.” The fact that we can train an ML system to perform this task doesn’t prove RankBrain does it, but it does at least show that it is well within Google’s ML capabilities.

When did RankBrain roll out?

Please note that RankBrain is often tied to the announcement date in October of 2015, but that article also says that RankBrain was in play “for the past few months.” Steven Levy’s article on ML in Google gives a date of April 2015 for the rollout, and we believe that timeline is accurate. RankBrain has probably been in play for at least 1 1/2 years at the time of this writing.

How do we adapt to RankBrain?

In a world where Google can understand stemming, synonyms, and even answers, how do we approach keyword research? Let’s go back to our Space Needle example. I’m going to use Moz’s Keyword Explorer as a backdrop for the rest of this discussion. Let’s say I fire up my trusty keyword research tool and enter the phrase “space needle height”:

Even out of the gate, we’ve got 1,000 keywords to deal with, many of which are fairly similar. How do we go about targeting these 1,000 variations?

Option 1 is to write 1,000 pages, each laser-targeted at a single phrase. We know, practically, that either this is going to be a huge amount of work or is going to lead to thin content. Sites filled with templated pages that only vary by a few keywords are a lousy user experience and prime bait for Google’s Panda algorithm.

Option 2 is to take as many of these phrases as possible and just stuff them into a single paragraph. I’ve done this for you, and here’s the kind of result you can expect:

SPACE NEEDLE HEIGHT
The Space Needle height (Seattle) is 605 feet. The Space Needle height in stories is just over 60. It’s interesting to note that the Space Needle height comparison to the Empire State Building is about half as high. In contrast, the Seattle Space Needle height comparison to Chicago’s Willis Tower is only about one-third the height.

The bolded phrases are my target phrases. I hope we can all agree that this isn’t optimal content crafting if our goal is to convince our audience that we’re a credible source of information.

I propose a third option. You may have noticed a pulldown in Keyword Explorer for [Group Keywords]. This does exactly what it sounds like it does. Let’s take all of these very similar keywords (and you could do this by hand as well, if you’re willing to put in the time) and try to group them. We end up with something like this:

The system has tried to bucket the keywords into broader, more useful groups, allowing us to ignore some of the minor variants. So, let’s pick three groups from this list:

“space needle height”
“space needle height in stories”
“space needle how tall”

What if we chose representative, natural language phrases within each of these groups? Think of them as exemplars of the group. We might pick something like this:

“height of the Space Needle”
“Space Needle is ___ stories”
“How tall is the Space Needle?”

Now, let’s craft a paragraph around these more natural, diverse phrases:

HOW TALL IS THE SPACE NEEDLE?
The height of the Space Needle in Seattle, Washington is 605 ft. (184 m), including the antenna. Interestingly, while the Space Needle is approximately 60 stories tall, it only occupies 6 floors, with most of the tower being structural. While it was once the tallest building in Seattle, the Space Needle now ranks only 7th.

Not only have we written a paragraph that might actually be valuable to humans, but we’ve covered our three target phrases and even had room for a fourth (“tallest building in Seattle”). What’s more, each of these phrases represent groups of dozens or hundreds of similar keywords. By writing to the groups or broader concepts instead of narrowly targeted phrases, we’re able to cover many keyword variants efficiently.

3 Gs: Gather, Group, Generate

I’ve taken to calling this approach to keyword research the 3 Gs, and it goes likes this:

Gather keywords
Group keywords into clusters
Generate exemplars

Another way to think of this process is that we’re grouping keywords into concepts, and then converting each concept back into a representative keyword/phrase: Keyword –> Concept –> Keyword*. The result is a specific search phrase to target, but that phrase represents potentially dozens or hundreds of similar keywords.

Let’s work through another example, but one with commercial intent. Pretend you’re working in the Seattle apartment space and are looking to write an article about rental costs. Just to pick a starting point, you enter “Seattle rental prices” into your keyword research tool of choice and gather your keyword list:

Naturally, we get back a list of related but sometimes very similar keywords. Even in this list, we can start to see some interesting variations (“average rent”, prices by year, mapped prices, etc.), but let’s take it to step two and group these keywords:

In a real-world keyword research scenario, we’d want to thoroughly explore all of the groups, but I’ve picked three for now that caught my eye (underlined in green). They are:

“Seattle average rent by neighborhood”
“Seattle housing prices skyrocket”
“cheapest Seattle apartments”

How do we go about generating an exemplar from each group? Sometimes, intuition is fine. For example, the keywords our system has grouped under #2 turn out to be a bit of an odd mix, but I really like how “skyrocket” resonates and “housing prices” is a good keyword variant, so I’ll pick a phrase. For something like #3, we may choose to just see what variation has the highest potential for traffic. In Keyword Explorer, we can simply expand that group, select the keywords, and add all of them to a list, like this:

Once the stats for the list are collected, we can take a look and see that “cheapest apartments in Seattle” has both the highest traffic volume and Keyword Potential, according to our metrics:

For the final group (“Seattle average rent by neighborhood”), I browsed the grouped keywords, and one caught my eye: “average rent downtown seattle.” I like this one because it’s specific to an actual neighborhood, although we might choose to craft content around some kind of neighborhood-by-neighborhood theme as well. What I like about trying to understand our keywords as groups/clusters is that it’s also a great process for generating content ideas.

So, let’s put some exemplars against our three groups. We might end up with something like this:

“average rent in downtown Seattle”
“Seattle housing prices are skyrocketing”
“cheapest apartments in Seattle”

These are all rich phrases that we can use to craft content, and they’re built on a logical framework of keyword research. Even using just this single list, our system claims these three groups represent at least 64 keyword phrases. Factoring in the long-tail, they potentially represent hundreds more.

Eventually, we may have ML tools that can take large groups of related phrases and help find the perfect exemplar. Even now, Keyword Explorer’s grouping engine is built on ML. There will come a time very soon when ML is part of our everyday work as SEOs.

There’s a fourth, unofficial G: Gap. As our British friends might say, mind the gap. The exemplars you build in this process are meant to be natural-language phrases that represent dozens of keywords, but our understanding of a concept and Google’s won’t always match, and some searches you hoped you’d rank for will fall through the cracks. It’s important to continue to monitor and track a large set of keywords. If you see that some aren’t improving, consider generating new exemplars or targeting them separately. This is an iterative process, and we still have to get our hands dirty with real searches every day.

Bonus: Keyword brainstorming

Here’s something fun to try. In Keyword Explorer, you can specifically request keyword phrases that contain none of the words in your original phrase. Why would you want to do this? It can help you find related concepts that you might not have considered.

From the [Display keyword suggestions that] pulldown, select “exclude your query terms to get broader ideas.” Here are some of the results I get on a search for “Seattle rental prices” with grouping on (I’ve edited this list a bit just to show some of the more interesting results in the space allowed):

Some of these are obvious (although still interesting), like searches that use specific neigbhorhood names (e.g. “best Capitol Hill apartments”). Some are less obvious and open up some new avenues. “Kirkland apartments under $1000” reminds us that both neighborhood and price sensitivity matter in similar searches. These are aspects we can’t ignore in our broader keyword research on this topic.

The second to the last is really interesting, IMO: “apartments near Amazon headquarters.” Being such a big employer (we know all too well, given the competition for talent in Seattle), a content focus on just apartments near Amazon’s headquarters could get a lot of traction. Finally, while it’s not the most useful topic or keyword to target, “too damn expensive” is certainly a good headline phrase to tuck away.

Why not just write for people?

If Google is really understanding natural language searches and becoming more intelligent, why don’t we just write content for people and forget about this whole process? It’s a fair question. If your choices are 2005-era keyword stuffing and thin content or writing for people, then please, for the love of all that this is holy, write for your human site users (and, by extension, search users).

There’s a problem, though, and it’s probably easier to show than tell…

Google has come a long way in their journey from a heuristic-based approach to a machine learning approach, but where we’re at in 2016 is still a long way from human language comprehension. To really be effective as SEOs, we still need to understand how this machine thinks, and where it falls short of human behavior. If you want to do truly next-level keyword research, your approach can be more human, but your process should replicate the machine’s understanding as much as possible.

I hope you’ll give the 3 Gs a try and let me know what you think. I’ll freely admit I’m biased and hope you’ll also give Keyword Explorer a try, if you haven’t yet (and if you have, test out some of the new tricks I’ve talked about).

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Posted by mark-johnstone

A few weeks ago, a post was published entitled The SEO Myth of Going Viral. It referenced 8 pieces of content across 4 different sites that went viral and, most importantly for SEO, gained hundreds of linking root domains. I was the creative director on a lot of those campaigns while working as the VP of Creative at Distilled. Today, I’d like to add some important context and detail to the original post.

I actually agree with much of what it said. However, it’s based on the assumption that one big viral piece of content would result in a visible jump in rankings across the domain within about 3 months of the content being released. There are a few challenges with this as a basis for measuring success.

I wouldn’t advise setting your hopes on one big viral hit boosting your rankings across the domain. Not by itself. However, if that viral hit is part of ongoing link building efforts in which you build lots of links to lots of pieces of content, you can begin to see an upwards trend.

“Trend” is the important word here. If you’re looking for a dramatic step or jump as a direct result of one piece of viral content, this could cause you to overlook a positive trend in the right direction, and even tempt you to conclude that this form of content-based link building doesn’t work.

With regards to this type of link building and its impact on domain-wide rankings, I’d like to focus on the follow 4 points:

How success really looksWhy success looks like it doesOther factors you need to considerHow we can improve our approachWhat successful link building really looks like

Simply Business was held up in the SEO myth post as an example of this kind of link building not working. I would argue the opposite, holding it up as an example of it working. So how can this be?

I believe it stems from a misunderstanding of what success looks like.

The post highlighted three of the most successful pieces of content Distilled created for Simply Business. However, focusing on those three pieces of content doesn’t provide the full picture. We didn’t make just three pieces of content; we made twenty-one. Here are the results of those pieces:

Note: Data missing for the first two pieces of content

That’s links from 1466 domains built to 19 pieces of content over a period of 3 years.

The myth in question is as follows:

Building lots of links to one piece of content will result in a jump in domain-wide rankings within a reasonable timeframe, e.g. 3 months.

Though this wasn’t the hypothesis explicitly stated at the start of the post, it was later clarified in a comment. However, that’s not necessarily how this works.

An accurate description of what works would be:

Building lots of links to lots of pieces of content sustainably, while taking other important factors into consideration, can result in an increase in domain-wide rankings over time.

To hold up, the myth required a directly attributable jump in rankings and organic traffic within approximately 3 months of the release of each piece of content. So where was the bump? The anticipated reward for all those links?

No. The movement we’re looking for is here:

Not a jump, but a general trend. Up and to the right.

Below is a SEMRush graph from the original post, showing estimated organic traffic to the Simply Business site:

At first glance, the graph between 2012 and 2014 might look unremarkable, but that’s because the four large spikes on the right-hand side push the rest of the chart down, creating a flattening effect. There’s actually a 170% rise in traffic from June 2012 to June 2014. To see that more clearly, here’s the same data (up to June 2014) on a different scale:

Paints quite a different picture, don’t you think?

Okay, but what did this do for the company? Did they see an increase in rankings for valuable terms, or just terms related to the content itself?

Over the duration of these link building campaigns, Simply Business saw their most important keywords (“professional indemnity insurance” and “public liability insurance”) move from positions 3 to 1 and 3 to 2, respectively. While writing this post, I contacted Jasper Martens, former Head of Marketing and Communications at Simply Business, now VP of Marketing at PensionBee. Jasper told me:

“A position change from 3 to 1 on our top keyword meant a 15% increase in sales.”

That translates to money. A serious amount of it!

Simply Business also saw ranking improvements for other commercial terms, too. Here’s a small sample:

Note: This data was taken from a third-party tool, Sistrix. Data from third-party tools, as used both in this post and the original post, should be taken with a grain of salt. They don’t provide a totally accurate picture, but they can give you some indication of the direction of movement.

I notice Simply Business still ranks #1 today for some of their top commercial keywords, such as “professional indemnity insurance.” That’s pretty incredible in a market filled with some seriously big players, household UK names with familiar TV ads and much bigger budgets.

Why success looks like it does

I remember the first time I was responsible for a piece of content going viral. The social shares, traffic, and links were rolling in. This was it! Link building nirvana! I was sitting back waiting for the rankings, organic traffic, and revenue to follow.

That day didn’t come.

I was gutted. I felt robbed!

I’ve come to terms with it now. But at the time, it was a blow.

I assume most SEOs know it doesn’t work that way. But maybe they don’t. Maybe there’s an assumption that one big burst in links will result in a jump in rankings, as discussed in the original post. That’s the myth it was seeking to dispel. I get it. I’ve been there, too.

It doesn’t necessarily work that way. And, actually, it makes sense that it doesn’t.

In two of the examples, the sites in question had one big viral hit, gaining hundreds of linking root domains, but this on its own didn’t result in a boost in domain-wide rankings. That’s true.Google would have pretty volatile search results if every time someone had a viral hit, they jumped up in the rankings for all their head terms. But if a site continues to build lots of links regularly over time, like Simply Business did, Google might want that site to be weighted more favorably and worthy of ranking higher.

The Google algorithm is an incredibly complex equation. It’s tempting to think that you put links in and you get rankings out, and a big jump in one will correspond to a big jump in the other. But the math involved is far more complicated than that. It’s not that linear.

Other factors to consider

Link building alone won’t improve your rankings.

There are a number of other influential factors at play. At a high level, these include:

A variety of on-site (and technical) SEO factorsAlgorithmic updates and penaltiesChanges to the SERPs, like the knowledge box and position of paid resultsCompetitor activity

I’m not going to go into great detail here, but I wanted to mention that you need to consider these factors and more when reviewing the impact of link building on a site’s rankings.

Below is the graph from SearchMetrics for Concert Hotels, also via the original post. This is another site to which Distilled built a high volume of links.

As you can possibly tell from the large drop before Distilled started working with Concert Hotels, the site was suffering from an algorithmic penalty. We proceeded under the hypothesis that building high-quality links, alongside other on-site activity, would be important in the site’s recovery.

However, after three or four large link building successes without any corresponding uplift, we recommended to the client that we stop building links and shift all resources to focus on other activities.

As you’ll see at the end of the chart, there appears to be some positive movement happening. If and when the site fully recovers, we’ll never be able to tell exactly what contribution, if any, link building made to the site’s eventual rankings.

You can’t take the above as proof that link building doesn’t work. You have to consider the other factors that might be affecting a site’s performance.

How can we improve our approach?

As I mentioned at the start of this post, I actually agree with a lot of the points raised in the original post. In particular, there were some strong points made about the topical relevance of the content you create and the way in which the content sits within the site architecture.

Ideally, the content you create to gain links would be:

Topically relevant to what you doIntegrated into the site architecture to distribute link equityValuable in its own right (even if it weren’t for links and SEO)

This can be a challenge, though, especially in certain industries, and you might not hit the sweet spot every time.

But let’s look at them in turn.

Topical relevance

If you can create a piece of content that gains links and is closely relevant to your product and what you do for customers, that’s great. That’s the ideal.

To give you an example of this, Distilled created a career aptitude test for Rasmussen, a career-focused college in America. This page earned links from 156 linking root domains (according to the Majestic Historic Index), and the site continues to rank well and drive relevant search traffic to the site.

Another example would be Moz’s own Search Engine Ranking Factors. Building lots of links to that page will certainly drive relevant and valuable traffic to the Moz site, as well as contributing to the overall strength of the domain.

However, your content doesn’t have to be about your product, as long as it’s relevant to your audience. In the case of Simply Business, the core audience (small business owners) doesn’t care about insurance as much as it cares about growing its businesses. That’s why we created several guides to small business marketing, which also gained lots of links.

As Jasper Martens explains:

“Before I left Simply Business, the guides we created attracted 15,000 unique visits a month with a healthy CTR to sign-up and sales. It was very effective to move prospects down the funnel and make them sales-ready. It also attracted a lot of small business owners not looking for insurance right now.”Integrating the content into the site architecture

Distilled often places content outside the main architecture of the site. I’ll accept this isn’t optimal, but just for context, let me explain the reasons behind it:

It creates a more immersive and compelling experience. Consider how impactful New York Times’ Snowfall would have been if it had to sit inside the normal page layout.It prevents conflicts between the site’s code and the interactive content’s code. This can be particularly useful for organizations that have restrictive development cycles, making live edits on the site difficult to negotiate. It also helps reduce the time, cost, and frustration on both the client-side and agency-side.It looks less branded. If a page looks too commercial, it can deter publishers from linking.

While it worked for Simply Business, it would make sense, where you can, to pull these things into the normal site architecture to help distribute link equity further.

Content that’s valuable in its own right (even if it weren’t for links and SEO)

Google is always changing. What’s working now and what’s worked in the past won’t necessarily continue to be the case. The most future-proof way you can build links to your site is via activity that’s valuable in its own right — activities like PR, branding, and growing your audience online.

So where do we go from here?

Link building via content marketing campaigns can still make a positive impact to domain-wide rankings. However, it’s important to enter any link building campaign with realistic expectations. The results might not be as direct and immediate as you might hope.

You need to be in it for the long haul, and build links to a number of pieces of content over time before you’ll really see results. When looking for results, focus on overall trends, not month-to-month movements.

Remember that link building alone won’t solve your SEO. You need to make sure you take other on-site, technical, and algorithmic factors into consideration.

It’s always worth refining the way you’re building links. The closer the topics are aligned with your product or core audience’s interests, the more the content is integrated into your site’s architecture, and the more the content you’re creating is valuable for reasons beyond SEO, the better.

It’s not easy to manage that every time, but if you can, you’ll be in a good position to sustainably build links and improve your site’s rankings over time.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

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Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

The post SearchCap: Google top searches, Cortana with Harman Kardon & image product schema appeared first on Search Engine Land.

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As a digital marketing firm and SEO service provider, a mistake we see made all too often by brands is failing to identify the audience they’re targeting. It may sound like a step significantly less important than others throughout the optimization process, but spending time getting to know and understanding your audience is one of the most important things to do.

The key to not just good, but successful and effective digital marketing is knowing who you’re directing your efforts at, how they will respond to those efforts, and so on. But not exploring your audience impacts user experience, and a negative user experience impacts conversions, and fewer conversions directly translates to less profit and reach. See the problem?

Don’t make the mistake of skipping this important optimization step, and if you already have, go back and get to know your users. Here are some starting points.

Research. 

This is a pretty obvious step, but in a world where most interactions are digitally stream lined, it’s become easy to forget. Conducting research to find out who your audience is should be a top priority, and it can be done in a number of ways. You can study the audience of your competitors and do some digging on social media platforms, get out in the field-wherever that field may be-and dispense surveys, and/or conduct interviews. All of these are great starting points to identifying your target audience.

Break the audience into groups.

You might have one big audience, but more often than not you have multiple groups within your audience at large. A perfect example is Target. Target has something for everyone, and there are multiple audience groups within their audience as a whole. After conducting your research, take a closer look at the types of people you’re targeting. Then, build your advertising and optimization efforts around them.

Find out who’s who.

Yes, I’m talking about the influential leaders that exist within your audience. For example, if your business is in the cosmetic industry, find a make-up tutorial guru who has thousands of followers on Youtube and start a partnership with him or her. There are respected authorities within every industry, and finding a way to reach their audience your audience too.

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Call it a case of too much of a good thing.

Google Analytics puts a lot of data at your fingertips. It’s a mountain of metrics, and a deluge of dimensions. With it, you can drill deep down into your website visitors, their demographics, and their behavior.

But by default, it just throws everything into one enormous bucket. The reports show stats, graphs, and charts for “All Users/Sessions”.

And while that’s perfect for getting a big-picture-at-a-glance snapshot, you need to break things down a little if you want to find the buried treasure within.

Mixing everything together is good for smoothies, metallurgy, and cocktails. For analytics, you want to separate and segment.

Segmentation in Analytics

Click on any report in Analytics – audience, acquisition, behavior, or conversions – and you get a wonderful presentation of the data collected for your site. For everyone that visited during the chosen time frame (the last 30 days by default).

The beauty of Analytics, though, is in whittling away from general to specific. Of finding the statue of David within the block of marble. Or the chewy core at the center of a Tootsie Pop.

With audience segments, you can break that pile of data into more manageable sections based on the criteria that matters most to you. What insights are you looking for?

Segments group visitors together who share common traits or behavior. And the best part? You get to decide exactly what traits and behavior to group.

Knowing how many visitors you had last week is useful. But how many visitors did you have on desktop devices, from the United States, who viewed at least three pages, but left without making a purchase? That’s the power of segments.

Segments give you ultra-targeted insight into audience behavior, like visitors arriving via mobile, from which country, number of sessions with a conversion, visitor type, demographics, traffic source, value, browser, users with multiple sessions, and so on.

Sounds fantastic, right? Yet 41% are not using audience segments at all. Get out of that group.

There’s no end to the possible combinations you could create (although that’s technically not true…you are limited to 1000 segments that can be edited in any View, and 100 segments for any one specific View). Create an uber-precise segment for tracking and analysis that represents your exact audience and serves your particular needs.

Hit vs Session vs User

When creating a segment, you can often assign the scope as either user, session, or hit, so it’s important to recognize the difference:

Hit – an individual interaction with your website (usually a page view); a visitor did this, a visitor did that, a visitor…
Session – a collection of hits; one complete visit to a website
User – a person’s entire journey with your website (may encompass multiple sessions)

Most experts suggest focussing on users and sessions. It’s all about the relationship you have with them.

Segments: A Simple How-to

Getting started is mercifully uncomplicated. Even an Analytics amateur can create, save, and use segments.

To begin, simply click on the +Add Segment button at the top of any report.

You’ll next see the Segment dashboard, and you’re presented with several options. Down the left-hand side, you’ll find the View Segments column. You can select All, System (the segments pre-loaded on Analytics, like Converters, New Users, and Organic Traffic), Custom (the segments you’ve made), Shared, Starred (you can save segments to this favorites list by clicking the star beside their name), and Selected (the segments you’re currently using).

Do you want to work with an existing segment (select from the list), import from the gallery (custom segments created and shared by others), or create your own new one (click +New Segment to start from scratch, or click on the Actions dropdown beside an existing one, and select Copy to use it as your foundation)?

Let’s copy Organic Traffic. This will allow us to build a custom segment for all incoming organic traffic.

The next dashboard displays the current segment parameters. Because we opted to copy and build upon the Organic Traffic segment, there’s already one criteria listed:

Medium > exactly matches > organic

On the right, you’ll see a segment summary (updated in real-time when you add or delete criteria), and down the left are the categories and filters you can use to define your segment.

Options include Demographics, Technology, Behavior, Date of First Session, Traffic Sources, Enhanced Ecommerce, Conditions, and Sequences.

So far so good. Still with me?

You’ll want to zero in, so we’ll add a few more filters to this segment. Click on the +Add Filter button.

Next, click on the Ad Content dropdown menu, and you’ll see a long list of possible filters and criteria. Click around. Explore. There’s also a handy search field at the top to save you some time.

We’re going to segment by users who’ve viewed at least two pages during a session:

Type “Page Depth” in the search field and select it (it’s found under Behavior if you want to locate it yourself)
Click on “=” and select “>” (greater than symbol)
In the blank field, enter 2

Your summary will update, and you’ll see Medium: organic and Page Depth > 2 listed under Conditions. This segment now includes all organic traffic visitors who look at three or more pages during their session.

Starting to get the hang of it? Try this one on your own: add a country filter for visitors from the United Kingdom.

Done? Excellent. You can include that filter by either adding another condition (Users > Country > Contains > United Kingdom), or clicking on Demographics > Location > Country > Contains > United Kingdom. Either way, your summary will update, and your segment will now only count visitors from the UK.

We can make “organic” even more precise by adding a filter under Traffic Sources > Source > contains > google

Finally, give the segment a name that reflects what it is, such as UK Page Depth > 2 or something similar (whatever works for you), and click Save.

Congratulations. You just created your first audience segment. Kudos! This one will only exhibit organic traffic from the UK arriving specifically from Google that viewed at least three pages. How’s that for precise data?! It’ll be listed with all the other available segments, ready to be called upon whenever you need it.

Basic Segments

But segments don’t have to be complex. They don’t have to include multiple criteria, either. They can be very simple and straightforward:

By traffic source or medium (email, social, paid, organic, direct, referral, google, facebook, twitter) to get insight into visitor offsite behavior. Where are they coming from, and how are they finding you?
By user type (new visitor vs returning visitor, mobile vs desktop, frequent vs infrequent visitor, long vs short sessions, multiple pageviews vs single page) for insight into their engagement with your brand and company.
By location or language to understand your customer demographics better.
By Content Viewed (product pages, checkout page, thank you page) to get insight into their onsite behavior.
By Engagement (more than x pages, more than x seconds) to see how well your content and presentation is appealing to them.
By revenue, product viewed or purchased, brands added to the cart, or even product variants like specific sizes and colors.

The basic categories along the left on the segments dashboard – Demographics through Enhanced Ecommerce (which does require the ec.js plugin) – are easy to navigate and implement. Try them out.

The Advanced Options

Both Conditions and Sequences are considered advanced options. But that shouldn’t scare you (you’ve already mastered conditions).

As we’ve already seen, Conditions simply set a series of criteria that must be met for a visitor to be included in that segment. It allows for a tremendous amount of customization. There’s a bit of a learning curve – finding the right criterion and definition, for example – but there are plenty of tutorials that can guide you from absolute beginner to pro in no time.

Sequences are a series of conditions that must be met in order (step 1, step 2, and so on), but the fundamental idea is the same. A sequence where users visit your cart (step 1) but then don’t go through with an actual purchase (step 2) gives you an audience ripe for a follow-up.

The More the Merrier

Once you have a few favorite segments, you can start to compare one against another. Go to any report, click on +Add Segment, choose up to four different ones, and click Apply. You’ll then see the report with all segments presented at one time…color-coded for your convenience (thanks Google). Compare and contrast.

Get Fancy

The more comfortable you get with segments, the more you can create ones for every possible group of your audience.

How about a segment that shows you frequent and recent visitors that still haven’t purchased something?

+Add Segment
Behavior – Days Since Last Session < 5 (less than five)
Behavior – Sessions > 4 (more than four)
Behavior – Transactions = 0 (no purchases), or even Conditions – Page Title – contains – thankyou.html (your order confirmation page, but make sure you select EXCLUDE rather than include)

Once you’ve created a segment like this one, you can then create an Audience from it, and target only that group with ads promoting your free shipping, or current sale, or special coupon to entice them to pull the trigger (they’re obviously interested).

Creating an Audience from a Segment

Segments give you valuable, targeted data. And that’s a very good thing.

But they can extend beyond the Analytics dashboard if you create an Audience from a segment.

For the UK Page Depth > 2 segment we created earlier, let’s assume you’d like a way to send them – and only them – a special ad for a discount available only to UK residents.

Head to the segments list, find that segment, select Actions, and click Build Audience.

For this to work, your Analytics and Adwords accounts need to be linked (but don’t worry, Google walks you through it if they’re not yet connected).

Choose the View (your ecommerce site) and Destination (your linked Adwords account), and define the Audience. The definitions (i.e. criteria) will be prepopulated based on the segment you selected.

On the right, you’ll see a few fields:

For segments scoped to hits or sessions, you’re limited to Users over last 7 days (this will present the estimated size of this audience from users that meet the criteria in the last week).
Membership duration sets how long visitors will remain a member of this audience once slotted into it, anywhere from 1 to 540 days. If you’re creating an Audience for visitors with a recent purchase, for example, you’d set this for a relatively short period of time.
Segments scoped to Users gives the option of lookback days, either 7, 14, or 30. This is a time frame for Analytics to go back and find users that qualify for the Audience.
Eligibility tells you where you can share this audience, such as search and display ads (Adwords), or Google Optimize (for testing and personalization).

Give the Audience a name to help you remember it over in Adwords, and click Save. Done.

When you’re next in your Adwords account, you can select this Audience and create a targeted ad for them about your exclusive coupon for those in the land of tea, bangers, and mash.

It may be the gentle push they need.

See how everything comes together in the Googleverse?

Segments allow you to identify strengths, weaknesses, and patterns, find reliable revenue sources, and provide the guidelines to improve where you’re falling short.

There are many stellar custom segments ready to import, you can experiment and create your own, or simply utilize the ready-to-go standard segments there already.

The Google Analytics Help portal provides everything you’d want to know about segments but were afraid to ask.

They’re often ignored, but always beneficial. You can use them to track your most lucrative markets, identify where, when, and how the big spenders are coming to you, remarket to specific groups at just the right moment (via Adwords and Audiences), and more.

Anything you can do to better understand your audience behavior and acquisition is time well spent. Grasp the basics of segments, move on to more advanced techniques, and know your audience like never before.

It’s big data broken down.

Have you jumped in the audience segmentation pool? What filters do you find most insightful? Leave your comments below.

About the Author: Aaron Agius is an experienced search, content and social marketer. He has worked with some of the world’s largest and most recognized brands to build their online presence. See more from Aaron at Louder Online, their Blog, Facebook, Twitter, Google+ and LinkedIn.

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Seth Godin once said: content marketing is the only marketing that’s left. Effectively done, it gives a brand an image and a voice that speaks volumes to the audience’s direct needs and problems, with a solution. Learn 8 strong statistics on why content marketing is today’s marketing.

The post 8 Statistics That Prove Content Marketing Is the Best Brand Marketing by @JuliaEMcCoy appeared first on Search Engine Journal.

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With 677 entries from 343 companies across 27 categories, ClickZ and Search Engine Watch’s inaugural mobile reader survey has been compiled and is now open for voting!

Designed to reward technology vendors and agencies at the cutting edge of delivering mobile solutions, services, content and technology, the power is now in you, our reader’s hands to select the winners!

Voting rules:

You may only vote for one company in each category.
You may not be an active employee of the company that you are voting for.
If you do work for one of the companies, you are encouraged to promote the survey to your clients and network, but you may not vote for your own company. You are eligible to vote in any of the categories which do not feature your company.
We will be collecting basic information to be able to authenticate your entry against these rules. Please ensure that you use your business email address.

Voting will close on January 20th, 2017 and a Shortlist of the winners will be announced during the Mobile Content takeover on January 24th, 2017. Winners will be announced and published in an exclusive ‘State of Mobile Report’ which will go live on February 6th, 2017.

For more information on how to get involved in our Mobile Content Takeover please contact sam.lawson@clickz.com.

The categories:

Data
Best CRM or Data Management Platform
Best Mobile Engagement Platform
Best Mobile-first Marketing Automation Platform

Agencies
Best Overall Mobile Agency
Best Mobile Agency (SMB)
Best Mobile Search Agency

Analytics
Best Mobile Analytics Platform/Provider
Best App Analytics Platform

Attribution
Best Cross Device Attribution Platform

Apps
Best App Developer

Augmented Reality
Best AR Solution (Delivery/Technology)
Best AR Content Creation Agency/Firm (Content

Virtual Reality
Best VR Solution (Delivery/Technology)
Best VR Content Creation Agency/Firm (Content)

Location
Best Location Data Provider
Best Location-based Services (LBS) or Proximity Based Technology
Best Mobile Ad Server
Best In-App Mobile Advertising Solution
Best Native Advertising Mobile Solution
Best Mobile Video Advertising Platform

Display
Best Mobile Ad Network
Best Supply Side Platform (SSP) /Exchange
Best Demand Side Platform (DSP)

Content
Best Mobile Content Creation Firm/Agency

M-Commerce
Best Mobile Billing or Payments Provider

Call Technology
Best Mobile Click to Call Technology

⋆ Overall ⋆*
Most Innovative Mobile Marketing Technology/Solution

*This is the only category where the winner will be decided by our editorial team.

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2016 marks HubSpot’s 10th anniversary. When my co-founder Dharmesh Shah and I first started talking about the concept of inbound marketing, it wasn’t an evolution — it was a revolution. Instead of big brands beating small companies with expensive TV commercials, massive billboards, and other types of outbound campaigns as a foregone conclusion, content and search marketing equalized the playing field. Marketing became much more about the width of your brain than the width of your wallet.

Fast forward to today. While the fundamental tenets of inbound marketing still ring true, many of the specific channels and methods prospects are using to find, evaluate, and purchase products are different than 10 years ago.

Even the supply and demand dynamics have changed. In 2006, prospects had an average of four to five choices for any product or service they were looking to purchase. In 2016, I’ve found that that number has spiked to approximately 14 or 15 different vendors — and this holds among most industries. Choices that once occupied a few inches on a shelf at a retail store now expand endlessly on the infinite shelf of ecommerce sites.

Supply is way up, and demand is relatively flat. The increased competition means understanding how your prospective buyers research and buy is even more critical.

I’ve spent the last year studying the changes in prospect behavior, and I’ve come to the conclusion that we’re looking at another revolution — which heralds the second act of inbound marketing.

Here’s what that means for you, your prospects, your business, and your 2017 marketing plan.

The Second Act of Inbound Marketing
How to Get Found in 2016 (or, Why Google Is Now Ask Jeeves)

Back in 2006, Google helped buyers find the answers to their questions by serving up links to relevant information. Increasingly today, there’s no click needed — Google just gives searchers the answer directly through a rich answer box. The numbers vary on this but research has found that between 20 and 35% of searches performed today result in a quick answer box populated on the SERP itself.

Need an answer fast? Just ask. That’s right, Google has finally fulfilled the original Ask Jeeves promise. In our own research we’ve found that clickthrough rates from these answer boxes are also higher than traditional links. RKG Merkle saw a 516% increase in sessions when a client’s page captured the snippet box.

To adapt, companies should start to create content not just intended to rank in search, but also to be pulled into Google’s quick answer box, and educate your buyer directly on the SERP. Here’s a blog post on how to do this.

Quick answer boxes are not the only way Google is changing. Ten years ago, paid AdWords results took up about 50% of the screen “above the fold.” Today, because of a layout shift Google introduced earlier this year, AdWords ads have moved from the right column to the center and take up nearly 100% of the “above the fold” space on both desktop and mobile.

All of which means: If you have the budget, it’s worth integrating ads into your inbound strategy. But how do you run ads in an “inbound” way? Well, bad ads are still useless. If you have a piece of content, however, that’s taken off organically, that’s a good indication it could be even more useful if amplified by some paid budget. I’d suggest using your ad budget to boost quality content that has already done well on social media.

Speaking of social media, Facebook’s Lead Ads product, which enables buyers to complete a form directly in their newsfeeds, is opening up new opportunities and levels of transparency into an ad campaign’s effectiveness. Whereas once we had PPC, we’re now seeing the early days of PPL: Pay Per Lead.

The ad industry is at a significant precipice. On the one hand we’re seeing a drastic rise in usage of ad-blocking technology; according to the 2016 State of Inbound report, mobile ad blocking has increased 90% year-over-year. On the other, ads are getting more space in search engine results and offering better clarity around their cost per lead ratios. All of this will inevitably influence the way prospective customers find your company.

How to Create Compelling Content in 2016 (or, Why the Silent Movie Is Back)

The way people find answers and discover companies is changing, but so is the format of content itself. Here’s what I’ve noticed about the types of content working well today.

In short: Reading is out, watching is in. Ten years ago, the power content marketing move was search + written content. Now, it’s social media + video content.

And these aren’t the hyper-produced, long-form videos from years past. The marketing teams winning the video game today are mastering live, off-the-cuff video. Additionally, Charlie Chaplin would likely raise an eyebrow to learn we’ve tƒurned away from talkies: A full 85% of videos today are watched without audio.

Increases in mobile usage have fueled much of this growth in video popularity. For content marketers, the takeaway here is simple. While text is still important, you may want to stop looking for that next blogger, and start looking for a video producer. At least 50% or more of your marketing content next year should be in video format, either hosted natively on social or created with social distribution in mind.

The other thing to know about content consumption is much of it is now happening away from the bounds of your website on social media. Social media has evolved from a promotion channel alone to a platform for content. Buyers aren’t just stopping by social media sites during work breaks anymore — they never leave.

Facebook in particular has started to dominate how we spend our free time. Four of the apps consumers use most — Facebook, Instagram, Messenger, and WhatsApp — are owned by Facebook. Today’s buyers are plugged into Mark Zuckerberg’s universe all day long. If you’re not meeting them there, you risk losing them.

How Buyers Buy in 2016 (or, Why Uber Wrecked Your Website)

Ten years ago, your website augmented your sales rep. Buyers would browse the website to get the basics, and then hop on the phone with a salesperson to get into the nitty gritty.

Today, it’s the opposite: Your sales rep augments your website. Buyers want a self-service experience, making a purchase directly onsite or contacting Sales when they’re ready, not the other way around. Indeed, only 19% of buyers in a HubSpot Research study said they wanted to talk to a salesperson during the “awareness” stage of their purchase process.

And the website of 2006 won’t cut it. Your prospects have been trained in every industry to expect automation, personalization, and self-service. They have been spoiled by Uber to expect a consumer-grade, on-demand buying experience. A website that doesn’t enable viewers to get on-demand information or instantly chat with Sales or Support frustrates your buyers and will send them into the arms of your competition.

Throughout the entire buying process — from your ads, to your website (mobile and desktop), to your live chat service — buyers expect personalization. They expect you to remember them, give them fantastic service, and enable them to serve themselves through automation.

My Advice For Marketers in 2017 and Beyond

Here are the four things I think every marketer should do next year:

Invest in video. Diversify your content playbook to include social + video in addition to text + search.
Live in social. We’re not B2B or B2C marketers — we’re business to human marketers. Humans live in Snapchat, Instagram, Messenger, and a host of other social apps. Find them there.
Accelerate your content. Combine content marketing and paid marketing. Repurpose your most successful content for multiple channels and boost it with budget.
Automate your buying process. Buyers expect to be able to buy on their timeline with either minimal or no human contact.

2017 marks the second act of inbound marketing. Are you ready?

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Columnist Stephan Spencer discusses how you can use SEO knowledge to build assets that make money for you, rather than having all the gains go to your clients.

The post Monetizing your SEO expertise appeared first on Search Engine Land.

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