​The 2015 Online Marketing Industry Survey

Posted by Dr-Pete

It’s been another wild year in search marketing. Mobilegeddon crushed our Twitter streams, but not our dreams, and Matt Cutts stepped out of the spotlight to make way for an uncertain Google future. Pandas and Penguins continue to torment us, but most days, like anyone else, we were just trying to get the job done and earn a living.

This year, over 3,600 brave souls, each one more intelligent and good-looking than the last, completed our survey. While the last survey was technically “2014”, we collected data for it in late 2013, so the 2015 survey reflects about 18 months of industry changes.

A few highlights

Let’s dig in. Almost half (49%) of our 2015 respondents involved in search marketing were in-house marketers. In-house teams still tend to be small – 71% of our in-house marketers reported only 1-3 people in their company being involved in search marketing at least quarter-time. These teams do have substantial influence, though, with 86% reporting that they were involved in purchasing decisions.

Agency search marketers reported larger teams and more diverse responsibilities. More than one-third (36%) of agency marketers in our survey reported working with more than 20 clients in the previous year. Agencies covered a wide range of services, with the top 5 being:

More than four-fifths (81%) of agency respondents reported providing both SEO and SEM services for clients. Please note that respondents could select more than one service/tool/etc., so the charts in this post will not add up to 100%.

The vast majority of respondents (85%) reported being directly involved with content marketing, which was on par with 2014. Nearly two-thirds (66%) of agency content marketers reported “Content for SEO purposes” as their top activity, although “Building Content Strategy” came in a solid second at 44% of respondents.

Top tools

Where do we get such wonderful toys? We marketers love our tools, so let’s take a look at the Top 10 tools across a range of categories. Please note that this survey was conducted here on Moz, and our audience certainly has a pro-Moz slant.

Up first, here are the Top 10 SEO tools in our survey:

Just like last time, Google Webmaster Tools (now “Search Console”) leads the way. Moz Pro and Majestic slipped a little bit, and Firebug fell out of the Top 10. The core players remained fairly stable.

Here are the Top 10 Content tools in our survey:

Even with its uncertain future, Google Alerts continues to be widely used. There are a lot of newcomers to the content tools world, so year-over-year comparisons are tricky. Expect even more players in this market in the coming year.

Following are our respondents’ Top 10 analytics tools:

For an industry that complains about Google so much, we sure do seem to love their stuff. Google Analytics dominates, crushing the enterprise players, at least in the mid-market. KISSmetrics gained solid ground (from the #10 spot last time), while home-brewed tools slipped a bit. CrazyEgg and WordPress Stats remain very popular since our last survey.

Finally, here are the Top 10 social tools used by our respondents:

Facebook Insights and Hootsuite retained the top spots from last year, but newcomer Twitter Analytics rocketed into the #3 position. LinkedIn Insights emerged as a strong contender, too. Overall usage of all social tools increased. Tweetdeck held the #6 spot in 2014, with 19% usage, but dropped to #10 this year, even bumping up slightly to 20%.

Of course, digging into social tools naturally begs the question of which social networks are at the top of our lists.

The Top 6 are unchanged since our last survey, and it’s clear that the barriers to entry to compete with the big social networks are only getting higher. Instagram doubled its usage (from 11% of respondents last time), but this still wasn’t enough to overtake Pinterest. Reddit and Quora saw steady growth, and StumbleUpon slipped out of the Top 10.

Top activities

So, what exactly do we do with these tools and all of our time? Across all online marketers in our survey, the Top 5 activities were:

For in-house marketers, “Site Audits” dropped to the #6 position and “Brand Strategy” jumped up to the #3 spot. Naturally, in-house marketers have more resources to focus on strategy.

For agencies and consultants, “Site Audits” bumped up to #2, and “Managing People” pushed down social media to take the #5 position. Larger agency teams require more traditional people wrangling.

Here’s a much more detailed breakdown of how we spend our time in 2015:

In terms of overall demand for services, the Top 5 winners (calculated by % reporting increase – % reporting decrease were):

Demand for CRO is growing at a steady clip, but analytics still leads the way. Both “Content Creation” (#2) and “Content Curation” (#6) showed solid demand increases.

Some categories reported both gains and losses – 30% of respondents reported increased demand for “Link Building”, while 20% reported decreased demand. Similarly, 20% reported increased demand for “Link Removal”, while almost as many (17%) reported decreased demand. This may be a result of overall demand shifts, or it may represent more specialization by agencies and consultants.

What’s in store for 2016?

It’s clear that our job as online marketers is becoming more diverse, more challenging, and more strategic. We have to have a command of a wide array of tools and tactics, and that’s not going to slow down any time soon. On the bright side, companies are more aware of what we do, and they’re more willing to spend the money to have it done. Our evolution has barely begun as an industry, and you can expect more changes and growth in the coming year.

Raw data download

If you’d like to take a look through the raw results from this year’s survey (we’ve removed identifying information like email addresses from all responses), we’ve got that for you here:

Download the raw results

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Reblogged 2 years ago from tracking.feedpress.it

Understand and Harness the Power of Archetypes in Marketing

Posted by gfiorelli1

Roger Dooley, neuromarketing expert, reminds us in his book Brainfluence that in 80% of cases we make a decision before being rationally aware of it.

Although Dooley explains this effect in terms of how our brain works, in my opinion, distinctly separating neuroscience and the theory of archetypes would be incorrect. On the contrary, I believe that these two aspects of the study of the human mind are complementary.

According to
Jung, archetypes are “[…] forms or images of a collective nature which occur practically all over the Earth as constituents of myths and—at the same time—as individual products of unconscious”. He then, added something that interests us greatly: “The [forms and images] are imprinted and hardwired into out psyches”.

Being able to design a brand personality around an archetype that connects unconsciously with our audience is a big first step for: brand loyalty, community creation, engagement, conversions.

The Slender Man is the “Internet age” version of the archetype figure of the Shadow

Archetypes can be also used for differentiating our brand and its messaging from others in our same market niche and to give that brand a unique voice.

If we put users at the center of our marketing strategy, then
we cannot limit ourselves in knowing how they search, how they talk on social media, what they like to share or what their demographics are.

No,
we should also understand the deep psychological reasons why they desire something they search for, talk the way they talk, share what they share, and their psychological relation with the environment and society they live in.

Knowing that,
we can use archetypes to create a deep emotional connection with our audience and earn their strong positive attitude toward us thanks to the empathy that is created between them and us.

Narrative modes, then, help us in shaping in a structured way a brand storytelling able to guide and engage the users, and not simply selling or creating content narrative doomed to fail.

The 12 archetypes




graph by Emily Bennet

The chart above presents the 12 Jungian archetypes (i.e: Hero), to what principal human desire (i.e.: leave a mark on the world) they correspond and what is the main behavior each one uses for achieving that desire (i.e.: mastery).


Remember: if the audience instinctively recognizes the archetypal figure of the brand and its symbolism and instinctively connect with it, then your audience is more ready to like and trust what your brand proposes
.

On the other hand, it is also a good exercise to experiment with archetypes that we would not think are our brand’s one, expanding the practice of A/B tests to make sure we’re working with the correct archetype. 

The Creator

In my last post I used Lego as example of a brand that is winning Internet marketing thanks to its holistic and synergistic use of offline and online marketing channels.

I explained also how part of its success is due to the fact Lego was able to shape its messages and brand personality around the Creator archetype (sometimes called the “Builder”) which is embodied by their tagline, “let’s build”.

Creators tend to be nonconformist and to enjoy self expression.
A Creator brand, then, will empower and prize its audience as much as it is able to express itself using its products.

The Ruler

The Ruler is the leader, the one setting the rules others will follow, even competitors. Usually it’s paired with an
idea of exclusiveness and status growth.

A brand that presents itself as a Ruler is suggesting to their audience that they can be rulers too.

A classic example of Ruler brand is Mercedes:

The Caregiver

Altruism, compassion, generosity.
Caregiver brands present themselves as someone to trust, because they care and empathize with their audience.

The Caregiver is one of the most positive archetypes, and it is obviously used by nonprofit organizations or governmental institutions like UNICEF, but brands like Johnson & Johnson have also shaped their personality and messages around this figure.

The Innocent

The Innocent finds positive sides in everyone and everything

It sees beauty even in things that others will not even consider, and feels in peace with its inner beauty.

Dove, obviously, is a good representation of the Innocent archetype.

The Sage

The Sages wants to know and understand things. 


The Sage is deeply humanist and believe in the power of humankind to shape a better world through knowledge
.

However, the Sage also has a shadowed side: intolerance to ideas others than their own.

Google, in both cases, is a good example a Sage brand.

The Explorer

The Explorer is adventurous, brave, and loves challenges. He tends to be an individualist too, and loves to challenge himself so as to find his real self.


Explorer brands prompt their audience to challenge themselves and to discover the Explorer within

Red Bull is a classic example of these kinds of brands, but REI and Patagonia are even better representations.

The Hero

In many aspects, the Hero archetype is similar to the Explorer and Outlaw ones, with the difference that the Hero many times never wanted to be the hero, but injustice and external events obliged him to find the courage, braveness, and the honor to become one.

Nike, and also its competitor Adidas, shapes its brand voice around this archetypal figure.

The Magician

The Magician is clever, intelligent, and sometimes his ability can be considered supernatural. 


The Magician is able to make the impossible possible
. Because of that some of the best known technology brands use this archetype as their own to showcase their innovation and how they use their advanced knowledge creatively.

Apple—even if you are not an Apple fan—created a powerful brand by shaping it around this archetype. 

The Outlaw


The Outlaw is the rebel, the one who breaks the rules in order to free his true self
.

The Outlaw goes against the canon and is very aware of the constrictions society creates.

A great example of a brand that very well represents the Outlaw archetype is Betabrand.

The Everyman

It is perfectly fine to be “normal,” and happiness can come from simply sharing things with people we love.


Brands targeting the Everyman audience (and painting themselves as such) craft their messages about the beauty of simple things and daily real life
.

Ikea is probably the brand that’s achieved mastery in the use of this archetype over the past few years.

The Jester 

Fun, irreverent, energetic, impulsive and against the established rules at the same time, the Jester is also the only one who is able to tell the truth with a joke. 

Jesters can be revolutionary too, and their motto could be “a laugh will bury you all.”


A brand that presents itself as the Jester is a brand that wants to make our lives easier and more bearable, providing us joy.

The Lover


Sensuality is the main characteristic of the Lover archetype
, as well as strong physicality, passion, and a need for deep and strong sensations.

But the Lover can be also the idealist, the romantic longing for the perfect love.

Archetypes and brand storytelling

Our brain, as many neuroscientists have proved, is
hard-wired for stories (I suggest you to watch this TEDx too).

Therefore, once we have decided what archetype figure best responds both to our audience and our values as a brand,
we must translate the psychology we created for our brand into
brand storytelling.
That storytelling must then be attuned to the psychology of our audience based on our psychographic analysis of them.

Good (brand) storytelling is very hard to achieve, and most of the time we see brands that miserably fail when trying to tell branded stories.

Introducing the Theory of Literary (or Narrative) Modes

In order to help my clients find the correct narrative, I rely on something that usually is not considered by marketers: the
Theory of Literary Modes.

I use this theory, presented first by
Northrop Frye in it essay Anatomy of Criticism, because it is close to our “technical marketer” mindset.

In fact:

  1. The theory is based on a objective and “scientific” analysis of data (the literary corpus produced by humans);
  2. It refuses “personal taste” as a metric, which in web marketing would be the same as creating a campaign with tactics you like but you don’t really know if your public is interested in. Even worse, it would be like saying “create great content” without defining what that means.

Moreover, the
Theory of Literary Modes is deeply structured and strongly relies on semiotics, which is going to be the natural evolution of how search engines like Google will comprehend the content published in the Internet. Semantic thinking is just the first step as well explained 
Isla McKetta here on Moz few months ago.

Finally, Northrop Fryed
considers also archetypes this theory because of the psychological and semiotic value of the symbolism attached to the archetypal figure.

Therefore, my election to use the Theory of Literary Modes responds 

  1. To the need to translate ideal brand storytelling into something real that can instinctively connect with the brand’s audience;
  2. To make the content based on that storytelling process understandable also by search engines.

The Theory of Literary Modes in marketing

To understand how this works in marketing, we need to dig a little deeper into the theory.

A literary work can be classified in two different but complementary ways:

1) Considering only the
relation between the nature of the main character (the Hero) and the ambient (or environment) where he acts.

2) Considering also
if the Hero is refused or accepted by society (Tragedy and Comedy).

In the
first case, as represented in the schema above, if the Hero:
  1. Is higher by nature than the readers and acts in a completely different ambient than theirs, we have a Romance;
  2. Is higher by nature than the readers, but acts in their same ambient, we have an Epic;
  3. Is someone like the reader and acts in the reader’s own ambient, we are in field of Realism;
  4. Is someone lower by nature than the readers and acts in a different or identical ambient, we are in the realm of Irony, which is meant as “distance.”
A fifth situation exists too, the
Myth, when the nature of the Hero is different than ours and acts in an ambient different than ours. The Hero, in this case, is the God.

If we consider also if society refuses or accepts the hero, we can discover the different versions of Tragedy and Comedy.

I will not enter in the details of Tragedy, because
we will not use its modes for brand storytelling (this is only common in specific cases of political marketing or propaganda, classic examples are the mythology of Nazism or Communism).

On the contrary,
the most common modes used in brand storytelling are related to Comedy, where the Hero, who usually is the target audience, is eventually accepted by society (the archetypal world designed by the brand).

In
Comedy we have several sub modes of storytelling:

  1. “The God Accepted.” The Hero is a god or god-like kind of person who must pass through trials in order to be accepted by the society;
  2. The Idyll, where the Hero uses his skills to explore (or conquer) an ideal world and/or become part of an ideal society. Far West and its heir, Space Opera (think of Interstellar) are classic examples. 
  3. Comedy sees the hero trying to impose his own view of the world, fighting for it and finally being awarded with acceptance of his worldview. A good example of this is every well ending biopic of an entrepreneur, and Comedy is the exact contrary of melodrama. 
  4. On a lower level we can find the Picaresque Comedy, where the hero is by nature inferior to the society, but – thanks to his cleverness – is able to elevate himself to society’s level. Some technology business companies use this narrative mode for telling their users that they can “conquer” their market niche despite not having the same economic possibilities as the big brands (this conquering usually involves the brand’s tools).
  5. Finally we have the Irony Mode of Comedy which is quite complex to define. 
    1. It can represent stories where the hero is actually an antihero, who finally fails in his integration into the society. 
    2. It can also be about inflicting pain on helpless victims, as in mystery novels. 
    3. It can also be Parody.

Some examples

The Magician, gamification, and the Idyllic mode

Consider this brand plot:

The user (the Hero) can become part of a community of users only if he or she passes through a series of tasks, which will award prizes and more capabilities. If the user is able to pass through all the tasks, he will not only be accepted but also may have the opportunity to be among the leaders of the community itself.

And now
consider sites, which are strongly centered on communities like GitHub and Code Academy. Consider also SAAS companies that present the freemium model like Moz or mobile games like Boom Beach, where you can unlock new weapons only if you pass a given trial (or you buy them).

The Magician is usually the archetype of reference for these kinds of brands. The Hero (the user) will be able to dominate a complex art thanks to the help of a Master (the brand), which will offer him instruments (i.e.: tools/courses/weapons). 

Trials are not necessarily tests. A trial can be doing something that will be awarded, for instance, with points (like commenting on a Moz blog post), and the more the points the more the recognition, with all the advantages that it may offer. 

Gamification, then, assumes an even stronger meaning and narrative function when tied to an archetype and literary mode.

Ikea, the Everyman, and the Comedic mode

Another
example is Ikea, which we cited before when talking of the Everyman archetype.

In this case, the Hero is someone like me or you who is not an interior designer or decorator or, maybe, who does not have the money for hiring those professionals or buying very expensive furniture and decoration.

But, faithful to its mission statements (“design for all”, “design your own life”…), Ikea is there to help Everyman kind of people like me and you in every way as we decorate our own houses.

On the practical side, this narrative is delivered in all the possible channels used by Ikea: web site, mobile app, social media (look at its
Twitter profile) and YouTube channel.

Betabrand, the Outlaw, and Picaresque Comedy

A third and last example can be
Betabrand.

In this case both the brand and the audience is portrayed using the
Outlaw archetype, and the brand narrative tend to use the Picaresque mode.

The Heroes is the Betabrand community who does not care what the mainstream concept of fashion is and designs and crowdfounds “its fashion.”

How to use archetypes and narrative modes in your brand storytelling

The first thing you must understand is what archetype best responds to your company tenets and mission. 

Usually this is not something an SEO can decide by him- or herself, but it is something that founders, CEOs, and directors of a company can inform.

Oftentimes a small to medium business company can achieve this with a long talk among those company figures and where they are asked to directly define the idealistic “why?” of their company.

In case of bigger companies, defining an archetype can seem almost impossible to do, but the same history of the company and hidden treasure pages like “About Us” can offer clear inspiration.

Look at REI:

Clearly the archetype figure that bests fits REI is the Explorer.

Then, using the information we retrieve when creating the
psychographic of our audience and buyer personas, matching with the characteristics each archetype has, and comparing it with the same brand core values, we can start to understand the archetype and narrative mode. If we look at REI’s audience, then we will see how it also has a certain affinity with the Everyman archetypal figure (and that also explains why REI also dedicates great attention to family as audience).

Once we have defined the best archetype commonly shared by our company and our audience, we must translate this figure and its symbolism into brand storytelling, which in web site includes design, especially the following:

  • Color pattern, because colors have a direct relation with psychological reaction (see this article, especially all the sources it links to)
  • Images, considering that in user-centric marketing the ideal is always to represent our targeted audience (or a credible approximation) as their main characters. I am talking of the so called “hero-shots”, about which Angie Shoetmuller brilliantly discussed in the deck I embed here below:

If you want to dig deeper in discovering the meaning and value of symbols worldwide, I suggest you become member of
Aras.org or to buy the Book of Symbols curated by Aras.

  • Define the best narrative mode to use. REI, again, does this well, using the Idyllic mode where the Hero explores and become part of an ideal society (the REI community, which literally means becoming a member of REI). 

We should, then:

  1. Continue investigating the archetypal nature of our audience conducting surveys
  2. Analyzing the demographic data Google Analytics offers us about our users 
  3. Using GA insights in combination with the data and demographic information offered by social networks’ ad platforms in order to create not only the interest graph of our audience but also to understand the psychology behind those interests 
  4. Doing A/B tests so to see whether symbols, images, and copywriting based on the targeted archetypes work better and if we have the correct archetype.

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!

Reblogged 3 years ago from tracking.feedpress.it

What Deep Learning and Machine Learning Mean For the Future of SEO – Whiteboard Friday

Posted by randfish

Imagine a world where even the high-up Google engineers don’t know what’s in the ranking algorithm. We may be moving in that direction. In today’s Whiteboard Friday, Rand explores and explains the concepts of deep learning and machine learning, drawing us a picture of how they could impact our work as SEOs.

For reference, here’s a still of this week’s whiteboard!

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we are going to take a peek into Google’s future and look at what it could mean as Google advances their machine learning and deep learning capabilities. I know these sound like big, fancy, important words. They’re not actually that tough of topics to understand. In fact, they’re simplistic enough that even a lot of technology firms like Moz do some level of machine learning. We don’t do anything with deep learning and a lot of neural networks. We might be going that direction.

But I found an article that was published in January, absolutely fascinating and I think really worth reading, and I wanted to extract some of the contents here for Whiteboard Friday because I do think this is tactically and strategically important to understand for SEOs and really important for us to understand so that we can explain to our bosses, our teams, our clients how SEO works and will work in the future.

The article is called “Google Search Will Be Your Next Brain.” It’s by Steve Levy. It’s over on Medium. I do encourage you to read it. It’s a relatively lengthy read, but just a fascinating one if you’re interested in search. It starts with a profile of Geoff Hinton, who was a professor in Canada and worked on neural networks for a long time and then came over to Google and is now a distinguished engineer there. As the article says, a quote from the article: “He is versed in the black art of organizing several layers of artificial neurons so that the entire system, the system of neurons, could be trained or even train itself to divine coherence from random inputs.”

This sounds complex, but basically what we’re saying is we’re trying to get machines to come up with outcomes on their own rather than us having to tell them all the inputs to consider and how to process those incomes and the outcome to spit out. So this is essentially machine learning. Google has used this, for example, to figure out when you give it a bunch of photos and it can say, “Oh, this is a landscape photo. Oh, this is an outdoor photo. Oh, this is a photo of a person.” Have you ever had that creepy experience where you upload a photo to Facebook or to Google+ and they say, “Is this your friend so and so?” And you’re like, “God, that’s a terrible shot of my friend. You can barely see most of his face, and he’s wearing glasses which he usually never wears. How in the world could Google+ or Facebook figure out that this is this person?”

That’s what they use, these neural networks, these deep machine learning processes for. So I’ll give you a simple example. Here at MOZ, we do machine learning very simplistically for page authority and domain authority. We take all the inputs — numbers of links, number of linking root domains, every single metric that you could get from MOZ on the page level, on the sub-domain level, on the root-domain level, all these metrics — and then we combine them together and we say, “Hey machine, we want you to build us the algorithm that best correlates with how Google ranks pages, and here’s a bunch of pages that Google has ranked.” I think we use a base set of 10,000, and we do it about quarterly or every 6 months, feed that back into the system and the system pumps out the little algorithm that says, “Here you go. This will give you the best correlating metric with how Google ranks pages.” That’s how you get page authority domain authority.

Cool, really useful, helpful for us to say like, “Okay, this page is probably considered a little more important than this page by Google, and this one a lot more important.” Very cool. But it’s not a particularly advanced system. The more advanced system is to have these kinds of neural nets in layers. So you have a set of networks, and these neural networks, by the way, they’re designed to replicate nodes in the human brain, which is in my opinion a little creepy, but don’t worry. The article does talk about how there’s a board of scientists who make sure Terminator 2 doesn’t happen, or Terminator 1 for that matter. Apparently, no one’s stopping Terminator 4 from happening? That’s the new one that’s coming out.

So one layer of the neural net will identify features. Another layer of the neural net might classify the types of features that are coming in. Imagine this for search results. Search results are coming in, and Google’s looking at the features of all the websites and web pages, your websites and pages, to try and consider like, “What are the elements I could pull out from there?”

Well, there’s the link data about it, and there are things that happen on the page. There are user interactions and all sorts of stuff. Then we’re going to classify types of pages, types of searches, and then we’re going to extract the features or metrics that predict the desired result, that a user gets a search result they really like. We have an algorithm that can consistently produce those, and then neural networks are hopefully designed — that’s what Geoff Hinton has been working on — to train themselves to get better. So it’s not like with PA and DA, our data scientist Matt Peters and his team looking at it and going, “I bet we could make this better by doing this.”

This is standing back and the guys at Google just going, “All right machine, you learn.” They figure it out. It’s kind of creepy, right?

In the original system, you needed those people, these individuals here to feed the inputs, to say like, “This is what you can consider, system, and the features that we want you to extract from it.”

Then unsupervised learning, which is kind of this next step, the system figures it out. So this takes us to some interesting places. Imagine the Google algorithm, circa 2005. You had basically a bunch of things in here. Maybe you’d have anchor text, PageRank and you’d have some measure of authority on a domain level. Maybe there are people who are tossing new stuff in there like, “Hey algorithm, let’s consider the location of the searcher. Hey algorithm, let’s consider some user and usage data.” They’re tossing new things into the bucket that the algorithm might consider, and then they’re measuring it, seeing if it improves.

But you get to the algorithm today, and gosh there are going to be a lot of things in there that are driven by machine learning, if not deep learning yet. So there are derivatives of all of these metrics. There are conglomerations of them. There are extracted pieces like, “Hey, we only ant to look and measure anchor text on these types of results when we also see that the anchor text matches up to the search queries that have previously been performed by people who also search for this.” What does that even mean? But that’s what the algorithm is designed to do. The machine learning system figures out things that humans would never extract, metrics that we would never even create from the inputs that they can see.

Then, over time, the idea is that in the future even the inputs aren’t given by human beings. The machine is getting to figure this stuff out itself. That’s weird. That means that if you were to ask a Google engineer in a world where deep learning controls the ranking algorithm, if you were to ask the people who designed the ranking system, “Hey, does it matter if I get more links,” they might be like, “Well, maybe.” But they don’t know, because they don’t know what’s in this algorithm. Only the machine knows, and the machine can’t even really explain it. You could go take a snapshot and look at it, but (a) it’s constantly evolving, and (b) a lot of these metrics are going to be weird conglomerations and derivatives of a bunch of metrics mashed together and torn apart and considered only when certain criteria are fulfilled. Yikes.

So what does that mean for SEOs. Like what do we have to care about from all of these systems and this evolution and this move towards deep learning, which by the way that’s what Jeff Dean, who is, I think, a senior fellow over at Google, he’s the dude that everyone mocks for being the world’s smartest computer scientist over there, and Jeff Dean has basically said, “Hey, we want to put this into search. It’s not there yet, but we want to take these models, these things that Hinton has built, and we want to put them into search.” That for SEOs in the future is going to mean much less distinct universal ranking inputs, ranking factors. We won’t really have ranking factors in the way that we know them today. It won’t be like, “Well, they have more anchor text and so they rank higher.” That might be something we’d still look at and we’d say, “Hey, they have this anchor text. Maybe that’s correlated with what the machine is finding, the system is finding to be useful, and that’s still something I want to care about to a certain extent.”

But we’re going to have to consider those things a lot more seriously. We’re going to have to take another look at them and decide and determine whether the things that we thought were ranking factors still are when the neural network system takes over. It also is going to mean something that I think many, many SEOs have been predicting for a long time and have been working towards, which is more success for websites that satisfy searchers. If the output is successful searches, and that’ s what the system is looking for, and that’s what it’s trying to correlate all its metrics to, if you produce something that means more successful searches for Google searchers when they get to your site, and you ranking in the top means Google searchers are happier, well you know what? The algorithm will catch up to you. That’s kind of a nice thing. It does mean a lot less info from Google about how they rank results.

So today you might hear from someone at Google, “Well, page speed is a very small ranking factor.” In the future they might be, “Well, page speed is like all ranking factors, totally unknown to us.” Because the machine might say, “Well yeah, page speed as a distinct metric, one that a Google engineer could actually look at, looks very small.” But derivatives of things that are connected to page speed may be huge inputs. Maybe page speed is something, that across all of these, is very well connected with happier searchers and successful search results. Weird things that we never thought of before might be connected with them as the machine learning system tries to build all those correlations, and that means potentially many more inputs into the ranking algorithm, things that we would never consider today, things we might consider wholly illogical, like, “What servers do you run on?” Well, that seems ridiculous. Why would Google ever grade you on that?

If human beings are putting factors into the algorithm, they never would. But the neural network doesn’t care. It doesn’t care. It’s a honey badger. It doesn’t care what inputs it collects. It only cares about successful searches, and so if it turns out that Ubuntu is poorly correlated with successful search results, too bad.

This world is not here yet today, but certainly there are elements of it. Google has talked about how Panda and Penguin are based off of machine learning systems like this. I think, given what Geoff Hinton and Jeff Dean are working on at Google, it sounds like this will be making its way more seriously into search and therefore it’s something that we’re really going to have to consider as search marketers.

All right everyone, I hope you’ll join me again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

Everything You Need to Know About Mobile App Search

Posted by Justin_Briggs

Mobile isn’t the future. It’s the present. Mobile apps are not only changing how we interact with devices and websites, they’re changing the way we search. Companies are creating meaningful experiences on mobile-friendly websites and apps, which in turn create new opportunities to get in front of users.

I’d like to explore the growth of mobile app search and its current opportunities to gain visibility and drive engagement.

Rise of mobile app search

The growth of mobile device usage has driven a significant lift in app-related searches. This is giving rise to mobile app search as a vertical within traditional universal search.

While it has been clear for some time that mobile search is important, that importance has been more heavily emphasized by Google recently, as they continue to push
mobile-friendly labels in SERPs, and are likely increasing mobile-friendliness’s weight as a ranking factor.

The future of search marketing involves mobile, and it will not be limited to optimizing HTML webpages, creating responsive designs, and optimizing UX. Mobile SEO is a world where apps, knowledge graph, and conversational search are front and center.

For the
top 10 leading properties online, 34% of visitors are mobile-only (comScore data), and, anecdotally, we’re seeing similar numbers with our clients, if not more.

Mobile device and app growth

It’s also worth noting that
72% of mobile engagement relies on apps vs. on browsers. Looking at teen usage, apps are increasingly dominant. Additionally,
55% of teens use voice search more than once per day

If you haven’t read it, grab some coffee and read
A Teenagers View on Social Media, which is written by a 19-year old who gives his perspective of online behavior. Reading between the lines shows a number of subtle shifts in behavior. I noticed that every time I expected him say website, he said application. In fact, he referenced application 15 times, and it is the primary way he describes social networks.

This means that one of the fasting growing segments of mobile users cannot be marketed to by optimizing HTML webpages alone, requiring search marketers to expand their skills into app optimization.

The mobile app pack

This shift is giving rise to the mobile app pack and app search results, which are triggered on searches from mobile devices in instances of high mobile app intent. Think of these as being similar to local search results. Considering
mobile searcher behavior, these listings dominate user attention.

Mobile app search results and mobile app pack

As with local search, mobile app search can reorder traditional results, completely push them down, or integrate app listings with traditional web results.

You can test on your desktop using a
user-agent switcher, or by searching on your iOS or Android device. 

There are slight differences between iPhone and Android mobile app results:

iOS and Android mobile search result listing

From what I’ve seen, mobile app listings trigger more frequently, and with more results, on Android search results when compared to iOS. Additionally, iOS mobile app listings are represented as a traditional website result listing, while mobile app listings on Android are more integrated.

Some of the differences also come from the differences in app submission guidelines on the two major stores, the Apple App Store and Google Play.

Overview of differences in mobile app results

  1. Title – Google uses the app listing page’s HTML title (which is the app’s title). iOS app titles can exceed 55-62 characters, which causes wrapping and title truncation like a traditional result. Android app title requirements are shorter, so titles are typically shorter on Android mobile app listings.
  2. URL – iOS mobile app listings display the iTunes URL to the App Store as part of the search result.
  3. Icon – iOS icons are square and Android icons have rounded corners.
  4. Design – Android results stand out more, with an “Apps” headline above the pack and a link to Google Play at the end.
  5. App store content – The other differences show up in the copy, ratings, and reviews on each app store.

Ranking in mobile app search results

Ranking in mobile app search results is a
combination of App Store Optimization (ASO) and traditional SEO. The on-page factors are dependent upon your app listing, so optimization starts with having solid ASO. If you’re not familiar with ASO, it’s the process of optimizing your app listing for internal app store search.

Basics of ASO

Ranking in the Apple App Store and in Google Play is driven by two primary factors: keyword alignment and app performance. Text fields in the app store listing, such as title, description, and keyword list, align the app with a particular set of keywords. Performance metrics including download velocity, app ratings, and reviews determine how well the app will rank for each of those keywords. (Additionally, the Google Play algorithm may include external, web-based performance metrics like citations and links as ranking factors.)

App store ranking factors

Mobile app listing optimization

While I won’t explore ASO in-depth here, as it’s very similar to traditional SEO,
optimizing app listings is primarily a function of keyword targeting.

Tools like
Sensor Tower, MobileDevHQ, and App Annie can help you with mobile app keyword research. However, keep in mind that mobile app search listings show up in universal search, so it’s important to leverage traditional keyword research tools like the AdWords Tool or Google Trends.

While there are similarities with ASO, optimizing for these mobile app search listings on the web has some slight differences.

Differences between ASO & mobile app SEO targeting

  1. Titles – While the Apple App Store allows relatively long titles, they are limited to the preview length in organic search. Titles should be optimized with Google search in mind, in addition to optimizing for the app store. Additionally, several apps aggressively target keywords in their app title, but caution should be used as spamming keywords could influence app performance in Google.
  2. Description – The app description on the App Store may not be a factor in internal search, but it will impact external app search results. Leverage keyword targeting best practices when writing your iOS app description, as well as your Android app description.
  3. Device and platform keywords – When targeting for app store search, it is not as important to target terms related to the OS or device. However, these terms can help visibility in external search. Include device and OS terms, such as Android, Samsung Note, iOS, iPad, and iPhone.

App performance optimization

Outside of content optimization, Google looks at the performance of the app. On the Android side, they have access to the data, but for iOS they have to rely on publicly available information.

App performance factors

  • Number of ratings
  • Average rating score
  • Content and sentiment analysis of reviews
  • Downloads / installs
  • Engagement and retention
  • Internal links on app store

For iOS, the primary public metrics are ratings and reviews. However, app performance can be inferred using the App Store’s ranking charts and search results, which can be leveraged as proxies of these performance metrics.


The following objectives will have the greatest influence on your mobile app search ranking:

  1. Increase your average rating number
  2. Increase your number of ratings
  3. Increase downloads

For app ratings and reviews, leverage platforms like
Apptentive to improve your ratings. They are very effective at driving positive ratings. Additionally, paid tactics are a great way to drive install volume and are one area where paid budget capacity could directly influence organic results in Google. Anecdotally, both app stores use rating numbers (typically above or below 4 stars) to make decisions around promoting an app, either through merchandising spots or co-branded campaigns. I suspect this is being used as a general cut-off for what is displayed in universal results. Increasing your rating above 4 stars should improve the likelihood you’ll appear in mobile app search results.

Lastly, think of merchandising and rankings in terms of 
internal linking structures. The more visible you are inside of the app store, the more visibility you have in external search.

App web performance optimization

Lastly, we’re talking Google rankings, so factors like links, citations, and social shares matter. You should be
conducting content marketing, PR, and outreach for your app. Focus on merchandising your app on your own site, as well as increasing coverage of your app (linking to the app store page). The basics of link optimization apply here.

App indexation – drive app engagement

Application search is not limited to driving installs via app search results. With app indexing, you can leverage your desktop/mobile website visibility in organic search to drive engagement with those who have your app installed. Google can discover and expose content deep inside your app directly in search results. This means that when a user clicks on your website in organic search, it can open your app directly, taking them to that exact piece of content in your app, instead of opening your website.

App indexation fundamentally changes technical SEO, extending SEO from server and webpage setup to the setup and optimization of applications.

App indexation on Google

This also fundamentally changes search. Your most avid and engaged user may choose to no longer visit your website. For example, on my Note 4, when I click a link to a site of a brand that I have an app installed for, Google gives me the option not only to open in the app, but to set opening the app as a default behavior.

If a user chooses to open your site in your app, they may never visit your site from organic search again.

App indexation is currently limited to Android devices, but there is evidence to suggest that it’s already in the works and is
soon to be released on iOS devices. There have been hints for some time, but markup is showing up in the wild suggesting that Google is actively working with Apple and select brands to develop iOS app indexing.

URI optimization for apps

The first step in creating an indexable app is to set up your app to support deep links. Deep links are URIs that are understood by your app and will open up a specific piece of content. They are effectively URLs for applications.

Once this URI is supported, a user can be sent to deep content in the app. These can be discovered as alternates to your desktop site’s URLs, similar to how
separate-site mobile sites are defined as alternate URLs for the desktop site. In instances of proper context (on an Android device with the app installed), Google can direct a user to the app instead of the website.

Setting this up requires working with your app developer to implement changes inside the app as well as working with your website developers to add references on your desktop site.

Adding intent filters

Android has
documented the technical setup of deep links in detail, but it starts with setting up intent filters in an app’s Android manifest file. This is done with the following code.

<activity android:name="com.example.android.GizmosActivity"
android:label="@string/title_gizmos" >
<intent-filter android:label="@string/filter_title_viewgizmos">
<action android:name="android.intent.action.VIEW" />
<data android:scheme="http"
android:host="example.com"
android:pathPrefix="/gizmos" />
<category android:name="android.intent.category.DEFAULT" />
<category android:name="android.intent.category.BROWSABLE" />
</intent-filter>
</activity>

This dictates the technical optimization of your app URIs for app indexation and defines the elements used in the URI example above.

  • The <intent-filter> element should be added for activities that should be launchable from search results.
  • The <action> element specifies the ACTION_VIEW intent action so that the intent filter can be reached from Google Search.
  • The <data> tag represents a URI format that resolves to the activity. At minimum, the <data> tag must include the android:scheme attribute.
  • Include the BROWSABLE category. The BROWSABLE category is required in order for the intent filter to be accessible from a web browser. Without it, clicking a link in a browser cannot resolve to your app. The DEFAULT category is optional, but recommended. Without this category, the activity can be started only with an explicit intent, using your app component name.

Testing deep links

Google has created tools to help test your deep link setup. You can use
Google’s Deep Link Test Tool to test your app behavior with deep links on your phone. Additionally, you can create an HTML page with an intent:// link in it.

For example
:

<a href="intent://example.com/page-1#Intent;scheme=http;package=com.example.android;end;"> <a href="http://example.com/page-1">http://example.com/page-1></a>

This link would open up deep content inside the app from the HTML page.

App URI crawl and discovery

Once an app has deep link functionality, the next step is to
ensure that Google can discover these URIs as part of its traditional desktop crawling.

Ways to get apps crawled

  1. Rel=”alternate” in HTML head
  2. ViewAction with Schema.org
  3. Rel=”alternate” in XML Sitemap

Implementing all three will create clear signals, but at minimum you should add the rel=”alternate” tag to the HTML head of your webpages.

Effectively, think of the app URI as being similar to a mobile site URL when
setting up a separate-site mobile site for SEO. The mobile deep link is an alternative way to view a webpage on your site. You map a piece of content on your site to a corresponding piece of content inside the app.

Before you get started, be sure to
verify your website and app following the guidelines here. This will verify your app in Google Play Developer Console and Google Webmaster Tools.

#1: Rel=”alternate” in HTML head

On an example page, such as example.com/page-1, you would add the following code to the head of the document. Again, very similar to separate-site mobile optimization.

<html>
<head> 
... 
<link rel="alternate" href="android-app://com.example.android/http/example.com/page-1" /> 
...
</head>
<body>
</body>
#2: ViewAction with Schema.org

Additionally, you can reference the deep link using Schema.org and JSON by using a 
ViewAction.

<script type="application/ld+json"> 
{ 
"@context": "http://schema.org", 
"@type": "WebPage", 
"@id": "http://example.com/gizmos", 
"potentialAction": { 
"@type": "ViewAction", 
"target": "android-app://com.example.android/http/example.com/gizmos" 
} 
} 
</script>
#3 Rel=”alternate” in XML sitemap

Lastly, you can reference the alternate URL in your XML Sitemaps, similar to using the rel=”alternate” for mobile sites.

<?xml version="1.0" encoding="UTF-8" ?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml"> 
<url> 
<loc>http://example.com/page-1</loc> 
<xhtml:link rel="alternate" href="android-app://com.example.android/http/example.com/page-1" /> 
</url> 
... 
</urlset>

Once these are in place, Google can discover the app URI and provide your app as an alternative way to view content found in search.

Bot control and robots noindex for apps

There may be instances where there is content within your app that you do not want indexed in Google. A good example of this might be content or functionality that is built out on your site, but has not yet been developed in your app. This would create an inferior experience for users. The good news is that we can block indexation with a few updates to the app.

First, add the following to your app resource directory (res/xml/noindex.xml).

<?xml version="1.0" encoding="utf-8"?> 
<search-engine xmlns:android="http://schemas.android.com/apk/res/android"> 
<noindex uri="http://example.com/gizmos/hidden_uri"/> 
<noindex uriPrefix="http://example.com/gizmos/hidden_prefix"/> 
<noindex uri="gizmos://hidden_path"/> 
<noindex uriPrefix="gizmos://hidden_prefix"/> 
</search-engine>

As you can see above, you can block an individual URI or define a URI prefix to block entire folders.

Once this has been added, you need to update the AndroidManifest.xml file to denote that you’re using noindex.html to block indexation.

<manifest xmlns:android="http://schemas.android.com/apk/res/android" package="com.example.android.Gizmos"> 
<application> 
<activity android:name="com.example.android.GizmosActivity" android:label="@string/title_gizmos" > 
<intent-filter android:label="@string/filter_title_viewgizmos"> 
<action android:name="android.intent.action.VIEW"/> 
... 
</activity> 
<meta-data android:name="search-engine" android:resource="@xml/noindex"/> 
</application> 
<uses-permission android:name="android.permission.INTERNET"/> 
</manifest>

App indexing API to drive re-engagement

In addition to URI discovery via desktop crawl, your mobile app can integrate
Google’s App Indexing API, which communicates with Google when users take actions inside your app. This sends information to Google about what users are viewing in the app. This is an additional method for deep link discovery and has some benefits.

The primary benefit is the ability to appear in
autocomplete. This can drive re-engagement through Google Search query autocompletions, providing access to inner pages in apps.

App auto suggest

Again, be sure to
verify your website and app following the guidelines here. This will verify your app in Google Play Developer Console and Google Webmaster Tools.

App actions with knowledge graph

The next, and most exciting, evolution of search is leveraging actions. These will be powerful when
combined with voice search, allowing search engines to take action on behalf of users, turning spoken language into executed actions.

App indexing allows you to take advantage of actions by allowing Google to not only launch an app, but execute actions inside of the app. Order me a pizza? Schedule my meeting? Drive my car? Ok, Google.

App actions work via entity detection and the application of the knowledge graph, allowing search engines to understand actions, words, ideas and objects. With that understanding, they can build an action graph that allows them to define common actions by entity type.

Here is a list of actions currently supported by Schema.org

For example, the PlayAction could be used to play a song in a music app. This can be achieve with the following markup.

<script type="application/ld+json">
{
"@context": "http://schema.org",
"@type": "MusicGroup",
"name": "Weezer", "potentialAction": {
"@type": "ListenAction",
"target": "android-app://com.spotify.music/http/we.../listen"
}
}
</script>
Once this is implemented, these app actions can begin to appear in search results and knowledge graph.

deep links in app search results

Overview of mobile app search opportunities

In summary, there are five primary ways to increase visibility and engagement for your mobile app in traditional organic search efforts.

Mobile apps in search results

The growth of mobile search is transforming how we define technical SEO, moving beyond front-end and back-end optimization of websites into the realm of structured data and application development. As app indexing expands to include iOS, I suspect the possibilities and opportunities associated with indexing applications, and their corresponding actions, to grow extensively. 

For those with Android apps, app indexing is a potential leapfrog style opportunity to get ahead of competitors who are dominant in traditional desktop search. Those with iOS devices should start by optimizing their app listings, while preparing to implement indexation, as I suspect it’ll be released for iOS this year.

Have you been leveraging traditional organic search to drive visibility and engagement for apps? Share your experiences in the comments below.

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!

Reblogged 3 years ago from tracking.feedpress.it