How Your Brand Can Create an Enviable Customer Experience for Mobile Web Searchers

Posted by ronell-smith

Not very edible corned beef hash

Here I am, seated in a Manhattan, New York restaurant, staring at corned beef hash that looks and tastes like what I imagine dog food to look and taste like.

I'm pissed for two reasons:

• It cost nearly $25 and was entirely inedible
• I should have known better given the visuals depicted after doing a Google image search to find the dish, which was offered at a nearby restaurant

In retrospect, I should have checked A and B on my phone before ordering the $25 plate of Alpo. And though I didn't do that, other would-be customers will, which means the business owner or SEO had better follow the steps below if they wish to stay in business.

The bad news is I no longer relish the thought of eating at high-end NY restaurants; the good news is this experience totally reshaped the way I view mobile, opening my eyes to simple but very effective tactics businesses of all types can immediately put to use for their brands.

My mobile education

We've all heard how mobile is transforming the web experience, reshaping the landscape for marketers, brands and consumers.

As marketers, we now have to account for how our content will be accessed and consumed on mobile devices, whether that's a phone, tablet or phablet. As brands, we realize our efforts will be judged not only on how well or high we show up in the SERPs, but also on much we can delight the on-the-go prospect who needs information that's (a) fast, (b) accurate and (c) available from any device.

As prospects and consumers, we've come to know and value customer experience in large part because brands that use mobile to deliver what we need when we need it and in a way that's easily consumed, have earned our attention — and maybe even our dollars.

But that's where the similarities seemingly end. Marketers and brands seem to get so wrapped up in the technology (responsive design, anyone?) they forget that, at the end of the day, prospects want what they want right now — in the easiest-to-access way possible.

I've come to believe that, while marketers appreciate the overall value of mobile, they have yet to realize how, for customers, it's all about what it allows them to accomplish.

At the customer/end-user level it's not about mobile-friendly or responsive design; it's about creating an enviable customer experience, one web searchers will reward you for with traffic, brand mentions and conversions.

I was alerted to the prominence of mobile phone use by noticing how many people sit staring at their phones while out at dinner, even as family members and friends are seated all around them. "How rude," I thought. Then I realized it wasn't only the people at restaurants; it's people everywhere: walking down the street, driving (sadly and dangerously), sitting in movie theaters, at work, even texting while they talk on the phone.

One of my favorite comments with regard to mobile's dominance comes with the Wizard of Moz himself, when he shared this tweet and accompanying image last year:

Mobile isn't killing desktop. It's killing all our free time. pic.twitter.com/pXb7F7aWsP
— Rand Fishkin (@randfish) December 20, 2015

But my "aha!" moment happened last year, in Manhattan, during the corned beef hash episode.

After working until brunch, I...

1. Opened iPhone to Google
2. Typed "Best corned beef hash near me"
3. Scanned list of restaurant by distance and reviews
4. Selected the closest restaurant having > 4-star review ratings
5. Ended up disappointed

That's when it hit me that I'd made errors of omission at every step, in large part by leaving one very important element out of the process, but also by not thinking like a smart web user.

Normally my process is as follows, when I wish to enjoy a specific meal while traveling:

1. Open iPhone to Google Search box
2. Type "Best _________ near me"
3. Scan list of restaurants by distance and reviews
4. Select restaurant having > 4-star review rating but has excellent reviews (> 4.5) of the dish I want and has great images of the dish online
5. Delight ensues

That's when three things occurred to me like a brickbat to the noggin':

• This is a process I use quite often and is one that has proved quite foolproof
• It's undoubtedly a process many other would-be customer are using to identify desirable products and services
• Marketers can reverse-engineer the process to bring the customers they're hoping for to their doors or websites.

(Eds. note: This post was created with small business owners (single or multiple location), or those doing Local SEO for SMBs, in mind, as I hope to inform them of how many individuals think about and use mobile, and how the marketers can get in front of them with relevant content. Also, I'd like to thank Cindy Krum of Mobile Moxie for encouraging me to write this post, and Local SEO savant Phil Rozek of Local Visibility System for making sure I colored within the lines.)

Five ways to create an enviable customer experience on mobile

#1 — Optimize your images

Image optimization is the quintessential low-hanging fruit of online marketing: easy to accomplish but typically overlooked.

For our purposes, we aren't so much making them "mobile-friendly" as we are making them search-friendly, increasing the likelihood that Google's crawlers can better decipher what they contain and deliver them for the optimal search query.

First and foremost, do not use a stock image if your goal is for searchers to find, read and enjoy your content. Just don't. Also, given how much of a factor website speed is, minify your images to ensure they don't hamper page speed load times.

But the three main areas I want us to focus on are file name, alt text and title text, and captions. My standard for each is summed up very well in a blog post from Ian Lurie, who proposes an ingenious idea:

The Blank Sheet of Paper Test: If you wrote this text on a piece of paper and showed it to a stranger, would they understand the meaning? Is this text fully descriptive?

With this thinking in mind, image optimization becomes far simpler:

• File name: We're all adults here — don't be thickheaded and choose something like "DSC9671 . png" when "cornedbeefhash . jpg" clearly works better.
• Alt text and title text: Given that, in Google's eyes, these two are the priorities, you must make certain they're as descriptive as possible. Clearly list what the image is and/or contains without weighing it down with unneeded text. Using the corned beef hash from above as a example, "corned beef hash with minced meat" would be great, but "corned beef hash with minced meat and diced potatoes" would work better, alerting me that the dish isn't what I'm looking for. (I prefer shredded beef and shredded potatoes.)

• Caption: Yes, I know these aren't necessary for every post, but why leave your visitors hanging, especially if an optimal customer experience is the goal? Were I to caption the corned beef, it'd be something along the lines of "Corned beef hash with minced meat and diced potatoes is one of the most popular dishes at XX." It says just enough without trying to say everything, which is the goal, says Lurie.

“'Fully descriptive' means 'describes the thing to which it’s attached,' not 'describe the entire universe,'" he adds.

Also, invite customers to take and share pictures online (e.g., websites, Instagram, Yelp, Google) and include as much rich detail as possible.

What's more, it might behoove you to have a Google Business View photo shoot, says Rozek. "Those show up most prominently (in the Knowledge Panel) for brand-name mobile searches in Google."

#2 — Make reviews a priority

Many prospects and customers use reviews as a make-or-break tactic when making purchases. Brands, realizing this, have taken note, making it their charge to get positive reviews.

But not all reviews are created equal.

Instead of making certain your brand gets positive reviews on the entirety of its products and services, redouble your efforts at getting positive reviews on your bread-and-butter services.

In many instances, what people have to say about your individual services and/or products matters more than your brand's overall review ratings.

I learned this from talking to several uber-picky foodie friends who shared that the main thing they look for is a brand having an overall rating (e.g., on Yelp, Google, Angie's List, Amazon, etc.) higher than 3.5, but who have customer comments glorifying the specific product they're hoping to enjoy.

"These days, everyone is gaming the system, doing what they can to get their customers to leave favorable reviews," said one friend, who lives in Dallas. "But discerning [prospects] are only looking at the overall rating as a beginning point. From there, they're digging into the comments, looking to see what people have to say about the very specific thing they want. [Smart brands] would focus more on getting people to leave comments about the particular service they used, how happy they work with the result and how it compares to other [such services they've used]. We may be on our phones, but we're still willing to dig into those comments."

To take advantage of this behavior,

• In addition to asking for a favorable review, ask customers to comment on the specific services they used, providing as much detail as possible
• Redouble your efforts at over-delivering on quality service when it comes to your core offerings
• Ask a few of your regulars, who have left comments on review sites, what they think meets the minimum expectation for provoking folks to leave a review (e.g., optimizing for the desired behavior)
• Encourage reviewers to upload photos with their reviews (or even just photos, if they don't want to review you). They're great "local content," they're useful as social-proof elements, and your customers may take better pictures than you do, in which case you can showcase them on your site.

Relevant content:

• 60+ Questions to Troubleshoot and Fix Your Local Reviews Strategy
• How to Execute the Perfect Local Reviews Strategy

#3 — Shorten your content

I serve as a horrible spokesperson for content brevity, but it matters a great deal to mobile searchers. What works fine on desktop is a clutter-fest on mobile, even for sites using responsive design.

As a general rule, simplicity wins.

For example, Whataburger's mobile experience is uncluttered, appealing to the eye and makes it clear what they want me to do: learn about their specials or make a purchase:

On the other hand, McDonald's isn't so sure what I'm looking for, apparently:

Are they trying to sell me potatoes, convince me of how committed they are to freshness or looking to learn as much as they can about me? Or all of the above?

Web searchers have specific needs and are typically short on time and patience, so you have to get in front of them with the right message to have a chance.

When it comes to the content you deliver, think tight (shorter), punchy (attention-grabbing) and valuable (on- message for the query).

# 4 — Optimize for local content

Like all of you, I've been using "near me" searches for years, especially when I travel. But over the last year, these searches have gotten more thorough and more accurate, in large part as a result of Google's Mobile Update and because the search giant is making customer intent a priority.

In 2015, Google reported that "near me" searches increased by 34-fold since 2011.

And though most of these "near me" searches are for durable goods/appliances and their associated retailers, services, including "surgeons near me," "plumbers near me," "jobs near me," etc., and other things that are typically in a high consideration set are growing considerably, according to Google via its website, thinkwithgoogle.com.

A recent case study of 82 websites (41, control group; 41, test group) shows just how dramatic the impact of optimizing a site for local intent can be. By tweaking the hours and directions page titles, descriptions and H1s to utilize the phrases "franchise dealer near me" and "nearest franchise dealer" the brand saw mobile impressions for “near me” more than double to 8,833 impressions and 46 clicks. (The control group's “near me” impression share only rose 11%.)

Image courtesy of CDK Global

Additional steps for optimizing your site for “near me” searches

• Prominently display your business name, address and phone number (aka, NAP) on your site
• Use schema markup in your NAP
• In addition to proper setup and optimization of your Google My Business listing, provide each location with its own listing and, just as important, ensure that the business name, address and phone number of each location matches what's listed on the site
• Consider embedding a Google Map prominently on your website. "It's good for user experience," says Rozek. "But it may also influence rankings."

#5 — Use Google App Deep Linking

We've all heard the statistics: The vast majority — in some circles the figure is 95% — of apps downloaded to mobile devices are never used. Don't be deceived, however, into believing apps are irrelevant.

Nearly half of all time spent on the web is in apps.

This means that the mobile searchers looking for products or services in your area are likely using an app or, at the very least, prompted to enter/use an app.

For example, when I type "thai restaurant near me," the first organic result is TripAdvisor.

Upon entering the site, the first (and preferred) action the brand would like for me to make is to download the TripAdvisor app:

Many times, a "near me" search will take us to content within an app, and we won't even realize it until we see the "continue in XX app or visit the mobile site" banner.

And if a searcher doesn't have the app installed, "Google can show an app install button. So, enabling your app for Google indexing could actually increase the installed base of the app," writes Eric Enge of Stone Temple Consulting.

For brands, App Deep Linking (ADL), which he defines as "the ability for Google to index content from within an app and then display it as mobile search results," has huge implications if utilized properly.

"Think about it," he writes. "If your app is not one of the fortunate few that get most of the attention, but your app content ranks high in searches, then you could end up with a lot more users in your app than you might have had otherwise."

(To access details on how to set up Google App Deep Linking, read Enge's Search Engine Land article: SMX Advanced recap: Advanced Google App Deep Linking)

If your brand has an app, this is information you shouldn't sleep on.

Typically, when I conduct a "near me" search, I click on/look through the images until I find one that fits what I'm looking for. Nine times out of ten (depending upon what I'm looking for), I'm either taken to content within an app or taken to a mobile site and prompted to download the app.

Seems to me that ADL would be a no-brainer.

Optimizing for mobile is simply putting web searchers first

For all the gnashing of teeth Google's many actions/inactions provoke, the search giant deserves credit for making the needs of web searchers a priority.

Too often, we, as marketers, think first and foremost in this fashion:

1. What do we have to sell?
2. Who needs it?
3. What's the cheapest, easiest way to deliver the product or service?

I think Google is saying to us that the reverse needs to occur:

1. Make it as fast and as easy for people to find what they want
2. Better understand who it is that's likely to be looking for it by better understanding our customers and their intent
3. The sales process must begin by thinking "what specific needs do web searchers have that my brand is uniquely qualified to fulfill?"

In this way, we're placing the needs of web searchers ahead of the needs of the brand, which will be the winning combination for successful companies in the days ahead.

Brands will either follow suit or fall by the wayside.

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!

How Your Brand Can Create an Enviable Customer Experience for Mobile Web Searchers

Posted by ronell-smith

Not very edible corned beef hash

Here I am, seated in a Manhattan, New York restaurant, staring at corned beef hash that looks and tastes like what I imagine dog food to look and taste like.

I'm pissed for two reasons:

• It cost nearly $25 and was entirely inedible
• I should have known better given the visuals depicted after doing a Google image search to find the dish, which was offered at a nearby restaurant

In retrospect, I should have checked A and B on my phone before ordering the $25 plate of Alpo. And though I didn't do that, other would-be customers will, which means the business owner or SEO had better follow the steps below if they wish to stay in business.

The bad news is I no longer relish the thought of eating at high-end NY restaurants; the good news is this experience totally reshaped the way I view mobile, opening my eyes to simple but very effective tactics businesses of all types can immediately put to use for their brands.

My mobile education

We've all heard how mobile is transforming the web experience, reshaping the landscape for marketers, brands and consumers.

As marketers, we now have to account for how our content will be accessed and consumed on mobile devices, whether that's a phone, tablet or phablet. As brands, we realize our efforts will be judged not only on how well or high we show up in the SERPs, but also on much we can delight the on-the-go prospect who needs information that's (a) fast, (b) accurate and (c) available from any device.

As prospects and consumers, we've come to know and value customer experience in large part because brands that use mobile to deliver what we need when we need it and in a way that's easily consumed, have earned our attention — and maybe even our dollars.

But that's where the similarities seemingly end. Marketers and brands seem to get so wrapped up in the technology (responsive design, anyone?) they forget that, at the end of the day, prospects want what they want right now — in the easiest-to-access way possible.

I've come to believe that, while marketers appreciate the overall value of mobile, they have yet to realize how, for customers, it's all about what it allows them to accomplish.

At the customer/end-user level it's not about mobile-friendly or responsive design; it's about creating an enviable customer experience, one web searchers will reward you for with traffic, brand mentions and conversions.

I was alerted to the prominence of mobile phone use by noticing how many people sit staring at their phones while out at dinner, even as family members and friends are seated all around them. "How rude," I thought. Then I realized it wasn't only the people at restaurants; it's people everywhere: walking down the street, driving (sadly and dangerously), sitting in movie theaters, at work, even texting while they talk on the phone.

One of my favorite comments with regard to mobile's dominance comes with the Wizard of Moz himself, when he shared this tweet and accompanying image last year:

Mobile isn't killing desktop. It's killing all our free time. pic.twitter.com/pXb7F7aWsP
— Rand Fishkin (@randfish) December 20, 2015

But my "aha!" moment happened last year, in Manhattan, during the corned beef hash episode.

After working until brunch, I...

1. Opened iPhone to Google
2. Typed "Best corned beef hash near me"
3. Scanned list of restaurant by distance and reviews
4. Selected the closest restaurant having > 4-star review ratings
5. Ended up disappointed

That's when it hit me that I'd made errors of omission at every step, in large part by leaving one very important element out of the process, but also by not thinking like a smart web user.

Normally my process is as follows, when I wish to enjoy a specific meal while traveling:

1. Open iPhone to Google Search box
2. Type "Best _________ near me"
3. Scan list of restaurants by distance and reviews
4. Select restaurant having > 4-star review rating but has excellent reviews (> 4.5) of the dish I want and has great images of the dish online
5. Delight ensues

That's when three things occurred to me like a brickbat to the noggin':

• This is a process I use quite often and is one that has proved quite foolproof
• It's undoubtedly a process many other would-be customer are using to identify desirable products and services
• Marketers can reverse-engineer the process to bring the customers they're hoping for to their doors or websites.

(Eds. note: This post was created with small business owners (single or multiple location), or those doing Local SEO for SMBs, in mind, as I hope to inform them of how many individuals think about and use mobile, and how the marketers can get in front of them with relevant content. Also, I'd like to thank Cindy Krum of Mobile Moxie for encouraging me to write this post, and Local SEO savant Phil Rozek of Local Visibility System for making sure I colored within the lines.)

Five ways to create an enviable customer experience on mobile

#1 — Optimize your images

Image optimization is the quintessential low-hanging fruit of online marketing: easy to accomplish but typically overlooked.

For our purposes, we aren't so much making them "mobile-friendly" as we are making them search-friendly, increasing the likelihood that Google's crawlers can better decipher what they contain and deliver them for the optimal search query.

First and foremost, do not use a stock image if your goal is for searchers to find, read and enjoy your content. Just don't. Also, given how much of a factor website speed is, minify your images to ensure they don't hamper page speed load times.

But the three main areas I want us to focus on are file name, alt text and title text, and captions. My standard for each is summed up very well in a blog post from Ian Lurie, who proposes an ingenious idea:

The Blank Sheet of Paper Test: If you wrote this text on a piece of paper and showed it to a stranger, would they understand the meaning? Is this text fully descriptive?

With this thinking in mind, image optimization becomes far simpler:

• File name: We're all adults here — don't be thickheaded and choose something like "DSC9671 . png" when "cornedbeefhash . jpg" clearly works better.
• Alt text and title text: Given that, in Google's eyes, these two are the priorities, you must make certain they're as descriptive as possible. Clearly list what the image is and/or contains without weighing it down with unneeded text. Using the corned beef hash from above as a example, "corned beef hash with minced meat" would be great, but "corned beef hash with minced meat and diced potatoes" would work better, alerting me that the dish isn't what I'm looking for. (I prefer shredded beef and shredded potatoes.)

• Caption: Yes, I know these aren't necessary for every post, but why leave your visitors hanging, especially if an optimal customer experience is the goal? Were I to caption the corned beef, it'd be something along the lines of "Corned beef hash with minced meat and diced potatoes is one of the most popular dishes at XX." It says just enough without trying to say everything, which is the goal, says Lurie.

“'Fully descriptive' means 'describes the thing to which it’s attached,' not 'describe the entire universe,'" he adds.

Also, invite customers to take and share pictures online (e.g., websites, Instagram, Yelp, Google) and include as much rich detail as possible.

What's more, it might behoove you to have a Google Business View photo shoot, says Rozek. "Those show up most prominently (in the Knowledge Panel) for brand-name mobile searches in Google."

#2 — Make reviews a priority

Many prospects and customers use reviews as a make-or-break tactic when making purchases. Brands, realizing this, have taken note, making it their charge to get positive reviews.

But not all reviews are created equal.

Instead of making certain your brand gets positive reviews on the entirety of its products and services, redouble your efforts at getting positive reviews on your bread-and-butter services.

In many instances, what people have to say about your individual services and/or products matters more than your brand's overall review ratings.

I learned this from talking to several uber-picky foodie friends who shared that the main thing they look for is a brand having an overall rating (e.g., on Yelp, Google, Angie's List, Amazon, etc.) higher than 3.5, but who have customer comments glorifying the specific product they're hoping to enjoy.

"These days, everyone is gaming the system, doing what they can to get their customers to leave favorable reviews," said one friend, who lives in Dallas. "But discerning [prospects] are only looking at the overall rating as a beginning point. From there, they're digging into the comments, looking to see what people have to say about the very specific thing they want. [Smart brands] would focus more on getting people to leave comments about the particular service they used, how happy they work with the result and how it compares to other [such services they've used]. We may be on our phones, but we're still willing to dig into those comments."

To take advantage of this behavior,

• In addition to asking for a favorable review, ask customers to comment on the specific services they used, providing as much detail as possible
• Redouble your efforts at over-delivering on quality service when it comes to your core offerings
• Ask a few of your regulars, who have left comments on review sites, what they think meets the minimum expectation for provoking folks to leave a review (e.g., optimizing for the desired behavior)
• Encourage reviewers to upload photos with their reviews (or even just photos, if they don't want to review you). They're great "local content," they're useful as social-proof elements, and your customers may take better pictures than you do, in which case you can showcase them on your site.

Relevant content:

• 60+ Questions to Troubleshoot and Fix Your Local Reviews Strategy
• How to Execute the Perfect Local Reviews Strategy

#3 — Shorten your content

I serve as a horrible spokesperson for content brevity, but it matters a great deal to mobile searchers. What works fine on desktop is a clutter-fest on mobile, even for sites using responsive design.

As a general rule, simplicity wins.

For example, Whataburger's mobile experience is uncluttered, appealing to the eye and makes it clear what they want me to do: learn about their specials or make a purchase:

On the other hand, McDonald's isn't so sure what I'm looking for, apparently:

Are they trying to sell me potatoes, convince me of how committed they are to freshness or looking to learn as much as they can about me? Or all of the above?

Web searchers have specific needs and are typically short on time and patience, so you have to get in front of them with the right message to have a chance.

When it comes to the content you deliver, think tight (shorter), punchy (attention-grabbing) and valuable (on- message for the query).

# 4 — Optimize for local content

Like all of you, I've been using "near me" searches for years, especially when I travel. But over the last year, these searches have gotten more thorough and more accurate, in large part as a result of Google's Mobile Update and because the search giant is making customer intent a priority.

In 2015, Google reported that "near me" searches increased by 34-fold since 2011.

And though most of these "near me" searches are for durable goods/appliances and their associated retailers, services, including "surgeons near me," "plumbers near me," "jobs near me," etc., and other things that are typically in a high consideration set are growing considerably, according to Google via its website, thinkwithgoogle.com.

A recent case study of 82 websites (41, control group; 41, test group) shows just how dramatic the impact of optimizing a site for local intent can be. By tweaking the hours and directions page titles, descriptions and H1s to utilize the phrases "franchise dealer near me" and "nearest franchise dealer" the brand saw mobile impressions for “near me” more than double to 8,833 impressions and 46 clicks. (The control group's “near me” impression share only rose 11%.)

Image courtesy of CDK Global

Additional steps for optimizing your site for “near me” searches

• Prominently display your business name, address and phone number (aka, NAP) on your site
• Use schema markup in your NAP
• In addition to proper setup and optimization of your Google My Business listing, provide each location with its own listing and, just as important, ensure that the business name, address and phone number of each location matches what's listed on the site
• Consider embedding a Google Map prominently on your website. "It's good for user experience," says Rozek. "But it may also influence rankings."

#5 — Use Google App Deep Linking

We've all heard the statistics: The vast majority — in some circles the figure is 95% — of apps downloaded to mobile devices are never used. Don't be deceived, however, into believing apps are irrelevant.

Nearly half of all time spent on the web is in apps.

This means that the mobile searchers looking for products or services in your area are likely using an app or, at the very least, prompted to enter/use an app.

For example, when I type "thai restaurant near me," the first organic result is TripAdvisor.

Upon entering the site, the first (and preferred) action the brand would like for me to make is to download the TripAdvisor app:

Many times, a "near me" search will take us to content within an app, and we won't even realize it until we see the "continue in XX app or visit the mobile site" banner.

And if a searcher doesn't have the app installed, "Google can show an app install button. So, enabling your app for Google indexing could actually increase the installed base of the app," writes Eric Enge of Stone Temple Consulting.

For brands, App Deep Linking (ADL), which he defines as "the ability for Google to index content from within an app and then display it as mobile search results," has huge implications if utilized properly.

"Think about it," he writes. "If your app is not one of the fortunate few that get most of the attention, but your app content ranks high in searches, then you could end up with a lot more users in your app than you might have had otherwise."

(To access details on how to set up Google App Deep Linking, read Enge's Search Engine Land article: SMX Advanced recap: Advanced Google App Deep Linking)

If your brand has an app, this is information you shouldn't sleep on.

Typically, when I conduct a "near me" search, I click on/look through the images until I find one that fits what I'm looking for. Nine times out of ten (depending upon what I'm looking for), I'm either taken to content within an app or taken to a mobile site and prompted to download the app.

Seems to me that ADL would be a no-brainer.

Optimizing for mobile is simply putting web searchers first

For all the gnashing of teeth Google's many actions/inactions provoke, the search giant deserves credit for making the needs of web searchers a priority.

Too often, we, as marketers, think first and foremost in this fashion:

1. What do we have to sell?
2. Who needs it?
3. What's the cheapest, easiest way to deliver the product or service?

I think Google is saying to us that the reverse needs to occur:

1. Make it as fast and as easy for people to find what they want
2. Better understand who it is that's likely to be looking for it by better understanding our customers and their intent
3. The sales process must begin by thinking "what specific needs do web searchers have that my brand is uniquely qualified to fulfill?"

In this way, we're placing the needs of web searchers ahead of the needs of the brand, which will be the winning combination for successful companies in the days ahead.

Brands will either follow suit or fall by the wayside.

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!

SEO Trek: The Search for Google RankBrain* [New Data]

Posted by larry.kim

Rand Fishkin posted another brilliant Whiteboard Friday last week on the topic of optimizing for RankBrain. In it, he explained how RankBrain helps Google select and prioritize signals it uses for ranking.

One of the most important signals Google takes into account is user engagement. As Rand noted, engagement is a "very, very important signal."

Engagement is a huge but often ignored opportunity. That's why I've been a bit obsessed with improving engagement metrics.

My theory has been that RankBrain *and/or other machine learning elements within Google's core algorithm are increasingly rewarding pages with high user engagement. Not always, but it's happening often enough that it's kind of a huge deal.

Google is looking for unicorns – and I think that machine learning is Google's ultimate Unicorn Detector.

Now, when I say unicorns, I mean those pages that have magical engagement rates that elevate them above the other donkey pages Google could show for a given query. Like if your page has a 5 percent click-through rate (CTR) when everyone else has a 1 percent CTR.

What is Google's mission? To provide the best results to searchers. One way Google does this is by looking at engagement data.

If most people are clicking on a particular search result – and then also engaging with that page – these are clear signals to Google that people think this page is fascinating. That it's a unicorn.

RankBrain: Into Darkness

RankBrain, much like Google's algorithm, is a great mystery. Since Google revealed (in a Bloomberg article just under a year ago) the important role of machine learning and artificial intelligence in its algorithm, RankBrain has been a surprisingly controversial topic, generating speculation and debate within the search industry.

Then, we found out in June that Google RankBrain was no longer just for long-tail queries. It was "involved in every query."

We learned quite a few things about RankBrain. We were told by Google that you can't optimize for it. Yet we also learned that Google's engineers don't really understand what RankBrain does or how it works.

Some people have even argued that there is absolutely nothing you can do to see Google's machine learning systems at work.

Give me a break! It's an algorithm. Granted, a more complex algorithm thanks to machine learning, but an algorithm nonetheless. All algorithms have rules and patterns.

When Google tweaked Panda and Penguin, we saw it. When Google tweaked its exact-match domain algorithm, we saw it. When Google tweaked its mobile algorithm, we saw it.

If you carefully set up an experiment, you should be able to isolate some aspect of what Google is proclaiming as the third most important ranking factor. You should be able to find evidence – a digital fingerprint.

Well, I say it's time to boldly go where no SEO has gone before. That's what I've attempted to do in this post. Let's look at some new data.

The search for RankBrain [New Data]

What you're about to look at is organic search click-through rate vs. the average organic search position for three separate 30-day periods ending April 30, July 12, and September 19 of this year. This data, obtained from the Google Search Console, tracked the same keywords in the Internet marketing niche.

I see some of the most compelling evidence of RankBrain (and/or other machine learning search algorithms!) at work.

The shape of CTR vs. ranking curve is changing every month – for the 30 days ending:

• April 30, 2016, the average CTR for top position was about 22 percent.
• July 12, 2016, the average CTR rose to about 24 percent.
• By September 19, 2016, the average CTR increased to about 27 percent.

The top, most prominent positions are getting even more clicks. Obviously, they were already getting a lot of clicks. But now they're getting more clicks than they have in recent history.

This is the winner-take-all nature of Google's organic SERPs today. It's coming at the expense of Positions 4–10, which are being clicked on much less over time.

Results that are more likely to attract engagement are pushed further up the SERP, while results with lower engagement get pushed further down. That's what we believe RankBrain is doing.

Going beyond the data

This data is showing us something very interesting. A couple thoughts:

• This is exactly the fingerprint you would expect to see for a machine learning-based algorithm doing query interpretation that impacts rank based on user engagement metrics, such as CTR.
• Essentially, machine learning systems move away from serving up 10 blue links and asking a user to choose one of them and toward providing the actual correct answers, further eliminating the need for lower positions.

Could anything else be causing this shift to the click curve? Could it have been the elimination of right rail ads?

No, that happened in February. I was careful to use date ranges that were after the right rail apocalypse.

Could it be more Knowledge Graph elements creeping into the SERPs? If that were the case, it would look like everything got pushed down by one position (e.g., Position 1 becomes Position 2, Position 2 becomes Position 3, and so on).

The data didn't show that happening. We see a bending of the click curve, not a shifting of the curve.

Behold the awesome power of CTR optimization!

OK, so we've looked at the big picture. Now let's look at the little picture to illustrate the remarkable power of CTR optimization.

Let's talk about guerrilla marketing. Here are two headlines. Which headline do you think has the higher CTR?

• Guerrilla Marketing: 20+ Examples and Strategies to Stand Out

This was the original headline for an article published on the WordStream blog in 2014.

• 20+ Jaw-Dropping Guerrilla Marketing Examples

This is the updated headline, which we changed just a few months ago, in the hopes of increasing the CTR. And yep, we sure did!

Before we updated the headline, the article had a CTR of 1 percent and was ranking in position 8. Nothing awesome.

Since we updated the headline, the article has had a CTR of 4.19 percent and is ranking in position 5. Pretty awesome, no?

Increasingly, we've been trying to move away from "SEO titles" that look like the original headline, where you have the primary keyword followed by a colon and the rest of your headline. They aren't catchy enough.

Yes, you still need to include keywords in your headline. But you don't have to use this tired format, which will deliver (at best) solid but unspectacular results.

To be clear: we only changed the title tag. No other optimization tactics were used.

We didn't point any links (internal or external) at it. We didn't add any images or anything else to the post. Nothing.

Changing the title tag changed the CTR. Which gave it "magical points" that resulted in 97 percent more organic traffic:

What does it all mean?

This example illustrates that if you increase your CTR, you'll see a nice boost in traffic. Ranking in a better position means more traffic, which means a higher CTR, which also means more traffic.

What's so remarkable is that this is on-page SEO. No link building was required! Besides, pointing new links to a page wouldn't result in a higher click-through rate – a catchier headline, however, would result in a higher CTR.

What's also interesting about this is that RankBrain isn't like other algorithms, say Panda or Penguin, where it was obvious when you got hit. You lost half your traffic!

If RankBrain or a machine learning algorithm impacts your site due to engagement metrics (positive or negative), it's a much more subtle shift. All your best pages do better. All your “upper class donkey” pages do slightly worse. Ultimately, the two forces cancel each other out, to some extent, so that the SEO alarms don’t go off.

The final frontier

When it comes to SEO, your mission is to seek out every advantage. It's my belief that organic CTR and website engagement rates impact organic rankings.

So boldly go where many SEOs are failing to go now. Hop aboard the USS Unicorn, make the jump to warp speed, and discover the wonders of those magical creatures.

Oh, and…

Are you optimizing your click-through rates? If not, why not? If so, what have you been seeing in your analytics?

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!

SEO Trek: The Search for Google RankBrain* [New Data]

Posted by larry.kim

Rand Fishkin posted another brilliant Whiteboard Friday last week on the topic of optimizing for RankBrain. In it, he explained how RankBrain helps Google select and prioritize signals it uses for ranking.

One of the most important signals Google takes into account is user engagement. As Rand noted, engagement is a "very, very important signal."

Engagement is a huge but often ignored opportunity. That's why I've been a bit obsessed with improving engagement metrics.

My theory has been that RankBrain *and/or other machine learning elements within Google's core algorithm are increasingly rewarding pages with high user engagement. Not always, but it's happening often enough that it's kind of a huge deal.

Google is looking for unicorns – and I think that machine learning is Google's ultimate Unicorn Detector.

Now, when I say unicorns, I mean those pages that have magical engagement rates that elevate them above the other donkey pages Google could show for a given query. Like if your page has a 5 percent click-through rate (CTR) when everyone else has a 1 percent CTR.

What is Google's mission? To provide the best results to searchers. One way Google does this is by looking at engagement data.

If most people are clicking on a particular search result – and then also engaging with that page – these are clear signals to Google that people think this page is fascinating. That it's a unicorn.

RankBrain: Into Darkness

RankBrain, much like Google's algorithm, is a great mystery. Since Google revealed (in a Bloomberg article just under a year ago) the important role of machine learning and artificial intelligence in its algorithm, RankBrain has been a surprisingly controversial topic, generating speculation and debate within the search industry.

Then, we found out in June that Google RankBrain was no longer just for long-tail queries. It was "involved in every query."

We learned quite a few things about RankBrain. We were told by Google that you can't optimize for it. Yet we also learned that Google's engineers don't really understand what RankBrain does or how it works.

Some people have even argued that there is absolutely nothing you can do to see Google's machine learning systems at work.

Give me a break! It's an algorithm. Granted, a more complex algorithm thanks to machine learning, but an algorithm nonetheless. All algorithms have rules and patterns.

When Google tweaked Panda and Penguin, we saw it. When Google tweaked its exact-match domain algorithm, we saw it. When Google tweaked its mobile algorithm, we saw it.

If you carefully set up an experiment, you should be able to isolate some aspect of what Google is proclaiming as the third most important ranking factor. You should be able to find evidence – a digital fingerprint.

Well, I say it's time to boldly go where no SEO has gone before. That's what I've attempted to do in this post. Let's look at some new data.

The search for RankBrain [New Data]

What you're about to look at is organic search click-through rate vs. the average organic search position for three separate 30-day periods ending April 30, July 12, and September 19 of this year. This data, obtained from the Google Search Console, tracked the same keywords in the Internet marketing niche.

I see some of the most compelling evidence of RankBrain (and/or other machine learning search algorithms!) at work.

The shape of CTR vs. ranking curve is changing every month – for the 30 days ending:

• April 30, 2016, the average CTR for top position was about 22 percent.
• July 12, 2016, the average CTR rose to about 24 percent.
• By September 19, 2016, the average CTR increased to about 27 percent.

The top, most prominent positions are getting even more clicks. Obviously, they were already getting a lot of clicks. But now they're getting more clicks than they have in recent history.

This is the winner-take-all nature of Google's organic SERPs today. It's coming at the expense of Positions 4–10, which are being clicked on much less over time.

Results that are more likely to attract engagement are pushed further up the SERP, while results with lower engagement get pushed further down. That's what we believe RankBrain is doing.

Going beyond the data

This data is showing us something very interesting. A couple thoughts:

• This is exactly the fingerprint you would expect to see for a machine learning-based algorithm doing query interpretation that impacts rank based on user engagement metrics, such as CTR.
• Essentially, machine learning systems move away from serving up 10 blue links and asking a user to choose one of them and toward providing the actual correct answers, further eliminating the need for lower positions.

Could anything else be causing this shift to the click curve? Could it have been the elimination of right rail ads?

No, that happened in February. I was careful to use date ranges that were after the right rail apocalypse.

Could it be more Knowledge Graph elements creeping into the SERPs? If that were the case, it would look like everything got pushed down by one position (e.g., Position 1 becomes Position 2, Position 2 becomes Position 3, and so on).

The data didn't show that happening. We see a bending of the click curve, not a shifting of the curve.

Behold the awesome power of CTR optimization!

OK, so we've looked at the big picture. Now let's look at the little picture to illustrate the remarkable power of CTR optimization.

Let's talk about guerrilla marketing. Here are two headlines. Which headline do you think has the higher CTR?

• Guerrilla Marketing: 20+ Examples and Strategies to Stand Out

This was the original headline for an article published on the WordStream blog in 2014.

• 20+ Jaw-Dropping Guerrilla Marketing Examples

This is the updated headline, which we changed just a few months ago, in the hopes of increasing the CTR. And yep, we sure did!

Before we updated the headline, the article had a CTR of 1 percent and was ranking in position 8. Nothing awesome.

Since we updated the headline, the article has had a CTR of 4.19 percent and is ranking in position 5. Pretty awesome, no?

Increasingly, we've been trying to move away from "SEO titles" that look like the original headline, where you have the primary keyword followed by a colon and the rest of your headline. They aren't catchy enough.

Yes, you still need to include keywords in your headline. But you don't have to use this tired format, which will deliver (at best) solid but unspectacular results.

To be clear: we only changed the title tag. No other optimization tactics were used.

We didn't point any links (internal or external) at it. We didn't add any images or anything else to the post. Nothing.

Changing the title tag changed the CTR. Which gave it "magical points" that resulted in 97 percent more organic traffic:

What does it all mean?

This example illustrates that if you increase your CTR, you'll see a nice boost in traffic. Ranking in a better position means more traffic, which means a higher CTR, which also means more traffic.

What's so remarkable is that this is on-page SEO. No link building was required! Besides, pointing new links to a page wouldn't result in a higher click-through rate – a catchier headline, however, would result in a higher CTR.

What's also interesting about this is that RankBrain isn't like other algorithms, say Panda or Penguin, where it was obvious when you got hit. You lost half your traffic!

If RankBrain or a machine learning algorithm impacts your site due to engagement metrics (positive or negative), it's a much more subtle shift. All your best pages do better. All your “upper class donkey” pages do slightly worse. Ultimately, the two forces cancel each other out, to some extent, so that the SEO alarms don’t go off.

The final frontier

When it comes to SEO, your mission is to seek out every advantage. It's my belief that organic CTR and website engagement rates impact organic rankings.

So boldly go where many SEOs are failing to go now. Hop aboard the USS Unicorn, make the jump to warp speed, and discover the wonders of those magical creatures.

Oh, and…

Are you optimizing your click-through rates? If not, why not? If so, what have you been seeing in your analytics?

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!

R Street Offers No “Bargain” on Carbon At All

A new report from R Street tries to convince conservatives that they should embrace a “carbon bargain” involving a new carbon tax, so long as several important principles are obeyed. Specifically, R Street insists that a new carbon tax be revenue neutral, with the funds devoted exclusively to reducing capital taxes, and it also insists that “[p]re-emption of the Environmental Protection Agency’s Clean Power Plan and other efforts – including CAFE standards, fuel taxes, the renewable fuel standard, the investment and production tax credits, efficiency standards and restrictions on fossil resource development – would be a necessary prerequisite.” So long as the deal included these (and some other) elements, R Street promises conservatives that a new carbon tax would actually be good for the economy, as well as correcting possible market failures from greenhouse gas emissions.

In the present post, I don’t want to rehash IER’s long-standing criticism of such “carbon tax swap” deals in terms of the technicalities. Indeed, the first half of the R Street report itself does a good job summarizing all of the problems with a carbon tax—especially with the dubious notion of the “social cost of carbon.” (Since the author of the R Street study, Catrina Rorke, explicitly cites my work for IER on the tax interaction effect—which I spell out here—I can hardly complain that she ignored our objections.) So R Street’s own study acknowledges many of the dangers in their own approach.

Rather than reiterating our list of technical objections, let me in this post instead simply step back and ask: What is the point of studies such as the R Street proposal? It’s not as if President Obama or Gina McCarthy are making a substantive offer here. Rather, R Street’s proposal (and others like it) are fantasy land bargains from people with no political power in order to get conservatives and libertarians to abandon their opposition to a massive new tax. What is the point of this exercise?

After all, it’s not as if even mainstream environmentalists are on board. No, the relevant wonk at Vox (to pick just one example) is actively telling the rank and file to reject any compromise that relies exclusively on a “carbon price” to “internalize the externalities.” To repeat, I’m not merely pointing to Joe Romm or Al Gore, I’m saying even progressives who are professional economists (such as Paul Krugman, who would presumably understand the benefits of a “market solution”) are telling their readers that a carbon tax isn’t enough. And when a progressive economist does bust his butt to get a revenue-neutral carbon tax on the ballot, environmental groups oppose it and Greg Ip at the Wall Street Journal is flummoxed as to why leftists aren’t embracing a good ol’ “market solution” when they see one.

Allow me to let Catrina Rorke and Greg Ip in on a dirty little secret: The typical progressive activist, and the typical administrator at the EPA, do not share their general admiration for the market economy. It is not as if the people of Greenpeace toss and turn at night, lamenting the Pareto inefficiencies in our economy and the fact that industry produces a bit above the “optimal” level of pollution. No, these people do not like capitalism, period, and think Americans are consuming too much.

Consider, for example, the Sierra Club’s opposition to nuclear power. If human-caused climate change is such a serious threat, it would make sense for the Sierra Club to support nuclear power to reduce CO₂ emissions, especially for the next few decades. Yet for the last forty years, the Sierra Club stated they would reject nuclear power until the “[d]evelopment of adequate national and global policies to curb energy over-use and unnecessary economic growth” (emphasis added). It’s true that earlier this month the Sierra Club eliminated this clause, but for forty years this has been the Sierra Club’s position—nuclear energy is bad because (among other reasons) it could foster unnecessary economic growth.

Look at the R Street study’s own table, showing that, for example, the 2011 change to CAFE standards translated into carbon emission reductions at a cost of $125 per ton. This was not an unnecessarily expensive way to reduce emissions, this was instead a completely unjustified power grab that must be explained using Public Choice or more broadly political science, not environmental economics.

For R Street, the Niskanen Center, and any other groups trying to convince conservatives and libertarians of the virtues of rolling back nutty top-down regulations and replacing them with a clean, simple carbon tax: They must first explain why those interventions exist in the first place. When these groups explain that, they will then see why no conservative or libertarian should believe offers of a “tax swap deal” that brushes away all of the power that the EPA and other organizations have accrued over the years.

 

In this context, it’s an irrelevant academic point of whether a new carbon tax, with all funds directed to tax breaks for corporations and capitalists, plus the elimination of all other federal regulations on emissions, would be economically beneficial on net. When I read studies like R Street’s, I feel like I’m a bank teller looking at guys storming in with shotguns and ski masks, and my colleague says, “Let’s offer them free checking.”

I’m glad to see that R Street’s energy analysts are admitting all of the tremendous problems with a carbon tax (such as leakage, the tax interaction effect, and the arbitrariness of the “social cost of carbon”), but they valiantly throw all of those concerns aside and still assume they can strike a deal with groups who are saying, in print, that they are not going to make such a deal. In closing, I simply repeat my initial reaction: I honestly don’t know what the point of these reflections is, except to weaken conservative and libertarian resistance to a massive new tax that R Street admits could bring in more than $1 trillion in the first ten years.

The post R Street Offers No “Bargain” on Carbon At All appeared first on IER.

Most SEOs Are No Better than a Coin-Flip at Predicting Which Page Will Rank Better. Can You?

Posted by willcritchlow

We want to be able to answer questions about why one page outranks another.

“What would we have to do to outrank that site?”

“Why is our competitor outranking us on this search?”

These kind of questions — from bosses, from clients, and from prospective clients — are a standard part of day-to-day life for many SEOs. I know I’ve been asked both in the last week.

It’s relatively easy to figure out ways that a page can be made more relevant and compelling for a given search, and it’s straightforward to think of ways the page or site could be more authoritative (even if it’s less straight-forward to get it done). But will those changes or that extra link cause an actual reordering of a specific ranking? That’s a very hard question to answer with a high degree of certainty.

When we asked a few hundred people to pick which of two pages would rank better for a range of keywords, the average accuracy on UK SERPs was 46%. That’s worse than you’d get if you just flipped a coin! This chart shows the performance by keyword. It’s pretty abysmal:

It’s getting harder to unpick all the ranking factors

I’ve participated in each iteration of Moz’s ranking factors survey since its inception in 2009. At one of our recent conferences (the last time I was in San Diego for SearchLove) I talked about how I used to enjoy it and feel like I could add real value by taking the survey, but how that's changed over the years as the complexity has increased.

While I remain confident when building strategies to increase overall organic visibility, traffic, and revenue, I’m less sure than ever which individual ranking factors will outweigh which others in a specific case.

The strategic approach looks at whole sites and groups of keywords

My approach is generally to zoom out and build business cases on assumptions about portfolios of rankings, but it’s been on my mind recently as I think about the ways machine learning should make Google rankings ever more of a black box, and cause the ranking factors to vary more and more between niches.

In general, "why does this page rank?" is the same as "which of these two pages will rank better?"

I've been teaching myself about deep neural networks using TensorFlow and Keras — an area I’m pretty sure I’d have ended up studying and working in if I’d gone to college 5 years later. As I did so, I started thinking about how you would model a SERP (which is a set of high-dimensional non-linear relationships). I realized that the litmus test of understanding ranking factors — and thus being able to answer “why does that page outrank us?” — boils down to being able to answer a simpler question:

Given two pages, can you figure out which one will outrank the other for a given query?

If you can answer that in the general case, then you know why one page outranks another, and vice-versa.

It turns out that people are terrible at answering this question.

I thought that answering this with greater accuracy than a coin flip was going to be a pretty low bar. As you saw from the sneak peak of my results above, that turned out not to be the case. Reckon you can do better? Skip ahead to take the test and find out.

(In fact, if you could find a way to test this effectively, I wonder if it would make a good qualifying question for the next moz ranking factors survey. Should you only listen only to the opinion of those experts who are capable of answering with reasonable accuracy? Note that my test that follows isn’t at all rigorous because you can cheat by Googling the keywords — it’s just for entertainment purposes).

Take the test and see how well you can answer

With my curiosity piqued, I put together a simple test, thinking it would be interesting to see how good expert SEOs actually are at this, as well as to see how well laypeople do.

I’ve included a bit more about the methodology and some early results below, but if you'd like to skip ahead and test yourself you can go ahead here.

Note that to simplify the adversarial side, I’m going to let you rely on all of Google’s spam filtering — you can trust that every URL ranks in the top 10 for its example keyword — so you're choosing an ordering of two pages that do rank for the query rather than two pages from potentially any domain on the Internet.

I haven’t designed this to be uncheatable — you can obviously cheat by Googling the keywords — but as my old teachers used to say: "If you do, you’ll only be cheating yourself."

Unfortunately, Google Forms seems to have removed the option to be emailed your own answers outside of an apps domain, so if you want to know how you did, note down your answers as you go along and compare them to the correct answers (which are linked from the final page of the test).

You can try your hand with just one keyword or keep going, trying anywhere up to 10 keywords (each with a pair of pages to put in order). Note that you don’t need to do all of them; you can submit after any number.

You can take the survey either for the US (google.com) or UK (google.co.uk). All results are considering only the "blue links" results — i.e. links to web pages — rather than universal search results / one-boxes etc.

Take the test!

What do the early responses show?

Before publishing this post, we sent it out to the @distilled and @moz networks. At the time of writing, almost 300 people have taken the test, and there are already some interesting results:

It seems as though the US questions are slightly easier

The UK test appears to be a little harder (judging both by the accuracy of laypeople, and with a subjective eye). And while accuracy generally increases with experience in both the UK and the US, the vast majority of UK respondents performed worse than a coin flip:

Some easy questions might skew the data in the US

Digging into the data, there are a few of the US questions that are absolute no-brainers (e.g. there's a question about the keyword [mortgage calculator] in the US that 84% of respondents get right regardless of their experience). In comparison, the easiest one in the UK was also a mortgage-related query ([mortgage comparisons]) but only 2/3 of people got that right (67%).

Compare the UK results by keyword...

...To the same chart for the US keywords:

So, even though the overall accuracy was a little above 50% in the US (around 56% or roughly 5/9), I’m not actually convinced that US SERPs are generally easier to understand. I think there are a lot of US SERPs where human accuracy is in the 40% range.

The Dunning-Kruger effect is on display

The Dunning-Kruger effect is a well-studied psychological phenomenon whereby people “fail to adequately assess their level of competence,” typically feeling unsure in areas where they are actually strong (impostor syndrome) and overconfident in areas where they are weak. Alongside the raw predictions, I asked respondents to give their confidence in their rankings for each URL pair on a scale from 1 (“Essentially a guess, but I’ve picked the one I think”) to 5 (“I’m sure my chosen page should rank better”).

The effect was most pronounced on the UK SERPs — where respondents answering that they were sure or fairly sure (4–5) were almost as likely to be wrong as those guessing (1) — and almost four percentage points worse than those who said they were unsure (2–3):

Is Google getting so me of these wrong?

The question I asked SEOs was “which page do you think ranks better?”, not “which page is a better result?”, so in general, most of the results say very little about whether Google is picking the right result in terms of user satisfaction. I did, however, ask people to share the survey with their non-SEO friends and ask them to answer the latter question.

If I had a large enough sample-size, you might expect to see some correlation here — but remember that these were a diverse array of queries and the average respondent might well not be in the target market, so it’s perfectly possible that Google knows what a good result looks like better than they do.

Having said that, in my own opinion, there are one or two of these results that are clearly wrong in UX terms, and it might be interesting to analyze why the “wrong” page is ranking better. Maybe that’ll be a topic for a follow-up post. If you want to dig into it, there’s enough data in both the post above and the answers given at the end of the survey to find the ones I mean (I don’t want to spoil it for those who haven’t tried it out yet). Let me know if you dive into the ranking factors and come up with any theories.

There is hope for our ability to fight machine learning with machine learning

One of the disappointments of putting together this test was that by the time I’d made the Google Form I knew too many of the answer to be able to test myself fairly. But I was comforted by the fact that I could do the next best thing — I could test my neural network (well, my model, refactored by our R&D team and trained on data they gathered, which we flippantly called Deeprank).

I think this is fair; the instructions did say “use whatever tools you like to assess the sites, but please don't skew the results by performing the queries on Google yourself.” The neural network wasn’t trained on these results, so I think that’s within the rules. I ran it on the UK questions because it was trained on google.co.uk SERPs, and it did better than a coin flip:

So maybe there is hope that smarter tools could help us continue to answer questions like “why is our competitor outranking us on this search?”, even as Google’s black box gets ever more complex and impenetrable.

If you want to hear more about these results as I gather more data and get updates on Deeprank when it’s ready for prime-time, be sure to add your email address when you:

Take the test (or just drop me your email here)

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!

Most SEOs Are No Better than a Coin-Flip at Predicting Which Page Will Rank Better. Can You?

Posted by willcritchlow

We want to be able to answer questions about why one page outranks another.

“What would we have to do to outrank that site?”

“Why is our competitor outranking us on this search?”

These kind of questions — from bosses, from clients, and from prospective clients — are a standard part of day-to-day life for many SEOs. I know I’ve been asked both in the last week.

It’s relatively easy to figure out ways that a page can be made more relevant and compelling for a given search, and it’s straightforward to think of ways the page or site could be more authoritative (even if it’s less straight-forward to get it done). But will those changes or that extra link cause an actual reordering of a specific ranking? That’s a very hard question to answer with a high degree of certainty.

When we asked a few hundred people to pick which of two pages would rank better for a range of keywords, the average accuracy on UK SERPs was 46%. That’s worse than you’d get if you just flipped a coin! This chart shows the performance by keyword. It’s pretty abysmal:

It’s getting harder to unpick all the ranking factors

I’ve participated in each iteration of Moz’s ranking factors survey since its inception in 2009. At one of our recent conferences (the last time I was in San Diego for SearchLove) I talked about how I used to enjoy it and feel like I could add real value by taking the survey, but how that's changed over the years as the complexity has increased.

While I remain confident when building strategies to increase overall organic visibility, traffic, and revenue, I’m less sure than ever which individual ranking factors will outweigh which others in a specific case.

The strategic approach looks at whole sites and groups of keywords

My approach is generally to zoom out and build business cases on assumptions about portfolios of rankings, but it’s been on my mind recently as I think about the ways machine learning should make Google rankings ever more of a black box, and cause the ranking factors to vary more and more between niches.

In general, "why does this page rank?" is the same as "which of these two pages will rank better?"

I've been teaching myself about deep neural networks using TensorFlow and Keras — an area I’m pretty sure I’d have ended up studying and working in if I’d gone to college 5 years later. As I did so, I started thinking about how you would model a SERP (which is a set of high-dimensional non-linear relationships). I realized that the litmus test of understanding ranking factors — and thus being able to answer “why does that page outrank us?” — boils down to being able to answer a simpler question:

Given two pages, can you figure out which one will outrank the other for a given query?

If you can answer that in the general case, then you know why one page outranks another, and vice-versa.

It turns out that people are terrible at answering this question.

I thought that answering this with greater accuracy than a coin flip was going to be a pretty low bar. As you saw from the sneak peak of my results above, that turned out not to be the case. Reckon you can do better? Skip ahead to take the test and find out.

(In fact, if you could find a way to test this effectively, I wonder if it would make a good qualifying question for the next moz ranking factors survey. Should you only listen only to the opinion of those experts who are capable of answering with reasonable accuracy? Note that my test that follows isn’t at all rigorous because you can cheat by Googling the keywords — it’s just for entertainment purposes).

Take the test and see how well you can answer

With my curiosity piqued, I put together a simple test, thinking it would be interesting to see how good expert SEOs actually are at this, as well as to see how well laypeople do.

I’ve included a bit more about the methodology and some early results below, but if you'd like to skip ahead and test yourself you can go ahead here.

Note that to simplify the adversarial side, I’m going to let you rely on all of Google’s spam filtering — you can trust that every URL ranks in the top 10 for its example keyword — so you're choosing an ordering of two pages that do rank for the query rather than two pages from potentially any domain on the Internet.

I haven’t designed this to be uncheatable — you can obviously cheat by Googling the keywords — but as my old teachers used to say: "If you do, you’ll only be cheating yourself."

Unfortunately, Google Forms seems to have removed the option to be emailed your own answers outside of an apps domain, so if you want to know how you did, note down your answers as you go along and compare them to the correct answers (which are linked from the final page of the test).

You can try your hand with just one keyword or keep going, trying anywhere up to 10 keywords (each with a pair of pages to put in order). Note that you don’t need to do all of them; you can submit after any number.

You can take the survey either for the US (google.com) or UK (google.co.uk). All results are considering only the "blue links" results — i.e. links to web pages — rather than universal search results / one-boxes etc.

Take the test!

What do the early responses show?

Before publishing this post, we sent it out to the @distilled and @moz networks. At the time of writing, almost 300 people have taken the test, and there are already some interesting results:

It seems as though the US questions are slightly easier

The UK test appears to be a little harder (judging both by the accuracy of laypeople, and with a subjective eye). And while accuracy generally increases with experience in both the UK and the US, the vast majority of UK respondents performed worse than a coin flip:

Some easy questions might skew the data in the US

Digging into the data, there are a few of the US questions that are absolute no-brainers (e.g. there's a question about the keyword [mortgage calculator] in the US that 84% of respondents get right regardless of their experience). In comparison, the easiest one in the UK was also a mortgage-related query ([mortgage comparisons]) but only 2/3 of people got that right (67%).

Compare the UK results by keyword...

...To the same chart for the US keywords:

So, even though the overall accuracy was a little above 50% in the US (around 56% or roughly 5/9), I’m not actually convinced that US SERPs are generally easier to understand. I think there are a lot of US SERPs where human accuracy is in the 40% range.

The Dunning-Kruger effect is on display

The Dunning-Kruger effect is a well-studied psychological phenomenon whereby people “fail to adequately assess their level of competence,” typically feeling unsure in areas where they are actually strong (impostor syndrome) and overconfident in areas where they are weak. Alongside the raw predictions, I asked respondents to give their confidence in their rankings for each URL pair on a scale from 1 (“Essentially a guess, but I’ve picked the one I think”) to 5 (“I’m sure my chosen page should rank better”).

The effect was most pronounced on the UK SERPs — where respondents answering that they were sure or fairly sure (4–5) were almost as likely to be wrong as those guessing (1) — and almost four percentage points worse than those who said they were unsure (2–3):

Is Google getting so me of these wrong?

The question I asked SEOs was “which page do you think ranks better?”, not “which page is a better result?”, so in general, most of the results say very little about whether Google is picking the right result in terms of user satisfaction. I did, however, ask people to share the survey with their non-SEO friends and ask them to answer the latter question.

If I had a large enough sample-size, you might expect to see some correlation here — but remember that these were a diverse array of queries and the average respondent might well not be in the target market, so it’s perfectly possible that Google knows what a good result looks like better than they do.

Having said that, in my own opinion, there are one or two of these results that are clearly wrong in UX terms, and it might be interesting to analyze why the “wrong” page is ranking better. Maybe that’ll be a topic for a follow-up post. If you want to dig into it, there’s enough data in both the post above and the answers given at the end of the survey to find the ones I mean (I don’t want to spoil it for those who haven’t tried it out yet). Let me know if you dive into the ranking factors and come up with any theories.

There is hope for our ability to fight machine learning with machine learning

One of the disappointments of putting together this test was that by the time I’d made the Google Form I knew too many of the answer to be able to test myself fairly. But I was comforted by the fact that I could do the next best thing — I could test my neural network (well, my model, refactored by our R&D team and trained on data they gathered, which we flippantly called Deeprank).

I think this is fair; the instructions did say “use whatever tools you like to assess the sites, but please don't skew the results by performing the queries on Google yourself.” The neural network wasn’t trained on these results, so I think that’s within the rules. I ran it on the UK questions because it was trained on google.co.uk SERPs, and it did better than a coin flip:

So maybe there is hope that smarter tools could help us continue to answer questions like “why is our competitor outranking us on this search?”, even as Google’s black box gets ever more complex and impenetrable.

If you want to hear more about these results as I gather more data and get updates on Deeprank when it’s ready for prime-time, be sure to add your email address when you:

Take the test (or just drop me your email here)

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