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After a holiday season full of delightful indulging, millions of people make an optimistic New Year’s resolution to stay fit or lose the extra pounds they put on from those festive parties and family get-togethers. To help, starting today we’re piloting a new way to easily book fitness and wellness classes. Reserve with Google will be available in Los Angeles, New York City and the San Francisco Bay Area, to make keeping up with your resolutions easier than ever.

To book a fitness class, visit the Reserve with Google site on desktop or mobile web. There you can search for fitness studios near you, get great recommendations for fun new classes, or book a spot in the session you already know and love.

Over the coming days, you’ll be able to do this right from Google Maps and Google Search, as well.

Reserve with Google is possible through deep partnerships with top scheduling providers you may already use, including MINDBODY, Full Slate, Front Desk, Appointy — and we’ll be adding more, like zingFit, MyTime, and Genbook soon.

So pull out your yoga mat, dust off your running shoes, and fill up that water bottle. Reserve with Google will help make completing your New Year’s resolution as easy as click, click, booked.

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Editor’s note: As part of the ExploreEDU event series, schools are working with Google for Education Premier Partners to throw open their doors and invite neighboring educators to learn first-hand from their own experiences using Google tools to innovate and improve. To see if there is an event near you, visit the ExploreEDU site. For those who can’t join in person, we’ll also share the schools’ experiences here. Today’s guest author is Nicholas Gooch, Director of Technology, from Grain Valley School District. The school is hosting an event on Dec. 15-16 with Best Buy.

It’s daunting to realize that many of us are preparing students for jobs that don’t yet exist and a world we can’t quite imagine. At Grain Valley School District, we’ve designed our schools to prepare students for the future workplace. We’ve created our curriculum to emphasize communication and creativity. We’ve done away with the traditional 100-point grading system to instead focus on helping students master their subjects. We’ve even started the process of redesigning our high school campus to create dedicated spaces for group and individual learning.

We see technology as critical to helping students master the skills they’ll need in the 21st-century workplace. We began integrating technology into everyday learning by launching G Suite for Education at our high school, two middle schools and four elementary schools during the 2014-15 school year. Last year our high school became the first of our schools to go 1:1 with Chromebooks. Each of our 1,250 high school students has access to a Chromebook that they can use in class and at home. With the help of technology and the support of our innovative teaching staff, here are a few ways we changed how we educate to better prepare students for the future:

Fostering teacher leadership and innovation

To empower our teachers to push the envelope and take more risks with technology in the classroom, we created Breakthrough Learning cohorts. The cohorts are made up of elementary and secondary teachers who applied for the program. One day each month, these teachers work with our instructional technology coaches to learn the best practices for integrating technology into their curriculum. These sessions go beyond typical “how-to” sessions and instead focus on helping teachers make learning, assessment, and digital citizenship a seamless component of the classroom. Not only does the program develop this capacity in our own teachers, it allows them to do the same for their fellow teachers.

High school students using TV screens to collaborate on shared assignment.

Emphasizing subject-mastery over grades

In 2006, school and district leadership reexamined the 100-point grading system. They decided to try a new approach — helping students achieve subject mastery rather than measuring them on a graded scale. Standards-based grading and feedback practices have been fully integrated at the high school since the 2013-2014 school year. Today, we personalize each student’s learning program. Google Classroom has also helped us emphasize the learning process rather than a final grade. Teachers can easily give feedback on an assignment, even before a student has turned it in.

Building for collaboration

The new architecture for our high school is designed to make it easier for students to study together outside of class. We’re creating communal spaces with whiteboard walls and TV screens. Students can use these TV screens to visualize projects they’re working on together — for instance, a presentation they’ve made in Google Slides. We’re also providing WiFi access and ample charging stations for laptops and mobile devices across the campus. Over the next few years we hope to introduce similar changes to other schools in the district.

We’re excited to be hosting an ExploreEDU event on December 15-16. If you’re in the area, join us to hear more about how we redesigned our approach to teaching and learning for the 21st century, supported by technology and an openness to change.

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SEO fixes tend to get pushed further down the development queue as their benefit is harder to put a number on. While you can usually put a definitive number on CRO or UX fixes, SEOs tend to fall into the trap of parroting back Google guidelines, or best practice recommendations, which quite frankly do not stand up to the scrutiny of hard and fast projections.

What if I told you I could give you a process which can put a definitive number on the returns you would get from SEO fixes? At Zazzle Media, we lead all our recommendations with data. We ground this with keywords, but then pull all the information we can about our competitors to make informed decisions. We then use past, current and predicted keyword rankings for the affected pages and project traffic levels based upon estimated click through rates.

Why Keywords?

Why do we use keywords to estimate traffic levels when we already have traffic in Google Analytics? GA is not as clean a data set. You’re not going to get an improvement to your branded traffic levels so it will need to be excluded from your data set, and this is pretty much going to be guesswork. Seasonal traffic also need to be removed; while search volumes are subject to seasonal volume alike, we’re looking to measure the impact excluding these factors. A yearly average of search volume is sufficient for this. Stripping out seasonality from GA traffic is trickier.

Keyword rankings exclude the white noise which affects Google Analytics, and allows us to sidestep the above tricky questions. Once completed you will be able to say:

“The current average ranking position of the four affected pages is 5.4. Should we fix the issue it will affect 78 keywords in ranking positions, moving our average position across the pages to 4.2, which will equate to an additional 6428 clicks per year. For a full breakdown of the affected pages and keywords please see my report.” Now that is how to win an argument!

Methodology

To complete this task, you will need a complete list of keywords, search volumes and rankings. From there we’ve got a great little template which puts it all together and which you can download here.

So to begin with you’ll need all the keywords you rank for. When I say all I mean all. You only get out what you put in and I cannot stress how critical it is to this task that you get every possible keyword you rank for. Here’s how I would do it. First I’d go into every rank tracking tool; SEMRush, AHrefs, Sistrix and others and export a full list of my domain’s keywords, ranking and search volumes.

Don’t neglect the free tools! Google Search Console and the Adwords Keyword Planner are both invaluable additions which some of you will have to lean on more should you have a limited toolset.

While the export feature in the GSCs Search Analytics report only allows up to 1000 rows at a time, you can get around this restriction with filters. Only select a single URL and pull an export. Make sure you’re keeping tracking of the URL in the export as you will need this later.

Another way of getting a ton of free keywords is with the keyword planner report. Take full advantage of every report available here; product/service (your keywords), top landing pages and the multiply keyword combination report.

By this point you should have a monster set of keywords. Get them all into a single sheet and remove duplicates. Here you want to sense check your keywords for irrelevant/branded keywords which won’t be delivering any targeted traffic. Without a clean data set our analysis is pretty much useless so make sure you’re hot on this.

You’ll need up to date rankings for every single one of your keywords – if you haven’t already get these you can use URL profilers simple serp scraper. You’ll also need search volumes which will be a bit of a pain if you haven’t got access to an active adwords account. I’m currently using the chrome bookmark Keywords Everywhere which is a good alternative.

Once you’ve got all this it’s time to fire up our keyword template. Go straight to the Keywords and Rankings sheet and copy your keywords, search volumes, rankings and ranking URLs into the relevant columns. From here on in drag the formulas present in rows G through to K down to the bottom of your keyword set and you should have something resembling the following:

I’ve hidden the categorisation tabs here as we don’t need it initially. This template is a great strategy tool with a large range of uses, but as we’re only concerned with traffic by URL for this task most of it isn’t required. If you would like to look into the other uses for this template you can read up on it at our blog.

Okay – now to explain what’s going on. The estimated traffic column will multiply the search volume by the relative position’s click-through rate (estimated CTRs are available in the CTR Ref sheet). So for example, if you were in position one for 1,000 searches a month keyword, you’d capture 26% of the searches which would equal 260 clicks per month.

The maximum traffic column simply multiplies the search volume by position one’s CTR to give you the total traffic you could ever capture for the associated keyword.

The incremental traffic column takes the current traffic away from the total traffic to give you an estimate on how much traffic is available for your keyword to capture should your rankings increase.

Position range and opportunity group pull in from their relative position on the CTR Ref sheet. We will come back to these later.

So the easiest way to sort through all this data is through a pivot table. Highlight all the columns, then insert a pivot table into a new sheet and use the following setup:

Sort your rows by URL and then keyword. This allows us to see a breakdown of the total performance of each of your URLs, while expanding the field can show individual keyword performance.

Columns are sorted by values. We can now see the average position of your URL, amount of keywords the URL ranks for, the total traffic going into this page and the total incremental traffic available.

It is important to filter your pivot table by opportunity group and exclude long terms and no ranking keywords. This allows you to only see keywords which are currently giving you traffic, making the incremental metric as relevant as possible as it won’t be skewed by keywords we have no chance to rank well for.

Finally, sort your URL by estimated traffic and you will be left with something like this:

I just ran a quick export of search engine watch’s keywords. You can see that I didn’t exclude any branded or irrelevant keywords straight away. Apparently SEW are getting a ton of traffic from the term Duck Duck Go. I don’t think so! Do you think they get traffic for the term Search Engine Land? That one is up for debate. I personally would go into GSC’s Search Analytics report for guidance here; if it shows up high on the list keep it in. Here is is updated pivot table which looks more accurate:

Now, you’ve got all the tools you need to build a business case. Technical fixes you’re chasing will broadly fall into two categories, proactive and reactive. You’ll need to use the tool differently in each instances so I will run through a few examples below.

Example 1

We’re going to use proteinworld.com as the example here. Let’s say we don’t think our internal linking is optimised for search. Key pages are several clicks from the homepage and we want to improve this but don’t know where to begin.

We begin as we always should, by following the above methodology. This allows us to benchmarking our current performance; we end up with an ordered list of amount of available growth by keywords which are already ranking well. This is our foundation.

I would then do a screaming frog crawl of the site and get the clicks from homepage metric. Add these onto the end of the Keywords and Rankings sheet and update your pivot table to include your results. We now have a table which looks like this:

Here we can see straight away, the top page isn’t even the homepage. It’s a whey protein product page which is three clicks from the homepage. In fact, only two (three with the homepage) out of the top ten pages are in the main navigation! We’ve now identified a significant opportunity to improve the visibility of multiple pages on site.

We now need to work through them individually to analyse how much growth we can achieve. We can’t predict a positional increase without understanding what we are up against. We need to compile our competitors’ rankings, technical implementation and website authority, then we need to find a competitor with similar metrics to us, just who have better internal linking. The difference in traffic should be a projected traffic increase; simple right?

I actually just started again with a fresh spreadsheet at this point, but I just like to have things clean. So I took every keyword which the whey protein page ranked for, I pulled off every ranking URL and their relative position and added it to my new spreadsheet. I quickly identified the top sites by estimated traffic and number of keywords ranking for and ran all the top sites through screaming frog to get the clicks from homepage metrics. Finally, I ran my domains through URL profiler to get their trust flow (Ahref’s domain rating is just as good metric for this).

The theory here is that, while you can build links to improve your site’s authority, the overall authority is relatively out of your control. Replicating a fix to move your site in line with Amazon technically is not going to elevate your rankings to their level if your domain’s authority sucks.

Here are my results, rows are ordered by domain and sorted by count of keywords:

As I have sorted columns by domain and then URL, expanding the sections allow me to see exactly where this traffic is coming from:

We can go one step further and see which keywords are driving the traffic:

They’re absolutely killing it on the head term. P.5 on the term whey protein with a product page, with lower authority metrics. How are they doing it? Surely it can’t be just the internal linking? Of course it’s not.

If you take a look, their on-page content is awesome:

http://www.theproteinworks.com/whey-protein-80-concentrate

They’ve got star ratings, reviews and an absolutely awesome FAQ content section at the bottom of the page:

This is great news for us though, as it is something we can go out and do a lot quicker than say, boosting your trust flow by 30 points. Reviews might need development time… if only you have a business case for that.

All we have to do is go to our pivot table. Grab our current traffic estimates and our competitors’ estimates, and then pull off affected URLs. So in this instance:

https://www.proteinworld.com/whey-protein-isolate.html

Average Position 11
Estimated Clicks Per Month: 416
Keywords we expect to see an increase on:
iso whey protein
isolate protein
isolate whey protein

(etc).

Projected Position 6
Projected Clicks Per Month: 2623
Projected Increase in Clicks Per Month: 2207

We then just repeat the process for every affected URL which we believe is under-optimised. This basic process applies to any proactive fix you want to push through:

Identify affected pages
Estimate improvements to rankings based upon competitor implementation and link metrics
Project traffic increases based upon estimate rankings improvements.

Example 2

Reactive fixes are a lot easier to project; the most important part of the exercise is to regularly run rankings, especially before and after technical fixes are deployed. Let’s say, for example, that your site does a redesign and despite your wishes, the new category template moves the main body of content below the fold.

In the template just duplicate your positional and estimated traffic volumes, and add in a new column which takes the new estimated traffic from the old.

Jump into the pivot table and update it, comparing your affected URLs pre and post launch traffic and build a business case from this. Here is an example of what this might look like:

We’ve got the average positions and lost traffic all laid out for you. If you sort the pivot rows by URL and then keyword you can highlight exactly where you have lost traffic at the click of a button.

A weak argument which parrots Google guidelines and best practices is unconvincing, especially to the uninitiated to SEO. We can all understand data, we can all understand competitor intelligence. Using this approach wins arguments and silences doubters.

Failure to justify your recommendations can see even the best recommendations fail to get off the ground.

 

Tom is a Search and Data Consultant at Zazzle Media and a contributor to Search Engine Watch

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Posted by MiriamEllis

Two dates to know: August 4, 2010 – the day Google enabled owner responses to Google My Business reviews; November 17, 2016 – the day Moz enabled incredibly easy GMB owner response functionality in the Moz Local dashboard. Why are these noteworthy events in Local Search history?

Because reviews and owner responses are direct reputation management, free marketing, free advertising, damage control, and quality control all wrapped up in one multi-voice song about your brand.

What’s missing from the picture of this free-for-all of voices caroling sentiment about your brand? You are — the conductor! If you’re not leading the tune — from setting customer service policies, to training staff, to managing complaints, to engaging directly with consumers online — you’re giving up available reputation management controls.

Make no mistake: No brand can prevent every sour note, but with owner response functionality, you can not only retune relationships with valuable customers, but can also protect revenue by keeping those customers instead of having to invest 25x as much in obtaining new ones. Owner response mastery is, indeed, smart business.

For the past six years, since Google launched owner responses as part of its local product, I’ve been studying them and acting as a consultant to a variety of local business owners and agencies regarding effective usage of this remarkable capability. Today, in celebration of Moz Local’s support of this function, I’m going to break down the types of reviews into 5 categories and offer you my tips for skilled management. With reputation and revenue on the line, every local brand needs an intelligent strategy!

Getting up-to-speed on owner responses

During our recent launch, a Moz community member let us know he’d never heard of owner responses before, so real quick: Many review platforms give you the option, as the business owner, to respond to reviews your customers have left you. This is normally done from within your dashboard on that platform, or, in some cases, via mobile apps.

In the Moz Local dashboard, the Google My Business owner response function is a real time-saver. We alert you when new reviews come in, and you simply click the ‘reply’ link to write your response. A little form pops up in which you can type away handily:

Now let’s delve into responding to the five basic types of reviews most local brands can expect to receive.

Type 1: “I love you!”
Real-world example:

Diagnosis: This is the customer every brand wants to have: the delighted evangelist who goes to the lengths of saying that nothing else on the local scene can compare to what the business offers. Honestly, reviews like this are like beautiful greeting cards validating that your business is getting it right on all points. Pure music to your ears!

Owner response strategy:

Many business owners ask if it’s necessary to respond to positive reviews. My short answer is yes, if you wish your business to come across as courteous and engaged. Part of conducting the flow of your reputation is acknowledging customer satisfaction and thanking them for the time they invest in writing such nice things about your company. It’s just good manners.

Having said this, I’ll qualify it by mentioning scale. If your enterprise has 100+ locations which each have 100+ positive Google My Business reviews, responding to every single one may not be the best use of your resources. Prevent the appearance of ungrateful neglect by aiming for a percentage — maybe 10% — of ‘thank yous’ in response to your best reviews.

Pro tips:

Your thanks can be brief, but avoid repetitiousness. Write a unique response each time. There are owner response profiles out there that have made me strongly suspect robots manage them, as in ‘thank you for your review’ written on 30 different responses. Avoid that.
Remember that owner responses are content consumers read. They are, in essence, free advertising space. Don’t go over the top with this, but if a customer mentions something they love, latch onto that. In our sample review above, the owner could mention that comments like this one inspired them to bottle their hot sauce for retail sales, or they could mention that they actually just won a best-in-Bay-Area award from X publication. Think products, services, and hyperlocal/local terminology. No, don’t put the hard sell on the customer in the owner response, but use this real estate with savvy. If there’s something you think a happy customer would be excited to know, promote it in a nice, friendly way!
Positive reviews indicate that a customer is already in a good, receptive mood. The more personable your owner response, the more of an impression your business can make, encouraging the customer to come back for more. Here, company culture, personality, and fun can shine. Your customer thinks you are special — act like it in the response.

Suggested owner response:

Hi Charley!

We were just thrilled by your review — in fact, we showed it to Chef Rosa, because the pique sauce you love is based on her grandmother’s traditional recipe brought from Puerto Rico in the 1930s. It’s the real deal, and we’re actually offering it bottled for retail now right next to the hostess stand at both our San Rafael and San Francisco locations, based on diner requests. Hint: one secret ingredient is apple cider vinegar, but that’s all we can say! We’d love to see you back soon, and Chef Rosa says, “Thank you for the lovely compliment.” ‘Best in the Bay Area’ makes us all proud!

Good Eating!

Marta Sanchez, Owner

Type 2: “My mind isn’t made up yet.”
Real-world example:

Diagnosis: A 3-star rating is the hallmark of the consumer who likes some things about your business, but isn’t totally loyal yet. They may/may not return and may/may not recommend you to others. Undecided patrons represent an exciting challenge to transform dissatisfactory aspects of your business and specific consumer sentiment, all at the same time.

Owner response strategy:

The honesty of a less-than-5-star review, when written in detail, delivers two valuable assets to your brand: it tells you where you’re hitting and where you’re missing, giving you the opportunity to improve and turn a lukewarm consumer into a loyal one.

Strategy for the owner response involves thanking for praise, accepting responsibility for faults, apologizing for disappointments, and making some kind of an offer. This offer, meant to sweeten the pitch that you hope the consumer will give your company a second chance, could be a comp or a coupon for future use, or it could simply be an explanation of how you have heard their feedback and made changes.

Pro tips:

Express gratitude for consumer complaints — they are valuable. Do not attempt to shift blame onto anyone else, including the customer or staff members.
Document both the positive and negative sentiment of so-so reviews and use it as your playbook for keeping what’s good and improving what isn’t excellent.
Be sure the customer feels heard. Cite their complaints back to them. By doing so, you are demonstrating to all future potential customers that your brand is responsive to feedback.

Suggested owner response:

Dear Yesenia,

We’re so grateful to you for letting us know that our prices, staff, and in-hotel restaurants pleased you, and, I also want to express my thanks to you for mentioning that the housekeeping wasn’t exceptional. I need to hear that, and take full responsibility for the dusty room. I have been trying a variety of cleaning services this past year, with the goal of finding the best.

While I want to be sure that every guest knows we honor any requests during their stay (just dial 9 on your in-room phone), I also want to let you know that, based on your comments, I held an all-staff meeting with our current cleaning service and have issued a new 10-point cleaning checklist (including dusting all surfaces) for each housekeeper. Should you honor us with a second stay, I personally guarantee you will find your room immaculate, and I would also like to offer your party a free breakfast in the Palm Room, as you enjoyed our restaurants. Just tell them Rob sent you, and it will be our pleasure to serve you! Thank you for your valuable and honest review.

Cordially,

Rob Brown, Owner

Type 3: “There was hair in my taco…”
Real-world example:

Diagnosis: The dreaded 1-star review! The customer has a specific, legitimate complaint, and your job as the owner is to address their dissatisfaction, take responsibility, and, whenever possible, make an offer to make things right. A negative review is likely the last life preserver an unhappy customer will throw you — a last chance to earn them back with superior responsiveness. Given the cost of replacing them, rewards for the effort can be great. When a customer ‘saves you’ by making their complaints known, an adept response from you may ‘save them’ in return, earning their repeat business.

Pro tips:

Apologize!!! Say the words, “I’m sorry, I apologize.”
No blame shifting, no lectures — just total accountability, humility, and a willingness to learn.
Be as honest as possible about whatever circumstance led to the customer’s bad experience, and state what you’re doing to improve that circumstance. Sometimes, the circumstances may include faults on the customer’s part. If you have to mention these in order to be honest, do so with great care and no blame, as in the sample response below.
Negative reviews often run on for agonizing paragraphs and chapters, but your response should not. Be thorough, but concise.
Offer something, even if it’s just a few minutes of your time on the phone, to try to make it right.
Aim for a ‘wow’ factor — as in you want future potential customers to say, “Wow, this business really cares!” when they read the response.
For more tips on managing negative reviews, please read Diagramming The Story of a 1 Star Review.
Document all complaints; they are incredibly valuable both in terms of damage control and quality control. Consider doing a full review audit on a set schedule to catch emerging problems and resolve them.

Suggested owner response:

Dear Vivi,

This is Dr. Tom, and I want to begin by apologizing for the inconvenience you experienced. I hate to think of you having wasted both time and gas on this. I’m so sorry.

I regret that you missed the message about hours for the shot clinic on our homepage, and your review has made me concerned that other patients may be missing it, too. Thanks for alerting me to this. Here’s what we’ve done:

Enlarged the homepage hours message + included those hours in the header of every website page
Put this at the top of our Facebook page
Updated our off-hours phone message to include the info that folks need to come in by 3:00 to ensure walk-in service.

Will you give me a second chance to make this right for you? It’s so important that your pet gets proper shots. Please phone and let my receptionist know Dr. Tom is offering you a priority appointment, any day of the week, and I’d like to make friends with your pup by treating him to one of our wonderful new chew toys. Hoping to have the great pleasure of caring for you and your awesome companion animal!

Kindly,

Dr. Tom

Type 4: “I’m actually your competitor.”
Real-world example:

Diagnosis: Unfortunately, fake reviews happen. They may stem from unscrupulous competitors, disgruntled past employees, or individuals with personal grudges against someone at the company. The line to walk here is whether the reviews are simply false (warranting a response + Google action) or citing such defamatory or illegal practices that you should consult with a lawyer before taking any further action. Our real-world example is of the former kind — it illustrates what a fake review might look like with sentiment that is negative but not accusing the business of criminal activity.

Pro tips:

If research has made you aware that a review has been left by a competitor or by someone who is not a customer, that’s a violation of Google’s Review Policy.
First, leave a brief owner response to the review (as shown in my sample response below) to alert consumers to the falsity of the review. Note: I don’t advise ‘outing’ the bad actor — it’s not professional.
Second, follow Google’s steps for flagging the review. I suggest waiting 24 hours after doing this before moving on.
Next, on that same page, you will see options for speaking directly with Google via phone, chat, or email. Contact Google to let them know about the fake review you have flagged. Hopefully, they will be able to rectify this for you and remove the review.
However, if you get a rep who doesn’t seem to understand your issue, turn to the Google My Business Community, post the complete details of your scenario, and beg for a Top Contributor to help escalate your issue.
Don’t expect a quick fix. You may have to be persistent to obtain resolution.
But, again, please don’t take these steps if a review accuses your business of something illegal. We’ll cover that, below, in Type 5.

Suggested owner response:

To Our Valued Customers,

Sadly, after researching this, our company discovered that this review was left by a competitor. We are taking the appropriate steps to report this to Google, and we hope having this fake review removed will encourage this unfortunate competitor to seek other, more honest forms of promoting his business. If he persists, we will engage appropriate legal counsel.

SMH,

Jim Davis, Owner

Type 5: “I’m citing illegal stuff.”
Real-world example:

Diagnosis: Whether a negative review is true or false, any time illegal or dangerous behavior is cited, it’s a cue to you that you need to speak with an attorney before taking any further steps. Don’t respond and don’t attempt to have the review removed, as both could be used as evidence in a court of law. Seek an attorney well-versed in cyber law and act on his or her advice, rather than on any advice you may read on the Internet or receive from marketers, friends, etc. And if you run an SEO agency, I urge you not to advise clients on Type 5 reviews — we’re SEOs, not attorneys, and shouldn’t be consulting on legal matters.

Orchestrating the ideal owner response environment

If you already have an excellent customer service training program in place at your business, chances are good that you will mostly be managing Type 1 and Type 2 reviews with only the occasional Type 3. Types 4 and 5 will hopefully be the exception rather than the rule. Given that one 2016 survey found that 57% of consumer complaints relate to employee behavior, we can estimate that at least half of your reputation is anchored to the quality of your staff hiring and training practices. So, definitely place first and fundamental focus there, and then manage the ensuing consumer sentiment as it flows in with these tips:

Observe the typical rate at which you normally receive reviews. It could be a few per week, or if you’re managing multiple locations, numerous reviews per day.
What you observe dictates how frequently you need to monitor your reviews. If you’re a Moz Local customer, we’ll conveniently alert you as each new review comes in, and you can check that as often as makes sense.
Avoid unnecessary customer frustration and bad reviews stemming from bad data. There must be literally millions of negative reviews on the web citing wrong phone numbers, wrong hours of operations, wrong addresses. Do a quick citation health check to see if your major local business listings are fomenting negative sentiment. Correct problems.
I’ve seen various theories about how quickly an owner should respond to reviews; my own opinion is ASAP, particularly when it comes to Type 2 and Type 3 reviews. If you are trying to catch complaints for the purpose of resolving them and winning back unhappy customers, there may be circumstances (like our example with the puppy shots) that make it vital to respond quickly to avoid customer loss.
While it may be ideal to have owners be the authors of all owner responses, scale may make that an untenable situation. If you are designating a staff member or marketer to represent the owner, prevent mistakes by clearly outlining company policies, voice, permissions, and objectives with that person.
Responsiveness can be a competitive difference maker. Observe your direct competitors; if they are careless about active management of reviews, you can take advantage by making your brand the one that always responds, demonstrating care and accessibility.
Know that expert owner responders experience thrilling victories, like having an unhappy customer update their review and raise their star ratings after receiving a great owner reply. These are rewards that make the input of effort well worth it!

Six years into Google’s rollout of the owner response function, I still encounter many business owners expressing fear of reviews. At the root of this, I often find that they feel powerless and overwhelmed by the prospect of managing their brand’s reputation.

It’s my hope that this post signals to every local business owner that you do, indeed, have significant power in this regard. Via the the right combination of skilled customer service and active review management, you can orchestrate an exceptional online reputation for your brand in concert with your customers, in harmony with your professional goals and dreams.

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

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Campus Exchange is a start-up exchange program hosted by Google for Entrepreneurs. We recently invited four start-ups from Korea and another four from Brazil, Ireland, Mexico, and Poland to join us at Campus Seoul for a week.  We gave them access to Startup:Con 2016, and hosted workshops on the topic of going global with their businesses. Two founders — Byungik Choi, CEO of CoolJam, the Korean start-up behind “Hum On!”, an app which transforms hummed melodies into musical soundtracks, and Pedro Matsumura Kayatt, co-founder of VRMonkey, a virtual reality start-up based in Brazil — share their insights from the exchange.  

What are some of the differences between running a start-up in your countries and elsewhere? 

Byungik of Cool Jamm (Korea): I’ve realized just how supportive the Korean government is of start-ups compared to some other countries. Mentors are readily available here—though I do think there’s always room for more role models who are open to sharing advice about their entrepreneurial journey. 

Pedro of VRMonkey (Brazil): Korea has a very tech-friendly environment that helps companies to perform better and even attract more investments. Brazil’s still an emerging country when it comes to technology—there aren’t many big tech players, and many people with a technical background end up going abroad. 

How do you think your experience at the Google Seoul Campus Exchange will shape your company?

Pedro: First thing we learned is to focus. Our VR studio has been doing a lot of things, and we should really focus on doing more in one particular area. We also learned how we can collaborate better with so many content providers to create really amazing VR experiences that’ll hopefully go mainstream.

Second, we now understand how important it is for our projects to be accessible in other languages. English is not enough. People in Korea and other countries want to play games in their own language. Plus, we understand that we can launch games and experiences that can also impact other regions, so we are exploring more themes—including Asian themes—to create more content. 

Byungik: First, it reminded me of the importance of English. For a global start-up, having fluent English skills are mandatory. Second, the dogfooding session taught us that we could get a lot of meaningful feedback from Googlers and other start-up members who are from different cultures. It was really helpful. We’ve now decided to do dogfooding sessions frequently.

Who was one of the most memorable entrepreneurs or people you met during this trip? 

Pedro: Wow, that is hard! I met so many amazing people during the program, but one guy who made me feel at home was Junsoo Kim, Chief Operating Officer of Reality Reflection. He invited me to their office and we spent a night there talking about the future of VR and sharing our companies’ projects over a pizza! It was a great time and made us rethink a lot of things about VRMonkey.

Byungik: All the participating start-ups are wonderful. FanFootage from Ireland was most impressive to me. I think it was a good example where a great idea and great technology are well-combined. And the Brazilian guys of VRMonkey were cool and friendly.

The week-long Campus Exchange in Seoul featured participants from all around the world, icnlduing FanFootage from Ireland, FEAT Sp. z o.o. from Poland,  Machina Wearable Technology from Mexico, VRMonkey from Brazil, and 1DAY1SONG, Cool Jamm, Maverick Co. and Busking TV all from Korea.

A panel on fundraising in Korea featuring Tim Chae of 500startups and Kihong Bae of Strong Ventures

What’s your advice to entrepreneurs seeking to expand their business?

Byungik: Staying humble. It is I who must ask for advice from other entrepreneurs because I just started my first start-up. Everything is new to me.

Pedro: My strongest advice would be to share. Be honest about what you are doing, do it in the best way you can imagine, and share your work with anyone interested in it. There are few secrets and almost no competitors when it comes to markets like mobile and VR because basically, the whole world is your client.

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It’s a week full of video launches! After Instagram’s debut of live video in the U.S. and the addition of 360 video to the Facebook Live experience comes Twitter’s launch of live video. Starting today, you can #GoLive on Twitter.

The post #GoLive Now: Twitter Launches Live Video by @rinadianewrites appeared first on Search Engine Journal.

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It is much more cost effective to keep a customer than it is to find a new one. In fact, according to a report by Adobe, returning customers account for up to 40% of a company’s revenue.

Trouble is, a 2016 small business survey revealed that while 72% of small businesses plan to allocate the majority of their marketing budget to customer acquisition, only 28% plan to allocate the majority to customer retention.

To help marketers refocus their retention plans — especially those in the ecommerce industry — I’ve put together six creative ways to keep your loyal customers coming back. Check them out below.

6 Little Ways to Reward Your Customers This Holiday Season
1) Offer a compelling discount.

By a compelling discount, I don’t simply mean “20% off your next order.” Instead, try a discount that will inspire your customer to continue shopping with you.

What does that look like? It depends. And there are a couple things to consider:

Focus on the value your product or service provides customers, not just the price you discount it by. In other words, position yourself in a way where buying your product is a complete no-brainer.
Choose your words carefully. According to research from Psychology Today, a “Get $ Off” promotion emphasizes achieving a gain, while the “Save $” wording emphasizes avoiding a loss — and customers recognize that difference.

For example, check out how T-Mobile positioned this holiday deal on the Samsung Gear S2:

2) Get personal.

Great customer service is about more than just solving your customer’s issue. Try going above and beyond by creating something awesome to remind your customers why they love your business.

For example, you might send out a personalized holiday card or thank you note to their home address. Even though we’re in a digital word, it means a lot to receive something handwritten, and capitalizing on the holiday spirit in this way can build a positive brand association.

Not only do the folks at Buffer engage their audience on social, but they also go out of their way to send handwritten cards, stickers, and t-shirts to their clients:

Wearing my @buffer t-shirt with pride. #bufferlove pic.twitter.com/bWpjalt2nW

— Jason Thompson (@nosaj_jason)
January 25, 2016

Their strategy works well because with every exchange, they are encouraging customers to become brand ambassadors.

3) Tap into referrals that reward.

Referrals are a great way to get customers to sell your products for you. It’s a simple evolution of word-of-mouth marketing, as customers usually refer friends or colleagues.

For example, when shopping with The Clymb, you are gently reminded to refer a friend in the website header:

Notice how it provides a clear call-to-action along with an incentive — $10 for your loyal customer and $10 for a future one — encouraging visitors to take action even before they start shopping.

4) Use premium packaging.

Anyone that’s spent any amount of time wresting with a roll of wrapping paper and tape dispenser knows the value of a pretty package. In fact, according to a 2015 Dotcom Distribution packaging study, 49% of online shoppers said that branded packaging made them more excited about receiving or opening an item, while 44% admitted that packaging reinforces that a product is worth its cost.

The lesson? First impressions make a huge difference in our fast-paced lives, so by using premium packaging you allow your product to stand out against others. Take Pad & Quill, for example. This tech accessories company packages its iPhone and iPad cases in paper with friendly messages printed on it. It even uses an authentic-looking Roman seal to give a more hand-wrapped feel.

5) Turn the small print into a feature.

Refund and exchange policies are often part of the small print on your website. During the holiday season, you have an opportunity to make them a feature. After all, these policies are in place to increase trust and promote sales, right? Make them known.

When buying gifts, customers prefer a return policy that ensures that if the recipient doesn’t like it, they can easily return it. If you can offer this, you have a better shot at increasing sales, while keeping your customers happy.

Nordstrom does a great job of displaying all of its holiday shopping, shipping, and return perks right on the homepage, giving potential and existing customers peace of mind during one of the more stressful times of the year:

6) Create a holiday-inspired video.

Why does everyone love and anticipate the John Lewis holiday advertisements year after year? Because they tell a story that evokes emotion.

Fortunately, you don’t need a million-dollar TV advertising budget to do this, just an idea for a great story. As video is becoming more and more popular, it’s key that you apply it to all areas of your marketing strategy.

Back in 2014, the Lowell Police Department went viral due to its “Christmas Surprise Traffic Stop” video. In the video, police officers asked the folks they pulled over for traffic violations questions about what they wanted for the holidays. Meanwhile, their behind-the-scenes crew quickly purchased the items and drove them to the scene. Watch the magic unfold below — trust us, it’s worth the four minutes:

The holiday season is a time of giving, so be creative with how you can implement goodwill into your marketing campaign.

Wrapping Up

Offering compelling discounts with creative call-to-actions will inspire your customers to buy, but great customer service is why they return.

Remember to focus on the value that you are bringing your customer, not just how low you can get your prices.

How are you creating the holiday cheer with your marketing this year? Share your ideas with us in the comments below.

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Hitting a wall in your SEO strategy can make planning your next move kind of hard. But if you’ve exhausted your current SEO strategy and feel like your recent efforts need a ramp-up, it might be time to start exploring the idea of paid search.

I know, it sounds expensive-and intimidating-and is it really more important than the SEO efforts I use to drive organic traffic?

The fact of the matter is, paid search plays an undeniable role in the success of many websites, and in order to rank among top tier competitors, you’re probably going to have to dabble in paid search at some point. Here are 5 reasons to make a PPC campaign your next move.

CTR of paid search is up, up, up!

You may recall last February, when Google eliminated right hand side advertisements. Since the elimination of those ads, paid search CTRs have risen 38%. For just 9 months, that’s a significant increase. The absence of that right hand side gives the paid ads you now see at the top of search results a red carpet for clickability.

Mobile is a must.

As you know by now, user behavior continues to reveal a major shift towards mobile browsing. Google has adjusted to and accounted for this shift by assuming a mobile first design to provide a better experience for users, giving you a great PPC mobile-friendly resource. Within your AdWords account you can adjust for optimized display across any device, including mobile phones.

PPC is a social affair.

Many major advertisers have increased the amount of targeted ads across social platforms. You’ve likely experienced how effective this form of paid search is firsthand, so it should come as no surprise that the share of social media visits produced across the major platforms (Facebook, Pinterest, Twitter, Instagram, etc.) has also increased significantly.

It works well for newbies.

If you have a relatively new website, PPC has the potential to get you a lot of bang for your buck. What many people overlook is the amount of time it takes to build up the necessary authority behind a domain to earn a top ranking spot. Organic search optimization alone will take a while, but paid search can put you right up in the top spots. You may have to pay more for keywords, but the ROI of having a top ranking website is well worth it.

You have more control.

Google can basically change what your listing says at any time, so there’s really no guarantee on what your meta, title tags, etc. will say. When you work through the paid search platform

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

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

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

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

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

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

Enjoy holiday music from your favorite apps

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

Keep track of Santa

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

Get YouTube Red FREE for six months

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

Make your holidays truly magical with Google Home.

*Netflix streaming membership required

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

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Posted by Dr-Pete

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

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

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

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

Natural language in action

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

“Space Needle height”

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

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

“What is the height of the Space Needle?”

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

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

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

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

Build great concepts!

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

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

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

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

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

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

Language in action, part 2

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

Plurals (“scarf” & “proxies”)

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

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

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

Google can easily understand even irregular plurals in both directions.

Stemming (“ballroom dancer”)

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

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

Abbreviations (“Dr. Who”)

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

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

Acronyms (“SNL skits” & “TARDIS”)

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

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

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

Acronyms+ (“NJ DMV”)

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

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

Synonyms (“discount airfare”)

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

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

Synonyms+ (“upscale department stores”)

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

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

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

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

These aren’t just exceptions

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

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

The Algorithm is learning

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

What, exactly, is deep learning?

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

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

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

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

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

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

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

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

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

How does Google use deep learning?

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

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

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

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

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

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

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

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

RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities – called vectors – that the computer can understand. If RankBrain sees a word or phrase it isn’t familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries.

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

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

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

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

RankBrain is NOT query translation

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

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

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

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

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

Are we sure it’s RankBrain?

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

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

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

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

When did RankBrain roll out?

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

How do we adapt to RankBrain?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3 Gs: Gather, Group, Generate

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

Gather keywords
Group keywords into clusters
Generate exemplars

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

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

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

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

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

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

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

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

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

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

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

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

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

Bonus: Keyword brainstorming

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

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

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

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

Why not just write for people?

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

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

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

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

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