Posted by BritneyMuller

There’s a lot of hype and misinformation about the new Google algorithm update. What actually is BERT, how does it work, and why does it matter to our work as SEOs? Join our own machine learning and natural language processing expert Britney Muller as she breaks down exactly what BERT is and what it means for the search industry.

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Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today we are talking about all things BERT and I’m super excited to attempt to really break this down for everyone. I don’t claim to be a BERT expert. I have just done lots and lots of research. I’ve been able to interview some experts in the field and my goal is to try to be a catalyst for this information to be a little bit easier to understand. 

There is a ton of commotion going on right now in the industry about you can’t optimize for BERT. While that is absolutely true, you cannot, you just need to be writing really good content for your users, I still think many of us got into this space because we are curious by nature. If you are curious to learn a little bit more about BERT and be able to explain it a little bit better to clients or have better conversations around the context of BERT, then I hope you enjoy this video. If not, and this isn’t for you, that’s fine too.

Word of caution: Don’t over-hype BERT!

I’m so excited to jump right in. The first thing I do want to mention is I was able to sit down with Allyson Ettinger, who is a Natural Language Processing researcher. She is a professor at the University of Chicago. When I got to speak with her, the main takeaway was that it’s very, very important to not over-hype BERT. There is a lot of commotion going on right now, but it’s still far away from understanding language and context in the same way that we humans can understand it. So I think that’s important to keep in mind that we are not overemphasizing what this model can do, but it’s still really exciting and it’s a pretty monumental moment in NLP and machine learning. Without further ado, let’s jump right in.

Where did BERT come from?

I wanted to give everyone a wider context to where BERT came from and where it’s going. I think a lot of times these announcements are kind of bombs dropped on the industry and it’s essentially a still frame in a series of a movie and we don’t get the full before and after movie bits. We just get this one still frame. So we get this BERT announcement, but let’s go back in time a little bit. 

Natural language processing

Traditionally computers have had an impossible time understanding language. They can store text, we can enter text, but understanding language has always been incredibly difficult for computers. So along comes natural language processing (NLP), the field in which researchers were developing specific models to solve for various types of language understanding. A couple of examples are named entity recognition, classification. We see sentiment, question answering. All of these things have traditionally been sold by individual NLP models and so it looks a little bit like your kitchen. 

If you think about the individual models like utensils that you use in your kitchen, they all have a very specific task that they do very well. But when along came BERT, it was sort of the be-all end-all of kitchen utensils. It was the one kitchen utensil that does ten-plus or eleven natural language processing solutions really, really well after it’s fine tuned. This is a really exciting differentiation in the space. That’s why people got really excited about it, because no longer do they have all these one-off things. They can use BERT to solve for all of this stuff, which makes sense in that Google would incorporate it into their algorithm. Super, super exciting. 

Where is BERT going?

Where is this heading? Where is this going? Allyson had said, 

“I think we’ll be heading on the same trajectory for a while building bigger and better variants of BERT that are stronger in the ways that BERT is strong and probably with the same fundamental limitations.”

There are already tons of different versions of BERT out there and we are going to continue to see more and more of that. It will be interesting to see where this space is heading.

How did BERT get so smart?

How about we take a look at a very oversimplified view of how BERT got so smart? I find this stuff fascinating. It is quite amazing that Google was able to do this. Google took Wikipedia text and a lot of money for computational power TPUs in which they put together in a V3 pod, so huge computer system that can power these models. And they used an unsupervised neural network. What’s interesting about how it learns and how it gets smarter is it takes any arbitrary length of text, which is good because language is quite arbitrary in the way that we speak, in the length of texts, and it transcribes it into a vector.

It will take a length of text and code it into a vector, which is a fixed string of numbers to help sort of translate it to the machine. This happens in a really wild and dimensional space that we can’t even really imagine. But what it does is it puts context and different things within our language in the same areas together. Similar to Word2vec, it uses this trick called masking. 

So it will take different sentences that it’s training on and it will mask a word. It uses this bi-directional model to look at the words before and after it to predict what the masked word is. It does this over and over and over again until it’s extremely powerful. And then it can further be fine-tuned to do all of these natural language processing tasks. Really, really exciting and a fun time to be in this space.

In a nutshell, BERT is the first deeply bi-directional. All that means is it’s just looking at the words before and after entities and context, unsupervised language representation, pre-trained on Wikipedia. So it’s this really beautiful pre-trained model that can be used in all sorts of ways. 

What are some things BERT cannot do? 

Allyson Ettinger wrote this really great research paper called What BERT Can’t Do. There is a Bitly link that you can use to go directly to that. The most surprising takeaway from her research was this area of negation diagnostics, meaning that BERT isn’t very good at understanding negation. 

For example, when inputted with a Robin is a… It predicted bird, which is right, that’s great. But when entered a Robin is not a… It also predicted bird. So in cases where BERT hasn’t seen negation examples or context, it will still have a hard time understanding that. There are a ton more really interesting takeaways. I highly suggest you check that out, really good stuff.

How do you optimize for BERT? (You can’t!)

Finally, how do you optimize for BERT? Again, you can’t. The only way to improve your website with this update is to write really great content for your users and fulfill the intent that they are seeking. And so you can’t, but one thing I just have to mention because I honestly cannot get this out of my head, is there is a YouTube video where Jeff Dean, we will link to it, it’s a keynote by Jeff Dean where he speaking about BERT and he goes into natural questions and natural question understanding. The big takeaway for me was this example around, okay, let’s say someone asked the question, can you make and receive calls in airplane mode? The block of text in which Google’s natural language translation layer is trying to understand all this text. It’s a ton of words. It’s kind of very technical, hard to understand.

With these layers, leveraging things like BERT, they were able to just answer no out of all of this very complex, long, confusing language. It’s really, really powerful in our space. Consider things like featured snippets; consider things like just general SERP features. I mean, this can start to have a huge impact in our space. So I think it’s important to sort of have a pulse on where it’s all heading and what’s going on in this field. 

I really hope you enjoyed this version of Whiteboard Friday. Please let me know if you have any questions or comments down below and I look forward to seeing you all again next time. Thanks so much.

Video transcription by Speechpad.com

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Posted by PhilNottingham

Video and podcasts are only growing in popularity, proving to be an engaging way to reach your audience and find ways to talk about your industry or product. But it’s a crowded market out there, and finding a good idea is only half the battle. Join video marketing extraordinaire Phil Nottingham from Wistia as he explores how we can both uncover great ideas for a podcast or video series and follow through on them in this week’s episode of Whiteboard Friday.

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Video Transcription

Howdy, Moz fans. My name is Phil Nottingham, and welcome to another edition of Whiteboard Friday. Today we’re going to talk about how to come up with a great idea for your video series or podcast. I think a lot of businesses out there understand that there’s just this great opportunity now to do a longer form series, a show in podcast or video form, but really struggle with that moment of finding what kind of idea could take them to the next level and help them stand out.

1. Audience

I think the most common error that businesses make is to start with the worst idea in the world, which is interviewing our customers about how they use our product. I’m sure many of you have accidentally fallen down this trap, where you’ve thought, “Ah, maybe that will be a good idea.” But the thing is even if you’re Ferrari or Christian Louboutin or the most desirable product in the world, it’s never going to be interesting for someone to sit there and just listen to your customers talking about your product.

The problem is that your customers are not a unique group of people, aside from the fact that they use your product. Usually there isn’t anything else that brings them together. For this kind of content, for a video series and podcast to really stand out and to grow in terms of their audience, we need to harness word of mouth. Word of mouth doesn’t grow through the way we often think about audience growth in marketing.

Many of us, particularly in the performance marketing space, are used to thinking about funnels. So we get more and more traffic into the funnel, get more people in there, and ultimately some of them convert. But the way word of mouth works is that a small group of people start communicating to another group of people who start communicating to another group of people. You have these ever-expanding circles of communication that ultimately allow you to grow your audience.

How to find a niche audience

But that means you need to start with a group of people who are talking to one another. Invariably, your customers are not talking to each other as a kind of rule of thumb. So what you need to do is find a group of people, an audience who are talking to each other, and that really means a subculture, a community, or maybe an interest group. So find your group of customers and work out what is a subset of customers, what kind of community, wider culture they’re part of, a group of people who you could actually speak to.

The way you might find this is using things like Reddit. If there’s a subculture, there’s going to be a subreddit. A tool like SparkToro will allow you to discover other topics that your customer base might be interested in. Slack communities can be a great source of this. Blogs, there’s often any sort of topic or a niche audience have a blog. Hashtags as well on social media and perhaps meetup groups as well.

So spend some time finding who this audience is for your show, a real group of people who are communicating with one another and who ultimately are someone who you could speak to in a meaningful way. 

2. Insight

Once you’ve got your audience, you then need to think about the insight. What the insight is, is this gap between desire and outcome. So what you normally find is that when you’re speaking to groups of people, they will have something they want to achieve, but there is a barrier in the way of them doing it.

This might be something to do with tools or hardware/software. It could be just to do with professional experience. It could be to do with emotional problems. It could be anything really. So you need to kind of discover what that might be. The essential way to do that is just through good, old-fashioned talking to people. 

• Focus groups, 
• Surveys, 
• Social media interactions, 
• Conversations, 
• Data that you have from search, like using Google Search Console, 
• Internal site search, 
• Search volume 

That kind of thing might tell you exactly what sort of topics, what problems people are having that they really try to solve in this interest group.

Solve for the barrier

So what we need to do is find this particular little nugget of wisdom, this gold that’s going to give us the insight that allows us to come up with a really good idea to try and solve this barrier, whatever that might be, that makes a difference between desire and outcome for this audience. Once we’ve got that, you might see a show idea starting to emerge. So let’s take a couple of examples.

A few examples

Let’s assume that we are working for like a DIY supplies company. Maybe we’re doing just sort of piping. We will discover that a subset of our customers are plumbers, and there’s a community there of plumbing professionals. Now what might we find about plumbers? Well, maybe it’s true that all plumbers are kind of really into cars, and one of the challenges they have is making sure that their car or their van is up to the job for their work.

Okay, so we now have an interesting insight there, that there’s something to do with improving cars that we could hook up for plumbers. Or let’s say we are doing a furniture company and we’re creating furniture for people. We might discover that a subset of our audience are actually amateur carpenters who really love wooden furniture. Their desire is to become professional.

But maybe the barrier is they don’t have the skills or the experience or the belief that they could actually do that with their lives and their career. So we see these sort of very personal problems that we can start to emerge an idea for a show that we might have. 

3. Format

So once we’ve got that, we can then take inspiration from existing TV and media. I think the mistake that a lot of us make is thinking about the format that we might be doing with a show in a very broad sense.

Don’t think about the format in a broad sense — get specific

So like we’re doing an interview show. We’re doing a talk show. We’re doing a documentary. We’re doing a talent show. Whatever it might be. But actually, if we think about the great history of TV and radio the last hundred years or so, all these really smart formats have emerged. So within talk show, there’s “Inside the Actors Studio,” a very sort of serious, long, in-depth interview with one person about their practice.

There’s “The Tonight Show Starring Jimmy Fallon,” which has got lots of kind of set pieces and sketches and things that intermingle with the interview. There’s “Ellen,” where multiple people are interviewed in one show. If we think about documentaries, there’s like fly-on-the-wall stuff, just run and gun with a camera, like “Diners, Drive-Ins and Dives.” Carrying on the food thing, there’s “Chef’s Table,” where it’s very planned and meticulously shot and is an exposé of one particular chef.

Or something like “Ugly Delicious,” which is a bit more like a kind of exploratory piece of documentary, where there’s kind of one protagonist going around the world and they piece it together at the end. So you can think about all these different formats and try to find an idea that maybe has been done before in TV in some format and find your way through that. 

A few more examples

So let’s think about our plumber example. Plumbers who love cars, well, we could do “Pimp My Ride for Tradesmen.”

That’s an interesting idea for a talk. Or let’s say we’re going after like amateur carpenters who would love to be professional. We could easily do “American Idol for Lumberjacks or Carpenters.” So we can start to see this idea emerge. Or let’s take a kind of B2B example. Maybe we are a marketing agency, as I’m sure many of you are. If you’re a marketing agency, maybe you know that some of your customers are in startups, and there’s this startup community.

One of the real problems that startups have is getting their product ready for market. So you could kind of think, well, the barrier is getting the product ready for market. We could then do “Queer Eye for Product Teams and Startups,”and we’ll bring in five specialists in different areas to kind of get their product ready and sort of iron out the details and make sure they’re ready to go to market and support marketing.

So you can start to see by having a clear niche audience and an insight into the problems that they’re having, then pulling together a whole list of different show ideas how you can bring together an idea for a potential, interesting TV show, video series, or podcast that could really make your business stand out. But remember that great ideas are kind of 10 a penny, and the really hard thing is finding the right one and making sure that it works for you.

So spend a lot of time coming up with lots and lots of different executions, trying them out, doing kind of little pilots before you work out and commit to the idea that works for you. The most important thing is to keep going and keep trying and teasing out those ideas rather than just settling on the first thing that comes to mind, because usually it’s not going to be the right answer. So I hope that was very useful, and we will see you again on another episode of Whiteboard Friday.

Take care.

Video transcription by Speechpad.com

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Posted by Jeff_Baker

Does reading that title give you a mini-panic attack?

Having gone through exactly as the title suggests, I can guarantee your anxiety is fully warranted.

If you care to relive my nightmare with me — perhaps as equal parts catharsis and SEO study — we will walk through the events chronologically.

Are you ready?

August 4th, 2019

It was a Sunday morning. I was drinking my coffee and screwing around in our SEO tools, like normal, not expecting a damned thing. Then … BAM!

What. The. Hell?

As SEOs, we’re all used to seeing natural fluctuations in rankings. Fluctuations, not disappearances.

Step 1: Denial

Immediately my mind goes to one place: it’s a mistake. So I jumped into some other tools to confirm whether or not Ahrefs was losing its mind.

Google Analytics also showed a corresponding drop in traffic, confirming something was definitely up. So as an SEO, I naturally assumed the worst…

Step 2: Algo panic

Algorithm update. Please, please don’t let it be an algo update.

I jumped into Barracuda’s Panguin Tool to see if our issue coincided with a confirmed update.

No updates. Phew.

Step 3: Diagnosis

Nobody ever thinks clearly when their reptile brain is engaged. You panic, you think irrationally and you make poor decisions. Zero chill.

I finally gathered some presence of mind to think clearly about what happened: It’s highly unusual for keywords rankings to disappear completely. It must be technical.

It must be indexing.

A quick Google search for the pages that lost keyword rankings confirmed that the pages had, in fact, disappeared. Search Console reported the same:

Notice the warning at the bottom:

No: ‘noindex’ detected in ‘robots’ meta tag

Now we were getting somewhere. Next, it was time to confirm this finding in the source code.

Our pages were marked for de-indexing. But how many pages were actually de-indexed so far?

Step 4: Surveying the damage

All of them. After sending a few frantic notes to our developer, he confirmed that a sprint deployed on Thursday evening (August 1, 2019), almost three days prior, had accidentally pushed the code live on every page.

But was the whole site de-indexed?

It’s highly unlikely, because in order for that to happen, Google would have had to crawl every page of the site within three days in order to find the ‘noindex’ markup. Search Console would be no help in this regard, as its data will always be lagging and may never pick up the changes before they are fixed.

Even looking back now, we see that Search Console only picked up a maximum of 249 affected pages, of over 8,000 indexed. Which is impossible, considering our search presence was cut by one-third an entire week after the incident was fixed.

Note: I will never be certain how many pages were fully de-indexed in Google, but what I do know is that EVERY page had ‘noindex’ markup, and I vaguely remember Googling ‘site:brafton.com’ and seeing roughly one-eighth of our pages indexed. Sure wish I had a screenshot. Sorry.

Step 1: Fix the problem

Once the problem was identified, our developer rolled back the update and pushed the site live as it was before the ‘noindex’ markup. Next came the issue of re-indexing our content.

Step 2: Get the site recrawled ASAP

I deleted the old sitemap, built a new one and re-uploaded to Search Console. I also grabbed most of our core product landing pages and manually requested re-indexing (which I don’t fully believe does anything since the most recent SC update).

Step 3: Wait

There was nothing else we could do at this point, other than wait. There were so many questions:

• Will the pages rank for the same keywords as they did previously?
• Will they rank in the same positions?
• Will Google “penalize” the pages in some way for briefly disappearing?

Only time would tell.

August 8th, 2019 (one week) – 33% drop in search presence

In assessing the damage, I’m going to use the date in which the erroring code was fully deployed and populated on live pages (August 2nd) as ground zero. So the first measurement will be seven days completed, August 2nd through August 8th.

Search Console would likely give me the best indication as to how much our search presence had suffered.

We had lost about 33.2% of our search traffic. Ouch.

Fortunately, this would mark the peak level of damage we experienced throughout the entire ordeal.

August 15th, 2019 (two weeks) – 23% drop in traffic

During this period I was keeping an eye on two things: search traffic and indexed pages. Despite re-submitting my sitemap and manually fetching pages in Search Console, many pages were still not being indexed — even core landing pages. This will become a theme throughout this timeline.

As a result of our remaining unindexed pages, our traffic was still suffering.

Two weeks after the incident and we were still 8% down, and our revenue-generating conversions fell with the traffic (despite increased conversion rates).

August 22nd, 2019 (three weeks) – 13% drop in traffic

Our pages were still indexing slowly. Painfully slowly, while I was watching my commercial targets drop through the floor.

At least it was clear that our search presence was recovering. But how it was recovering was of particular interest to me.

Were all the pages re-indexed, but with decreased search presence?

Were only a portion of the pages re-indexed with fully restored search presence?

To answer this question, I took a look at pages that were de-indexed, and re-indexed, individually. Here is an example of one of those pages:

Here’s an example of a page that was de-indexed for a much shorter period of time:

In every instance I could find, each page was fully restored to its original search presence. So it didn’t seem to be a matter of whether or not pages would recover, it was a matter of when pages would be re-indexed.

Speaking of which, Search Console has a new feature in which it will “validate” erroring pages. I started this process on August 26th. After this point, SC slowly recrawled (I presume) these pages to the tune of about 10 pages per week. Is that even faster than a normally scheduled crawl? Do these tools in SC even do anything?

What I knew for certain was there were a number of pages still de-indexed after three weeks, including commercial landing pages that I counted on to drive traffic. More on that later.

August 29th, 2019 (four weeks) – 9% drop in traffic

At this point I was getting very frustrated, because there were only about 150 pages remaining to be re-indexed, and no matter how many times I inspected and requested a new indexing in Search Console, it wouldn’t work.

These pages were fully capable of being indexed (as reported by SC URL inspection), yet they wouldn’t get crawled. As a result, we were still 9% below baseline, after nearly a month.

One particular page simply refused to be re-indexed. This was a high commercial value product page that I counted on for conversions.

In my attempts to force re-indexing, I tried:

• URL inspection and requesting indexing (15 times over the month).
• Updating the publish date, then requesting indexing.
• Updating the content and publish date, then requesting indexing.
• Resubmitting sitemaps to SC.

Nothing worked. This page would not re-index. Same story for over one hundred other less commercially impactful URLs.

Note: This page would not re-index until October 1st, two full months after it was de-indexed.

By the way, here’s what our overall recovery progress looked like after four weeks:

September 5th, 2019 (five weeks) – 10.4% drop in traffic

The great plateau. At this point we had reindexed all of our pages, save for the ~150 or so supposedly being “validated.”

They weren’t. And they weren’t being recrawled either.

It seemed that we would likely fully recover, but the timing was in Google’s hands, and there was nothing I could do to impact it.

September 12th, 2019 (six weeks) – 5.3% gain in traffic

It took about six weeks before we fully recovered our traffic.

But in truth, we still hadn’t fully recovered our traffic, in that some content overperformed and was overcompensating for a number of pages that were not yet indexed. Notably, our product page that wouldn’t be indexed for another ~2.5 weeks.

On balance, our search presence recovered after six weeks. But our content wasn’t fully re-indexed until eight-plus weeks after fixing the problem.

Conclusion

For starters, definitely don’t de-index your site on accident, for an experiment, or any other reason. It stings. I estimate that we purged about 12% of all organic traffic amounting to an equally proportionate drop on commercial conversions.

What did we learn??

Once pages re-indexed, they were fully restored in terms of search visibility. The biggest issue was getting them re-indexed.

Some main questions we answered with this accidental experiment:

Did we recover?

Yes, we fully recovered and all URLs seem to drive the same search visibility.

How long did it take?

Search visibility returned to baseline after six weeks. All pages re-indexed after about eight to nine weeks.

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Posted by MiriamEllis

File this under fresh ideas for stagnant clients.

It’s 10:45 at night and I’m out of:

• Tortillas
• Avocados
• Salsa

Maybe I just got off of work, like millions of other non-nine-to-fivers. Maybe I was running around with my family all day and didn’t get my errands done. Maybe I was feeling too sick to appear in a public grocery store wrapped in the ratty throw from my sofa.

And now, most of the local shops are closed for the night and I’m sitting here, taco-less and sad.

But what if it didn’t have to be that way? What if I could search Google and find a kiosk just a couple of blocks away that would vend me solutions, no matter what time of night or day?

Something old is becoming new again, just like home delivery. And for your agency’s local business clients, the opportunity could become an amazing competitive advantage.

What’s up with kiosks?

Something old

The automat was invented in Germany in the late 19th century and took off in the US in the decades following, with industry leader Horn & Hardart’s last New York location only closing in 1991. These famous kiosks fed thousands of Americans on a daily basis with on-demand servings of macaroni, fish cakes, baked beans, and chicory coffee. The demise of the automat is largely blamed on the rise of the fast food industry, with Burger Kings even opening doors at former automat locations.

Something new

A couple of weeks ago, I was watching an episode of my favorite local SEO news roundup in which Ignitor Digital’s Carrie Hill mentioned a meat vending kiosk. I was immediately intrigued and wanted to know more about this. What I learned sparked my imagination on behalf of local businesses which are always benefitted by at least considering fresh ideas, even if those ideas are actually just taking a page from history and editing it a bit.

Something inspirational

What I learned from my research is that the Applestone Meat Company is distinguishing itself from the competition by offering a 24/7 butcher shop via two vending installations in the state of New York. They also have a drive-up service window from 11am–6pm, but for the countless potential customers who are at work or elsewhere during so-called “normal business hours,” the meat kiosks are ever-ready to serve.

CEO Joshua Applestone says he was inspired by the memory of Horn & Hardart and he must be one smart local business owner to have taken this bold plunge. The company has already earned some pretty awesome unstructured citations from the likes of Bloomberg with this product marketing strategy and they’re planning to open ten more kiosks in the near future.

But Applestone isn’t alone. A kiosk can technically just be a fancy vending machine. Check out Chicago startup Farmer’s Fridge. They recently closed a $30 million Series C round led by one-time Google CEO Eric Schmidt’s Innovation Endeavors. Their 200+ midwestern units provide granola, Greek yogurt, pasta, wraps, beverages, and similar on-the-go fare, and they donate leftovers to local food pantries.

Americans have long been accustomed to ATM machines. DVD and game rental stations are old news to us. We are nowhere near Japan, with its sixty-billion-dollar-a-year, national vending machine density of one machine per 23 citizens, and its automated sales of everything from ramen to socks to umbrellas. Geography and economics don’t point to the need to go to such a level in the US, but where convenience is truly absent, opportunity may reside. What might that look like?

Use your imagination

My corner of the world is famous for its sourdough bread. There are hundreds of regional bakeries competing with one another for the crustiest, lightest, most indulgent loaf. But, if you don’t make it to the local stores by early afternoon, your favorite brand is likely to have sold out. And if you’re working the 47-hour American work week, or gigging California night and day but don’t want to live on fast food, you’d likely be quite grateful to have your access to artisan baguettes restored.

Just imagine every bread bakery around the SF Bay Area installing a kiosk outside its front door, and you can hear the satisfied after-hours crunching, can’t you?

Applestone is selling unprepared meat, Farmer’s Fridge is selling prepared meals, and almost anything people nosh could be a candidate for a kiosk, but why should on-demand products be limited to food? I let my imagination meander and jotted down a quick list of things people might buy at various off-hours, if a machine existed outside the storefront:

• Books/magazines
• Weather-appropriate basic apparel (sweatshirts, socks, t-shirts)
• First aid supplies
• Baby care supplies
• Emergency electronics (chargers, batteries, flashlights)
• Basic auto repair supplies (headlight bulbs, wipers, puncture kits)
• Personal care products (bathroom tissue, toiletries)
• Office supplies (printer ink, paper, envelopes, stamps)
• Household goods (lightbulbs, laundry soap, pantry basics)
• Pet supplies
• Travel/camping/athletic supplies
• Basic craft supplies, small games, gifts, etc.

What if customers who do their morning bike ride at 5 AM knew they could stop by your client’s kiosk to fix a punctured tire? What if night workers knew they could pick up a box of light bulbs or bandages or cat food on their way to their shift? Think of the convenience — in some instances even life-saving help — that could be provided to travelers on the road at all hours, members of your community who are housing-insecure, or whole neighborhoods that lack access to basic goods?

Not every local business has the right model for a kiosk, but once I started to think about it, I realized just how many of them could. I’m initially envisioning these machines being installed at the place of business, but, where the scenario is right, a company with the right type of inventory could certainly place additional kiosks in strategic locations around the communities they wish to serve.

Kiosk Local SEO

Clearly, kiosks can generate revenue, but what could they do for clients’ online presence? The guidelines for representing your business on Google already support the creation of local business listings for ATMs, video rental stations, and express mail dropboxes. But I went straight to Google with the Applewood example to ask if this emerging type of kiosk would be permitted to create listings. They were kind enough to reply:

The link in the Twitter DM reply just pointed to the general guidelines, and I can find no reference to the term “Food Kiosk listing” in them. It’s the first time I’ve ever heard this terminology. But, clearly this representative is naming food kiosks as a “thing.” Google, it seems, is already quite aware of this business model. And the proof of their support is in the Maps pudding:

My, my! Talk about having the ability to hyperlocalize your local search marketing to fit Google’s extreme emphasis on user-to-business proximity. Enough to make any local SEO agency see conversions and dollar signs for clients.

Tip #1: Helpline phone numbers

I’ve written about ATM SEO in the past for financial publications, and so I’ll add one important tip for creating eligible Google listings for kiosks: guidelines require that you have a helpline phone number for kiosk users. I would post this number both on the listings and on the units, themselves. Note that this will likely mean you have a shared phone number on multiple listings, which isn’t typically deemed ideal for local search marketing, but if kiosks become your model and you avoid any semblance of creating fake listings, Google can likely handle it.

Tip #2: Unique local landing pages for your kiosks

I can also see value in creating unique location landing pages on client websites for their kiosks, especially if they aren’t stationed at your physical location. These pages could give excellent driving and walking directions for each unit, explain how to use the machine, feature reviews and testimonials for that location, and perhaps highlight new inventory.

Tip #3: Capitalize on your social media

Social media will also be an excellent vehicle for letting particular neighborhoods know about client kiosks and engaging with communities to understand their sentiments. Seek abundant feedback about what is and isn’t working for customers and how inventory could better serve their needs. And, of course, be sure every client is monitoring reviews like a low-flying hawk.

Is there an appetite for kiosks?

Image credit: Ben Chun

I’m a longtime observer of rural local SEO. I’ve learned that being intentional in noticing small things can lead to big ideas, and almost any novel concept is worth floating to clients. The tiny, free book lending kiosks sometimes officially branded “Little Free Libraries” are everywhere in my county, have become a non-profit initiative, and are driving Etsy sales of cute wooden contraptions. Moreover, my region is dotted with unstaffed farm stands that operate on the honor system, trusting neighbors to pay for what they take. I’d say our household purchases about half of our produce from them.

Within recent recall, the milkman and the grocery delivery boy seemed as distant as the phonograph. Now, consumers are showing interest in having whole meal kits, entire wardrobes, and just about everything delivered. The point being: don’t discount anything that renders convenience; not the traveling salesman, not the automat.

The decision to experiment with a kiosk isn’t a simple one. There will be financial aspects, like how to access a unit that works for the inventory being sold. There will be security questions, as most businesses probably won’t feel comfortable operating on the honor system.

But if the question is whether there is an appetite for the right kiosk, selling the right goods, in the right place, I’ll close today with a look at these provocative, illuminating reviews from just one location of Farmer’s Fridge:

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Posted by Emily.Potter

A/B testing your SEO changes can bring you a competitive edge and dodge the bullet of negative changes that could lower your traffic. In this episode of Whiteboard Friday, Emily Potter shares not only why A/B testing your changes is important, but how to develop a hypothesis, what goes into collecting and analyzing the data, and thoughts around drawing your conclusions.

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Video Transcription

Howdy, Moz fans. I’m Emily Potter, and I work at Distilled over in our London office. Today I’m going to talk to you about hypothesis testing in SEO and statistical significance.

At Distilled, we use a platform called ODN, which is the Distilled Optimization Delivery Network, to do SEO A/B testing. Now, in that, we use hypothesis testing. You may not be able to deploy ODN, but I still think today that you can learn something valuable from what I’m talking about.

Hypothesis testing

The four main steps of hypothesis testing

So when we’re using hypothesis testing, we use four main steps:

1. First, we formulate a hypothesis.
2. Then we collect data on that hypothesis.
3. We analyze the data, and then… 
4. We draw some conclusions from that at the end.

The most important part of A/B testing is having a strong hypothesis. So up here, I’ve talked about how to formulate a strong SEO hypothesis.

1. Forming your hypothesis

Three mechanisms to help formulate a hypothesis

Now we need to remember that with SEO we are trying to look to impact three things to increase organic traffic.

1. We’re either trying to improve organic click-through rates. So that’s any change you make that makes yours appearance in the SERPs seem more appealing to your competitors and therefore more people will click your ad.
2. Or you can improve your organic ranking so you’re moving higher up.
3. Or we could also rank for more keywords.

You could also be impacting a mixture of all three of these things. But you just want to make sure that one of these is clearly being targeted or else it’s not really an SEO test.

2. Collecting the data

Now next, we collect our data. Again, at Distilled, we use the ODN platform to do this. Now, with the ODN platform, we do A/B testing, and we split pages up into statistically similar buckets. 

A/B test with your control and your variant

So once we do that, we take our variant group and we use a mathematical analysis to decide what we think the variant group would have done had we not made that change.

So up here, we have the black line, and that’s what that’s doing. It’s predicting what our model thought the variant group would do if we had not made any change. This dotted line here is when the test began. So you can see after the test there was a separation. This blue line is actually what happened. 

Now, because there’s a difference between these two lines, we can see a change. If we move down here, we’ve just plotted the difference between those two lines.

Because the blue line is above the black line, we call this a positive test. Now this green part here is our confidence interval, and this one, as a standard, is a 95% confidence interval. Now we use that because we use statistical testing. So when the green lines are all above the zero line, or all below it for a negative test, we can call this a statistically significant test.

For this one, our best estimate is that this would have increased sessions by 12%, and that roughly turns out to be about 7,000 monthly organic sessions. Now, on either side here, you can see I have written 2.5%. That’s to make this all add up to 100, and the reason for that is that you never get a 100% confident result. There’s always the opportunity that there’s a random chance and you have a false negative or positive. That’s why we then say we are 97.5% confident this was positive. That’s because we have 95 plus 2.5.

Tests without statistical significance

Now, at Distilled, we’ve found that there are a lot of circumstances where we have tests that are not statistically significant, but there’s pretty strong evidence that they had an uplift. If we move down here, I have an example of that. So this is an example of something that wasn’t statistically significant, but we saw a strong uplift.

Now you can see our green line still has an area in it that is negative, and that’s saying there’s still a chance that, at 95% confidence interval, this was a negative test. Now if we drop down again below, I’ve done our pink again. So we have 5% on both sides, and we can say here that we’re 95% confident there was a positive result. That’s because this 5% is always above as well.

3. Analyze the data to test hypothesis

Now the reason we do this is to try and be able to implement changes that we have a strong hypothesis with and be able to get those wins from those instead of just rejecting it completely. Now part of the reason for this is also that we say we’re doing business and not science.

Here I’ve created a chart of when we would maybe deploy a test that was not statistically significant, and this is based off how strong or weak the hypothesis is and how cheap or expensive the change is.

Strong hypothesis / cheap change

Now over here, in your top right corner, when we have a strong hypothesis and a cheap change, we’d probably deploy that. For example, we had a test like this recently with one of our clients at Distilled, where they added their main keyword to the H1.

This final result looked something like this graph here. It was a strong hypothesis. It wasn’t an expensive change to implement, and we decided to deploy that test because we were pretty confident that that would still be something that would be positive.

Weak hypothesis / cheap change

Now on this other side here, if you have a weak hypothesis but it’s still cheap, then maybe evidence of an uplift is still reason to deploy that. You’d have to communicate with your client.

Strong hypothesis / expensive change

On the expensive change with strong hypothesis point, you’re going to have to weigh out the benefit that you might get from your return on investment if you calculate your expected revenue based off that percentage change that you’re getting there.

Weak hypothesis / cheap change

When it’s a weak hypothesis and expensive change, we would only want to deploy that if it’s statistically significant.

4. Drawing conclusions

Now we need to remember that when we’re doing hypothesis testing, all we’re doing is trying to test the null hypothesis. That does not mean that a null result means that there was no effect at all. All that that means is that we cannot accept or reject the hypothesis. We’re saying that this was too random for us to say whether this is true or not.

Now 95% confidence interval is being able to accept or reject the hypothesis, and we’re saying our data is not noise. When it’s less than 95% confidence, like this one over here, we can’t claim that we learned something the way that we would with a scientific test, but we could still say we have some pretty strong evidence that this would produce a positive effect on these pages.

The advantages of testing

Now when we talk to our clients about this, it’s because we’re aiming really here to give a competitive advantage over other people in their verticals. Now the main advantage of testing is to avoid those negative changes.

We want to just make sure that changes we’re making are not really plummeting traffic, and we see that a lot. At Distilled, we call that a dodged bullet.

Now this is something I hope that you can bring into your work and to be able to use with your clients or with your own website. Hopefully, you can start formulating hypotheses, and even if you can’t deploy something like ODN, you can still use your GA data to try and get a better idea if changes that you’re making are helping or hurting your traffic. That’s all that I have for you today. Thank you.

Video transcription by Speechpad.com

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Posted by FeliciaCrawford

MozCon 2019 was an absolute blast. There were endless snacks. There were Roger hugs. There were networking opportunities and Birds of a Feather tables and search epiphanies galore. And there were a ton of folks in our community who watched it all unfold from the perspective of a Twitter hashtag — fun to follow along with, but not quite the same impact as seeing the talks unfold in real-time.

If you’re still wishing you could’ve joined us in Seattle this past July, you’ll be happy to know that you can recreate the MozCon experience from the comfort of your home or office (or your home office, but hopefully not your office-home — seriously, Karen, the quarterly reports will still be there in the morning!).

Yep, you got it: the MozCon 2019 Video Bundle is available for your purchasing and viewing pleasure!

Get the MozCon 2019 video bundle

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Tell me about the video bundle!

For those of you who attended in-person, good news: you’ve already got access! The video bundle is always included in the price of your MozCon ticket, so you can relive your three jam-packed days of learning as many times as you want — and if you aren’t too bummed that they already made you share your MozCon swag with them, be sure to share the vids with your team!

For the rest of us, the video bundle lets us enjoy the presentations at our own pace. It’s condensed MozCon-caliber information in a neat, on-demand package that you can — have we mentioned this? — share with your team. Seriously, we think they’ll like it. We were humbled to host some of the very brightest minds in SEO and digital marketing on our stage. With topics ranging from content marketing to technical SEO, PPC to local SEO, and just about everything in between, there are presentations to inspire just about any role in marketing (and your web dev just might be interested in a few talks, too).

What’s covered in the videos:

1. The Golden Age of Search, Sarah Bird
2. Web Search 2019: The Essential Data Marketers Need, Rand Fishkin
3. Human > Machine > Human: Understanding Human-Readable Quality Signals and Their Machine-Readable Equivalents, Ruth Burr Reedy
4. Improved Reporting & Analytics Within Google Tools, Dana DiTomaso
5. Local Market Analytics: The Challenges and Opportunities, Rob Bucci
6. Keywords Aren’t Enough: How to Uncover Content Ideas Worth Chasing, Ross Simmonds
7. How to Supercharge Link Building with a Digital PR Newsroom, Shannon McGuirk
8. From Zero to Local Ranking Hero, Darren Shaw
9. Esse Quam Videri: When Faking it is Harder than Making It, Russ Jones
10. Building a Discoverability Powerhouse: Lessons From Merging an Organic, Paid, & Content Practice, Heather Physioc
11. Brand Is King: How to Rule in the New Era of Local Search, Mary Bowling
12. Making Memories: Creating Content People Remember, Casie Gillette
13. 20 Years in Search & I Don’t Trust My Gut or Google, Wil Reynolds
14. Super-Practical Tips for Improving Your Site’s E-A-T, Marie Haynes
15. Fixing the Indexability Challenge: A Data-Based Framework, Areej AbuAli
16. What Voice Means for Search Marketers: Top Findings from the 2019 Report, Christi Olson
17. Redefining Technical SEO, Paul Shapiro
18. How Many Words Is a Question Worth?, Dr. Peter J. Meyers
19. Fraggles, Mobile-First Indexing, & the SERP of the Future, Cindy Krum
20. Killer E-commerce CRO and UX Wins Using A SEO Crawler, Luke Carthy
21. Content, Rankings, and Lead Generation: A Breakdown of the 1% Content Strategy, Andy Crestodina
22. Running Your Own SEO Tests: Why It Matters & How to Do It Right, Rob Ousbey
23. Dark Helmet’s Guide to Local Domination with Google Posts and Q&A, Greg Gifford
24. How to Audit for Inclusive Content, Emily Triplett Lentz
25. Image & Visual Search Optimization Opportunities, Joelle Irvine
26. Factors that Affect the Local Algorithm that Don’t Impact Organic, Joy Hawkins
27. Featured Snippets: Essentials to Know & How to Target, Britney Muller

What you’ll get:

For just $299, you’ll get all of the MozCon education and inspiration with none of the air travel or traffic. The bundle includes:

• 27 full-length presentation videos chock full of leading SEO innovations, thought leadership, and tips & tricks
• Instant downloads and streaming to your computer, tablet, or mobile device
• Downloadable slide decks for all presentations

If we could include a download of a Top Pot doughnut and some piping hot Starbucks, we would in a heartbeat. Alas, they don’t have the technology for that… yet.

Free preview – Running Your Own SEO Tests: Why It Matters & How to Do It Right by Rob Ousbey

Speaking of doughnuts, we wouldn’t expect you to buy a dozen sweet treats without taking a little taste first to see if you like ‘em. It’s important to know that your doughnuts are both delicious, shareable, and relevant to your everyday work as an SEO — almost exactly like the MozCon video bundle. And just like the feeling of warmth and goodwill you receive when you come back to the office with a fragrant baker’s dozen, your teammates will thank you when you’ve got twenty-seven highly actionable talks to share with them — presentations that’ll hone your skills and level up your understanding of modern SEO and digital marketing.

That’s why we’ve released a talk we’re super proud of as your free preview of all the juicy goodness you can look forward to in the video bundle: Running Your Own SEO Tests: Why It Matters & How to Do It Right, presented by our very own Rob Ousbey. 

Google’s algorithms have undergone significant changes in recent years. Traditional ranking signals don’t hold the same sway they used to, and they’re being usurped by factors like UX and brand that are becoming more important than ever before. What’s an SEO to do? The answer lies in testing. Sharing original data and results from clients, Rob highlights the necessity of testing, learning, and iterating your work, from traditional UX testing to weighing the impact of technical SEO changes, tweaking on-page elements, and changing up content on key pages. Actionable processes and real-world results abound in this thoughtful presentation on why you should be testing SEO changes, how and where to run them, and what kinds of tests you ought to consider for your circumstances.

Gather the team, grab some snacks, and get ready to binge these presentations Netflix-Original-Series-style. 

Get the MozCon 2019 video bundle

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Posted by MiriamEllis

Can franchises make good digital marketing agency clients? There are almost 750,000 of them in the US alone, employing some 9 million Americans. Chances are good you’ll have the opportunity to market a business with this specialized model at some point. In this structure:

The Franchisor grants permission to others to operate under its trademark, selling approved goods and services supported by an operating system and marketing.

The Franchisee is the person or group paying the franchisor for the right to use the trademark and the benefits of the operating system and marketing.

Seems simple enough. But it’s this structure that gives franchise marketing its unique complexities. For your agency, the challenge is that you can’t enter these marketing relationships equipped solely with your knowledge of corporate or local search marketing.

You need to deeply understand the setup to avoid bewilderment over why implementation bogs down with franchise clients and why players lose track of their roles, or even overwrite one another’s efforts.

In this post, we’ll give you some quick and useful coaching on the franchise model, but if your agency just got a phone call from Orangetheory or Smoothie King, you can get the bigger playbook right away.

Download The Practical Guide to Franchise Marketing

Roles and goals make franchises unique clients

Imagine a post-game locker room scene. On the field, all players seemed united by the goal of winning. But now, at different press conferences, the owner is saying the coach failed to meet standards, the coach is saying the owner should keep his opinions to himself, and several of the star players are saying they didn’t get the ball enough.

Franchises can be just like that when there’s confusion over roles and goals. Read on to get a peek into the playbook we’ve prepared to help the team as a whole work better together:

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This post is excerpted from our new primer: The Practical Guide to Franchise Marketing.

Franchise marketing is a unique kind of activity. It does share a lot of qualities with corporate marketing (on the awareness side) and with SMB marketing (on the local side) but as we noted earlier, it’s sort of a joint custody arrangement that — like all custody arrangements — can get contentious at times.

Everyone wants the best for the brand, but everyone’s “best” is very much a matter of their own perspective and goals. Typically in this arrangement, there are at least two stakeholders, though sometimes there are more. The stakeholders and their goals tend to play out as follows:

Corporate Franchisor goals

• Creating a strong brand to license more franchisors.
• Controlling that brand so it isn’t negatively impacted.
• Supporting franchisees with strong branding and resources so they succeed.

Master Franchisor goals

• Working with corporate to protect the brand.
• Licensing more local franchisors.
• Supporting franchisees with resources so they succeed.

Regional or Area Franchisee goals

• Driving customer traffic and revenue at individual locations.
• Growing their portfolio of locations.
• Supporting location managers with resources so they succeed.

Owner/Operator Franchisee goals

• Increasing location(s) foot traffic.
• Increasing location(s) revenue.
• Building customer loyalty at the location(s).

In what ways is franchise marketing different from corporate or standard SMB marketing? There are some unique challenges that franchisors and franchisees face which are worth unpacking. Some of them are:

• Conflicting goals between franchisor/franchisee
• Faster turnover of locations and addresses
• Different opening hours, menus and promotions from location to location
• Unique local sales and marketing opportunities and challenges
• Competitors on both the brand side but also among local SMBs
• Lack of clearly defined marketing roles causing work to be overwritten, duplicated, or even neglected

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Getting your agency’s head in the game

Your agency can be a better coach to franchises by having a playbook that respects how they differ from corporate or SMB clients at the very outset. But differences don’t have to equal weaknesses. Are you ready to draft a game plan that draws from the strengths of both franchisors and franchisees? 

The Practical Guide to Franchise Marketing

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Posted by MiriamEllis

We couldn’t do it without you! In 2018, over 1,400 marketers responded to our State of Local SEO industry survey. We all learned so much from your responses about the day-to-day realities of marketing local businesses. This year, we can do even better because your answers will give us all valuable comparative data to analyze, YoY.

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Who can take the survey?

Anyone who markets local businesses in any way is eagerly invited. Whether you market a single location, work for an agency with some local business clients, or are an in-house SEO for a brand with thousands of locations, we would love your participation! Whether you do just a little local search marketing or a lot, are a novice or an adept, your insights have value.

What is the survey about?

Unlike a typical local ranking factors poll, The Local Search Marketing Industry Survey digs deep into marketers’ experiences with tactics, challenges, clients, Google, and the working environment. For example, we learned last year that:

• 90% of respondents felt Google’s emphasis on proximity was detrimental to SERP quality
• 62% felt there aren’t enough quality local search marketing training materials available
• 60% lacked a comprehensive review management strategy
• 49% felt utilization of Google Business Profile features were impacting local rank
• 35% had no link building strategy in place
• 17% of enterprises had no in-house SEO staff

With your help, we’ll see what’s changed and what hasn’t. There are fresh questions, too, which we hope will uncover new stories to spark new strategies for local brands and their marketers.

There will be four lucky winners!

Everyone is a winner with access to the data we’ll be sharing from this large survey. But we’d like to offer a little extra thank-you for your time and knowledge.

Every respondent who completes the full survey will be automatically entered for a chance to win one of four $50 Visa gift cards. Winners will be selected at random, and we hope they will use these gift cards to shop someplace local and awesome this holiday season!

Take the survey

Look forward to seeing the results in early 2020, when we compile them into our State of Local SEO 2020 Industry Report. Curious about last year’s insights? Check them out here, and thank you for participating!

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Posted by BritneyMuller

Earlier this week, I hosted a webinar all about featured snippets covering essential background info, brand-new research we’ve done, the results of all the tests I’ve performed, and key takeaways. Things didn’t quite go as planned, though. We had technical difficulties that interfered with our ability to broadcast live, and lots of folks were left with questions after the recording that we weren’t able to answer in a follow-up Q&A.

The next best thing to a live webinar Q&A? A digital one that you can bookmark and come back to over and over again! We asked our incredibly patient, phenomenally smart attendees to submit their questions via email and promised to answer them in an upcoming blog post. We’ve pulled out the top recurring questions and themes from those submissions and addressed them below. If you had a question and missed the submission window, don’t worry! Ask it down in the comments and we’ll keep the conversation going.

If you didn’t get a chance to sign up for the original webinar, you can register for it on-demand here:

Watch the webinar

And if you’re here to grab the free featured snippets cheat sheet we put together, look no further — download the PDF directly here. Print it off, tape it to your office wall, and keep featured snippets top-of-mind as you create and optimize your site content. 

Now, let’s get to those juicy questions!

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1. Can I win a featured snippet with a brand-new website?

If you rank on page one for a keyword that triggers a featured snippet (in positions 1–10), you’re a contender for stealing that featured snippet. It might be tougher with a new website, but you’re in a position to be competitive if you’re on page one — regardless of how established your site is.

We’ve got some great Whiteboard Fridays that cover how to set a new site up for success:

• Launching a New Website: Your SEO Checklist
• 10 Basic SEO Tips to Index + Rank New Content Faster
• How to Rank: The SEO Checklist

2. Does Google provide a tag that identifies traffic sources from featured snippets? Is there a GTM tag for this?

Unfortunately, Google does not provide a tag to help identify traffic from featured snippets. I’m not aware of a GTM tag that helps with this, either, but would love to hear any community suggestions or ideas in the comments!

It’s worth noting that it’s currently impossible to determine what percentage of your traffic comes from the featured snippet versus the duplicate organic URL below the featured snippet.

3. Do you think it’s worth targeting longer-tail question-based queries that have very low monthly searches to gain a featured snippet?

Great question! My advice is this: don’t sleep on low-search-volume keywords. They often convert really well and in aggregate they can do wonders for a website. I suggest prioritizing long tail keywords that you foresee providing a high potential ROI.

For example, there are millions of searches a month for the keyword “shoes.” Very competitive, but that query is pretty vague. In contrast, the keyword “size 6 red womens nike running shoes” is very specific. This searcher knows what they want and they’re dialing in their search to find it. This is a great example of a long tail keyword phrase that could provide direct conversions.

4. What’s the best keyword strategy for determining which queries are worth creating featured snippet-optimized content for?

Dr. Pete wrote a great blog post outlining how to perform keyword research for featured snippets back in 2016. Once you’ve narrowed down your list of likely queries, you need to look at keywords that you rank on page one for, that trigger a snippet, and that you don’t yet own. Next, narrow your list down further by what you envision will have the highest ROI for your goals. Are you trying to drive conversions? Attract top-of-funnel site visitors? Make sure the queries you target align with your business goals, and go from there. Both Moz Pro and STAT can be a big help with this process.

A tactical pro tip: Use the featured snippet carousel queries as a starting point. For instance, if there’s a snippet for the query “car insurance” with a carousel of “in Florida,” “in Michigan,” and so on, you might consider writing about state-specific topics to win those carousel snippets. For this technique, the bonus is that you don’t really need to be on page one for the root term (or ranking at all) — often, carousel snippets are taken from off-SERP links.

5. Do featured snippets fluctuate according to language, i.e. if I have several versions of my site in different languages, will the snippet display for each version?

This is a great question! Unfortunately, we haven’t been able to do international/multi-language featured snippet research just yet, but hope to in the future. I would suspect the featured snippet could change depending on language and search variation. The best way to explore this is to do a search in an incognito (and un-logged-in) browser window of Google Chrome.

If you’ve performed research along these lines, let us know what you found out down in the comments!

6. Why do featured snippet opportunities fluctuate in number from day to day?

Change really is the only constant in search. In the webinar, I discussed the various tests I did that caused Moz to lose a formerly won featured snippet (and what helped it reappear once again). Changes as simple as an extra period at the end of a sentence were enough to lose us the snippet. With content across the web constantly being created and edited and deprecated and in its own state of change, it’s no wonder that it’s tough to win and keep a featured snippet — sometimes even from one day to the next.

The SERPs are incredibly volatile things, with Google making updates multiple times every day. But when it comes down to the facts, there are a few things that reliably cause volatility (is that an oxymoron?):

• If a snippet is pulling from a lower-ranking URL (not positions 1–3); this could mean Google is testing the best answer for the query
• Google regularly changing which scraped content is used in each snippet
• Featured snippet carousel topics changing

The best way to change-proof yourself is to become an authority in your particular niche (E-A-T, remember?) and strive to rank higher to increase your chances of capturing and keeping a featured snippet.

7. How can I use Keyword Lists to find missed SERP feature opportunities? What’s the best way to use them to identify keyword gaps?

Keyword Lists are a wonderful area to uncover feature snippet (and other SERP feature) opportunity gaps. My favorite way to do this is to filter the Keyword List by your desired SERP feature. We’ll use featured snippets as an example. Next, sort by your website’s current rank (1–10) to determine your primary featured snippet gaps and opportunities.

The filters are another great way to tease out additional gaps:

• Which keywords have high search volume and low competition? 
• Which keywords have high organic CTR that you currently rank just off page one for?

8. What are best practices around reviewing the structure of content that’s won a snippet, and how do I know whether it’s worth replicating?

Content that has won a featured snippet is definitely worth reviewing (even if it doesn’t hold the featured snippet over time). Consider why Google might have provided this as a featured snippet:

• Does it succinctly answer the query? 
• Might it sound good as a voice answer? 
• Is it comprehensive for someone looking for additional information? 
• Does the page provide additional answers or information around the topic? 
• Are there visual elements? 

It’s best to put on your detective hat and try to uncover why a piece of content might be ranking for a particular featured snippet:

• What part of the page is Google pulling that featured snippet content from? 
• Is it marked up in a certain way? 
• What other elements are on the page? 
• Is there a common theme? 
• What additional value can you glean from the ranking featured snippet?

9. Does Google identify and prioritize informational websites for featured snippets, or are they determined by a correlation between pages with useful information and frequency of snippets? 

In other words, would being an e-commerce site harm your chances of winning featured snippets, all other factors being the same?

I’m not sure whether Google explicitly categorizes informational websites. They likely establish a trust metric of sorts for domains and then seek out information or content that most succinctly answers queries within their trust parameters, but this is just a hypothesis.

While informational sites tend to do overwhelmingly better than other types of websites, it’s absolutely possible for an e-commerce website to find creative ways of snagging featured snippets.

It’s fascinating how various e-commerce websites have found their way into current featured snippets in extremely savvy ways. Here’s a super relevant example: after our webinar experienced issues and wasn’t able to launch on time, I did a voice search for “how much do stamps cost” to determine how expensive it would be to send apology notes to all of our hopeful attendees. 

This was the voice answer:

“According to stamps.com the cost of a one ounce first class mail stamp is $0.55 at the Post Office, or $.047 if you buy and print stamps online using stamps.com.”

Pretty clever, right? I believe there are plenty of savvy ways like this to get your brand and offers into featured snippets.

10. When did the “People Also Ask” feature first appear? What changes to PAAs do you anticipate in the future?



People Also Ask boxes first appeared in July 2015 as a small-scale test. Their presence in the SERPs grew over 1700% between July 2015 and March 2017, so they certainly exploded in popularity just a few years ago. Funny enough, I was one of the first SEOs to come across Google’s PAA testing — you can read about that stat and more in my original article on the subject: Infinite “People Also Ask” Boxes: Research and SEO Opportunities

We recently published some great PAA research by Samuel Mangialavori on the Moz Blog, as well: 5 Things You Should Know About “People Also Ask” & How to Take Advantage

And there are a couple of great articles cataloging the evolution of PAAs over the years here:

• What’s the deal with “People also ask” boxes? (2016)
• How the “People also ask” box is evolving (2017)

When it comes to predicting the future of PAAs, well, we don’t have a crystal ball yet, but featured snippets continue to look more and more like PAA boxes with their new-ish accordion format. Is it possible Google will merge them into a single feature someday? It’s hard to say, but as SEOs, our best bet is to maintain flexibility and prepare to roll with the punches the search engines send our way.

11. Can you explain what you meant by “15% of image URLs are not in organic”?

Sure thing! The majority of images that show up in featured snippet boxes (or to be more accurate, the webpage those images live on) do not rank organically within the first ten pages of organic search results for the featured snippet query.

12. How should content creators consider featured snippets when crafting written content? Are there any tools that can help?

First and foremost, you’ll want to consider the searcher. 

• What is their intent? 
• What desired information or content are they after? 
• Are you providing the desired information in the medium in which they desire it most (video, images, copy, etc)? 

Look to the current SERPs to determine how you should be providing content to your users. Read all of the results on page one:

• What common themes do they have? 
• What topics do they cover? 
• How can you cover those better?

Dr. Pete has a fantastic Whiteboard Friday that covers how to write content to win featured snippets. Check it out: How to Write Content for Answers Using the Inverted Pyramid



You might also get some good advice from this classic Whiteboard Friday by Rand Fishkin: How to Appear in Google’s Answer Boxes

13. “Write quality content for people, not search engines” seems like great advice. But should I also be using any APIs or tools to audit my content? 

The only really helpful tool that comes to mind is the Flesch-Kincaid readability test, but even that can be a bit disruptive to the creative process. The very best tool you might have for reviewing your content might be a real person. I would ensure that your content can be easily understood when read out loud to your targeted audience. It may help to consider whether your content, as a featured snippet, would make for an effective, helpful voice search result.

14. What’s the best way to stay on top of trends when it comes to Google’s featured snippets?

Find publications and tools that resonate, and keep an eye on them. Some of my favorites include:

1. MozCast to keep a pulse on the Google algorithm
2. Monitoring tools like STAT (email alerts when you win/lose a snippet? Awesome.)
3. Cultivating a healthy list of digital marketing heroes to follow on Twitter
4. Industry news publications like Search Engine Journal and, of course, the Moz Blog 😉
5. Subscribing to SEO newsletters like the Moz Top 10

One of the very best things you can do, though, is performing your own investigative featured snippet research within your space. Publishing the trends you observe helps our entire community grow and learn. 

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Thank you so much to every attendee who submitted their questions. Digging into these follow-up thoughts and ideas is one of the best parts of putting on a presentation. If you’ve got any lingering questions after the webinar, I would love to hear them — leave me a note in the comments and I’ll be on point to answer you. And if you missed the webinar sign-up, you can still access it on-demand whenever you want.

We also promised you some bonus content, yeah? Here it is — I compiled all of my best tips and tricks for winning featured snippets into a downloadable cheat sheet that I hope is a helpful reference for you:

Free download: The Featured Snippets Cheat Sheet

There’s no reason you shouldn’t be able to win your own snippets when you’re armed with data, drive, and a good, solid plan! Hopefully this is a great resource for you to have on hand, either to share around with colleagues or to print out and keep at your desk:

Grab the cheat sheet

Again, thank you so much for submitting your questions, and we’ll see you in the comments for more.

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!

Posted by BritneyMuller

Machine learning is only growing in importance for anyone working in the digital world, but it can often feel like an inaccessible subject. It doesn’t have to be — and you don’t have to miss out on the competitive edge it can give you when it comes to SEO task automation. Put on your technical SEO cap and get ready to take notes, because Britney Muller is walking us through Machine Learning 101 in this week’s episode of Whiteboard Friday.

Click on the whiteboard image above to open a high-resolution version in a new tab!

Video Transcription

Hey, Moz fans. Welcome to another edition of Whiteboard Friday. Today I’m talking about all things machine learning, something, as many of you know, I’m super passionate about and love to talk about. So hopefully, this sparks a seed in some of you to explore it a bit further, because it is truly one of the most powerful things to happen in our space in a very long time. 

What is machine learning?

So a brief overview, in a nutshell, machine learning is actually a subset of AI, and some would argue we still haven’t really reached artificial intelligence. But it’s just one facet of the overall AI. 

Traditional programming

The best way to think about it is in comparison to traditional programming. So traditional programming, you input data and a program into a computer and out comes the output, whether that be a web page or calculator you built online, whatever that might be.

Machine learning

With machine learning, what you do is you put in the data and the desired output and put this into a computer, and you get a program, otherwise known as a machine learning model. So it’s a bit flipped, and it works extremely well. There are two primary types of machine learning:

1. You have supervised, which is where you’re basically feeding a model labeled training data, 
2. And then unsupervised, which is where you’re feeding a program data and letting it create clusters or associations between data points. 

The supervised is a bit more common. You’ll see things like classification, linear regression, and image recognition. Things like that are all very common. If you think about machine learning in terms of, okay, there’s all of this data that you’re putting into the model, data is the biggest part of machine learning. A lot of people would argue that if machine learning was a vehicle, data would be the fuel.

It’s a really important part to understand, because unless you have the right types of data to feed a model, you’re not going to get the desired outcome that you would like. 

A machine learning model example

So let’s look at an example. If you wanted to build a machine learning model that predicts housing prices, you might have all of this information.

You might have the current price, square foot of these homes, land, the number of bathrooms, the number of bedrooms, you name it. It goes on and on. These are also known as features. So what a model is going to try to do, when you put in all of this data, it’s going to try to understand associations between this information and come up with a model that best predicts home prices in the future.

The most basic of these machine learning models is linear regression. So if you think about inputting the data where maybe you just put in the price and the square foot, and you can kind of see the data like this. 

You see that as the square foot goes up, so does the price. A model over time, in looking at this data, is going to start to find the smoothest line through the data to have the most accurate predictions in the future.

What you don’t want it to do is to fit every single data point and have a line that looks like that — that’s also known as overfitting — because it doesn’t play nice for new data points. You don’t want a model to get so calculated to your dataset that it doesn’t predict accurately in the future.

A way to look at that is by the loss function. That’s maybe getting a bit deeper in this, but that’s how you would measure how the line is being fit. Let’s see. 

What are the machine learning possibilities in SEO?

So what are some of the possibilities in SEO? How can we leverage machine learning in the SEO space?

Automate meta descriptions

So there are couple ways that people are already doing this. You can automate meta descriptions by looking at the page content and using a machine model to summarize the text. So this literally summarizes the content for you and pares it down to a meta description length. Pretty incredible. 

Automate titles

You could similarly do this for titles, although I don’t suggest you do this for primary pages. This isn’t going to be perfect. But if you have a huge, huge website, with hundreds of thousands of pages, it gets you halfway there. It’s really interesting to start playing around in that space with these large websites.

Automate image alt text

You can also automate alt text for images. We see these models getting really good at understanding what’s in an image. 

Automate 301 redirects

301 redirects, Paul Shapiro has an incredible write-up and basically process for that already. 

Automate content creation

Content creation, and if that scares some of you or if you doubt that these models can currently create content that is decent, I challenge you to go check out Talk to Transformer.

It is a pared-back version of OpenAI, which was founded by Elon Musk. It’s pretty incredible and a little scary as to how good the content is just from that pared back model. So that is for sure possible in the future and even today. 

Automate product/page suggestions

In addition to product and page suggestions.

So this is just going to get better. Imagine us providing content and UX specifically for the unique users that come to our site, highly personalized content, highly personalized experiences. Really exciting stuff moving forward. 

Resources

I’ve got some resources I highly suggest you check out.

Google Codelabs is one of my favorites, just because it walks you through the steps. So if you go to Google Codelabs, filter by TensorFlow or machine learning, you can see the possible examples there. Colab notebooks or Jupyter notebooks are where you’ll likely be doing any of the machine learning that you want to do on your own.

Kaggle.com is the number one resource for data science competitions. So you get to really see what are the examples, how are people using machine learning today. You’ll see things like TSA has put up over $1 million for a data science team to come up with a model that predicts potential threats from security footage.

This stuff gets really interesting really fast. It’s also so important to have diversity and inclusion in this space to avoid really dangerous models in the future. So it’s something to definitely think about. 

TensorFlow is a great resource. It’s what Google put out, and it’s what a lot of their machine learning models is built off of. They’ve got a really great JavaScript platform that you can play around with. 

Andrew Ng has an incredible machine learning course. I highly suggest you check that out. 

Then Algorithmia is sort of a one-stop shop for models. So if you don’t care to dip your toes into machine learning and you just want say a summarizer model or a particular type of model, you could potentially find one there and do a plug-and-play of sorts.



So that’s pretty interesting and fun to explore. The last thing is a machine learning model is only as good as the data. I can’t express that enough. So a lot of machine learning and data scientists, it’s all data cleaning and parsing, and that’s the bulk of the work in this field.

It’s important to be aware of that. So that’s it for Machine Learning 101. Thank you so much for joining me, and I hope to see you all again soon. Thanks.

Video transcription by Speechpad.com

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If you enjoyed this episode of Whiteboard Friday, you’ll be delighted by all the cutting-edge SEO knowledge you’ll get from our newly released MozCon 2019 video bundle. Catch more useful technical tips in Britney’s talk, plus 26 additional future-focused topics from our top-notch speakers:

Grab the sessions now!

We suggest scheduling a good old-fashioned knowledge share with your colleagues to educate the whole team — after all, who didn’t love movie day in school? 😉

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