Jonathan Alonso

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.

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

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 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:

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

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 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!

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. 

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 Purple-Toolz

I’m a self-funded start-up business owner. As such, I want to get as much as I can for free before convincing our finance director to spend our hard-earned bootstrapping funds. I’m also an analyst with a background in data and computer science, so a bit of a geek by any definition.

What I try to do, with my SEO analyst hat on, is hunt down great sources of free data and wrangle it into something insightful. Why? Because there’s no value in basing client advice on conjecture. It’s far better to combine quality data with good analysis and help our clients better understand what’s important for them to focus on.

In this article, I will tell you how to get started using a few free resources and illustrate how to pull together unique analytics that provide useful insights for your blog articles if you’re a writer, your agency if you’re an SEO, or your website if you’re a client or owner doing SEO yourself.

The scenario I’m going to use is that I want analyze some SEO attributes (e.g. backlinks, Page Authority etc.) and look at their effect on Google ranking. I want to answer questions like “Do backlinks really matter in getting to Page 1 of SERPs?” and “What kind of Page Authority score do I really need to be in the top 10 results?” To do this, I will need to combine data from a number of Google searches with data on each result that has the SEO attributes in that I want to measure.

Let’s get started and work through how to combine the following tasks to achieve this, which can all be setup for free:

• Querying with Google Custom Search Engine
• Using the free Moz API account
• Harvesting data with PHP and MySQL
• Analyzing data with SQL and R

Querying with Google Custom Search Engine

We first need to query Google and get some results stored. To stay on the right side of Google’s terms of service, we’ll not be scraping Google.com directly but will instead use Google’s Custom Search feature. Google’s Custom Search is designed mainly to let website owners provide a Google like search widget on their website. However, there is also a REST based Google Search API that is free and lets you query Google and retrieve results in the popular JSON format. There are quota limits but these can be configured and extended to provide a good sample of data to work with.

When configured correctly to search the entire web, you can send queries to your Custom Search Engine, in our case using PHP, and treat them like Google responses, albeit with some caveats. The main limitations of using a Custom Search Engine are: (i) it doesn’t use some Google Web Search features such as personalized results and; (ii) it may have a subset of results from the Google index if you include more than ten sites.

Notwithstanding these limitations, there are many search options that can be passed to the Custom Search Engine to proxy what you might expect Google.com to return. In our scenario, we passed the following when making a call:

https://www.googleapis.com/customsearch/v1?key=<google_api_id>&userIp=
<ip_address>&cx<custom_search_engine_id>&q=iPhone+X&cr=countryUS&start=
1</custom_search_engine_id></ip_address></google_api_id>

Where:

• https://www.googleapis.com/customsearch/v1 – is the URL for the Google Custom Search API
• key=<GOOGLE_API_ID> – Your Google Developer API Key
• userIp=<IP_ADDRESS> – The IP address of the local machine making the call
• cx=<CUSTOM_SEARCH_ENGINE_ID> – Your Google Custom Search Engine ID
• q=iPhone+X – The Google query string (‘+’ replaces ‘ ‘)
• cr=countryUS – Country restriction (from Goolge’s Country Collection Name list)
• start=1 – The index of the first result to return – e.g. SERP page 1. Successive calls would increment this to get pages 2–5.

Google has said that the Google Custom Search engine differs from Google .com, but in my limited prod testing comparing results between the two, I was encouraged by the similarities and so continued with the analysis. That said, keep in mind that the data and results below come from Google Custom Search (using ‘whole web’ queries), not Google.com.

Using the free Moz API account

Moz provide an Application Programming Interface (API). To use it you will need to register for a Mozscape API key, which is free but limited to 2,500 rows per month and one query every ten seconds. Current paid plans give you increased quotas and start at $250/month. Having a free account and API key, you can then query the Links API and analyze the following metrics:

NOTE: Since this analysis was captured, Moz documented that they have deprecated these fields. However, in testing this (15-06-2019), the fields were still present.

Moz API Codes are added together before calling the Links API with something that looks like the following:

www.apple.com%2F?Cols=103616137253&AccessID=MOZ_ACCESS_ID&
Expires=1560586149&Signature=<MOZ_SECRET_KEY>

Where:

• http://lsapi.seomoz.com/linkscape/url-metrics/” class=”redactor-autoparser-object”>http://lsapi.seomoz.com/linksc… – Is the URL for the Moz API
• http%3A%2F%2Fwww.apple.com%2F – An encoded URL that we want to get data on
• Cols=103616137253 – The sum of the Moz API codes from the table above
• AccessID=MOZ_ACCESS_ID – An encoded version of the Moz Access ID (found in your API account)
• Expires=1560586149 – A timeout for the query – set a few minutes into the future
• Signature=<MOZ_SECRET_KEY> – An encoded version of the Moz Access ID (found in your API account)

Moz will return with something like the following JSON:

Array
( [ut] => Apple [uu] => <a href="http://www.apple.com/" class="redactor-autoparser-object">www.apple.com/</a> [ueid] => 13078035 [uid] => 14632963 [uu] => www.apple.com/ [ueid] => 13078035 [uid] => 14632963 [umrp] => 9 [umrr] => 0.8999999762 [fmrp] => 2.602215052 [fmrr] => 0.2602215111 [us] => 200 [upa] => 90 [pda] => 100
)

For a great starting point on querying Moz with PHP, Perl, Python, Ruby and Javascript, see this repository on Github. I chose to use PHP.

Harvesting data with PHP and MySQL

Now we have a Google Custom Search Engine and our Moz API, we’re almost ready to capture data. Google and Moz respond to requests via the JSON format and so can be queried by many popular programming languages. In addition to my chosen language, PHP, I wrote the results of both Google and Moz to a database and chose MySQL Community Edition for this. Other databases could be also used, e.g. Postgres, Oracle, Microsoft SQL Server etc. Doing so enables persistence of the data and ad-hoc analysis using SQL (Structured Query Language) as well as other languages (like R, which I will go over later). After creating database tables to hold the Google search results (with fields for rank, URL etc.) and a table to hold Moz data fields (ueid, upa, uda etc.), we’re ready to design our data harvesting plan.

Google provide a generous quota with the Custom Search Engine (up to 100M queries per day with the same Google developer console key) but the Moz free API is limited to 2,500. Though for Moz, paid for options provide between 120k and 40M rows per month depending on plans and range in cost from $250–$10,000/month. Therefore, as I’m just exploring the free option, I designed my code to harvest 125 Google queries over 2 pages of SERPs (10 results per page) allowing me to stay within the Moz 2,500 row quota. As for which searches to fire at Google, there are numerous resources to use from. I chose to use Mondovo as they provide numerous lists by category and up to 500 words per list which is ample for the experiment.

I also rolled in a few PHP helper classes alongside my own code for database I/O and HTTP.

In summary, the main PHP building blocks and sources used were:

• Google Custom Search Engine – Ash Kiswany wrote an excellent article using Jacob Fogg’s PHP interface for Google Custom Search;
• Mozscape API – As mentioned, this PHP implementation for accessing Moz on Github was a good starting point;
• Website crawler and HTTP – At Purple Toolz, we have our own crawler called PurpleToolzBot which uses Curl for HTTP and this Simple HTML DOM Parser;
• Database I/O – PHP has excellent support for MySQL which I wrapped into classes from these tutorials.

One factor to be aware of is the 10 second interval between Moz API calls. This is to prevent Moz being overloaded by free API users. To handle this in software, I wrote a “query throttler” which blocked access to the Moz API between successive calls within a timeframe. However, whilst working perfectly it meant that calling Moz 2,500 times in succession took just under 7 hours to complete.

Analyzing data with SQL and R

Data harvested. Now the fun begins!

It’s time to have a look at what we’ve got. This is sometimes called data wrangling. I use a free statistical programming language called R along with a development environment (editor) called R Studio. There are other languages such as Stata and more graphical data science tools like Tableau, but these cost and the finance director at Purple Toolz isn’t someone to cross!

I have been using R for a number of years because it’s open source and it has many third-party libraries, making it extremely versatile and appropriate for this kind of work.

Let’s roll up our sleeves.

I now have a couple of database tables with the results of my 125 search term queries across 2 pages of SERPS (i.e. 20 ranked URLs per search term). Two database tables hold the Google results and another table holds the Moz data results. To access these, we’ll need to do a database INNER JOIN which we can easily accomplish by using the RMySQL package with R. This is loaded by typing “install.packages(‘RMySQL’)” into R’s console and including the line “library(RMySQL)” at the top of our R script.

We can then do the following to connect and get the data into an R data frame variable called “theResults.”

library(RMySQL)
# INNER JOIN the two tables
theQuery <- " SELECT A.*, B.*, C.* FROM ( SELECT cseq_search_id FROM cse_query ) A -- Custom Search Query INNER JOIN ( SELECT cser_cseq_id, cser_rank, cser_url FROM cse_results ) B -- Custom Search Results ON A.cseq_search_id = B.cser_cseq_id INNER JOIN ( SELECT * FROM moz ) C -- Moz Data Fields ON B.cser_url = C.moz_url ; "
# [1] Connect to the database
# Replace USER_NAME with your database username
# Replace PASSWORD with your database password
# Replace MY_DB with your database name
theConn <- dbConnect(dbDriver("MySQL"), user = "USER_NAME", password = "PASSWORD", dbname = "MY_DB")
# [2] Query the database and hold the results
theResults <- dbGetQuery(theConn, theQuery)
# [3] Disconnect from the database
dbDisconnect(theConn)

NOTE: I have two tables to hold the Google Custom Search Engine data. One holds data on the Google query (cse_query) and one holds results (cse_results).

We can now use R’s full range of statistical functions to begin wrangling.

Let’s start with some summaries to get a feel for the data. The process I go through is basically the same for each of the fields, so let’s illustrate and use Moz’s ‘UEID’ field (the number of external equity links to a URL). By typing the following into R I get the this:

> summary(theResults$moz_ueid) Min. 1st Qu. Median Mean 3rd Qu. Max. 0 1 20 14709 182 2755274 > quantile(theResults$moz_ueid, probs = c(1, 5, 10, 25, 50, 75, 80, 90, 95, 99, 100)/100) 1% 5% 10% 25% 50% 75% 80% 90% 95% 99% 100% 0.0 0.0 0.0 1.0 20.0 182.0 337.2 1715.2 7873.4 412283.4 2755274.0 

Looking at this, you can see that the data is skewed (a lot) by the relationship of the median to the mean, which is being pulled by values in the upper quartile range (values beyond 75% of the observations). We can however, plot this as a box and whisker plot in R where each X value is the distribution of UEIDs by rank from Google Custom Search position 1-20.

Note we are using a log scale on the y-axis so that we can display the full range of values as they vary a lot!

Box and whisker plots are great as they show a lot of information in them (see the geom_boxplot function in R). The purple boxed area represents the Inter-Quartile Range (IQR) which are the values between 25% and 75% of observations. The horizontal line in each ‘box’ represents the median value (the one in the middle when ordered), whilst the lines extending from the box (called the ‘whiskers’) represent 1.5x IQR. Dots outside the whiskers are called ‘outliers’ and show where the extents of each rank’s set of observations are. Despite the log scale, we can see a noticeable pull-up from rank #10 to rank #1 in median values, indicating that the number of equity links might be a Google ranking factor. Let’s explore this further with density plots.

Density plots are a lot like distributions (histograms) but show smooth lines rather than bars for the data. Much like a histogram, a density plot’s peak shows where the data values are concentrated and can help when comparing two distributions. In the density plot below, I have split the data into two categories: (i) results that appeared on Page 1 of SERPs ranked 1-10 are in pink and; (ii) results that appeared on SERP Page 2 are in blue. I have also plotted the medians of both distributions to help illustrate the difference in results between Page 1 and Page 2.

The inference from these two density plots is that Page 1 SERP results had more external equity backlinks (UEIDs) on than Page 2 results. You can also see the median values for these two categories below which clearly shows how the value for Page 1 (38) is far greater than Page 2 (11). So we now have some numbers to base our SEO strategy for backlinks on.

# Create a factor in R according to which SERP page a result (cser_rank) is on
> theResults$rankBin <- paste("Page", ceiling(theResults$cser_rank / 10))
> theResults$rankBin <- factor(theResults$rankBin)
# Now report the medians by SERP page by calling ‘tapply’
> tapply(theResults$moz_ueid, theResults$rankBin, median) Page 1 Page 2 38 11 

From this, we can deduce that equity backlinks (UEID) matter and if I were advising a client based on this data, I would say they should be looking to get over 38 equity-based backlinks to help them get to Page 1 of SERPs. Of course, this is a limited sample and more research, a bigger sample and other ranking factors would need to be considered, but you get the idea.

Now let’s investigate another metric that has less of a range on it than UEID and look at Moz’s UPA measure, which is the likelihood that a page will rank well in search engine results.

> summary(theResults$moz_upa) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.00 33.00 41.00 41.22 50.00 81.00 > quantile(theResults$moz_upa, probs = c(1, 5, 10, 25, 50, 75, 80, 90, 95, 99, 100)/100) 1% 5% 10% 25% 50% 75% 80% 90% 95% 99% 100% 12 20 25 33 41 50 53 58 62 75 81 

UPA is a number given to a URL and ranges between 0–100. The data is better behaved than the previous UEID unbounded variable having its mean and median close together making for a more ‘normal’ distribution as we can see below by plotting a histogram in R.

We’ll do the same Page 1 : Page 2 split and density plot that we did before and look at the UPA score distributions when we divide the UPA data into two groups.

# Report the medians by SERP page by calling ‘tapply’
> tapply(theResults$moz_upa, theResults$rankBin, median) Page 1 Page 2 43 39 

In summary, two very different distributions from two Moz API variables. But both showed differences in their scores between SERP pages and provide you with tangible values (medians) to work with and ultimately advise clients on or apply to your own SEO.

Of course, this is just a small sample and shouldn’t be taken literally. But with free resources from both Google and Moz, you can now see how you can begin to develop analytical capabilities of your own to base your assumptions on rather than accepting the norm. SEO ranking factors change all the time and having your own analytical tools to conduct your own tests and experiments on will help give you credibility and perhaps even a unique insight on something hitherto unknown.

Google provide you with a healthy free quota to obtain search results from. If you need more than the 2,500 rows/month Moz provide for free there are numerous paid-for plans you can purchase. MySQL is a free download and R is also a free package for statistical analysis (and much more).

Go explore!

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 Cyrus-Shepard

A good, solid competitive analysis provides you with priceless insights into what’s working for other folks in your industry, but it’s not always easy to do right. In this week’s edition of Whiteboard Friday, Cyrus walks you through how to perform a full competitive analysis, including:

• How to identify your true competitors
• Keyword gap analysis
• Link gap analysis
• Top content analysis

Plus, don’t miss the handy tips on which tools can help with this process and our brand-new guide (with free template) on SEO competitive analysis. Give it a watch and let us know your own favorite tips for performing a competitive analysis in the comments!

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

Video Transcription

Howdy, Moz fans. Welcome to another edition of Whiteboard Friday. I’m Cyrus Shepard. Today we’re talking about a really cool topic — competitive analysis. This is an introduction to competitive analysis.

What is competitive analysis for SEO?

It’s basically stealing your competitors’ traffic. If you’re new to SEO or you’ve been around awhile, this is a very valuable tactic to earn more traffic and rankings for your site.

Instead of researching blindly what to go after, competitive analysis can tell you certain things with a high degree of accuracy that you won’t find other ways, such as:

• what keywords to target, 
• what content to create, 
• how to optimize that content, and 
• where to get links.

How to do an SEO competitive analysis

How does it do this?

Well, instead of researching just in a keyword tool or a link tool, with competitive analysis you look at what’s actually working for your competitors and use those tactics for yourself.

This often works so much better than the old-style ways of research, because you can actually improve upon what other people are actually doing and make those tactics work for you.

1. Identify your top competitors

So to get started with competitive analysis, the first challenge is to actually identify your top competitors.

This sounds easy. You probably think you know who your competitors are because you type a keyword into Google and you see who’s ranking for your desired keyword. This does work, to certain degree.

Another way to do it is to look at the keywords you rank for, because the challenge is you probably rank for far more keywords than you believe you do.

Moz, for instance, ranks for hundreds of thousands or possibly even millions of keywords, and we want to know at scale who are all the competitors ranking for all those different queries. This is very hard to do manually.

Fortunately there are a lot of SEO tools out there — Ahrefs, SEMrush — many tools that can tell you look at all the keywords that you rank for across thousands of SERPs and then calculate, using advanced metrics, exactly who your true competitors are.

I’m happy to announce that Moz just released a tool that does exactly this. We’re going to link to it in the transcript below.

It’s called Domain Analysis. It’s a free tool. Anybody can use it.

You simply type in your domain, and we look through all the keywords that your site ranks for in our database, we look at all the competitors, and we use some advanced heuristics and we match those up and we tell you who your true competitors are. Once you know your true competitors, you can continue with the rest of the analysis.

2. Perform a keyword gap analysis

The first step that most people take in doing an SEO competitive analysis is identifying the keyword gap. Now for a long time, when I was new to SEO, I heard this term “keyword gap” and I didn’t really know what it meant. But it’s actually really simple.

It’s simply what keywords do my competitors rank for that I don’t rank for, and that’s the gap. The idea is that we want to close that gap if the keyword is valuable or high volume. The trick is you can do this on your own manually. You can see all the keywords you rank for using an advanced keyword tool and then list all the keywords your competitors rank for and then combine those lists in Excel. It’s a long, tedious process.

Fortunately, again, major SEO tools, such as Moz, can do this at scale for you within seconds. If you go to Moz Keyword Explorer, you simply enter your domain, enter your top competitor’s domain that we found in this first step, and it will list all the keywords that your competitors rank for that you don’t rank for. 

You can then pull this into a spreadsheet and find keywords with high volume or keywords that are valuable and relevant to your business.

This is an important point. You don’t just want to go willy-nilly after any keyword your competitor ranks for. You want to actually find the keywords that are relevant to your business.

3. Perform a link gap analysis

So after you do that, we also have the cousin of a keyword gap analysis — link gap analysis.

This is a very similar concept, because you need links to rank. But where do you find the links? So you want to ask, “Who links to my competitors but does not link to me?”

The theory here is that if someone is linking to your competitor on a similar topic, they are more likely to link to you because they are in that business of linking out to that type of content.

An advanced tip is you often want to look at two or more competitors. The idea is that if someone is linking to multiple sources but not to you, it’s more likely they’ll link to you if you have superior content.

Again, SEO tools can provide something like this. You can list all the backlinks to yourself or your competitors and combine them in a spreadsheet. But the tools make it much easier.

In Moz’s Link Explorer, you simply enter your competitor, you enter another competitor and yours, and you can find all the people who are linking to those competitors but not to you.

An advanced tip that I like to use is do it at the page level. Don’t look for domains that are linking to your competitors. Look for specific pages and you can do this in Link Explorer. We’re going to show you in a little more detail in a guide I’m going to link to at the bottom of this post.

4. Perform a top content analysis

So we understand links, we understand the keywords. But what content do we want to create?

Top content analysis, this is very easy to do these days. You’re basically looking for content that earns your competitors a lot of traffic or a lot of links.

The idea is if other people are linking to these things, then it’s highly probable that you can earn links with similar but better content. So the idea is you go to a tool like Link Explorer. You can sort by top pages, and you pick out the content that has the most links for your competitor. Then don’t just re-create the content, but make it better. This is called the skyscraper technique, the idea of finding content that does really well and then making it better.

Then once you have this, you go back to your link gap analysis and you reach out to those people who are linking to that content and you ask them for links, showing them the better content.

So that’s it in a nutshell. When we put it all together, we have a very valuable process. We can go back to our individual pages, look at those pages that are ranking for our competitors. When you’re all done, you can actually take your page, plug it into your keyword gap, and see all the keywords the page is ranking for.

Our original keyword gap analysis looked at the domain, but now we just want to know what the page is ranking for. We can add that into our own page and make the page even better. We can again reach out to the same people who are linking to this page, show them our better content, and that is the process.

New Guide & Free Template

Whew, I’m exhausted. This is a huge process. I went over it really quickly. Fortunately, if it went by a little fast for you, we just released a guide, “An Introduction to SEO Competitive Analysis.” We’re going to link to it.

Get the Guide + Free Template



It explains all these processes in much more detail. It’s free to use. I hope you enjoy it.

Hey, I really enjoyed making this video. If you found value in it, give it a thumbs up. Please share on social media and we’ll talk to you next time. Thanks, everybody.

Video transcription by Speechpad.com

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