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Advanced Hack: How to Improve Your SEO in Less Than 30 Minutes

This may be of some interest.

digital marketing

I’ve been testing a new SEO hack and it works no matter how old or how new your site is.

Heck, you can have barely any links, and I’ve found it to work as well.

Best of all, unlike most SEO changes, it doesn’t take months or years to see results from this… you can literally see results in less than 30 minutes.

And here’s what’s crazy: I had my team crawl 10,000 sites to see how many people are leveraging this SEO technique and it was only 17.

In other words, your competition doesn’t know about this yet!

So what is this hack that I speak of?

Google’s ever-changing search results

Not only is Google changing its algorithm on a regular basis, but they also test out new design elements.

For example, if you search for “food near me”, you’ll not only see a list of restaurants but you also see their ratings.

food near me

And if you look up a person, Google may show you a picture of that person and a quick overview.

elon musk

Over the years, Google has adapted its search results to give you the best experience. For example, if you search “2+2” Google will show you the answer of “4” so you don’t have to click through and head over to a webpage.

2 plus 2

But you already know this.

Now, what’s new that no one is really using are FAQ-rich results and Answer Cards.

Here’s what I mean… if you search “digital marketing” you’ll see that I rank on Google. But my listing doesn’t look like most people’s…

digital marketing

As you can see from the image above, Google has pulled FAQ rich results from my site.

And best of all, I was able to pull it off in less than 30 minutes. That’s how quickly Google picked it up and adjusted their SERP listing.

Literally all within 30 minutes.

And you can do the same thing through Answer Cards anytime you have pages related to question and answers.

qa example

So how can you do this?

Picking the right markup

Before we get this going with your site, you have to pick the right schema markup.

FAQpage schema is used when you offer a Frequently Asked Question page or have a product page that contains frequently asked questions about the product itself. This will let you be eligible for a collapsible menu under your SERP with the question, that when clicked on, reveals the answer.

faq rich result

It can also let you be eligible for FAQ Action that is shown on Google Assistant. This can potentially help get you noticed by people using voice search to find out an answer!

faq action

Q&A schema is used when people are contributing different types of answers and can vote for which answer they think is the best. This will provide the rich result cads under your SERP and shows all the answers, with the top answer highlighted.

qa rich result

After making sure you understand what these are used for, Google also has additional guidelines on when you can and can’t use these schema’s for:

Google’s guidelines

Google has a list of FAQpage schema guidelines.

Only use FAQPage if your page has a list of questions with answers. If your page has a single question and users can submit alternative answers, use QAPage instead. Here are some examples:

Valid use cases:

  • An FAQ page was written by the site itself with no way for users to submit alternative answers
  • A product support page that lists FAQs with no way for users to submit alternative answers 

Invalid use cases:

  • A forum page where users can submit answers to a single question
  • A product support page where users can submit answers to a single question
  • A product page where users can submit multiple questions and answers on a single page
  • Don’t use FAQPagefor advertising purposes
  • Make sure each Question includes the entire text of the question and make sure each answer includes the entire text of the answer. The entire question text and answer text may be displayed.
  • Question and answer content may not be displayed as a rich result if it contains any of the following types of content: obscene, profane, sexually explicit, graphically violent, promotion of dangerous or illegal activities, or hateful or harassing language.
  • All FAQcontent must be visible to the user on the source page.

And here are the guidelines for Q&A schema:

Only use the QAPage markup if your page has information in a question and answer format, which is one question followed by its answers.

Users must be able to submit answers to the question. Don’t use QAPage markup for content that has only one answer for a given question with no way for users to add alternative answers; instead, use FAQPage. Here are some examples:

Valid use cases:

  • A forum page where users can submit answers to a single question
  • A product support page where users can submit answers to a single question 

Invalid use cases:

  • An FAQ page was written by the site itself with no way for users to submit alternative answers
  • A product page where users can submit multiple questions and answers on a single page
  • A how-to guide that answers a question
  • A blog post that answers a question
  • An essay that answers a question
  • Don’t apply QAPagemarkup to all pages on a site or forum if not all the content is eligible. For example, a forum may have lots of questions posted, which are individually eligible for the markup. However, if the forum also has pages that are not questions, those pages are not eligible.
  • Don’t use QAPagemarkup for FAQ pages or pages where there are multiple questions per page. QAPagemarkup is for pages where the focus of the page is a single question and its answers.
  • Don’t use QAPagemarkup for advertising purposes.
  • Make sure each Questionincludes the entire text of the question and make sure each Answer includes the entire text of the answer.
  • Answermarkup is for answers to the question, not for comments on the question or comments on other answers. Don’t mark up non-answer comments as an answer.
  • Question and answer content may not be displayed as a rich result if it contains any of the following types of content: obscene, profane, sexually explicit, graphically violent, promotion of dangerous or illegal activities, or hateful or harassing language.

If your content meets these guidelines, the next step is to figure out how to implement the schema onto your website and which type to use.

How do I implement Schema and which to use? 

There are two ways to implement it… either through JSON-LD or Microdata.

I recommend choosing one style and sticking to it throughout your webpage, and I also recommend not using both types on the same page.

JSON-LD is what Google recommends wherever possible and Google has been in the process of adding support for markup-powered features. JSON-LD can be implemented into the header of your content and can take very little time to implement.

The other option is Microdata, which involves coding elements into your website. This can be a challenging process for some odd reason, I prefer it. Below are examples of how each work.

FAQpage Schema JSON-LD:

<html>

<head>

<title>Digital Marketing Frequently Asked Questions (FAQ) – Neil Patel</title>

</head>

<body>

<script type=”application/ld+json”>

“@context”: “//schema.org”,

“@type”: “FAQPage”,

“mainEntity”: [

“@type”: “Question”,

“name”: “What is digital marketing?”,

“acceptedAnswer”:

“@type”: “Answer”,

“text”:”Digital marketing is any form of marketing products or services that involves electronic device”

]

</script>

</body>

</html>

FAQpage Schema Microdata:

<html itemscope itemtype=”//schema.org/FAQPage”>

<head>

<title>Digital Marketing Frequently Asked Questions (FAQ) – Neil Patel</title>

</head>

<body>

<div itemscope itemprop=”mainEntity” itemtype=”//schema.org/Question”>

<h3 itemprop=”name”>What is digital marketing?</h3>

<div itemscope itemprop=”acceptedAnswer” itemtype=”//schema.org/Answer”>

<div itemprop=”text”>

<p>Digital marketing is any form of marketing products or services that involves electronic device.</p>

</div>

</div>

</div>

</body>

</html>

Q&A Schema JSON-LD:

“@context”: “//schema.org”,

“@type”: “QAPage”,

“mainEntity”:

“@type”: “Question”,

“name”: “Can I tie my shoe with one hand?”,

“text”: “I currently have taken a hobby to do many actions with one hand and I’m currently stuck on how to tie a shoe with one hand. Is it possible to tie my shoe with one hand?”,

“answerCount”: 2,

“upvoteCount”: 20,

“dateCreated”: “2019-07-23T21:11Z”,

“author”:

“@type”: “Person”,

“name”: “Expert at Shoes”

,

“acceptedAnswer”:

“@type”: “Answer”,

“text”: “It is possible to tie your shoe with one hand by using your teeth to hold the other lace”,

“dateCreated”: “2019-11-02T21:11Z”,

“upvoteCount”: 9000,

“url”: “//example.com/question1#acceptedAnswer”,

“author”:

“@type”: “Person”,

“name”: “AnotherShoeMan”

,

“suggestedAnswer”: [

“@type”: “Answer”,

“text”: “It is not possible to tie your shoe with one hand”,

“dateCreated”: “2019-11-02T21:11Z”,

“upvoteCount”: 2,

“url”: “//example.com/question1#suggestedAnswer1”,

“author”:

“@type”: “Person”,

“name”: “Best Shoe Man”

]

Q&A Schema Microdata:

<div itemprop=”mainEntity” itemscope itemtype=”//schema.org/Question”>

<h2 itemprop=”name”>Can I tie my shoe with one hand?</h2>

<div itemprop=”upvoteCount”>20</div>

<div itemprop=”text”>I currently have taken a hobby to do many actions with one hand and I’m currently stuck on how to tie a shoe with one hand. Is it possible to tie my shoe with one hand?</div>

<div>asked <time itemprop=”dateCreated” datetime=”2019-07-23T21:11Z”>July 23’19 at 21:11</time></div>

<div itemprop=”author” itemscope itemtype=”//schema.org/Person”><span

itemprop=”name”>Expert at Shoes</span></div>

<div>

<div><span itemprop=”answerCount”>2</span> answers</div>

<div><span itemprop=”upvoteCount”>20</span> votes</div>

<div itemprop=”acceptedAnswer” itemscope itemtype=”//schema.org/Answer”>

<div itemprop=”upvoteCount”>9000</div>

<div itemprop=”text”>

It is possible to tie your shoe with one hand by using your teeth to hold the other lace.

</div>

<a itemprop=”url” href=”//example.com/question1#acceptedAnswer”>Answer Link</a>

<div>answered <time itemprop=”dateCreated” datetime=”2019-11-02T22:01Z”>Nov 2 ’19 at 22:01</time></div>

<div itemprop=”author” itemscope itemtype=”//schema.org/Person”><span itemprop=”name”>AnotherShoeMan</span></div>

</div>

<div itemprop=”suggestedAnswer” itemscope itemtype=”//schema.org/Answer”>

<div itemprop=”upvoteCount”>2</div>

<div itemprop=”text”>

It is not possible to tie your shoe with one hand

</div>

<a itemprop=”url” href=”//example.com/question1#suggestedAnswer1″>Answer Link</a>

<div>answered <time itemprop=”dateCreated”datetime=”2019-11-02T21:11Z”>Nov 2 ’19 at 21:11</time></div>

<div itemprop=”author” itemscope itemtype=”//schema.org/Person”><span

itemprop=”name”>Best Shoe Man</span></div>

</div>

</div>

</div>

When you are implementing it on your website, feel free and just use the templates above and modify them with your content.

If you are unsure if your code is correctly implemented or not, use Google’s Structured Data Testing Tool and you can add your code snippet or the page that you implemented the schema on and it will tell you if you did it right or wrong.

Plus it will give you feedback on if there are any errors or issues with your code.

google structure data testing

You can also try Google’s Rich Result Tester. This will give you a brief look at how your structured data will look like in the results!

google rich snippet

Getting results in under 30 minutes

Once you make the changes to any page that you think is a good fit, you’ll want to log into Google Search Console and enter the URL of the page you modified in the top search bar.

add url

You’ll then want to have Google crawl that page so they can index the results. All you have to do is click “request indexing”.

request indexing

And typically within 10 minutes, you’ll notice it kick in and when you perform a Google search you’ll see your updated listing.

Now the key to making this work is to do this with pages and terms that already rank on page 1. That’s where I’ve seen the biggest improvement.

Will Schema get me to rank for People Also Ask and Featured Snippets?

Will this help with People Also Ask and Featured Snippets? So far, there has been no correlation between schema markup and People Also Ask or Featured Snippets and you do not need them to be featured in them.

Optimizing your content for this will not hurt you though and can potentially improve your chances to be on here.

Google has been testing out how they can show these types of Q&A, FAQ, and How-To results and looking at structured data to help understand them.

It’s better to be early to the game and help Google understand your pages, as well as possibly participating in any of Google’s experiments.

snippet

Will this get me on voice search?

With more and more people using mobile devices to find answers to questions, this is a very relevant question!

Especially considering that over half of the searches on Google will be from voice search in the near future.

Answers from voice search get most of their answers from featured snippets.

And adding structured data on your website increases the chances of getting you into featured snippets, which increases the chance of you getting featured on voice search.

Conclusion

This simple hack can potentially increase the visibility of your brand and help improve the authority of your website. It’s a simple solution that can take a single day to implement across your main question, product, or FAQ page.

I’ve been using it heavily for the last week or so and as long as I pick keywords that I already rank on page 1 for, I am seeing great results.

And as I mentioned above, when my team analyzed 10,000 sites we only found 17 to be using FAQ and QA schema. In other words, less than 1% of the sites are using this, which means you if you take advantage now, you’ll have the leg up on your competition.

So what do you think about this tactic? Are you going to use it?

The post Advanced Hack: How to Improve Your SEO in Less Than 30 Minutes appeared first on Neil Patel.

Thank you for reading.

Advanced Facebook Retargeting: How to Up Your Ads Game

This may be of some interest.

Do you want to run successful Facebook retargeting ads? Wondering what tactics can improve your Facebook campaign performance? To explore advanced Facebook retargeting, I interview Susan Wenograd. Susan is a Facebook ads expert, a regular columnist at Search Engine Journal, and an account director for AimClear, an integrated digital agency. You’ll discover the biggest mistakes […]

The post Advanced Facebook Retargeting: How to Up Your Ads Game appeared first on Social Media Marketing | Social Media Examiner.

Thank you for reading.

Advanced Linkbuilding: How to Find the Absolute Best Publishers and Writers to Pitch

This may be of some interest.

Posted by KristinTynski

In my last post, I explained how using network visualization tools can help you massively improve your content marketing PR/Outreach strategy —understanding which news outlets have the largest syndication networks empowers your outreach team to prioritize high-syndication publications over lower syndication publications. The result? The content you are pitching enjoys significantly more widespread link pickups.

Today, I’m going to take you a little deeper — we’ll be looking at a few techniques for forming an even better understanding of the publisher syndication networks in your particular niche. I’ve broken this technique into two parts:

  • Technique One — Leveraging Buzzsumo influencer data and twitter scraping to find the most influential journalists writing about any topic
  • Technique Two — Leveraging the Gdelt Dataset to reveal deep story syndication networks between publishers using in-context links.

Why do this at all?

If you are interested in generating high-value links at scale, these techniques provide an undeniable competitive advantage — they help you to deeply understand how writers and news publications connect and syndicate to each other.

In our opinion at Fractl, data-driven content stories that have strong news hooks, finding writers and publications who would find the content compelling, and pitching them effectively is the single highest ROI SEO activity possible. Done correctly, it is entirely possible to generate dozens, sometimes even hundreds or thousands, of high-authority links with one or a handful of content campaigns.

Let’s dive in.

Using Buzzsumo to understand journalist influencer networks on any topic

First, you want to figure out who your topc influencers are your a topic. A very handy feature of Buzzsumo is its “influencers” tool. You can locate it on the influences tab, then follow these steps:

  • Select only “Journalists.” This will limit the result to only the Twitter accounts of those known to be reporters and journalists of major publications. Bloggers and lower authority publishers will be excluded.
  • Search using a topical keyword. If it is straightforward, one or two searches should be fine. If it is more complex, create a few related queries, and collate the twitter accounts that appear in all of them. Alternatively, use the Boolean “and/or” in your search to narrow your result. It is critical to be sure your search results are returning journalists that as closely match your target criteria as possible.
  • Ideally, you want at least 100 results. More is generally better, so long as you are sure the results represent your target criteria well.
  • Once you are happy with your search result, click export to grab a CSV.

The next step is to grab all of the people each of these known journalist influencers follows — the goal is to understand which of these 100 or so influencers impacts the other 100 the most. Additionally, we want to find people outside of this group that many of these 100 follow in common.

To do so, we leveraged Twint, a handy Twitter scraper available on Github to pull all of the people each of these journalist influencers follow. Using our scraped data, we built an edge list, which allowed us to visualize the result in  Gephi.

Here is an interactive version for you to explore, and here is a screenshot of what it looks like:

This graph shows us which nodes (influencers) have the most In-Degree links. In other words: it tells us who, of our media influencers, is most followed. 

    These are the top 10 nodes:

    • Maia Szalavitz (@maiasz) Neuroscience Journalist, VICE and TIME
    • Radley Balko (@radleybalko) Opinion journalist, Washington Post
    • Johann Hari (@johannhari101) New York Times best-selling author
    • David Kroll (@davidkroll) Freelance healthcare writer, Forbes Heath
    • Max Daly (@Narcomania) Global Drugs Editor, VICE
    • Dana Milbank (@milbank)Columnist, Washington Post
    • Sam Quinones (@samquinones7), Author
    • Felice Freyer (@felicejfreyer), Boston Globe Reporter, Mental health and Addiction
    • Jeanne Whalen (@jeannewhalen) Business Reporter, Washington Post
    • Eric Bolling (@ericbolling) New York Times best-selling author

    Who is the most influential?

      Using the “Betweenness Centrality” score given by Gephi, we get a rough understanding of which nodes (influencers) in the network act as hubs of information transfer. Those with the highest “Betweenness Centrality” can be thought of as the “connectors” of the network. These are the top 10 influencers:

      • Maia Szalavitz (@maiasz) Neuroscience Journalist, VICE and TIME
      • David Kroll (@davidkroll) Freelance healthcare writer, Forbes Heath
      • Jeanne Whalen (@jeannewhalen) Business Reporter, Washington Post
      • Travis Lupick (@tlupick), Journalist, Author
      • Johann Hari (@johannhari101) New York Times best-selling author
      • Radley Balko (@radleybalko) Opinion journalist, Washington Post
      • Sam Quinones (@samquinones7), Author
      • Eric Bolling (@ericbolling) New York Times best-selling author
      • Dana Milbank (@milbank)Columnist, Washington Post
      • Mike Riggs (@mikeriggs) Writer & Editor, Reason Mag 

          @maiasz, @davidkroll, and @johannhari101 are standouts. There’s considerable overlap between the winners in “In-Degree” and “Betweenness Centrality” but they are still quite different. 

            What else can we learn?

              The middle of the visualization holds many of the largest sized nodes. The nodes in this view are sized by “In-Degree.” The large, centrally located nodes are disproportionately followed by other members of the graph and enjoy popularity across the board (from many of the other influential nodes). These are journalists commonly followed by everyone else. Sifting through these centrally located nodes will surface many journalists who behave as influencers of the group initially pulled from BuzzSumo.

              So, if you had a campaign about a niche topic, you could consider pitching to an influencer surfaced from this data —according to our the visualization, an article shared in their network would have the most reach and potential ROI

              Using Gdelt to find the most influential websites on a topic with in-context link analysis

              The first example was a great way to find the best journalists in a niche to pitch to, but top journalists are often the most pitched to overall. Often times, it can be easier to get a pickup from less known writers at major publications. For this reason, understanding which major publishers are most influential, and enjoy the widest syndication on a specific theme, topic, or beat, can be majorly helpful.

              By using Gdelt’s massive and fully comprehensive database of digital news stories, along with Google BigQuery and Gephi, it is possible to dig even deeper to yield important strategic information that will help you prioritize your content pitching.

              We pulled all of the articles in Gdelt’s database that are known to be about a specific theme within a given timeframe. In this case (as with the previous example) we looked at “behaviour health.” For each article we found in Gdelt’s database that matches our criteria, we also grabbed links found only within the context of the article.

              Here is how it is done:

              • Connect to Gdelt on Google BigQuery — you can find a tutorial here.
              • Pull data from Gdelt. You can use this command: SELECT DocumentIdentifier,V2Themes,Extras,SourceCommonName,DATE FROM [gdelt-bq:gdeltv2.gkg] where (V2Themes like ‘%Your Theme%’).
              • Select any theme you find, here — just replace the part between the percentages.
              • To extract the links found in each article and build an edge file. This can be done with a relatively simple python script to pull out all of the <PAGE_LINKS> from the results of the query, clean the links to only show their root domain (not the full URL) and put them into an edge file format.

              Note: The edge file is made up of Source–>Target pairs. The Source is the article and the Target are the links found within the article. The edge list will look like this:

              • Article 1, First link found in the article.
              • Article 1, Second link found in the article.
              • Article 2, First link found in the article.
              • Article 2, Second link found in the article.
              • Article 2, Third link found in the article.

              From here, the edge file can be used to build a network visualization where the nodes publishers and the edges between them represent the in-context links found from our Gdelt data pull around whatever topic we desired.

              This final visualization is a network representation of the publishers who have written stories about addiction, and where those stories link to.

                What can we learn from this graph?

                This tells us which nodes (Publisher websites) have the most In-Degree links. In other words: who is the most linked. We can see that the most linked-to for this topic are:

                • tmz.com
                • people.com
                • cdc.gov
                • cnn.com
                • go.com
                • nih.gov
                • ap.org
                • latimes.com
                • jamanetwork.com
                • nytimes.com

                Which publisher is most influential? 

                Using the “Betweenness Centrality” score given by Gephi, we get a rough understanding of which nodes (publishers) in the network act as hubs of information transfer. The nodes with the highest “Betweenness Centrality” can be thought of as the “connectors” of the network. Getting pickups from these high-betweenness centrality nodes gives a much greater likelihood of syndication for that specific topic/theme. 

                • Dailymail.co.uk
                • Nytimes.com
                • People.com
                • CNN.com
                • Latimes.com
                • washingtonpost.com
                • usatoday.com
                • cvslocal.com
                • huffingtonpost.com
                • sfgate.com

                What else can we learn?

                  Similar to the first example, the higher the betweenness centrality numbers, number of In-degree links, and the more centrally located in the graph, the more “important” that node can generally be said to be. Using this as a guide, the most important pitching targets can be easily identified. 

                  Understanding some of the edge clusters gives additional insights into other potential opportunities. Including a few clusters specific to different regional or state local news, and a few foreign language publication clusters.

                  Wrapping up

                  I’ve outlined two different techniques we use at Fractl to understand the influence networks around specific topical areas, both in terms of publications and the writers at those publications. The visualization techniques described are not obvious guides, but instead, are tools for combing through large amounts of data and finding hidden information. Use these techniques to unearth new opportunities and prioritize as you get ready to find the best places to pitch the content you’ve worked so hard to create.

                  Do you have any similar ideas or tactics to ensure you’re pitching the best writers and publishers with your content? Comment below!

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