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Content optimization using entities: An actionable guide

December 24, 2019 No Comments

They’re by no means a secret, and entities’ role in SEO has been heavily documented – entity optimization just isn’t the trendy topic you might see every time you check your Twitter timeline.

We’d much rather discuss less impactful concepts, like whether content within a subfolder will rank better than a subdomain or whether it’s important for an SEO to learn Python (am I right?).

But entity optimization should be getting the same amount of press as the other topics and concepts we SEO’s drive into the ground week after week. I want to help us understand why, and how to approach content with entities in mind.

What is an entity?

Google defines an entity as, “A thing or concept that is singular, unique, well-defined and distinguishable.” An entity can be an event, idea, book, person, company, place, brand, a domain, and so much more. You might ask, “Isn’t that the definition of a keyword? What’s the difference?”

An entity isn’t bound by language or spelling, but rather a universally understood concept or thing. And at the core of an entity is its relation to other entities. Google uses an illustration of “nodes” and “edges” to explain entities, with entities as nodes and relationships as edges. Let’s look at a search to see how this plays out:

How Google uses entities example Justin Trudeau search

How Google uses entities example 2 Justin Trudeau search

A search for “Justin Trudeau” displays a knowledge panel where he carries the title “Prime Minister of Canada”. And a search for “prime minister of Canada” displays a knowledge panel of Justin Trudeau. So we know that Justin Trudeau is associated with Prime Minister of Canada and vice versa. Trudeau is the current prime minister, so what if we search for the same entities with a different relationship?

Example searching same entities with a different relationship

Here we see a different set of results, based on a different relationship between the nodes.

How are entities used by search engines?

We believe Google uses a model called Word2Vec (referenced in this patent regarding keyword extraction) to break down entities, map them to a graph, and assign a unique ID. In a sense, Word2Vec turns language into a mathematical computation, allowing Google to properly identify concepts and map them appropriately – regardless of language – in a way traditional models simply can’t.

We don’t know exactly how entities fit into search results right now but based on a model introduced in a patent titled “Ranking search results based on entity metrics“, we know one of the biggest factors is relatedness.

Relatedness is judged primarily by something called co-occurrence (the linked patent is still pending, but helpful in understanding co-occurrence). Co-occurrence judges the strength of relationships based on the frequency of the entities appearing together in documents around the web. The more frequently two entities are mentioned together, and the more authoritative the document that mentions them, the stronger the relation.

Are entities a ranking factor?

Entities aren’t necessarily a ranking factor – at least in the traditional sense. And we don’t really know exactly how much weight they carry as quality signals. But we know there are two key categories of ranking factors (among many others) heavily influenced by the entity graph.

Content

Keywords have historically been the judge of the relevance and quality of content. Keywords aren’t dead, but entities give better insight to search engines on the relationship between words in a search.

For example, let’s look at the search “best shoes for basketball in Atlanta.” Sure, we could create a post and stuff it with the keyphrase. But in a world of entity-based indexing, Google is looking for semantics around each of these entities, and signals that indicate their relationships.

You might recall the explosion of “LSI keywords”. Whether or not latent semantic indexing is used in Google’s algorithm, this fascination with semantics is rooted in entities. All search is now semantic.

Links

It’s pretty common knowledge in the world of SEO that not all links are created equal. Entity-based indexing amplifies this sentiment. A post aiming to rank for “best shoes for basketball in Atlanta” needs links and references from authoritative sources on shoes, basketball, and the city of Atlanta in order to really own that SERP.

How long have entities been used in algorithms?

We’ve seen patents on entities surfacing for over ten years, and most believe entities have played a role in search algorithms for quite a long time. The question is when did entities become core to indexing?

Cindy Crum of Mobile Moxie wrote a brilliant five-part series on entities. She makes a strong case for entities becoming a strong ranking signal at the same time as Google rolled out Mobile-First Indexing. In fact, she terms the entire update Entity-First Indexing.

BERT and entities

Did BERT have anything to do with entities? Though I believe BERT got a little more attention than it probably deserved, its use in Google’s algorithm can help us understand the importance of entities.

BERT (Bidirectional Encoder Representations from Transformers) is a Natural Language Processing model that Google introduced in 2018 and began rolling out in October 2019. BERT has the ability to consider the full context of a word based on the words that come before or after named entities.

We won’t dive deep, but we’ll look at an example Google gave to help us understand what BERT means for search. Google called out the query “2019 Brazil traveler to USA needs a visa” in a recent post. The preposition “to” is crucial here, and more crucial is its relationship to the entities found before and after it. Before BERT, Google would have returned results about US citizens traveling to Brazil. Post-BERT, Google can recognize that nuance and return a more relevant and helpful result:

2019 Brazil traveler to USA needs a visa before and after BERT

Source

Entities are at the core of Natural Language Processing models like BERT.

How to optimize content for entities

Before we dive into some actionable tips, know that entities have far more implications than content. Entity optimization is crucial for building brands, establishing domains, and all kinds of other online endeavors. Having said that, there are massive implications for content.

*Quick preface: I’ve used this approach to rank articles and have seen success, but this is by no means foolproof and battle-tested. I don’t at this time have or know of research that proves a direct correlation between an approach like this and high rankings. Nonetheless, I believe in it and believe a knowledge of entities gives SEOs a leg up.

Choose and research a topic

For starters, we need a topic and keyphrase for which we want to rank. We won’t dive into how to do keyword research or topic research, but let’s stick with our example above and aim to rank for “best shoes for basketball.”

If we want to aim to rank for this keyphrase, we need to gather insight on what other topics and concepts Google deems related in their entity graph. Where can we gain insight like this? A few places:

Wikipedia: We know entities are the foundation of Google’s Knowledge Graph – and we know Wikipedia fuels a lot of their knowledge on entities. We can assume that if Google leans on Wikipedia to help them understand topics, the attributes and sources found within Wikipedia may help guide our content.

Google images is another goldmine for entity insight:

Using Google images for entities

Beneath the search bar, we find entities Google positively associates with “best shoes for basketball.” These aren’t the shoes or attributes of shoes you must list in your article, but logic would say the mentioning of these topics will help Google associate your article with them.

“People Also Ask” is another helpful source for entity optimization. These are the other topics and questions Google associates with your target keyphrase:

Example of using "People Also Ask"

Use Google’s NLP API demo to analyze the competition

Identify the top two or three ranking articles for your target keyphrase. Now we will look at how Google views the entities found within their articles. We’re going to use Google’s NLP API demo:

Google's NLP API demo

This is just a sample demo of their NLP cloud product. Nonetheless, it provides really valuable data. Before we dive in, we need to define a key term.

Google’s API demo looks at a handful of things: salience, sentiment, syntax, and categories. We’re really only focusing on salience in this article.

Salience is a score of how important the entity is in the context of the whole text. The higher the score, the more salient the entity is. We’ll use salience to help guide our content. Here’s what to do:

  1. Click on one of your competing posts in the SERP
  2. Copy and paste the content into the demo editor
  3. Click “Analyze”
  4. Check out for which entities Google reveals high salience

Google's NLP API demo

We see the entities with the highest salience are “player,” “best basketball shoes,” and “basketball shoes.” Seeing as Google ranks this page well for the keyphrase we desire, we can conclude these are entities we should seek to optimize for in our post.

Provide context throughout

How can you optimize for these entities? As you begin writing, your goal should be to establish the relationship between the entities you’re targeting in your keyphrase and give Google all the context you can to associate your target keywords with their entity graph. This isn’t done by keyword stuffing, but by using some of the language and semantics we’ve gleaned from the above sources.

Google Images and Wikipedia should help you choose semantically related keywords and language to use throughout your article, while “People Also Ask” can help guide your overall topics and headings. Again, the aim is not to stuff keywords in, but to have a toolbox of individual words, phrases, language, and topics to guide our writing in a way that prioritizes our target entities.

Once you’ve finished writing, run your own article through Google’s NLP API demo to get a feel for how you stack up. If the desired entities show low salience, it may be worth going back to the drawing board. At the very least, you can analyze articles that show more entity success to gain insight into how Google associates your targets.

Update content as needed

Because entity optimization is a bit more complex than keyword optimization, there’s a stronger case for updating content on a regular basis as new topics arise around your entities. For example, as new basketball shoes come out, and Google establishes their place in the entity graph, it would help the salience of your entities to add them to your post.

BERT is another great example. As it blew up across the internet, if you had a post on Natural Language Processing, Google would expect to see mention of it.

The future of search

There is still a lot myself and the industry have to learn on the topic of entity optimization. And again, the implications expand far beyond content optimization.

But I do believe a focus on entities has already begun, and the signals will only grow in prominence for Google and other search engines.

Here’s to better content, more relevant SERPs, and the future of search.

Brooks Manley is a Digital Marketing Specialist and SEO Lead at Engenius, a marketing agency in Greenville, SC. When he’s not panicking about ranking drops and algorithm updates, you can find him watching NBA games and eating tacos.

The post Content optimization using entities: An actionable guide appeared first on Search Engine Watch.

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The finite era of “actionable insights”

December 4, 2019 No Comments

For years, “actionable insights” have been the Holy Grail for data analytics companies. Actionable insights, the thinking goes, are the end product of data collection, aggregation, analysis, and judgment. They enable a decision-maker to modify behavior and achieve desired outcomes.

The process begins with data collection, which can take many forms. There’s a big difference between collecting data and aggregating it in a meaningful way that can provide a picture of reality. That’s the “insights” part of the puzzle. First, you need high-quality data, then you need the technological prowess to clean and organize it.

With high-quality data that’s been cleaned and organized, the next step is to provide context. This is the realm of companies like Tableau, which provide tools that translate machine-friendly data points into human-friendly visualizations that strive to depict an objective picture of current conditions.

But whereas a snapshot of current conditions may, in fact, yield new and meaningful insights (for example, if I look ‘sales numbers’ across an organization I can see which channels are over- or under-performing), human judgment has always been paramount in choosing a particular action. A perfect picture of static conditions doesn’t by itself offer any suggestions as to how to achieve particular outcomes. We still rely on management to tweak sales incentives or redistribute resources.

Or at least we did, up until recently. Machine learning is now shifting the balance of institutional decision-making. Advances in processing and algorithmic self-improvement mean that computers can now anticipate future outcomes and take steps to maximize particular ones. Intelligent systems can now see the world in shades of gray and evaluate likelihoods from multitudes of variables far beyond human comprehension.

That’s the world we currently live in, and the evidence is all around us. Machine learning algorithms have swayed elections by stoking targeted outrage. Our clothes, food, and consumer products are designed according to data-driven analytics. Every design feature in your favorite app is being constantly optimized according to how computers anticipate your future behavior. It’s why YouTube is actually pretty good at showing you videos that keep you engaged.

The day is coming when we will no longer require “actionable insights,” because the action will have already been taken. Nobody at YouTube is looking at your viewing history to determine what to recommend next. Computers do that. The value of the stock market is now largely driven by automated trading algorithms, and as a consequence, there are fewer stock analysts than there used to be. Not only can computers process information far better than humans, but they’ve also demonstrated better financial judgment.

The day will soon arrive when “actionable insights” will seem like a quaint notion from a simpler time. Computers will be smart enough to act on insights by themselves. In doing so, they may, in fact, diminish the need for human oversight.

Until then, however, human enterprise is still structured around hierarchies of decision-making and judgment. The CEO of a company still needs to delegate day-to-day responsibilities to human actors whose knowledge and judgment have proven sound.

And so, for now, we still need actionable insights. Data analytics companies will continue to build better mousetraps, until the day when there are no longer mice.

Gil Rachlin, SVP of Products and Partnerships at Synup.

The post The finite era of “actionable insights” appeared first on Search Engine Watch.

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Three tools providing actionable competitive research insight

July 2, 2019 No Comments

Spying on your competitors’ marketing efforts is one of the most efficient ways to come up with your our marketing campaigns, learn new tactics as well as predict the future of your niche.

The powerful driving source of any business is its competition. Competitors force us to move forward, implement changes and evolve.

I am not a big fan of competitive research unless you use the data right away to create your own marketing plan. Furthermore, I like plans that are pretty quickly turned into reality. I like action, so here are three competitive research tools that provide highly actionable insights:

1. Spy on your competitor’s traffic

Haven’t you wanted to know where exactly your competitors’ traffic and sales are coming from. Or how their landing pages convert? Or what are their best-performing keywords?

Nacho Analytics is a unique and innovative tool that will help you find answers to all the above questions and more. It uses its own database of “millions” of people who agreed to share their browsing history with the tool to help you spy on how they find and interact with your competitors’ pages.

Now, the most important part about the tool is that domain monitoring starts only when you add it to the tool. There’s no historical data available until you start monitoring any domain, so the earlier to add your competitors, the more data you’ll end up accumulating, with time.

Now, what sort of data is it?

Nacho Analytics looks exactly as Google Analytics, so you’ll see all the reports there. The difference is, there’s no tracking code to install and that’s not your site you are tracking, which sounds even too good to be true, only it’s both good and real. Some of the competitive insights you’ll be able to obtain include:

  • Traffic sources (including conversions, page engagement metrics, etc. for each)
  • Lead sources
  • Conversion funnel analysis

In essence, you can do the same things with anyone else’s data that you do with your own: You can explore traffic acquisition tactics, see real-time data, identify best-performing CTAs, and more.

Screenshot of monitoring competitor's traffic

You can also share any data with anyone for free, similarly to how it works inside Google Analytics.

Nacho Analytics cheapest package is $ 39 and it allows you to track one competitor. For $ 79 a month, you’ll be able to monitor five competitors of yours.

2. Spy on your competitor’s backlinks

Ok, this one isn’t new: Monitoring competitors’ backlink acquisition methods help you create your own link building strategy as well as learn what not to do and how to avoid penalties.

But until now that was a pretty expensive and time-consuming process. Unless it’s your own site you are investigating, you had to pay for link research tools. And if you were dealing with a successful competitor, you had to go through thousands of lines before realizing how to sort and filter them to make sense of the data.

Therefore, I was pretty excited to discover this new backlink checker tool by Neil Patel which is free, available for use without the need to register and extremely usable.

Simply copy-paste your competitor’s domain and the tool will generate the list of backlinks including:

  • The referring page title and URL
  • The target page URL
  • The link anchor text and type (text and image)

Now, there’s also a domain and link scores available but since I am not a fan of any link scoring (to put it mildly), I ignore those for the most part. Yet, if you come to think of those, you can actually use these numbers for sorting purposes. So, unless you miss a good link because of that, they might turn useful.

screenshot of monitoring competitor's back links

The advanced filtering is where the tool really shines. They make it so much easier to filter backlinks by identified patterns, including/excluding:

  • Keywords in the anchor text
  • Domains
  • Words in the URL

screenshot of exporting backlink reports in the Neil Patel backlinks tool

Finally, you can easily export the whole list in a CSV file to keep playing with the data.

Again, this tool is absolutely free, with no registration required.

3. Spy on your own missed keyword opportunities

This, again, is not such an innovative tactic but it’s the format and the tool that can make all the difference.

The best way to improve and diversify your rankings is to expand the list of keywords you are targeting, and the easiest way to discover new keywords is to see what your competitors are ranking for.

“Keyword gap” tactic is about identifying queries one or more of your competitors’ domains is ranking fairly high, while yours is nowhere to be found.

This Domain vs Domain tool takes this tactic to a new level:

  • It suggests competing domains for you to analyze
  • It generates a handy venn diagram showing how close the selected competitors are and how many more opportunities you can explore. The venn graphic is clickable allowing you to instantly load the keyword lists based on the overlap:

venn diagram showing the selected competitors' missed keyword opportunities

The tool also shows current rankings of each domain for each query, as well as its recent movement. The latter should be a signal for you to go ahead and check what they possibly did recently to see the ranking change.

The chart also shows Google search volume for each query. However, sadly, you cannot sort results by it. You can export the whole chart to a CSV or Excel file to obtain more sorting and filtering flexibility.

You can also select a search engine for the analysis which is a pretty amazing feature. I find it absolutely invaluable when clients are trying to enter a new market, especially the same-language market, like Google.ca or Google.co.uk

screenshot of selecting the region-wise domain to generate results

Being able which queries your competitors are ranking for in various versions of Google makes international SEO much easier and more predictable. There’s some more info on how to make the most of the feature.

The tool currently costs $ 19 per month but they are giving up on the cheapest plan this summer.

Which tools are you using to explore your competition and, more importantly, make use of the data? Please share in the comments!

Ann Smarty is the Brand and Community Manager at InternetMarketingNinjas.com. She can be found on Twitter @seosmarty.

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Getting To The Point – How To Create Actionable PPC Reports

July 5, 2017 No Comments

Learn how to build insightful reports that drive action items for your PPC accounts.

Read more at PPCHero.com
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