Tag Archive | "Research"

How To Know Exactly What People Want (And Will Pay For) Using Your Most Powerful Research Tool

When I started teaching people how to build blogs that could potentially make money, I faced a difficult challenge, a big question that begins the entire process of blogging that had to be answered – How can I teach people to pick the right blog topic or niche? It’s the hardest decision to make and […]

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Entrepreneurs-Journey.com by Yaro Starak

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The One-Hour Guide to SEO, Part 2: Keyword Research – Whiteboard Friday

Posted by randfish

Before doing any SEO work, it’s important to get a handle on your keyword research. Aside from helping to inform your strategy and structure your content, you’ll get to know the needs of your searchers, the search demand landscape of the SERPs, and what kind of competition you’re up against.

In the second part of the One-Hour Guide to SEO, the inimitable Rand Fishkin covers what you need to know about the keyword research process, from understanding its goals to building your own keyword universe map. Enjoy!


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

Video Transcription

Howdy, Moz fans. Welcome to another portion of our special edition of Whiteboard Friday, the One-Hour Guide to SEO. This is Part II – Keyword Research. Hopefully you’ve already seen our SEO strategy session from last week. What we want to do in keyword research is talk about why keyword research is required. Why do I have to do this task prior to doing any SEO work?

The answer is fairly simple. If you don’t know which words and phrases people type into Google or YouTube or Amazon or Bing, whatever search engine you’re optimizing for, you’re not going to be able to know how to structure your content. You won’t be able to get into the searcher’s brain, into their head to imagine and empathize with them what they actually want from your content. You probably won’t do correct targeting, which will mean your competitors, who are doing keyword research, are choosing wise search phrases, wise words and terms and phrases that searchers are actually looking for, and you might be unfortunately optimizing for words and phrases that no one is actually looking for or not as many people are looking for or that are much more difficult than what you can actually rank for.

The goals of keyword research

So let’s talk about some of the big-picture goals of keyword research. 

Understand the search demand landscape so you can craft more optimal SEO strategies

First off, we are trying to understand the search demand landscape so we can craft better SEO strategies. Let me just paint a picture for you.

I was helping a startup here in Seattle, Washington, a number of years ago — this was probably a couple of years ago — called Crowd Cow. Crowd Cow is an awesome company. They basically will deliver beef from small ranchers and small farms straight to your doorstep. I personally am a big fan of steak, and I don’t really love the quality of the stuff that I can get from the store. I don’t love the mass-produced sort of industry around beef. I think there are a lot of Americans who feel that way. So working with small ranchers directly, where they’re sending it straight from their farms, is kind of an awesome thing.

But when we looked at the SEO picture for Crowd Cow, for this company, what we saw was that there was more search demand for competitors of theirs, people like Omaha Steaks, which you might have heard of. There was more search demand for them than there was for “buy steak online,” “buy beef online,” and “buy rib eye online.” Even things like just “shop for steak” or “steak online,” these broad keyword phrases, the branded terms of their competition had more search demand than all of the specific keywords, the unbranded generic keywords put together.

That is a very different picture from a world like “soccer jerseys,” where I spent a little bit of keyword research time today looking, and basically the brand names in that field do not have nearly as much search volume as the generic terms for soccer jerseys and custom soccer jerseys and football clubs’ particular jerseys. Those generic terms have much more volume, which is a totally different kind of SEO that you’re doing. One is very, “Oh, we need to build our brand. We need to go out into this marketplace and create demand.” The other one is, “Hey, we need to serve existing demand already.”

So you’ve got to understand your search demand landscape so that you can present to your executive team and your marketing team or your client or whoever it is, hey, this is what the search demand landscape looks like, and here’s what we can actually do for you. Here’s how much demand there is. Here’s what we can serve today versus we need to grow our brand.

Create a list of terms and phrases that match your marketing goals and are achievable in rankings

The next goal of keyword research, we want to create a list of terms and phrases that we can then use to match our marketing goals and achieve rankings. We want to make sure that the rankings that we promise, the keywords that we say we’re going to try and rank for actually have real demand and we can actually optimize for them and potentially rank for them. Or in the case where that’s not true, they’re too difficult or they’re too hard to rank for. Or organic results don’t really show up in those types of searches, and we should go after paid or maps or images or videos or some other type of search result.

Prioritize keyword investments so you do the most important, high-ROI work first

We also want to prioritize those keyword investments so we’re doing the most important work, the highest ROI work in our SEO universe first. There’s no point spending hours and months going after a bunch of keywords that if we had just chosen these other ones, we could have achieved much better results in a shorter period of time.

Match keywords to pages on your site to find the gaps

Finally, we want to take all the keywords that matter to us and match them to the pages on our site. If we don’t have matches, we need to create that content. If we do have matches but they are suboptimal, not doing a great job of answering that searcher’s query, well, we need to do that work as well. If we have a page that matches but we haven’t done our keyword optimization, which we’ll talk a little bit more about in a future video, we’ve got to do that too.

Understand the different varieties of search results

So an important part of understanding how search engines work — we’re going to start down here and then we’ll come back up — is to have this understanding that when you perform a query on a mobile device or a desktop device, Google shows you a vast variety of results. Ten or fifteen years ago this was not the case. We searched 15 years ago for “soccer jerseys,” what did we get? Ten blue links. I think, unfortunately, in the minds of many search marketers and many people who are unfamiliar with SEO, they still think of it that way. How do I rank number one? The answer is, well, there are a lot of things “number one” can mean today, and we need to be careful about what we’re optimizing for.

So if I search for “soccer jersey,” I get these shopping results from Macy’s and soccer.com and all these other places. Google sort has this sliding box of sponsored shopping results. Then they’ve got advertisements below that, notated with this tiny green ad box. Then below that, there are couple of organic results, what we would call classic SEO, 10 blue links-style organic results. There are two of those. Then there’s a box of maps results that show me local soccer stores in my region, which is a totally different kind of optimization, local SEO. So you need to make sure that you understand and that you can convey that understanding to everyone on your team that these different kinds of results mean different types of SEO.

Now I’ve done some work recently over the last few years with a company called Jumpshot. They collect clickstream data from millions of browsers around the world and millions of browsers here in the United States. So they are able to provide some broad overview numbers collectively across the billions of searches that are performed on Google every day in the United States.

Click-through rates differ between mobile and desktop

The click-through rates look something like this. For mobile devices, on average, paid results get 8.7% of all clicks, organic results get about 40%, a little under 40% of all clicks, and zero-click searches, where a searcher performs a query but doesn’t click anything, Google essentially either answers the results in there or the searcher is so unhappy with the potential results that they don’t bother taking anything, that is 62%. So the vast majority of searches on mobile are no-click searches.

On desktop, it’s a very different story. It’s sort of inverted. So paid is 5.6%. I think people are a little savvier about which result they should be clicking on desktop. Organic is 65%, so much, much higher than mobile. Zero-click searches is 34%, so considerably lower.

There are a lot more clicks happening on a desktop device. That being said, right now we think it’s around 60–40, meaning 60% of queries on Google, at least, happen on mobile and 40% happen on desktop, somewhere in those ranges. It might be a little higher or a little lower.

The search demand curve

Another important and critical thing to understand about the keyword research universe and how we do keyword research is that there’s a sort of search demand curve. So for any given universe of keywords, there is essentially a small number, maybe a few to a few dozen keywords that have millions or hundreds of thousands of searches every month. Something like “soccer” or “Seattle Sounders,” those have tens or hundreds of thousands, even millions of searches every month in the United States.

But people searching for “Sounders FC away jersey customizable,” there are very, very few searches per month, but there are millions, even billions of keywords like this. 

The long-tail: millions of keyword terms and phrases, low number of monthly searches

When Sundar Pichai, Google’s current CEO, was testifying before Congress just a few months ago, he told Congress that around 20% of all searches that Google receives each day they have never seen before. No one has ever performed them in the history of the search engines. I think maybe that number is closer to 18%. But that is just a remarkable sum, and it tells you about what we call the long tail of search demand, essentially tons and tons of keywords, millions or billions of keywords that are only searched for 1 time per month, 5 times per month, 10 times per month.

The chunky middle: thousands or tens of thousands of keywords with ~50–100 searches per month

If you want to get into this next layer, what we call the chunky middle in the SEO world, this is where there are thousands or tens of thousands of keywords potentially in your universe, but they only have between say 50 and a few hundred searches per month.

The fat head: a very few keywords with hundreds of thousands or millions of searches

Then this fat head has only a few keywords. There’s only one keyword like “soccer” or “soccer jersey,” which is actually probably more like the chunky middle, but it has hundreds of thousands or millions of searches. The fat head is higher competition and broader intent.

Searcher intent and keyword competition

What do I mean by broader intent? That means when someone performs a search for “soccer,” you don’t know what they’re looking for. The likelihood that they want a customizable soccer jersey right that moment is very, very small. They’re probably looking for something much broader, and it’s hard to know exactly their intent.

However, as you drift down into the chunky middle and into the long tail, where there are more keywords but fewer searches for each keyword, your competition gets much lower. There are fewer people trying to compete and rank for those, because they don’t know to optimize for them, and there’s more specific intent. “Customizable Sounders FC away jersey” is very clear. I know exactly what I want. I want to order a customizable jersey from the Seattle Sounders away, the particular colors that the away jersey has, and I want to be able to put my logo on there or my name on the back of it, what have you. So super specific intent.

Build a map of your own keyword universe

As a result, you need to figure out what the map of your universe looks like so that you can present that, and you need to be able to build a list that looks something like this. You should at the end of the keyword research process — we featured a screenshot from Moz’s Keyword Explorer, which is a tool that I really like to use and I find super helpful whenever I’m helping companies, even now that I have left Moz and been gone for a year, I still sort of use Keyword Explorer because the volume data is so good and it puts all the stuff together. However, there are two or three other tools that a lot of people like, one from Ahrefs, which I think also has the name Keyword Explorer, and one from SEMrush, which I like although some of the volume numbers, at least in the United States, are not as good as what I might hope for. There are a number of other tools that you could check out as well. A lot of people like Google Trends, which is totally free and interesting for some of that broad volume data.



So I might have terms like “soccer jersey,” “Sounders FC jersey”, and “custom soccer jersey Seattle Sounders.” Then I’ll have these columns: 

  • Volume, because I want to know how many people search for it; 
  • Difficulty, how hard will it be to rank. If it’s super difficult to rank and I have a brand-new website and I don’t have a lot of authority, well, maybe I should target some of these other ones first that are lower difficulty. 
  • Organic Click-through Rate, just like we talked about back here, there are different levels of click-through rate, and the tools, at least Moz’s Keyword Explorer tool uses Jumpshot data on a per keyword basis to estimate what percent of people are going to click the organic results. Should you optimize for it? Well, if the click-through rate is only 60%, pretend that instead of 100 searches, this only has 60 or 60 available searches for your organic clicks. Ninety-five percent, though, great, awesome. All four of those monthly searches are available to you.
  • Business Value, how useful is this to your business? 
  • Then set some type of priority to determine. So I might look at this list and say, “Hey, for my new soccer jersey website, this is the most important keyword. I want to go after “custom soccer jersey” for each team in the U.S., and then I’ll go after team jersey, and then I’ll go after “customizable away jerseys.” Then maybe I’ll go after “soccer jerseys,” because it’s just so competitive and so difficult to rank for. There’s a lot of volume, but the search intent is not as great. The business value to me is not as good, all those kinds of things.
  • Last, but not least, I want to know the types of searches that appear — organic, paid. Do images show up? Does shopping show up? Does video show up? Do maps results show up? If those other types of search results, like we talked about here, show up in there, I can do SEO to appear in those places too. That could yield, in certain keyword universes, a strategy that is very image centric or very video centric, which means I’ve got to do a lot of work on YouTube, or very map centric, which means I’ve got to do a lot of local SEO, or other kinds like this.

Once you build a keyword research list like this, you can begin the prioritization process and the true work of creating pages, mapping the pages you already have to the keywords that you’ve got, and optimizing in order to rank. We’ll talk about that in Part III next week. Take care.

Video transcription by Speechpad.com

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How to Research, Monitor, and Optimize for Questions

Posted by AnnSmarty

Have you been optimizing your content for questions? There are a few powerful reasons for you to start doing it now:

  • Niche question research is the most powerful content inspiration source
  • Questions are highly engaging: Asking a question triggers a natural answering reflex in human beings. Using questions on your landing pages and / or social media will improve engagement
  • Questions are very useful for niche and audience research: What can’t people figure out in your industry and how can you best help them?
  • Question research allows you to understand natural language better and optimize for voice search
  • Question optimization allows for increased organic search visibility through both featured snippets and Google’s “People Also Ask” results.

Just to reinforce the latter point, Google is going a bit insane with understanding and featuring questions in SERPs. Here’s just one of their recent experiments showing a multifaceted featured snippet, addressing a possible follow-up question (courtesy of Barry Schwartz):

multifaceted featured snippets

Types of niche questions and how to group them

  • Basic questions (these usually relate to defining concepts). In most cases you don’t need to write lengthy explanations because people searching for those seek quick easy-to-understand answers.
  • How-to questions (these usually relate to step-by-step instructions). Adding videos to better explain the process is almost always a good idea here
  • Branded questions (those usually include your or your competitor’s brand name or a product name). Like any branded queries**, these should be further categorized into:
  • ROPO questions (“research online, buy online / offline”). These are specific questions discussing your product, its pros and cons, reviews, etc.
    • High-intent questions: for example, questions asking how to buy your product.
    • Navigational questions: those addressing your site navigation, e.g. “How to login,” “How to cancel,” etc.
    • Competitive research questions: those comparing your brand to your competitors.
    • Reputational questions: those questions relating to your brand history, culture, etc.

Type of Questions

All branded questions may also be labeled based on possible sentiment.

** Most basic and how-to questions are going to have informational intent (simply due to the essence of the question format: most people asking questions seek to find an answer, i.e. information). But there’s always a chance there’s a transactional intent there that you may want to make note of, too.

For example, “What’s the best CRM” may be a query reflecting a solid commercial intent. Same goes about “How do you use a CRM?” Both can be asked by someone who is willing to give the software a try, and this needs to be reflected within your copy and on-page layout.

Tools to discover questions

1. People Also Ask

“People Also Ask” is a newer Google search element containing related questions to a given query. It’s not clear how Google is generating these (it might be due to enough people typing each question into the search box), but what we do know for sure is:

  • Google is smart: It would only show things to a user when they have found enough evidence that’s helpful and something their users engage with
  • “People Also Ask” boxes present more SERPs real estate which we may want to dominate for maximum organic search visibility

People Also Ask

With that in mind, People Also Ask results are important for content marketers on two fronts:

  • They allow us lots of insight into what our target audience wants to know
  • They allow us additional organic search visibility

To collect as many People Also Ask results as you can, give Featured Snippet Tool a try (disclaimer: This tool has been developed by the company I work for). It checks your domain’s important search queries and generates “People Also Ask” results for all of them:

People Also Ask results

The tool ranks “People Also Ask” questions by the number of queries they were triggered by. This enables you to quickly see most popular questions on your topic.

2. Google / Bing SERPs

Search results give us lots of cues beyond People Also Ask boxes, provided you use smart tools to analyze them. Text Optimizer is a tool that extracts terms and concepts from SERPs and uses semantic analysis to come up with the list of questions you may want to include in your content:

I believe that is partly what Google is doing to generate those “People Also Ask” suggestions, but this tool will give you more ideas than “People Also Ask” boxes alone.

It supports Google and Bing. You can also copy-paste your text in the tool and it will suggest terms and questions to add to optimize your content better for either search engine.

3. Google Suggest

Google Suggest is another search-based tool for content marketers. Google Suggest auto-completes a user’s query based on how other users tend to complete it. This way, we can safely assume that all Google Suggest results have a solid search volume / demand, simply because they ended up in the suggest index.

The problem with this one is that you need to know how to start typing the question to see it properly completed:

Google Suggest

There’s a workaround that forces Google to autocomplete the middle of the query:

  • Type your core query and hit search
  • Put your cursor back at the beginning of the query
  • Type “how” and Google will suggest more popular queries:

Google Suggest middle of the query

Another way to discover more question-type Google Suggest results is to play with the following tools:

Serpstat Questions is a solid keyword research tool allowing you to generate hundreds of niche questions based on your core query. What’s helpful is that Serpstat allows you to sort results by the question word:

Serpstat Questions

…and filter questions by a popular term in the tag cloud, making it easier to make sense of those multiple results (and optimize for several questions within one content asset):

Serpstat questions filter

Ahrefs is another multi-feature SEO platform that allows users to research related questions with one of its recent updates:

Ahrefs questions

If you end up with too many Google-suggested questions, run your list through Serpstat’s clustering tool to break those questions into meaningful groups based on relevancy.

The screenshot is based on the following settings: Linkage strength - Medium, Type of Clustering - Soft. Once you run it, you can re-run the clustering tool for free with different settings within the project. Don’t forget to export your first set of results before re-running it.

4. Quora and discussion boards

Quora is undoubtedly one of the largest sources of questions out there. In fact, it forces users to post new discussions in a question format, so everything you see there is questions.

Quora’s search functionality is highly confusing though. It has an intricate architecture based on topics (many of which overlap) and it won’t show you most popular questions over time. Its search ranking algorithm is a weird mix of personalization (based on your chosen interests and connections), recency, activity, and probably something else.

Because of this, I rarely use Quora itself. Instead I use Buzzsumo Question Analyzer. It aggregates results from all kinds of discussion boards, including Quora and Amazon Q&A. Furthermore, it analyzes your query and generates results for related keywords allowing you to expand your search and see the bigger picture:

/buzzsumo question analyzer

5. Twitter questions

Twitter is an amazing source of content inspiration few content marketers are really using. One of the must-have Twitter search tricks I always use within my social media monitoring dashboard is Twitter’s question search:

Type [brandname ?] (with the space in-between) into Twitter’s search box and you’ll see all questions people are asking when discussing your topic / brand / product.

If you want to get a bit trickier, monitor your bigger competitor’s tweeted questions, too. This will enable your team to be on top of everything your potential customers cannot figure out when buying from your competitor:

Twitter questiio

Cyfe (disclaimer: this is my content marketing client) is a social media dashboard providing an easy way to monitor multiple Twitter search results within one dashboard. You can use it to monitor all kinds of tweeted questions around your core term or brand name:

Cyfe Twitter Monitoring

6. Reddit AMA

Reddit AMAs offer another great way to pick up some interesting questions to use in your content. Unfortunately, I haven’t found a good reliable way to monitor Reddit for keywords (while restricting to a particular Subreddit) but I’ve been using Twitter monitoring for that.

You can use Cyfe to monitor the #redditama hashtag in combination with your core term. Or you can set up an alert inside My Tweet Alerts. The tool has a pretty unique set of options allowing you to find tweets based on keywords, hashtags, and even words in users’ bios. It sends email digests of most recent tweets making the alerts harder to miss.

For Reddit AMA monitoring, you can set it up to search for tweets that have the #redditama hashtag in them together with your main keyword. Or, to make it more targeted, you can only monitor those tweets published by Twitter users with your keyword in the bio:

MyTweetAlerts Settings

Here’s an example of the announced AMA on a related topic of my interest:

mytweetalerts

All I need to do is to open the AMA thread and scroll through comments in search for interesting questions to note for my future content ideas:

Reddit AMA

How to add questions to your (content) marketing strategy

Niche question research provides an almost unending source of content opportunities. To name a few, here are some ideas on how you can use questions:

  • Create a separate FAQ section to address and explain basic questions
  • Identify and optimize existing content to cover the identified questions
  • Add Q&A to important landing pages (this may help get product pages featured in Google).

But it’s not really only about content:

Different actions + teams for different types of questions

Keeping our initial question categorization above in mind, here’s how question research may (or rather, should) involve multiple departments within your company:

You can download this worksheet with clickable links here.

Basic (what-is) questions:

  • Types of content to answer these questions: Glossary, FAQ
  • Specific SEO considerations:
    • Clickable table of contents (see sample)
    • Implement QAPage Schema
  • Other teams to get involved: Customer support and sales team (including for training). You want those teams to use jargon your customers use

How-to questions:

  • Types of content to answer these questions: FAQ (+ videos)
  • Specific SEO considerations: Use HowTo Schema (Including Yoast for WP)
  • Other teams to get involved: Include your CRO expert because these could be transactional

Branded ROPO questions:

  • Types of content to answer these questions: Blog content (+ video tutorials)
  • Specific SEO considerations: Optimize for as many related branded terms as possible
  • Other teams to get involved: Include your product management team for them to collect answers (feedback) and implement required product updates / improvements). Add these to your editorial schedule as high-priority

Branded high-intent questions:

  • Types of content to answer these questions: Product Q&A
  • Specific SEO considerations: Implement QAPage Schema
  • Other teams to get involved: Include your CRO expert and A/B testing expert for optimum on-page conversion optimization

Branded navigational questions:

  • Types of content to answer these questions: Product-specific knowledge base (+ video tutorials)
  • Specific SEO considerations: Implement QAPage Schema or use a Q&A-optimized solution (like this one)
  • Other teams to get involved: Include your design and usability teams to solve navigational issues

Branded competitive research questions:

  • Types of content to answer these questions: Create specific landing pages + videos to explain your product benefits
  • Specific SEO considerations: Optimize for as many related branded terms as possible
  • Other teams to get involved: Include your product management team for them to collect answers (feedback) and implement required product updates / improvements. Include your sales team for them to know how to best explain your product benefits to clients

Branded competitive reputational questions

  • Types of content to answer these questions: Create specific landing pages + videos
  • Specific SEO considerations: Optimize for as many related branded terms as possible
  • Other teams to get involved: Include your reputation management + social media teams to address these questions properly when they have to

Takeaways:

  • Questions are useful on many levels, from audience research to conversion optimization and product development
  • As far as SEO is concerned, optimizing for questions helps you develop better-targeted copy and gain more organic search visibility (especially through appearing in featured and “People Also Ask” boxes)
  • Researching questions is an ongoing process: You need to be constantly discovering new ones and monitoring social media for real-time ideas
  • There are lots of tools to help you discover and organize niche questions (when it comes to organizing them, using your favorite tools or even simply spreadsheets is always a good idea)
  • Question research is not just for SEO or content ideation. It can help improve social media engagement, help you develop a better product, train your internal teams to better explain product advantages to clients, etc.

Are you researching and optimizing for niche questions yet? Please share your tips and tricks in the comments below!

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Evolving Keyword Research to Match Your Buyer’s Journey

Posted by matthew_jkay

Keyword research has been around as long as the SEO industry has. Search engines built a system that revolves around users entering a term or query into a text entry field, hitting return, and receiving a list of relevant results. As the online search market expanded, one clear leader emerged — Google — and with it they brought AdWords (now Google Ads), an advertising platform that allowed organizations to appear on search results pages for keywords that organically they might not.

Within Google Ads came a tool that enabled businesses to look at how many searches there were per month for almost any query. Google Keyword Planner became the de facto tool for keyword research in the industry, and with good reason: it was Google’s data. Not only that, Google gave us the ability to gather further insights due to other metrics Keyword Planner provided: competition and suggested bid. Whilst these keywords were Google Ads-oriented metrics, they gave the SEO industry an indication of how competitive a keyword was.

The reason is obvious. If a keyword or phrase has higher competition (i.e. more advertisers bidding to appear for that term) it’s likely to be more competitive from an organic perspective. Similarly, a term that has a higher suggested bid means it’s more likely to be a competitive term. SEOs dined on this data for years, but when the industry started digging a bit more into the data, we soon realized that while useful, it was not always wholly accurate. Moz, SEMrush, and other tools all started to develop alternative volume and competitive metrics using Clickstream data to give marketers more insights.

Now industry professionals have several software tools and data outlets to conduct their keyword research. These software companies will only improve in the accuracy of their data outputs. Google’s data is unlikely to significantly change; their goal is to sell ad space, not make life easy for SEOs. In fact, they’ve made life harder by using volume ranges for Google Ads accounts with low activity. SEO tools have investors and customers to appease and must continually improve their products to reduce churn and grow their customer base. This makes things rosy for content-led SEO, right?

Well, not really.

The problem with historical keyword research is twofold:

1. SEOs spend too much time thinking about the decision stage of the buyer’s journey (more on that later).

2. SEOs spend too much time thinking about keywords, rather than categories or topics.

The industry, to its credit, is doing a lot to tackle issue number two. “Topics over keywords” is something that is not new as I’ll briefly come to later. Frameworks for topic-based SEO have started to appear over the last few years. This is a step in the right direction. Organizing site content into categories, adding appropriate internal linking, and understanding that one piece of content can rank for several variations of a phrase is becoming far more commonplace.

What is less well known (but starting to gain traction) is point one. But in order to understand this further, we should dive into what the buyer’s journey actually is.

What is the buyer’s journey?

The buyer’s or customer’s journey is not new. If you open marketing text books from years gone by, get a college degree in marketing, or even just go on general marketing blogs you’ll see it crop up. There are lots of variations of this journey, but they all say a similar thing. No matter what product or service is bought, everyone goes through this journey. This could be online or offline — the main difference is that depending on the product, person, or situation, the amount of time this journey takes will vary — but every buyer goes through it. But what is it, exactly? For the purpose of this article, we’ll focus on three stages: awareness, consideration, & decision.

Awareness

The awareness stage of the buyer’s journey is similar to problem discovery, where a potential customer realizes that they have a problem (or an opportunity) but they may not have figured out exactly what that is yet.

Search terms at this stage are often question-based — users are researching around a particular area.

Consideration

The consideration stage is where a potential consumer has defined what their problem or opportunity is and has begun to look for potential solutions to help solve the issue they face.

Decision

The decision stage is where most organizations focus their attention. Normally consumers are ready to buy at this stage and are often doing product or vendor comparisons, looking at reviews, and searching for pricing information.

To illustrate this process, let’s take two examples: buying an ice cream and buying a holiday.

Being low-value, the former is not a particularly considered purchase, but this journey still takes place. The latter is more considered. It can often take several weeks or months for a consumer to decide on what destination they want to visit, let alone a hotel or excursions. But how does this affect keyword research, and the content which we as marketers should provide?

At each stage, a buyer will have a different thought process. It’s key to note that not every buyer of the same product will have the same thought process but you can see how we can start to formulate a process.

The Buyer’s Journey – Holiday Purchase

The above table illustrates the sort of queries or terms that consumers might use at different stages of their journey. The problem is that most organizations focus all of their efforts on the decision end of the spectrum. This is entirely the right approach to take at the start because you’re targeting consumers who are interested in your product or service then and there. However, in an increasingly competitive online space you should try and find ways to diversify and bring people into your marketing funnel (which in most cases is your website) at different stages.

I agree with the argument that creating content for people earlier in the journey will likely mean lower conversion rates from visitor to customer, but my counter to this would be that you’re also potentially missing out on people who will become customers. Further possibilities to at least get these people into your funnel include offering content downloads (gated content) to capture user’s information, or remarketing activity via Facebook, Google Ads, or other retargeting platforms.

Moving from keywords to topics

I’m not going to bang this drum too loudly. I think many in of the SEO community have signed up to the approach that topics are more important than keywords. There are quite a few resources on this listed online, but what forced it home for me was Cyrus Shepard’s Moz article in 2014. Much, if not all, of that post still holds true today.

What I will cover is an adoption of HubSpot’s Topic Cluster model. For those unaccustomed to their model, HubSpot’s approach formalizes and labels what many search marketers have been doing for a while now. The basic premise is instead of having your site fragmented with lots of content across multiple sections, all hyperlinking to each other, you create one really in-depth content piece that covers a topic area broadly (and covers shorter-tail keywords with high search volume), and then supplement this page with content targeting the long-tail, such as blog posts, FAQs, or opinion pieces. HubSpot calls this “pillar” and “cluster” content respectively.

Source: Matt Barby / HubSpot

The process then involves taking these cluster pages and linking back to the pillar page using keyword-rich anchor text. There’s nothing particularly new about this approach aside from formalizing it a bit more. Instead of having your site’s content structured in such a way that it’s fragmented and interlinking between lots of different pages and topics, you keep the internal linking within its topic, or content cluster. This video explains this methodology further. While we accept this model may not fit every situation, and nor is it completely perfect, it’s a great way of understanding how search engines are now interpreting content.

At Aira, we’ve taken this approach and tried to evolve it a bit further, tying these topics into the stages of the buyer’s journey while utilizing several data points to make sure our outputs are based off as much data as we can get our hands on. Furthermore, because pillar pages tend to target shorter-tail keywords with high search volume, they’re often either awareness- or consideration-stage content, and thus not applicable for decision stage. We term our key decision pages “target pages,” as this should be a primary focus of any activity we conduct.

We’ll also look at the semantic relativity of the keywords reviewed, so that we have a “parent” keyword that we’re targeting a page to rank for, and then children of that keyword or phrase that the page may also rank for, due to its similarity to the parent. Every keyword is categorized according to its stage in the buyer’s journey and whether it’s appropriate for a pillar, target, or cluster page. We also add two further classifications to our keywords: track & monitor and ignore. Definitions for these five keyword types are listed below:

Pillar page

A pillar page covers all aspects of a topic on a single page, with room for more in-depth reporting in more detailed cluster blog posts that hyperlink back to the pillar page. A keyword tagged with pillar page will be the primary topic and the focus of a page on the website. Pillar pages should be awareness- or consideration-stage content.

A great pillar page example I often refer to is HubSpot’s Facebook marketing guide or Mosi-guard’s insect bites guide (disclaimer: probably don’t click through if you don’t like close-up shots of insects!).

Cluster page

A cluster topic page for the pillar focuses on providing more detail for a specific long-tail keyword related to the main topic. This type of page is normally associated with a blog article but could be another type of content, like an FAQ page.

Good examples within the Facebook marketing topic listed above are HubSpot’s posts:

For Mosi-guard, they’re not utilizing internal links within the copy of the other blogs, but the “older posts” section at the bottom of the blog is referencing this guide:

Target page

Normally a keyword or phrase linked to a product or service page, e.g. nike trainers or seo services. Target pages are decision-stage content pieces.

HubSpot’s target content is their social media software page, with one of Mosi-guard’s target pages being their natural spray product.

Track & monitor

A keyword or phrase that is not the main focus of a page, but could still rank due to its similarity to the target page keyword. A good example of this might be seo services as the target page keyword, but this page could also rank for seo agency, seo company, etc.

Ignore

A keyword or phrase that has been reviewed but is not recommended to be optimized for, possibly due to a lack of search volume, it’s too competitive, it won’t be profitable, etc.

Once the keyword research is complete, we then map our keywords to existing website pages. This gives us a list of mapped keywords and a list of unmapped keywords, which in turn creates a content gap analysis that often leads to a content plan that could last for three, six, or twelve-plus months.

Putting it into practice

I’m a firm believer in giving an example of how this would work in practice, so I’m going to walk through one with screenshots. I’ll also provide a template of our keyword research document for you to take away.

1. Harvesting keywords

The first step in the process is similar, if not identical, to every other keyword research project. You start off with a batch of keywords from the client or other stakeholders that the site wants to rank for. Most of the industry call this a seed keyword list. That keyword list is normally a minimum of 15–20 keywords, but can often be more if you’re dealing with an e-commerce website with multiple product lines.

This list is often based off nothing more than opinion: “What do we think our potential customers will search for?” It’s a good starting point, but you need the rest of the process to follow on to make sure you’re optimizing based off data, not opinion.

2. Expanding the list

Once you’ve got that keyword list, it’s time to start utilizing some of the tools you have at your disposal. There are lots, of course! We tend to use a combination of Moz Keyword Explorer, Answer the Public, Keywords Everywhere, Google Search Console, Google Analytics, Google Ads, ranking tools, and SEMrush.

The idea of this list is to start thinking about keywords that the organization may not have considered before. Your expanded list will include obvious synonyms from your list. Take the example below:

Seed Keywords

Expanded Keywords

ski chalet

ski chalet

ski chalet rental

ski chalet hire

ski chalet [location name]

etc

There are other examples that should be considered. A client I worked with in the past once gave a seed keyword of “biomass boilers.” But after keyword research was conducted, a more colloquial term for “biomass boilers” in the UK is “wood burners.” This is an important distinction and should be picked up as early in the process as possible. Keyword research tools are not infallible, so if budget and resource allows, you may wish to consult current and potential customers about which terms they might use to find the products or services being offered.

3. Filtering out irrelevant keywords

Once you’ve expanded the seed keyword list, it’s time to start filtering out irrelevant keywords. This is pretty labor-intensive and involves sorting through rows of data. We tend to use Moz’s Keyword Explorer, filter by relevancy, and work our way down. As we go, we’ll add keywords to lists within the platform and start to try and sort things by topic. Topics are fairly subjective, and often you’ll get overlap between them. We’ll group similar keywords and phrases together in a topic based off the semantic relativity of those phrases. For example:

Topic

Keywords

ski chalet

ski chalet

ski chalet rental

ski chalet hire

ski chalet [location name]

catered chalet

catered chalet

luxury catered chalet

catered chalet rental

catered chalet hire

catered chalet [location name]

ski accommodation

ski accommodation

cheap ski accommodation

budget ski accommodation

ski accomodation [location name]

Many of the above keywords are decision-based keywords — particularly those with rental or hire in them. They’re showing buying intent. We’ll then try to put ourselves in the mind of the buyer and come up with keywords towards the start of the buyer’s journey.

Topic

Keywords

Buyer’s stage

ski resorts

ski resorts

best ski resorts

ski resorts europe

ski resorts usa

ski resorts canada

top ski resorts

cheap ski resorts

luxury ski resorts

Consideration

skiing

skiing

skiing guide

skiing beginner’s guide

Consideration

family holidays

family holidays

family winter holidays

family trips

Awareness

This helps us cater to customers that might not be in the frame of mind to purchase just yet — they’re just doing research. It means we cast the net wider. Conversion rates for these keywords are unlikely to be high (at least, for purchases or enquiries) but if utilized as part of a wider marketing strategy, we should look to capture some form of information, primarily an email address, so we can send people relevant information via email or remarketing ads later down the line.

4. Pulling in data

Once you’ve expanded the seed keywords out, Keyword Explorer’s handy list function enables your to break things down into separate topics. You can then export that data into a CSV and start combining it with other data sources. If you have SEMrush API access, Dave Sottimano’s API Library is a great time saver; otherwise, you may want to consider uploading the keywords into the Keywords Everywhere Chrome extension and manually exporting the data and combining everything together. You should then have a spreadsheet that looks something like this:

You could then add in additional data sources. There’s no reason you couldn’t combine the above with volumes and competition metrics from other SEO tools. Consider including existing keyword ranking information or Google Ads data in this process. Keywords that convert well on PPC should do the same organically and should therefore be considered. Wil Reynolds talks about this particular tactic a lot.

5. Aligning phrases to the buyer’s journey

The next stage of the process is to start categorizing the keywords into the stage of the buyer’s journey. Something we’ve found at Aira is that keywords don’t always fit into a predefined stage. Someone looking for “marketing services” could be doing research about what marketing services are, but they could also be looking for a provider. You may get keywords that could be either awareness/consideration or consideration/decision. Use your judgement, and remember this is subjective. Once complete, you should end up with some data that looks similar to this:

This categorization is important, as it starts to frame what type of content is most appropriate for that keyword or phrase.

The next stage of this process is to start noticing patterns in keyphrases and where they get mapped to in the buyer’s journey. Often you’ll see keywords like “price” or ”cost” at the decision stage and phrases like “how to” at the awareness stage. Once you start identifying these patterns, possibly using a variation of Tom Casano’s keyword clustering approach, you can then try to find a way to automate so that when these terms appear in your keyword column, the intent automatically gets updated.

Once completed, we can then start to define each of our keywords and give them a type:

  • Pillar page
  • Cluster page
  • Target page
  • Track & monitor
  • Ignore

We use this document to start thinking about what type of content is most effective for that piece given the search volume available, how competitive that term is, how profitable the keyword could be, and what stage the buyer might be at. We’re trying to find that sweet spot between having enough search volume, ensuring we can actually rank for that keyphrase (there’s no point in a small e-commerce startup trying to rank for “buy nike trainers”), and how important/profitable that phrase could be for the business. The below Venn diagram illustrates this nicely:

We also reorder the keywords so keywords that are semantically similar are bucketed together into parent and child keywords. This helps to inform our on-page recommendations:

From the example above, you can see “digital marketing agency” as the main keyword, but “digital marketing services” & “digital marketing agency uk” sit underneath.

We also use conditional formatting to help identify keyword page types:

And then sheets to separate topics out:

Once this is complete, we have a data-rich spreadsheet of keywords that we then work with clients on to make sure we’ve not missed anything. The document can get pretty big, particularly when you’re dealing with e-commerce websites that have thousands of products.

5. Keyword mapping and content gap analysis

We then map these keywords to existing content to ensure that the site hasn’t already written about the subject in the past. We often use Google Search Console data to do this so we understand how any existing content is being interpreted by the search engines. By doing this we’re creating our own content gap analysis. An example output can be seen below:

The above process takes our keyword research and then applies the usual on-page concepts (such as optimizing meta titles, URLs, descriptions, headings, etc) to existing pages. We’re also ensuring that we’re mapping our user intent and type of page (pillar, cluster, target, etc), which helps us decide what sort of content the piece should be (such as a blog post, webinar, e-book, etc). This process helps us understand what keywords and phrases the site is not already being found for, or is not targeted to.

Free template

I promised a template Google Sheet earlier in this blog post and you can find that here.

Do you have any questions on this process? Ways to improve it? Feel free to post in the comments below or ping me over on Twitter!

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Moz Blog

Posted in IM NewsComments Off

Ranking the 6 Most Accurate Keyword Research Tools

Posted by Jeff_Baker

In January of 2018 Brafton began a massive organic keyword targeting campaign, amounting to over 90,000 words of blog content being published.

Did it work?

Well, yeah. We doubled the number of total keywords we rank for in less than six months. By using our advanced keyword research and topic writing process published earlier this year we also increased our organic traffic by 45% and the number of keywords ranking in the top ten results by 130%.

But we got a whole lot more than just traffic.

From planning to execution and performance tracking, we meticulously logged every aspect of the project. I’m talking blog word count, MarketMuse performance scores, on-page SEO scores, days indexed on Google. You name it, we recorded it.

As a byproduct of this nerdery, we were able to draw juicy correlations between our target keyword rankings and variables that can affect and predict those rankings. But specifically for this piece…

How well keyword research tools can predict where you will rank.

A little background

We created a list of keywords we wanted to target in blogs based on optimal combinations of search volume, organic keyword difficulty scores, SERP crowding, and searcher intent.

We then wrote a blog post targeting each individual keyword. We intended for each new piece of blog content to rank for the target keyword on its own.

With our keyword list in hand, my colleague and I manually created content briefs explaining how we would like each blog post written to maximize the likelihood of ranking for the target keyword. Here’s an example of a typical brief we would give to a writer:

This image links to an example of a content brief Brafton delivers to writers.

Between mid-January and late May, we ended up writing 55 blog posts each targeting 55 unique keywords. 50 of those blog posts ended up ranking in the top 100 of Google results.

We then paused and took a snapshot of each URL’s Google ranking position for its target keyword and its corresponding organic difficulty scores from Moz, SEMrush, Ahrefs, SpyFu, and KW Finder. We also took the PPC competition scores from the Keyword Planner Tool.

Our intention was to draw statistical correlations between between our keyword rankings and each tool’s organic difficulty score. With this data, we were able to report on how accurately each tool predicted where we would rank.

This study is uniquely scientific, in that each blog had one specific keyword target. We optimized the blog content specifically for that keyword. Therefore every post was created in a similar fashion.

Do keyword research tools actually work?

We use them every day, on faith. But has anyone ever actually asked, or better yet, measured how well keyword research tools report on the organic difficulty of a given keyword?

Today, we are doing just that. So let’s cut through the chit-chat and get to the results…

This image ranks each of the 6 keyword research tools, in order, Moz leads with 4.95 stars out of 5, followed by KW Finder, SEMrush, AHREFs, SpyFu, and lastly Keyword Planner Tool.

While Moz wins top-performing keyword research tool, note that any keyword research tool with organic difficulty functionality will give you an advantage over flipping a coin (or using Google Keyword Planner Tool).

As you will see in the following paragraphs, we have run each tool through a battery of statistical tests to ensure that we painted a fair and accurate representation of its performance. I’ll even provide the raw data for you to inspect for yourself.

Let’s dig in!

The Pearson Correlation Coefficient

Yes, statistics! For those of you currently feeling panicked and lobbing obscenities at your screen, don’t worry — we’re going to walk through this together.

In order to understand the relationship between two variables, our first step is to create a scatter plot chart.

Below is the scatter plot for our 50 keyword rankings compared to their corresponding Moz organic difficulty scores.

This image shows a scatter plot for Moz's keyword difficulty scores versus our keyword rankings. In general, the data clusters fairly tight around the regression line.

We start with a visual inspection of the data to determine if there is a linear relationship between the two variables. Ideally for each tool, you would expect to see the X variable (keyword ranking) increase proportionately with the Y variable (organic difficulty). Put simply, if the tool is working, the higher the keyword difficulty, the less likely you will rank in a top position, and vice-versa.

This chart is all fine and dandy, however, it’s not very scientific. This is where the Pearson Correlation Coefficient (PCC) comes into play.

The PCC measures the strength of a linear relationship between two variables. The output of the PCC is a score ranging from +1 to -1. A score greater than zero indicates a positive relationship; as one variable increases, the other increases as well. A score less than zero indicates a negative relationship; as one variable increases, the other decreases. Both scenarios would indicate a level of causal relationship between the two variables. The stronger the relationship between the two veriables, the closer to +1 or -1 the PCC will be. Scores near zero indicate a weak or no relatioship.

Phew. Still with me?

So each of these scatter plots will have a corresponding PCC score that will tell us how well each tool predicted where we would rank, based on its keyword difficulty score.

We will use the following table from statisticshowto.com to interpret the PCC score for each tool:

Coefficient Correlation R Score

Key

.70 or higher

Very strong positive relationship

.40 to +.69

Strong positive relationship

.30 to +.39

Moderate positive relationship

.20 to +.29

Weak positive relationship

.01 to +.19

No or negligible relationship

0

No relationship [zero correlation]

-.01 to -.19

No or negligible relationship

-.20 to -.29

Weak negative relationship

-.30 to -.39

Moderate negative relationship

-.40 to -.69

Strong negative relationship

-.70 or higher

Very strong negative relationship

In order to visually understand what some of these relationships would look like on a scatter plot, check out these sample charts from Laerd Statistics.

These scatter plots show three types of correlations: positive, negative, and no correlation. Positive correlations have data plots that move up and to the right. Negative correlations move down and to the right. No correlation has data that follows no linear pattern

And here are some examples of charts with their correlating PCC scores (r):

These scatter plots show what different PCC values look like visually. The tighter the grouping of data around the regression line, the higher the PCC value.

The closer the numbers cluster towards the regression line in either a positive or negative slope, the stronger the relationship.

That was the tough part – you still with me? Great, now let’s look at each tool’s results.

Test 1: The Pearson Correlation Coefficient

Now that we’ve all had our statistics refresher course, we will take a look at the results, in order of performance. We will evaluate each tool’s PCC score, the statistical significance of the data (P-val), the strength of the relationship, and the percentage of keywords the tool was able to find and report keyword difficulty values for.

In order of performance:

#1: Moz

This image shows a scatter plot for Moz's keyword difficulty scores versus our keyword rankings. In general, the data clusters fairly tight around the regression line.

Revisiting Moz’s scatter plot, we observe a tight grouping of results relative to the regression line with few moderate outliers.

Moz Organic Difficulty Predictability

PCC

0.412

P-val

.003 (P<0.05)

Relationship

Strong

% Keywords Matched

100.00%

Moz came in first with the highest PCC of .412. As an added bonus, Moz grabs data on keyword difficulty in real time, rather than from a fixed database. This means that you can get any keyword difficulty score for any keyword.

In other words, Moz was able to generate keyword difficulty scores for 100% of the 50 keywords studied.

#2: SpyFu

This image shows a scatter plot for SpyFu's keyword difficulty scores versus our keyword rankings. The plot is similar looking to Moz's, with a few larger outliers.

Visually, SpyFu shows a fairly tight clustering amongst low difficulty keywords, and a couple moderate outliers amongst the higher difficulty keywords.

SpyFu Organic Difficulty Predictability

PCC

0.405

P-val

.01 (P<0.05)

Relationship

Strong

% Keywords Matched

80.00%

SpyFu came in right under Moz with 1.7% weaker PCC (.405). However, the tool ran into the largest issue with keyword matching, with only 40 of 50 keywords producing keyword difficulty scores.

#3: SEMrush

This image shows a scatter plot for SEMrush's keyword difficulty scores versus our keyword rankings. The data has a significant amount of outliers relative to the regression line.

SEMrush would certainly benefit from a couple mulligans (a second chance to perform an action). The Correlation Coefficient is very sensitive to outliers, which pushed SEMrush’s score down to third (.364).

SEMrush Organic Difficulty Predictability

PCC

0.364

P-val

.01 (P<0.05)

Relationship

Moderate

% Keywords Matched

92.00%

Further complicating the research process, only 46 of 50 keywords had keyword difficulty scores associated with them, and many of those had to be found through SEMrush’s “phrase match” feature individually, rather than through the difficulty tool.

The process was more laborious to dig around for data.

#4: KW Finder

This image shows a scatter plot for KW Finder's keyword difficulty scores versus our keyword rankings. The data also has a significant amount of outliers relative to the regression line.

KW Finder definitely could have benefitted from more than a few mulligans with numerous strong outliers, coming in right behind SEMrush with a score of .360.

KW Finder Organic Difficulty Predictability

PCC

0.360

P-val

.01 (P<0.05)

Relationship

Moderate

% Keywords Matched

100.00%

Fortunately, the KW Finder tool had a 100% match rate without any trouble digging around for the data.

#5: Ahrefs

This image shows a scatter plot for AHREF's keyword difficulty scores versus our keyword rankings. The data shows tight clustering amongst low difficulty score keywords, and a wide distribution amongst higher difficulty scores.

Ahrefs comes in fifth by a large margin at .316, barely passing the “weak relationship” threshold.

Ahrefs Organic Difficulty Predictability

PCC

0.316

P-val

.03 (P<0.05)

Relationship

Moderate

% Keywords Matched

100%

On a positive note, the tool seems to be very reliable with low difficulty scores (notice the tight clustering for low difficulty scores), and matched all 50 keywords.

#6: Google Keyword Planner Tool

This image shows a scatter plot for Google Keyword Planner Tool's keyword difficulty scores versus our keyword rankings. The data shows randomly distributed plots with no linear relationship.

Before you ask, yes, SEO companies still use the paid competition figures from Google’s Keyword Planner Tool (and other tools) to assess organic ranking potential. As you can see from the scatter plot, there is in fact no linear relationship between the two variables.

Google Keyword Planner Tool Organic Difficulty Predictability

PCC

0.045

P-val

Statistically insignificant/no linear relationship

Relationship

Negligible/None

% Keywords Matched

88.00%

SEO agencies still using KPT for organic research (you know who you are!) — let this serve as a warning: You need to evolve.

Test 1 summary

For scoring, we will use a ten-point scale and score every tool relative to the highest-scoring competitor. For example, if the second highest score is 98% of the highest score, the tool will receive a 9.8. As a reminder, here are the results from the PCC test:

This bar chart shows the final PCC values for the first test, summarized.

And the resulting scores are as follows:

Tool

PCC Test

Moz

10

SpyFu

9.8

SEMrush

8.8

KW Finder

8.7

Ahrefs

7.7

KPT

1.1

Moz takes the top position for the first test, followed closely by SpyFu (with an 80% match rate caveat).

Test 2: Adjusted Pearson Correlation Coefficient

Let’s call this the “Mulligan Round.” In this round, assuming sometimes things just go haywire and a tool just flat-out misses, we will remove the three most egregious outliers to each tool’s score.

Here are the adjusted results for the handicap round:

Adjusted Scores (3 Outliers removed)

PCC

Difference (+/-)

SpyFu

0.527

0.122

SEMrush

0.515

0.150

Moz

0.514

0.101

Ahrefs

0.478

0.162

KWFinder

0.470

0.110

Keyword Planner Tool

0.189

0.144

As noted in the original PCC test, some of these tools really took a big hit with major outliers. Specifically, Ahrefs and SEMrush benefitted the most from their outliers being removed, gaining .162 and .150 respectively to their scores, while Moz benefited the least from the adjustments.

For those of you crying out, “But this is real life, you don’t get mulligans with SEO!”, never fear, we will make adjustments for reliability at the end.

Here are the updated scores at the end of round two:

Tool

PCC Test

Adjusted PCC

Total

SpyFu

9.8

10

19.8

Moz

10

9.7

19.7

SEMrush

8.8

9.8

18.6

KW Finder

8.7

8.9

17.6

AHREFs

7.7

9.1

16.8

KPT

1.1

3.6

4.7

SpyFu takes the lead! Now let’s jump into the final round of statistical tests.

Test 3: Resampling

Being that there has never been a study performed on keyword research tools at this scale, we wanted to ensure that we explored multiple ways of looking at the data.

Big thanks to Russ Jones, who put together an entirely different model that answers the question: “What is the likelihood that the keyword difficulty of two randomly selected keywords will correctly predict the relative position of rankings?”

He randomly selected 2 keywords from the list and their associated difficulty scores.

Let’s assume one tool says that the difficulties are 30 and 60, respectively. What is the likelihood that the article written for a score of 30 ranks higher than the article written on 60? Then, he performed the same test 1,000 times.

He also threw out examples where the two randomly selected keywords shared the same rankings, or data points were missing. Here was the outcome:

Resampling

% Guessed correctly

Moz

62.2%

Ahrefs

61.2%

SEMrush

60.3%

Keyword Finder

58.9%

SpyFu

54.3%

KPT

45.9%

As you can see, this tool was particularly critical on each of the tools. As we are starting to see, no one tool is a silver bullet, so it is our job to see how much each tool helps make more educated decisions than guessing.

Most tools stayed pretty consistent with their levels of performance from the previous tests, except SpyFu, which struggled mightily with this test.

In order to score this test, we need to use 50% as the baseline (equivalent of a coin flip, or zero points), and scale each tool relative to how much better it performed over a coin flip, with the top scorer receiving ten points.

For example, Ahrefs scored 11.2% better than flipping a coin, which is 8.2% less than Moz which scored 12.2% better than flipping a coin, giving AHREFs a score of 9.2.

The updated scores are as follows:

Tool

PCC Test

Adjusted PCC

Resampling

Total

Moz

10

9.7

10

29.7

SEMrush

8.8

9.8

8.4

27

Ahrefs

7.7

9.1

9.2

26

KW Finder

8.7

8.9

7.3

24.9

SpyFu

9.8

10

3.5

23.3

KPT

1.1

3.6

-.4

.7

So after the last statistical accuracy test, we have Moz consistently performing alone in the top tier. SEMrush, Ahrefs, and KW Finder all turn in respectable scores in the second tier, followed by the unique case of SpyFu, which performed outstanding in the first two tests (albeit, only returning results on 80% of the tested keywords), then falling flat on the final test.

Finally, we need to make some usability adjustments.

Usability Adjustment 1: Keyword Matching

A keyword research tool doesn’t do you much good if it can’t provide results for the keywords you are researching. Plain and simple, we can’t treat two tools as equals if they don’t have the same level of practical functionality.

To explain in practical terms, if a tool doesn’t have data on a particular keyword, one of two things will happen:

  1. You have to use another tool to get the data, which devalues the entire point of using the original tool.
  2. You miss an opportunity to rank for a high-value keyword.

Neither scenario is good, therefore we developed a penalty system. For each 10% match rate under 100%, we deducted a single point from the final score, with a maximum deduction of 5 points. For example, if a tool matched 92% of the keywords, we would deduct .8 points from the final score.

One may argue that this penalty is actually too lenient considering the significance of the two unideal scenarios outlined above.

The penalties are as follows:

Tool

Match Rate

Penalty

KW Finder

100%

0

Ahrefs

100%

0

Moz

100%

0

SEMrush

92%

-.8

Keyword Planner Tool

88%

-1.2

SpyFu

80%

-2

Please note we gave SEMrush a lot of leniency, in that technically, many of the keywords evaluated were not found in its keyword difficulty tool, but rather through manually digging through the phrase match tool. We will give them a pass, but with a stern warning!

Usability Adjustment 2: Reliability

I told you we would come back to this! Revisiting the second test in which we threw away the three strongest outliers that negatively impacted each tool’s score, we will now make adjustments.

In real life, there are no mulligans. In real life, each of those three blog posts that were thrown out represented a significant monetary and time investment. Therefore, when a tool has a major blunder, the result can be a total waste of time and resources.

For that reason, we will impose a slight penalty on those tools that benefited the most from their handicap.

We will use the level of PCC improvement to evaluate how much a tool benefitted from removing their outliers. In doing so, we will be rewarding the tools that were the most consistently reliable. As a reminder, the amounts each tool benefitted were as follows:

Tool

Difference (+/-)

Ahrefs

0.162

SEMrush

0.150

Keyword Planner Tool

0.144

SpyFu

0.122

KWFinder

0.110

Moz

0.101

In calculating the penalty, we scored each of the tools relative to the top performer, giving the top performer zero penalty and imposing penalties based on how much additional benefit the tools received over the most reliable tool, on a scale of 0–100%, with a maximum deduction of 5 points.

So if a tool received twice the benefit of the top performing tool, it would have had a 100% benefit, receiving the maximum deduction of 5 points. If another tool received a 20% benefit over of the most reliable tool, it would get a 1-point deduction. And so on.

Tool

% Benefit

Penalty

Ahrefs

60%

-3

SEMrush

48%

-2.4

Keyword Planner Tool

42%

-2.1

SpyFu

20%

-1

KW Finder

8%

-.4

Moz

-

0

Results

All told, our penalties were fairly mild, with a slight shuffling in the middle tier. The final scores are as follows:

Tool

Total Score

Stars (5 max)

Moz

29.7

4.95

KW Finder

24.5

4.08

SEMrush

23.8

3.97

Ahrefs

23.0

3.83

Spyfu

20.3

3.38

KPT

-2.6

0.00

Conclusion

Using any organic keyword difficulty tool will give you an advantage over not doing so. While none of the tools are a crystal ball, providing perfect predictability, they will certainly give you an edge. Further, if you record enough data on your own blogs’ performance, you will get a clearer picture of the keyword difficulty scores you should target in order to rank on the first page.

For example, we know the following about how we should target keywords with each tool:

Tool

Average KD ranking ≤10

Average KD ranking ≥ 11

Moz

33.3

37.0

SpyFu

47.7

50.6

SEMrush

60.3

64.5

KWFinder

43.3

46.5

Ahrefs

11.9

23.6

This is pretty powerful information! It’s either first page or bust, so we now know the threshold for each tool that we should set when selecting keywords.

Stay tuned, because we made a lot more correlations between word count, days live, total keywords ranking, and all kinds of other juicy stuff. Tune in again in early September for updates!

We hope you found this test useful, and feel free to reach out with any questions on our math!

Disclaimer: These results are estimates based on 50 ranking keywords from 50 blog posts and keyword research data pulled from a single moment in time. Search is a shifting landscape, and these results have certainly changed since the data was pulled. In other words, this is about as accurate as we can get from analyzing a moving target.

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Rewriting the Beginner’s Guide to SEO, Chapter 3: Keyword Research

Posted by BritneyMuller

Welcome to the draft of Chapter Three of the new and improved Beginner’s Guide to SEO! So far you’ve been generous and energizing with your feedback for our outline, Chapter One, and Chapter Two. We’re asking for a little more of your time as we debut the our third chapter on keyword research. Please let us know what you think in the comments!


Chapter 3: Keyword Research

Understand what your audience wants to find.

Now that you’ve learned how to show up in search results, let’s determine which strategic keywords to target in your website’s content, and how to craft that content to satisfy both users and search engines.

The power of keyword research lies in better understanding your target market and how they are searching for your content, services, or products.

Keyword research provides you with specific search data that can help you answer questions like:

  • What are people searching for?
  • How many people are searching for it?
  • In what format do they want that information?

In this chapter, you’ll get tools and strategies for uncovering that information, as well as learn tactics that’ll help you avoid keyword research foibles and build strong content. Once you uncover how your target audience is searching for your content, you begin to uncover a whole new world of strategic SEO!

What terms are people searching for?

You may know what you do, but how do people search for the product, service, or information you provide? Answering this question is a crucial first step in the keyword research process.

Discovering keywords

You likely have a few keywords in mind that you would like to rank for. These will be things like your products, services, or other topics your website addresses, and they are great seed keywords for your research, so start there! You can enter those keywords into a keyword research tool to discover average monthly search volume and similar keywords. We’ll get into search volume in greater depth in the next section, but during the discovery phase, it can help you determine which variations of your keywords are most popular amongst searchers.

Once you enter in your seed keywords into a keyword research tool, you will begin to discover other keywords, common questions, and topics for your content that you might have otherwise missed.

Let’s use the example of a florist that specializes in weddings.

Typing “wedding” and “florist” into a keyword research tool, you may discover highly relevant, highly searched for related terms such as:

  • Wedding bouquets
  • Bridal flowers
  • Wedding flower shop

In the process of discovering relevant keywords for your content, you will likely notice that the search volume of those keywords varies greatly. While you definitely want to target terms that your audience is searching for, in some cases, it may be more advantageous to target terms with lower search volume because they’re far less competitive.

Since both high- and low-competition keywords can be advantageous for your website, learning more about search volume can help you prioritize keywords and pick the ones that will give your website the biggest strategic advantage.

Pro tip: Diversify!

It’s important to note that entire websites don’t rank for keywords, pages do. With big brands, we often see the homepage ranking for many keywords, but for most websites, this isn’t usually the case. Many websites receive more organic traffic to pages other than the homepage, which is why it’s so important to diversify your website’s pages by optimizing each for uniquely valuable keywords.

How often are those terms searched?

Uncovering search volume

The higher the search volume for a given keyword or keyword phrase, the more work is typically required to achieve higher rankings. This is often referred to as keyword difficulty and occasionally incorporates SERP features; for example, if many SERP features (like featured snippets, knowledge graph, carousels, etc) are clogging up a keyword’s result page, difficulty will increase. Big brands often take up the top 10 results for high-volume keywords, so if you’re just starting out on the web and going after the same keywords, the uphill battle for ranking can take years of effort.

Typically, the higher the search volume, the greater the competition and effort required to achieve organic ranking success. Go too low, though, and you risk not drawing any searchers to your site. In many cases, it may be most advantageous to target highly specific, lower competition search terms. In SEO, we call those long-tail keywords.

Understanding the long tail

It would be great to rank #1 for the keyword “shoes”… or would it?

It’s wonderful to deal with keywords that have 50,000 searches a month, or even 5,000 searches a month, but in reality, these popular search terms only make up a fraction of all searches performed on the web. In fact, keywords with very high search volumes may even indicate ambiguous intent, which, if you target these terms, it could put you at risk for drawing visitors to your site whose goals don’t match the content your page provides.

Does the searcher want to know the nutritional value of pizza? Order a pizza? Find a restaurant to take their family? Google doesn’t know, so they offer these features to help you refine. Targeting “pizza” means that you’re likely casting too wide a net.

The remaining 75% lie in the “chunky middle” and “long tail” of search.

Don’t underestimate these less popular keywords. Long tail keywords with lower search volume often convert better, because searchers are more specific and intentional in their searches. For example, a person searching for “shoes” is probably just browsing. Whereas, someone searching for “best price red womens size 7 running shoe,” practically has their wallet out!

Pro tip: Questions are SEO gold!

Discovering what questions people are asking in your space, and adding those questions and their answers to an FAQ page, can yield incredible organic traffic for your website.

Getting strategic with search volume

Now that you’ve discovered relevant search terms for your site and their corresponding search volumes, you can get even more strategic by looking at your competitors and figuring out how searches might differ by season or location.

Keywords by competitor

You’ll likely compile a lot of keywords. How do you know which to tackle first? It could be a good idea to prioritize high-volume keywords that your competitors are not currently ranking for. On the flip side, you could also see which keywords from your list your competitors are already ranking for and prioritize those. The former is great when you want to take advantage of your competitors’ missed opportunities, while the latter is an aggressive strategy that sets you up to compete for keywords your competitors are already performing well for.

Keywords by season

Knowing about seasonal trends can be advantageous in setting a content strategy. For example, if you know that “christmas box” starts to spike in October through December in the United Kingdom, you can prepare content months in advance and give it a big push around those months.

Keywords by region

You can more strategically target a specific location by narrowing down your keyword research to specific towns, counties, or states in the Google Keyword Planner, or evaluate “interest by subregion” in Google Trends. Geo-specific research can help make your content more relevant to your target audience. For example, you might find out that in Texas, the preferred term for a large truck is “big rig,” while in New York, “tractor trailer” is the preferred terminology.

Which format best suits the searcher’s intent?

In Chapter 2, we learned about SERP features. That background is going to help us understand how searchers want to consume information for a particular keyword. The format in which Google chooses to display search results depends on intent, and every query has a unique one. While there are thousands of of possible search types, there are five major categories to be aware of:

1. Informational queries: The searcher needs information, such as the name of a band or the height of the Empire State Building.

2. Navigational queries: The searcher wants to go to a particular place on the Internet, such as Facebook or the homepage of the NFL.

3. Transactional queries: The searcher wants to do something, such as buy a plane ticket or listen to a song.

4. Commercial investigation: The searcher wants to compare products and find the best one for their specific needs.

5. Local queries: The searcher wants to find something locally, such as a nearby coffee shop, doctor, or music venue.

An important step in the keyword research process is surveying the SERP landscape for the keyword you want to target in order to get a better gauge of searcher intent. If you want to know what type of content your target audience wants, look to the SERPs!

Google has closely evaluated the behavior of trillions of searches in an attempt to provide the most desired content for each specific keyword search.

Take the search “dresses,” for example:

By the shopping carousel, you can infer that Google has determined many people who search for “dresses” want to shop for dresses online.

There is also a Local Pack feature for this keyword, indicating Google’s desire to help searchers who may be looking for local dress retailers.

If the query is ambiguous, Google will also sometimes include the “refine by” feature to help searchers specify what they’re looking for further. By doing so, the search engine can provide results that better help the searcher accomplish their task.

Google has a wide array of result types it can serve up depending on the query, so if you’re going to target a keyword, look to the SERP to understand what type of content you need to create.

Tools for determining the value of a keyword

How much value would a keyword add to your website? These tools can help you answer that question, so they’d make great additions to your keyword research arsenal:

  • Moz Keyword Explorer – Our own Moz Keyword Explorer tool extracts accurate search volume data, keyword difficulty, and keyword opportunity metrics by using live clickstream data. To learn more about how we’re producing our keyword data, check out Announcing Keyword Explorer.
  • Google Keyword Planner – Google’s AdWords Keyword Planner has historically been the most common starting point for SEO keyword research. However, Keyword Planner does restrict search volume data by lumping keywords together into large search volume range buckets. To learn more, check out Google Keyword Planner’s Dirty Secrets.
  • Google Trends – Google’s keyword trend tool is great for finding seasonal keyword fluctuations. For example, “funny halloween costume ideas” will peak in the weeks before Halloween.
  • AnswerThePublic – This free tool populates commonly searched for questions around a specific keyword. Bonus! You can use this tool in tandem with another free tool, Keywords Everywhere, to prioritize ATP’s suggestions by search volume.
  • SpyFu Keyword Research Tool – Provides some really neat competitive keyword data.

Download our free keyword research template!

Keyword research can yield a ton of data. Stay organized by downloading our free keyword research template. Customize the template to fit your unique needs. Happy keyword researching!

Now that you know how to uncover what your target audience is searching for and how often, it’s time to move onto the next step: crafting pages in a way that users will love and search engines can understand.

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Measuring the quality of popular keyword research tools

Contributor JR Oakes measures the quality of popular keyword research tools against data found in Google search results and performing page data from Google Search Console.



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New Research: 35% of Competitive Local Keywords Have Local Pack Ads

Posted by Dr-Pete

Over the past year, you may have spotted a new kind of Google ad on a local search. It looks something like this one (on a search for “oil change” from my Pixel phone in the Chicago suburbs):

These ads seem to appear primarily on mobile results, with some limited testing on desktop results. We’ve heard rumors about local pack ads as far back as 2016, but very few details. How prevalent are these ads, and how seriously should you be taking them?

11,000 SERPs: Quick summary

For this study, we decided to look at 110 keywords (in 11 categories) across 100 major US cities. We purposely focused on competitive keywords in large cities, assuming, based on our observations as searchers, that the prevalence rate for these ads was still pretty low. The 11 categories were as follows:

  • Apparel
  • Automotive
  • Consumer Goods
  • Finance
  • Fitness
  • Hospitality
  • Insurance
  • Legal
  • Medical
  • Services (Home)
  • Services (Other)

We purposely selected terms that were likely to have local pack results and looked for the presence of local packs and local pack ads. We collected these searches as a mobile user with a Samsung Galaxy 7 (a middle-ground choice between iOS and a “pure” Google phone).

Why 11 categories? Confession time – it was originally 10, and then I had the good sense to ask Darren Shaw about the list and realized I had completely left out insurance keywords. Thanks, Darren.

Finding #1: I was very wrong

I’ll be honest – I expected, from casual observations and the lack of chatter in the search community, that we’d see fewer than 5% of local packs with ads, and maybe even numbers in the 1% range.

Across our data set, roughly 35% of SERPs with local packs had ads.

Across industry categories, the prevalence of pack ads ranged wildly, from 10% to 64%:

For the 110 individual keyword phrases in our study, the presence of local ads ranged from 0% to 96%. Here are the keywords with >=90% local pack ad prevalence:

  • “car insurance” (90%)
  • “auto glass shop” (91%)
  • “bankruptcy lawyer” (91%)
  • “storage” (92%)
  • “oil change” (95%)
  • “mattress sale” (95%)
  • “personal injury attorney” (96%)

There was no discernible correlation between the presence of pack ads and city size. Since our study was limited to the top 100 US cities by population, though, this may simply be due to a restricted data range.

Finding #2: One is the magic number

Every local pack with ads in our study had one and only one ad. This ad appeared in addition to regular pack listings. In our data set, 99.7% of local packs had three regular/organic listings, and the rest had two listings (which can happen with or without ads).

Finding #3: Pack ads land on Google

Despite their appearance, local packs ads are more like regular local pack results than AdWords ads, in that they’re linked directly to a local panel (a rich Google result). On my Pixel phone, the Jiffy Lube ad at the beginning of this post links to this result:

This is not an anomaly: 100% of the 3,768 local pack ads in our study linked back to Google. This follows a long trend of local pack results linking back to Google entities, including the gradual disappearance of the “Website” link in the local pack.

Conclusion: It’s time to get serious

If you’re in a competitive local vertical, it’s time to take local pack ads seriously. Your visitors are probably seeing them more often than you realize. Currently, local pack ads are an extension of AdWords, and require you to set up location extensions.

It’s also more important than ever to get your Google My Business listing in order and make sure that all of your information is up to date. It may be frustrating to lose the direct click to your website, but a strong local business panel can drive phone calls, foot traffic, and provide valuable information to potential customers.

Like every Google change, we ultimately have to put aside whether we like or dislike it and make the tough choices. With more than one-third of local packs across the competitive keywords in our data set showing ads, it’s time to get your head out of the sand and get serious.

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SearchCap: Google to sell Zagat, videos in Google My Business & new research

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

The post SearchCap: Google to sell Zagat, videos in Google My Business & new research appeared first on Search Engine Land.



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How to Use the "Keywords by Site" Data in Tools (Moz, SEMrush, Ahrefs, etc.) to Improve Your Keyword Research and Targeting – Whiteboard Friday

Posted by randfish

One of the most helpful functions of modern-day SEO software is the idea of a “keyword universe,” a database of tens of millions of keywords that you can tap into and discover what your site is ranking for. Rankings data like this can be powerful, and having that kind of power at your fingertips can be intimidating. In today’s Whiteboard Friday, Rand explains the concept of the “keyword universe” and shares his most useful tips to take advantage of this data in the most popular SEO tools.

How to use keywords by site

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

Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat about the Keywords by Site feature that exists now in Moz’s toolset — we just launched it this week — and SEMrush and Ahrefs, who have had it for a little while, and there are some other tools out there that also do it, so places like KeyCompete and SpyFu and others.

In SEO software, there are two types of rankings data:

A) Keywords you’ve specifically chosen to track over time

Basically, the way you can think of this is, in SEO software, there are two kinds of keyword rankings data. There are keywords that you have specifically selected or your marketing manager or your SEO has specifically selected to track over time. So I’ve said I want to track X, Y and Z. I want to see how they rank in Google’s results, maybe in a particular location or a particular country. I want to see the position, and I want to see the change over time. Great, that’s your set that you’ve constructed and built and chosen.

B) A keyword “universe” that gives wide coverage of tens of millions of keywords

But then there’s what’s called a keyword universe, an entire universe of keywords that’s maintained by a tool provider. So SEMrush has their particular database, their universe of keywords for a bunch of different languages, and Ahrefs has their keyword universe of keywords that each of those two companies have selected. Moz now has its keyword universe, a universe of, I think in our case, about 40 million keywords in English in the US that we track every two weeks, so we’ll basically get rankings updates. SEMrush tracks their keywords monthly. I think Ahrefs also does monthly.

Depending on the degree of change, you might care or not care about the various updates. Usually, for keywords you’ve specifically chosen, it’s every week. But in these cases, because it’s tens of millions or hundreds of millions of keywords, they’re usually tracking them weekly or monthly.

So in this universe of keywords, you might only rank for some of them. It’s not ones you’ve specifically selected. It’s ones the tool provider has said, “Hey, this is a broad representation of all the keywords that we could find that have some real search volume that people might be interested in who’s ranking in Google, and we’re going track this giant database.” So you might see some of these your site ranks for. In this case, seven of these keywords your site ranks for, four of them your competitors rank for, and two of them both you and your competitors rank for.

Remarkable data can be extracted from a “keyword universe”

There’s a bunch of cool data, very, very cool data that can be extracted from a keyword universe. Most of these tools that I mentioned do this.

Number of ranking keywords over time

So they’ll show you how many keywords a given site ranks for over time. So you can see, oh, Moz.com is growing its presence in the keyword universe, or it’s shrinking. Maybe it’s ranking for fewer keywords this month than it was last month, which might be a telltale sign of something going wrong or poorly.

Degree of rankings overlap

You can see the degree of overlap between several websites’ keyword rankings. So, for example, I can see here that Moz and Search Engine Land overlap here with all these keywords. In fact, in the Keywords by Site tool inside Moz and in SEMrush, you can see what those numbers look like. I think Moz actually visualizes it with a Venn diagram. Here’s Distilled.net. They’re a smaller website. They have less content. So it’s no surprise that they overlap with both. There’s some overlap with all three. I could see keywords that all three of them rank for, and I could see ones that only Distilled.net ranks for.

Estimated traffic from organic search

You can also grab estimated traffic. So you would be able to extract out — Moz does not offer this, but SEMrush does — you could see, given a keyword list and ranking positions and an estimated volume and estimated click-through rate, you could say we’re going to guess, we’re going to estimate that this site gets this much traffic from search. You can see lots of folks doing this and showing, “Hey, it looks this site is growing its visits from search and this site is not.” SISTRIX does this in Europe really nicely, and they have some great blog posts about it.

Most prominent sites for a given set of keywords

You can also extract out the most prominent sites given a set of keywords. So if you say, “Hey, here are a thousand keywords. Tell me who shows up most in this thousand-keyword set around the world of vegetarian recipes.” The tool could extract out, “Okay, here’s the small segment. Here’s the galaxy of vegetarian recipe keywords in our giant keyword universe, and this is the set of sites that are most prominent in that particular vertical, in that little galaxy.”

Recommended applications for SEOs and marketers

So some recommended applications, things that I think every SEO should probably be doing with this data. There are many, many more. I’m sure we can talk about them in the comments.

1. Identify important keywords by seeing what you rank for in the keyword universe

First and foremost, identify keywords that you probably should be tracking, that should be part of your reporting. It will make you look good, and it will also help you keep tabs on important keywords where if you lost rankings for them, you might cost yourself a lot of traffic.

Monthly granularity might not be good enough. You might want to say, “Hey, no, I want to track these keywords every week. I want to get reporting on them. I want to see which page is ranking. I want to see how I rank by geo. So I’m going to include them in my specific rank tracking features.” You can do that in the Moz Keywords by Site, you’d go to Keyword Explorer, you’d select the root domain instead of the keyword, and you’d plug in your website, which maybe is Indie Hackers, a site that I’ve been reading a lot of lately and I like a lot.

You could see, “Oh, cool. I’m not tracking stock trading bot or ark servers, but those actually get some nice traffic. In this case, I’m ranking number 12. That’s real close to page one. If I put in a little more effort on my ark servers page, maybe I could be on page one and I could be getting some of that sweet traffic, 4,000 to 6,000 searches a month. That’s really significant.” So great way to find additional keywords you should be adding to your tracking.

2. Discover potential keywords targets that your competitors rank for (but you don’t)

Second, you can discover some new potential keyword targets when you’re doing keyword research based on the queries your competition ranks for that you don’t. So, in this case, I might plug in “First Round.” First Round Capital has a great content play that they’ve been doing for many years. Indie Hackers might say, “Gosh, there’s a lot of stuff that startups and tech founders are interested in that First Round writes about. Let me see what keywords they’re ranking for that I’m not ranking for.”

So you plug in those two to Moz’s tool or other tools. You could see, “Aha, I’m right. Look at that. They’re ranking for about 4,500 more keywords than I am.” Then I could go get that full list, and I could sort it by volume and by difficulty. Then I could choose, okay, these keywords all look good, check, check, check. Add them to my list in Keyword Explorer or Excel or Google Docs if you’re using those and go to work.

3. Explore keywords sets from large, content-focused media sites with similar audiences

Then the third one is you can explore keyword sets. I’m going to urge you to. I don’t think this is something that many people do, but I think that it really should be, which is to look outside of your little galaxy of yourself and your competitors, direct competitors, to large content players that serve your audience.

So in this case, I might say, “Gosh, I’m Indie Hackers. I’m really competing maybe more directly with First Round. But you know what? HBR, Harvard Business Review, writes about a lot of stuff that my audience reads. I see people on Twitter that are in my audience share it a lot. I see people in our forums discussing it and linking out to their articles. Let me go see what they are doing in the content world.”

In fact, when you look at the Venn diagram, which I just did in the Keywords by Site tool, I can see, “Oh my god, look there’s almost no overlap, and there’s this huge opportunity.” So I might take HBR and I might click to see all their keywords and then start looking through and sort, again, probably by volume and maybe with a difficulty filter and say, “Which ones do I think I could create content around? Which ones do they have really old content that they haven’t updated since 2010 or 2011?” Those types of content opportunities can be a golden chance for you to find an audience that is likely to be the right types of customers for your business. That’s a pretty exciting thing.

So, in addition to these, there’s a ton of other uses. I’m sure over the next few months we’ll be talking more about them here on Whiteboard Friday and here on the Moz blog. But for now, I would love to hear your uses for tools like SEMrush and the Ahrefs keyword universe feature and Moz’s keyword universe feature, which is called Keywords by Site. Hopefully, we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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