Tag Archive | "Keyword"

How to Get More Keyword Metrics for Your Target Keywords

Posted by Bill.Sebald

If you’re old in SEO years, you remember the day [not provided] was introduced. It was a dark, dark day. SEOs lost a vast amount of trusty information. Click data. Conversion data. This was incredibly valuable, allowing SEOs to prioritize their targets.

Google said the info was removed for security purposes, while suspicious SEOs thought this was a push towards spending more on AdWords (now Google Ads). I get it — since AdWords would give you the keyword data SEOs cherished, the “controversy” was warranted, in my opinion. The truth is out there.

But we’ve moved on, and learned to live with the situation. Then a few years later, Google Webmaster Tools (now Search Console) started providing some of the keyword data in the Search Analytics report. Through the years, the report got better and better.

But there’s still a finite set of keywords in the interface. You can’t get more than 999 in your report.

Search Analytics Report

Guess what? Google has more data for you!

The Google Search Console API is your friend. This summer it became even friendlier, providing 16 months worth of data. What you may not know is this API can give you more than 999 keywords. By way of example, the API provides more than 45,000 for our Greenlane site. And we’re not even a very large site. That’s right — the API can give you keywords, clicks, average position, impressions, and CTR %.

Salivating yet?

How to easily leverage the API

If you’re not very technical and the thought of an API frightens you, I promise there’s nothing to fear. I’m going to show you a way to leverage the data using Google Sheets.

Here is what you will need:

  1. Google Sheets (free)
  2. Supermetrics Add-On (free trial, but a paid tool)

If you haven’t heard of Google Sheets, it’s one of several tools Google provides for free. This directly competes with Microsoft Excel. It’s a cloud-based spreadsheet that works exceptionally well.

If you aren’t familiar with Supermetrics, it’s an add-on for Google Sheets that allows data to be pulled in from other sources. In this case, one of the sources will be Google Search Console. Now, while Supermetrics has a free trial, paid is the way to go. It’s worth it!

Installation of Supermetrics:

  1. Open Google Sheets and click the Add-On option
  2. Click Get Add-Ons
  3. A window will open where you can search for Supermetrics. It will look like this:

How To Install Supermetrics

From there, just follow the steps. It will immediately ask to connect to your Google account. I’m sure you’ve seen this kind of dialog box before:

Supermetrics wants to access your Google Account

You’ll be greeted with a message for launching the newly installed add-on. Just follow the prompts to launch. Next you’ll see a new window to the right of your Google Sheet.

Launch message

At this point, you should see the following note:

Great, you’re logged into Google Search Console! Now let’s run your first query. Pick an account from the list below.

Next, all you have to do is work down the list in Supermetrics. Data Source, Select Sites, and Select Dates are pretty self-explanatory. When you reach the “Select metrics” toggle, choose Impressions, Clicks, CTR (%), and Average Position.

Metrics

When you reach “Split by,” choose Search Query as the Split to rows option. And pick a large number for number of rows to fetch. If you also want the page URLs (perhaps you’d like your data divided by the page level), you just need to add Full URL as well.

Split By

You can play with the other Filter and Options if you’d like, but you’re ready to click Apply Changes and receive the data. It should compile like this:

Final result

Got the data. Now what?

Sometimes optimization is about taking something that’s working, and making it work better. This data can show you which keywords and topics are important to your audience. It’s also a clue towards what Google thinks you’re important for (thus, rewarding you with clicks).

SEMrush and Ahrefs can provide ranking keyword data with their estimated clicks, but impressions is an interesting metric here. High impression and low clicks? Maybe your title and description tags aren’t compelling enough. It’s also fun to VLOOKUP their data against this, to see just how accurate they are (or are not). Or you can use a tool like PowerBI to append other customer or paid search metrics to paint a bigger picture of your visitors’ mindset.

Conclusion

Sometimes the littlest hacks are the most fun. Google commonly holds some data back through their free products (the Greenlane Indexation Tester is a good example with the old interface). We know Search Planner and Google Analytics have more than they share. But in those cases, where directional information can sometimes be enough, digging out even more of your impactful keyword data is pure gold.

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Ranking the 6 Most Accurate Keyword Difficulty 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. You can customize the template to fit your unique needs (ex: remove the “Seasonal Trends” column), sort keywords by volume, and categorize by Priority Score. 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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Why Google AdWords’ Keyword Volume Numbers Are Wildly Unreliable – Whiteboard Friday

Posted by randfish

Many of us rely on the search volume numbers Google AdWords provides, but those numbers ought to be consumed with a hearty helping of skepticism. Broad and unusable volume ranges, misalignment with other Google tools, and conflating similar yet intrinsically distinct keywords — these are just a few of the serious issues that make relying on AdWords search volume data alone so dangerous. In this edition of Whiteboard Friday, we discuss those issues in depth and offer a few alternatives for more accurate volume data.

why it's insane to rely on Google adwords' keyword volume numbers

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


Video Transcription

Howdy, Moz fans. Welcome to another edition of Whiteboard Friday. This week we’re going to chat about Google AdWords’ keyword data and why it is absolutely insane as an SEO or as a content marketer or a content creator to rely on this.

Look, as a paid search person, you don’t have a whole lot of choice, right? Google and Facebook combine to form the duopoly of advertising on the internet. But as an organic marketer, as a content marketer or as someone doing SEO, you need to do something fundamentally different than what paid search folks are doing. Paid search folks are basically trying to figure out when will Google show my ad for a keyword that might create the right kind of demand that will drive visitors to my site who will then convert?

But as an SEO, you’re often driving traffic so that you can do all sorts of other things. The same with content marketers. You’re driving traffic for multitudes of reasons that aren’t directly or necessarily directly connected to a conversion, at least certainly not right in that visit. So there are lots reasons why you might want to target different types of keywords and why AdWords data will steer you wrong.

1. AdWords’ “range” is so broad, it’s nearly useless

First up, AdWords shows you this volume range, and they show you this competition score. Many SEOs I know, even really smart folks just I think haven’t processed that AdWords could be misleading them in this facet.

So let’s talk about what happened here. I searched for types of lighting and lighting design, and Google AdWords came back with some suggestions. This is in the keyword planner section of the tool. So “types of lighting,” “lighting design”, and “lighting consultant,” we’ll stick with those three keywords for a little bit.

I can see here that, all right, average monthly searches, well, these volume ranges are really unhelpful. 10k to 100k, that’s just way too giant. Even 1k to 10k, way too big of a range. And competition, low, low, low. So this is only true for the quantity of advertisers. That’s really the only thing that you’re seeing here. If there are many, many people bidding on these keywords in AdWords, these will be high.

But as an example, for “types of light,” there’s virtually no one bidding, but for “lighting consultant,” there are quite a few people bidding. So I don’t understand why these are both low competition. There’s not enough granularity here, or Google is just not showing me accurate data. It’s very confusing.

By the way, “types of light,” though it has no PPC ads right now in Google’s results, this is incredibly difficult to rank for in the SEO results. I think I looked at the keyword difficulty score. It’s in the 60s, maybe even low 70s, because there’s a bunch of powerful sites. There’s a featured snippet up top. The domains that are ranking are doing really well. So it’s going to be very hard to rank for this, and yet competition low, it’s just not telling you the right thing. That’s not telling you the right story, and so you’re getting misled on both competition and monthly searches.

2. AdWords doesn’t line up to reality, or even Google Trends!

Worse, number two, AdWords doesn’t line up to reality with itself. I’ll show you what I mean.

So let’s go over to Google Trends. Great tool, by the way. I’m going to talk about that in a second. But I plugged in “lighting design,” “lighting consultant,” and “types of lighting.” I get the nice chart that shows me seasonality. But over on the left, it also shows average keyword volume compared to each other — 86 for “lighting design,” 2 for “lighting consultant,” and 12 for “types of lighting.” Now, you tell me how it is that this can be 43 times as big as this one and this can be 6 times as big as that one, and yet these are all correct.

The math only works in some very, very tiny amounts of circumstances, like, okay, maybe if this is 1,000 and this is 12,000, which technically puts it in the 10k, and this is 86,000 — well, no wait, that doesn’t quite work — 43,000, okay, now we made it work. But you change this to 2,000 or 3,000, the numbers don’t add up. Worse, it gets worse, of course it does. When AdWords gets more specific with the performance data, things just get so crazy weird that nothing lines up.

So what I did is I created ad groups, because in AdWords in order to get more granular monthly search data, you have to actually create ad groups and then go review those. This is in the review section of my ad group creation. I created ad groups with only a single keyword so that I could get the most accurate volume data I could, and then I maximized out my bid until I wasn’t getting any more impressions by bidding any higher.

Well, whether that truly accounts for all searches or not, hard to say. But here’s the impression count — 2,500 a day, 330 a day, 4 a day. So 4 a day times 30, gosh, that sounds like 120 to me. That doesn’t sound like it’s in the 1,000 to 10,000 range. I don’t think this could possibly be right. It just doesn’t make any sense.

What’s happening? Oh, actually, this is “types of lighting.” Google clearly knows that there are way more searches for this. There’s a ton more searches for this. Why is the impression so low? The impressions are so low because Google will rarely ever show an ad for that keyword, which is why when we were talking, above here, about competition, I didn’t see an ad for that keyword. So again, extremely misleading.

If you’re taking data from AdWords and you’re trying to apply it to your SEO campaigns, your organic campaigns, your content marketing campaigns, you are being misled and led astray. If you see numbers like this that are coming straight from AdWords, “Oh, we looked at the AdWords impression,” know that these can be dead f’ing wrong, totally misleading, and throw your campaigns off.

You might choose not to invest in content around types of lighting, when in fact that could be an incredibly wonderful lead source. It could be the exact right keyword for you. It is getting way more search volume. We can see it right here. We can see it in Google Trends, which is showing us some real data, and we can back that up with our own clickstream data that we get here at Moz.

3. AdWords conflates and combines keywords that don’t share search intent or volume

Number three, another problem, Google conflates keywords. So when I do searches and I start adding keywords to a list, unless I’m very careful and I type them in manually and I’m only using the exact ones, Google will take all three of these, “types of lights,” “types of light” (singular light), and “types of lighting” and conflate them all, which is insane. It is maddening.

Why is it maddening? Because “types of light,” in my opinion, is a physics-related search. You can see many of the results, they’ll be from Energy.gov or whatever, and they’ll show you the different types of wavelengths and light ranges on the visible spectrum. “Types of lights” will show you what? It will show you types of lights that you could put in your home or office. “Types of lighting” will show you lighting design stuff, the things that a lighting consultant might be interested in. So three different, very different, types of results with three different search intents all conflated in AdWords, killing me.

4. AdWords will hide relevant keyword suggestions if they don’t believe there’s a strong commercial intent

Number four, not only this, a lot of times when you do searches inside AdWords, they will hide the suggestions that you want the most. So when I performed my searches for “lighting design,” Google never showed me — I couldn’t find it anywhere in the search results, even with the export of a thousand keywords — “types of lights” or “types of lighting.”

Why? I think it’s the same reason down here, because Google doesn’t believe that those are commercial intent search queries. Well, AdWords doesn’t believe they’re commercial intent search queries. So they don’t want to show them to AdWords customers because then they might bid on them, and Google will (a) rarely show those, and (b) they’ll get a poor return on that spend. What happens to advertisers? They don’t blame themselves for choosing faulty keywords. They blame Google for giving them bad traffic, and so Google knocks these out.

So if you are doing SEO or you’re doing content marketing and you’re trying to find these targets, AdWords is a terrible suggestion engine as well. As a result, my advice is going to be rely on different tools.

Instead:

There are a few that I’ve got here. I’m obviously a big fan of Moz’s Keyword Explorer, having been one of the designers of that product. Ahrefs came out with a near clone product that’s actually very, very good. SEMrush is also a quality product. I like their suggestions a little bit more, although they do use AdWords keyword data. So the volume data might be misleading again there. I’d be cautious about using that.

Google Trends, I actually really like Google Trends. I’m not sure why Google is choosing to give out such accurate data here, but from what we’ve seen, it looks really comparatively good. Challenge being if you do these searches in Google Trends, make sure you select the right type, the search term, not the list or the topic. Topics and lists inside Google Trends will aggregate, just like this will, a bunch of different keywords into one thing.

Then if you want to get truly, truly accurate, you can go ahead and run a sample AdWords campaign, the challenge with that being if Google chooses not to show your ad, you won’t know how many impressions you potentially missed out on, and that can be frustrating too.

So AdWords today, using PPC as an SEO tool, a content marketing tool is a little bit of a black box. I would really recommend against it. As long as you know what you’re doing and you want to find some inspiration there, fine. But otherwise, I’d rely on some of these other tools. Some of them are free, some of them are paid. All of them are better than AdWords.

All right, everyone. Look forward to your comments and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Keyword Research Beats Nate Silver’s 2016 Presidential Election Prediction

Posted by BritneyMuller

100% of statisticians would say this is a terrible method for predicting elections. However, in the case of 2016’s presidential election, analyzing the geographic search volume of a few telling keywords “predicted” the outcome more accurately than Nate Silver himself.

The 2016 US Presidential Election was a nail-biter, and many of us followed along with the famed statistician’s predictions in real time on FiveThirtyEight.com. Silver’s predictions, though more accurate than many, were still disrupted by the election results.

In an effort to better understand our country (and current political chaos), I dove into keyword research state-by-state searching for insights. Keywords can be powerful indicators of intent, thought, and behavior. What keyword searches might indicate a personal political opinion? Might there be a common denominator search among people with the same political beliefs?

It’s generally agreed that Fox News leans to the right and CNN leans to the left. And if we’ve learned anything this past year, it’s that the news you consume can have a strong impact on what you believe, in addition to the confirmation bias already present in seeking out particular sources of information.

My crazy idea: What if Republican states showed more “fox news” searches than “cnn”? What if those searches revealed a bias and an intent that exit polling seemed to obscure?

The limitations to this research were pretty obvious. Watching Fox News or CNN doesn’t necessarily correlate with voter behavior, but could it be a better indicator than the polls? My research says yes. I researched other media outlets as well, but the top two ideologically opposed news sources — in any of the 50 states — were consistently Fox News and CNN.

Using Google Keyword Planner (connected to a high-paying Adwords account to view the most accurate/non-bucketed data), I evaluated each state’s search volume for “fox news” and “cnn.”

Eight states showed the exact same search volumes for both. Excluding those from my initial test, my results accurately predicted 42/42 of the 2016 presidential state outcomes including North Carolina and Wisconsin (which Silver mis-predicted). Interestingly, “cnn” even mirrored Hillary Clinton, similarly winning the popular vote (25,633,333 vs. 23,675,000 average monthly search volume for the United States).

In contrast, Nate Silver accurately predicted 45/50 states using a statistical methodology based on polling results.

Click for a larger image

This gets even more interesting:

The eight states showing the same average monthly search volume for both “cnn” and “fox news” are Arizona, Florida, Michigan, Nevada, New Mexico, Ohio, Pennsylvania, and Texas.

However, I was able to dive deeper via GrepWords API (a keyword research tool that actually powers Keyword Explorer’s data), to discover that Arizona, Nevada, New Mexico, Pennsylvania, and Ohio each have slightly different “cnn” vs “fox news” search averages over the previous 12-month period. Those new search volume averages are:

“fox news” avg monthly search volume

“cnn” avg monthly search volume

KWR Prediction

2016 Vote

Arizona

566333

518583

Trump

Trump

Nevada

213833

214583

Hillary

Hillary

New Mexico

138833

142916

Hillary

Hillary

Ohio

845833

781083

Trump

Trump

Pennsylvania

1030500

1063583

Hillary

Trump

Four out of five isn’t bad! This brought my new prediction up to 46/47.

Silver and I each got Pennsylvania wrong. The GrepWords API shows the average monthly search volume for “cnn” was ~33,083 searches higher than “fox news” (to put that in perspective, that’s ~0.26% of the state’s population). This tight-knit keyword research theory is perfectly reflected in Trump’s 48.2% win against Clinton’s 47.5%.

Nate Silver and I have very different day jobs, and he wouldn’t make many of these hasty generalizations. Any prediction method can be right a couple times. However, it got me thinking about the power of keyword research: how it can reveal searcher intent, predict behavior, and sometimes even defy the logic of things like statistics.

It’s also easy to predict the past. What happens when we apply this model to today’s Senate race?

Can we apply this theory to Alabama’s special election in the US Senate?

After completing the above research on a whim, I realized that we’re on the cusp of yet another hotly contested, extremely close election: the upcoming Alabama senate race, between controversy-laden Republican Roy Moore and Democratic challenger Doug Jones, fighting for a Senate seat that hasn’t been held by a Democrat since 1992.

I researched each Alabama county — 67 in total — for good measure. There are obviously a ton of variables at play. However, 52 out of the 67 counties (77.6%) 2016 presidential county votes are correctly “predicted” by my theory.

Even when giving the Democratic nominee more weight to the very low search volume counties (19 counties showed a search volume difference of less than 500), my numbers lean pretty far to the right (48/67 Republican counties):

It should be noted that my theory incorrectly guessed two of the five largest Alabama counties, Montgomery and Jefferson, which both voted Democrat in 2016.

Greene and Macon Counties should both vote Democrat; their very slight “cnn” over “fox news” search volume is confirmed by their previous presidential election results.

I realize state elections are not won by county, they’re won by popular vote, and the state of Alabama searches for “fox news” 204,000 more times a month than “cnn” (to put that in perspective, that’s around ~4.27% of Alabama’s population).

All things aside and regardless of outcome, this was an interesting exploration into how keyword research can offer us a glimpse into popular opinion, future behavior, and search intent. What do you think? Any other predictions we could make to test this theory? What other keywords or factors would you look at? Let us know in the comments.

Also, if you’ve enjoyed this post, check out Sam Wang’s Google-Wide Association Studies! –Fascinating read.

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Are you changing keyword bids too often?

It’s great to proactively manage your paid search accounts, but columnist Ted Ives makes the case for backing off a little when it comes to bid adjustments.

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SearchCap: Local ranking factors, keyword bidding & more

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

The post SearchCap: Local ranking factors, keyword bidding & more appeared first on Search Engine Land.



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How to Do a Keyword-Driven Content Audit (with Keyword Explorer)

Posted by Dr-Pete

As content marketers, we frequently suffer from What Have You Done For Me Lately Syndrome (WHYDFMLS). As soon as we’re done with one piece of content, we’re on to the next one, barely stopping to check analytics for a couple of days. Analytics themselves are to blame, in part. Our default window into traffic-based analytics is somewhere in the realm of 30 days, leading us to neglect older content that’s still performing well but may not be competing day-to-day with the latest and greatest.

I’m a big believer in digging back into your hidden gems and looking for content that’s still performing but may be due for an update, rewrite, or even just testing a better title/headline. How do we find this content, which is often buried in our this-week-focused analytics?

Let’s think like SEOs. One approach is to find older content that’s still ranking for a solid number of keywords, but may be out of date or under-performing. This is content that’s still driving traffic, but we may be overlooking. We don’t have to fight an uphill battle to get it ranking – we just have to better tap the potential this content is already demonstrating.

Step 0 – The “Exact Page” filter

Before we begin, I’m going to jump to the end. You may know that we recently launched Keywords By Site in Keyword Explorer, which allows you to peer into a keyword “universe” of millions of searches to see how a given domain is ranking. What you may not know is that you can also look up a specific page with the “Exact Page” filter. Go to the Keyword Explorer home page, and it’s the last entry in the pull-down:

Here’s a zoom-in. I’ve entered a popular post from my personal website:

Click the search (magnifying glass) button and you’ll get back something like this:

Even for my small blog, I’ve got a healthy list of keywords here, and some ranking in the top 50 that have solid volume. I also know that this post still gets decent traffic, even though it was written in 2009. If I were still active in the usability space, this would be a prime candidate for a rewrite, and I’d know exactly what keywords to target.

This is all well and good when you have an exact page in mind, but how do you audit an entire site or blog when you don’t know what’s performing for you? I’m going to outline a 6-step process below.

Step 1 – Get all rankings

Let’s say I want to find some buried content treasure right here on the Moz Blog. In the Keyword Explorer menu, I’ll select “root domain” and enter our root domain, “moz.com”:

I’ll get a similar report as in Step 0. Under “Top Ranking Keywords”, I’m going to select “See all ranking keywords”. In this case, I get back a table of more than 53,000 keywords that moz.com currently ranks

for. Not too shabby. These are not just keywords I actively track, but all of the keywords moz.com ranks for in Keyword Explorer’s “universe” of roughly 40 million keywords.

Step 2 – Export keywords

So, how does a keyword list help us to better understand our content? Above the keyword table, you’ll see two options, “Export CSV” and “Add to…”:

I’m going to choose the export – we’re going to want the whole, beautiful mess for this job. What I’ll get back is a file with every keyword and the following columns:

  • Keyword
  • Minimum Volume
  • Maximum Volume
  • Keyword Difficulty
  • Top Rank
  • Top Ranking URL

That last column is the important one. The export contains the top ranking URL for moz.com for each of the keywords (note: your maximum export size does vary with your Moz Pro membership level). This is where we can start forging the content connection.

Step 3 – Filter pages

I ended up with 30K keyword/URL pairings in the CSV. So that the viewers at home can follow along, I’m going to do the next few steps in Google Sheets. The first thing I want to do is filter out just what I’m interested in. In the “Data” menu, select “Filter”. You’ll see green arrows appear next to each column header. Click on the one next to “Top Ranking URL” (the last column). I’m going to use “Filter by condition” with “Text contains” and isolate all ranking URLs with “/blog/” in them:

This leaves me with 13,266 keyword/URL pairings. Personally, I like to copy and paste the filtered data to a new worksheet, just because working with filtered data tends to be a bit unpredictable. So, now I’ve got a separate worksheet (named “Filtered”) with just the keywords where the Moz blog ranks.

Step 4 – Pivot pages

If you haven’t used pivot tables, I’d strongly encourage you to check them out. Annie Cushing has a great Excel tutorial on pivot tables, and I’ll walk you through a couple of basics in Google Sheets. Generally, you use pivot tables when you want to group data and calculate statistics on those groups very quickly. In this case, what I want to do is group all of the matching URLs in my data set and get the counts. In other words, how many keywords is each unique blog post ranking on?

After selecting all of the data on that new “Filtered” tab, click the “Data” menu again, and then “Pivot tables…” at the bottom. This opens up a new sheet with a blank table. On the right are some slightly cryptic options. Under “Rows”, I’m going to add “Top Ranking URL”. This tells Google Sheets that each row in the pivot table should be a unique (grouped) URL from the top ranking URLs. Next, I’ll select the “Values”::

The COUNTA() function just tells Google Sheets to return the total count for each URL (for some reason, COUNT() only works on numeric values). As a bonus, I’ve also selected the SUM() of Max Volume. This will total up the volume for all of the ranking keywords in our data set for each URL.

Pivot table results can be a bit hard to work with (in both Excel and Google Sheets), so I’m going to copy and paste the data (as values only) into a new sheet called “Audit”.

Step 5 – Find candidates

Let’s get to the good stuff. When I group the URLs, I’m left with 1,604 unique blog posts in this particular data set. I can easily sort by posts ranking for the most keywords or posts with the most potential search volume (under “Data” / “Sort range”). I’m going to stick to raw keyword count. Here’s just a sample:

Obviously, there’s a ton here to dig into, but right away I noticed that two of the posts in the top 10 seemed to have some connection to graphics and/or image search. This stood out, because it’s not a topic we write about a lot. Turns out the first one is a video from May 2017, so not a great candidate for an update. The second, however (highlighted), is a tools post from early 2013. This post was surprisingly popular, and given how many new tools have come out in the past 4-1/2 years, is a perfect candidate to rewrite.

Here’s a link to the full Google Sheet. Feel free to make a copy and play around.

Step 6 – Back to Step 0

Remember that “Exact URL” option I talked about at the beginning of this post? Well, now I’ve got a URL to plug back into that feature and learn more about. Our data dump showed 170 ranking keywords, but when I target that exact URL, I’m likely to get even more data. Here’s just a sample:

Sure enough, I get almost double that count (348) with an exact URL search, and now I have an entire treasure trove to sort through. I sorted by volume (descending) here, just to get a sense of some of the more interesting keywords. I can, of course, repeat Step 6 with any of the URLs from Step 5 until I narrow down my best prospects.

Next steps (for the adventurous)

If I were going to rewrite the post I found, I’d want to make sure that I’m targeting two sets of keywords: (1) the important keywords I currently rank highly on (don’t want to lose that traffic) and (2) higher volume keywords I have the potential to rank on if I target them better. I might target, for example, a few choice keywords where I currently rank in the top 20 results and have a Page Authority that’s better than (or, at least, not too far from) the listed Keyword Difficulty.

Of course, you can also feed any of these keywords back into Keyword Explorer for more suggestions. Ideally, you’re looking for a handful of solid keyword concepts to target. The goal isn’t to stuff every variation into your rewritten post. The goal is to create a better, newer, more useful post that also happens to intelligently incorporate highly relevant keywords.

Bonus: Walk-through video

If you’d like to learn more about the Keyword Explorer features discussed in this post, I’ve created a short (roughly 2 minute) walk-through video:

Give it a try and let me know what you find. While I’ve chosen to focus on Keyword Explorer in this post (hey, we have to pay the bills), this same process should work with a handful of other popular keyword research tools, as well.

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How to Use Keyword Explorer to Identify Competitive Keyword Opportunities

Posted by hayleysherman

You may have heard by now that Moz launched a new feature within Keyword Explorer last week. We heard your requests, and we’re super-excited for you to check out the new addition. The tool has been expanded to allow you to search by URL: an easy way to understand what keywords an exact URL, subdomain, or entire domain is ranking for.

As Rand pointed out, this feature of Keyword Explorer is multifunctional and can solve a lot of different problems. For this blog post, I’ll cover a workflow for identifying low-hanging fruit when it comes to your competitors’ keywords.

The question of “How do I utilize competitive data to my advantage?” is one we hear a lot as SEOs. How do we know what a competitor is ranking for, and how can we use that to help direct our own strategy? Many great SEO tools out there tap into what can be described as a keyword universe — a database of keywords the tool maintains that a given site can rank for. In this universe of keywords, you can search to see how your site performs. You can also search any other site to see how it performs, which is where the competitive data comes into play. Our new feature does just that.

If you want to follow along, hop into Keyword Explorer! The search bar will allow you to:

  • Search by keyword (as you always have!)
  • Search by root domain
  • Search by subdomain
  • Search by exact page

Follow along in Keyword Explorer

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Find keyword opportunities at the intersection point

For this example, I’ll use local Seattle doughnut shop Top Pot Doughnuts. Since we know the doughnut game can be a competitive one, Top Pot might want to get an idea of the keywords that a few other Seattle shops are ranking for. The competitors I’ve used are in a similar geographical area and sell similarly delicious products.

Start by entering the URL into Keyword Explorer. To keep it broad, I’d recommend beginning with the “root domain” function. You’ll be pulled into a Site Overview for your domain — including the number of ranking keywords each site has, the top positions the keywords sit in, as well as the Page Authority and Domain Authority of the site you searched for. You’ll see a sneak peek of the top ranking keywords beneath that.

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Drop two competitors into the two boxes up at the top, and click “Compare sites.” The tables will populate with data on the two competitors’ sites, and the top ranking keywords for all three.

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Click through to the full report of Top Ranking Keywords. You’ll see a Venn diagram and two columns added in with competitors’ data. Click on any of the overlapping areas in the Venn Diagram to see the keywords that you and one or both competitors have in common.

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We’ve now entered into an ideal spot in that keyword universe we talked about earlier — a list of keywords that your site is ranking for that your competitors are also ranking for. This is the intersection point where you can find perfect keyword opportunities. Where is the competitor doing well that you are not?

(Side note: You’re not starting from scratch here, because you’re already ranking for these keywords. This means there’s a great opportunity for improvement in an area where you likely have some content or some authority.)

A great next step is to click on the header to sort by one of your competitor’s highest rankings. Identify the keywords that each competitor is ranking best for — those might be an area for you to focus on. Are these keywords applicable to what you do? If the answer is yes, there are a couple good courses of action: Add them straight into a Moz Pro campaign to start tracking your ranking progress, or add them into a Keyword Explorer list for further investigation.

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If you do add these into a Keyword List, you might want to pop into the list and sort by metrics like Difficulty or Organic CTR. This will help you determine how to prioritize the new keywords.

Tracking and taking action in Moz Pro

Once you’ve discovered these competitive keywords, push them into a Moz Pro campaign! That way, you can measure a baseline for keyword performance and get ready to track your improvements against it over time. You can either add them to a campaign manually in the Add & Manage Keywords section, or add them to a campaign directly from Keyword Explorer.

Stay organized by labeling your keywords. You may want to label them by product, service, or even by the name of the competitor that was ranking for them back in Keyword Explorer. Once a label (or multiple labels) are in place, you can filter by those labels within the campaign to see which keywords are seeing movement, and which ones you may still need to spend more time on.

Jump into the SERP features section of your campaign, and filter by label to view the new keywords you’ve added in. Do any of the new keywords have a featured snippet opportunity? Use that knowledge to dictate how you structure the content for those topics. (Don’t know what I’m talking about? Not to worry. Here’s a great glossary of SERP features, what they mean, and how to become featured.)

And there you have it! We hope Keyword Explorer’s new addition will help you through the journey of keyword research, from start to finish. Let us know how this flow is working for you.

Start exploring Keywords by Site

Can’t get enough keyword research in your life? Check out our workshops through Moz Training for a deeper dive into best practices and strategies.

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