Tag Archive | "Keyword"

Here’s how Google Ads’ new keyword selection preferences work

A look at the potential impact of same-meaning close variants for exact match, phrase match and broad match modifier on your keyword matching.



Please visit Search Engine Land for the full article.


Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing

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Using STAT: How to Uncover Additional Value in Your Keyword Data

Posted by TheMozTeam

Changing SERP features and near-daily Google updates mean that single keyword strategies are no longer viable. Brands have a lot to keep tabs on if they want to stay visible and keep that coveted top spot on the SERP.

That’s why we asked Laura Hampton, Head of Marketing at Impressionto share some of the ways her award-winning team leverages STAT to surface all kinds of insights to make informed decisions.

Snag her expert tips on how to uncover additional value in your keyword data — including how Impression’s web team uses STAT’s API to improve client reporting, how to spot quick wins with dynamic tags, and what new projects they have up their sleeves. Take it away, Laura!

Spotting quick wins 

We all remember the traditional CTR chart. It suggests that websites ranking in position one on the SERPs can expect roughly 30 percent of the clicks available, with position two getting around 12 percent, position three seeing six percent, and so on (disclaimer: these may not be the actual numbers but, let’s face it, this formula is way outdated at this point anyway).

Today, the SERP landscape has changed, so we know that the chances of any of the above-suggested numbers being correct are minimal — especially when you consider the influence of elements like featured snippets on click-through rates.

But the practical reality remains that if you can improve your ranking position, it’s highly likely you’ll get at least some uplift in traffic for that term. This is where STAT’s dynamic tags can really help. Dynamic tags are a special kind of tag that automatically populates keywords based on changeable filter criteria.

We like to set up dynamic tags based on ranking position. We use this to flag keywords which are sitting just outside of the top three, top five, or top 10 positions. Layer into this some form of traffic benchmark, and you can easily uncover keywords with decent traffic potential that just need an extra bit of work to tip them into a better position.

Chasing position zero with featured snippets and PAAs 

There’s been a lot of chat in our industry about the growing prevalence of SERP features like featured snippets and “People also ask” (PAA) boxes. In fact, STAT has been instrumental in leading much of the research into the influence of these two SERP features on brand visibility and CTRs.

If your strategy includes a hunt for the coveted position zero, you’re in luck. We like to use STAT’s dynamic tagging feature to monitor the keywords that result in featured snippets. This way, we can track keywords where our client owns the snippet and where they don’t. We can also highlight new opportunities to create optimized content and attempt to capture the spot from their competitors.

This also really helps guide our overall content strategy, since STAT is able to provide quick feedback on the type of content (and, therefore, the assumed intent) that will perform best amongst a keyword set.

Making use of data views 

Data views are one of the most fundamental elements of STAT. They are tools that allow you to organize your data in ways that are meaningful to you. Holding multiple keyword segments (tags) and producing aggregate metrics, they make it possible for us to dissect keyword information and then implement strategically driven decisions.

For us at Impression, data views are essential. They reflect the tactical aspirations of the client. While you could create a single templated dashboard for all your clients with the same data views, our strategists will often set up data views that mirror the way each client and account work.

Even if we’re not yet actively working on a keyword set, we usually create data views to enable us to quickly spot opportunities and report back on the strategic progression.

Here are just some of the data views we’ve grouped our keyword segments into:

The conversion funnel

Segmenting keywords into the stages of the conversion funnel is a fairly common strategy for search marketers — it makes it possible to focus in on and prioritize higher intent queries and then extrapolate out.

Many of our data views are set up to monitor keywords tagged as “conversion,” “education,” and “awareness.”

Client goals

Because we believe successful search marketing is only possible when it integrates with wider business goals, we like to spend time getting to know our clients’ audiences, as well as their specific niches and characteristics.

This way, we can split our keywords into those which reflect the segments that our clients wish to target. For example, in some cases, this is based on sectors, such as our telecommunications client who targets audiences in finance, marketing, IT, and general business. In others, it’s based on locations, in which case we’ll leverage STAT’s location capabilities to track the visibility of our clients to different locales.

Services and/or categories

For those clients who sell online — whether it’s products or services — data views are a great way to track their visibility within each service area or product category.

Our own dashboard (for Impression) uses this approach to split out our service-based keywords, so our data view is marked “Services” and the tags we track within are “SEO,” “PPC,” “web,” and so on. For one of our fashion clients, the data view relates to product categories, where the tracked tags include “footwear,” “accessories,” and “dresses.”

At-a-glance health monitoring

A relatively new feature in STAT allows us to see the performance of tags compared to one another: the Tags tab.

Because we use data views and tags a lot, this has been a neat addition for us. The ability to quickly view those tags and how the keywords within are progressing is immensely valuable.

Let’s use an example from above. For Impression’s own keyword set, one data view contains tags that represent different service offerings. When we click on that data view and choose “Tags” in the tabbed options, we can see how well each service area is performing in terms of its visibility online.

This means we can get very quick strategic insights that say our ranking positions for SEO are consistently pretty awesome, while those around CRO (which we are arguably less well known for), tend to fluctuate more. We can also make a quick comparison between them thanks to the layout of the tab.

Identifying keyword cannibalization risk through duplicate landing pages 

While we certainly don’t subscribe to any notion of a content cannibalization penalty per se, we do believe that having multiple landing pages for one keyword or keyword set is problematic.

That’s where STAT can help. We simply filter the keywords table to show a given landing page and we’re able to track instances where it’s ranking for multiple keywords.

By exporting that information, we can then compare the best and worst ranking URLs. We can also highlight where the ranking URL for a single keyword has changed, signaling internal conflict and, therefore, an opportunity to streamline and improve.

Monitoring the competitive landscape 

No search strategy is complete without an understanding of the wider search landscape. Specifically, this means keeping track of your and/or your client’s rankings when compared to others ranking around them.

We like to use STAT’s Competitive Landscape tab to view this information for a specific data view, or across the whole account. In particular, the Share of Voice: Current Leaders board tells us very quickly who we’re up against for a keyword set.

This leads to insights such as the competitiveness of the keyword set, which makes it easier to set client expectations. It also surfaces relevance of the keywords tracked, where, if the share of voice is going to brands that aren’t your own, it may indicate the keywords you’re targeting are not that relevant to your own audience.

You can also take a look at the Share of Voice: Top 10 Trending to see where competitors are increasing or decreasing their visibility. This can be indicative of changes on the SERPs for that industry, or in the industry as a whole.

Creating a custom connector for GDS 

Reporting is a fundamental part of agency life. Our clients appreciate formalized insights into campaign progression (on top of regular communications throughout the month, of course) and one of our main challenges in growing our agency lies in identifying the best way to display reports.

We’ll be honest here: There was a point where we had started to invest in building our own platform, with all sorts of aspirations of bespoke builds and highly branded experiences that could tie into a plethora of other UX considerations for our clients.

But at the same time, we’re also big believers that there’s no point in trying to reinvent the wheel if an appropriate solution already exists. So, we decided to use Google Data Studio (GDS) as it was released in Beta and moved onto the platform in 2017.

Of course, ranking data — while we’d all like to reserve it for internal insight to drive bigger goals — is always of interest to clients. At the time, the STAT API was publicly available, but there was no way to pull data into GDS.

That’s why we decided to put some of our own time into creating a GDS connector for STAT. Through this connector, we’re able to pull in live data to our GDS reports, which can be easily shared with our clients. It was a relatively straightforward process and, because GDS caches the data for a short amount of time, it doesn’t hammer the STAT API for every request.

Though our clients do have access to STAT (made possible through their granular user permissions), the GDS integration is a simpler way for them to see top-level stats at a glance.

We’re in the process of building pipelines through BigQuery to feed into this and facilitate date specific tracking in GDS too — keep an eye out for more info and get access to the STAT GDS connector here.

Want more? 

Ready to learn how to get cracking and tracking some more? Reach out to our rad team and request a demo to get your very own tailored walkthrough of STAT. 

If you’re attending MozCon this year, you can see the ins and outs of STAT in person — grab your ticket before they’re all gone! 

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!


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How to Automate Keyword Ranking with STAT and Google Data Studio

Posted by TheMozTeam

This blog post was originally published on the STAT blog.


We asked SEO Analyst, Emily Christon of Ovative Group, to share how her team makes keyword rank reporting easy and digestible for all stakeholders. Read on to see how she combines the power of STAT with Google Data Studio for streamlined reporting that never fails to impress her clients.

Why Google Data Studio

Creating reports for your clients is a vital part of SEO. It’s also one of the most daunting and time-consuming tasks. Your reports need to contain all the necessary data, while also having clear visuals, providing quick wins, and being easy to understand.

At Ovative Group, we’re big advocates for reporting tools that save time and make data easier to understand. This is why we love Google Data Studio.

This reporting tool was created with the user in mind and allows for easy collaboration and sharing with teams. It’s also free, and its reporting dashboard is designed to take the complexity and stress out of visualizing data.

Don’t get us wrong. We still love our spreadsheets, but tools like Excel aren’t ideal for building interactive dashboards. They also don’t allow for easy data pulls — you have to manually add your data, which can eat up a lot of time and cause a lot of feelings.

Data Studio, however, pulls all your data into one place from multiple sources, like spreadsheets, Google Analytics accounts, and Adwords. You can then customize how all that data is viewed so you can surface quick insights.

How does this relate to keyword reporting?

Creating an actionable keyword report that is beneficial for both SEO and your stakeholders can be a challenge. Data Studio makes things a bit easier for us at Ovative in a variety of ways:

Automated data integration

Our team uses the STAT API — which can be connected to Data Studio through a little technical magic and Google Big Query — to pull in all our raw data. You can select what data points you want to be collected from the API, including rank, base rank, competitors, search volume, local information, and more.

Once your data is collected and living in Big Query, you can access it through the Data Studio interface. If you want to learn more about STAT’s API, go here.

Customization

Do you care about current rank? Rank over time? Major movers – those that changed +20 positions week over week? Or are you just after how many keywords you have ranking number one?

All of this is doable — and easy — once you’re comfortable in Data Studio. You can easily customize your reports to match your goals.

“Our team uses the STAT API — which can be connected to Data Studio through a little technical magic and Google Big Query — to pull in all our raw data.” — Emily Christon, SEO Analyst at Ovative Group

Custom dashboards make reporting and insights efficient and client-facing, transforming all that raw data into easy-to-understand metrics, which tell a more compelling story.

How to build your custom Google Data Studio 

There are a myriad of ways to leverage Google Data Studio for major insights. Here are just a few features we use to help visualize our data.

Keyword rank

This report gives you a snapshot of how many keywords you have in each ranking group and how things are trending. You can also scroll through your list of keywords to see what the traffic-driving queries are.

One cool feature of Data Studio when it comes to rank is period over period comparisons. For example, if you set the date range to the previous week, it will automatically pull week over week rank change. If you set the date range to the previous month, it pulls a month over month rank change.

At Ovative, we do weekly, monthly, and yearly keyword rank change reporting.

Keyword look-up tool

If you notice that traffic has declined in a specific keyword set, pop down to the keyword look-up tool to track rank trends over time. This view is extremely helpful — it shows the progress or decline of rank to help explain traffic variability.

Campaign or priority tracker

To support newly launched pages or priority keywords, create a separate section just for these keywords. This will make it easy for you to quickly check the performance and trends of chosen keyword sets.

What’s next? 

Google Data Studio is only as powerful as you make it.

The STAT API integration in Google Data Studio represents one page of our typical client’s reporting studio; we make sure to add in a page for top-level KPI trends, a page for Search Console keyword performance, and other relevant sources for ease of use for ourselves and the client.

Want more? 

Want to dive deeper into STAT? Got questions about our API? You can book a demo with us and get a personalized walk through. 

You can also chat with our rad team at MozCon this July 15–17 to see how you can go seriously deep with your data. Ask about our specialty API — two additional services to give you everything a 100-result SERP has to offer, and perfect if you’ve built your own connector.

Grab my MozCon ticket now!

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Keyword Not Provided, But it Just Clicks

When SEO Was Easy

When I got started on the web over 15 years ago I created an overly broad & shallow website that had little chance of making money because it was utterly undifferentiated and crappy. In spite of my best (worst?) efforts while being a complete newbie, sometimes I would go to the mailbox and see a check for a couple hundred or a couple thousand dollars come in. My old roommate & I went to Coachella & when the trip was over I returned to a bunch of mail to catch up on & realized I had made way more while not working than what I spent on that trip.

What was the secret to a total newbie making decent income by accident?

Horrible spelling.

Back then search engines were not as sophisticated with their spelling correction features & I was one of 3 or 4 people in the search index that misspelled the name of an online casino the same way many searchers did.

The high minded excuse for why I did not scale that would be claiming I knew it was a temporary trick that was somehow beneath me. The more accurate reason would be thinking in part it was a lucky fluke rather than thinking in systems. If I were clever at the time I would have created the misspeller’s guide to online gambling, though I think I was just so excited to make anything from the web that I perhaps lacked the ambition & foresight to scale things back then.

In the decade that followed I had a number of other lucky breaks like that. One time one of the original internet bubble companies that managed to stay around put up a sitewide footer link targeting the concept that one of my sites made decent money from. This was just before the great recession, before Panda existed. The concept they targeted had 3 or 4 ways to describe it. 2 of them were very profitable & if they targeted either of the most profitable versions with that page the targeting would have sort of carried over to both. They would have outranked me if they targeted the correct version, but they didn’t so their mistargeting was a huge win for me.

Search Gets Complex

Search today is much more complex. In the years since those easy-n-cheesy wins, Google has rolled out many updates which aim to feature sought after destination sites while diminishing the sites which rely one “one simple trick” to rank.

Arguably the quality of the search results has improved significantly as search has become more powerful, more feature rich & has layered in more relevancy signals.

Many quality small web publishers have went away due to some combination of increased competition, algorithmic shifts & uncertainty, and reduced monetization as more ad spend was redirected toward Google & Facebook. But the impact as felt by any given publisher is not the impact as felt by the ecosystem as a whole. Many terrible websites have also went away, while some formerly obscure though higher-quality sites rose to prominence.

There was the Vince update in 2009, which boosted the rankings of many branded websites.

Then in 2011 there was Panda as an extension of Vince, which tanked the rankings of many sites that published hundreds of thousands or millions of thin content pages while boosting the rankings of trusted branded destinations.

Then there was Penguin, which was a penalty that hit many websites which had heavily manipulated or otherwise aggressive appearing link profiles. Google felt there was a lot of noise in the link graph, which was their justification for the Penguin.

There were updates which lowered the rankings of many exact match domains. And then increased ad load in the search results along with the other above ranking shifts further lowered the ability to rank keyword-driven domain names. If your domain is generically descriptive then there is a limit to how differentiated & memorable you can make it if you are targeting the core market the keywords are aligned with.

There is a reason eBay is more popular than auction.com, Google is more popular than search.com, Yahoo is more popular than portal.com & Amazon is more popular than a store.com or a shop.com. When that winner take most impact of many online markets is coupled with the move away from using classic relevancy signals the economics shift to where is makes a lot more sense to carry the heavy overhead of establishing a strong brand.

Branded and navigational search queries could be used in the relevancy algorithm stack to confirm the quality of a site & verify (or dispute) the veracity of other signals.

Historically relevant algo shortcuts become less appealing as they become less relevant to the current ecosystem & even less aligned with the future trends of the market. Add in negative incentives for pushing on a string (penalties on top of wasting the capital outlay) and a more holistic approach certainly makes sense.

Modeling Web Users & Modeling Language

PageRank was an attempt to model the random surfer.

When Google is pervasively monitoring most users across the web they can shift to directly measuring their behaviors instead of using indirect signals.

Years ago Bill Slawski wrote about the long click in which he opened by quoting Steven Levy’s In the Plex: How Google Thinks, Works, and Shapes our Lives

“On the most basic level, Google could see how satisfied users were. To paraphrase Tolstoy, happy users were all the same. The best sign of their happiness was the “Long Click” — This occurred when someone went to a search result, ideally the top one, and did not return. That meant Google has successfully fulfilled the query.”

Of course, there’s a patent for that. In Modifying search result ranking based on implicit user feedback they state:

user reactions to particular search results or search result lists may be gauged, so that results on which users often click will receive a higher ranking. The general assumption under such an approach is that searching users are often the best judges of relevance, so that if they select a particular search result, it is likely to be relevant, or at least more relevant than the presented alternatives.

If you are a known brand you are more likely to get clicked on than a random unknown entity in the same market.

And if you are something people are specifically seeking out, they are likely to stay on your website for an extended period of time.

One aspect of the subject matter described in this specification can be embodied in a computer-implemented method that includes determining a measure of relevance for a document result within a context of a search query for which the document result is returned, the determining being based on a first number in relation to a second number, the first number corresponding to longer views of the document result, and the second number corresponding to at least shorter views of the document result; and outputting the measure of relevance to a ranking engine for ranking of search results, including the document result, for a new search corresponding to the search query. The first number can include a number of the longer views of the document result, the second number can include a total number of views of the document result, and the determining can include dividing the number of longer views by the total number of views.

Attempts to manipulate such data may not work.

safeguards against spammers (users who generate fraudulent clicks in an attempt to boost certain search results) can be taken to help ensure that the user selection data is meaningful, even when very little data is available for a given (rare) query. These safeguards can include employing a user model that describes how a user should behave over time, and if a user doesn’t conform to this model, their click data can be disregarded. The safeguards can be designed to accomplish two main objectives: (1) ensure democracy in the votes (e.g., one single vote per cookie and/or IP for a given query-URL pair), and (2) entirely remove the information coming from cookies or IP addresses that do not look natural in their browsing behavior (e.g., abnormal distribution of click positions, click durations, clicks_per_minute/hour/day, etc.). Suspicious clicks can be removed, and the click signals for queries that appear to be spmed need not be used (e.g., queries for which the clicks feature a distribution of user agents, cookie ages, etc. that do not look normal).

And just like Google can make a matrix of documents & queries, they could also choose to put more weight on search accounts associated with topical expert users based on their historical click patterns.

Moreover, the weighting can be adjusted based on the determined type of the user both in terms of how click duration is translated into good clicks versus not-so-good clicks, and in terms of how much weight to give to the good clicks from a particular user group versus another user group. Some user’s implicit feedback may be more valuable than other users due to the details of a user’s review process. For example, a user that almost always clicks on the highest ranked result can have his good clicks assigned lower weights than a user who more often clicks results lower in the ranking first (since the second user is likely more discriminating in his assessment of what constitutes a good result). In addition, a user can be classified based on his or her query stream. Users that issue many queries on (or related to) a given topic T (e.g., queries related to law) can be presumed to have a high degree of expertise with respect to the given topic T, and their click data can be weighted accordingly for other queries by them on (or related to) the given topic T.

Google was using click data to drive their search rankings as far back as 2009. David Naylor was perhaps the first person who publicly spotted this. Google was ranking Australian websites for [tennis court hire] in the UK & Ireland, in part because that is where most of the click signal came from. That phrase was most widely searched for in Australia. In the years since Google has done a better job of geographically isolating clicks to prevent things like the problem David Naylor noticed, where almost all search results in one geographic region came from a different country.

Whenever SEOs mention using click data to search engineers, the search engineers quickly respond about how they might consider any signal but clicks would be a noisy signal. But if a signal has noise an engineer would work around the noise by finding ways to filter the noise out or combine multiple signals. To this day Google states they are still working to filter noise from the link graph: “We continued to protect the value of authoritative and relevant links as an important ranking signal for Search.”

The site with millions of inbound links, few intentional visits & those who do visit quickly click the back button (due to a heavy ad load, poor user experience, low quality content, shallow content, outdated content, or some other bait-n-switch approach)…that’s an outlier. Preventing those sorts of sites from ranking well would be another way of protecting the value of authoritative & relevant links.

Best Practices Vary Across Time & By Market + Category

Along the way, concurrent with the above sorts of updates, Google also improved their spelling auto-correct features, auto-completed search queries for many years through a featured called Google Instant (though they later undid forced query auto-completion while retaining automated search suggestions), and then they rolled out a few other algorithms that further allowed them to model language & user behavior.

Today it would be much harder to get paid above median wages explicitly for sucking at basic spelling or scaling some other individual shortcut to the moon, like pouring millions of low quality articles into a (formerly!) trusted domain.

Nearly a decade after Panda, eHow’s rankings still haven’t recovered.

Back when I got started with SEO the phrase Indian SEO company was associated with cut-rate work where people were buying exclusively based on price. Sort of like a “I got a $ 500 budget for link building, but can not under any circumstance invest more than $ 5 in any individual link.” Part of how my wife met me was she hired a hack SEO from San Diego who outsourced all the work to India and marked the price up about 100-fold while claiming it was all done in the United States. He created reciprocal links pages that got her site penalized & it didn’t rank until after she took her reciprocal links page down.

With that sort of behavior widespread (hack US firm teaching people working in an emerging market poor practices), it likely meant many SEO “best practices” which were learned in an emerging market (particularly where the web was also underdeveloped) would be more inclined to being spammy. Considering how far ahead many Western markets were on the early Internet & how India has so many languages & how most web usage in India is based on mobile devices where it is hard for users to create links, it only makes sense that Google would want to place more weight on end user data in such a market.

If you set your computer location to India Bing’s search box lists 9 different languages to choose from.

The above is not to state anything derogatory about any emerging market, but rather that various signals are stronger in some markets than others. And competition is stronger in some markets than others.

Search engines can only rank what exists.

“In a lot of Eastern European – but not just Eastern European markets – I think it is an issue for the majority of the [bream? muffled] countries, for the Arabic-speaking world, there just isn’t enough content as compared to the percentage of the Internet population that those regions represent. I don’t have up to date data, I know that a couple years ago we looked at Arabic for example and then the disparity was enormous. so if I’m not mistaken the Arabic speaking population of the world is maybe 5 to 6%, maybe more, correct me if I am wrong. But very definitely the amount of Arabic content in our index is several orders below that. So that means we do not have enough Arabic content to give to our Arabic users even if we wanted to. And you can exploit that amazingly easily and if you create a bit of content in Arabic, whatever it looks like we’re gonna go you know we don’t have anything else to serve this and it ends up being horrible. and people will say you know this works. I keyword stuffed the hell out of this page, bought some links, and there it is number one. There is nothing else to show, so yeah you’re number one. the moment somebody actually goes out and creates high quality content that’s there for the long haul, you’ll be out and that there will be one.” – Andrey Lipattsev – Search Quality Senior Strategist at Google Ireland, on Mar 23, 2016


Impacting the Economics of Publishing

Now search engines can certainly influence the economics of various types of media. At one point some otherwise credible media outlets were pitching the Demand Media IPO narrative that Demand Media was the publisher of the future & what other media outlets will look like. Years later, after heavily squeezing on the partner network & promoting programmatic advertising that reduces CPMs by the day Google is funding partnerships with multiple news publishers like McClatchy & Gatehouse to try to revive the news dead zones even Facebook is struggling with.

“Facebook Inc. has been looking to boost its local-news offerings since a 2017 survey showed most of its users were clamoring for more. It has run into a problem: There simply isn’t enough local news in vast swaths of the country. … more than one in five newspapers have closed in the past decade and a half, leaving half the counties in the nation with just one newspaper, and 200 counties with no newspaper at all.”

As mainstream newspapers continue laying off journalists, Facebook’s news efforts are likely to continue failing unless they include direct economic incentives, as Google’s programmatic ad push broke the banner ad:

“Thanks to the convoluted machinery of Internet advertising, the advertising world went from being about content publishers and advertising context—The Times unilaterally declaring, via its ‘rate card’, that ads in the Times Style section cost $ 30 per thousand impressions—to the users themselves and the data that targets them—Zappo’s saying it wants to show this specific shoe ad to this specific user (or type of user), regardless of publisher context. Flipping the script from a historically publisher-controlled mediascape to an advertiser (and advertiser intermediary) controlled one was really Google’s doing. Facebook merely rode the now-cresting wave, borrowing outside media’s content via its own users’ sharing, while undermining media’s ability to monetize via Facebook’s own user-data-centric advertising machinery. Conventional media lost both distribution and monetization at once, a mortal blow.”

Google is offering news publishers audience development & business development tools.

Heavy Investment in Emerging Markets Quickly Evolves the Markets

As the web grows rapidly in India, they’ll have a thousand flowers bloom. In 5 years the competition in India & other emerging markets will be much tougher as those markets continue to grow rapidly. Media is much cheaper to produce in India than it is in the United States. Labor costs are lower & they never had the economic albatross that is the ACA adversely impact their economy. At some point the level of investment & increased competition will mean early techniques stop having as much efficacy. Chinese companies are aggressively investing in India.

“If you break India into a pyramid, the top 100 million (urban) consumers who think and behave more like Americans are well-served,” says Amit Jangir, who leads India investments at 01VC, a Chinese venture capital firm based in Shanghai. The early stage venture firm has invested in micro-lending firms FlashCash and SmartCoin based in India. The new target is the next 200 million to 600 million consumers, who do not have a go-to entertainment, payment or ecommerce platform yet— and there is gonna be a unicorn in each of these verticals, says Jangir, adding that it will be not be as easy for a player to win this market considering the diversity and low ticket sizes.

RankBrain

RankBrain appears to be based on using user clickpaths on head keywords to help bleed rankings across into related searches which are searched less frequently. A Googler didn’t state this specifically, but it is how they would be able to use models of searcher behavior to refine search results for keywords which are rarely searched for.

In a recent interview in Scientific American a Google engineer stated: “By design, search engines have learned to associate short queries with the targets of those searches by tracking pages that are visited as a result of the query, making the results returned both faster and more accurate than they otherwise would have been.”

Now a person might go out and try to search for something a bunch of times or pay other people to search for a topic and click a specific listing, but some of the related Google patents on using click data (which keep getting updated) mentioned how they can discount or turn off the signal if there is an unnatural spike of traffic on a specific keyword, or if there is an unnatural spike of traffic heading to a particular website or web page.

And, since Google is tracking the behavior of end users on their own website, anomalous behavior is easier to track than it is tracking something across the broader web where signals are more indirect. Google can take advantage of their wide distribution of Chrome & Android where users are regularly logged into Google & pervasively tracked to place more weight on users where they had credit card data, a long account history with regular normal search behavior, heavy Gmail users, etc.

Plus there is a huge gap between the cost of traffic & the ability to monetize it. You might have to pay someone a dime or a quarter to search for something & there is no guarantee it will work on a sustainable basis even if you paid hundreds or thousands of people to do it. Any of those experimental searchers will have no lasting value unless they influence rank, but even if they do influence rankings it might only last temporarily. If you bought a bunch of traffic into something genuine Google searchers didn’t like then even if it started to rank better temporarily the rankings would quickly fall back if the real end user searchers disliked the site relative to other sites which already rank.

This is part of the reason why so many SEO blogs mention brand, brand, brand. If people are specifically looking for you in volume & Google can see that thousands or millions of people specifically want to access your site then that can impact how you rank elsewhere.

Even looking at something inside the search results for a while (dwell time) or quickly skipping over it to have a deeper scroll depth can be a ranking signal. Some Google patents mention how they can use mouse pointer location on desktop or scroll data from the viewport on mobile devices as a quality signal.

Neural Matching

Last year Danny Sullivan mentioned how Google rolled out neural matching to better understand the intent behind a search query.

The above Tweets capture what the neural matching technology intends to do. Google also stated:

we’ve now reached the point where neural networks can help us take a major leap forward from understanding words to understanding concepts. Neural embeddings, an approach developed in the field of neural networks, allow us to transform words to fuzzier representations of the underlying concepts, and then match the concepts in the query with the concepts in the document. We call this technique neural matching.

To help people understand the difference between neural matching & RankBrain, Google told SEL: “RankBrain helps Google better relate pages to concepts. Neural matching helps Google better relate words to searches.”

There are a couple research papers on neural matching.

The first one was titled A Deep Relevance Matching Model for Ad-hoc Retrieval. It mentioned using Word2vec & here are a few quotes from the research paper

  • “Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements.”
  • “the interaction-focused model, which first builds local level interactions (i.e., local matching signals) between two pieces of text, and then uses deep neural networks to learn hierarchical interaction patterns for matching.”
  • “according to the diverse matching requirement, relevance matching is not position related since it could happen in any position in a long document.”
  • “Most NLP tasks concern semantic matching, i.e., identifying the semantic meaning and infer”ring the semantic relations between two pieces of text, while the ad-hoc retrieval task is mainly about relevance matching, i.e., identifying whether a document is relevant to a given query.”
  • “Since the ad-hoc retrieval task is fundamentally a ranking problem, we employ a pairwise ranking loss such as hinge loss to train our deep relevance matching model.”

The paper mentions how semantic matching falls down when compared against relevancy matching because:

  • semantic matching relies on similarity matching signals (some words or phrases with the same meaning might be semantically distant), compositional meanings (matching sentences more than meaning) & a global matching requirement (comparing things in their entirety instead of looking at the best matching part of a longer document); whereas,
  • relevance matching can put significant weight on exact matching signals (weighting an exact match higher than a near match), adjust weighting on query term importance (one word might or phrase in a search query might have a far higher discrimination value & might deserve far more weight than the next) & leverage diverse matching requirements (allowing relevancy matching to happen in any part of a longer document)

Here are a couple images from the above research paper

And then the second research paper is

Deep Relevancy Ranking Using Enhanced Dcoument-Query Interactions
“interaction-based models are less efficient, since one cannot index a document representation independently of the query. This is less important, though, when relevancy ranking methods rerank the top documents returned by a conventional IR engine, which is the scenario we consider here.”

That same sort of re-ranking concept is being better understood across the industry. There are ranking signals that earn some base level ranking, and then results get re-ranked based on other factors like how well a result matches the user intent.

Here are a couple images from the above research paper.

For those who hate the idea of reading research papers or patent applications, Martinibuster also wrote about the technology here. About the only part of his post I would debate is this one:

“Does this mean publishers should use more synonyms? Adding synonyms has always seemed to me to be a variation of keyword spamming. I have always considered it a naive suggestion. The purpose of Google understanding synonyms is simply to understand the context and meaning of a page. Communicating clearly and consistently is, in my opinion, more important than spamming a page with keywords and synonyms.”

I think one should always consider user experience over other factors, however a person could still use variations throughout the copy & pick up a bit more traffic without coming across as spammy. Danny Sullivan mentioned the super synonym concept was impacting 30% of search queries, so there are still a lot which may only be available to those who use a specific phrase on their page.

Martinibuster also wrote another blog post tying more research papers & patents to the above. You could probably spend a month reading all the related patents & research papers.

The above sort of language modeling & end user click feedback compliment links-based ranking signals in a way that makes it much harder to luck one’s way into any form of success by being a terrible speller or just bombing away at link manipulation without much concern toward any other aspect of the user experience or market you operate in.

Pre-penalized Shortcuts

Google was even issued a patent for predicting site quality based upon the N-grams used on the site & comparing those against the N-grams used on other established site where quality has already been scored via other methods: “The phrase model can be used to predict a site quality score for a new site; in particular, this can be done in the absence of other information. The goal is to predict a score that is comparable to the baseline site quality scores of the previously-scored sites.”

Have you considered using a PLR package to generate the shell of your site’s content? Good luck with that as some sites trying that shortcut might be pre-penalized from birth.

Navigating the Maze

When I started in SEO one of my friends had a dad who is vastly smarter than I am. He advised me that Google engineers were smarter, had more capital, had more exposure, had more data, etc etc etc … and thus SEO was ultimately going to be a malinvestment.

Back then he was at least partially wrong because influencing search was so easy.

But in the current market, 16 years later, we are near the infection point where he would finally be right.

At some point the shortcuts stop working & it makes sense to try a different approach.

The flip side of all the above changes is as the algorithms have become more complex they have went from being a headwind to people ignorant about SEO to being a tailwind to those who do not focus excessively on SEO in isolation.

If one is a dominant voice in a particular market, if they break industry news, if they have key exclusives, if they spot & name the industry trends, if their site becomes a must read & is what amounts to a habit … then they perhaps become viewed as an entity. Entity-related signals help them & those signals that are working against the people who might have lucked into a bit of success become a tailwind rather than a headwind.

If your work defines your industry, then any efforts to model entities, user behavior or the language of your industry are going to boost your work on a relative basis.

This requires sites to publish frequently enough to be a habit, or publish highly differentiated content which is strong enough that it is worth the wait.

Those which publish frequently without being particularly differentiated are almost guaranteed to eventually walk into a penalty of some sort. And each additional person who reads marginal, undifferentiated content (particularly if it has an ad-heavy layout) is one additional visitor that site is closer to eventually getting whacked. Success becomes self regulating. Any short-term success becomes self defeating if one has a highly opportunistic short-term focus.

Those who write content that only they could write are more likely to have sustained success.

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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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The Basics of Building an Intent-Based Keyword List

Posted by TheMozTeam

This post was originally published on the STAT blog.


In this article, we’re taking a deep dive into search intent.

It’s a topic we’ve covered before with some depth. This STAT whitepaper looked at how SERP features respond to intent, and a few bonus blog posts broke things down even further and examined how individual intent modifiers impact SERP features, the kind of content that Google serves at each stage of intent, and how you can set up your very own search intent projects. (And look out for Seer’s very own Scott Taft’s upcoming post this week on how to use STAT and Power BI to create your very own search intent dashboard.)

Search intent is the new demographics, so it only made sense to get up close and personal with it. Of course, in order to bag all those juicy search intent tidbits, we needed a great intent-based keyword list. Here’s how you can get your hands on one of those.

Gather your core keywords

First, before you can even think about intent, you need to have a solid foundation of core keywords in place. These are the products, features, and/or services that you’ll build your search intent funnel around.

But goodness knows that keyword list-building is more of an art than a science, and even the greatest writers (hi, Homer) needed to invoke the muses (hey, Calliope) for inspiration, so if staring at your website isn’t getting the creative juices flowing, you can look to a few different places for help.

Snag some good suggestions from keyword research tools

Lots of folks like to use the Google Keyword Planner to help them get started. Ubersuggest and Yoast’s Google Suggest Expander will also help add keywords to your arsenal. And Answer The Public gives you all of that, and beautifully visualized to boot.

Simply plunk in a keyword and watch the suggestions pour in. Just remember to be critical of these auto-generated lists, as odd choices sometimes slip into the mix. For example, apparently we should add [free phones] to our list of [rank tracking] keywords. Huh.

Spot inspiration on the SERPs

Two straight-from-the-SERP resources that we love for keyword research are the “People also ask” box and related searches. These queries are Google-vetted and plentiful, and also give you some insight into how the search engine giant links topics.

If you’re a STAT client, you can generate reports that will give you every question in a PAA box (before it gets infinite), as well as each of the eight related searches at the bottom of a SERP. Run the reports for a couple of days and you’ll get a quick sense of which questions and queries Google favours for your existing keyword set.

A quick note about language & location

When you’re in the UK, you push a pram, not a stroller; you don’t wear a sweater, you wear a jumper. This is all to say that if you’re in the business of global tracking, it’s important to keep different countries’ word choices in mind. Even if you’re not creating content with them, it’s good to see if you’re appearing for the terms your global searchers are using.

Add your intent modifiers

Now it’s time to tackle the intent bit of your keyword list. And this bit is going to require drawing some lines in the sand because the modifiers that occupy each intent category can be highly subjective — does “best” apply transactional intent instead of commercial?

We’ve put together a loose guideline below, but the bottom line is that intent should be structured and classified in a way that makes sense to your business. And if you’re stuck for modifiers to marry to your core keywords, here’s a list of 50+ to help with the coupling.

Informational intent

The searcher has identified a need and is looking for the best solution. These keywords are the core keywords from your earlier hard work, plus every question you think your searchers might have if they’re unfamiliar with your product or services.

Your informational queries might look something like:

  • [product name]
  • what is [product name]
  • how does [product name] work
  • how do I use [product name]

Commercial intent

At this stage, the searcher has zeroed in on a solution and is looking into all the different options available to them. They’re doing comparative research and are interested in specific requirements and features.

For our research, we used best, compare, deals, new, online, refurbished, reviews, shop, top, and used.

Your commercial queries might look something like:

  • best [product name]
  • [product name] reviews
  • compare [product name]
  • what is the top [product name]
  • [colour/style/size] [product name]

Transactional intent (including local and navigational intent)

Transactional queries are the most likely to convert and generally include terms that revolve around price, brand, and location, which is why navigational and local intent are nestled within this stage of the intent funnel.

For our research, we used affordable, buy, cheap, cost, coupon, free shipping, and price.

Your transactional queries might look something like:

  • how much does [product name] cost
  • [product name] in [location]
  • order [product name] online
  • [product name] near me
  • affordable [brand name] [product name]

A tip if you want to speed things up

A super quick way to add modifiers to your keywords and save your typing fingers is by using a keyword mixer like this one. Just don’t forget that using computer programs for human-speak means you’ll have to give them the ol’ once-over to make sure they still make sense.

Audit your list

Now that you’ve reached for the stars and got yourself a huge list of keywords, it’s time to bring things back down to reality and see which ones you’ll actually want to keep around.

No two audits are going to look the same, but here are a few considerations you’ll want to keep in mind when whittling your keywords down to the best of the bunch.

  1. Relevance. Are your keywords represented on your site? Do they point to optimized pages
  2. Search volume. Are you after highly searched terms or looking to build an audience? You can get the SV goods from the Google Keyword Planner.
  3. Opportunity. How many clicks and impressions are your keywords raking in? While not comprehensive (thanks, Not Provided), you can gather some of this info by digging into Google Search Console.
  4. Competition. What other websites are ranking for your keywords? Are you up against SERP monsters like Amazon? What about paid advertising like shopping boxes? How much SERP space are they taking up? Your friendly SERP analytics platform withshare of voice capabilities (hi!) can help you understand your search landscape.
  5. Difficulty. How easy is your keyword going to be to win? Search volume can give you a rough idea — the higher the search volume, the stiffer the competition is likely to be — but for a different approach, Moz’s Keyword Explorer has a Difficulty score that takes Page Authority, Domain Authority, and projected click-through-rate into account.

By now, you should have a pretty solid plan of attack to create an intent-based keyword list of your very own to love, nurture, and cherish.

If, before you jump headlong into it, you’re curious what a good chunk of this is going to looks like in practice, give this excellent article by Russ Jones a read, or drop us a line. We’re always keen to show folks why tracking keywords at scale is the best way to uncover intent-based insights.

Read on, readers!

More in our search intent series:

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SearchCap: Quora keyword targets, Angular SEO & old stock SEO

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



Please visit Search Engine Land for the full article.


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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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The Advanced Guide to Keyword Clustering

Posted by tomcasano

If your goal is to grow your organic traffic, you have to think about SEO in terms of “product/market fit.”

Keyword research is the “market” (what users are actually searching for) and content is the “product” (what users are consuming). The “fit” is optimization.

To grow your organic traffic, you need your content to mirror the reality of what users are actually searching for. Your content planning and creation, keyword mapping, and optimization should all align with the market. This is one of the best ways to grow your organic traffic.

Why bother with keyword grouping?

One web page can rank for multiple keywords. So why aren’t we hyper-focused on planning and optimizing content that targets dozens of similar and related keywords?

Why target only one keyword with one piece of content when you can target 20?

The impact of keyword clustering to acquire more organic traffic is not only underrated, it is largely ignored. In this guide, I’ll share with you our proprietary process we’ve pioneered for keyword grouping so you can not only do it yourself, but you can maximize the number of keywords your amazing content can rank for.

Here’s a real-world example of a handful of the top keywords that this piece of content is ranking for. The full list is over 1,000 keywords.

17 different keywords one page is ranking for

Why should you care?

It’d be foolish to focus on only one keyword, as you’d lose out on 90%+ of the opportunity.

Here’s one of my favorite examples of all of the keywords that one piece of content could potentially target:

List of ~100 keywords one page ranks for

Let’s dive in!

Part 1: Keyword collection

Before we start grouping keywords into clusters, we first need our dataset of keywords from which to group from.

In essence, our job in this initial phase is to find every possible keyword. In the process of doing so, we’ll also be inadvertently getting many irrelevant keywords (thank you, Keyword Planner). However, it’s better to have many relevant and long-tail keywords (and the ability to filter out the irrelevant ones) than to only have a limited pool of keywords to target.

For any client project, I typically say that we’ll collect anywhere from 1,000 to 6,000 keywords. But truth be told, we’ve sometimes found 10,000+ keywords, and sometimes (in the instance of a local, niche client), we’ve found less than 1,000.

I recommend collecting keywords from about 8–12 different sources. These sources are:

  1. Your competitors
  2. Third-party data tools (Moz, Ahrefs, SEMrush, AnswerThePublic, etc.)
  3. Your existing data in Google Search Console/Google Analytics
  4. Brainstorming your own ideas and checking against them
  5. Mashing up keyword combinations
  6. Autocomplete suggestions and “Searches related to” from Google

There’s no shortage of sources for keyword collection, and more keyword research tools exist now than ever did before. Our goal here is to be so extensive that we never have to go back and “find more keywords” in the future — unless, of course, there’s a new topic we are targeting.

The prequel to this guide will expand upon keyword collection in depth. For now, let’s assume that you’ve spent a few hours collecting a long list of keywords, you have removed the duplicates, and you have semi-reliable search volume data.

Part 2: Term analysis

Now that you have an unmanageable list of 1,000+ keywords, let’s turn it into something useful.

We begin with term analysis. What the heck does that mean?

We break each keyword apart into its component terms that comprise the keyword, so we can see which terms are the most frequently occurring.

For example, the keyword: “best natural protein powder” is comprised of 4 terms: “best,” “natural,” “protein,” and “powder.” Once we break apart all of the keywords into their component parts, we can more readily analyze and understand which terms (as subcomponents of the keywords) are recurring the most in our keyword dataset.

Here’s a sampling of 3 keywords:

  • best natural protein powder
  • most powerful natural anti inflammatory
  • how to make natural deodorant

Take a closer look, and you’ll notice that the term “natural” occurs in all three of these keywords. If this term is occurring very frequently throughout our long list of keywords, it’ll be highly important when we start grouping our keywords.

You will need a word frequency counter to give you this insight. The ultimate free tool for this is Write Words’ Word Frequency Counter. It’s magical.

Paste in your list of keywords, click submit, and you’ll get something like this:

List of keywords and how frequently they occur

Copy and paste your list of recurring terms into a spreadsheet. You can obviously remove prepositions and terms like “is,” “for,” and “to.”

You don’t always get the most value by just looking at individual terms. Sometimes a two-word or three-word phrase gives you insights you wouldn’t have otherwise. In this example, you see the terms “milk” and “almond” appearing, but it turns out that this is actually part of the phrase “almond milk.”

To gather these insights, use the Phrase Frequency Counter from WriteWords and repeat the process for phrases that have two, three, four, five, and six terms in them. Paste all of this data into your spreadsheet too.

A two-word phrase that occurs more frequently than a one-word phrase is an indicator of its significance. To account for this, I use the COUNTA function in Google Sheets to show me the number of terms in a phrase:

=COUNTA(SPLIT(B2," "))

Now we can look at our keyword data with a second dimension: not only the number of times a term or phrase occurs, but also how many words are in that phrase.

Finally, to give more weighting to phrases that recur less frequently but have more terms in them, I put an exponent on the number of terms with a basic formula:

=(C4^2)*A4

In other words, take the number of terms and raise it to a power, and then multiply that by the frequency of its occurrence. All this does is give more weighting to the fact that a two-word phrase that occurs less frequently is still more important than a one-word phrase that might occur more frequently.

As I never know just the right power to raise it to, I test several and keep re-sorting the sheet to try to find the most important terms and phrases in the sheet.

Spreadsheet of keywords and their weighted importance

When you look at this now, you can already see patterns start to emerge and you’re already beginning to understand your searchers better.

In this example dataset, we are going from a list of 10k+ keywords to an analysis of terms and phrases to understand what people are really asking. For example, “what is the best” and “where can i buy” are phrases we can absolutely understand searchers using.

I mark off the important terms or phrases. I try to keep this number to under 50 and to a maximum of around 75; otherwise, grouping will get hairy in Part 5.

Part 3: Hot words

What are hot words?

Hot words are the terms or phrases from that last section that we have deemed to be the most important. We’ve explained hot words in greater depth here.

Why are hot words important?

We explain:

This exercise provides us with a handful of the most relevant and important terms and phrases for traffic and relevancy, which can then be used to create the best content strategies — content that will rank highly and, in turn, help us reap traffic rewards for your site.

When developing your hot words list, we identify the highest frequency and most relevant terms from a large range of keywords used by several of your highest-performing competitors to generate their traffic, and these become “hot words.”

When working with a client (or doing this for yourself), there are generally 3 questions we want answered for each hot word:

  1. Which of these terms are the most important for your business? (0–10)
  2. Which of these terms are negative keywords (we want to ignore or avoid)?
  3. Any other feedback about qualified or high-intent keywords?

We narrow down the list, removing any negative keywords or keywords that are not really important for the website.

Once we have our final list of hot words, we organize them into broad topic groups like this:

Organized spreadsheet of hot words by topic

The different colors have no meaning, but just help to keep it visually organized for when we group them.

One important thing to note is that word stems play an important part here.

For example, consider that all of these words below have the same underlying relevance and meaning:

  • blog
  • blogs
  • blogger
  • bloggers
  • blogging

Therefore, when we’re grouping keywords, to consider “blog” and “blogging” and “bloggers” as part of the same cluster, we’ll need to use the word stem of “blog” for all of them. Word stems are our best friend when grouping. Synonyms can be organized in a similar way, which are basically two different ways of saying the same thing (and the same user intent) such as “build” and “create” or “search” and “look for.”

Part 4: Preparation for keyword grouping

Now we’re going to get ourselves set up for our Herculean task of clustering.

To start, copy your list of hot words and transpose them horizontally across a row.

Screenshot of menu in spreadsheet

List your keywords in the first column.

Screenshot of keyword spreadsheet

Now, the real magic begins.

After much research and noodling around, I discovered the function in Google Sheets that tells us whether a stem or term is in a keyword or not. It uses RegEx:

=IF(RegExMatch(A5,"health"),"YES","NO")

This simply tells us whether this word stem or word is in that keyword or not. You have to individually set the term for each column to get your “YES” or “NO” answer. I then drag this formula down to all of the rows to get all of the YES/NO answers. Google Sheets often takes a minute or so to process all of this data.

Next, we have to “hard code” these formulas so we can remove the NOs and be left with only a YES if that terms exists in that keyword.

Copy all of the data and “Paste values only.”

Screenshot of spreadsheet menu

Now, use “Find and replace” to remove all of the NOs.

Screenshot of Find and Replace popup

What you’re left with is nothing short of a work of art. You now have the most powerful way to group your keywords. Let the grouping begin!

Screenshot of keyword spreadsheet

Part 5: Keyword grouping

At this point, you’re now set up for keyword clustering success.

This part is half art, half science. No wait, I take that back. To do this part right, you need:

  • A deep understanding of who you’re targeting, why they’re important to the business, user intent, and relevance
  • Good judgment to make tradeoffs when breaking keywords apart into groups
  • Good intuition

This is one of the hardest parts for me to train anyone to do. It comes with experience.

At the top of the sheet, I use the COUNTA function to show me how many times this word step has been found in our keyword set:

=COUNTA(C3:C10000)

This is important because as a general rule, it’s best to start with the most niche topics that have the least overlap with other topics. If you start too broadly, your keywords will overlap with other keyword groups and you’ll have a hard time segmenting them into meaningful groups. Start with the most narrow and specific groups first.

To begin, you want to sort the sheet by word stem.

The word stems that occur only a handful of times won’t have a large amount of overlap. So I start by sorting the sheet by that column, and copying and pasting those keywords into their own new tab.

Now you have your first keyword group!

Here’s a first group example: the “matcha” group. This can be its own project in its own right: for instance, if a website was all about matcha tea and there were other tangentially related keywords.

Screenshot of list of matcha-related keywords

As we continue breaking apart one keyword group and then another, by the end we’re left with many different keyword groups. If the groups you’ve arrived at are too broad, you can subdivide them even more into narrower keyword subgroups for more focused content pieces. You can follow the same process for this broad keyword group, and make it a microcosm of the same process of dividing the keywords into smaller groups based on word stems.

We can create an overview of the groups to see the volume and topical opportunities from a high level.

Screenshot of spreadsheet with keyword group overview

We want to not only consider search volume, but ideally also intent, competitiveness, and so forth.

Voilà!

You’ve successfully taken a list of thousands of keywords and grouped them into relevant keyword groups.

Wait, why did we do all of this hard work again?

Now you can finally attain that “product/market fit” we talked about. It’s magical.

You can take each keyword group and create a piece of optimized content around it, targeting dozens of keywords, exponentially raising your potential to acquire more organic traffic. Boo yah!

All done. Now what?

Now the real fun begins. You can start planning out new content that you never knew you needed to create. Alternatively, you can map your keyword groups (and subgroups) to existing pages on your website and add in keywords and optimizations to the header tags, body text, and so forth for all those long-tail keywords you had ignored.

Keyword grouping is underrated, overlooked, and ignored at large. It creates a massive new opportunity to optimize for terms where none existed. Sometimes it’s just adding one phrase or a few sentences targeting a long-tail keyword here and there that will bring in that incremental search traffic for your site. Do this dozens of times and you will keep getting incremental increases in your organic traffic.

What do you think?

Leave a comment below and let me know your take on keyword clustering.

Need a hand? Just give me a shout, I’m happy to help.

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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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