seo

Posted by willcritchlow

If you’re a big site competing to rank for popular head terms, where’s the best place to focus your content strategy? According to a hypothesis by the good folks at Distilled, the answer may lie in perfectly satisfying searcher intent.

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If you haven’t heard the news, the Domain Authority metric discussed in this episode will be updated on March 5th, 2019 to better correlate with Google algorithm changes. Learn about what’s changing below:

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Video Transcription

Hi, Whiteboard Friday fans. I’m Will Critchlow, one of the founders at Distilled, and what I want to talk about today is joining the dots between some theoretical work that some of my colleagues have been doing and some of the client work that we’ve been doing recently and the results that we’ve been seeing from that in the wild and what I think it means for strategies for different-sized sites going on from here.

Correlations and a hypothesis

The beginning of this I credit to one of my colleagues, Tom Capper, THCapper on Twitter, who presented at our Search Love London conference a presentation entitled “The Two-Tiered SERP,” and I’m going to describe what that means in just a second. But what I’m going to do today is talk about what I think that the two-tiered SERP means for content strategy going forward and base that a little bit on some of what we’re seeing in the wild with some of our client projects.

What Tom presented at Search Love London was he started by looking at the fact that the correlation between domain authority and rankings has decreased over time. So he pulled out some stats from February 2017 and looked at those same stats 18 months later and saw a significant drop in the correlation between domain authority and rankings. This ties into a bunch of work that he’s done and presented elsewhere around potentially less reliance on links going forward and some other data that Google might be using, some other metrics and ranking factors that they might be using in their place, particularly branded metrics and so forth.

But Tom saw this drop and had a hypothesis that it wasn’t just an across-the-board drop. This wasn’t just Google not using links anymore or using links less. It was actually a more granular effect than that. This is the two-tiered SERP or what we mean by the two-tiered SERP. So a search engine result page, a SERP, you’ve got some results at the top and some results further down the page.

What Tom found — he had this hypothesis that was born out in the data — was that the correlation between domain authority and rankings was much higher among the positions 6 through 10 than it was among the top half of the search results page and that this can be explained by essentially somewhat traditional ranking factors lower down the page and in lower competition niches and that at the top of the page, where there’s more usage data, greater search volume and so forth in these top positions, that traditional ranking factors played less of a part.

They maybe get you into the consideration set. There are no domains ranking up here that are very, very weak. But once you’re in the consideration set, there’s much less of a correlation between these different positions. So it’s still true on average that these positions 1 through 5 are probably more authoritative than the sites that are appearing in lower positions. But within this set there’s less predictive value.

The domain authority is less predictive of ranking within this set than it is of ranking within this set. So this is the two-tiered SERP, and this is consistent with a bunch of data that we’ve seen across the place and in particular with the outcomes that we’re seeing among content campaigns and content strategies for different kinds of sites.

At Distilled, we get quite a lot of clients coming to us wanting either a content strategy put together or in some cases coming to us essentially with their content strategy and saying, “Can you execute this? Can you help us execute this plan?” It’s very common for that plan to be, “We want to create a bunch of big pieces of content that get a ton of links, and we’re going to use that link authority to make our site more authoritative and that is going to result in our whole site doing better and ranking better.”

An anonymized case study

We’ve seen that that is performing differently in different cases, and in particular it’s performing better on smaller sites than it is on big sites. So this is a little anonymized case study. This is a real example of a story that happened with one of our consulting clients where we put in place a content strategy for them that did include a plan to build the domain authority because this was a site that came to us with a domain authority significantly below that of their key competitors, also with all of these sites not having a ton of domain authority.

This was working in a B2B space, relatively small domains. They came to us with that, and we figured that actually growing the authority was a key part of this content strategy and over the next 18 months put out a bunch of pieces that have done really well and generated a ton of press coverage and traction and things. Over that time, they’ve actually outstripped their key competitors in the domain authority metrics, and crucially we saw that tie directly to increases in traffic that went hand-in-hand with this increase in domain authority.

But this contrasts to what we’ve seen with some much larger sites in much more competitive verticals where they’re already very, very high domain authority, maybe they’re already stronger than some of their competitors and adding to that. So adding big content pieces that get even more big authoritative links has not moved the needle in the way that it might have done a few years ago.

That’s totally consistent with this kind of setup, where if you are currently trying to edge in the bottom or you’re competing for less competitive search terms, then this kind of approach might really work for you and it might, in fact, be necessary to get into the consideration set for the more competitive end. But if you’re operating on a much bigger site, you’ve already got the competitive domain authority, you and your competitors are all very powerful sites, then our kind of hypothesis is that you’re going to be needing to look more towards the user experience, the conversion rate, and intent research.

Are you satisfying searcher intent for competitive head terms?

What is somebody who performs this search actually looking to do? Can you satisfy that intent? Can you make sure that they don’t bounce back to the search results and click on a competitor? Can you make sure that in fact they stay on your site, they get done the thing they want to get done, and it all works out for them, because we think that these kinds of things are going to be much more powerful for moving up through the very top end of the most competitive head terms.

So when we’re working on a content strategy or putting our creative team to work on these kinds of things on bigger sites, we’re more likely to be creating content directly designed to rank. We might be creating content based off a ton of this research, and we’re going to be incrementally improving those things to try and say, “Have we actually satisfied the perfect intent for this super competitive head term?”

What we’re seeing is that’s more likely to move the needle up at this top end than growing the domain authority on a big site. So I hope you found that interesting. I’m looking forward to a vigorous discussion in the comments on this one. But thank you for joining me for this week’s Whiteboard Friday. I’ve been Will Critchlow from Distilled. Take care.

Video transcription by Speechpad.com

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This post was originally published on the STAT blog.

We all know that featured snippets provide easy-to-read, authoritative answers and that digital assistants love to say them out loud when asked questions.

This means that featured snippets have an impact on voice search — bad snippets, or no snippets at all, and digital assistants struggle. By that logic: Create a lot of awesome snippets and win the voice search race. Right?

Right, but there’s actually a far more interesting angle to examine — one that will help you nab more snippets and optimize for voice search at the same time. In order to explore this, we need to make like Doctor Who and go back in time.

From typing to talking

Back when dinosaurs roamed the earth and queries were typed into search engines via keyboards, people adapted to search engines by adjusting how they performed queries. We pulled out unnecessary words and phrases, like “the,” “of,” and, well, “and,” which created truncated requests — robotic-sounding searches for a robotic search engine.

Of course, as search engines have evolved, so too has their ability to understand natural language patterns and the intent behind queries. Google’s 2013 Hummingbird update helped pave the way for such evolution. This algorithm rejigging allowed Google’s search engine to better understand the whole of a query, moving it away from keyword matching to conversation having.

This is good news if you’re a human person: We have a harder time changing the way we speak than the way we write. It’s even greater news for digital assistants, because voice search only works if search engines can interpret human speech and engage in chitchat.

Digital assistants and machine learning

By looking at how digital assistants do their voice search thing (what we say versus what they search), we can see just how far machine learning has come with natural language processing and how far it still has to go (robots, they’re just like us!). We can also get a sense of the kinds of queries we need to be tracking if voice search is on the SEO agenda.

For example, when we asked our Google Assistant, “What are the best headphones for $100,” it queried [best headphones for $100]. We followed that by asking, “What about wireless,” and it searched [best wireless headphones for $100]. And then we remembered that we’re in Canada, so we followed that with, “I meant $100 Canadian,” and it performed a search for [best wireless headphones for $100 Canadian].

We can learn two things from this successful tête-à-tête: Not only does our Google Assistant manage to construct mostly full-sentence queries out of our mostly full-sentence asks, but it’s able to accurately link together topical queries. Despite us dropping our subject altogether by the end, Google Assistant still knows what we’re talking about.

Of course, we’re not above pointing out the fumbles. In the string of: “How to bake a Bundt cake,” “What kind of pan does it take,” and then “How much do those cost,” the actual query Google Assistant searched for the last question was [how much does bundt cake cost].

Just after we finished praising our Assistant for being able to maintain the same subject all the way through our inquiry, we needed it to be able to switch tracks. And it couldn’t. It associated the “those” with our initial Bundt cake subject instead of the most recent noun mentioned (Bundt cake pans).

In another important line of questioning about Bundt cake-baking, “How long will it take” produced the query [how long does it take to take a Bundt cake], while “How long does that take” produced [how long does a Bundt cake take to bake].

They’re the same ask, but our Google Assistant had a harder time parsing which definition of “take” our first sentence was using, spitting out a rather awkward query. Unless we really did want to know how long it’s going to take us to run off with someone’s freshly baked Bundt cake? (Don’t judge us.)

Since Google is likely paying out the wazoo to up the machine learning ante, we expect there to be less awkward failures over time. Which is a good thing, because when we asked about Bundt cake ingredients (“Does it take butter”) we found ourselves looking at a SERP for [how do I bake a butter].

Not that that doesn’t sound delicious.

Snippets are appearing for different kinds of queries

So, what are we to make of all of this? That we’re essentially in the midst of a natural language renaissance. And that voice search is helping spearhead the charge.

As for what this means for snippets specifically? They’re going to have to show up for human speak-type queries. And wouldn’t you know it, Google is already moving forward with this strategy, and not simply creating more snippets for the same types of queries. We’ve even got proof.

Over the last two years, we’ve seen an increase in the number of words in a query that surfaces a featured snippet. Long-tail queries may be a nuisance and a half, but snippet-having queries are getting longer by the minute.

When we bucket and weight the terms found in those long-tail queries by TF-IDF, we get further proof of voice search’s sway over snippets. The term “how” appears more than any other word and is followed closely by “does,” “to,” “much,” “what,” and “is” — all words that typically compose full sentences and are easier to remove from our typed searches than our spoken ones.

This means that if we want to snag more snippets and help searchers using digital assistants, we need to build out long-tail, natural-sounding keyword lists to track and optimize for.

Format your snippet content to match

When it’s finally time to optimize, one of the best ways to get your content into the ears of a searcher is through the right snippet formatting, which is a lesson we can learn from Google.

Taking our TF-IDF-weighted terms, we found that the words “best” and “how to” brought in the most list snippets of the bunch. We certainly don’t have to think too hard about why Google decided they benefit from list formatting — it provides a quick comparative snapshot or a handy step-by-step.

From this, we may be inclined to format all of our “best” and “how to” keyword content into lists. But, as you can see in the chart above, paragraphs and tables are still appearing here, and we could be leaving snippets on the table by ignoring them. If we have time, we’ll dig into which keywords those formats are a better fit for and why.

Get tracking

You could be the Wonder Woman of meta descriptions, but if you aren’t optimizing for the right kind of snippets, then your content’s going to have a harder time getting heard. Building out a voice search-friendly keyword list to track is the first step to lassoing those snippets.

Want to learn how you can do that in STAT? Say hello and request a tailored demo.

Need more snippets in your life? We dug into Google’s double-snippet SERPs for you — double the snippets, double the fun.

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Posted by BrankoK

Too often do you see SEO analyses and decisions being made without considering the context of the marketing channel mix. Equally as often do you see large budgets being poured into paid ads in ways that seem to forget there’s a whole lot to gain from catering to popular search demand.

Both instances can lead to leaky conversion funnels and missed opportunity for long term traffic flows. But this article will show you a case of an SEO context analysis we used to determine the importance and role of SEO.

This analysis was one of our deliverables for a marketing agency client who hired us to inform SEO decisions which we then turned into a report template for you to get inspired by and duplicate.

Case description

The included charts show real, live data. You can see the whole SEO channel context analysis in this Data Studio SEO report template.

The traffic analyzed is for of a monetizing blog, whose marketing team also happens to be one of most fun to work for. For the sake of this case study, we’re giving them a spectacular undercover name — “The Broze Fellaz.”

For context, this blog started off with content for the first two years before they launched their flagship product. Now, they sell a catalogue of products highly relevant to their content and, thanks to one of the most entertaining Shark Tank episodes ever aired, they have acquired investments and a highly engaged niche community.

As you’ll see below, organic search is their biggest channel in many ways. Facebook also runs both as organic and paid and the team spends many an hour inside the platform. Email has elaborate automated flows that strive to leverage subscribers that come from the stellar content on the website. We therefore chose the three — organic Search, Facebook, and email — as a combination that would yield a comprehensive analysis with insights we can easily act on.

Ingredients for the SEO analysis

This analysis is a result of a long-term retainer relationship with “The Broze Fellaz” as our ongoing analytics client. A great deal was required in order for data-driven action to happen, but we assure you, it’s all doable.

From the analysis best practice drawer, we used:

• 2 cups of relevant channels for context and analysis via comparison.
• 3 cups of different touch points to identify channel roles — bringing in traffic, generating opt-ins, closing sales, etc.
• 5 heads of open-minded lettuce and readiness to change current status quo, for a team that can execute.
• 457 oz of focus-on-finding what is going on with organic search, why it is going on, and what we can do about it (otherwise, we’d end up with another scorecard export).
• Imperial units used in arbitrary numbers that are hard to imagine and thus feel very large.
• 1 to 2 heads of your analyst brain, baked into the analysis. You’re not making an automated report — even a HubSpot intern can do that. You’re being a human and you’re analyzing. You’re making human analysis. This helps avoid having your job stolen by a robot.
• Full tray of Data Studio visualizations that appeal to the eye.
• Sprinkles of benchmarks, for highlighting significance of performance differences.

From the measurement setup and stack toolbox, we used:

• Google Analytics with tailored channel definitions, enhanced e-commerce and Search Console integration.
• Event tracking for opt-ins and adjusted bounce rate via MashMetrics GTM setup framework.
• UTM routine for social and email traffic implemented via Google Sheets & UTM.io.
• Google Data Studio. This is my favorite visualization tool. Despite its flaws and gaps (as it’s still in beta) I say it is better than its paid counterparts, and it keeps getting better. For data sources, we used the native connectors for Google Analytics and Google Sheets, then Facebook community connectors by Supermetrics.
• Keyword Hero. Thanks to semantic algorithms and data aggregation, you are indeed able to see 95 percent of your organic search queries (check out Onpage Hero, too, you’ll be amazed).

Inspiration for my approach comes from Lea Pica, Avinash, the Google Data Studio newsletter, and Chris Penn, along with our dear clients and the questions they have us answer for them.

Ready? Let’s dive in.

Analysis of the client’s SEO on the context of their channel mix

1) Insight: Before the visit

What’s going on and why is it happening?

Organic search traffic volume blows the other channels out of the water. This is normal for sites with quality regular content; yet, the difference is stark considering the active effort that goes into Facebook and email campaigns.

The CTR of organic search is up to par with Facebook. That’s a lot to say when comparing an organic channel to a channel with high level of targeting control.

It looks like email flows are the clear winner in terms of CTR to the website, which has a highly engaged community of users who return fairly often and advocate passionately. It also has a product and content that’s incredibly relevant to their users, which few other companies appear to be good at.

There’s a high CTR on search engine results pages often indicates that organic search may support funnel stages beyond just the top.

As well, email flows are sent to a very warm audience — interested users who went through a double opt-in. It is to be expected for this CTR to be high.

What’s been done already?

There’s an active effort and budget allocation being put towards Facebook Ads and email automation. A content plan has been put in place and is being executed diligently.

What we recommend next

1. Approach SEO in a way as systematic as what you do for Facebook and email flows.
2. Optimize meta titles and descriptions via testing tools such as Sanity Check. The organic search CTR may become consistently higher than that of Facebook ads.
3. Assuming you’ve worked on improving CTR for Facebook ads, have the same person work on the meta text and titles. Most likely, there’ll be patterns you can replicate from social to SEO.
4. Run a technical audit and optimize accordingly. Knowing that you haven’t done that in a long time, and seeing how much traffic you get anyway, there’ll be quick, big wins to enjoy.

Results we expect

You can easily increase the organic CTR by at least 5 percent. You could also clean up the technical state of your site in the eyes of crawlers -— you’ll then see faster indexing by search engines when you publish new content, increased impressions for existing content. As a result, you may enjoy a major spike within a month.

2) Insight: Engagement and options during the visit

With over 70 percent of traffic coming to this website from organic search, the metrics in this analysis will be heavily skewed towards organic search. So, comparing the rate for organic search to site-wide is sometimes conclusive, other times not conclusive.

Adjusted bounce rate — via GTM events in the measurement framework used, we do not count a visit as a bounce if the visit lasts 45 seconds or longer. We prefer this approach because such an adjusted bounce rate is much more actionable for content sites. Users who find what they were searching for often read the page they land on for several minutes without clicking to another page. However, this is still a memorable visit for the user. Further, staying on the landing page for a while, or keeping the page open in a browser tab, are both good indicators for distinguishing quality, interested traffic, from all traffic.

We included all Facebook traffic here, not just paid. We know from the client’s data that the majority is from paid content, they have a solid UTM routine in place. But due to boosted posts, we’ve experienced big inaccuracies when splitting paid and organic Facebook for the purposes of channel attribution.

What’s going on and why is it happening?

It looks like organic search has a bounce rate worse than the email flows — that’s to be expected and not actionable, considering that the emails are only sent to recent visitors who have gone through a double opt-in. What is meaningful, however, is that organic has a better bounce rate than Facebook. It is safe to say that organic search visitors will be more likely to remember the website than the Facebook visitors.

Opt-in rates for Facebook are right above site average, and those for organic search are right below, while organic is bringing in a majority of email opt-ins despite its lower opt-in rate.

Google’s algorithms and the draw of the content on this website are doing better at winning users’ attention than the detailed targeting applied on Facebook. The organic traffic will have a higher likelihood of remembering the website and coming back. Across all of our clients, we find that organic search can be a great retargeting channel, particularly if you consider that the site will come up higher in search results for its recent visitors.

What’s been done already?

The Facebook ad campaigns of “The Broze Fellaz” have been built and optimized for driving content opt-ins. Site content that ranks in organic search is less intentional than that.

Opt-in placements have been tested on some of the biggest organic traffic magnets.

Thorough, creative and consistent content calendars have been in place as a foundation for all channels.

What we recommend next

1. It’s great to keep using organic search as a way to introduce new users to the site. Now, you can try to be more intentional about using it for driving opt-ins. It’s already serving both of the stages of the funnel.
2. Test and optimize opt-in placements on more traffic magnets.
3. Test and optimize opt-in copy for top 10 traffic magnets.
4. Once your opt-in rates have improved, focus on growing the channel. Add to the content work with a 3-month sprint of an extensive SEO project
5. Assign Google Analytics goal values to non-e-commerce actions on your site. The current opt-ins have different roles and levels of importance and there’s also a handful of other actions people can take that lead to marketing results down the road. Analyzing goal values will help you create better flows toward pre-purchase actions.
6. Facebook campaigns seem to be at a point where you can pour more budget into them and expect proportionate increase in opt-in count.

Results we expect

Growth in your opt-ins from Facebook should be proportionate to increase in budget, with a near-immediate effect. At the same time, it’s fairly realistic to bring the opt-in rate of organic search closer to site average.

3) Insight: Closing the deal

For channel attribution with money involved, you want to make sure that your Google Analytics channel definitions, view filters, and UTM’s are in top shape.

What’s going on and why is it happening?

Transaction rate, as well as per session value, is higher for organic search than it is for Facebook (paid and organic combined).

Organic search contributes to far more last-click revenue than Facebook and email combined. For its relatively low volume of traffic, email flows are outstanding in the volume of revenue they bring in.

Thanks to the integration of Keyword Hero with Google Analytics for this client, we can see that about 30 percent of organic search visits are from branded keywords, which tends to drive the transaction rate up.

So, why is this happening? Most of the product on the site is highly relevant to the information people search for on Google.

Multi-channel reports in Google Analytics also show that people often discover the site in organic search, then come back by typing in the URL or clicking a bookmark. That makes organic a source of conversions where, very often, no other channels are even needed.

We can conclude that Facebook posts and campaigns of this client are built to drive content opt-ins, not e-commerce transactions. Email flows are built specifically to close sales.

What’s been done already?

There is dedicated staff for Facebook campaigns and posts, as well a thorough system dedicated to automated email flows.

A consistent content routine is in place, with experienced staff at the helm. A piece has been published every week for the last few years, with the content calendar filled with ready-to-publish content for the next few months. The community is highly engaged, reading times are high, comment count soaring, and usefulness of content outstanding. This, along with partnerships with influencers, helps “The Broze Fellaz” take up a half of the first page on the SERP for several lucrative topics. They’ve been achieving this even without a comprehensive SEO project. Content seems to be king indeed.

Google Shopping has been tried. The campaign looked promising but didn’t yield incremental sales. There’s much more search demand for informational queries than there is for product.

What we recommend next

1. Organic traffic is ready to grow. If there is no budget left, resource allocation should be considered. In paid search, you can often simply increase budgets. Here, with stellar content already performing well, a comprehensive SEO project is begging for your attention. Focus can be put into structure and technical aspects, as well as content that better caters to search demand. Think optimizing the site’s information architecture, interlinking content for cornerstone structure, log analysis, and technical cleanup, meta text testing for CTR gains that would also lead to ranking gains, strategic ranking of long tail topics, intentional growing of the backlink profile.
2. Three- or six-month intensive sprint of comprehensive SEO work would be appropriate.

Results we expect

Increasing last click revenue from organic search and direct by 25 percent would lead to a gain as high as all of the current revenue from automated email flows. Considering how large the growth has been already, this gain is more than achievable in 3–6 months.

Wrapping it up

Organic search presence of “The Broze Fellaz” should continue to be the number-one role for bringing new people to the site and bringing people back to the site. Doing so supports sales that happen with the contribution of other channels, e.g. email flows. The analysis points out is that organic search is also effective at playing the role of the last-click channel for transactions, often times without the help of other channels.

We’ve worked with this client for a few years, and, based on our knowledge of their marketing focus, this analysis points us to a confident conclusion that a dedicated, comprehensive SEO project will lead to high incremental growth.

Your turn

In drawing analytical conclusions and acting on them, there’s always more than one way to shoe a horse. Let us know what conclusions you would’ve drawn instead. Copy the layout of our SEO Channel Context Comparison analysis template and show us what it helped you do for your SEO efforts — create a similar analysis for a paid or owned channel in your mix. Whether it’s comments below, tweeting our way, or sending a smoke signal, we’ll be all ears. And eyes.

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!

Posted by TheMozTeam

This blog post was originally published on the STAT blog.

The first step to getting the most out of your SERP data is smart keyword segmentation — it surfaces targeted insights that will help you make data-driven decisions.

But knowing what to segment can feel daunting, especially when you’re working with thousands of keywords. That’s why we’re arming you with a handful of must-have tags.

Follow along as we walk through the different kinds of segments in STAT, how to create them, and which tags you’ll want to get started with. You’ll be a fanciful segment connoisseur by the time we’re through!

Segmentation in STAT

In STAT, keyword segments are called “tags” and come as two different types: standard or dynamic.

Standard tags are best used when you want to keep specific keywords grouped together because of shared characteristics — like term (brand, product type, etc), location, or device. Standard tags are static, so the keywords that populate those segments won’t change unless you manually add or remove them.

Dynamic tags, on the other hand, are a fancier kind of tag based on filter criteria. Just like a smart playlist, dynamic tags automatically populate with all of the keywords that meet said criteria, such as keywords with a search volume over 500 that rank on page one. This means that the keywords in a dynamic tag aren’t forever — they’ll filter in and out depending on the criteria you’ve set.

How to create a keyword segment

Tags are created in a few easy steps. At the Site level, pop over to the Keywords tab, click the down arrow on any table column header, and then select Filter keywords. From there, you can select the pre-populated options or enter your own metrics for a choose-your-own-filter adventure.

Once your filters are in place, simply click Tag All Filtered Keywords, enter a new tag name, and then pick the tag type best suited to your needs — standard or dynamic — and voila! You’ve created your very own segment.

Segments to get you started

Now that you know how to set up a tag, it’s time to explore some of the different segments you can implement and the filter criteria you’ll need to apply.

Rank and rank movement

Tracking your rank and ranking movements with dynamic tags will give you eyeballs on your keyword performance, making it easy to monitor and report on current and historical trends.

There’s a boatload of rank segments you can set up, but here’s just a sampling to get you started:

• Keywords ranking in position 1–3; this will identify your top performing keywords.
• Keywords ranking in position 11–15; this will suss out the low-hanging, top of page two fruit in need of a little nudge.
• Keywords with a rank change of 10 or more (in either direction); this will show you keywords that are slipping off or shooting up the SERP.

Appearance and ownership of SERP features

Whether they’re images, carousels, or news results, SERP features have significantly altered the search landscape. Sometimes they push you down the page and other times, like when you manage to snag one, they can give you a serious leg up on the competition and drive loads more traffic to your site.

Whatever industry-related SERP features that you want to keep apprised of, you can create dynamic tags that show you the prevalence and movement of them within your keyword set. Segment even further for tags that show which keywords own those features and which have fallen short.

Below are a few segments you can set up for featured snippets and local packs.

Featured snippets

Everyone’s favourite SERP feature isn’t going anywhere anytime soon, so it wouldn’t be a bad idea to outfit yourself with a snippet tracking strategy. You can create as many tags as there are snippet options to choose from:

• Keywords with a featured snippet.
• Keywords with a paragraph, list, table, and/or carousel snippet.
• Keywords with an owned paragraph, list, table, and/or carousel snippet.
• Keywords with an unowned paragraph, list, table, and/or carousel snippet.

The first two will allow you to see over-arching snippet trends, while the last two will chart your ownership progress.

If you want to know the URL that’s won you a snippet, just take a peek at the URL column.

Local packs

If you’re a brick and mortar business, we highly advise creating tags for local packs since they provide a huge opportunity for exposure. These two tags will show you which local packs you have a presence in and which you need to work on

• Keywords with an owned local pack.
• Keywords with an unowned local pack.

Want all the juicy data squeezed into a local pack, like who’s showing up and with what URL? We created the Local pack report just for that.

Landing pages, subdomains, and other important URLs

Whether you’re adding new content or implementing link-building strategies around subdomains and landing pages, dynamic tags allow you to track and measure page performance, see whether your searchers are ending up on the pages you want, and match increases in page traffic with specific keywords.

For example, are your informational intent keywords driving traffic to your product pages instead of your blog? To check, a tag that includes your blog URL will pull in each post that ranks for one of your keywords.

Try these three dynamic tags for starters:

• Keywords ranking for a landing page URL.
• Keywords ranking for a subdomain URL.
• Keywords ranking for a blog URL.

Is a page not indexed yet? That’s okay. You can still create a dynamic tag for its URL and keywords will start appearing in that segment when Google finally gets to it.

Location, location, location

Google cares a lot about location and so should you, which is why keyword segments centred around location are essential. You can tag in two ways: by geo-modifier and by geo-location.

For these, it’s better to go with the standard tag as the search term and location are fixed to the keyword.

Geo-modifier

A geo-modifier is the geographical qualifier that searchers manually include in their query — like in [sushi near me]. We advocate for adding various geo-modifiers to your keywords and then incorporating them into your tagging strategy. For instance, you can segment by:

• Keywords with “in [city]” in them.
• Keywords with “near me” in them.

The former will show you how you fare for city-wide searches, while the latter will let you see if you’re meeting the needs of searchers looking for nearby options.

Geo-location

Geo-location is where the keyword is being tracked. More tracked locations mean more searchers’ SERPs to sample. And the closer you can get to searchers standing on a street corner, the more accurate those SERPs will be. This is why we strongly recommend you track in multiple pin-point locations in every market you serve.

Once you’ve got your tracking strategy in place, get your segmentation on. You can filter and tag by:

• Keywords tracked in specific locations; this will let you keep tabs on geographical trends.
• Keywords tracked in each market; this will allow for market-level research.

Search volume & cost-per-click

Search volume might be a contentious metric thanks to Google’s close variants, but having a decent idea of what it’s up to is better than a complete shot in the dark. We suggest at least two dynamic segments around search volume:

• Keywords with high search volume; this will show which queries are popular in your industry and have the potential to drive the most traffic.
• Keywords with low search volume; this can actually help reveal conversion opportunities — remember, long-tail keywords typically have lower search volumes but higher conversion rates.

Tracking the cost-per-click of your keywords will also bring you and your PPC team tonnes of valuable insights — you’ll know if you’re holding the top organic spot for an outrageously high CPC keyword.

As with search volume, tags for high and low CPC should do you just fine. High CPC keywords will show you where the competition is the fiercest, while low CPC keywords will surface your easiest point of entry into the paid game — queries you can optimize for with less of a fight.

Device type

From screen size to indexing, desktop and smartphones produce substantially different SERPs from one another, making it essential to track them separately. So, filter and tag for:

• Keywords tracked on a desktop.
• Keywords tracked on a smartphone.

Similar to your location segments, it’s best to use the standard tag here.

Go crazy with multiple filters

We’ve shown you some really high-level segments, but you can actually filter down your keywords even further. In other words, you can get extra fancy and add multiple filters to a single tag. Go as far as high search volume, branded keywords triggering paragraph featured snippets that you own for smartphone searchers in the downtown core. Phew!

Want to make talk shop about segmentation or see dynamic tags in action? Say hello (don’t be shy) and request a demo.

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Posted by scott.taft

We’ve been talking a lot about search intent this week, and if you’ve been following along, you’re likely already aware of how “search intent” is essential for a robust SEO strategy. If, however, you’ve ever laboured for hours classifying keywords by topic and search intent, only to end up with a ton of data you don’t really know what to do with, then this post is for you.

I’m going to share how to take all that sweet keyword data you’ve categorized, put it into a Power BI dashboard, and start slicing and dicing to uncover a ton insights — faster than you ever could before.

Building your keyword list

Every great search analysis starts with keyword research and this one is no different. I’m not going to go into excruciating detail about how to build your keyword list. However, I will mention a few of my favorite tools that I’m sure most of you are using already:

• Search Query Report — What better place to look first than the search terms already driving clicks and (hopefully) conversions to your site.
• Answer The Public — Great for pulling a ton of suggested terms, questions and phrases related to a single search term.
• InfiniteSuggest — Like Answer The Public, but faster and allows you to build based on a continuous list of seed keywords.
• MergeWords — Quickly expand your keywords by adding modifiers upon modifiers.
• Grep Words — A suite of keyword tools for expanding, pulling search volume and more.

Please note that these tools are a great way to scale your keyword collecting but each will come with the need to comb through and clean your data to ensure all keywords are at least somewhat relevant to your business and audience.

Once I have an initial keyword list built, I’ll upload it to STAT and let it run for a couple days to get an initial data pull. This allows me to pull the ‘People Also Ask’ and ‘Related Searches’ reports in STAT to further build out my keyword list. All in all, I’m aiming to get to at least 5,000 keywords, but the more the merrier.

For the purposes of this blog post I have about 19,000 keywords I collected for a client in the window treatments space.

Categorizing your keywords by topic

Bucketing keywords into categories is an age-old challenge for most digital marketers but it’s a critical step in understanding the distribution of your data. One of the best ways to segment your keywords is by shared words. If you’re short on AI and machine learning capabilities, look no further than a trusty Ngram analyzer. I love to use this Ngram Tool from guidetodatamining.com — it ain’t much to look at, but it’s fast and trustworthy.

After dropping my 19,000 keywords into the tool and analyzing by unigram (or 1-word phrases), I manually select categories that fit with my client’s business and audience. I also make sure the unigram accounts for a decent amount of keywords (e.g. I wouldn’t pick a unigram that has a count of only 2 keywords).

Using this data, I then create a Category Mapping table and map a unigram, or “trigger word”, to a Category like the following:

You’ll notice that for “curtain” and “drapes” I mapped both to the Curtains category. For my client’s business, they treat these as the same product, and doing this allows me to account for variations in keywords but ultimately group them how I want for this analysis.

Using this method, I create a Trigger Word-Category mapping based on my entire dataset. It’s possible that not every keyword will fall into a category and that’s okay — it likely means that keyword is not relevant or significant enough to be accounted for.

Creating a keyword intent map

Similar to identifying common topics by which to group your keywords, I’m going to follow a similar process but with the goal of grouping keywords by intent modifier.

Search intent is the end goal of a person using a search engine. Digital marketers can leverage these terms and modifiers to infer what types of results or actions a consumer is aiming for.

For example, if a person searches for “white blinds near me”, it is safe to infer that this person is looking to buy white blinds as they are looking for a physical location that sells them. In this case I would classify “near me” as a “Transactional” modifier. If, however, the person searched “living room blinds ideas” I would infer their intent is to see images or read blog posts on the topic of living room blinds. I might classify this search term as being at the “Inspirational” stage, where a person is still deciding what products they might be interested and, therefore, isn’t quite ready to buy yet.

There is a lot of research on some generally accepted intent modifiers in search and I don’t intent to reinvent the wheel. This handy guide (originally published in STAT) provides a good review of intent modifiers you can start with.

I followed the same process as building out categories to build out my intent mapping and the result is a table of intent triggers and their corresponding Intent stage.

Intro to Power BI

There are tons of resources on how to get started with the free tool Power BI, one of which is from own founder Will Reynold’s video series on using Power BI for Digital Marketing. This is a great place to start if you’re new to the tool and its capabilities.

Note: it’s not about the tool necessarily (although Power BI is a super powerful one). It’s more about being able to look at all of this data in one place and pull insights from it at speeds which Excel just won’t give you. If you’re still skeptical of trying a new tool like Power BI at the end of this post, I urge you to get the free download from Microsoft and give it a try.

Setting up your data in Power BI

Power BI’s power comes from linking multiple datasets together based on common “keys.” Think back to your Microsoft Access days and this should all start to sound familiar.

Step 1: Upload your data sources

First, open Power BI and you’ll see a button called “Get Data” in the top ribbon. Click that and then select the data format you want to upload. All of my data for this analysis is in CSV format so I will select the Text/CSV option for all of my data sources. You have to follow these steps for each data source. Click “Load” for each data source.

Step 2: Clean your data

In the Power BI ribbon menu, click the button called “Edit Queries.” This will open the Query Editor where we will make all of our data transformations.

The main things you’ll want to do in the Query Editor are the following:

• Make sure all data formats make sense (e.g. keywords are formatted as text, numbers are formatted as decimals or whole numbers).
• Rename columns as needed.
• Create a domain column in your Top 20 report based on the URL column.

Close and apply your changes by hitting the “Edit Queries” button, as seen above.

Step 3: Create relationships between data sources

On the left side of Power BI is a vertical bar with icons for different views. Click the third one to see your relationships view.

In this view, we are going to connect all data sources to our ‘Keywords Bridge’ table by clicking and dragging a line from the field ‘Keyword’ in each table and to ‘Keyword’ in the ‘Keywords Bridge’ table (note that for the PPC Data, I have connected ‘Search Term’ as this is the PPC equivalent of a keyword, as we’re using here).

The last thing we need to do for our relationships is double-click on each line to ensure the following options are selected for each so that our dashboard works properly:

• The cardinality is Many to 1
• The relationship is “active”
• The cross filter direction is set to “both”

We are now ready to start building our Intent Dashboard and analyzing our data.

Building the search intent dashboard

In this section I’ll walk you through each visual in the Search Intent Dashboard (as seen below):

Top domains by count of keywords

Visual type: Stacked Bar Chart visual

Axis: I’ve nested URL under Domain so I can drill down to see this same breakdown by URL for a specific Domain

Value: Distinct count of keywords

Legend: Result Types

Filter: Top 10 filter on Domains by count of distinct keywords

Keyword breakdown by result type

Visual type: Donut chart

Legend: Result Types

Value: Count of distinct keywords, shown as Percent of grand total

Metric Cards

Sum of Distinct MSV

Because the Top 20 report shows each keyword 20 times, we need to create a calculated measure in Power BI to only sum MSV for the unique list of keywords. Use this formula for that calculated measure:

Sum Distinct MSV = SUMX(DISTINCT('Table'[Keywords]), FIRSTNONBLANK('Table'[MSV], 0))

Keywords

This is just a distinct count of keywords

Slicer: PPC Conversions

Visual type: Slicer

Drop your PPC Conversions field into a slicer and set the format to “Between” to get this nifty slider visual.

Tables

Visual type: Table or Matrix (a matrix allows for drilling down similar to a pivot table in Excel)

Values: Here I have Category or Intent Stage and then the distinct count of keywords.

Pulling insights from your search intent dashboard

This dashboard is now a Swiss Army knife of data that allows you to slice and dice to your heart’s content. Below are a couple examples of how I use this dashboard to pull out opportunities and insights for my clients.

Where are competitors winning?

With this data we can quickly see who the top competing domains are, but what’s more valuable is seeing who the competitors are for a particular intent stage and category.

I start by filtering to the “Informational” stage, since it represents the most keywords in our dataset. I also filter to the top category for this intent stage which is “Blinds”. Looking at my Keyword Count card, I can now see that I’m looking at a subset of 641 keywords.

Note: To filter multiple visuals in Power BI, you need to press and hold the “Ctrl” button each time you click a new visual to maintain all the filters you clicked previously.

The top competing subdomain here is videos.blinds.com with visibility in the top 20 for over 250 keywords, most of which are for video results. I hit ctrl+click on the Video results portion of videos.blinds.com to update the keywords table to only keywords where videos.blinds.com is ranking in the top 20 with a video result.

From all this I can now say that videos.blinds.com is ranking in the top 20 positions for about 30 percent of keywords that fall into the “Blinds” category and the “Informational” intent stage. I can also see that most of the keywords here start with “how to”, which tells me that most likely people searching for blinds in an informational stage are looking for how to instructions and that video may be a desired content format.

Where should I focus my time?

Whether you’re in-house or at an agency, time is always a hit commodity. You can use this dashboard to quickly identify opportunities that you should be prioritizing first — opportunities that can guarantee you’ll deliver bottom-line results.

To find these bottom-line results, we’re going to filter our data using the PPC conversions slicer so that our data only includes keywords that have converted at least once in our PPC campaigns.

Once I do that, I can see I’m working with a pretty limited set of keywords that have been bucketed into intent stages, but I can continue by drilling into the “Transactional” intent stage because I want to target queries that are linked to a possible purchase.

Note: Not every keyword will fall into an intent stage if it doesn’t meet the criteria we set. These keywords will still appear in the data, but this is the reason why your total keyword count might not always match the total keyword count in the intent stages or category tables.

From there I want to focus on those “Transactional” keywords that are triggering answer boxes to make sure I have good visibility, since they are converting for me on PPC. To do that, I filter to only show keywords triggering answer boxes. Based on these filters I can look at my keyword table and see most (if not all) of the keywords are “installation” keywords and I don’t see my client’s domain in the top list of competitors. This is now an area of focus for me to start driving organic conversions.

Wrap up

I’ve only just scratched the surface — there’s tons that can can be done with this data inside a tool like Power BI. Having a solid data set of keywords and visuals that I can revisit repeatedly for a client and continuously pull out opportunities to help fuel our strategy is, for me, invaluable. I can work efficiently without having to go back to keyword tools whenever I need an idea. Hopefully you find this makes building an intent-based strategy more efficient and sound for your business or clients.

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