- Build a Search Intent Dashboard to Unlock Better Opportunities
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.
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.
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.
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.
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.
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
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))
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.
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.
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.
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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- Detecting Link Manipulation and Spam with Domain Authority
Posted by rjonesx.
Over 7 years ago, while still an employee at Virante, Inc. (now Hive Digital), I wrote a post on Moz outlining some simple methods for detecting backlink manipulation by comparing one's backlink profile to an ideal model based on Wikipedia. At the time, I was limited in the research I could perform because I was a consumer of the API, lacking access to deeper metrics, measurements, and methodologies to identify anomalies in backlink profiles. We used these techniques in spotting backlink manipulation with tools like Remove'em and Penguin Risk, but they were always handicapped by the limitations of consumer facing APIs. Moreover, they didn't scale. It is one thing to collect all the backlinks for a site, even a large site, and judge every individual link for source type, quality, anchor text, etc. Reports like these can be accessed from dozens of vendors if you are willing to wait a few hours for the report to complete. But how do you do this for 30 trillion links every single day?
Since the launch of Link Explorer and my residency here at Moz, I have had the luxury of far less filtered data, giving me a far deeper, clearer picture of the tools available to backlink index maintainers to identify and counter manipulation. While I in no way intend to say that all manipulation can be detected, I want to outline just some of the myriad surprising methodologies to detect spam.
The general methodology
You don't need to be a data scientist or a math nerd to understand this simple practice for identifying link spam. While there certainly is a great deal of math used in the execution of measuring, testing, and building practical models, the general gist is plainly understandable.
The first step is to get a good random sample of links from the web, which you can read about here. But let's assume you have already finished that step. Then, for any property of those random links (DA, anchor text, etc.), you figure out what is normal or expected. Finally, you look for outliers and see if those correspond with something important - like sites that are manipulating the link graph, or sites that are exceptionally good. Let's start with an easy example, link decay.
Link decay and link spam
Link decay is the natural occurrence of links either dropping off the web or changing URLs. For example, if you get links after you send out a press release, you would expect some of those links to eventually disappear as the pages are archived or removed for being old. And, if you were to get a link from a blog post, you might expect to have a homepage link on the blog until that post is pushed to the second or third page by new posts.
But what if you bought your links? What if you own a large number of domains and all the sites link to each other? What if you use a PBN? These links tend not to decay. Exercising control over your inbound links often means that you keep them from ever decaying. Thus, we can create a simple hypothesis:
Hypothesis: The link decay rate of sites manipulating the link graph will differ from sites with natural link profiles.
The methodology for testing this hypothesis is just as we discussed before. We first figure out what is natural. What does a random site's link decay rate look like? Well, we simply get a bunch of sites and record how fast links are deleted (we visit a page and see a link is gone) vs. their total number of links. We then can look for anomalies.
In this case of anomaly hunting, I'm going to make it really easy. No statistics, no math, just a quick look at what pops up when we first sort by Lowest Decay Rate and then sort by Highest Domain Authority to see who is at the tail-end of the spectrum.
Success! Every example we see of a good DA score but 0 link decay appears to be powered by a link network of some sort. This is the Aha! moment of data science that is so fun. What is particularly interesting is we find spam on both ends of the distribution — that is to say, sites that have 0 decay or near 100% decay rates both tend to be spammy. The first type tends to be part of a link network, the second part tends to spam their backlinks to sites others are spamming, so their links quickly shuffle off to other pages.
Of course, now we do the hard work of building a model that actually takes this into account and accurately reduces Domain Authority relative to the severity of the link spam. But you might be asking...
These sites don't rank in Google — why do they have decent DAs in the first place?
Well, this is a common problem with training sets. DA is trained on sites that rank in Google so that we can figure out who will rank above who. However, historically, we haven't (and no one to my knowledge in our industry has) taken into account random URLs that don't rank at all. This is something we're solving for in the new DA model set to launch in early March, so stay tuned, as this represents a major improvement on the way we calculate DA!
Spam Score distribution and link spam
One of the most exciting new additions to the upcoming Domain Authority 2.0 is the use of our Spam Score. Moz's Spam Score is a link-blind (we don't use links at all) metric that predicts the likelihood a domain will be indexed in Google. The higher the score, the worse the site.
Now, we could just ignore any links from sites with Spam Scores over 70 and call it a day, but it turns out there are fascinating patterns left behind by common link manipulation schemes waiting to be discovered by using this simple methodology of using a random sample of URLs to find out what a normal backlink profile looks like, and then see if there are anomalies in the way Spam Score is distributed among the backlinks to a site. Let me show you just one.
It turns out that acting natural is really hard to do. Even the best attempts often fall short, as did this particularly pernicious link spam network. This network had haunted me for 2 years because it included a directory of the top million sites, so if you were one of those sites, you could see anywhere from 200 to 600 followed links show up in your backlink profile. I called it "The Globe" network. It was easy to look at the network and see what they were doing, but could we spot it automatically so that we could devalue other networks like it in the future? When we looked at the link profile of sites included in the network, the Spam Score distribution lit up like a Christmas tree.
Most sites get the majority of their backlinks from low Spam Score domains and get fewer and fewer as the Spam Score of the domains go up. But this link network couldn't hide because we were able to detect the sites in their network as having quality issues using Spam Score. If we relied only on ignoring the bad Spam Score links, we would have never discovered this issue. Instead, we found a great classifier for finding sites that are likely to be penalized by Google for bad link building practices.
DA distribution and link spam
We can find similar patterns among sites with the distribution of inbound Domain Authority. It's common for businesses seeking to increase their rankings to set minimum quality standards on their outreach campaigns, often DA30 and above. An unfortunate outcome of this is that what remains are glaring examples of sites with manipulated link profiles.
Let me take a moment and be clear here. A manipulated link profile is not necessarily against Google's guidelines. If you do targeted PR outreach, it is reasonable to expect that such a distribution might occur without any attempt to manipulate the graph. However, the real question is whether Google wants sites that perform such outreach to perform better. If not, this glaring example of link manipulation is pretty easy for Google to dampen, if not ignore altogether.
A normal link graph for a site that is not targeting high link equity domains will have the majority of their links coming from DA0–10 sites, slightly fewer for DA10–20, and so on and so forth until there are almost no links from DA90+. This makes sense, as the web has far more low DA sites than high. But all the sites above have abnormal link distributions, which make it easy to detect and correct — at scale — link value.
Now, I want to be clear: these are not necessarily examples of violating Google's guidelines. However, they are manipulations of the link graph. It's up to you to determine whether you believe Google takes the time to differentiate between how the outreach was conducted that resulted in the abnormal link distribution.
What doesn't work
For every type of link manipulation detection method we discover, we scrap dozens more. Some of these are actually quite surprising. Let me write about just one of the many.
The first surprising example was the ratio of nofollow to follow links. It seems pretty straightforward that comment, forum, and other types of spammers would end up accumulating lots of nofollowed links, thereby leaving a pattern that is easy to discern. Well, it turns out this is not true at all.
The ratio of nofollow to follow links turns out to be a poor indicator, as popular sites like facebook.com often have a higher ratio than even pure comment spammers. This is likely due to the use of widgets and beacons and the legitimate usage of popular sites like facebook.com in comments across the web. Of course, this isn't always the case. There are some sites with 100% nofollow links and a high number of root linking domains. These anomalies, like "Comment Spammer 1," can be detected quite easily, but as a general measurement the ratio does not serve as a good classifier for spam or ham.
So what's next?
Moz is continually traversing the the link graph looking for ways to improve Domain Authority using everything from basic linear algebra to complex neural networks. The goal in mind is simple: We want to make the best Domain Authority metric ever. We want a metric which users can trust in the long run to root out spam just like Google (and help you determine when you or your competitors are pushing the limits) while at the same time maintaining or improving correlations with rankings. Of course, we have no expectation of rooting out all spam — no one can do that. But we can do a better job. Led by the incomparable Neil Martinsen-Burrell, our metric will stand alone in the industry as the canonical method for measuring the likelihood a site will rank in Google.
We're launching Domain Authority 2.0 on March 5th! Check out our helpful resources here, or sign up for our webinar this Thursday, February 21st for more info on how to communicate changes like this to clients and stakeholders:
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- 4 Ways to Improve Your Data Hygiene - Whiteboard Friday
Posted by DiTomaso
We base so much of our livelihood on good data, but managing that data properly is a task in and of itself. In this week's Whiteboard Friday, Dana DiTomaso shares why you need to keep your data clean and some of the top things to watch out for.
Hi. My name is Dana DiTomaso. I am President and partner at Kick Point. We're a digital marketing agency, based in the frozen north of Edmonton, Alberta. So today I'm going to be talking to you about data hygiene.
What I mean by that is the stuff that we see every single time we start working with a new client this stuff is always messed up. Sometimes it's one of these four things. Sometimes it's all four, or sometimes there are extra things. So I'm going to cover this stuff today in the hopes that perhaps the next time we get a profile from someone it is not quite as bad, or if you look at these things and see how bad it is, definitely start sitting down and cleaning this stuff up.
So what we're going to start with first are filters. By filters, I'm talking about analytics here, specifically Google Analytics. When go you into the admin of Google Analytics, there's a section called Filters. There's a section on the left, which is all the filters for everything in that account, and then there's a section for each view for filters. Filters help you exclude or include specific traffic based on a set of parameters.
Filter out office, home office, and agency traffic
So usually what we'll find is one Analytics property for your website, and it has one view, which is all website data which is the default that Analytics gives you, but then there are no filters, which means that you're not excluding things like office traffic, your internal people visiting the website, or home office. If you have a bunch of people who work from home, get their IP addresses, exclude them from this because you don't necessarily want your internal traffic mucking up things like conversions, especially if you're doing stuff like checking your own forms.
You haven't had a lead in a while and maybe you fill out the form to make sure it's working. You don't want that coming in as a conversion and then screwing up your data, especially if you're a low-volume website. If you have a million hits a day, then maybe this isn't a problem for you. But if you're like the rest of us and don't necessarily have that much traffic, something like this can be a big problem in terms of the volume of traffic you see. Then agency traffic as well.
So agencies, please make sure that you're filtering out your own traffic. Again things like your web developer, some contractor you worked with briefly, really make sure you're filtering out all that stuff because you don't want that polluting your main profile.
Create a test and staging view
The other thing that I recommend is creating what we call a test and staging view. Usually in our Analytics profiles, we'll have three different views. One we call master, and that's the view that has all these filters applied to it.
So you're only seeing the traffic that isn't you. It's the customers, people visiting your website, the real people, not your office people. Then the second view we call test and staging. So this is just your staging server, which is really nice. For example, if you have a different URL for your staging server, which you should, then you can just include that traffic. Then if you're making enhancements to the site or you upgraded your WordPress instance and you want to make sure that your goals are still firing correctly, you can do all that and see that it's working in the test and staging view without polluting your main view.
Test on a second property
That's really helpful. Then the third thing is make sure to test on a second property. This is easy to do with Google Tag Manager. What we'll have set up in most of our Google Tag Manager accounts is we'll have our usual analytics and most of the stuff goes to there. But then if we're testing something new, like say the content consumption metric we started putting out this summer, then we want to make sure we set up a second Analytics view and we put the test, the new stuff that we're trying over to the second Analytics property, not view.
So you have two different Analytics properties. One is your main property. This is where all the regular stuff goes. Then you have a second property, which is where you test things out, and this is really helpful to make sure that you're not going to screw something up accidentally when you're trying out some crazy new thing like content consumption, which can totally happen and has definitely happened as we were testing the product. You don't want to pollute your main data with something different that you're trying out.
So send something to a second property. You do this for websites. You always have a staging and a live. So why wouldn't you do this for your analytics, where you have a staging and a live? So definitely consider setting up a second property.
2. Time zones
The next thing that we have a lot of problems with are time zones. Here's what happens.
Let's say your website, basic install of WordPress and you didn't change the time zone in WordPress, so it's set to UTM. That's the default in WordPress unless you change it. So now you've got your data for your website saying it's UTM. Then let's say your marketing team is on the East Coast, so they've got all of their tools set to Eastern time. Then your sales team is on the West Coast, so all of their tools are set to Pacific time.
So you can end up with a situation where let's say, for example, you've got a website where you're using a form plugin for WordPress. Then when someone submits a form, it's recorded on your website, but then that data also gets pushed over to your sales CRM. So now your website is saying that this number of leads came in on this day, because it's in UTM mode. Well, the day ended, or it hasn't started yet, and now you've got Eastern, which is when your analytics tools are recording the number of leads.
But then the third wrinkle is then you have Salesforce or HubSpot or whatever your CRM is now recording Pacific time. So that means that you've got this huge gap of who knows when this stuff happened, and your data will never line up. This is incredibly frustrating, especially if you're trying to diagnose why, for example, I'm submitting a form, but I'm not seeing the lead, or if you've got other data hygiene issues, you can't match up the data and that's because you have different time zones.
So definitely check the time zones of every product you use --website, CRM, analytics, ads, all of it. If it has a time zone, pick one, stick with it. That's your canonical time zone. It will save you so many headaches down the road, trust me.
The next thing is attribution. Attribution is a whole other lecture in and of itself, beyond what I'm talking about here today.
Different tools have different ways of showing attribution
But what I find frustrating about attribution is that every tool has its own little special way of doing it. Analytics is like the last non-direct click. That's great. Ads says, well, maybe we'll attribute it, maybe we won't. If you went to the site a week ago, maybe we'll call it a view-through conversion. Who knows what they're going to call it? Then Facebook has a completely different attribution window.
You can use a tool, such as Supermetrics, to change the attribution window. But if you don't understand what the default attribution window is in the first place, you're just going to make things harder for yourself. Then there's HubSpot, which says the very first touch is what matters, and so, of course, HubSpot will never agree with Analytics and so on. Every tool has its own little special sauce and how they do attribution. So pick a source of truth.
Pick your source of truth
This is the best thing to do is just say, "You know what? I trust this tool the most." Then that is your source of truth. Do not try to get this source of truth to match up with that source of truth. You will go insane. You do have to make sure that you are at least knowing that things like your time zones are clear so that's all set.
Be honest about limitations
But then after that, really it's just making sure that you're being honest about your limitations.
Know where things are necessarily going to fall down, and that's okay, but at least you've got this source of truth that you at least can trust. That's the most important thing with attribution. Make sure to spend the time and read how each tool handles attribution so when someone comes to you and says, "Well, I see that we got 300 visits from this ad campaign, but in Facebook it says we got 6,000.
Why is that? You have an answer. That might be a little bit of an extreme example, but I mean I've seen weirder things with Facebook attribution versus Analytics attribution. I've even talked about stuff like Mixpanel and Kissmetrics. Every tool has its own little special way of recording attributions. It's never the same as anyone else's. We don't have a standard in the industry of how this stuff works, so make sure you understand these pieces.
Then the last thing are what I call interactions. The biggest thing that I find that people do wrong here is in Google Tag Manager it gives you a lot of rope, which you can hang yourself with if you're not careful.
GTM interactive hits
One of the biggest things is what we call an interactive hit versus a non-interactive hit. So let's say in Google Tag Manager you have a scroll depth.
You want to see how far down the page people scroll. At 25%, 50%, 75%, and 100%, it will send off an alert and say this is how far down they scrolled on the page. Well, the thing is that you can also make that interactive. So if somebody scrolls down the page 25%, you can say, well, that's an interactive hit, which means that person is no longer bounced, because it's counting an interaction, which for your setup might be great.
Gaming bounce rate
But what I've seen are unscrupulous agencies who come in and say if the person scrolls 2% of the way down the page, now that's an interactive hit. Suddenly the client's bounce rate goes down from say 80% to 3%, and they think, "Wow, this agency is amazing." They're not amazing. They're lying. This is where Google Tag Manager can really manipulate your bounce rate. So be careful when you're using interactive hits.
Absolutely, maybe it's totally fair that if someone is reading your content, they might just read that one page and then hit the back button and go back out. It's totally fair to use something like scroll depth or a certain piece of the content entering the user's view port, that that would be interactive. But that doesn't mean that everything should be interactive. So just dial it back on the interactions that you're using, or at least make smart decisions about the interactions that you choose to use. So you can game your bounce rate for that.
Then goal setup as well, that's a big problem. A lot of people by default maybe they have destination goals set up in Analytics because they don't know how to set up event-based goals. But what we find happens is by destination goal, I mean you filled out the form, you got to a thank you page, and you're recording views of that thank you page as goals, which yes, that's one way to do it.
But the problem is that a lot of people, who aren't super great at interneting, will bookmark that page or they'll keep coming back to it again and again because maybe you put some really useful information on your thank you page, which is what you should do, except that means that people keep visiting it again and again without actually filling out the form. So now your conversion rate is all messed up because you're basing it on destination, not on the actual action of the form being submitted.
So be careful on how you set up goals, because that can also really game the way you're looking at your data.
Ad blockers could be anywhere from 2% to 10% of your audience depending upon how technically sophisticated your visitors are. So you'll end up in situations where you have a form fill, you have no corresponding visit to match with that form fill.
It just goes into an attribution black hole. But they did fill out the form, so at least you got their data, but you have no idea where they came from. Again, that's going to be okay. So definitely think about the percentage of your visitors, based on you and your audience, who probably have an ad blocker installed and make sure you're comfortable with that level of error in your data. That's just the internet, and ad blockers are getting more and more popular.
Stuff like Apple is changing the way that they do tracking. So definitely make sure that you understand these pieces and you're really thinking about that when you're looking at your data. Again, these numbers may never 100% match up. That's okay. You can't measure everything. Sorry.
Then the last thing I really want you to think about — this is the bonus tip — audit regularly.
So at least once a year, go through all the different stuff that I've covered in this video and make sure that nothing has changed or updated, you don't have some secret, exciting new tracking code that somebody added in and then forgot because you were trying out a trial of this product and you tossed it on, and it's been running for a year even though the trial expired nine months ago. So definitely make sure that you're running the stuff that you should be running and doing an audit at least on an yearly basis.
If you're busy and you have a lot of different visitors to your website, it's a pretty high-volume property, maybe monthly or quarterly would be a better interval, but at least once a year go through and make sure that everything that's there is supposed to be there, because that will save you headaches when you look at trying to compare year-over-year and realize that something horrible has been going on for the last nine months and all of your data is trash. We really don't want to have that happen.
So I hope these tips are helpful. Get to know your data a little bit better. It will like you for it. Thanks.
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- A Guide to Setting Up Your Very Own Search Intent Projects
Posted by TheMozTeam
This post was originally published on the STAT blog.
Whether you’re tracking thousands or millions of keywords, if you expect to extract deep insights and trends just by looking at your keywords from a high-level, you’re not getting the full story.
Smart segmentation is key to making sense of your data. And you’re probably already applying this outside of STAT. So now, we’re going to show you how to do it in STAT to uncover boatloads of insights that will help you make super data-driven decisions.
To show you what we mean, let’s take a look at a few ways we can set up a search intent project to uncover the kinds of insights we shared in our whitepaper, Using search intent to connect with consumers.
Before we jump in, there are a few things you should have down pat:
1. Picking a search intent that works for you
Search intent is the motivating force behind search and it can be:
- Informational: The searcher has identified a need and is looking for information on the best solution, ie. [blender], [food processor]
- Commercial: The searcher has zeroed in on a solution and wants to compare options, ie. [blender reviews], [best blenders]
- Transactional: The searcher has narrowed their hunt down to a few best options, and is on the precipice of purchase, ie. [affordable blenders], [blender cost]
- Local (sub-category of transactional): The searcher plans to do or buy something locally, ie. [blenders in dallas]
- Navigational (sub-category of transactional): The searcher wants to locate a specific website, ie. [Blendtec]
We left navigational intent out of our study because it’s brand specific and didn’t want to bias our data.
Our keyword set was a big list of retail products — from kitty pooper-scoopers to pricey speakers. We needed a straightforward way to imply search intent, so we added keyword modifiers to characterize each type of intent.
As always, different strokes for different folks: The modifiers you choose and the intent categories you look at may differ, but it’s important to map that all out before you get started.
2. Identifying the SERP features you really want
For our whitepaper research, we pretty much tracked every feature under the sun, but you certainly don’t have to.
You might already know which features you want to target, the ones you want to keep an eye on, or questions you want to answer. For example, are shopping boxes taking up enough space to warrant a PPC strategy?
In this blog post, we’re going to really focus-in on our most beloved SERP feature: featured snippets (called “answers” in STAT). And we’ll be using a sample project where we’re tracking 25,692 keywords against Amazon.com.
3. Using STAT’s segmentation tools
Setting up projects in STAT means making use of the segmentation tools. Here’s a quick rundown of what we used:
- Standard tag: Best used to group your keywords into static themes — search intent, brand, product type, or modifier.
- Dynamic tag: Like a smart playlist, automatically returns keywords that match certain criteria, like a given search volume, rank, or SERP feature appearance.
- Data view: House any number of tags and show how those tags perform as a group.
Learn more about tags and data views in the STAT Knowledge Base.
Now, on to the main event…
1. Use top-level search intent to find SERP feature opportunities
To kick things off, we’ll identify the SERP features that appear at each level of search intent by creating tags.
Our first step is to filter our keywords and create standard tags for our search intent keywords (read more abou tfiltering keywords). Second, we create dynamic tags to track the appearance of specific SERP features within each search intent group. And our final step, to keep everything organized, is to place our tags in tidy little data views, according to search intent.
Here’s a peek at what that looks like in STAT:
What can we uncover?
Our standard tags (the blue tags) show how many keywords are in each search intent bucket: 2,940 commercial keywords. And our dynamic tags (the sunny yellow stars) show how many of those keywords return a SERP feature: 547 commercial keywords with a snippet.
This means we can quickly spot how much opportunity exists for each SERP feature by simply glancing at the tags. Boom!
By quickly crunching some numbers, we can see that snippets appear on 5 percent of our informational SERPs (27 out of 521), 19 percent of our commercial SERPs (547 out of 2,940), and 12 percent of our transactional SERPs (253 out of 2,058).
From this, we might conclude that optimizing our commercial intent keywords for featured snippets is the way to go since they appear to present the biggest opportunity. To confirm, let’s click on the commercial intent featured snippet tag to view the tag dashboard…
Voilà! There are loads of opportunities to gain a featured snippet.
Though, we should note that most of our keywords rank below where Google typically pulls the answer from. So, what we can see right away is that we need to make some serious ranking gains in order to stand a chance at grabbing those snippets.
2. Find SERP feature opportunities with intent modifiers
Now, let’s take a look at which SERP features appear most often for our different keyword modifiers.
To do this, we group our keywords by modifier and create a standard tag for each group. Then, we set up dynamic tags for our desired SERP features. Again, to keep track of all the things, we contained the tags in handy data views, grouped by search intent.
What can we uncover?
Because we saw that featured snippets appear most often for our commercial intent keywords, it’s time to drill on down and figure out precisely which modifiers within our commercial bucket are driving this trend.
Glancing quickly at the numbers in the tag titles in the image above, we can see that “best,” “reviews,” and “top” are responsible for the majority of the keywords that return a featured snippet:
- 212 out of 294 of our “best” keywords (72%)
- 109 out of 294 of our “reviews” keywords (37%)
- 170 out of 294 of our “top” keywords (59%)
This shows us where our efforts are best spent optimizing.
By clicking on the “best — featured snippets” tag, we’re magically transported into the dashboard. Here, we see that our average ranking could use some TLC.
There is a lot of opportunity to snag a snippet here, but we (actually, Amazon, who we’re tracking these keywords against) don’t seem to be capitalizing on that potential as much as we could. Let’s drill down further to see which snippets we already own.
We know we’ve got content that has won snippets, so we can use that as a guideline for the other keywords that we want to target.
3. See which pages are ranking best by search intent
In our blog post How Google dishes out content by search intent, we looked at what type of pages — category pages, product pages, reviews — appear most frequently at each stage of a searcher’s intent.
What we found was that Google loves category pages, which are the engine’s top choice for retail keywords across all levels of search intent. Product pages weren’t far behind.
By creating dynamic tags for URL markers, or portions of your URL that identify product pages versus category pages, and segmenting those by intent, you too can get all this glorious data. That’s exactly what we did for our retail keywords
What can we uncover?
Looking at the tags in the transactional page types data view, we can see that product pages are appearing far more frequently (526) than category pages (151).
When we glanced at the dashboard, we found that slightly more than half of the product pages were ranking on the first page (sah-weet!). That said, more than thirty percent appeared on page three and beyond. So despite the initial visual of “doing well”, there’s a lot of opportunity that Amazon could be capitalizing on.
We can also see this in the Daily Snapshot. In the image above, we compare category pages (left) to product pages (right), and we see that while there are less category pages ranking, the rank is significantly better. Amazon could take some of the lessons they’ve applied to their category pages to help their product pages out.
Wrapping it up
So what did we learn today?
- Smart segmentation starts with a well-crafted list of keywords, grouped into tags, and housed in data views.
- The more you segment, the more insights you’re gonna uncover.
- Rely on the dashboards in STAT to flag opportunities and tell you what’s good, yo!
Want to see it all in action? Get a tailored walkthrough of STAT, here.
Or get your mitts on even more intent-based insights in our full whitepaper: Using search intent to connect with consumers.
Read on, readers!
More in our search intent series:
- How SERP features respond to intent modifiers
- How Google dishes out content by search intent
- The basics of building an intent-based keyword list
- 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.
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]
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.
- Relevance. Are your keywords represented on your site? Do they point to optimized pages
- 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.
- 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.
- 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 with share of voice capabilities (hi!) can help you understand your search landscape.
- 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:
- Whitepaper: Whitepaper: Using search intent to connect with consumers
- How SERP features respond to intent modifiers
- How Google dishes out content by search intent
- A guide to setting up your very own search intent projects
- Do Businesses Really Use Google My Business Posts? A Case Study
Posted by Ben_Fisher
Google My Business (GMB) is one of the most powerful ways to improve a business’ local search engine optimization and online visibility. If you’re a local business, claiming your Google My Business profile is one of the first steps you should take to increase your company’s online presence.
As long as your local business meets Google’s guidelines, your Google My Business profile can help give your company FREE exposure on Google’s search engine. Not only can potential customers quickly see your business’ name, address and phone number, but they can also see photos of your business, read online reviews, find a description about your company, complete a transaction (like book an appointment) and see other information that grabs a searcher’s attention — all without them even visiting your website. That’s pretty powerful stuff!
Google My Business helps with local rankings
Not only is your GMB Profile easily visible to potential customers when they search on Google, but Google My Business is also a key Google local ranking factor. In fact, according to local ranking factor industry research, Google My Business “signals” is the most important ranking factor for local pack rankings. Google My Business signals had a significant increase in ranking importance between 2017 and 2018 — rising from 19% to 25%.
Claiming your Google My Business profile is your first step to local optimization — but many people mistakenly think that just claiming your Google My Business profile is enough. However, optimizing your Google My Business profile and frequently logging into your Google My Business dashboard to make sure that no unwanted updates have been made to your profile is vital to improving your rankings and ensuring the integrity of your business profile’s accuracy.
Google My Business features that make your profile ROCK!
Google offers a variety of ways to optimize and enhance your Google My Business profile. You can add photos, videos, business hours, a description of your company, frequently asked questions and answers, communicate with customers via messages, allow customers to book appointments, respond to online reviews and more.
One of the most powerful ways to grab a searcher’s attention is by creating Google My Business Posts. GMB Posts are almost like mini-ads for your company, products, or services.
Google offers a variety of posts you can create to promote your business:
- What's New
Posts also allow you to include a call to action (CTA) so you can better control what the visitor does after they view your post — creating the ultimate marketing experience. Current CTAs are:
- Order Online
- Learn More
- Sign Up
- Get Offer
- Call Now
Posts use a combination of images, text and a CTA to creatively show your message to potential customers. A Post shows in your GMB profile when someone searches for your business’ name on Google or views your business’ Google My Business profile on Google Maps.
Once you create a Post, you can even share it on your social media channels to get extra exposure.
Despite the name, Google My Business Posts are not actual social media posts. Typically the first 100 characters of the post are what shows up on screen (the rest is cut off and must be clicked on to be seen), so make sure the most important words are at the beginning of your post. Don’t use hashtags — they’re meaningless. It’s best if you can create new posts every seven days or so.
Google My Business Posts are a great way to show off your business in a unique way at the exact time when a searcher is looking at your business online.
But there’s a long-standing question: Are businesses actually creating GMB Posts to get their message across to potential customers? Let’s find out...
The big question: Are businesses actively using Google My Business Posts?
There has been a lot of discussion in the SEO industry about Google My Business Posts and their value: Do they help with SEO rankings? How effective are they? Do posts garner engagement? Does where the Posts appear on your GMB profile matter? How often should you post? Should you even create Google My Business Posts at all? Lots of questions, right?
As industry experts look at all of these angles, what do average, everyday business owners actually do when it comes to GMB Posts? Are real businesses creating posts? I set out to find the answer to this question using real data. Here are the details.
Google My Business Post case study: Just the facts
When I set out to discover if businesses were actively using GMB Posts for their companies’ Google My Business profiles, I first wanted to make sure I looked at data in competitive industries and markets. So I looked at a total of 2,000 Google My Business profiles that comprised the top 20 results in the Local Finder. I searched for highly competitive keyword phrases in the top ten cities (based on population density, according to Wikipedia.)
For this case study, I also chose to look at service type businesses.
Here are the results.
New York, Los Angeles, Chicago, Philadelphia, Dallas, San Jose, San Francisco, Washington DC, Houston, and Boston.
real estate agent, mortgage, travel agency, insurance or insurance agents, dentist, plastic surgeon, personal injury lawyer, plumber, veterinarian or vet, and locksmith
Surprise! Out of the industries researched, Personal Injury Lawyers and Locksmiths posted the most often.
For the case study, I looked at the following:
- How many businesses had an active Google My Business Post (i.e. have posted in the last seven days)
- How many had previously made at least one post
- How many have never created a post
Do businesses create Google My Business Posts?
Based on the businesses, cities, and keywords researched, I discovered that more than half of the businesses are actively creating Posts or have created Google My Business Posts in the past.
- 17.5% of businesses had an active post in the last 7 days
- 42.1% of businesses had previously made at least one post
- 40.4% have never created a post
Highlight: A total of 59.60% of businesses have posted a Google My Business Post on their Google My Business profile.
NOTE: If you want to look at the raw numbers, you can check out the research document that outlines all the raw data. (NOTE: Credit for the research spreadsheet template I used and inspiration to do this case study goes to SEO expert Phil Rozek.)
Do searchers engage with Google My Business Posts?
If a business takes the time to create Google My Business Posts, do searchers and potential customers actually take the time to look at your posts? And most importantly, do they take action and engage with your posts?
This chart represents nine random clients, their total post views over a 28-day period, and the corresponding total direct/branded impressions on their Google My Business profiles. When we look at the total number of direct/branded views alongside the number of views posts received, the number of views for posts appears to be higher. This means that a single user is more than likely viewing multiple posts.
This means that if you take the time to create a GMB Post and your marketing message is meaningful, you have a high chance of converting a potential searcher into a customer — or at least someone who is going to take the time to look at your marketing message. (How awesome is that?)
Do searchers click on Google My Business Posts?
So your GMB Posts show up in your Knowledge Panel when someone searches for your business on Google and Google Maps, but do searchers actually click on your post to read more?
When we evaluated the various industry post views to their total direct/branded search views, on average the post is clicked on almost 100% of the time!
Google My Business insights
When you log in to your Google My Business dashboard you can see firsthand how well your Posts are doing. Below is a side-by-side image of a business’ post views and their direct search impressions. By checking your GMB insights, you can find out how well your Google My Business posts are performing for your business!
GMB Posts are worth it
After looking at 2,000 GMB profiles, I discovered a lot of things. One thing is for sure. It's hard to tell on a week-by-week basis how many companies are using GMB Posts because posts “go dark” every seven business days (unless the Post is an event post with a start and end date.)
Also, Google recently moved Posts from the top of the Google My Business profile towards the bottom, so they don’t stand out as much as they did just a few months ago. This may mean that there’s less incentive for businesses to create posts.
However, what this case study does show us is that businesses that are in a competitive location and industry should use Google My Business optimizing strategies and features like posts if they want to get an edge on their competition.
- How to Identify and Tackle Keyword Cannibalization in 2019
Posted by SamuelMangialavori
If you read the title of this blog and somehow, even only for a second, thought about the iconic movie “The Silence of the Lambs”, welcome to the club — you are not alone!
Despite the fact that the term “cannibalization” does not sound very suitable for digital marketing, this core concept has been around for a long time. This term simply identifies the issue of having multiple pages competing for the same (or very similar) keywords/keyword clusters, hence the cannibalization.
What do we mean by cannibalization in SEO?
This unfortunate and often unnoticed problem harms the SEO potential of the pages involved. When more than one page has the same/similar keyword target, it creates “confusion” in the eyes of the search engine, resulting in a struggle to decide what page to rank for what term.
For instance, say my imaginary e-commerce website sells shoes online and I have created a dedicated category page that targets the term ‘ankle boots’: www.distilledshoes.com/boots/ankle-boots/
Knowing the importance of editorial content, over time I decide to create two blog posts that cover topics related to ankle boots off the back of a keyword research: one post on how to wear ankle boots and another about the top 10 ways to wear ankle boots in 2019:
One month later, I realize that some of my blog pages are actually ranking for a few key terms that my e-commerce category page was initially visible for.
Now the question is: is this good or bad for my website?
Drum roll, please...and the answer is — It depends on the situation, the exact keywords, and the intent of the user when searching for a particular term.
Keyword cannibalization is not black or white — there are multiple grey areas and we will try and go though several scenarios in this blog post. I recommend you spend 5 minutes checking this awesome Whiteboard Friday which covers the topic of search intent extremely well.
How serious of a problem is keyword cannibalization?
Much more than what you might think — almost every website that I have worked on in the past few years have some degree of cannibalization that needs resolving. It is hard to estimate how much a single page might be held back by this issue, as it involves a group of pages whose potential is being limited. So, my suggestion is to treat this issue by analyzing clusters of pages that have some degree of cannibalization rather than single pages.
Where is most common to find cannibalization problems in SEO?
Normally, you can come across two main placements for cannibalization:
1) At meta data level:
When two or more pages have meta data (title tags and headings mainly) which target the same or very similar keywords, cannibalization occurs. This requires a less labour-intensive type of fix, as only meta data needs adjusting.
For example: my e-commerce site has three boots-related pages, which have the following meta data:
Page URL Title tag Header 1 /boots/all /Women’s Boots - Ankle & Chelsea Boots | Distilled Shoes Women’s Ankle & Chelsea Boots /boots/ankle-boots/ Women’s Ankle Boots | Distilled Shoes Ankle Boots boots/chelsea-boots/ Women’s Chelsea Boots | Distilled Shoes Chelsea Boots
These types of keyword cannibalization often occurs on e-commerce sites which have many category (or subcategory) pages with the intention to target specific keywords, such as the example above. Ideally, we would want to have a generic boots page to target generic boots related terms, while the other two pages should be focusing on the specific types of boots we are selling on those pages: ankle and chelsea.
Why not try the below instead?
Page URL New Title Tag New Header 1 /boots/all Women’s Boots - All Types of Winter Boots | Distilled Shoes Women’s Winter Boots /boots/ankle-boots/ Women’s Ankle Boots | Distilled Shoes Ankle Boots boots/chelsea-boots/ Women’s Chelsea Boots | Distilled Shoes Chelsea Boots
More often than not, we fail to differentiate our e-commerce site’s meta data to target the very specific subgroup of keywords that we should aim for — after all, this is the main point of having so many category pages, no? If interested in the topic, find here a blog post I wrote on the subject.
The fact that e-commerce pages tend to have very little text on them makes meta data very important, as it will be one of the main elements search engines look at to understand how a page differs from the other.
2) At page content level
When cannibalization occurs at page content level (meaning two or more pages tend to cover very similar topics in their body content), it normally needs more work than the above example, since it requires the webmaster to first find all the competing pages and then decide on the best approach to tackle the issue.
For example: say my e-commerce has two blog pages which cover the following topics:
Page URL Objective of the article /blog/how-to-clean-leather-boots/ Suggests how to take care of leather boots so they last longer /blog/boots-cleaning-guide-2019/ Shows a 121 guide on how to clean different types of boots
These types of keyword cannibalization typically occurs on editorial pages, or transactional pages provided with substantial amount of text.
It is fundamental to clarify something: SEO is often not the main driver when producing editorial content, as different teams are involved in producing content for social and engagement reasons, and fairly so. Especially in larger corporations, it is easy to underestimate how complex it is to find a balance between all departments and how easily things can be missed.
From a pure SEO standpoint, I can assure you that the two pages above are very likely to be subject to cannibalization. Despite the fact they have different editorial angles, they will probably display some degree of duplicated content between them (more on this later).
In the eyes of a search engine, how different are these two blog posts, both of which aim to address a fairly similar intent? That is the main question you should ask yourself when going through this task. My suggestion is the following: Before investing time and resources into creating new pages, make the effort to review your existing content.
What are the types of cannibalization in SEO?
Simply put, you could come across 2 main types:
1) Two or more landing pages on your website that are competing for the same keywords
For instance, it could be the case that, for the keyword "ankle boots", two of my pages are ranking at the same time:
Page URL Title tag Ranking for the keyword “ankle boots” Page A: /boots/all Women’s Boots - Ankle & Chelsea Boots | Distilled Shoes Position 8 Pabe B: /boots/ankle-boots/ Women’s Ankle Boots | Distilled Shoes Position 5
Is this a real cannibalization issue? The answer is both yes and no.
If multiple pages are ranking for the same term, it is because a search engine finds elements of both pages that they think respond to the query in some way — so technically speaking, they are potential ‘cannibals’.
Does it mean you need to panic and change everything on both pages? Surely not. It very much depends on the scenario and your objective.
In the instances where both pages have really high rankings on the first page of the SERPS, this could work in your advantage: More space occupied means more traffic for your pages, so treat it as "good" cannibalization.
If this is the case, I recommend you do the following:
- Consider changing the meta descriptions to make them more enticing and unique from each other. You do not want both pages to show the same message and fail to impress the user.
- In case you realise that amongst the two pages, the “secondary/non-intended page” is ranking higher (for example: Page A /boots/all ranks higher than Page B /boots/ankle-boots/ for the term ‘ankle boots’), you should check on Google Search Console (GSC) to see which page is getting the most amount of clicks for that single term. Then, decide if it is worth altering other elements of your SEO to better address that particular keyword.
For instance, what would happen if I removed the term ankle boots from my /boots/all (Page A) title tag and page copy? If Google reacts by favouring my /boots/ankle-boots/ page instead (Page B), which may gain higher positions, then great! If not, the worst case scenario is you can revert the changes back and keep enjoying the two results on page one of the SERP.
Page URL Title tag Ranking for the keyword “ankle boots” Page A: /boots/all Women’s Boots - Chelsea Boots & many more types | Distilled Shoes Test and decide
In the instances where page A has high rankings page one of the SERPS and page B is nowhere to be seen (beyond the top 15–20 results), it is up to you to decide if this minor cannibalization is worth your time and resources, as this may not be an urgency.
If you decide that it is worth pursuing, I recommend you do the following:
- Keep monitoring the keywords for which the two pages seem to show, in case Google might react differently in the future.
- Come back to this minor cannibalization point after you have addressed your most important issues.
In the instances where both pages are ranking in page two or three of the SERP, then it might be the case that your cannibalization is holding one or both of them back.
If this is the case, I recommend you do the following:
- Check on GSC to see which of your pages is getting the most amount of clicks for that single keyword. You should also check on similar terms, since keywords on page two or three of the SERP will show very low clicks in GSC. Then, decide which page should be your primary focus — the one that is better suited from a content perspective — and be open to test changes for on-page SEO elements of both pages.
- Review your title tags, headings, and page copies and try to find instances where both pages seem to overlap. If the degree of duplication between them is really high, it might be worth consolidating/canonicalising/redirecting one to the other (I'll touch on this below).
2) Two or more landing pages on your website that are flip-flopping for the same keyword
It could be the case that, for instance, the keyword “ankle boots” for two of my pages are ranking at different times, as Google seems to have a difficult time deciding which page to choose for the term.
Page URL Ranking for the keyword “ankle boots” on 1st of January Ranking for the keyword “ankle boots” on 5th of January Page A: /boots/all Position 6 Not ranking Pabe B: /boots/ankle-boots/ Not ranking Position 8
If this happens to you, try and find an answer to the following questions:This is a common issue that I am sure many of you have encountered, in which landing pages seem to be very volatile and rank for a group of keywords in a non-consistent manner.
When did this flip-flopping start?
Pinpointing the right moment in time where this all began might help you understand how the problem originated in the first place. Maybe a canonical tag occurred or went missing, maybe some changes to your on-page elements or an algorithm update mixed things up?
How many pages flip-flop between each other for the same keyword?
The fewer pages subject to volatility, the better and easier to address. Try to identify which pages are involved and inspect all elements that might have triggered this instability.
How often do these pages flip-flop?
Try and find out how often the ranking page for a keyword has changed: the fewer times, the better. Cross reference the time of the changes with your knowledge of the site in case it might have been caused by other adjustments.
If the flip-flop has occurred only once and seems to have stopped, there is probably nothing to worry about, as it's likely a one-off volatility in the SERP. At the end of the day, we need to remember that Google runs test and changes almost everyday.
How to identify which pages are victims of cannibalization
I will explain what tools I normally use to detect major cannibalization fluxes, but I am sure there are several ways to reach the same results — if you want to share your tips, please do comment below!
Tools to deploy for type 1 of cannibalization: When two of more landing pages are competing for the same keyword
I know we all love tools that help you speed up long tasks, and one of my favorites is Ahrefs. I recommend using their fantastic method which will find your ‘cannibals’ in minutes.
Watch their five minute video here to see how to do it.
I am certain SEMrush, SEOMonitor, and other similar tools offer the same ability to retrieve that kind of data, maybe just not as fast as Ahrefs’ method listed above. If you do not have any tools at your disposal, Google Search Console and Google Sheets will be your friends, but it will be more of a manual process.
Tools to deploy for Type 2 of cannibalization: When two or more landing pages are flip-flopping for the same keyword
Ideally, most rank tracking tools will be able to do this functionally discover when a keyword has changed ranking URL over time. Back in the day I used tracking tools like Linkdex and Pi Datametrics to do just this.
At Distilled, we use STAT, which displays this data under History, within the main Keyword tab — see screenshot below as example.
One caveat of these kinds of ranking tools is that this data is often accessible only by keyword and will require data analysis. This means it may take a bit of time to check all keywords involved in this cannibalization, but the insights you'll glean are well worth the effort.
Google Data Studio Dashboard
If you're looking for a speedier approach, you can build a Google Data Studio dashboard that connects to your GSC to provide data in real time, so you don’t have to check on your reports when you think there is a cannibalization issue (credit to my colleague Dom).
Our example of a dashboard comprises two tables (see screenshots below):
The table above captures the full list of keyword offenders for the period of time selected. For instance, keyword 'X' at the top of the table has generated 13 organic clicks (total_clicks) from GSC over the period considered and changed ranking URL approximately 24 times (num_of_pages).
The second table (shown above) indicates the individual pages that have ranked for each keyword for the period of time selected. In this particular example, for our keyword X (which, as we know, has changed URLs 24 times in the period of time selected) the column path would show the list of individual URLs that have been flip flopping.
What solutions should I implement to tackle cannibalization?
It is important to distinguish the different types of cannibalization you may encounter and try to be flexible with solutions — not every fix will be the same.
I started touching on possible solutions when I was talking about the different types of cannibalization, but let’s take a more holistic approach and explain what solutions are available.
Ask yourself this question: do I really need all the pages that I found cannibalizing each other?
In several instances the answer is no, and if that is the case, 301 redirects are your friends.
For instance, you might have created a new (or very similar) version of the same article your site posted years ago, so you may consider redirecting one of them — generally speaking, the older URL might have more equity in the eyes of search engines and potentially would have attracted some backlinks over time.
Page URL Date of blog post Page A: blog/how-to-wear-ankle-boots May 2016 Page B: blog/how-to-wear-ankle-boots-in-2019 December 2018
Check if page A has backlinks and, if so, how many keywords it is ranking for (and how well it is ranking for those keywords)
What to do:
- If page A has enough equity and visibility, do a 301 redirect from page B to page A, change all internal links (coming from the site to page B) to page A, and update metadata of page A if necessary (including the reference of 2019 for instance)
- If not, do the opposite: complete a 301 redirect from page A to page B and change all internal links (coming from the site to page A) to page B.
In case you do need all the pages that are cannibalizing for whatever reason (maybe PPC, social, or testing purposes, or maybe it is just because they are still relevant) then canonical tags are your friends. The main difference with a 301 redirect is that both pages will still exist, while the equity from page A will be transferred to page B.
Let's say you created a new article that covers a similar topic to another existing one (but has a different angle) and you find out that both pages are cannibalizing each other. After a quick analysis, you may decide you want Page B to be your "primary", so you can use a canonical tag from page A pointing to page B. You would want to use canonicalization if the content of the two pages is diverse enough that users should see it but not so much that search engines should think it's different.
Page URL Date of blog post Page A: blog/how-to-wear-ankle-boots-with-skinny-jeans December 2017 Page B: blog/how-to-wear-high-ankle-boots January 2019
What to do:
- Use a canonical tag from page A to page B. As a reinforcement to Google, you could also use a self-referencing canonical tag on page B.
- After having assessed accessibility and internal link equity of both pages, you may want to change all/some internal links (coming from the site to page A) to page B if you deem it useful.
As already touched on, it primarily involves a metadata type of cannibalization, which is what I named as type 1 in this article. After identifying the pages whose meta data seem to overlap or somehow target the same/highly similar keywords, you will need to decide which is your primary page for that keyword/keyword group and re-optimize the competing pages.
See the example earlier in the blog post to get a better idea.
This type of solution involves consolidating a part or the entire content of a page into another. Once that has happened, it is down to you to decide if it is worth keeping the page you have stripped content from or just 301 redirect it to the other.
You would use consolidation as an option if you think the cannibalization is a result of similar or duplicated content between multiple pages, which is more likely to be the type 2 of cannibalization, as stated earlier. It is essential to establish your primary page first so you are able to act on the competing internal pages. Content consolidation requires you to move the offending content to your primary page in order to stop this problem and improve your rankings.
For example, you might have created a new article that falls under a certain content theme (in this instance, boots cleaning). You then realize that a paragraph of your new page B touches on leather boots and how to take care of them, which is something you have covered in page A. In case both articles respond to similar intents (one targeting cleaning leather only, the other targeting cleaning boots in general), then it is worth consolidating the offending content from page B to page A, and add an internal link to page A instead of the paragraph that covers leather boots in page B.
Page URL Date of blog post Page A: blog/how-to-clean-leather-boots December 2017 Page B: /blog/boots-cleaning-guide-2019/ January 2019
What to do:
- Find the offending part of content on page B, review it and consolidate the most compelling bits to page A
- Replace the stripped content on page B with a direct internal link pointing to page A
- Often after having consolidated the content of a page to another, there is no scope for the page where content has been stripped from so it should just be redirected (301).
How can I avoid cannibalization in the first place?
The best way to prevent cannibalization from happening is a simple, yet underrated task, that involves keyword mapping. Implementing a correct mapping strategy for your site is a key part of your SEO, as important as your keyword research.
Carson Ward has written an awesome Moz Blog post about the topic, I recommend you have a look.
Don’t take 'intent' for granted
Another way to avoid cannibalization, and the last tip I want to share with you, involves something most of you are familiar with: search intent.
Most of the time, we take things for granted, assuming Google will behave in a certain way and show certain type of results. What I mean by this is: When you work on your keyword mapping, don’t forget to check what kind of results search engines display before assuming a certain outcome. Often, even Google is not sure and will not always get intent right.
For instance, when searching for ‘shoes gift ideas’ and ‘gift ideas for shoe lovers’ I get two very different SERPs despite the fact that my intent is kind of the same: I am looking for ideas for a gift which involves shoes.
The SERP on the left shows a SERP for a query of "shoes gift ideas". It displays a row of pictures from Google Images with the link to see more, one editorial page (informational content), and then the rest of results are all transactional/e-commerce pages for me to buy from. Google has assumed that I’d like to see commercial pages as I might be close to a conversion.
The SERP on the right shows a SERP for a query of "gift ideas for show loves", displaying a row of Google Shopping ads and then a featured snippet, taken from an editorial page, while the rest are a mix of transactional and editorial pages, with Pinterest ranking twice in the top 10. Clearly Google is not sure what I would prefer to see here. Am I still in the consideration phase or am I moving to conversion?
The example above is just one of the many I encountered when going through my keyword research and mapping task. Before going after a certain keyword/keyword cluster, try and address all these points:
- Check if one of your existing pages has already covered it.
- If so, how well have you covered the keyword target? What can you do to improve my focus? Is there any cannibalization that is holding you back?
- If you do not have a page for it, is it worth creating one and what implications will it have on your existing pages?
- Check what results Google is displaying for that keyword target, as it might be different from your expectations.
- Once you have created a new page/s, double check this has not created unintentional and unplanned cannibalization further down the line by using the tips in this post.
Keyword cannibalization is an underrated, but rather significant, problem, especially for sites that have been running for several years and end up having lots of pages. However, fear not — there are simple ways to monitor this issue and hopefully this post can help you speed up the whole process to find such instances.
Most of the times, it is just a matter of using the most logical approach while considering other SEO elements such as backlinks, crawlability, and content duplication. If possible, always test your changes first before applying it at site-wide level or making them permanent.
If you, like me, are a fan of knowledge sharing and you think there are better ways to help with cannibalization, please comment below!