Posted by matthew_jkay
Keyword research has been around as long as the SEO industry has. Search engines built a system that revolves around users entering a term or query into a text entry field, hitting return, and receiving a list of relevant results. As the online search market expanded, one clear leader emerged — Google — and with it they brought AdWords (now Google Ads), an advertising platform that allowed organizations to appear on search results pages for keywords that organically they might not.
Within Google Ads came a tool that enabled businesses to look at how many searches there were per month for almost any query. Google Keyword Planner became the de facto tool for keyword research in the industry, and with good reason: it was Google’s data. Not only that, Google gave us the ability to gather further insights due to other metrics Keyword Planner provided: competition and suggested bid. Whilst these keywords were Google Ads-oriented metrics, they gave the SEO industry an indication of how competitive a keyword was.
The reason is obvious. If a keyword or phrase has higher competition (i.e. more advertisers bidding to appear for that term) it’s likely to be more competitive from an organic perspective. Similarly, a term that has a higher suggested bid means it’s more likely to be a competitive term. SEOs dined on this data for years, but when the industry started digging a bit more into the data, we soon realized that while useful, it was not always wholly accurate. Moz, SEMrush, and other tools all started to develop alternative volume and competitive metrics using Clickstream data to give marketers more insights.
Now industry professionals have several software tools and data outlets to conduct their keyword research. These software companies will only improve in the accuracy of their data outputs. Google’s data is unlikely to significantly change; their goal is to sell ad space, not make life easy for SEOs. In fact, they've made life harder by using volume ranges for Google Ads accounts with low activity. SEO tools have investors and customers to appease and must continually improve their products to reduce churn and grow their customer base. This makes things rosy for content-led SEO, right?
Well, not really.
The problem with historical keyword research is twofold:
1. SEOs spend too much time thinking about the decision stage of the buyer’s journey (more on that later).
2. SEOs spend too much time thinking about keywords, rather than categories or topics.
The industry, to its credit, is doing a lot to tackle issue number two. “Topics over keywords” is something that is not new as I’ll briefly come to later. Frameworks for topic-based SEO have started to appear over the last few years. This is a step in the right direction. Organizing site content into categories, adding appropriate internal linking, and understanding that one piece of content can rank for several variations of a phrase is becoming far more commonplace.
What is less well known (but starting to gain traction) is point one. But in order to understand this further, we should dive into what the buyer’s journey actually is.
What is the buyer’s journey?
The buyer’s or customer’s journey is not new. If you open marketing text books from years gone by, get a college degree in marketing, or even just go on general marketing blogs you’ll see it crop up. There are lots of variations of this journey, but they all say a similar thing. No matter what product or service is bought, everyone goes through this journey. This could be online or offline — the main difference is that depending on the product, person, or situation, the amount of time this journey takes will vary — but every buyer goes through it. But what is it, exactly? For the purpose of this article, we’ll focus on three stages: awareness, consideration, & decision.
The awareness stage of the buyer’s journey is similar to problem discovery, where a potential customer realizes that they have a problem (or an opportunity) but they may not have figured out exactly what that is yet.
Search terms at this stage are often question-based — users are researching around a particular area.
The consideration stage is where a potential consumer has defined what their problem or opportunity is and has begun to look for potential solutions to help solve the issue they face.
The decision stage is where most organizations focus their attention. Normally consumers are ready to buy at this stage and are often doing product or vendor comparisons, looking at reviews, and searching for pricing information.
To illustrate this process, let’s take two examples: buying an ice cream and buying a holiday.
Being low-value, the former is not a particularly considered purchase, but this journey still takes place. The latter is more considered. It can often take several weeks or months for a consumer to decide on what destination they want to visit, let alone a hotel or excursions. But how does this affect keyword research, and the content which we as marketers should provide?
At each stage, a buyer will have a different thought process. It’s key to note that not every buyer of the same product will have the same thought process but you can see how we can start to formulate a process.
The Buyer’s Journey - Holiday Purchase
The above table illustrates the sort of queries or terms that consumers might use at different stages of their journey. The problem is that most organizations focus all of their efforts on the decision end of the spectrum. This is entirely the right approach to take at the start because you’re targeting consumers who are interested in your product or service then and there. However, in an increasingly competitive online space you should try and find ways to diversify and bring people into your marketing funnel (which in most cases is your website) at different stages.
I agree with the argument that creating content for people earlier in the journey will likely mean lower conversion rates from visitor to customer, but my counter to this would be that you're also potentially missing out on people who will become customers. Further possibilities to at least get these people into your funnel include offering content downloads (gated content) to capture user’s information, or remarketing activity via Facebook, Google Ads, or other retargeting platforms.
Moving from keywords to topics
I’m not going to bang this drum too loudly. I think many in of the SEO community have signed up to the approach that topics are more important than keywords. There are quite a few resources on this listed online, but what forced it home for me was Cyrus Shepard’s Moz article in 2014. Much, if not all, of that post still holds true today.
What I will cover is an adoption of HubSpot’s Topic Cluster model. For those unaccustomed to their model, HubSpot’s approach formalizes and labels what many search marketers have been doing for a while now. The basic premise is instead of having your site fragmented with lots of content across multiple sections, all hyperlinking to each other, you create one really in-depth content piece that covers a topic area broadly (and covers shorter-tail keywords with high search volume), and then supplement this page with content targeting the long-tail, such as blog posts, FAQs, or opinion pieces. HubSpot calls this "pillar" and "cluster" content respectively.
The process then involves taking these cluster pages and linking back to the pillar page using keyword-rich anchor text. There’s nothing particularly new about this approach aside from formalizing it a bit more. Instead of having your site’s content structured in such a way that it's fragmented and interlinking between lots of different pages and topics, you keep the internal linking within its topic, or content cluster. This video explains this methodology further. While we accept this model may not fit every situation, and nor is it completely perfect, it’s a great way of understanding how search engines are now interpreting content.
At Aira, we’ve taken this approach and tried to evolve it a bit further, tying these topics into the stages of the buyer’s journey while utilizing several data points to make sure our outputs are based off as much data as we can get our hands on. Furthermore, because pillar pages tend to target shorter-tail keywords with high search volume, they're often either awareness- or consideration-stage content, and thus not applicable for decision stage. We term our key decision pages “target pages,” as this should be a primary focus of any activity we conduct.
We’ll also look at the semantic relativity of the keywords reviewed, so that we have a “parent” keyword that we’re targeting a page to rank for, and then children of that keyword or phrase that the page may also rank for, due to its similarity to the parent. Every keyword is categorized according to its stage in the buyer’s journey and whether it's appropriate for a pillar, target, or cluster page. We also add two further classifications to our keywords: track & monitor and ignore. Definitions for these five keyword types are listed below:
A pillar page covers all aspects of a topic on a single page, with room for more in-depth reporting in more detailed cluster blog posts that hyperlink back to the pillar page. A keyword tagged with pillar page will be the primary topic and the focus of a page on the website. Pillar pages should be awareness- or consideration-stage content.
A great pillar page example I often refer to is HubSpot’s Facebook marketing guide or Mosi-guard’s insect bites guide (disclaimer: probably don’t click through if you don’t like close-up shots of insects!).
A cluster topic page for the pillar focuses on providing more detail for a specific long-tail keyword related to the main topic. This type of page is normally associated with a blog article but could be another type of content, like an FAQ page.
Good examples within the Facebook marketing topic listed above are HubSpot’s posts:
For Mosi-guard, they’re not utilizing internal links within the copy of the other blogs, but the "older posts" section at the bottom of the blog is referencing this guide:
Normally a keyword or phrase linked to a product or service page, e.g. nike trainers or seo services. Target pages are decision-stage content pieces.
Track & monitor
A keyword or phrase that is not the main focus of a page, but could still rank due to its similarity to the target page keyword. A good example of this might be seo services as the target page keyword, but this page could also rank for seo agency, seo company, etc.
A keyword or phrase that has been reviewed but is not recommended to be optimized for, possibly due to a lack of search volume, it’s too competitive, it won’t be profitable, etc.
Once the keyword research is complete, we then map our keywords to existing website pages. This gives us a list of mapped keywords and a list of unmapped keywords, which in turn creates a content gap analysis that often leads to a content plan that could last for three, six, or twelve-plus months.
Putting it into practice
I’m a firm believer in giving an example of how this would work in practice, so I’m going to walk through one with screenshots. I’ll also provide a template of our keyword research document for you to take away.
1. Harvesting keywords
The first step in the process is similar, if not identical, to every other keyword research project. You start off with a batch of keywords from the client or other stakeholders that the site wants to rank for. Most of the industry call this a seed keyword list. That keyword list is normally a minimum of 15–20 keywords, but can often be more if you’re dealing with an e-commerce website with multiple product lines.
This list is often based off nothing more than opinion: “What do we think our potential customers will search for?” It’s a good starting point, but you need the rest of the process to follow on to make sure you’re optimizing based off data, not opinion.
2. Expanding the list
Once you’ve got that keyword list, it’s time to start utilizing some of the tools you have at your disposal. There are lots, of course! We tend to use a combination of Moz Keyword Explorer, Answer the Public, Keywords Everywhere, Google Search Console, Google Analytics, Google Ads, ranking tools, and SEMrush.
The idea of this list is to start thinking about keywords that the organization may not have considered before. Your expanded list will include obvious synonyms from your list. Take the example below:
There are other examples that should be considered. A client I worked with in the past once gave a seed keyword of “biomass boilers.” But after keyword research was conducted, a more colloquial term for “biomass boilers” in the UK is “wood burners.” This is an important distinction and should be picked up as early in the process as possible. Keyword research tools are not infallible, so if budget and resource allows, you may wish to consult current and potential customers about which terms they might use to find the products or services being offered.
3. Filtering out irrelevant keywords
Once you’ve expanded the seed keyword list, it’s time to start filtering out irrelevant keywords. This is pretty labor-intensive and involves sorting through rows of data. We tend to use Moz’s Keyword Explorer, filter by relevancy, and work our way down. As we go, we’ll add keywords to lists within the platform and start to try and sort things by topic. Topics are fairly subjective, and often you’ll get overlap between them. We’ll group similar keywords and phrases together in a topic based off the semantic relativity of those phrases. For example:
Many of the above keywords are decision-based keywords — particularly those with rental or hire in them. They're showing buying intent. We’ll then try to put ourselves in the mind of the buyer and come up with keywords towards the start of the buyer’s journey.
This helps us cater to customers that might not be in the frame of mind to purchase just yet — they're just doing research. It means we cast the net wider. Conversion rates for these keywords are unlikely to be high (at least, for purchases or enquiries) but if utilized as part of a wider marketing strategy, we should look to capture some form of information, primarily an email address, so we can send people relevant information via email or remarketing ads later down the line.
4. Pulling in data
Once you’ve expanded the seed keywords out, Keyword Explorer’s handy list function enables your to break things down into separate topics. You can then export that data into a CSV and start combining it with other data sources. If you have SEMrush API access, Dave Sottimano’s API Library is a great time saver; otherwise, you may want to consider uploading the keywords into the Keywords Everywhere Chrome extension and manually exporting the data and combining everything together. You should then have a spreadsheet that looks something like this:
You could then add in additional data sources. There’s no reason you couldn’t combine the above with volumes and competition metrics from other SEO tools. Consider including existing keyword ranking information or Google Ads data in this process. Keywords that convert well on PPC should do the same organically and should therefore be considered. Wil Reynolds talks about this particular tactic a lot.
5. Aligning phrases to the buyer’s journey
The next stage of the process is to start categorizing the keywords into the stage of the buyer’s journey. Something we’ve found at Aira is that keywords don’t always fit into a predefined stage. Someone looking for “marketing services” could be doing research about what marketing services are, but they could also be looking for a provider. You may get keywords that could be either awareness/consideration or consideration/decision. Use your judgement, and remember this is subjective. Once complete, you should end up with some data that looks similar to this:
This categorization is important, as it starts to frame what type of content is most appropriate for that keyword or phrase.
The next stage of this process is to start noticing patterns in keyphrases and where they get mapped to in the buyer’s journey. Often you’ll see keywords like “price” or ”cost” at the decision stage and phrases like “how to” at the awareness stage. Once you start identifying these patterns, possibly using a variation of Tom Casano’s keyword clustering approach, you can then try to find a way to automate so that when these terms appear in your keyword column, the intent automatically gets updated.
Once completed, we can then start to define each of our keywords and give them a type:
- Pillar page
- Cluster page
- Target page
- Track & monitor
We use this document to start thinking about what type of content is most effective for that piece given the search volume available, how competitive that term is, how profitable the keyword could be, and what stage the buyer might be at. We’re trying to find that sweet spot between having enough search volume, ensuring we can actually rank for that keyphrase (there’s no point in a small e-commerce startup trying to rank for “buy nike trainers”), and how important/profitable that phrase could be for the business. The below Venn diagram illustrates this nicely:
We also reorder the keywords so keywords that are semantically similar are bucketed together into parent and child keywords. This helps to inform our on-page recommendations:
From the example above, you can see "digital marketing agency" as the main keyword, but “digital marketing services” & “digital marketing agency uk” sit underneath.
We also use conditional formatting to help identify keyword page types:
And then sheets to separate topics out:
Once this is complete, we have a data-rich spreadsheet of keywords that we then work with clients on to make sure we’ve not missed anything. The document can get pretty big, particularly when you’re dealing with e-commerce websites that have thousands of products.
5. Keyword mapping and content gap analysis
We then map these keywords to existing content to ensure that the site hasn’t already written about the subject in the past. We often use Google Search Console data to do this so we understand how any existing content is being interpreted by the search engines. By doing this we’re creating our own content gap analysis. An example output can be seen below:
The above process takes our keyword research and then applies the usual on-page concepts (such as optimizing meta titles, URLs, descriptions, headings, etc) to existing pages. We’re also ensuring that we’re mapping our user intent and type of page (pillar, cluster, target, etc), which helps us decide what sort of content the piece should be (such as a blog post, webinar, e-book, etc). This process helps us understand what keywords and phrases the site is not already being found for, or is not targeted to.
I promised a template Google Sheet earlier in this blog post and you can find that here.
Do you have any questions on this process? Ways to improve it? Feel free to post in the comments below or ping me over on Twitter!
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