If you’re a technical marketer, you likely spend your days knee-deep in the minutiae of your site’s data, analyzing conversions, optimal keywords, volume, and cost per click. In which case, you might have some trouble seeing the forest for the trees.
It may be time for you to take a step back, look at your overall content, and ask yourself, does my content do a good job of covering all relevant topics that signal to Google that I am an authority in my industry?
Keyword relevance isn’t just about choosing terms that are applicable to your business (though that’s part of it). It’s also about using all related terms and topics in your content to signal to Google that you’re not just publishing surface-level content, that you’re getting into the nitty gritty with your posts because you’re an expert and that’s just what you do.
Why Keyword Relevance and Topic Comprehensiveness Are Important
Conventional SEO wisdom tells you to focus on your best-ranking keywords and have dedicated pages for each of them, and while that’s a good strategy, it isn’t enough. If you want to rank for a term, you need to have many pages that mention that term and its related topics.
The reason behind this is twofold: Google’s Hummingbird algorithm looks beyond keyword use to gauge the semantic meaning behind content, and it also collects user data to analyze the path that people take from search query to landing on the right content.
As such, having comprehensive topic coverage and relevant content sends both direct and indirect signals to the search engine. Hummingbird helps you rank when you focus on relevance, then rewards you further when search users realize the value you provide, leading to exponential improvement in ranking.
“As SEOs, we've got to be asking ourselves, ‘Okay, how do we build up an association between our brand or our domain and the broad keywords, terms, topics, phrases, so that we can rank for all of the long tail and chunky middle terms around those topics?’ This is now part of our job. We need to build up that brand association,” said Rand Fishkin.
Right, so how do we identify those chunky middle terms and related topics? Next, we’ll talk about what Google does – and doesn’t – tell you, and how to work around these gaps in knowledge.
Search Engines and Keyword Relevancy: What We Know and What We Don’t
Industry experts, and Google itself, urges marketers to write high-quality content and to focus on your target audience, but identifying which topics are relevant to your target market is up to you.
Google is certainly calculating relevance and scoring it in a quantitative manner; these relevance calculations are core to determining Topical Authority, PageRank, which synonyms are matched by their internal engine and more. But the search engine giant is not providing the relevance data back to marketers, so as not to tip their hand. Without that relevance data, however, marketers simply lack the necessary information to make data-driven decisions on the relevance of their content.
Without Google giving us clear direction on how to model relevance, marketers have found workarounds when developing content strategies. As pointed out by Moz, one good method of determining semantic relevance is to build a topical hub, using relevance-based data sources such as Wikipedia, and to then look at entities to determine which is the most relevant.
This is certainly a valid way to do it, since Wikipedia is bound to have high-quality, relevant content for basically anything you'd want to market. As you'd expect, however, it's difficult to execute this at scale. Marketers have to manually build topical hubs for each topic that they're marketing and, for agencies, each client as well, which can quickly get out of hand. This is one of the main reasons we built MarketMuse, and next we’ll discuss how our solution can assist you in search engine optimization.
How to Relevant Topics and Keywords in Your Content
SEO tools today also fail to provide relevance-based metrics. Great keyword research tools such as SEMRush and SpyFu return keywords that have some degree of relevance but they can't quantify the degree of relevance, so their data can only be sorted on Volume. For example, at the time of this writing, SEMRush will generate 10,000 keywords for each query. But once you have the list, it's up you to manually determine relevance for each topic, and combing through 10,000 keywords for each topic is no small task.
Another issue is the way that the 'relevant' keywords are generated. The easiest way for a keyword tool to generate suggestions is to look at the seed term you've entered (e.g. "canned dog food") and expand it to a list of niche keywords that contain the seed (e.g. "best canned dog food for senior dogs"). Long-tail keyword campaigns have always relied on this type of expansion.
However, as Google moves toward semantic search, it's begun making adjustments to the way it lists Search Engine Result Pages (SERPs): Google is now lumping results, meaning that it shows the same SERP for many long-term keywords. As a result, there is less of a discernible difference between ranking for "canned dog food" and "best canned dog food for senior dogs.”
We started MarketMuse to bridge this gap and to provide marketers the relevance data that they need to be effective. Working with a team of PhD statisticians, we've built the MarketMuse Keyword Relevance Engine, the core technology that powers our set of content analysis tools. MarketMuse differs from any other keyword research tool in two important ways:
- MarketMuse generates topically related keywords that don't contain the seed term. As an example, our technology draws the connection between "dog food" and "pet food.” We'll serve both search synonyms ('proof terms') and related keywords ('related terms'), depending on which terms will rank you highest in organic search. (For those who have read Cyrus Shepard's great blog post on the Moz blog, think along the lines of TF-IDF and co-occurrence measures).
- For each related keyword, we calculate a Relevance score, which measures the degree of topical relevance. This relevance score is critical to prioritizing how you spend your time. Instead of having to comb through thousands of keywords, MarketMuse just tells you the most important keywords you should focus on right now.
When you create content that draws a relevant audience, that content is much more likely to drive real results to your bottom line. A central question in organic search marketing is conversion: you're drawing pageviews on your website, but how many visitors will convert to customers? By prioritizing relevance, you're more likely to draw the right audience that will actually buy your products and services.
Luckily, creating relevant, targeted content can be made easy with content outlines that instruct your writers with data-based recommendations. We did it for Neil Patel, and we can do it for you. Check out our case study on how we helped Patel double his traffic and rankings: