The power of search engine keywords is waning. Since the introduction of semantic search with Google’s hummingbird search rewrite, they no longer have a decisive influence in search ranking. At best, they are simply one of dozens of factors involved with semantic search results. Perceptions about keywords have been slow to change for authors and marketers who don’t specialize in SEO, and even for some SEO consultants. Google throttled the flow of keyword information to content producers, but many people still consider search keywords important or even essential. Search keywords have become a crutch on which brands and authors rely to try to communicate with audiences.
It’s a challenge to reverse a decade or more of group-think relating to keywords. For a keyword loyalist, giving up old habits can be hard, even habits that no longer make sense — especially when there are no obvious replacement tactics. Keywords are more often used unthinkingly than used constructively. The good news is that although keywords offer limited value to improve SEO, they can improve content quality in selective cases. It’s important to know the difference between the fetish use of keywords in content, and the creative application of keyword insights to improve the quality of content offered to audiences. The difference between keyword hacks and keyword understanding is methodology.
Search Keywords Shouldn’t Describe Page Titles
The SEO industry has responded to Google’s introduction of semantic search with confusing advice. Although Google doesn’t match exact keywords on a page with keywords used in search queries, numerous SEO consultants still maintain search engine keywords are vital to how Google understands content. Sure, Google can reinterpret search queries; but they argue if you write natively using the most popular keywords used in search queries, it’s simpler and more effective. These people suggest that things have changed less than they seem. They note that Google still indexes keywords in search, and still has a keyword planner writers can use.
As Google has altered its behavior over time, SEO has deformed into an incoherent set of tactics. Many ordinary content producers have lost the ability to understand what these tactics really deliver. They consult Google’s AdWords keyword planner to guide the creation of content, often at the urging of SEO consultants who encourage the practice. The AdWords keyword tool may present forecasts of impressions associated with a search keyword. But ad impressions are not the same as search impressions (an impression being the existence of an item on a page accessed by a user, not necessarily an indication that the person noticed the item). The algorithm Google uses to prioritize the display of paid advertising based entirely around keywords is different from the algorithm it uses to prioritize organic search results based on search terms and contextual information. It’s a mistake to use Google’s keyword planner for advertising and assume it will deliver a better search ranking or more qualified audience. But content producers make this assumption all the time, because it is convenient and they lack a conceptually sound process for developing and writing about content.
Significantly, Google’s AdWords encourage the decoupling of search keyword terms from the specific terms used in the content displayed in an ad. Ad content can be related to the keyword bought without using the actual phrase. The mandate that you are supposed to use the exact term in your writing doesn’t even apply to advertising, the one area where Google encourages keyword research. Search engine keywords aren’t magic: they are simply a pricing mechanism for ads.
Search Engine Keywords Mask User Intent
Another common use of search engine keywords is to research popular topics. SEO consultants and writers believe that search keywords provide them with data-rich market research that will tell them what content they should produce. But search keywords have never been very solid as data to understand audiences. No matter what tool one uses, the tool won’t illuminate who is seeking information or necessarily why. Making bold assumptions about people, their motivations, and their likely behavior based on a few search engine keywords is a risky thing to do.
Consider the case of people searching for the phrase “dead Wi-Fi.” This example is fairly typical of search terms: short, inelegant — and ambiguous. Who are the people typing this phrase and what is their intent? Is the phrase “dead Wi-Fi” more likely to be entered by a 20 year old or a 60 year old? What might the phrase suggest about their level of understanding of wireless routers? And most importantly, what can we infer about the intentions of the numerous people entering this phrase? Are they all the same, or do different people have different goals when using the exact same phrase? Why, when presenting search results matching the exact same search phrase, will different people make different choices about which article titles to click on? Rather than providing answers, the search keyword raises questions.
Google prioritizes results to show the most popular pages that seem to match what Google interprets the search to be about. To illustrate, let’s suppose the first search result presents an article about Wi-Fi dead zones in your house. Google presents a specific popular article on reception problems by interpreting dead to mean “dead zones.” The eighth search result might provide an article on resolving general Wi-Fi problems, perhaps discussing when the Wi-Fi antenna on a phone or computer isn’t functioning. Here Google presents a popular page on fixing malfunctioning Wi-Fi equipment by interpreting the term dead to mean “not working.” The 15th search result might be entitled “Freedom from Dead Wi-Fi.” This article title exactly matches the search term, but its purpose is not clear. It is actually a page promoting the sale of new Wi-Fi equipment rather than a help article to fix existing equipment. It features images and copy describing a futuristic looking box with many antennas that might appeal to the gamer crowd.
The search ranking for the article “Freedom from Dead Wi-Fi” was determined by two factors: people who entered a different phrase but decided to click on the title, and those who entered the exact phrase. Those who entered a different search query may have been attracted to the aspirational, if vague, promise of having a hassle-free experience. The term “dead” might resonate with gamers in particular, who don’t want to be on the dead side of anything. Those who entered “dead Wi-Fi” as a search phrase probably clicked on the title because of confirmation bias: it exactly matched what they thought they were looking for. Confirmation bias is the tendency to identify with things that confirm our preexisting impressions or concepts. So if you have content that has intrinsic popularity— it ranks highly anyway because it gets many page views — including a popular search keyword in the title may spur some additional page views due to the confirmation bias factor. On the other hand, a title that merely sounds like it is helpful can run the risk of disappointing the viewer. Some people viewing the “Freedom From Dead Wi-Fi” page wanted help on their current Wi-Fi problems. Pages viewed are not the same as audience interest in the content.
Without actually looking through numerous results, it’s not possible to infer much from the search keywords. Viewing the content within the pages, one can find that the search keywords don’t represent a coherent set of user intentions.
Rethinking Keywords from an Audience Perspective
The purpose of any keyword research should be to understand the language of your audience, not to guess what will rank high on search engines. And it is important to know what specific audience segments matter most to your organization.
Many people have a naive belief that aggregated, unsegmented Google keyword data provides a perfect mirror of their audience. SEO consultants and writers may believe they are promoting audience interests by using search engine keywords, but they are being data-focused rather than audience-centric. They aggregate activity to create figures to justify content decisions, rather than start with the more granular needs of individuals and then identify common patterns. They put blind faith in often dubious numbers.
The Myth of the Undifferentiated Audience
People in different roles, from marketing to technical writing, want to believe their audience is undifferentiated. They want to believe that “everyone wants the same thing.” It’s simpler to do so. This mentality is common in marketing in particular: some marketing managers believe they need to talk to everyone and that everyone will want to listen to the brand.
There are a few brands that only care about page views, and care less about who the audience is. Advertising-supported publishers don’t care who visits their page: the ad shown will programmatically change according to who the person is. Businesses that are purely transactional, such as hotel booking sites, similarly don’t care so much about audience segmentation: they want as wide an audience as possible to generate transaction fees. But most businesses seek to capture value based on targeting specific kinds of customers, and providing products tailored to their needs. If some of your customers a more profitable than others — because they buy more, pay more, or are cheaper to serve — simply pursuing page views will skew your brand value.
When brands act as if everyone is equally important, it generally signals a problem in business strategy, or poor operational oversight. They don’t know, or at least don’t communicate internally, who are their most valuable customers and the need to focus on them. As a consequence, we have situations where SEO consultants dictate editorial choices, or copywriters rely on keywords to write generic copy because they don’t understand precisely who the audience is, and how they think about the topic.
Shifting the Role of Keywords from Discovery to Understanding
Popular keywords that aren’t specific to the audience segment a brand wants to attract, only provide the illusion of data. To provide value, keywords need indicate information to authors that is better than what they can get relying on available subject expertise.
Brands too often expect keywords to tell them what to say. They focus on target keywords instead of target audiences. They get fixated on the circular logic of “discovery”: they hope to discover the right keyword so audiences can discover the right content (theirs). If keywords exist to promote discovery, they can’t at the same time be the object of discovery. When this happens, the keyword becomes the end, instead of a means to an end. The keyword defines the audience, instead of the audience being the party defining appropriate keywords.
If instead we shift the role of keyword away from “discovery” toward understanding, we get a more realistic goal. Brands need to understand which audience keywords will promote understanding of their content. Here we assume the brand already knows what they want to say, they just need to know exactly how to phrase it. The target is a message; the keywords are simply guidelines for presenting the message. The keywords relate to terms used by a specific audience, rather than a magic box of gold at the end of a rainbow.
Understanding Audience Segments Through Language
Audience keywords — the specific terminology used by an audience segment — is not something available from Google search data. But audience keywords can be derived from various sources, and brands can find it worthwhile to understand linguistic differences.
One outcome of the vast quantities of text data that are now available is a growing understanding of language differences among groups of people. Social media scholars, for example, notice words and even neologisms being used frequently by people associated with one another, while these same terms aren’t used widely in the general population. Our language usage seems to be drifting back into distinct linguistic dialects, a consequence of both our online social connectivity and our selectively accessing content (the filter bubble). Now that the age of mass media is over, we no longer expect everyone to talk about things the same way.
Some writers may object to being concerned with linguistic differences. For example, advocates of plain language argue that all content should be written in a way that anyone can understand it. While such a goal is surely admirable for some sectors — government in particular — it is not true that all parties are equally satisfied with plain language descriptions. I’ve seen scientists frustrated by the quality of writing using plain language to describe a topic that required more specialized words, which were not allowed. They complain that a discussion is oversimplified or key details are missing. Similarly, writers may insist they are writing about a topic of narrow interest, so that anyone interested in the topic is likely to talk about it in the same way. But even for niche topics, there can be novices and experts. I am not suggesting that the vocabulary of all topics need to be segmented by audience; I am simply noting that it can be presumptuous to act as if no differences in audience needs exist.
Audience keywords involve a different set of tools and data than search engine keywords. Audience keyword analysis basically involves comparing the frequency of words in a target texts (corpus) of an audience, with the frequency of words used in another set of texts, often representing the general population. This comparison allows a writer to understand what vocabulary is most unique to the audience, and how they use this vocabulary. There are commercial SaaS products that provide these capabilities, such as Sketch Engine. There are also desktop software programs that one can use. I’ve used the popular Antconc program, for example. For those wanting to process large sets of data, text analysis libraries in Python and in the R statistical software can be used.
The next task is identifying what content exists that can reveal the vocabulary your audience uses to discuss a topic. A range of sources offer a rich corpus of content to identify the vocabulary used by your customers:
- For audiences who belong to topic focused communities of interest, the texts of publications they read regularly, such as hobby magazines (for understanding keywords of enthusiasts), or specialized trade publications (for understanding the keywords of a B2B vertical segment)
- Transcripts of focus groups of a target audience segment
- User comments from audiences in social media, or community forum discussions
- Terms used in internal search.
By analyzing such source content, writers identify words with special significance that are used more frequently by an audience segment than by the population as a whole. They can understand their audience’s preferred terminology, and nuances in how they describe things, especially adjectives. These can uncover value propositions.
Tribal publications — publications dedicated to distinct tribes such as specific professions or groups of avid fans of an activity — are different from general publications that don’t have such a tight audience focus. They are more likely to use lingo or jargon, and reflect the internalized language of the audience who read these publications. They are also likely to be read more loyally, and therefore promote the usage of words in a particular way.
A special comment about using internal search terms (also known as vertical search). Why are internal search terms are okay, but external search engine terms not? People using site search are more likely to be your target audience. They have seen your site, got of sense of who you are, and feel motivated to explore further. Vertical search was once considered an indication of UX or information architecture failures. Now vertical search is it a key differentiator for brands to guide their customers to find their products. Search logs from internal searches can provide information about the terminology that people coming to your brand use.
Keywords are Clues, not Facts
Keywords can reveal interesting clues about audiences. Clues suggest something, but they should not dictate it. A hint in a crossword puzzle is different from the answer. Internal search keywords, for example, can provide hints about dimensions of topics, and ways to discuss topics, but are not themselves the answer to what you should be writing. Not being clear about this distinction results in the clueless, fatalistic question: “What does the data say we should do?” Being data driven may be virtuous, but running on autopilot isn’t. Clues aren’t facts.
Keywords Aren’t Market Data
Keywords may provide clues to audience interests, but don’t provide direct data. One can’t infer directly from keywords who is using them. You need other forms of data to tie the reader to the keyword. So if you find an odd kind of search query showing up on your internal search logs, it does not automatically indicate that you should be producing content using that keyword. Search keywords are reliable indications of interest only when the search keywords match the keywords of the audience that you want to attract. Perhaps a number of people who aren’t your target audience mistakenly came to your content and are trying to find something you don’t offer, or care to offer. Your own internal analytics data will probably provide a better indication of what content you should produce than relying on internal search logs. There can be a role for search terms to gauge potential interest on topics about which you have not written previously, but your internal content usage analytics will in most cases be a better indication of what resonates with the audiences you attract.
Relying On Keywords Can Distort Meaning
Algorithmic assessments should never be a substitute for judgment in writing. Two terms that seem similar, but have different frequencies, are not necessarily identical in meaning. Related and similar-sounding words can have subtly different meanings, or different connotations. One shouldn’t use the most popular term simply because it’s the most popular. Make sure the term chosen is exactly equivalent to the term not chosen.
Sometimes more formal (and less popular) terms carry more precise meanings. The best way to connect a term that’s popular with your audience with a more precise term that you need to use in your content, is through cross referencing.
Keywords Can Help Brands Develop a Preferred Terminology for Topics and Audiences
If you routinely write about a certain topic, it may be worth your effort to analyze audience discussion relating to the topic. Text analysis programs can help brands determine the audience-preferred terminology relating to a particular domain. While this is obviously entails cost and effort, it may pay dividends.
Ideally all writers will have sufficient subject domain expertise internalized to know the preferred vocabulary for an audience segment. But writers often need to write about varied topics, and writing is often outsourced to others. Having a list of audience-preferred terminology with associated definitions can enable any author to write appropriately on a given topic. Text analysis can even support development of a style guide. For fields such as health and wellness, where words have precise meaning, a preferred domain terminology is helpful if some writers are not deep subject experts.
In the not too distant future, I can imagine commercial firms will offer tailored keyword products. Brands will be able to get a list of “keywords of 18 – 24 year old skateboarders” or “vacation-related keywords of upper income 50 – 60 year olds.” For now, content strategists will need to do the legwork themselves.
— Michael Andrews