SEO for the Future: To Keyword Clusters, and Beyond | Pulse | Industry Insights | All MKC Content | ANA

SEO for the Future: To Keyword Clusters, and Beyond


SEO was a lot simpler in a less-crowded marketplace; you could optimize by keyword for your product, industry, or customer demographics and watch your traffic grow. In 2021, however, not only is the online space left with little metaphorical keyword elbow room, but search engines have gotten smarter to keep up with consumer demand. People search more conversationally due to voice assistants like Alexa and Siri, and AI-powered chatbots learn more as they're being searched, which also contributes to a natural semantic search style.

If marketers want to make sure their SEO efforts are being noticed, there are several ways to do this, all of which embrace this more casual way of searching consumers are employing – "long-tail keywords," those with fewer search results but a higher conversion rate; "keyword clusters," which group product identifiers that can single out your brand; "natural language processing (or NLP)" which rely on qualifiers like "what" and "where" to make a search less-robotic, and the overall conversational style that voice search and chatbots have made more prominent as of late. The resources collected here discuss how marketers can utilize each of these strategies to stand out in the digital crowd.

Check out the resources below.

Tools For Finding Long-Tail Keywords. HubSpot, March 2021.
Search results pages are crowded these days, with everyone vying for a spot at the top. This means that if you want good placement for a simple, generic search term, you're going to face a lot of competition. That's why, when conducting keyword research, it's important to target highly-specific keywords — these are known as long-tail keywords. Long-tail keywords are usually well-thought-out queries in the form of questions, phrases, and sentences. They're typically three or more words long. The term "long-tail" refers to the "long tail" of a search's demand curve. As queries around a topic become more specific, the search volume decreases while the likelihood of conversion increases. This creates a skewed distribution:

Long-tail keywords are especially valuable in retail because their high specificity indicates high purchase intent. Also, it's much easier to rank for long-tail keywords versus their more general counterparts, as long-tail keywords comprise 92 percent of all keywords typed into search engines. This post from HubSpot provides examples of strategies for finding these keywords, as well as some tools that exist to help you uncover them.

Keyword Clusters: How to Level up Your SEO Content Strategy. Search Engine Journal, March 2021.
Over the last few years, Google's engineering team has directed its focus toward natural language processing (NLP) and a deeper understanding of how on-page content interrelates. Neural matching helped Google understand synonyms, and BERT helped Google understand tricky prepositions. With every core update, Google gets more literary. But despite Google getting smarter, many site owners still optimize their websites with only a few keyword targets in mind. This is an outdated practice, especially when we know our landing pages often end up ranking for hundreds of keywords anyway.

As Google's NLP capabilities continue to improve, our on-page SEO strategy also needs to evolve to reflect those advancements in search, and keyword clustering is the answer. Keyword clusters are groups of keywords that represent searchers with similar purchase intent. Let's say your brand sells linen curtains. If you only try to rank for the first keyword, you end up limiting your market share. If you instead get your web page ranking for your primary keyword and the long-tail variants and related subtopics, your page will often end up ranking for 10 to 20 times the amount of keywords and pick up significantly more traffic.

Understanding Natural Language Search. Brightspot, March 2021.
Content Marketing Company Brightspot explains that "Search engines like Google and Bing are working to deliver search results that respond more accurately to questions phrased in conversational language. The search language characterized by robotic, keyword-heavy phrasing ("Empire State height") may have satisfied our needs until now, but meeting complex requests phrased in our natural language will be key to maintaining optimal search performance in the future. Search is trending in the natural language direction due to several key shifts:

  • Search engine evolution means users expect perfect results on their first attempt.
  • Search technology has advanced enough to be able to handle complex reasoning.
  • The rise of voice-activated technology.

In other words, the trend toward natural language underlying future search parameters means we're teaching computers how to think like us. In turn, publishers stand to benefit from the advancing work in this area to make their content more effective."

Going Beyond Keywords: How Conversational Insights Take the Guesswork Out of Marketing. Search Engine Watch, April 2021.
Keywords represent the tip of the iceberg when it comes to understanding consumer intent; using AI-powered chatbots, conversational data that occurs over messaging channels like Facebook Messenger and Instagram Messaging can give businesses a deeper understanding of what consumers want. Search Engine Watch discusses how conversational marketing platforms use natural language processing (NLP) and artificial intelligence (AI) to guide customers through the buying funnel. A robust conversational marketing platform makes it possible for companies to build chatbots that engage and convert customers on the websites, apps, and social platforms where people spend their time.

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Josch Chodakowsky is a senior manager of research and innovation at ANA.

The views and opinions expressed in Marketing Futures Pulse are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.

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