Artificial intelligence (AI) can make content more user-friendly and empower marketers to work efficiently. However, discussions about AI and content marketing often center on the potential for machines to eventually replace writers. While the technology is already used to generate sports and financial news updates, creating bots smart enough to write with nuance, creativity, original research and critical thinking is far off.
Still, AI's major role in search has significant implications for content marketers.
How Google Uses Machine Learning to Rank Content
Machine learning is a type of AI in which models and algorithms learn to make predictions based on statistics, producing reliable results and uncovering insights through trend analysis. Google uses a machine learning algorithm called RankBrain to optimize search results. RankBrain observes user behavior to determine if the results delivered by Google help the user get the information they need, known as "searcher task accomplishment."
If a searcher inputs a query and jumps around from link to link on a SERP, this signals to RankBrain that the results may not be helping the user complete their task efficiently. In this case, RankBrain might adjust the weighing of different ranking factors — of which there are over 200, according to Search Engine Journal — to produce more relevant results.
When RankBrain observes a user clicking on a link and remaining on that page for a while (a factor known as dwell time), it assumes searcher task accomplishment has been achieved, and it ups the ranking of that page. Engineers used to perform this task and still do, but the algorithm assesses the data much more effectively.
From a content marketing perspective, you need to be creating pages that answer questions, which is distinct from creating pages optimized for keywords. Luckily, adjusting your content strategy won't require an overhaul — just a change in point of view.
Achieving Searcher Task Accomplishment
It may seem counterintuitive, but the integration of AI and content marketing actually allows marketers and strategists to start thinking more like humans again. You can stop trying to crack the code of Google's ranking factors and rest assured that searcher task accomplishment is number one.
Search Engine Journal states that marketers should look at the terms for which they want to rank and start thinking of them as topic groups instead of keywords. It might seem awkward if you're used to traditional keyword research, but once you get into it, you'll find it's an intuitive process.
For example, let's say you're marketing kitchen equipment. Your primary target keywords are the products you sell: pots, pans, plates, flatware and so on, perhaps with descriptors. Your secondary keywords might get more specific and include the product's brand name, materials, purpose or special features.
To use a topic group method, you'd create a core page around your primary keywords and build out supporting pages relating to your secondary keywords. To rank for "pots" and all the queries related to shopping for and purchasing pots, you would create a broad page describing everything you have to offer in terms of pots. You'd then link to supporting pages that answer questions on related topics, like copper pots, cast iron pots, caring for pots, etc.
Creating content as topic groups ensures you are answering any and all questions your visitors may have about your product or service. Including both broad overviews and specific coverage of your offerings allows you to organize your content in a way that makes sense to users — and search engines.
AI Tools for Search Engine Optimization
If machine learning can help Google rank content, it makes sense that AI can also help you uncover insights about your content and rankings.
Tried-and-true keyword planning platforms, like Moz and SEMrush, now include smart features that find opportunities to outrank the competition. They likely do this with semantic analysis of top-ranking pages, cross-checking them to identify gaps in topic coverage. There are many more players in the game, including Acrolinx and BrightEdge. You can even use AI tools to help you spot grammatical and spelling errors in your content or catalog the topics you've already covered.
The goal of using artificial intelligence in content marketing — whether you're Google, a software engineer or a marketer — is to build scalable processes that result in high-quality, relevant pieces of content. Even with the help of machine learning and other technologies, getting a handle on your content marketing strategy can prove challenging. Enlisting the help of an expert team may give you the perspective and prowess you need to create content that continually ranks in search and engages readers.