Microcontent: Authoring, Managing, and Publishing Content in 2022 and Beyond
By Rob Hanna, President & Co-Founder at Precision Content
Think back to the last time you Googled something. How many results were you offered? How long did it take you to find the answer you were looking for? We are increasingly offered more and more content as we search for the relevant results we need. From searching on Google to asking technical questions at work, we are looking for answers in an increasingly data-rich and complex information landscape. In this landscape, microcontent provides a profound opportunity to power conversational user interfaces (UI), like chatbots, voice platforms, and artificial intelligence systems.
Microcontent is the increasingly smaller components that make up larger compositions. Microcontent is the modular, agile components that can be “stitched” together to provide the best possible answers. These new combinations move together and work in different permutations that may not have been envisioned by the original creator. Microcontent addresses a need within the content management industry as we rethink how we author, manage, and publish content.
“It will no longer be good enough to simply provide the right information, at the right time, to the right people on the right device in the right locale. It’s not information that people are looking for: it’s answers”, said Rob Hanna, CEO of Precision Content.
The way we create and manage content has been a product of the era and the technology of the time. From the transition from oral communication to the first written language, the development of the Gutenberg Press, to our current digital age, content durability and portability has been crucial. We have now come full circle back to oral communication–we are talking to machines, and machines are talking to us. As a result, we need to have content that is understandable for both humans and machines. To adapt to the machine learning and AI phase of the digital revolution, technical communication enters the “age of micro”.
The Age of Micro
Micromoments
A few years ago Google introduced the idea of micromoments. Micromoments describe the delivery of snippets of highly contextualized, personalized information, and user experiences based on user intent. Micromoments highlight the trend towards content designed for those “I want to…” moments that provide directly actionable information for users.
Microlearning
In the education space, we are seeing microlearning via small, agile pieces of content. Microlearning is making information available to learners when they need it most. Rather than trying to teach an entire course, microlearning delivers a lesson a day, or a moment at a time. Putting that learning at the user’s fingertips, makes it highly contextual to their situation.
Microformats
Microformats are a form of markup that can be used to combine content from multiple sources. Taking snippets from websites, for example, and combining them together to form a new piece of information. Using microformat markup for web content makes information machine-readable and presents it in a way that it can be predictably reused. In this way, microformats structure content so it can be syndicated across multiple web properties.
Microcontent
The term microcontent was coined by Jakob Nielsen in the 1990s. The central concept of microcontent is to create easily scannable labels for clear identification of meaning. This often involves writing, organization, and formatting strategies so content can be easily used anywhere, anytime it is needed. But it’s not microcontent just because it’s small! To make microcontent effective you have to have the right structure in place, including rich metadata that makes it discoverable.
Let’s look at a few examples of microcontent. Google Featured Snippets deliver the answer we’ve queried based on information Google algorithms have found after indexing websites. Featured snippets deliver a concise answer and complete set of information based on pieces of microcontent. Looking at information streams, we can see that RSS feeds, for example, are good examples of microcontent. Feeds are able to deliver selective content to entice us to read more. Similarly, if you’ve ever searched for a particular product, you know that product titles on websites also deliver relevant snippets of information and are useful for more than website navigation.
Conversational User Interfaces
Looking ahead, one of the most talked about emerging technologies is conversational platforms, like chatbots. In a recent study, conducted by Precision Content and The Content Wrangler, indicated that many technical publication organizations are examining their role within the chatbot space. Our research revealed that chatbots are the tip of the iceberg for conversational platforms because they’re the most accessible part of the technology we have today. But they’re not the only piece of technology. Voice assistants are also part of the conversational user interface experience.
Chatbots and voice interfaces, interestingly enough, can be drawn from the same source. Each has different purposes, but both enable better functionality. Whether it’s learning or exploring, chatbots with a GUI (graphic user interface) and some amount of text are much easier to use when we’re trying to navigate the Wall Street Journal or book travel. But for activities like ordering pizza or working with your Fitbit, voice automated assistants are far more effective. In fact, it’s much easier to work with voice when trying to get information unless you are learning or exploring, which is easier through a text based interface. Both these interfaces are drawing from the same source of microcontent.
The next iteration of this interface is, of course, artificial intelligence (AI). AI, combining deep learning and natural language processing, also powers conversational UIs. Looking at the two methods for building an AI, the first, semantic reasoning, relies on a corpus of content and on subject matter domain in order to learn. We have to teach artificial intelligence the core of this subject matter area expertise. The second, machine learning, can work with large swaths of data and analyze that data in order to become more intelligent. Semantic reasoning relies on domain knowledge and content models or knowledge models, making semantic reasoning a much harder hill to climb. It’s going to be the one that our businesses are going to rely on most for working with content and information. We’re going to need to know how to package information so that our artificial intelligence systems can deploy it the right way.
Currently, the biggest barrier to employing a semantic reasoning system is the availability of microcontent. In a recent survey with over 100 participants, we asked who was best equipped to create microcontent or chatbot content within their organization. Less than half (45%) responded that technical communicators are responsible for creating technical content for chatbots. This result indicates that we’re going to miss a big opportunity for transformational change. The central question is: how are we going to keep the lights on, create microcontent, still create content for our traditional channels? We need to find a way that we can repurpose our existing content as microcontent for the enterprise to use. To avoid creating noise rather than useful content, we need a strategy where we build content from microcontent. Or, in other words, we want to write pieces that encapsulate sections of microcontent throughout.
How do we do that? We can start by thinking of microcontent as building blocks of information. As very precise, and very concise, consistent blocks of content that we use to serve as microcontent. We can use these building blocks to create any number of different traditional types of information products such as knowledge bases, user guides, and PDFs, all from the same source. When we work with structured content, we’re not just talking about technology, but we’re talking about making information more accessible to humans as well making it easier for us to be able to navigate that content, understand it, use it, retain it, and enable technical systems, like conversational UIs, to use that content.
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About the Author
Rob Hanna co-founded Precision Content in 2015 to pursue his goals to produce tools, training, and methods that will help organizations make their high-value content instantly available to all that need it including customers, staff, partners, and even other information systems that need to consume that content. Driving this development is the Precision Content® Writing Methods, based on the best-available research over the last 50 years into how the brain works with information. Today Rob leads his highly-skilled team of content strategists, information architects, writers, trainers, and developers to serve the needs for digital transformation for businesses across North America.