Capturing the terms that make content findable: How Knowledge Centered Support (KCS℠) can help

Knowledge Centered Support is one of the most powerful tools you can use to improve your content for a wealth of reasons. By incorporating KCS principles into your knowledge workflow, you can increase the likelihood that your audience will be able to locate the information they need to address their issue or answer their question.

What’s the question?

First, you must place yourself in the shoes of the content seeker to try and anticipate what question they might ask to locate the content.  Typically, this comes from a customer or an employee.  Herein lies the first challenge.  If the person trying to locate the content did not write the content, how can you possibly know which words to type in the search box to maximize your chances of finding the exact content you are looking for?

The answer lies within the KCS practices

Knowledge Capture The first step in writing a piece of content is to “capture” it.  Usually, that comes from communicating with a customer about a question or problem.  People who are not practicing KCS will usually try to translate what the customer is asking or saying into what they think the problem is and then write an article about the translated problem.

This presents an issue because generally the people searching for the content (ie. other customers) think similarly and may describe the problem or question in a similar way.  Therefore, part of capturing using KCS practices is to also put it in the exact context as was described by the initial customer who reported it.

This is called capturing in the customer’s context.  If customers describe the screen as blue, but a more accurate description is azure, make sure blue is still in the article, preferably in the title (along with azure).  This will help someone else immediately identify the article as a possible candidate for the answer if they spot it in a list with one of the words they have searched on.

Structure

The next step is to structure the content.  Not only does it need to be accurate, clearly stated, and easy to read, it also needs to contain ALL the words that someone else might use to 1) describe the problem and 2) search for it.  This doesn’t always happen as the article is being written.  It is often an iterative process.  However, you don’t really want a support agent taking the extra time to think of all the words that could be used to describe say the word blue in the example above.

So many words

This is where a Knowledge Management tool with a built in dictionary can make things much easier to practice KCS and make things much more findable.  Instead of placing the words within each article, certain tools contain a global dictionary where you can add related words.  Again from our example above, if the article only contains the word blue, you could add this to the dictionary and list the related words like azure, turquoise, navy, etc.  Then, when a search contains any of those words, it will find all the articles with the related words and place them in the search results list.  And best of all, as new articles get added with any of those “blue” type words, they will be returned in searches and therefore customers and employees will have an easier time finding them.

Why search is so important

Inaccurate or incorrect search results will stifle KM adoption and content reuse even in the best of KCS organizations. The KM search engine must adapt not only to how users ask questions using natural language processing, filtering selections, content metadata, customer dictionary synonyms, and proximity/ordering of words in the users’ query, but also to who the user is and which content is of most interest and importance to them in real time. Additionally, having a strong KM analytics focused KCS program will enlighten KM administrators on exactly how users are searching, which content is being used most and least often, and perhaps most importantly, what content is missing from the knowledge base. When an organization intimately understands how users search, what content they are using, and what content they need, they can better configure their KM system to be more responsive and accurate, all while improving the overall perception, acceptance, and reliance on KM and the industry proven KCS practices.


This post was co-authored by Link Black, manager of the Knova KM product line for Aptean. Link has been working with Knova KM as a technical consultant and manager for more than 15 years and is based in Pittsburgh, PA.