Knowledge Management Best Practices Today’s Contact Centers Should Follow

by | Jun 14, 2021 | Knowledge Automation, Knowledge Management

There is more content, on more platforms, in more locations, than at any time in the past. Sifting through this content quickly and accurately to return useful answers is an extraordinary information challenge.

Today’s Knowledge Management is focused on ensuring that the right content is delivered at the right time. It is a recognition that one of the most important resources available to everyone involved is time. Accuracy, speed, and relevancy all point back to time. Every process that exists in current knowledge management practices should return an investment against time for both the agent and the client.

There is no single solution that fits every knowledge management issue. The processes and principles of good knowledge management will apply differently to different projects, and part of the methodology behind good knowledge management is deciding how to employ those processes and principles.

This blog will cover elements of practical knowledge management and highlight the processes and practices used to execute those elements.

Centralize Key Knowledge in a Single Location

Speed of use and content findability are two highly beneficial results of good knowledge management. Centralizing important or often accessed content to a single location contributes to those results. A single location is easier to locate and is easier to maintain.

Design for Both Search & Browse Behavior

When users need to locate content, their approach can be broadly classified into two seeking behaviors, browse and search. In general, browse behavior is when a user traverses a taxonomy incrementally with a rough idea of where they need to go to find what they need, using a clearly articulated information architecture (for example: folder names) to guide them. Search behavior is when a user applies their knowledge of the name or contents of what they seek. In short, users browse when they know where content should be and users search when they know what content contains.

Create a Standard Taxonomy

One of the core principles of good knowledge management is leveraging routine processes and familiar landscapes to promote participation and enhance ease of use. The language used to describe a KM environment is a taxonomy. These descriptions are found in folder names, file naming conventions, keyword tags, process titles, and anywhere else good digital signage would benefit a user of the system.

The taxonomy of a KM system needs to use the same lexicon as the environment in which it operates. Simply put, the words used in a taxonomy should be the same words your company uses. Team members should not have to fight through a language barrier to find what they are looking for, and when a taxonomy matches practical working language, retrieval times drop and seeking accuracy improves.

Use Tags and Categories to Create Better Findability

Tags provide a powerful boost for searchers and browsers. Tags are a critical balancing point between managing the two finding behaviors: search and browse. While good browsing behavior is highlighted by an awareness of where a seeker is in a knowledge base, searching doesn’t always have this level of awareness. This is where tags pick up the slack. Either as a pre-search filter or a post-search refinement, tags aid seekers in determining the “aboutness” of a search result. Therefore it is crucial to apply tags as soon as possible; and this is also why reinforcing descriptors that focus on the aboutness of a piece of content is a great use for tags.

Categories within a knowledge management context are the highest level indicators of content. They are typically tied closely to the central needs or functions of the company or operating environment. Categories are often used to indicate a life-cycle status of a piece of content, a type, or a priority. Select the most important aspect of your content to serve as your category. When developing a knowledge management solution, categories create an elevated and visible differentiator within content to express differences in location, purpose, type or status. They serve as an immediate aid for seeking behaviors and have a positive effect on findability in terms of both speed and accuracy.

Establish a Consistent Review Process

At its core, good knowledge management is about completing routine, established processes. Sustainability is a goal for every KM effort. One of the best ways to achieve this is to perform regular review cycles, including folder maintenance, spot checks on content, an examination of a tag cloud, and category reviews. This is made easier through automation technology which is mentioned below.

Overall, a monthly review process is critical to a healthy KM system. It combats issues like Tag Rot and Tag Bloat. When reviews occur on a schedule, they help to maximize user adoption and overall system health.

Leverage Technology for Automation

Technology now exists that takes the traditional burdens off maintaining a knowledge management system—by placing the onus on the system instead of the administrator in terms of gaining insights. Automatically notifying administrators of critical tasks saves time, reduces stress, and keeps the entire system more accurate, up-to-date, and trusted.

Key areas to automate in a knowledge management system include: when content is going out of date, what content is being abandoned/not read, when tags are duplicated, when file names are similar or the same, and when search queries are being abandoned. By keeping track and automating the notification of certain tasks it is exponentially easier to maintain the system.

Make Your Knowledge Base Smarter with Technology

Leveraging AI in your knowledge management system improves the overall findability of key content reducing handle time and escalation, while improving first contact resolution. If knowledge management is enabling the right information to be found, at the right time, by the people who need it, then AI is an accelerator of that promise.

Leveraging recommendation and suggestion engines closes the gap between expected seeking behavior and actual results by giving the user a shortcut to key information based on similar content they were looking for. For a practical example of this in practice, refer to Amazon. When you’re on a particular product page, the website automatically provides other books or products that have “You Might Also Like” and “Recommendations” sections.

Implementing AI also helps users who know what they are looking for, but do not know exactly where it is, by providing a quicker route to the content. Using advanced findability technologies applies a formal structure to a knowledge base that aids both browse and search behavior.


Implementing knowledge management best practices can have a profound impact on many key metrics of a contact center such as: reducing workload through greater self-service and shorter handle times, improving First Contact Resolution (FCR) rate, and enabling agents to handle increasingly diverse, complex inquiries without needing extensive training and specialization.
Leveraging knowledge management practices reduces the maintenance burden and time demands on managers, while also providing a single trusted source that is accurate, consistent, and up-to-date. The investment will pay dividends on the bottom line, impacting areas such as: handle time, contact resolution, call transfers, training time, and time-to-proficiency.

Thanks to the cloud and new innovative technologies, contact centers can pursue KM without the inertia that hinders knowledge cleanup, development, maintenance, integration into the desktop, and user adoption. The result will be a well-organized and highly searchable knowledge base that transforms agent-assisted service and creates a new opportunity for continuous improvement.