Knowledge-Centered Support Support Methodology: The Best Practices

3 min read
Jump to section

    Customers have access to a seemingly unlimited amount of information, right at their fingertips. With an increasing number of game-changing services (such as Amazon Prime) that get customers what they want, when they want it, customers expect service to be timely. They also expect a seamless experience across all channels, and they want it to be personalized. With all of these expectations, how does your organization keep up with, or better yet, stand out from your competitors? And how to do you that while being mindful of your staffing and budget resources? The answer is Knowledge-Centered Support Methodology.

    Knowledge-Centered Support (KCS) is a set of nonlinear best practices aimed to create and maintain knowledge in work environments that are knowledge-centric. It’s about recognizing the value of collective company knowledge, and capturing that knowledge so it can be used to empower customer support reps to provide top-notch service.

    Why Use a Knowledge-Centered Support Methodology?

    According to a survey conducted by the Consortium for Service Innovation, there are both qualitative and quantitative benefits to implementing a Knowledge-Centered Support methodology. Companies who have implemented KCS enable support reps to find the answers they are looking for faster and more frequently, resulting in a 50-60 percent increase in time to resolution. Additionally, KCS helps speed onboarding and training, resulting in a 70 percent increase in time to job proficiency for new employees.

    There are also benefits related to the job satisfaction and retention rates of support employees. The same study found a 20-30 percent increase in employee retention and a 20-40 percent improvement in employee job satisfaction among customer support employees using the Knowledge-Centered Support methodology.

    KCS allows organizations to capitalize on one of the biggest resources they already have: knowledge. However, for many teams, this is a very transformative initiative that relies on having the right technology and a collaborative culture before implementing KCS practices.

    Knowledge-Centered Support Best Practices

    1. Produce New Content

    Every time a customer support employee resolves an issue, they should produce new documentation. They should record each detail of the process and explain the issue, in the customer’s words. How was the problem solved? What tools were utilized? Was the customer satisfied with the solution? Capturing this knowledge will help future customer support employees resolve the same issue with heightened efficiency.

    2. Continuously Update That Content Based On Demand

    Typically, the same customer support issues will come up more than once. Each time you or a team member encounters an issue, update pre-existing documentation pertaining to that issue. As customer support evolves, so should your content.

    3. Centralize Your Updated And Relevant Customer Support Knowledge

    What’s the use of capturing knowledge from your customers if customer support employees can’t find that knowledge when they need it? A knowledge management solution that is easily searchable allows employees to recall information and apply it to their everyday interactions with customers.

    4. Incentivize Social Learning, Knowledge Sharing, And Collaboration

    A Knowledge-Centered Support methodology will not succeed without collaboration, because it fundamentally relies on learning from the experiences of others. Motivate customer support employees to share knowledge by rewarding employees who go above and beyond to be team players.

    KCS addresses the way organizations view, maintain, and develop knowledge. And it’s proven to increase operational effectiveness for support organizations. If you want to learn more about implementing KCS at your organization, download our white paper, The Secret to Successful Knowledge-Centered Support.

    how to optimize content for generative AI
    How to Optimize Content for Generative AI
    How to Prepare Data for Machine Learning Tools
    How to Prepare Data for Machine Learning: A Comprehensive Guide
    The importance of data quality for AI
    The Importance of Data Quality for AI
    Request a Demo

    Start working smarter with Bloomfire

    See how Bloomfire helps companies find information, create insights, and maximize value of their most important knowledge.

    Schedule a Meeting
    Take a self guided Tour

    Take a self guided Tour

    See Bloomfire in action across several potential configurations. Imagine the potential of your team when they stop searching and start finding critical knowledge.

    Take a Test Drive