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Article 6 min read

What is knowledge-centered service (KCS)?

Knowledge-centered service is a philosophy that leverages the KM process to drive greater understanding of how an organization is solving knowledge and improve the organization as a result.

By Vishal Sharma, Chief Technology Officer, SearchUnify

Last updated July 14, 2022

Customers want resolutions, and they want them yesterday. Your support teams are generally swamped with a variety of tasks ranging from simple service requests to major incidents that require immediate attention. That’s why KCS®, knowledge-centered service, is a widely adopted approach to steer service processes in the right direction.

What is knowledge-centered service (KCS)?

Knowledge-centered service is the continuous generation of demand-driven and self-improving knowledge by many as a by-product of solving customer issues. It is a framework for collecting, structuring, reusing, and improving knowledge consistently to leverage it for maximizing support outcomes. It mandates that the onus of knowledge management is a collective effort, where the creation and improvement of knowledge are proactively performed across the firm. As a result, organizations, regardless of size, can expect improvements that will have a snowball effect and spur:

  • A demand-driven and self-improving knowledge base. And when everyone contributes to the collective good, a culture of knowledge sharing, not hoarding, is developed
  • Improved customer service workflows leading to better agent efficiency, turnaround time, first contact resolution (FCR), etc.

  • The ability to scale the service organization without breaking a bank or lowering the quality of service interactions

  • And reduced agent onboarding time as they won’t have to be extensively trained for various issues. Instead, they could be better instructed to deal with advanced issues

Check out these five great knowledge management examples to see how brands like Vend and Canva are doing it.

How does KCS work?

Knowledge management is about capturing, managing, and maintaining intellectual property throughout the content lifecycle. Knowledge-centered support is a philosophy that leverages the KM process to not only drive greater understanding of how an organization is solving knowledge, but to also learn and improve the organization as a result.

Service managers tend to focus on the total number of tickets getting resolved. That’s why even the agents prefer to quickly close a ticket and hop on to the next one. Understandably, sometimes knowledge creation and knowledge-centered service get the cold shoulder. As a result, though, new solutions aren’t added to the knowledge base, and the wheel is reinvented every time a similar issue arises.

Start by asking your team some questions.

  • Common issues: What do you know your customers are always struggling with? Could the answer be easily put into writing

  • What are some frequently asked questions? This is oftentimes low-hanging fruit. What are your agents consistently answering?

  • Are there any known issues that impact a lot of customers? Get in front of the inevitable requests with documentation or expected timelines for fixes.

  • What are the most time-consuming issues? Is it possible to put an explanation directly into an article to help customers find the answer for themselves?

The average large business in the U.S. loses $47 million in productivity annually as a direct result of poor knowledge sharing

Principles of knowledge-centered support

1. Capture

Every interaction with customers is an opportunity to learn. You’ll want to be sure to capture knowledge in the customer’s words, not yours. In other words, don’t translate knowledge to company jargon and acronyms that the customer does not use.

2. Structure

Consider how content will be templated, written, reviewed, verified, and published. Determine how customers will access your content as self-serve and how it will serve their needs with searchability and language choices

3. Reuse

This principle is about seeking existing knowledge immediately to reduce handle time and reusing what’s already been learned: never solve the same issue twice.

4. Improve

Content fine-tuning is a continuous part of KCS. Determine how your content will be updated and remain relevant to maintain trust scores and reduce escalation rates.

Benefits of KCS

You need a successful implementation to reap all the benefits of knowledge-centered service. A cognitive search engine can help with that. Here’s how:

1. Accelerates knowledge creation

As mentioned earlier, knowledge creation is generally not an agent’s top daily priority. Hence, it’s considered extra work. That’s why investing in the right technology that facilitates knowledge creation is paramount.

Leading cognitive engines come with AI-powered apps that embed content creation in the process of issue resolution itself. As a result, agents don’t have to spend extra time to document a solution. The engine proactively picks up vital information from the agent’s response and populates a knowledge article on a predefined template; thereby integrating KCS practices into the workflow and keeping employees and managers happy.

2. Integrates content and processes

To ingrain KCS practices in the DNA of your service agents, your enterprise platforms (such as CRM) should be integrated with support tools and knowledge base. That way your agents won’t have to switch between platforms for information retrieval and sharing.

Equipped with a cross-channel search, a cognitive engine helps agents find relevant information from all KBs inside their support console. This drives the usage of existing knowledge. On top of that, it stops wastage of time on creating articles that already exist.

3. Powers contextual relevance

Lauren Freedman once said, “Customers remember the service a lot longer than they remember the price.” Do you know what upsets a customer more than not having an answer? Having the wrong answer. And if more than half of customers won’t do business with a company after one negative experience, there’s just too much at stake.

Cognitive search uses artificial intelligence (AI) and natural language processing (NLP) to actually understand the context behind a search query. This allows the agents to find highly relevant information for the customers on the fly.

4. Quantifies KCS efforts

We can’t emphasize how extremely important it is to evaluate the knowledge articles your teams produce. Luckily, cognitive search also packs an insights engine that reveals how the generated content performs in the real world.

Because let’s face it, your KB articles are only good if they are actually solving problems. Cognitive search offers user-friendly reports that unveil how often the KB articles are shared and attached to support tickets. This helps gauge the impact of the knowledge management initiative. Additionally, things like top contributors help gamify the service organization and keep the agents motivated and happy.

Challenges of KCS

So, we have established that KCS strengthens all your terrific institutional knowledge — and helps take care of disorganization, miscommunication, and customer frustration. It’s time to address the next big question: how do I implement KCS successfully? Well, many factors function as a deterrent to a successful KCS implementation and adoption, such as:

  • Culture of knowledge hoarding

  • Lack of ownership and dedicated resources

  • Technical hurdles such as a multitude of enterprise platforms that limit access and waste time

  • Manual processes and outdated technology

How can you implement KCS in your organization

SearchUnify is a Zendesk solution provider and its partnership with Zendesk brings unified cognitive search to the table. It enhances Zendesk properties by revolutionizing information discovery and enabling support agents to access relevant case-resolving information from enterprise-wide content repositories (like Lithium, Jira, MadCap Flare, Confluence, MindTouch, etc.) within their Zendesk console.

SearchUnify’s cognitive platform combines artificial intelligence & machine learning to analyze past tickets and suggest helpful articles, top SMEs, etc., to agents, thus reducing the overall turnaround time. Its rich insights engine provides customer journey details that empower Zendesk agents to personalize interactions with customers at scale and improve the first call resolution (FCR) rate as well as MTTR. Learn more here.

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