Quick Guide to Knowledge Centered Service

The old adage, “knowledge is power” rings especially true now, as companies look for ways to increase customer loyalty and advocacy as we see the current pressure on the economy continue to build

When you give your customer service agents access to all the knowledge they need to help customers, you’re at once improving their day-to-day working experience and helping them deliver excellent customer service. And this has an impact. In fact, a recent study by Bain & Company reveals that interacting with knowledgeable staff can increase NPS scores by as much as 87 points. 

But it’s not enough to simply create a knowledge base and walk away. You’ll get the best results by implementing Knowledge Centered Service (KCS) at the same time. In my 10+ years as a customer service leader, implementing KCS has had the most significant impact in two main areas: improving the agent experience and seeing customer satisfaction skyrocket.

KCS is not something you do in addition to solving problems… KCS becomes the way you solve problems.

– KCS Academy

What is Knowledge Centered Service?

By way of introducing KCS, I’ll turn to an organization that has championed it from the very beginning, the Consortium for Service Innovation, which writes:

The starting premise for KCS was to capture structure, and re-use support knowledge.

In short, Knowledge Centered Service is a methodology and a set of practices and processes that focus on knowledge as the single most crucial element of the support organization. KCS is not a tool in itself, but the use of a specific tool—the knowledge base—is a key ingredient to working with KCS.

They go on to say:

Knowledge Centered Service seeks to…

  • Make documentation of problem-solving part of the problem-solving process
  • Evolve content based on demand, quality, and usage
  • Develop a knowledge base that reflects an organization’s collective experience to-date
  • Reward learning, collaboration, sharing, and improving

What Are the Benefits of Knowledge Centered Service? 

I’ll dive into the many benefits of implementing Knowledge Centered Service in a moment, but, first off, it’s important to note that it’s not for everyone. If you have a small support team of just a couple of agents, paired with a low contact volume, you may not be ready for it – yet. But if your starting point is somewhere close to this description:

“We are fairly specialized. We have no self-service solution for customers—or what we do have is outdated and has many gaps—we spend at least several weeks training new agents, and only some of our agents are multiskilled.”

… you’ll find that Knowledge Centered Service enables your agents to solve cases faster and often the first time around (your First Contact Resolution rate really does see a big jump) quickly becoming multiskilled. You’ll also see a decreased training period—at my previous employer, we reduced our training period from 14 to two days—and, finally, increased agent satisfaction as they get to grow their knowledge and abilities.

KCS will also allow you to implement self-service options for customers, as useful agent-facing knowledge is captured and repurposed as customer-facing knowledge. This will deflect contacts and decrease your contact volume.

Finally, Knowledge Centered Service makes root cause analysis possible, which will help you rally the rest of your organization to help improve customer satisfaction through improvements to communication, processes, products, and services.

I’ve seen some out-of-this-world numbers cited after successfully implementing Knowledge Centered Service (and our training period reduction certainly was just that: out-of-this-world). Still, I won’t mention them here for fear of being branded a liar or a KCS-fanatic. Instead, I’ll paint a picture of the potential benefits of Knowledge Centered Service and then let you gauge the potential impact for yourself.

But to fully understand what makes Knowledge Centered Service brilliant, I need to take you through some knowledge management theory. Bear with me, please.

The Support Demand Curve

Almost any issue which involves multiple contacts to a contact center follows the support demand curve: a demand rises, peaks, and then recedes.

Traditional knowledge engineering graph

How quickly demand recedes is very different for each issue. You could be dealing with a one-off incident – for instance, if you made a mistake when billing many customers simultaneously. Alternatively, it could be something entirely different that will only recede after several initiatives have been taken to decrease demand via self-service or a change to your product or service. But, in any case, over a long enough timeline, demand will recede.

Traditional Knowledge Engineering

Using traditional knowledge engineering, quite a few identical incidents must occur before a support article is written and published internally (and often much longer before said article is published externally where customers can access it). It usually takes multiple agents to identify the pattern of a repeated incident, with each having come to a solution independently. That solution may or may not be the same, sometimes to the detriment of those of your customers who might not have gotten the best solution to their problem.

It takes a lot of time and effort and usually many incidents before your knowledge is trusted, validated, and published for everyday use.

Dynamic Knowledge Management

Knowledge Centered Service proposes working with Dynamic Knowledge Management, in which a support article is created as part of the problem-solving process. That information is immediately made available for reuse by other agents in your knowledge base.

Dynamic knowledge management graph

Every subsequent incident then validates the article or finds it lacking and prompts your agents to update and fix it. In this way, your knowledge base always stays up-to-date. Plus, that same knowledge can then be published to customers after a compliance review. It’s the quickest route to easy and efficient self-service.

This also means that agents trust knowledge a lot sooner, and the return on investment for the time spent on creating the support article is amortized over each subsequent incident. It eliminates a lot of rework and redundancy.

Knowledge Centered Service Meets Machine-Learning 

Despite your best efforts, it can be tricky to keep a traditional knowledge base up-to-date (even when you subscribe to Dynamic Knowledge Management), and encouraging your agents to actually use your knowledge base rather than falling back on old habits can be just as difficult. Enter, machine learning. 

By working with a knowledge base that uses machine learning, the knowledge base does a lot of the administrative work of maintaining and creating knowledge articles for you. What this looks like in practice is automatically sending knowledge prompts to agents with article suggestions, so they don’t need to search the knowledge base, and sending automated reminders to content managers when an article needs to be updated. This results in a knowledge base that’s always up-to-date; self-regulating to keep its content fresh.

The Agent Workflow: How to Work with Knowledge Centered Service

Just to make it clear how your agents’ process changes with KCS, I’ll refer to this simple diagram:

knowledge centered service agent workflow graph

With traditional KCS, the goal over time is for more and more agents to reach a level where they are given editing rights. This helps to distribute the workload as well as ensure fixes and updates are implemented quickly. That being said, ultimately, you will need to assign responsibility for the state and health of your knowledge base to someone.

If you have questions about the agent workflow diagram, read the following step-by-step walkthrough – otherwise, you can skip it.

Use it:

For any question, agents have to search the knowledge base. Every time. Alternatively, if your knowledge base uses machine learning, suggestions will be automatically delivered to them. This ensures that they become aware of changes to procedures or products that they were not previously aware of.

Flag it:

If something in the article is incorrect and the agent cannot correct it, they have to be able to flag the article, so someone with editing rights can update the content.

Fix it:

When the agent obtains new information about the contents of an article, they should immediately correct any discrepancies to ensure best practice for the next agent (and customer!) who needs the information.

Add it:

Not all customer questions can be anticipated, and therefore not all relevant support articles will have been written. When an agent gets a customer question where no support article exists, it rests with the agent to create a support article answering that particular question. This is where the Work In Progress (WIP) article process comes into play. 

Note on WIP articles: 

Agents shouldn’t wait until they’ve found a solution for an article to be created and searchable. If an agent notices the need for an article, they should immediately create a WIP article that at least outlines the issue at hand, so if other agents also see the same issue they can find the WIP article and add their take to it. Then, once the solution is found, it can be added to the WIP article, marked as done, and all parties are notified. This removes the possibility of duplicate work, where multiple agents have created similar first draft articles for the same incident.

Watch Your Knowledge Base Grow

When working with this kind of Dynamic Knowledge Management for three months or more, the knowledge base will get to a point where it contains articles that answer almost any question a customer might ask. It will continue to grow organically with each new question. 

Its maturity can be measured by looking at the rate in which customer queries are resolved with the help of an existing article, versus the amount of queries that need a new article created.

Frontloading the creation of support articles to the problem-solving process means that it does take longer to resolve the issue the first time. Still, it’s time is well spent as the handling time for each subsequent customer request is reduced and solved correctly. It’s simply a good investment in the long run.

There’s more to KCS than creating and maintaining a knowledge base that your agents use. So let’s dive a bit deeper to get to some more benefits.

The Knowledge Centered Service Process: Solve & Evolve

KCS follows a continuous loop of gathering, structuring, and recycling content:

Knowledge Centered Service Process: Solve & Evolve Loop

The Solve and Evolve loop as an entire process does much more than just dealing with customer service and content. While customer service and content are the focus of this article, I’d like to point out that the Solve & Evolve loop is inherently a demand and usage-driven feedback mechanism meant to optimize your customer service and provide feedback for product improvement. I’ve written another blog post about using customer contacts to improve both service and product.

But, I digress. Continuing with the focus of this post, let’s go through each of the first four steps:

Step 1: Gather knowledge

Knowledge always starts with the customer. After all, what they’re experiencing is vital to your business. When a customer asks a question, a support article is created to answer that customer. Your agents write articles based on the customer’s context and are at the same time making that knowledge relevant and searchable.

Step 2: Structure knowledge

The best way to write a support article is from the customer’s perspective. To do this properly, it’s often easiest to start from a template. This also ensures that the agent provides the required information and that knowledge articles are uniform and easy to read.

Step 3: Reuse knowledge

When a customer asks a question, the agent has to search the knowledge base. Each time a support article is reused, you’re providing consistent service, which is so essential to a great customer experience.

Step 4: Improve knowledge

The next step is to improve your collective knowledge. Make sure your agents know this and take part in it; ownership of it is vital to the success of KCS. It’s not just to help them –  it benefits their colleagues, your business, and your customers.

So, Just How Effective Is Knowledge Centered Service, Really?

One of our customers, a gaming company with a popular online multiplayer game, had documented only 29% of their processes before they implemented KCS. This lack of a single source of truth for knowledge meant that agents searched in internal knowledge bases and external sites like Google and Stackoverflow for answers to players’ questions. It also meant agents were sending solutions out that might work for the player. After implementing KCS, they had 85% of their processes documented in just two months – that’s right, they went from 29% to 85% in two months! Agents were now sending solutions they knew worked, and players were no longer dependent on the searching skills of the agent handling their issue. When you receive hundreds of thousands of contacts a week, this makes a massive difference in agent satisfaction, player satisfaction, and efficiency.

And, it’s not uncommon for companies to keep their knowledge spread out in different folders and drives, with no formal structure or knowledge base. In fact, in a survey conducted by Dixa late last year, 47% of customer service agents, out of a cohort of 1500 across the US and UK, said that they have no formal knowledge base, with more than 29% responding that they often turned to Google to try and find answers. Read the full report here.

Empowered Agents and Consistent Answers

When agents can find answers to customer inquiries in an internal knowledge base, they feel empowered to solve issues because they know the information they’re providing to customers has been verified and is coming from a trusted source. 

When no article exists to solve an issue, agents have the power to create a new article right away and know that it will benefit not only their team members but also potentially themselves at a later point in time. If a solution is incorrect or things have changed since the article was published, they can flag the article and provide the correct information so the article can be updated. From the customers’ side, they will experience consistent answers across both agents and channels because the information is coming from the same source.

Closing the Loop

When you’re using a knowledge base that’s built-in to your customer service software, after a conversation has been resolved, agents then mark what article was used to solve the issue. This information is then added to the conversation as an internal note, which, if the customer reaches out again, ensures the next agent won’t try that same solution again. Instead, the agent will either use a different article or flag the used article to consider updating it to shed light on this new situation. 

Knowledge Health

Part of Knowledge Centered Service involves keeping an eye on your knowledge base’  analytics: which agents are creating the articles used the most to solve customer issues? Usage of which articles results in a high CSAT? Or, conversely, a low CSAT? There is a wealth of knowledge to be gained by analyzing the quality and health of the articles in the knowledge base. 

Get Started with Knowledge Centered Service Today

I hope this guide has proven useful and made the prospect of introducing Knowledge Centered Service to your support organization seem both exciting and possible. 

If I can add one final word of advice, it’s to start today. You’ll see immediate benefits, and, as for the results, well, I’ll let them speak for themselves.

If you’d like to hear more about KCS and Dixa’s knowledge basebook a demo with us today.

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Author

Tue Søttrup

Tue Søttrup

Tue brings over 20 years of experience in customer service to his role as VP CX Excellence at Dixa.

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