How knowledge-centred service benefits customer support teams
The customer service landscape is evolving at an unprecedented pace, but two constant customer expectations remain the same: speed and precision
Last updated February 15, 2021
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-centred service, is a widely adopted approach to steer service processes in the right direction.
66% of adults feel that the most important thing a company can do to provide them with a good online customer experience: value their time
What is KCS?
Knowledge-centred 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 maximising support outcomes. It mandates that the heart of KM is collective effort, where the creation and improvement of knowledge are proactively performed across the firm. As a result, organisations, regardless of size, can expect improvements that will have a snowball effect and spur:
- A demand-driven, self-improving knowledge base. 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, turnround time, first contact resolution (FCR), etc.
- The ability to scale the service organisation without breaking the 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.
What are the basic concepts of knowledge-centred support, or KCS?
Service managers tend to focus on the total number of tickets being 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-centred service get the cold shoulder. As a result, though, new solutions are not added to the knowledge base, and the wheel has to be reinvented every time a similar issue arises.
The average large business in the USA loses $47 billion in productivity annually as a direct result of poor knowledge sharing
How to successfully set up KCS
So, we have established that KCS strengthens all your terrific institutional knowledge — and helps take care of disorganisation, miscommunication and customer frustration. It’s time to address the next big question: how do I implement KCS successfully? Many factors can deter 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
The top 4 benefits of KCS
You need a successful implementation to reap all the benefits of knowledge-centred service. A cognitive search engine can help with that. Here’s how:
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 which 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.
Integrates content and processes
To embed 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 will not 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 creating articles that already exist.
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 will not 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 understand the context behind a search query. This allows the agents to find highly relevant information for the customers on the fly.
Quantifies KCS efforts
We can’t emphasise 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.
Your KB articles are only good if they are actually solving problems! Cognitive search offers user-friendly reports which unveil how often the KB articles are shared and attached to support tickets. This helps gauge the impact of the knowledge management initiative. In addition, ideas like top contributors help gamify the service organisation and keep agents motivated and happy.
Why SearchUnify and Zendesk work well together
SearchUnify is a Zendesk solution provider and its partnership with Zendesk brings unified cognitive search to the table. It enhances Zendesk properties by revolutionising information discovery and enabling support agents to access relevant case-resolving information from enterprise-wide content repositories (such as Lithium, Jira, MadCap Flare, Confluence, MindTouch etc.) within their Zendesk console.
SearchUnify’s cognitive platform combines artificial intelligence and machine learning to analyse past tickets and suggest helpful articles, top SMEs etc., to agents, thus reducing the overall turnround time. Its rich insights engine provides customer journey details that empower Zendesk agents to personalise interactions with customers at scale and improve the first call resolution (FCR) rate as well as MTTR. Learn more here.