Imagine you open an airline app to search for your flight information and it’s missing your gate location. Or you pull up an online form to apply for a permit with your local government and you are not sure where to begin.
These everyday information gaps are also common in customer service scenarios, like when a customer searches a portal for shipping status but can only find basic order details like amount paid. Or when a call center agent searches a knowledge base for an answer to a common customer question, only to find the response hasn’t been updated for over a year.
If the process isn’t seamless, experience suffers, and brands risk losing customers.
Customers need to be able to solve problems on their own using the channels of their choice. And if they can’t, agents need access to the right information, resources, and tools to help.
If the process isn’t seamless, experience suffers, and brands risk losing customers. In fact, according to research from Qualtrics and ServiceNow, 80% of customers said they have switched brands because of a poor customer experience.
As companies continue to compete on experience, quickly finding and filling missing information is key to customer and agent satisfaction and retention. We refer to these as knowledge gaps.
Artificial Intelligence (AI) and Machine Learning (ML) are fast-tracking knowledge management. But identifying knowledge gaps is easier said than done.
Knowledge managers are responsible for making sure customers and agents have the information they need, and they know that a lack of the right knowledge content can make or break a great experience. But they don’t always know what’s missing or have the tools to easily find this information. The burning question often on their minds is: “what knowledge content should I create?”
AI and ML are quickly becoming the unsung heroes of this story, helping identify and surface knowledge gaps to better serve agents and customers, driving operational efficiencies, and increasing customer satisfaction. Here’s how.
1. Powering seamless, consumer-grade search experiences. Online search is ubiquitous in everyday life. We use it to find recipes, check the weather, stay up to date on news, and more. The beauty of online search is that it’s optimized for the end-user, surfacing the most relevant and popular articles based on keywords.
Unfortunately, that seamless search experience hasn’t fully reached the customer service and knowledge space.
When a customer or agent searches for an answer to a question, they’re often served static information that isn’t relevant to their query and are forced to dig through multiple results to find what they need. This slows time to resolution and can even lead to no resolution at all.
AI can be key to improving the search experience. AI-powered search helps surface the most relevant articles. It also learns over time to improve the relevancy through ML, so that the most popular articles bubble to the top.
The most modern AI search engines provide the most definitive results in an answer box. You’ve likely seen this in your favorite online search engine where, for example, when you search for the weather it shows you the exact forecast in the result as an answer box at the top.
You can see how this will benefit both the agent and customer. As a call center agent searches a knowledge base for content to help solve a customer’s issue, AI-powered search can surface the most pertinent information, meaning they solve problems faster and the first time.
Increases in first call resolution means agents spend less time on the phone and, thus, operational costs decrease. For the customer, time is precious. Fast issue resolution can result in higher customer satisfaction and increased customer loyalty.
2. Automating and scaling processes. Perhaps the most tedious part of a knowledge manager’s job is keeping knowledge base content fresh and healthy.
Organizations have historically relied on manual, time-intensive processes to review and update knowledge articles. This has led to missed opportunities for content updates and outdated information.
As organizations transform their customer service and call center operations, they’ve started relying on predictive intelligence to automate and scale knowledge management.
Using AI and ML, predictive intelligence reviews historical customer cases and compares them to existing articles in the knowledge base. It also identifies common words and phrases that make articles easily searchable.
The result? Outdated or incorrect information is automatically surfaced, making it easier for knowledge managers to update articles with accurate information. Common cases are clustered so knowledge managers can create new, relevant content. Agents can stay focused on the most impactful and meaningful work, and when they are serving a customer they can provide consistent and accurate resolutions. Customers, in turn, can find the right answers to their questions faster when they self-serve. This is a huge time saver for organizations with thousands of knowledge articles.
AI and ML can find issues days or weeks before a pattern is obvious to call center agents. This often occurs when new products are launched. Now, with access to updated knowledge sooner, agents are less stressed and can concentrate on providing personalized service.
But the only way AI technology can succeed in solving real world problems is if it is easily accessible to business users.
Knowledge managers can’t afford to wait for engineers and data scientists to perform the analysis needed to fill these knowledge gaps. They know better than anyone the problems they need to solve and need to be able to easily solve them. Low code configuration puts the power of AI and ML directly in the hands of a business user.
3. Enlisting agents and customers. As knowledge managers leverage AI and ML to identify and fill knowledge gaps, it’s also important to enlist customers and agents in the effort. You then need to set up a system that allows agents to update knowledge content as they resolve cases to help tap into hidden or dormant knowledge.
Enabling agents to create knowledge in the context of their work drives two great outcomes. First, other agents working on solving similar cases will solve them faster, because those new knowledge articles will be surfaced for them automatically, speeding their time to resolution.
Second, once articles are approved and published as customer facing, customers will be empowered to solve their own problems — both reducing case volume and increasing customer satisfaction. It’s not just about technology – it’s about having a good process to capture knowledge from the people who know best – your agents.
Finally, ask customers for suggestions and to share the solutions they found most helpful in an online forum. It takes a village to create a fulsome knowledge management strategy.
Knowledge can’t work in a vacuum
AI and ML are critical pieces to improving knowledge management and filling information gaps. But simply filling the gaps won’t drive the experience forward for customers or agents, and knowledge can’t work alone.
Customer service, with knowledge built in, needs to be delivered to customers proactively, wherever they are on their journey.
For example, consider a customer who is in the middle of placing an order for a product. If they have a question – about sizing, specs, return policy, etc. – they shouldn’t have to toggle to a new screen to search for an answer on a customer service portal or make a phone call to get help. Customer service should be delivered to them when and where they need it.
Savvy organizations recognize the need to proactively meet customers where they are and allow them to self-serve. They’re doing this by embedding self-service throughout their websites, as an overlaying widget, to deliver service easily, effectively, and proactively to customers regardless of where they are on their journeys.
As a bonus, with the right technology, organizations can add recommendations for customers as they self-serve.
For example, when a customer does open a case, the system can capture additional context, like the product page they were viewing, while ML can help match the issue at hand to other similar cases, finding relevant content, and offering solutions as a response. This, once again, improves the customer experience by enabling quick and seamless service from wherever the customer is working.
The digital transformation imperative
Organizations need to adjust to ever increasing customer expectations, technology advances, and disruption on an ongoing basis. The ability to navigate through disruption and better serve customers is a competitive differentiator.
Key to this is knowledge management that supports agents as they work to support customers and helps customers self-serve – from wherever – to solve their own issues and requests.
According to research from ESI Thought Lab, organizations are moving fast to improve the customer experience in key areas, including unifying customer service operations, building intuitive experiences through AI, and using automation to optimize customer service.
The companies that embrace digital transformation to scale knowledge management and improve customer service are the ones that stand to win as they compete for customer loyalty.