Knowledge automation helps you simplify existing business processes and boost call center productivity. It’s a great way to ensure call center optimization and reduce or eliminate unnecessary costs while:
- Increasing customer satisfaction
- Optimizing resources
- Improving first call resolution rate
- Enhancing employee satisfaction
- Increasing customer reach
- Offering 24/7 customer support
A potential downside to automation is the lack of human interaction, which can leave customers unhappy. So, how do you implement AI-driven call center solutions while also providing a great customer experience?
This article looks at what call center automation is and how you can improve call center quality using data analytics. We’ll also offer suggestions on how to transition to automation with ease.
What is Call Center Automation?
Call center automation uses technology to perform daily repetitive tasks across various business processes without human intervention. For instance, you can employ automatic speech recognition (ASR) to upgrade interactive voice response (IVR) systems that let customers use self-service options to do things like check account balances or make a payment.
Multiple surveys show that up to 75% of consumers appreciate automated technology—if it saves them time. However, the same percentage says they prefer talking with a live agent to get their issue resolved. The trick, then, is to provide an automated experience to your customers that boosts call center efficiency and makes the most of your agents’ time.
There are countless automated solutions a business can use to help its customers. Six popular options include:
- Automated interactions like chatbots and virtual assistants that gather information about customer intent. They provide call center agents with pertinent information to help them better respond to a customer’s needs during a call or live chat. An automated knowledge base for customer support gives agents better context about a customers based on factors like previous interactions and purchase history.
- Forecasting automation uses customer and product data to understand trends in existing datasets. It then makes predictions that help you better understand your call center’s data and gain actionable insights.
- Workflow automation allows AI to handle all repetitive and mundane agent tasks that are often prone to human error, including interacting with clients, sending emails, entering data, and working on multiple applications. Agents save time, avoid mistakes, and can work on more demanding tasks.
- Automated agent guidance provides call center agents with real-time assistance to handle specific situations, including those that come with compliance rules. For example, agents who handle sensitive medical information must comply with HIPAA protocols.
- Sales automation tools help automate sales and marketing-related tasks, speeding up the sales cycle and improving conversion rates. Agents can send announcements and post-sale emails and provide personalized customer experiences.
- Scheduling automation ensures your call center is never over or understaffed, both of which can affect your bottom line. AI-based scheduling lets you check each agent’s availability, align time slots, and fill gaps between shifts.
Why Make the Shift to Knowledge Automation
Call centers depend on delivering accurate, consistent information that keeps customers satisfied and coming back. Automation can make life easier for call center agents, helping them become more efficient and productive. It guarantees high-quality and timely service, so customers don’t get pushed from agent to agent or receive dated or inaccurate information.
Transitioning to knowledge automation also prepares you for the future of customer service by optimizing omnichannel services for your customers and agents. You can use it:
- Behind the scenes to assist agents in making better, quicker decisions.
- Scale up digital knowledge capture initiatives.
- Enhance customer service quality over the long term.
And because your knowledge base is continually updated, employees are always up to date on common customer queries and how best to resolve issues.
How You Can Improve Call Center Quality With Data Analytics
AI-powered call center analytics significantly speeds up the process of gathering real-time, accurate, and integrated call center data. It also enables you to more quickly realize business goals and strategies, improving KPIs, customer satisfaction, and employee engagement while reducing costs.
Call center data analytics improves call center performance in many ways, including predicting agent quality scores, improving monitoring, and embedding data analytics so you can identify opportunities for personalized coaching, eLearning, and best practices.
Data’s value relies vastly on how it’s analyzed. Therefore, the key to effective call center data analysis lies in understanding available data and how you can use it to improve call center operation. And the better your analytics tools and processes, the better you can reveal customer insights and agent performance.
Measuring essential call center metrics is pivotal to keeping customers satisfied. After all, you can’t analyze things you can’t measure. Your organization’s unique goals and objectives will determine which KPIs to measure, but these metrics can set your business up for continuous improvement and success.
- Average time in queue provides an accurate indication of how well agents are serving customers and can be used for more than calls; it’s also a good metric for live chat and social media comments and can even be used in email response time.
- Average abandonment rate tells you how often a caller or live chatter gives up on trying to connect with your support team.
- Average handle time relates to customer satisfaction and indicates how well agents resolve issues.
- First contact resolution is often considered the most critical call center KPI, representing a significant measure of customer satisfaction. How many calls, emails, chats, or social media messages did it take to solve a customer’s concern or problem?
- Tracking agent absenteeism helps identify root causes and promote positive behavior.
- Agent turnover rate helps to identify agent challenges and training gaps that can be better managed.
- CSAT or customer satisfaction is obtained through analytic tools like surveys and tracks how satisfied customers are with your brand and its service.
- Schedule adherence measures productivity and performance and lets you know how well call center agents adhere to their schedules and complete their tasks.
- A contact quality management score measures product knowledge, technical knowledge, and communication and problem-solving abilities while allowing you to see how well agents help customers based on their skills.
One of the best ways to improve call center performance with data analytics is to put your agents at the center of quality in their interactions with customers. When agents can provide real-time feedback through live analytics visualizations, everyone can see how to do their best work. The result? Agents are deployed appropriately, they apply their talents most effectively, and you can track their growth transparently.
By putting agents at the forefront of exceptional customer service, you create happier employees, which has numerous benefits, including reducing call center turnover and onboarding costs. It’s a win-win-win situation for customers, agents, and companies alike.
Key Considerations for a Successful Knowledge Automation Deployment
While keeping call center costs low might be your priority, finding a balance between satisfying your bottom line and customers is essential. To ensure successful knowledge automation at your organization:
- Be sure to deliver a user experience that provides consistent, real-time, and reliable experience across all channels.
- Give your agents the support and training they need to handle customer-facing interactions.
- Gather client feedback to identify areas that require improvement to enhance the automated experience.
Lastly, be sure to partner with the right solution provider, one that helps you add value and generate growth with innovative and intuitive tools.
Automated Solutions for Call Center Improvement
Knowledge automation allows your call center agents to focus on offering seamless, effortless customer journeys and reduces the amount of time wasted on handling requests. It also boosts agent morale and helps your brand stand out from the competition.
When you implement knowledge automation into your existing customer support system, you retain current customers, gain new ones, and increase long-term customer loyalty. It also helps you minimize error rates exponentially.
American-based multinational conglomerate GE Healthcare’s call center manages nearly a half-million calls annually in four distinct lines of business ranging from patient monitoring systems to MRIs. Because the organization operates in a highly-regulated environment, its maintenance and repair requests are tightly monitored by the FDA. This web of processes often proves challenging for customer service reps.
GE Healthcare came to KMS Lighthouse looking to reduce agent error rates and cut agent learning curves by half. One year after implementing Lighthouse, the average error rate was reduced from 1.6% to 0.20%. Most importantly, agent performance continued to improve, even after the goal was met. The company eventually reached a record-low error rate of 0.12 percent. Learning curves were reduced from six months to 90 days, with agents saying they loved how easy Lighthouse is for them to use.
KMS Lighthouse is an automated knowledge-based solution that helps you transform how your call center agents deliver customer support. It also makes it easy to access customer-centric data that allows you to create better customer experiences. Done right, it helps you seamlessly integrate efficiency with personalized, authentic human interactions.
While knowledge automation does not replace your agents, it can support them in countless ways, making the difference between staying stagnant and growing your business.
