Heard the Hype? How AI Improves Call Center Efficiency
Discover how AI can reduce costs and improve call center efficiency.
Patrick Martin, general manager of service solutions of Coveo, emphasizes the importance of enhancing the customer service experience amidst tightening budgets. To meet expectations and retain customers, companies should consider investing in AI solutions that enhance self-service capabilities and equip call centers with the necessary tools to improve efficiency and customer satisfaction.
As macroeconomic conditions squeeze budgets, many companies are challenged to do more with less while still meeting customer expectations. This can take many forms, but in the end, the result will pretty much be an increased workload for agents, thus having an impact on the customer experience. To avoid this, investments need to be made, and investing in AI solutions that can help alleviate the impacts budget constraints will have. Since retaining customers is cheaper than acquiring new ones, brands must keep customers satisfied, even cutting costs.
Ninety-six percent of people say a negative customer service experience affects whether or not they would buy from that company again. Equipping call center agents with the right tools can boost efficiency and customer satisfaction – but organizations must first identify barriers to providing exceptional service.
Unifying the Customer Experience
Providing a unified customer experience is still one of the most challenging aspects of running a business, even with significant investments. A Salesforce report found 53% of customers think most of their support interactions are fragmented. They are often forced to explain and then re-explain their issues when the context of their problem does not transfer from self-service to assisted support.
The solution? Implement AI to provide a relevant and unified experience. Customers can find the answers to their questions on their own while enabling agents to find the information needed to solve complex issues quickly.
The Case for Self-Service
Most customers today prefer to avoid contacting a support team for help with simple issues. They usually submit a case only after exhausting their options and failing to find the answer they need. In fact, 59% of Gen X, 66% of Millennials, and 61% of Gen Z prefer self-service for simple cases, according to Salesforce.
Self-service allows customers to find the information they need without having to contact the help center. Instead, customers can use search to find the answers they need or ask a chatbot for guidance.
Self-service is a complement to call centers, not a replacement. Agents are best suited to handle non-routine situations where human creativity is needed to solve problems. However, leaving simple inquiries to self-service helps lower the cost of service while providing a frictionless digital customer experience. Customers can quickly get answers to questions like “How much time do I have to return a purchase?” without having to jump through hoops.
How AI-Powered Search Improves Call Center Efficiency
In self-service, AI-powered search offers customers the most personalized recommendations. However, self-service will not be the best option when faced with complex or unique situations. Customers then need to rely on assisted support. Can AI-powered search have similar impacts on providing a great customer experience when interacting with agents? Certainly! Machine learning can provide recommendations to agents when issues require a human touch, reducing the time required to solve the customer’s problematic situation.
A strong database of information is essential for agents to answer customer questions, but institutional knowledge is often held by senior members of a team – and lost when these individuals leave. Organizations can empower their agents by implementing a knowledge management program to solve this issue. This type of framework will allow agents to draw on the collective knowledge of the organization and other colleagues with the help of AI. Intelligent swarming, which is a collaboration model leveraging people profiles, allows agents to “phone a friend” to collaborate and find solutions within their department.
Seamlessly Providing Information In The Flow Of Work
Embedding AI-powered search to quickly find answers to customer questions or issues in an agent’s screen view can increase first contact resolution (FCR) and reduce average handle time (AHT). The use of an insight panel that uses machine learning to provide personalized recommendations for case-solving content is a great example of how AI contributes to delivering amazing customer experiences.
Monitoring Customer Behavior And Measuring Success
Machine learning can use data from each customer interaction and call center metrics to self-improve its recommendations.
The top metrics for customer service digital experience are:
- Self-Service Success: the percentage of sessions in which customers consumed a document without reverting to assisted channels.
- Implicit Case Deflection: the percentage of visits in which customers consulted knowledge articles without submitting a case. Since KB articles are created following Support tickets, we can imply that a case would have been submitted if the customer would not find their answer.
- Explicit Case Deflection: the percentage of visits to the case creation page/flow in which customers clicked on content without submitting a case
See More: Why Intelligent Automation, And Not AI Drives Contact Center Efficiency
Knowledge Managers can use the following metrics to improve the self-service experience continuously:
- Visit click-through rate: the percentage of visits with one or more clicks.
- Search event click-through rate: the percentage of searches with one or more clicks on search results.
- Average click rank: the average position of clicked items in the search results. For example, a value of 1 means users always click the first result.
- Content gap: content that is missing or unsearchable.
By tracking these metrics and continuously improving the customer experience, AI-powered search improves its overall operational efficiency and customer satisfaction by analyzing which recommendations optimize these metrics. As customers’ affinity for relevant recommendations grows, AI can deliver highly personalized and unified service experiences.
Forming a culture of continuous improvement in contact center management
Although no search solution is perfect right off the bat, search experiences improve based on customer and agent feedback. Call centers can track key performance indicators to show opportunities for growth in agent training and customer experience. These include top queries by call center staff and customers, most opened documents by source and search queries leading to call volume.
Joining the AI Movement
With the explosion of large-language models and Generative AI, many businesses are examining how they can integrate AI into their customer experience. Some of these new technologies, like ChatGPT, are not ready for the enterprise, facing issues with hallucinations, data privacy/security, lack of freshness and do not offer a link to the source. Enterprises looking at integrating these technologies should think about using a closed index of documentation, who should have access to which information, and how to maintain the privacy and security of your customer data.
Global enterprises have tried and tested other AI capabilities and have proven to be a powerful tool, providing brands with a competitive advantage to exceed customer expectations by providing hyper-relevant digital experiences and resolving issues quickly and at scale. AI is already revolutionizing customer service, and organizations should securely implement this technology or risk falling behind.
What AI tool can marketers use to boost call center efficiency? Share with us on Facebook, X, and LinkedIn. We’d love to hear from you!
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