Customer contact center managers want to keep engagement costs down, which often means live agents spending as little time on the phone by deflecting as many customers as possible.
Meanwhile, customers who want answers or solutions often will “zero out” to skip the menu tree and reach an agent.
Can’t managers and customers find middle ground?
A new solution has arrived to help improve engagement, without sacrificing results and customer satisfaction. Using the latest automated, self-service interactive voice response (IVR) solutions, empowered with artificial intelligence, customer contact centers can improve satisfaction, while keeping costs in check.
Artificial Intelligence, or AI, utilizes layers higher-level “thinking” atop traditional IVR. It capitalizes on the natural speech recognition through deep learning using artificial intelligence to improve interpretation and actually predict caller requests. By anticipating customer needs, engagement time is reduced, as is the caller’s probability of hitting zero to reach a live agent.
Think of the relationship between IVR and AI as being symbiotic. The solution uses the traditional call mapping of IVR solutions, and layers even more intelligent call paths crafted to meet customer needs. As customers progress through the decision tree, AI often is able to predicts the customer’s needs and deliver the likely solution.
Consider an example from the travel or hospitality industry. When the customer calls the reservation center, the IVR solution anticipates the most common requests, like “Please tell us in a few words why you’re calling today. You can say, book a new reservation, change my reservation, or something else.” Using algorithms behind speech-to-text technology, the solution maps the call.
It intuitively predicts the expected path. If it is correct, it moves to the next logical step along the call path. If the response was, “No,” the AI solution can offer different options.
To be sure, AI providers would offer than such misses are more rare than common, with deflection happening seven in 10 times. A more realistic figure, at least with current solutions, is about 10 percent. Moreover, effectiveness varies by industry. Travel and hospitality, where the options can be more limited than healthcare or insurance, for example, might see higher deflection rates. Also, audiences can affect efficacy; sectors that target more educated customers, like business professionals or native English speakers, likely will see stronger AI performance.
While AI’s time might not have arrived across the customer contact industry, the rationale for exploring AI as a viable solution has. Every minute an agent doesn’t spend on the line with a customer, and the customer has a positive experience, AI has served its purpose of lowering costs and delivering on the promise of heightened customer satisfaction.
While AI currently is a costly solution suitable for arguably only a select subset of the contact center industry, in the future AI will earn its place as an irreplaceable component of all interactive voice response offerings. As costs of the software continue to fall, and customer contact center managers pursue ways to keep engagement costs in check while ensuring customers remain satisfied, AI will be that solution that avoids the “zero out” helps both sides reach that desired middle ground.
About the Author
Curt Gooden is the Senior Vice President and Chief Information Officer at C3/CustomerContactChannels with nearly two decades of experience leading IT departments for global companies. He has successfully managed all aspects of information technology across international boundaries while lowering costs, implementing controls, mitigating risks and adding tangible value to clients. In his role, he is responsible for providing strategic direction and management of the C3 technology environment which spans clients on multiple continents and nearly a dozen customer contact centers throughout the world as well as its corporate headquarters in Plantation, Florida. Contact Curt at: email@example.com.