Call deflection is the process of routing a customer enquiry to an alternative service channel. The aim for companies wondering how to reduce call center volume is both ensure to customers receive the guidance they need in the most efficient manner and to cut the volume of inbound calls handled by human agents. Instead, enquiries may be routed to self-service channels and resources including FAQs, live chat, community forums, knowledge center databases and virtual agents.
Measuring the call reflection rate is complicated. One method is to estimate both the percentage of users who are successful with self-service and the percentage of users who would have contacted customer service. The product of those two percentages represents the deflection rate.
Measuring call deflection with absolute precision is almost impossible as the metric essentially measures what didn’t happen. A more useful answer to the question of how to reduce call center volume is to monitor the metric over time, as deflection should increase as self-service channels improve, and customers become more comfortable with the available technologies and resources.
To increase their call deflection rates, companies are increasingly turning to Computer Vision AI-powered solutions to streamline their contact center operations and communicate visually with their customers.
Computer Vision AI refers to the processing and analysis of digital images and videos to automatically understand their meaning and context. The technology is used to recognize customers’ faces for the purpose of identification and to gauge their reactions, enabling emotion analytics.
It helps self-driving cars read traffic signs and avoid pedestrians, and allows factory robots to monitor problems on the production line. In customer service, it helps virtual assistants “see” a customer’s problem, driving efficiencies in the resolution process.
Computer Vision AI allows the virtual assistant to quickly identify a customer’s issue via their smart phone camera, enabling it to easily diagnose the problem and visually interact with the customer in self-service mode. The virtual assistant can then use Augmented Reality to automatically guide the customer toward a resolution via a precise, visual step-by-step process. It can also detect motion, enabling the virtual assistant to correct the customer in case of errors, ensuring that the resolution is successful.
For enterprises asking how to reduce call center volume, it’s worth bearing in mind that the technology works across a wide range of use cases, from the unboxing, setting up and troubleshooting of devices to onboarding, insurance and billing issues.
Asking a customer to simply point his smart phone camera at their device – allowing Computer Vision AI to identify it automatically – is a much more efficient (and impressive!) method than asking customers to verbally provide the model number of the item.
With the device identified, Computer Vision AI can associate images with similar historical cases, allowing specific issues or problems to be detected.
Once the system identifies the customer’s device and its issue, it can determine whether the case can be successfully handled via a self-service channel or if it is more complex and must be routed to a human agent.
This visual automation process enables a greater number of tasks to be performed in self-service mode. By seeing the issue, the virtual assistant can guide the customer to a resolution with a significantly higher rate of success than a “blind’ chatbot.
Still pondering the question of how to reduce call center volume? Download this datasheet to learn more about Computer Vision AI and how the technology can improve your contact center’s call deflection rate without affecting the quality of support. The resource also explores the future of self-service and the impact increased call deflection can have on other call center KPIs.