No business wants to operate in the dark. However, without a complete understanding of the customer experience, poor customer sentiment and agent behaviors can go unnoticed. Supervisors do their best to evaluate agent performance, but manually reviewing every call is not feasible. Instead, they typically only review a very small amount of calls per day.
The impact? An incomplete view of agent performance, and ultimately customer satisfaction. Suppose an agent handles 100 calls per day. Ninety-nine of those calls were resolved in line with customer and company expectations. However, one call was from a customer that had called multiple times to solve the same problem and was already frustrated before connecting with the agent. While the agent did their best to help the customer, she was put in a no-win situation with the customer. No matter how well she performed, there was no way the customer was going to be satisfied.
What if that was the one call Anne’s supervisor, Brielle, was able to review? With that limited view into Anne’s performance, Brielle may give Anne a poor review, put her in a remedial training program that she doesn’t need, or worse.
This is one type of problem AI Quality Management was built to solve. With the scale to review and automatically score every interaction, it provides a complete view of agent performance. However, the challenge with traditional quality management solutions was that it was designed for large enterprises with thousands of agents and large budgets in order to deploy. Enter RingSense AI Quality Management — democratizing quality management for everyone.
A streamlined approach to quality management
RingSense was designed to make AI Quality Management a reality for any sized business. Available for all RingCX customers, RingSense AI Quality Management is out-of-the-box AI that is instantly deployable, automatically scoring 100% of calls.
AI Quality Management automates the scoring of every call, so agents can instantly understand their performance. Supervisors can streamline their review process by focusing on calls below a specific score (for example, anything below a 7 out of 10) or using the call review workflow to focus on specific sets of calls where certain keywords appear and analyze the customer experience at scale—ensuring positive customer experiences that improve over time.
With AI Coach, agents receive actionable feedback on soft skills such as keeping their composure during difficult situations, asking engaging questions, and using active listening. Supervisors can also provide time-based annotations to pinpoint specific segments of calls where issues occur.
Going beyond quality management
While traditional quality management solutions provide insights into how agents are performing, they do not provide an easy way to gauge customer sentiment or overall customer satisfaction. RingSense AI Quality Management includes powerful Conversation Analytics that provide conversational intelligence for precise decision making.
While you can get a good picture of KPIs such as Average Speed of Answer, Average Handle Time, and Abandonment Rate using RingCX Analytics, Conversation Analytics provides deeper performance insights that reporting metrics don’t show such as customer sentiment, how often do specific words and phrases appear, and what issues frustrate customers the most.
Supervisors can see all these insights through a single interface—from competitor mentions to how your teams handle specific objections. Supervisors and business leaders gain valuable insights that aid in business decision making and ongoing customer satisfaction improvements.
Attending Enterprise Connect 2024? Come visit us at Booth 1818 to see it in action!
Learn more about AI Quality Management at https://www.ringcentral.com/ringcx/workforce-engagement-management.html.
Originally published Mar 26, 2024, updated Mar 25, 2024