No business wants to operate in the dark. However, without a complete understanding of the customer experience, poor customer sentiment and agent behaviours 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 — democratising 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 analyse 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.
Learn more about AI Quality Management at https://www.ringcentral.com/ringcx/workforce-engagement-management.html.
Originally published Apr 12, 2024
