Conversational Analytics / 13.02.2015

5 ways to Improve Contact Center Agent’s quality using Speech Analytics

5 ways to improve agent’s quality using Speech Analytics

Virtually, every organization has its own contact center or outsources their contact center operations to manage and monetize their customer relationship. To achieve their objectives and deliver superlative customer service, contact centers entrust customer support agents. Improving every single call center agent’s quality to meet the established quality metrics is one of the biggest challenges faced by quality assurance analysts and managers in a contact center.

Uniphore’s Speech Analytics tool-suite, auMina, serves as the magical wand that helps quality assurance teams to extract customer-agent interactions in monitoring agents’ in-call movements, and optimizing their training programs. It helps them to achieve it in simple 5 ways:

1. Agent scorecard – auMina has an automatic quality scoring engine which compares agent’s utterances against preordained scoring metrics to provide a score for every call. It compares parameters like average call duration, average quality score, percentage silence, CSAT score, talkover, overtalk, etc. to help agents analyze their performance, improvement areas and understand their strengths and weakness.

2. Recommendations – auMina’s agent recommendation dashboard helps agents to improve their quality and performance by acting as a valet to the agent. It monitors the call in real-time and based on the call navigation, it shows recommendation pop-ups – for example: customer escalation language has been detected; it would be advisable to transfer the call to supervisor – to help the agent navigate the call smoothly and improve CSAT score.

3. Transcription – auMina’s highly accurate multi-lingual transcription engine captures agent-customer interactions in real-time. This enables the agents to go through their conversation word by word post-call and gain insights from the call, to improve their quality.

4. Sentiment analyzer – auMina’s sentiment analyzer calculates the overall emotion of the call by capturing positive and negative keyword utterances in the call. It classifies the customer’s mood in four categories: happy, confused, irate, and angry, and post notifications to the agent. This helps the agent to predict the customer’s mood and serve them better or stay away from the red-alert areas.

5. Real-time chat support – auMina’s real-time chat support allows supervisors to instantly chat with the agents and help them handle difficult situations. By monitoring agent-customer conversation, supervisors can get-hold on the situations, manipulate customers and alert agents through the chat to act accordingly.

Through these five ways, auMina helps customer support agents to handle withering situations and deliver exemplary service to their customers. For more information on how auMina helps leading contact centers improve their customer agent’s quality, watch this video. Please feel free to mail me at kaviarasan@uniphore.com for white papers and case studies of auMina.