Transforming Voice Data Into Opportunities through speech analytics ai: - aishasharma238/Speech-Analytics GitHub Wiki

Enterprises today are aware that data is of utmost importance to survive in the current business landscape. Having customer data is essential for firms to keep them engaged. With the right customer data, they can offer contextual, cost-effective and impactful service. Today, even if a contact center agent is unable to observe that a caller is annoyed—the software examining the call would not fail. As automated solutions such as chatbots and IVR respond to simple customer queries, businesses are using AI-powered voice and real-time speech analytics tools to gather insights into the more complex chats that fall on contact center agents to resolve. However, while both voice and Speech Analytics AI offer insights into a customer’s voice, their methods are different. Speech Analytics and Its Relationship with Voice Analytics Speech Analytics revolve around chats. The solutions are built to evaluate what was said by customers and agents and the conversation’s context. This is achieved by converting speech into text through phonetic indexing. Speech Analytics assesses what was said during a chat while voice analytics focuses on how it was said. Voice Analytics looks at certain qualities of a person’s voice, such as pitch, tone and tempo to gauge his mood. Speech Analytics Variants There are two major variants of Speech Analytics: Archival Analysis: This is attained by digging intelligence from hundreds of recorded conversations needed for identifying cost drivers, opportunities, and trends, recognizing weaknesses and strengths with products and processes; and knowing how your services are seen in the marketplace.

Synchronous Analysis: This is achieved by continuous real-time monitoring and tracking, allowing service center the capability to offer excellent customer engagement, manage situations at the right time to alleviate escalations and attrition. Core functions of Speech Analytics Here are some of the key speech analytics functions:

Topic Detection: Offer advanced conversational analytics to automatically recognize, categorize and manage phrases and words spoken during calls into themes, while revealing growing developments and areas of concern or opportunity. The solution must recognize themes and refine them continually and include new themes with time.

Voice Transcription: Convert speech to text via statistical machine translation methods and customer sentiment. Service Center Performance Analysis: Should process a wide array of unstructured data quickly. Assessment of this information must offer information about cross-sell and up-sell opportunities and customer sentiment.

Agent Quality Scoring – Assign a score to service center agents on a variety of compliance issues to boost quality assurance procedures. This will also aid in recognize resource and training re-allocation requirements.

Real-time Mood Analysis – Evaluate customer’s mood in a chat. Customer service will improve enormously by recognizing customers’ moods and the intensity of the discussion that will permit taking essential measures to deliver better customer service.

With real-time speech analytics, contact centers can obtain insights quickly. Since it observes and reports on all chats, it routinely takes raw data, manages, and evaluates it, and produces insights based on what businesses wish to track. When this data is applied, it can lead to extraordinary enhancements in agent productivity, customer satisfaction, contact center efficiency, and cut costs and both agent and customer attrition.