Powering the Voice of Customer Insights Through Real-Time Speech Analytics

1. Powering the Voice of Customer Insights Through Real-Time Speech Analytics For contact centre agents, real-time speech analytics is among the most potent tools to serve customers. Its importance lies in recognizing customers’ exact objective and pushing actionable intelligence. Conventional speech analytics tools rely on spotting phonetics, phrases, keywords and fail to catch the relevancy, context and therefore, the specific meaning or intent of the interaction. Next-generation, enterprise virtual assistant tools can reveal profound information and offer actionable insights into agent and customer behaviours and expectations. Moving to Next-gen Speech Analytics Ensuring an exceptional customer experience has changed, in the eyes of businesses, from an outlay that must be reduced to a critical competitive differentiator. Companies today must collect, identify and act quickly on customer intelligence while augmenting longevity, satisfaction, and profitability. One way that contemporary contact centres have indulged in this complex mission is by adding another level of analysis to the process of data gathering. There is an emphasis on amorphous voice recordings. Conventional speech analytics tools depend on phonetics and keywords that overlook relevancy and context, which are crucial to understanding what individuals say. Today speech tools are deployed for complex tasks that could fulfil more varied and demanding expectations of the customers. They are progressively using mobile devices, adding to their sense of urgency before and in the course of their interactions. All such shifts lead to more data that can be gathered by the centre and then dug out for insight. Speech Analytics Versus Voice Analytics Speech analytics tools revolve around the content of a chat. The solutions are devised to evaluate what was stated by associates and customers, and in what background. It is accomplished through phonetic indexing or by transforming speech into text. The result is a searchable database of what customers and associates said during the conversation. A customer, for example, makes a call to a service line to register a complaint about an inaccurate charge on a bill. Completing the search for words that the customer speaks such as, “wrong fee” together with a search for the partner saying “settled”, the reason for the chat can be pinpointed and if the customer’s problem was resolved. By tactically identifying phrases used during a conversation, speech analytics assist firms in making informed judgments to enhance quality assurance. On the other hand, effective voice analytics concentrates on how it was said. Voice analytics considers specific attributes of a speaker’s voice, such as pitch, tempo, and tone to form evaluation and quantify the customer’s feeling. Exploiting Real-time Speech Analytics for Enhanced Insights into Customer Engagement AI has substantially enhanced the capabilities of real-time speech analytics tools by augmenting insight generation, measurement of sentiment and tone, speech-to-text precision, and quality management. There are several ways for supervisors and agents to make the most of AI-powered speech and voice analytics to enhance the overall customer journey and customer experiences. Here are a few examples: • Self-managed learning: Speech analytics post a call can also boost employee engagement. It can be done by providing agents access to insights about call performance. Agents can take a look at their performance and make changes, thereby accelerating the learning process. • Agent training: Likewise, real-time voice analytics empowers managers to supervise calls and pinpoint opportunities to offer support. • Guided suggestions: Using voice analytics or real-time speech analytics, agents can get advice to recommend relevant products to customers based on the nature of the call and their mood. • Anticipate Net Promoter Score (NPS): Integrating NPS results with speech and predictive analytics will allow firms to foretell customer responses and expectations. • Better insights on customer satisfaction: Some solutions use deep learning that employs neural nets to provide better insights to detect emotions and to obtain insights from speech. • Alleviate compliance issues and liability risks: Through real-time speech analytics, agents can get reminders about critical disclosures that must be shared before closing the chat.

2. • Fraud detection: AI together with speech analytics solutions, can be taught to evaluate and score calls for possible fraud and alert agents before they publish classified customer data. Improving customer experience is key to business success. Companies that understand customer intent and expectations and manage to leverage these insights will be successful.