Companies are constantly collecting very large amounts of data from every channel they use. In the Call Center industry, tools for collecting and storing data are used, but their amount makes reliable assessment and analysis in terms of customer satisfaction almost impossible. Speech and text analysis, and more specifically tools based on artificial intelligence to do this, can effectively change this. They work hand in hand with quality management processes and methodologies, while facilitating the “extraction” of data from each interaction and the collection of relevant insights. This enables business owners to identify current trends and find the best solutions in contact with consultants and customers. How can speech and text analytics gather information from a Call Center?

Understand How it Works!

To understand how speech and text analytics can help you gather data from a Call Center, you should start by understanding what each is about. Voice transcription transforms unstructured audio into text and structured data. These, in turn, in combination with prior non-voice interactions, various machine learning algorithms and natural language processing detect specific meaning. Such automation makes it easier for managers to analyze information and streamline operations. 

Sentiment analysis, in turn, interprets the attitudes expressed by customers and employees through various interactions and is grouped according to positive, negative or neutral emotions. This reflects the entire context in which the interaction took place. Next up is Topic Detection – it organizes and understands an entire collection of text data to highlight specific groups of text data. It thus classifies a series of words and phrases that represent a given topic. Speech and text analysis allows you to discover hidden insights and trends among customers and consultants.

Types of Speech and Text Analysis

We can distinguish 3 types of speech and text analysis in a contact center.

  • Descriptive analysis

This type of analysis collects unstructured text data to identify conversation motives and trends. It will give you a clearer picture of customer satisfaction, shopping habits, and support issues over time. 

  • Predictive analysis

It focuses on forecasting future events by interpreting text and speech with the ultimate goal in mind. For example, it can streamline the viewing of open tickets in customer service to propose the ideal number of agents needed for the task described.

  • Prescriptive analysis

This speech-to-text analytics uses the previous one – predictive analytics – to create contingency plans for the future outcomes you want to achieve. 

Leveraging customer feedback through conversations is essential to know how and why customers are using your products. Knowing what they think about your offer, you can make important changes, adjust business processes and services to the specific needs of customers.

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