Machine learning – understanding humans
Contact centers handling high volumes of calls inevitably produce huge quantities of data, and businesses are increasingly focusing on new ways to capture and utilize this untapped wealth of information.
While the aim is usually to maximize efficiency and reduce costs, businesses can simultaneously improve the customer experience, which leads to increased sales and revenue. To do this, they must first understand their customer interactions.
Businesses seeking to use their system data to improve customer care and provide a more proactive service are turning to Natural Language Processing (NLP) and BigData analysis to gain critical insights and optimize their call center operations. This process begins with speech recognition.
Sentient Machines uses deep learning to bring an understanding of customers to the call center industry. Their cloud-based platform, Sentient Analytics, interprets calls and learns from customer-agent interactions with real-time monitoring.
Using Speechmatics’ Automatic Speech Recognition (ASR) software, natural language interactions between customers and agents are transcribed and analysed for insights into customer emotions, motivation and frustrations.
Sentient Analytics investigates how to improve the customer experience by analyzing callers’ sentiments and emotional shifts – after transcribing the voice file, it interprets any hidden meaning. The software can even highlight customers who are about to leave the call in real time, sending the agent an alert telling them why.