How AI-Powered Automatic Speech Recognition Can Transform the Value of Call Center Data

Despite the meteoric rise of Omni-digital customer engagement, Voice interactions are still the primary method for engaging with customers. The 2018 Microsoft State of global customer service survey reported that nearly 40% of global consumers preferred using phone or voice channels when dealing with contact centers. Call centers around the globe are sitting on exabytes of Voice data, much of which isn’t being analyzed as well as it could be or at all. Today’s call centers aren’t simply focussed on increasing revenue figures – there’s now a new emphasis around creating and sustaining high levels of customer satisfaction – or CSAT ­– as well as delivering the best customer experience possible. Recent advances in Machine Learning and Artificial Intelligence have paved the way for Automatic Speech Recognition (ASR) technology to unlock rich and invaluable insights from Voice call data.

ASR technology enables the accurate conversion of voice audio into meaningful text, regardless of context. Mission-critical, accurate Speech Recognition enables companies to rapidly build applications that can detect and transcribe recorded voice or in real-time.

The current state-of-the-art sees ASR neural networks consider variables such as acoustics, multiple languages, dialects, punctuation, capitalization, context and implicit meanings from Voice data. The Speech Recognition API industry itself is experiencing rapid growth, and it’s expected to be valued at over four billion US dollars by 2024.

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Untapped Business Potential

The call center industry has a tremendous opportunity to harness the opportunities presented by embracing and investing in ASR technology. These automated systems, when trained properly, can help to generate valuable business insights. By converting Voice call data into text, organizations can understand the mood, sentiment, and tone of customer conversations throughout the customer journey.

Best-in-class ASR technology solutions can be used to understand an organization’s challenges and objectives. Crucially, these systems learn through a combination of human-led Machine Learning solutions, coupled with neural networks that optimize their effectiveness over time.

For example, in the Telecommunications industry, a network operator such as EE could deploy ASR across a call center network to monitor specific trigger words, such as ‘contract termination’, ‘upgrade’, or real-time identification of angry callers, and deploy solutions to deal with these various scenarios. This would provide instant, tangible value to the company.

Providing a Bird’s-Eye View With Instant Access

Any-context Speech Recognition can also be used for quality control and to assess the effectiveness of a customer agent workforce. The technology can be used to identify the optimum level of agent effectiveness, attentiveness and responsiveness. Management and team leads can analyze bulk volumes of real-time Voice data and assess whether or not quality thresholds for these aforementioned elements are being met or not, and deploy solutions accordingly.

Agent effectiveness is, of course, imperative because dissatisfied agents often lead to angry customers, which in turn leads to abandoned calls, decreased satisfaction, and loss in revenues. The ongoing monitoring and analysis of a customer agent workforce is fundamental, and ASR can play a strong role in helping to boost agent morale, reduce stress and increase performance across the agent workforce.

Insights from legacy data can also be gleaned using ASR. Organizations across both public and private sectors have millions of hours of recorded Voice data, much of which is left untouched. Common practice sees companies occasionally dipping into legacy data, with manual transcriptions conducted in situations such as conflict resolutions and disputes. ASR can effectively automate this process, thereby increasing efficiency and saving time. Critically, ASR can be used to analyze significant datasets of legacy Voice call data, such as a few years’ worth of calls, and deliver analysis and trends based on recorded call data. For example, analyzing conversations could identify new purchasing opportunities in the customer journey.

While there is, of course, a huge amount of financial value in enhancing customer experience and retention, having immediate access to historic Voice data also represents a significant advantage to institutions’ compliance with the regulation. For example, although many businesses currently struggle to investigate cases of potentially-fraudulent behavior present in customer calls due to the volume of data that needs analyzing alongside the risk of simultaneously unearthing sensitive data that would result in GDPR fines, the efficiency of ASR systems turning voice into text would instantly enable pin-point extraction of specific instances of conversation. This not only eliminates major losses for businesses, but would also improve reputation, build customer trust and deter criminal activity going forward.

Conclusion

The integration of Machine Learning technologies such as Speech Recognition is another sign of the ongoing Digital Transformation of the call center industry. Forward-thinking organizations have an incredible opportunity to reap the benefits around the analysis of real-time and legacy Voice call data, which can in turn drive up efficiency, increase responsiveness and improve customer satisfaction and experience.

Read more: Humans vs. Machines – the Future of Communication