2020 sees the contact center industry entering a new phase of Digital Transformation, one that is being powered by AI, Machine Learning and Automatic Speech Recognition (ASR). Unprecedented advancements in NLU (National Language Understanding) are enabling companies to not only understand words with intent but also derive meaning and emotion from voice data. This leap around understanding the nuances of the human voice and emotion means companies can listen, monitor, analyze and engage with their customers at scale. The ability to capture millions of voice interactions offers deep call center analytics capabilities, improved call data searchability, building knowledge bases from real-life interactions, and creating best practices in a time of increased regulatory and compliance scrutiny.
Speechmatics produced an industry report, entitled Trends and Predictions for Voice Technology in 2020. Almost a third (30%) of the survey respondents said they believed that Voice will have the largest commercial impact on contact centers. The ability to transcribe voice calls empowers agents to optimize customer experiences. This can be used in combination with Natural Language Processing (NLP) to deliver detailed customer and agent insights, which of course can be to optimize the customer experiences.
Faster Dispute Resolutions
Improving customer experiences is critical for the ongoing success of contact center companies. The market is integrating ASR to improve CX with advanced Voice data analytics and other strategies. By using speech recognition to transform customer voice calls into actionable insights to support practices like dispute/issue resolution and provide agents with wider knowledge bases.
Predictive Call Center Analytics
By harvesting and analyzing Voice call data, contact centers can derive meaningful insights, and extrapolate and predict future scenarios and outcomes. In most companies, the customer experience is the primary metric for success, with dissatisfied customers equating to losses of revenue, which is why real-time and post-analysis is critical for short- and long-term success.
More Languages Being Offered by ASR Vendors
From 2020, respondents from the report said they expected to see multi-language ASR support for languages such as Spanish, Cantonese, Malay, Swiss-German, Hebrew, Brazilian and Welsh. The survey respondents also noted the importance of ‘any accent’ solutions, for example, Spanish, which is spoken in many different dialects and varying accents across the globe.
Value of ASR to Contact Centers
Some key drivers and motivations for contact centers adopting Voice technology include improving the productivity of support staff, increasing customer satisfaction, and improving overall operational knowledge. ASR providers and organizations are finding that Voice technology delivers significant value, reduces cost and augments workforces through the introduction of machine processes, enhanced customer experience, and customer analytics.
Voice call transcribing has many benefits for call centers; interactions are easier to index and search. Transcribed calls can be exported into natural language processing engines to extract insight from omnichannel strategies like the use of bots, instant messaging (e.g. Whatsapp messaging), social media engagements and email interactions with customers. In 2020, we’ll also see more contact centers conducting deep-dive analysis around legacy Voice call data, as companies will be able to derive great value for historic call data.
The Future Contact Center Is Digitally Transformed
The ability to transcribe Voice empowers contact center agents and optimizes the customer experience. While Digital Transformation plans are executed most commonly within manufacturing industries and business models for enterprises, contact centers are also utilizing digitalization tools like ASR to both enable better performance and automate the capturing of interactions.
In tomorrow’s world of Big Data in Business Intelligence, transcribed interactions can be used in conjunction with sophisticated Natural Language Processing (NLP) tools. In the future, we’ll see Voice used in common technological applications including conversational interfaces, work assistants, human-robot interaction, wearables, and home appliances. For ASR, this can even mean unsupervised learning for machines. This future starts with adopting automatic Voice-to-text today.