Add TechGraph on Google
Contact centres have fast emerged as a way for brands to connect with their existing and potential customers. These centres provide a range of means such as voice calls, interactive voice response systems, instant messengers, emails, and chatbots to enable customers to reach out to them.
At the back end, the contact centres can deploy a range of artificial intelligence and machine learning-based technology solutions to automate the response to these incoming queries to a great extent.
Despite various options to communicate, consumers frequently choose voice calls to reach out to the contact centres. This is because of the convenience of using the most natural means of communication.
Moreover, voice calls do not require typing, following any particular format for transmission, and truly convey the emotions such as anxiety, curiosity, happiness, and frustration, etc., of the consumers.
Therefore, an illiterate person can use voice as an effective medium for communication. Moreover, the growing prominence of voice-based digital solutions such as Alexa, Siri, Google Assistant has further helped consumers choose calls over emails or texts.
For contact centres, automating the response to voice calls is more challenging than any other written medium. Speech recognition technology is still at a nascent stage. Voice communication also comes with challenges such as inapt recording equipment, background noise, variety of accents, dialects, and tones. In other words, we need to listen to more than words when trying to interpret voice-based communication.
Despite these challenges, the voice continues to be the most effective medium for understanding consumers and offering them a different experience every time they connect with a brand. Companies can solve this ‘voice problem’ by bringing a technology partner on board to deploy AI-based voice automation solutions.
Such a solution can use a sophisticated AI-powered contact centre engine to provide a customised voice analysis process. It collects historical data from customer interactions, such as transcripts and recordings, to feed the AI-Powered Contact Center engines. The system cleans up the noise, analyzes the accent, dialect, etc., and converts the voice to a high-quality transcript in real-time.
The algorithm uses this data to decipher the query and consumers’ emotional state, just like any human would do. This understanding of emotional state could be beneficial if the consumer has a complex problem, is in a state of emergency, and needs a faster resolution. In such cases, the call could be further routed to a human agent.
On the other hand, an iterative call like price inquiry could be left entirely to the machines. Thus, a caller gets a frictionless, accurate, and effective response depending on the situation. Moreover, the system uses machine learning capabilities to get better over some time.
Voice automation systems are also flexible enough to be deployed on other text and messenger channels. It also has the capabilities to integrate with CRM platforms to help automation throughout the value chain.
Thus, a customised voice automation platform provides a tangible return on investments and a significantly enhanced consumer experience over its lifecycle.
Given the growing preference for voice-based communication, brands need to prioritise voice automation at their contact centres to stay competitive. The first step on this journey is to find a trusted automation partner.
— Dr. Rashi