Voice recognition is filling up the gaps call centres have deep within their support function. We show you how.
It wouldn’t be a stretch to affirm that modern-day call centres are, indeed, “experience centres.” They’re what you could readily associate the buzz acronyms CX, UX, EX, or ROX with, and understandably so.
From the moment customers approach a call centre, they’re subconsciously testing to see if they are able to connect with the brands they’re buying from.
As such, across every touchpoint, they’re (again subconsciously):
- Scrutinising the level of effortlessness and convenience they experience
- Deciphering the tone and nature of the brand’s messaging
- Cognising whether the overall experience is fluid and natural
- Assessing if their own engagement is welcomed and rewarded with appropriate recognition
- Interpreting if they’re treated as individuals or numbers
- Evaluating how wait times, speed of service, and overall interactions reflect the brand’s perceived value to them
In other words, the modern-day call centre is a technology-enabled touchpoint that needs to move beyond “voice recognition” to have a meaningful role in driving brand equity.
What lies at the thick of this evolution is “emotion and context recognition” — the mechanisms facilitated by voice recognition technology. Understandably, Statista projects the voice recognition market to be worth $27.16 billion by 2026 – up from $10.7 billion in 2020.
In all honesty, that doesn’t sound too far-fetched, considering how effectively voice inputs are being applied and what’s in store for the future.
That said, let’s dive right in and examine how voice recognition technology will drive a decisive shift in the way call centres interact with customers.
Voice Recognition Technology – A primer
In essence, voice or speech recognition allows a machine to interpret and comprehend speech patterns and respond to the user on par with an unaided human agent.
The most widely known and utilised form of speech recognition is the “speech-to-text” function, which converts an audio recording of a person’s voice into text. The user’s spoken words are transformed into digital data and processed by machine learning algorithms to create a model for understanding and predicting the user’s actions.
This model is employed to accurately anticipate and communicate responses delivered through natural language instead of preset prompts or instructions.
Naturally, call centres present themselves as viable candidates for realising this balance of speed, accuracy, and efficiency using voice recognition technology — especially AI- and ML-powered automatic speech recognition (ASR).
3 ways voice recognition facilitates the evolution from call to experience centres
A typical day at a call centre revolves around ringing phones and chat notifications buzzing around the room. Least to say, modern call centres struggle to keep up with a digital demand at a record high. A customer service voicebot with a competent ASR can fix most, if not all, of your contact centre troubles. Here’s how:
1. Content discovery and contextual awareness
Inferring behavioural patterns from customer enquiries helps aggregate invaluable insights into customer preferences. With a trained ASR, you are able to establish synergy between you and your customers by highlighting curated content relevant to their queries.
On the back of deep learning, ASR extracts meaningful insights by:
- Taking note of the voice’s tone, cadence and inflexion
- Classifying the components of speech (such as phonemes, intonation and stress)
- Recognising the word associations and predicting word sets based on a user’s request for
- Expediting immensely accurate transcripts by considering all these while translating the interactions into textual form
As a result, brands can tap into personalised content-based engagements and enrich the customer experience with genuinely relevant content and intuitive, highly contextualised interactions.
2. Prompt, intelligent call routing
Customarily, call centres follow a highly structured and linear experience. Competent agents follow pre-defined steps while providing general and specific information throughout. But, this doesn’t cut it anymore.
For instance, traditional IVR has come under scrutiny for engendering a lack of customer engagement and overwhelming stringency in responses.
In fact, Vonage published an article titled “RIP IVR: 1980-2020,” where the brand highlighted how IVR inhibits more than 50% of the customers from completing the call.
Thankfully, conversational AI has changed the game’s rules with regards to reporting, recording and resolving incoming requests by addressing people’s needs before they even reach an agent. AI-powered IVR places empathy at the heart of interactions and prioritises accuracy and efficiency in the delivery of responses.
Voice recognition technology today is perceptive. After gauging the nature of the query, it can transfer the query to the relevant department so it’s handled by the most qualified experts.
Voice AI can also read between the lines. Since it can detect emotion and urgency in the speaker’s voice, it knows just when to route the call to an available agent. For example, if the AI is, for some reason, unable to accurately handle the query evidently making the user more frustrated, the bot picks up on that right away. To avoid making the user more agitated, the AI will instantly hand off the ticket to the agent.
3. Differentiated customer experience
As elucidated above, voice recognition technology has the potential to create a brand-customer congruity and differentiate service delivery. This is made possible by leveraging colloquial language and recognising emotional context.
For instance, Verloop’s ASR, trained over 1000 hours of customer interactions, can understand the subtle differences in speech patterns and trigger different responses.
Also, consider the case of a customer calling to enquire about a product or service. The agent’s responses to this call can be mapped to three contextualised clusters — “discovery,” “expectation”, and “resolution.”
Each of these clusters holds uncanny potential for accentuating the customer’s experience. And the agent can make relevant adjustments in their responses to further augment the experience.
In fact, call centres can employ these ASR-driven conversations across multiple channels to help advance the notion of “omnichannel experience.” This also means that channel response times will be more synchronised, reducing potential friction points.
Indeed, the implementation of voice recognition technology can contribute immensely towards making otherwise linear, rigid and predictable calls into intuitive, efficient and engaging experiences.
The bottom line
It’s noteworthy that comprehending speech, together with its unique dialect, as well as the localised nuance, can be a challenging feat. Mistakes can occur in interpreting what’s said. But, conversational AI, by using deep learning neural networks, manages this complexity with a considerable degree of efficiency.
Our Voice AI technology, for example, supports an in-depth analysis of the emotional dimension of speech by detecting intent, accent, and dialect. It also complements this data by helping navigate through the case history — all on top of multilingual support (more than 20 languages).
Drop us a line or schedule a demo to learn more about how you can leverage Voice AI in your call centre.