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AI-enhanced automation is getting more and more popular both in the eyes of contact center leaders and consumers. Although some are still skeptical about relying on these technologies, 80% of consumers are already open to using self-service or talking to chatbots to avoid long wait times.
The benefits of AI-powered features are undeniable: these systems can save a lot of time for agents by understanding and reacting to human input, analyzing large amounts of data and recognizing customers simply by their voices.
Read on to learn more about five AI-powered capabilities thatâd surely make any contact center more productive.
AI voicebots use natural language processing and machine learning algorithms to understand and respond to spoken language.
Before answering to human input, an AI voicebot has to follow a step-by-step process. First, it captures the input and filters out insignificant sounds through automated speech recognition. This helps the bot focus on the intent and the customerâs accent. Next, the voicebot needs to remove any background noises like traffic or chattering so that the message doesnât get distorted.
After this, the system analyzes each element of the spoken input with natural language processing and understanding (NLP, NLU) models to decipher the meaning behind the message. A semantic analysis also assists the bot to pinpoint any hidden context it might miss in the sentences, so that itâs easier to formulate and narrow down the final reply the customer is going to receive. After all these steps are completed, the AI voicebot formulates the answer in writing and uses a text-to-speech system to convert it into audio.
Since AI voicebots can handle various types of inquiries without any human assistance, implementing them will save time for your agents. As routine tasks (such as answering frequently asked questions) can be automated, your workforce can focus on more complex tasks and issues. Consequently, your contact center will be able to handle more inquiries as resources are better allocated.
Voicebots can also decrease wait times for callers as many contact centers will offer talking to a bot (or using any other self-service option) first in their IVR menu, and offer connecting to a person only later on.
Another advantage AI voicebots offer is providing 24/7 availability on the phone, which can be a demanding task for contact centers, especially if all their agents are based in the same time zone.
This AI function is used by various industries in a multitude of ways. In e-commerce, voicebots assist customers in tracking orders or answering product-related questions. In healthcare, they can support appointment scheduling and provide further information on medical services. Similarly, airlines and hotels may use this AI capability to handle inquiries about reservations, flight statuses and check-in processes. Financial institutions can use bots for balance inquiries and transaction history. As for telecommunication companies, AI voicebots can assist with bill inquiries, plan changes and troubleshooting common issues.
This AI-powered capability converts text to speech (TTS) and speech to text (STT) automatically, enhancing efficiency and flexibility in contact centers.
In order to convert written text into spoken words, TTS uses neural networks and deep learning techniques to generate natural-sounding voices. In its deep learning stage, the system examines large samples of human speech including a diverse set of linguistic patterns and speech dynamics. The AI model learns how to recognize patterns and correlations between written input and spoken output. To sound lifelike, the system utilizes advanced voice synthesis techniques to have the most suitable pitch and accent based on the situation. Besides real-time speech, VCC Liveâs TTS feature can also generate a .wav audio file optionally.
First, the system captures the audio input to be transcribed – in a contact center, it usually comes from phone calls. Then the audio input is broken down into small sections to identify patterns. To ensure accuracy, language modeling and decoding is also utilized – these techniques predict the likelihood of word sequences based on training data.
Contact centers can use speech-to-text for the real-time transcription of customer-agent interactions. These text files can be valuable for monitoring and quality assurance purposes. Transcribed data can also be used to get new insights on customers, their demographic characteristics and unique needs, enabling call centers to pinpoint personalized offers easier.
As for text-to-speech, it enables the creation of IVR menus without pre-recorded audio files – the system can simply read out the entries you provide via text. If you opt for the same characteristics for all your TTS needs, youâll have a consistent brand voice customers can recognize. Thanks to machine learning, TTS capabilities tend to support many languages, allowing call centers to serve a diverse customer base. This feature can even be used to create personalized audio messages by dynamically inserting customer information into scripted responses.
In general, TTS supports contact centers in creating and changing IVR menus easily, while STT enables them to utilize call transcripts for advanced insights on customers. For example, an e-commerce business can provide information on products and answer FAQs automatically by using TTS in their IVR. Similarly, hotels, restaurants and hospitals may use this capability for handling inquiries or to automate over-the-phone appointment reminders.
Additionally, with the use of speech synthesis, agents can conduct surveys without relying on pre-recorded voice messages. The dynamic script of the survey can be updated anytime, and the system will automatically read out the most recent version to contacts.
The plain transcripts of conversations already provide useful information for contact centers, but they might not be able to fully reveal the emotional tone of the call. This is where sentiment analysis comes into play.
In the past, contact center sentiment analysis tools transcribed calls, broke each sentence down into small sections and gave sentiment scores based on what words were used in them. At the end, the tool would sum up the scores to evaluate how the conversation went. Of course, this method is not that different from simple speech-to-text analysis and doesnât detect nuances such as sarcasm.
More recent models are able to analyze vocal inflections and the tones that are being used during the call. Besides the content of the conversation, they also take laughter, crosstalk, sudden pitch or speaking pace changes into account, providing a much more detailed picture of each interaction.
Utilizing AI-powered sentiment analysis in the contact center gives you a better understanding of customer opinions. Besides CSAT and NPS scores, youâll have another, fully automated indicator of how your agent-customer interactions went.
Sentiment analysis tools can also find recurring trends in large samples of data, potentially pinpointing to indicators that typically lead to positive or negative calls. Advanced solutions can also give accurate tips and suggestions on how to improve your script or what personalized offers agents should give to your customers for better results.
Letâs take the example of a company offering subscription-based services. The team recognizes that customer retention rates are dropping, but theyâre unsure why. A sentiment analysis tool could easily analyze large amounts of call recordings and find the reasons behind the churn – perhaps the companyâs subscription renewal process is too long which frustrates people, leading them to switch providers.
Similarly, if an e-commerce companyâs sentiment analysis tool finds âslow deliveryâ a recurring phrase in negative conversations, itâs a pretty good hint that their logistics system needs improvement in order to retain customers.
When your business receives a large amount of emails from customers all around the world, manually checking and determining which language theyâre written in quickly adds up. To minimize language barriers while saving time, AI-powered email language detection can be a valuable feature.
The system pre-processes the email to omit irrelevant parts, such as headers and signatures. To accurately predict the emailâs language, this feature relies on statistical language models, machine learning or recurrent neural networks in its training phase. During training, the model learns the patterns and relationships between linguistic features and the corresponding languages.
Once the model is trained, it can determine the language the message was written in – in VCC Liveâs email language detection feature, the system can accurately predict it by analyzing the first 100 characters. Some models even provide a confidence score, indicating how certain the system is about its prediction of the language.
Once an emailâs language is detected, it can be added to the contact centerâs routing system, ensuring that the right department or agent can reply. Some solutions can even send an automated response in the customerâs language, reducing response times, increasing productivity and ensuring multilingual support. On top of this, email language detection also helps contact centers gain new insights on customer demographics.
Imagine a contact center that receives a large volume of emails in different languages, and some of them are urgent. Relying on agents only, determining the language and the urgency of each message might take too long. AI systems can determine both, come up with a priority order and route messages in a way that everyone gets a response in their preferred tongue in time.
Caller verification is an essential part of call center security – but when done manually, it can also become a hassle for both agents and customers. AI speeds up this process through automated voice recognition, making verification a much quicker yet secure process.
First and foremost, the system needs stored samples of the customerâs voice, which will serve as the basis for comparison later on. For this reason, new customers might need to enroll in the voice biometrics system by repeating specific phrases or sentences. During enrollment, a unique voiceprint is created, representing the speakerâs pitch, tone, rhythm and cadence.
The voiceprint is securely stored in a database, typically protected by advanced encryption techniques. When customers contact the call center in the future, the system compares their current voice against their stored voiceprint. Advanced systems incorporate adaptive learning mechanisms, so the feature can spot natural changes in the customerâs voice due to aging or illness for example.
In contact centers, voice biometrics can be integrated with IVR systems, allowing seamless authentication when a customer wants to handle processes via self-service. Itâs also a powerful tool to diminish identity theft and fraud risks. Advanced systems can even store a database of known fraudulent voiceprints and send alerts for further investigation when encountering them, taking an extra step towards protecting customer data.
In most cases, when a customer calls their utility provider to report an issue, they need to go through a lengthy manual identification process, often requiring them to read their customer ID out loud or answer a set of questions about their personal information. Letâs be honest: when someone experiences a power outage or has had no running water for hours, looking for their contract to find a long list of numbers is the last thing they want to do. Similarly, rescheduling an appointment or delivery date could be because of an unexpected change of plans.
With the use of voice biometrics, tedious manual checkups can be omitted, saving a lot of unnecessary hassle for both agents and customers. Some systems can even connect voiceprints with CRM data to truly personalize callersâ experience in the IVR.
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