It’s surprising that Chatbots were likely to generate over $8 billion in global savings by 2022 in accordance with Juniper Research.
If you consider the expenditure on AI-based systems worldwide, it is likely to touch $154 Billion in 2023, according to IDC.
These all researches are indirectly related to data collection, whose biggest source is call centre in many other countries. This is why remote customer service jobs are soaring from January 2020 to January 2021 in Indian call centres. The statistics clearly show a sharp upping, which is 498.40 per cent in job postings and jobseekers’ interest.
Now that you know these centres have a crucial role in data collection directly from users, it’s easy to connect this fact with AI. It’s artificial intelligence that feeds on data to create automatic and an immersive customer experience. This is why the call centre industry in India or anywhere is under transformation. Automation has evolved quicker ways to enhance customer experiences, and improve operational efficiency.
Let’s get through some ways wherein AI is fuelling call centres.
Contents
• Intelligent Virtual Agents (IVAs)
IVAs are AI-run chatbots and you can call them virtual agents. Since the day artificially intelligent technologies have stormed into, handheld customer support has started shifting to artificially generate fast and accurate responses. These virtual artificial agents can understand human language and address problems like a human to a certain extent. Artificial General Intelligence is a step ahead that can understand natural language as we do, interpret customer queries, and provide relevant information spontaneously. It minimizes the role of a human being and simultaneously turns the response times laser-fast.
• Speech Analytics
The introduction of artificial intelligence (AI) technologies in call centres can help in analyzing and deriving insights. This happens via extracting & analyzing customer interactions. By using speech recognition and natural language processing, this advanced technology can automatically convert queries into any requisite language and analyze calls. With the help of identified keywords, sentiments, and trends, this practice is carried out. This facilitates call centers with valuable insights that help in improving customer experience and identifying training needs.
Understand it with the example of IBM Watson tool, whose Speech to Text feature converts spoken language into written text. On the other hand, Watson Text to Speech converts text into natural-sounding speech. With Watson Studio, predictive analyis turns easier as it has capabilities for building and deploying machine learning models. Watson Natural Language Understanding offers sentiment analysis features to understand the emotional tone of text.
• Predictive Analytics
Predictive analytics is all about forecasting what’s likely to happen. With AI algorithms, it’s like a walkover to analyze customer data, call histories, and other relevant information, which points at how customers behave and prefer. Once understood, the business owner can win the battle of understanding what customers likely to have. If outsourced, the hired call center becomes able to personalize interactions, anticipate customer needs, and provide targeted recommendations. This eventually results in enhanced customer satisfaction and increased sales opportunities.
Azure Machine Learning, for instance, guides to build predictive models in no time.
• Call Routing and Intelligent Routing
Typically, a call routing system refers to a process directing incoming calls to the right-fit agent or destination within a call centre. This can be done on the basis of predetermined rules or criteria such as IVR (Interactive Voice Response) selections, time of day, caller’s geographic location, or specific agent skills.
AI-based call routing systems are infused with this capacity. They use machine learning algorithms to route calls to the most appropriate agent based on customer profiles, past interactions, and agent expertise. These criteria ensure that customers are connected to the right person without further ado, reducing wait times and improving first-call resolution rates. This is called intelligent routing.
• Sentiment Analysis
This is also called opinion mining, which requires the deep processing of data that are connected to sentiments and emotions. Every bit of that data is thoroughly examined to discover the sentiments of customers. As AI is in, a natural language processing (NLP) technique is used to determine the sentiment or emotional tones. It further moves to processing, sentiment classifications, opt-in approaches as of lexicon-based, machine learning-based, & emotions-based to categorize text and identify whether it expresses a positive, negative, or neutral sentiment. The goal of sentiment analysis is to understand and extract subjective information from text, such as opinions, attitudes, emotions, or evaluations.
Hugging Face Transformers, for example, is an open-source library that comes up with pre-trained models for natural language processing tasks. It also includes sentiment analysis and text classification. Simply put, it offers a wide range of models that can be fine-tuned for specific use cases, which enable developers to perform sentiment analysis.
In essence, AI-powered sentiment analysis can determine the sentiment and emotional tone of customer interactions. By analyzing voice tones, speech patterns, and word choices, call centres can find dissatisfied or frustrated customers in real-time. This finding ensures them to take proactive steps or measures and address dissatisfaction of customers.
• Customer Self-Service
Interestingly, artificial intelligence promotes self-servicing tools or software like interactive voice response (IVR) system and chatbots. With them, organizations can easily come across problems that are common among users. Moreover, these systems can be introduced to minimize human assistance, especially in transactions. It will certainly reduce call volumes, waiting time, and cost for call centre owners.
• Agent Assistance
With these tools, call representatives can get real-time guidance and suggestions when they are on calls with customers. By leveraging AI-powered tools, they can get the access of relevant information, product details, troubleshooting guides, and personalized recommendations in no time. This happening enables them to assist accurately, quickly, and efficiently.
Conclusion
The AI-driven advancements can change the way the call center industry in India is working. It is transforming customer service by providing fast support in optimizing operations and enhancing overall customer experiences. These extremely effective technologies can be introduced in call centers to better efficiency, reduce costs, and deliver more personalized and efficient services.
