Creating a bank of delighted customers is what customer service companies are battling for ages. Customer-centric companies are already taking the initiative, some of which include empowering agents with caller details, history, and more. Artificial Intelligence (AI) is yet another booming technology readily applied to create a memorable customer experience but misunderstood as ‘just chatbots.’ I don’t disagree with the contribution of bots in revolutionizing the customer service industry, but let me unravel behind the scenes brainy processes happening in the universe of AI in addition to chatting.
AI-enabled chatbots can, of course, share workload of agents, increases the company’s revenue and efficiency. These can work day and night without leaves. Chatbots act as a savior when handling basic or repetitive queries, but they are no superhero when it comes to dealing with complex issues. It is because chatbots can’t perform future analysis.
So, AI has got a better application, which is machine learning that further offers sentimental analysis, to step beyond just chatting and predict human behavior to empower agents with profound insights on the customer’s journey for higher engagement. Sentimental analysis is no rocket science to understand. With the use of natural language processing (NLP), it predicts the human satisfaction level with the product/service/brand. It uses customer and agent behavior or information to predict the outcome of the conversation. Such an analysis is a miracle for businesses that can save customers from going away.
NLP utilizes speech analysis and survey data to identify the characteristics that impact customer behavior. It also helps in the calculation of response time and agents’ performance. It enables contact center businesses to have more informed decisions and use messages/statements that have a long-lasting impact on the customers.
Another element of machine learning is the predictive evaluation for quality. It considers the above parameters of phonetic speech for every conversation to check for the quality scores and manage it accordingly. A contact center can now identify the best or the right calls, loopholes, training needs, and more. So, you can be free from randomly picking calls for analysis.
Technology under AI uses every available data on agent and customer behavior for predications and strategy making. Here, data includes analysis on the customer satisfaction level, reasons for churn, faked conversations, etc. AI allows digging deep into the customer journey and insights.
Every customer service industry has immense data and this acts as a food for AI, enabling them to evolve their predictions constantly. AI is just getting started with its applications. There are still many pages of this book to be unfolded, read and implemented in customer-focused industries. In addition to AI and RPA, there are existing contact center solutions such as unified agent desktops, CTI connectors, wallboards and more that have their places intact. I am surely going to keep myself and my readers updated on what new is going to happen in the contact center universe.