By HGS SVP of Solutions and Capabilities, Parikshit Kalra
HGS recently released a white paper on this year’s top 10 trends in customer service. Over the course of the year, we’ll dedicate a blog post to each of these CX game changers. Here, we dissect CX Trend No.5: Artificial Intelligence, highlighting strategies designed to drive the right answer, fast, for your customers—to ultimately result in a higher CSAT and NPS score for your business.
Artificial intelligence (AI) is revolutionizing industries across the globe, including customer service. According to Gartner, by 2020, 25% of customer service and support operations will integrate virtual customer assistant technology across engagement channels, up from less than 2% in 2017. Virtual customer assistants are being used across several channels such as contact center Interactive Voice Response (IVR) systems, mobile and messaging apps, social media platforms, online communities, kiosks, Internet of Things (IoT) devices, and websites.
Technology has always been intertwined with customer service. With the advent of AI and other technological advances, the capabilities and demands on CX has changed by leaps and bounds with each trying to play catch up with the other. Implementing AI technologies can be an outright win-win situation for customers, companies, and CX agents. AI helps counter and resolve complex, evolving customer queries that are tough to address on the spot by envisioning multiple scenarios and providing the right answers.
AI + Human Agents = Increased Efficiency and Improved Performance
AI is definitely a buzzword, but in terms of real outcomes, there is not a clear understanding. While businesses are aware of the benefits of this technology, according to a Tech Pro Research survey, only 28% of companies have some experience with AI or machine learning, and more than 40% said their enterprise lacks the skills required to implement and support AI and machine learning. That’s why, this year, BPOs are increasingly called on to help client partners employ machine learning to help brands deliver fast, accurate, and consistent customer care responses.
Thanks to the rise of digital channels and the advancement of AI and machine learning technology, we now have the ability to predict (with a high level of confidence) the right answer to a customer’s inquiry in any digital channel — email, SMS, chat, or social. This advancement in technology makes our agents much more efficient and saves time, helping them attend and resolve more increasingly complex queries in shorter time spans. No more looking up answers, spell checking, tagging or categorizing conversations. The only action agents have to take is to hit “approve” or “personalize” on the predicted responses. These new customer care solutions bring together the empathy of agents with the efficiency of bots, for AI-assisted conversations that present the facts of a product or brand while adding the personalization of a human touch. Blending the technological capabilities of AI and human agent empathy can reap rich rewards when delivering quality personalized customer experiences. AI technology will even be able to automatically tag and categorize posts, saving agents more time. It’s a win for customers, because they receive accurate and faster responses; a win for agents, because they save time hunting and searching for the right answer; and it’s a win for the company, which will now need fewer human resources as AI does most of the heavy lifting.
AI can take up the mantle of repetitive, everyday tasks and help speed them up or automate them taking time off agents’ hands freeing them to focus on more complex issues that need human intervention. This can lead to huge cost savings—for one HGS client, we employ a web portal to drive down operational costs by 70%. This solution has improved performance on multiple parameters for companies including but not limited to excellent customer experience.
AI is only as good as what you train the model to become. It takes lots of historical transcripts to build and train any AI model — the more data, the more accurate the predictive models become. Even if you have the best of what AI can offer in terms of technology, the system can only perform as well as you support it.