Imagine this scenario. A customer calls his bank’s support center. A virtual response system answers him, but with a startling difference. The voice at the other end does not struggle to understand what the customer is asking. Neither does it refer to a database to answer questions. It understands not just the question precisely, but the emotional nuances of the caller as well. It processes the natural language commands to provide meaningful answers, and tops them up with relevant, value-adding suggestions. It intuitively understands the complexity of additional issues and addresses them in one go, leaving in its wake a hugely satisfied customer.
Artificial Intelligence (AI), with all its cognitive and intuitive brilliance, is shaking up the business landscape. Banks, specifically, are upping the ante in customer experience strategy with the astute integration of AI and analytics. This allows machine learning-enabled systems to draw on vast volumes of structured and unstructured data in a game-changing way to elevate customer engagement.
Santander already provides secure transactions using voice recognition via its banking app. Luvo, RBS’ customer service assistant, uses a combination of intuition and reasoning when answering questions. With 30,000 conversations per month and 78 percent first-contact resolution, Swedbank’s web assistant Nina can, according to the bank, handle over 350 different customer questions and answers.
What Turns AI into an Intelligent Assistant?
When AI leverages the power of advanced analytics to gain cognitive capabilities, it not only hears or understands, it trains itself to learn and keep learning. The more it interacts with the customer, the more it learns about how the customer thinks, expresses and even emotes.
Analytics provides AI the ability to go beyond a customer’s obvious question or statement. The evolution of humanoid robots is an example of the progress brought about by advanced analytics. ‘Pepper’ deployed by Mizuho Financial Group Inc bank and ‘Nao’ implemented by Mitsubishi UFJ Financial Group have been developed by SoftBank Robotics to act smarter by understanding human language and emotions. The use of advanced analytics enables Pepper to even tap into customers’ relevant social media data, videos, images and text to understand them more accurately.
With AI, analytics and cognitive computing converging to an exciting stage of maturity, let us look at what more banks and their customers can expect.
Data can be processed across all channels and analyzed with greater depth for incisive insights—at unprecedented speeds and volumes. Machine-learning tools can personalize marketing initiatives better for new products and services, micro-pricing and customer retention. The intelligent cognitive front office agent can deliver a richer customer experience with ‘always-on’ and real-time collaboration between all enterprise systems. Self-service features such as ATMs, websites and mobile banking applications can be further enhanced through cognitive analytics.
Besides answering requests, seeking information and aligning customers with the right services, AI can also play the role of:
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Automated financial advisor: Monitoring events, stock and bond price trends, and analyzing them against the financial requirements of customers to make the right recommendations
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Smart wallet: Tracking and learning customers’ requirements and habits, and alerting and educating them on spending and saving
If this is what AI and analytics can do on their own, imagine the possibilities when intelligent assistance augments human touch. With both the human and artificial minds learning from and contributing to each other, customer service could rise to even higher levels.
Such a model could well be the game-changer that elevates customer management centers into revenue spinning profit centers. Each customer agent could well be a sales person with a team of chat bots and humanoid robots.
Exceptional customer experience is not about that one magic button to press. It is about understanding the customer’s changing needs across all touch points and providing distinct value. And this what the AI-Analytics combine directly addresses. It brings together cloud, social, mobile, automation and Internet of Things to understand patterns, predict outcomes, recommend the best possible actions, automate processes and deliver unimaginable customer experience.
In other words, it is all about becoming smarter about customers and their journeys.