Bots enter the world of customer care…
…thanks to machine learning and artificial intelligence
Whether we’re driving, cooking, or simply having a busy day, many of us are starting to enjoy using hands-free technology. Intelligent virtual assistants (IVAs), like Apple’s “Siri,” respond to voice commands and help us multitask with ease: texting or calling our friends, checking the weather or searching the internet, all without having to stop what we’re doing.
Those IVAs are one of the most visible applications of artificial intelligence (AI). Less visible, but just as powerful, is the deep penetration of analytics, AI and machine learning into the systems used by communications service providers (CSPs) to provide customer care.
Enter bots. Bots can be interactive and humanoid like, leveraging Natural Language Processing (NLP) capabilities; or invisible, working behind the scenes to monitor services and infrastructure, and to apply automated improvement actions to provide the customer with a “wow” experience.
AI simulates human intelligence: predicting outcomes, making plans and solving problems. AI can be continually enhanced using machine learning. Machine learning enables algorithms to learn from new data. NLP uses AI to find patterns within large datasets to recognize language, allowing consumers to use voice commands when interacting with bots. Natural Language Understanding (NLU) determines the customer’s intent so that the bot can understand and act upon the request.
Creating a smarter & autonomous customer care agent
When applying those bots to Customer Care operations, Nokia in general talks about Autonomous Care. That is where human and machine work seamlessly together for better outcomes.
The benefits to customers are obvious: no more waiting in call center queues, searching web sites, or finding the right app on their smartphone. No more dependency on the skill level of the customer service representative (CSR), no more frustration when repeating information endlessly as problems are bounced between communication channels.
For the operator, bots help significantly reduce the OPEX involved in maintaining a high quality service and increasing the Net Promotor Scores and/or Customer Effort Scores, as human labour is augmented with smart technology.
Here’s how we use artificial intelligence in Autonomous Care
AI allows workflows to become dynamic, as they adapt in real time to all contextual information. Pockets of flexibility in the workflow take the next best action — depending on the service, network, and customer-specific conditions. In short, the customer spends less time in the conversation and has a higher degree of success upon his or her first contact.
Secondly, as mentioned, Augmented Care leverages the IVA’s humanoid capabilities to take the customer through the care journey. This combines AI applied to both communication (NLP) as well as the understanding (NLU) of the customer. The understanding leads to grasping the intent of the customer, which itself gets translated into a next action based on a growing and auto-learning extensive library of use cases, known as the Knowledge System. It will be the ability to match subscribers’ intents to the appropriate remediation procedures (found in the Knowledge System) that will provide the key to unlocking the evolution toward autonomous care.
Finally, Proactive Care leverages big data and machine learning to detect anomalies in the network and derive potential service and customer impact, root cause observations, and recommendations for auto-resolution. These Proactive bots kick into action before anomalies inconvenience the customer. This prevents technical support calls and leads to an always-up-and-running service experience.
At the end of the day, whether we’re using our Amazon Echo to check the weather or engaging with a bot to be reminded of our Wi-Fi password, all of this technology is designed to make life easier.
Seeing is believing at TMForum Live 2017
To learn more about Autonomous Care and customer-centric technology, join me at TMForum Live! 2017, where I’ll provide further insight on “Integrating machine-learning and artificial intelligence into the Fabric of your Core Customer Care.”
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