All organizations have some form of a vision for their contact centers. At least, they have a rough sense of what they are trying to achieve and a general-purpose behind their customer experience initiatives.
According to recent CCW Digital research, for most contact center executives, reducing customer effort to provide a frictionless experience across channels is the #1 priority. The idea of reducing agent effort and empowering them to spend less time preparing and more time connecting with customers is also a common objective.
Other popular priorities include customer journey mapping/orchestration, better measurement of customer sentiment, better understanding of customer intent, predictive/proactive engagement, improving interaction analytics, and customer engagement using bots and virtual assistants.
Taking meaningful action to realize these objectives is as important as setting key goals and aspirations for the customer contact function. Coincidentally, in so many ways, the use of artificial intelligence has turned out to be the silver bullet for contact centers to achieve their objectives quickly and easily. At its core, artificial intelligence consists of several parallel-functioning technologies that can automate tasks that are time-intensive and often expensive to do manually.
When armed with AI capabilities that enable agents to understand customer intent, customer sentiment, preferences, and propensity to churn or buy — and be prepared to act on the opportunity, agents gain an extra edge to provide better levels of service and care proactively. AI enables agents to be more efficient—to provide knowledge, service, and experiences that deliver CX like never before.
Know your customer
Though most AI adopters recognize that it is too complicated for AI to handle customer interactions autonomously, AI can be of great use for contact centers as intelligent tools in aiding agents to provide superior experience to their customers.
The more agents know about the customers (what they’re looking for, and what conversations they already had with other agents), the easier it is to provide better and faster service. AI can anticipate what a customer needs based on their customer history or predict customer intent by listening in on the call and providing recommendations on the next best actions for agents.
Contextual intelligence about the customers saves the customers from repeating themselves to each agent, ultimately contributing to superior experiences. It helps not only the customers and live agents, but businesses in empowering their self-help tools, like IVRs, to better respond to customer needs more humanly. With this contextual insight shared across all departments, the customer-agent conversations will be more personalized and consistent.
Through predictive routing, AI increases an agent’s chances of success by transferring customers to agents with relevant skill sets, expertise, or similar personalities to the customer. AI also analyzes caller emotions and provides real-time feedback and coaching to agents, enabling them to improve more quickly.
Know your data
Aside from people, data is perhaps the most valuable resource in any organization. But in most cases, data is closed and static, locked inside various separate systems and channels like voice, e-mail, SMS, chat, and messaging apps such as WhatsApp. Optimizing contact center operations such as automatically routing customers to appropriate channels/agents involves analyzing complex datasets, including personal traits, demographic information, psychographic details, interaction history, and other transactional data. By taking advantage of artificial intelligence and machine learning, contact centers can easily map customer interactions across multiple channels to optimize the communications flow and provide a consistent, personalized and unified experience to the end customers in their customer journey.
Fully automated self-service
Unlike previous programs that require explicit instructions into computers for them to perform tasks, machine learning allows software to learn from examples. In contact centers, this could mean that instead of scripting responses for every possible question from the customers, managers can feed the software with a dataset of millions of queries and map to millions of appropriate answers from previous phone calls. The software can eventually address 30% or more of customer inquiries seamlessly and even create responses that a programmer did not explicitly input. It comes with many exciting benefits such as 24/7 productivity and freeing agents to allow them to focus on tasks that require more human touch.
Despite these notable benefits, AI has certain shortcomings. The major one is that AI is not a technology that one can develop or purchase from a vendor and start using it. Incorporating AI means a significant investment in resources, substantial backend programming, and time. The simplest version of a chatbot, for instance, costs approximately $30,000, while more advanced versions can cost $250,000 or more. Scaling up the chatbot implies additional, substantial cost.
Secondly, the customer acceptance of AI-powered applications varies widely, with some customers insisting on interacting with a human, while others are perfectly content to engage with a chatbot or virtual agent. Most people wind up asking for actual agents either because their issues are not appropriately addressed or responding to a programmed system is too frustrating. Third, since AI’s cognitive abilities depend on machine learning, which depends on adequate data, erroneous or insufficient data can lead to AI mistakes, which are hard to diagnose and rectify. Despite these challenges, AI is emerging as a better means to enhance the agents’ role in contact centers. After all, AI is about working smarter and helping agents do more with less.