In today’s technology-obsessed world, making life simpler, faster, and more seamless is a priority for both businesses and individuals trying to keep up with an always evolving digital landscape. Read any publication and you’re sure to find conversations revolving around Artificial Intelligence (AI) helping to power (if not completely replace, in some instances) human interaction. In the contact center, one thing is certain: positive human connections between agents and customers are critical to building brand loyalty. In an industry that places an emphasis on human contact, what role does AI play in the contact center and how can data gathered from it be used to improve the customer experience?
Besides Chatbots and Virtual Assistants, which operate on a more obvious human interaction level, AI in the contact center is gaining momentum in other ways including the routing, prioritization, and handling of calls. Artificial Intelligence includes a wide range of capabilities such as Natural Language Processing (NLP), Machine Learning and Predictive Analytics that are crucial elements to gathering and utilizing data in the contact center. Together, these technologies not only provide information on data directly available in an interaction (the text or speech), but also leverage data available from three other sources: the context of the interaction, specific history for that customer/prospect, and the “demographics” of that type of customer/prospect.
Context and History
The context of the interaction allows an AI system to gather insights about a call, such as a number dialed, speech recognition utterances, or time of day, to provide information on the reason for the interaction and specific customer needs.
This data is then added to the history for that customer including products or purchase patterns, previous interactions and contact frequency, and lifecycle events that recently occurred. Information gathered from a wider net, such as social media or other publicly available data, can also be leveraged to provide additional insights on the interests of the customer. All of this data provides some excellent context for each unique customer that can be used to create a personalized and differentiated experience.
The “demographics” refers to more than just the age/zip code and other data that we associate with demographics but extends to the constantly evolving data that an organization is gathering about similar clients and prospects. For example, customers that have been calling about X are also interested in Y, and that knowledge can enhance the way your organization can tailor the interaction and overall experience to add significantly more value for your customers.
If that interaction does require routing to a live agent for further processing, the system should transfer all of that relevant information and guide the agent to create a seamless, personalized, and genuine interaction that enables a more efficient, and higher-valued experience.
Where can AI help?
Chatbots, Virtual Agents, and dynamic routing are examples of how AI can help during customer interactions. Additionally, AI can help after the interaction. Progressive contact centers are utilizing voice recordings, which in the past served as rarely accessed archives, as rich sources of information. By converting speech to text and analyzing data using speech analytics solutions, clients can derive greater insights from the content of the interactions, the sentiment of the clients, and the behavior and attitudes of the agents servicing those interactions. The content of the calls is a rich source of operational data that can help organizations prioritize process improvements that will provide the greatest return. The sentiments and attitudes captured in specific interactions can also provide targeted insights into the performance and training requirements of the agents servicing the calls.
What are the downsides/limits of Artificial Intelligence?
AI is a powerful tool that can be leveraged to improve customer service and efficiency, but it must also be monitored, tuned, and calibrated so that it doesn’t lead clients and service awry. Tools must continually be “trained” to recognize the appropriate context in Natural Language Processing and adapted to new marketplace trends, business solutions, or customer language that is constantly evolving. This is a step that many businesses skip and are frequently disappointed in years 2 and 3 of implementation. The time and effort that is dedicated to continue nurturing the system are well worth it as the insights and action plans depend on the accuracy of the foundational data that comes from the AI platform.
How to proceed
An organization can prepare to use AI in its contact center in three steps. If your organization is not already recording interactions, this would be the first place to start. The key is then to use these recordings, not just as an archive, but to transcribe that speech to text data and use it as a rich source of information to learn about your customers, their unique needs, and ways to improve your business and create differentiated experiences.
Another simple step available to many organizations is to use the data that is already being captured in their IVR/Website/ERP/social media and other feeds to derive insights about what is driving the contacts into the centers and about how to create a more seamless, effortless, and value-added experience.
Finally, take the step to leverage both internal and external data to use Predictive Analytics to identify relevant ways to tailor solutions or proactively offer additional service where it adds value to the customer. Predictive Analytics also enables the agent to more effectively create a personalized interaction and brand loyalty for their customers. Ultimately, it is about getting the best response in place – potentially even before the customer asks for it.
The explosion of AI in the customer service center is just beginning to flourish, and as more and more people demand faster and easier interactions, organizations will have to strive to meet client demands or be left behind.
The original article was published here.