Learn how a recent innovation called Conversational Process Automation is truly making it possible to understand customer intent and do so cost-effectively.
We’re now living in an on-demand economy in which consumers insist on convenience, speed, and personalization. From ordering dinner with the push of a button to listening to a song by merely speaking its title, customer expectations are soaring higher and higher – and modern consumers aren’t inclined to wait for the companies they interact with to catch up.
For the customer service industry, this presents a substantial challenge. Not only are expectations of service rising, but demand within the contact center is as well. An increasing number of communication channels available through technology means a rapidly growing incoming ticket volume for customer service departments – one in which the cost of customer interactions is also ballooning.
With all of this increased demand, expectation, and cost falling upon the shoulders of customer service as an outcome of evolving technology, businesses are turning to technology for solutions of their own.
One of the key challenges that this tech will need to face is cost-effectively determining what the customer is hoping to accomplish when they reach out to the contact center. Whether by email, chat, web form, social media or otherwise, AI is already being adopted by many companies across the industry to support sustainable processes, but a recent innovation called Conversational Process Automation is truly making it possible to understand customer intent and do so cost-effectively.
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In order to serve the customer, the service department must first determine what the customer needs. The process to extract this information, when using traditional methods, is costly and can be inaccurate. It’s not financially feasible for business owners to hire and train enough human agents to answer and address each incoming customer call, so many have turned to other methods to try to remedy their situation.
For example, many companies use phone trees or web forms to narrow the customer’s intent. These types of tools help to reduce the possible scope of the need, but they are not an optimal solution. Many unique customer queries will not fit into a prescribed category, leaving the customer frustrated and the agent no better prepared to assist them.
It’s not only the dialogue where customer service reps need help. Our customers tell us that only 30 percent of customer service interactions in their contact centers involve actual “conversations.” When a consumer reaches out with an issue, chances are they aren’t just looking to chat about it, but for a transaction that provides them with a full solution to their issue.
Solving their issue requires the agent to take some type of action, often in one or more backend systems, which adds even more time and cost to the interaction. Customer service departments need to find a way to intelligently connect the conversations that determine customer need with the backend customer service processes that fulfill that need.
Conversational Process Automation provides that solution.
Unlike traditional customer service tools that work by deflecting a customer’s question, Conversational Process Automation (CPA) leverages machine learning to understand the request. For the most repetitive, time-consuming tickets, CPA can reach a point where it is able to determine the customer’s need, then resolve that need using all of the necessary systems without any agent involvement. This provides such a distinct advantage for both the customer and the agent because it doesn’t just delay a costly interaction, it can phase it out entirely.
With these monotonous tasks taken off of their plates, agents are able to tackle more complex issues with customers. While the CPA is tending to thousands of refund requests (or account lookups, or cancellations, etc.), agents can take more time on calls that require a more personal touch.
CPA is able to entirely take over these transactions because it employs open APIs that can interface with front-end CRM systems, as well as with backend software where ticket resolution actually occurs. Traditionally, customer service reps needed to switch between multiple programs in order to successfully resolve a ticket. They might have initially recorded information in their CRM system, yet when they needed to issue a refund or change a password, they were forced to pivot to another piece of software, and then back again. All of these extra steps can be avoided by streamlining the entire process with AI, with the results rapidly becoming evident in the bottom line.
The benefits of a collaborative relationship between the agent and the practical AI they’re using aren’t merely operational; it is also a great boost for morale. As they’ve taken on more challenging projects with more free time to be proactive, customer service reps are experiencing higher job satisfaction. Agents have self-reported higher employee satisfaction (ESAT) scores when using AI tools, which puts them at a lower risk of burnout, which in turn helps business owners manage turnover, decreasing recruiting and training expenses within the department.
With customer expectations and ticket volume on the rise, companies can’t afford to waste precious resources trying to extract customer intent. Businesses will increasingly turn to AI tools that utilize CPA to resolve the most repetitive and costly types of tickets they experience end-to-end.
As customer service becomes more accurate and timely, customer expectations of service will continue to rise, requiring companies to further step up their customer service operations. AI that gets smarter and learns over time is a crucial and necessary tool that facilitates their ability to do just that.