As a customer support manager, it’s your job to regularly improve service quality by evaluating and redesigning processes. To optimize service, you have to continuously adapt to consumer trends and meet shifting standards of customer satisfaction. Now is the time to move from systems of records to systems of action by adding an AI layer to your existing customer support systems.
You might not realize that the type of support system being used vastly affects agent productivity and customer satisfaction. How calls are routed, how quickly it reaches an agent and the quality of resolution customers receive all depend on which Customer Experience Management system (CXM) is being used.
CXMs have gone through several iterations over the years — incrementally improving as time goes on. The evolution of CXMs has progressed linearly, and each improvement has addressed the faults from the past, but not all companies are up-to-date on the newest technology. As a result, there is huge variance in quality when it comes to the customer support experience. Put simply: not all CXMs are the same, and this has a huge impact on resolution efficiency.
Phase 1: Knowledge and Ticket Based Routing
At the start of the customer support journey, companies use knowledge and ticket based routing. A customer reaches out, a ticket is created, and the ticket is sent to an alias or to customer support agents. Agents then leverage documentation in their knowledge base to answer the question as best they can. This is the most basic iteration of customer service systems, and while it progresses rather rigidly and linearly, it is a natural first step for small businesses.
However, as a company scales it becomes difficult to keep the knowledge base documentation up-to-date. Agent responses are decided by pre-approved guidelines, so as a company grows and experiences a greater variety of inquires, knowledge base documentation is quickly rendered obsolete. This process is especially challenging for agents that experience the brunt of dissatisfied customers who experience long wait times due to a lack of ticket prioritization or adaptive routing protocols.
Phase 2: Rules Based Routing
By now, most CXMs allow rules based routing. These systems run on a static, predetermined “if/then” structure to resolve tickets. For example, if certain keywords appear in an inquiry then a certain action is executed. Rules based workflows either direct inquiries to the correct agents or deliver standard responses to common cases.
Despite its prevalence, the obsolescence of rules based routing becomes very clear as a company grows: it is not sustainable. Rules are developed manually, so as business processes change or new products are introduced, routing protocols have to be modified by hand.
Another issue facing the rules based routing system is that, by the nature of its design, parameters are too narrow. Strict rules lack the interpretive capability to maneuver the nuances of human language. This means it cannot account for natural human intricacies like context, homonyms, outliers, etc. This lapse in utility led to the next, and currently most popular, iteration of CXMs.
Phase 3: Supervised Learning
To account for issues associated with rules based routing, CXMs had to adapt. Cutting-edge companies are now enlisting the help of technology partners to leverage supervised learning as a way of improving the adaptive capability of their systems and streamline the customer support process. Supervised learning is the new standard for CXMs, and can be achieved through a technology partner.
Supervised learning adjusts according to how previous tickets were resolved, and learns through the actions agents take. Using natural language processing, it continuously learns and mimics previous interactions. Supervised learning is unfettered by static rules and is much more adaptable to shifting customer needs. The supervised learning process improves with every inquiry — optimizing routing and responses based on past results — resulting in a more client tailored support experience. Every ticket is an opportunity for supervised learning to become more educated on customer needs and improve the accuracy of responses.
Supervised learning observes customer support processes and learns how to resolve tickets. The system will offer recommended responses to agents and prime a customer interaction with all of the knowledge it has acquired from previous interactions. This makes for a more efficient, productive support experience for the agents and customers.
While this is a marked improvement from rules based routing, it is still imperfect. Supervised learning is limited in functionality because it can only answer tickets that have a history. Any unique case is not subject to supervised learning — there must be a record of resolution for the system to draw from. Supervised learning is certainly a step in the right direction but it is limited by its dependence on ticket history.
The Future: Predictive and Prescriptive Intelligence
The next evolution of CXMs is predictive and prescriptive intelligence. This cutting-edge solution, also known as “unsupervised learning,” resolves many of the procedural issues of the previous CXM installments and generally streamlines the support process. Technological limitations stand in the way of implementing unsupervised learning today, but it is the future of CXMs and an exciting prospect on the horizon.
Predictive intelligence uses machine learning technology to adapt without the need for historical data. Rather than teaching a machine to resolve tickets, the machine teaches itself. This advancement has major implications for CXM efficiency. With every ticket, predictive intelligence learns to improve ticket accuracy — narrowing the margin of error with every customer interaction and making for a more helpful service for customers.
Predictive intelligence improves CXMs for agents too. Machine learning identifies common customer cases, develops a routing procedure, and offers suggested responses — taking out the guesswork for agents and allowing them to better focus their attention on providing quality, personalized support.
Are you behind or ahead of the curve?
CXMs have taken several forms over the years but have finally reached peak potential; innovative customer support teams should be using supervised learning through AI technology. AI technology is making customer support more intuitive, efficient, and better tailored to customer needs. The value of any given CXM is measured by customer satisfaction, and in order to reach a high level of service, a CXM must be smart enough to keep up with the complexity of customer needs. This is where a technology partner can help you stay ahead of the curve.
Contact AnswerIQ to learn how upgrading your CXM through machine learning can improve system efficiency and maximize customer satisfaction.