So your business is growing? Great! But, your customer support system remains the same? Well that means, like many newly prosperous businesses, your organization is on a collision course toward low CSAT scores.
The fact of the matter is many businesses are simply not equipped to handle increased ticket volume or newly complex inquiries. As organizations grow, CSAT scores suffer greatly which ultimately hurts the prosperity businesses. Why is customer satisfaction in such disarray as businesses grow? It’s simple: businesses are employing outdated CXM technology.
Most CXMs allow rules based routing, which run static protocols — based on keywords — to resolve tickets. Rules based workflows allow a basic level of automation, and are an important first step toward improved customer support efficiency. This is common, standard CXM technology — but the status-quo is simply not good enough anymore.
Despite the initial utility of rules based routing, it is no longer sustainable for growing businesses. As processes, products and customer volume grow, rules must be altered manually, which is inefficient and greatly restricts company advancement. AI technology had to evolve to be smarter, faster and adaptable. Supervised learning is the new age of AI technology, and customer support directors need to take note.
The advantages of supervised learning
Today, supervised learning has picked up where rule based routing fell short by offering greater interpretive capability. Due to the static nature of rules based routing, the nuances of human language were being overlooked. For example, customers with homonym names have an especially tough time navigating a rules based routing system.
Consider a customer named Will: Will calls into a rules based CXM and could be met with an assortment of challenges. An untrained, rules based system might mistake his name for several verbs (this will happen, a will to succeed, will you please, etc) or even a different noun (last will and testament). This leaves the Robs that have been robbed, or Jims that go to the gym in a convoluted, unhelpful predicament. Language is tricky — even highly educated professionals mix up homonyms — so CXMs had to become smarter. This is all possible with the aid of natural language processing and supervised learning.
Natural language processing studies previously resolved tickets, learns content and context, understands the problem and provides a solution independently. By studying agent approved responses, machine learning technology learns every time a customer inquiry is resolved and automatically adjusts rules. Supervised learning is much more suited to growing businesses because a CXM can teach itself and scale to organization processes on its own.
Growing your customer support system the right way
The emergence of AI technology is great news for growing businesses. With the help of NLP, machine learning, automation, historically-derived recommended agent responses and other efficiency tools offered by AI technology, agent productivity can be maximized.
The best way to meet increased customer support demand is to improve current agent productivity with AI rather than hiring more agents. By delegating menial tasks, such as triage or repetitive responses, to AI, you will not only accomplish better CSAT scores with less agents but also save valuable time and money on onboarding.
The most efficient way to grow a customer support system is through AI integration — it’s a fact. Automating even a portion of responses will reduce the workload of agents by thousands of tickets. As for the tickets that require human interactions, recommended responses offered by machine learning technology will cut down handle time by 30 - 40%. Integrating AI into CXMs streamlines routing, reduce handle time and provides more accurate, consistent and speedy responses.
Contact AnswerIQ to learn more about how machine learning technology is changing the way customers interact with support, and why leveraging AI is the best way to scale a CXM to growing businesses.