Ai Conquers Customer Service
Future of Customer Service Bots
In the United States alone, businesses lose an estimated sixty-two billion dollars per year to poor customer service. Sometimes all it takes is a bad mood, minor misunderstanding, or external stress to nix a purchase.
The objective of the customer service representative is to find out what a customer needs and how they can help. Obtaining this information is generally done by asking the customer questions and coding the answers, then directing them to a specialist if necessary. This is a fairly menial task for a person to do.
An artificial intelligence robot can do these menial tasks all day. In fact, AI ‘chat bots’ can answer up to 80% of routine questions, saving companies up to 30% as of 2017. From refunding cancelled tickets to refilling prescriptions to directing you to a specialist, AI customer service agents are changing the world of customer service for the better.
We can go even further. Building on advancements in AI ‘chat bots,’ companies can start to automate entire processes, all while trying to increase the human elements of the robot through machine learning.
Machine learning, a subcategory of artificial intelligence, allows a machine to pick up new tricks without having their algorithm modified by a person. The evolution is coded into the machine. Cost projections for this technology would far exceed the 30% mark and focus on the entire customer service industry as a whole.
One of the companies exploring the automation process in depth is tech conglomerate DXC Technology. Recently, the end-to-end tech solutions giant has experienced financial struggles, leadership changes and structural issues. The way to revive the state of their company, however, may lie in a recent machine learning project that could significantly help the customer service industry. During my time at DXC, I learned of their plans to take over the customer service branch of a company and improve it with machine learning technology.
They are using a newly developed machine learning system to direct consumers to a representative, while also providing the rep with machine-coded data on the customer based on the customer-robot conversation, which increases efficiency and avoids double-asking questions.
This reduces the time of calls, which means customers can get solutions faster and get on with their busy lives. Additionally, call-time reduction frees up representatives and cuts ‘hold’ time. Minimizing the time customers are on ‘hold’ is extremely important to retaining customer positivity and loyalty, as “a recent survey found that 60% of customers felt that being on hold for just one minute was too long.
Further, the longer that a customer is on hold the worse his or her perception of how long they are on hold becomes.” While the fledgling integration of ‘chat bots’ is a start for improving customer service, as long as there is hold time, there are angry customers.
The automation can be taken to further lengths, and smart human employee interaction with their “AI colleagues” is the path to vastly improving the customer service experience while reducing losses in revenue.
In the era of big data, it has become human nature to analyze any data set we can get. The verbal coding powers of a machine learning AI colleague in customer service give firms ready access to a whole new set of data. Upon implementing customer service AI technology, companies can more precisely analyze this data and figure out where they can improve their business model. Efficiency is at the crux of the modern business, and this new technological development provides that.
The trade-off for efficiency, as is often the case, is privacy. While there can always be a privacy policy that prefaces the call (and is honored), the coding robots may scare some customers off. This is where companies have to assess their values. In customer service, where this information is already being shared with a customer service representative, privacy concerns should not be a huge issue.
Allowing a company to improve sectors of their business with the goal of strengthening the areas that their customers need help with most is a good thing. It becomes bad when analysis becomes exploitation, but the already problem-sharing nature of customer service should erase major concern. Companies know that it is the customer, after all, that drives business forward.
Every day, we see how technological advancements and applications improve our standard of living. Machine Learning and Artificial Intelligence can be applied in so many new ways to cut costs, relieve monotony, and make people smarter. Thus, we must continue to aggressively advance and apply new technology so that in the customer service industry, we can turn previous losses into future gains.
Written by Jason Kauppila, Edited by Alexander Fleiss & Bryan Xiao
Note: The DXC content was ascertained based on the notes I took when I spent time shadowing the company.