Strategies for Using Machine Learning in Customer Support
Strategies for Using Machine Learning in Customer Support
By: Harmeet Singh
Five years ago, if anyone weighed upon the association of machine learning and customer service, they deserved a round of applause.
Machine learning (ML), as a concept, isn't new. In fact, it has been there for quite some while now—68 years to be precise.
Woah! You didn't know that, did you?
But you can say that the perception of ML, in the present day, has been shaped in the past decade. While it no longer surprises with its capabilities, the concept does tend to astonish when it is linked with personalization in customer service.
For this reason, we're going to explore how machine learning can assist the customer support industry and how it can prepare everyone for a very interacting and intriguing future.
Difficulties in procuring the desired customer experience
Catering to the needs of thousands of consumers is an altogether challenging scenario. Even if the human workforce manages it well, a deviation from the "normal" can disrupt the entire workflow and hurt the outcome.
It's just like acquiring reviews for your website. Four 5 STAR ratings and you're good, one 1 STAR and down you go.
The problem doesn't lie with the competency modeling of the organizations but the increasing and diversifying consumer base. A lot of unstructured data lurks online, which cannot be utilized by the human agents for drawing patterns. The vastness of the data makes it impossible for the human brain to track trends and help in forming personal recommendations.
In addition to this, human agents cannot track each conversation with the consumer so that a bigger picture of the problem can be brought forth. Last but not least, for startups and SMEs, it is essential to save money, something that cannot be achieved through mass recruitment.
encourages as well as entails the use of software to optimize the limited workforce.
When all these issues persist, fine customer experience, more often than not, is out of the league. In that scenario, machine learning appears to be the go-to technology as it encourages as well as entails the use of software to optimize the limited workforce.
Employing Machine Learning strategies for optimizing customer support
Machine learning, a component of AI, is a technology that can develop self-learning computer programs. These programs are so designed that they can observe the data, learn the patterns within it, and use them to recognize the trends in alien data sets. In simple words, a machine learns with experience and, thus, enhances its decision-making capability every time it churns a data set.
For all the aforementioned reasons, Machine learning is considered the most prominent comrade of customer service. From task automation to effort reduction and guidance to prediction, it has a lot in the store to keep your business up and running. However, the latter optimism is only reasonable when you know how and where to consolidate the technology within your customer support base.
Personalization and recommendation systems
It is said that machine learning in customer service is a game-changer predominantly due to the technology's rendering of personalized recommendations. Favorably, this saying is unquestionably true.
Machine learning algorithms are so designed that they recognize the interests of the users based on their web interactions. Collaborative Filtering, for instance, is an ML-powered algorithm that is widely used for recommending worthwhile content to the end-users. As the name suggests, it endeavors to recognize the interests of one of the users by considering the interests of others.
The figure below represents the likings of two users. Both of them highly rate Support Genie's services. Also, both of them are inclined towards regularly reading Support Genie's articles. Hence, the algorithm will presumably recommend to user B that they try Support Genie's customer support product.
Likewise, numerous algorithms work in favor of bringing the recommendations to light and drawing user's attention. In straightforward terms, you can know what the user wants even before the user approaches you. Interestingly, this is precisely what the user aspires before reaching out to you. Thus, the problem with customer service agents not able to align the recommendations is SOLVED.
Predictive analytics and insights generation
What if you knew whether the customer is loyal to your company or is deciding on leaving? You would run campaigns that can soothe the senses of those leaving and continue providing exceptional services to those staying. Isn't it?
Well, that's exactly what you should do because predictive analytics allows you to comprehend the customer's active or passive status pertaining to your company. It is a concept made up of machine learning, artificial intelligence, statistical modeling, and data mining.
With lots of customer data gathered from social networking sites and the web being analyzed, companies can develop methods that can help them sustain their customer base. In other words, predictive analytics makes your customer service proactive and efficient.
Most importantly, predictive analytics is capable of customer segmentation. From the vast database of unstructured conversations, machine learning algorithms are able to classify customers, thus, enabling the companies to
—react to different scenarios in the appropriate way
—gather insights about customer's response to a certain stimulus
—understand the purchase decision
—increase productivity by altering the mode of operation
Chatbots using machine learning
It won't be wrong to assert that machine learning is a relationship booster. The conventional thought process regarding the inefficiency of robots to garner engagement doesn't comply with the contemporary effectiveness of chatbots.
Just like all the other facets defined above, chatbots can also be equipped with the power to learn with experience. Whenever the customer visits the website, he/she can be entertained by the chatbot that can deliver an ideal response to almost every question being put up. By tagging the conversation according to priority and transferring it to the human agent, it can further bridge the gap between technology and the human workforce.
Machine learning and customer service: A revolution in the making
Can the relationship between machine learning and customer service be attributed to a revolution? Quite fairly, yes. With several opportunities from automation to prediction, machine learning has all that it requires to revolutionize the way the customer support industry works. It, however, remains to be seen how profoundly machine learning applications can infiltrate customer service.