Much has been said about artificial intelligence. And it’s much deserved. The implications and potential of artificial intelligence are both large and profound. And not just for a few industries, but all of them.
Among AI-related technologies that are particularly of interest is machine learning. Even now, machine learning algorithms are being employed and tested out by various companies.
Basically put, machine learning enables computer systems to learn certain skills that used to be done exclusively by humans. Data is fed into the machine, which is then utilized by the system to “learn” a specific task. For example, machine learning can differentiate objects. Or detect if an email or message is real or just spam. It can use context to interpret words.
Traditional computer software uses code that already instructs and has a set outcome. Machine learning utilizes information fed into it to interpret data by itself, meaning that it’s more predictive.
Machine learning is also different from AI in itself, since the latter is designed to mimic human intelligence. AI has a much broader scope; machine learning is just one of the pathways to AI.
This “learning” capability of machine learning is actually being applied right now. It’s proven to be useful in tasks like email filtering, network intrusion detection, and computer vision (analyzing videos and images).
There are basically three types of machine learning. Supervised learning involves teaching machines through example. Data is labelled, and the machine analyzes a large amount of examples in order to perform tasks. Unsupervised learning is somewhat the opposite, utilizing algorithms to identify patterns in data.
The last kind, semi-supervised learning, is somewhat a hybrid of supervised and unsupervised learning. Here, the two processes are mixed (using algorithms and analyzing labelled data) to fulfil specific tasks.
The potential of this technology is tremendous. For example, machine learning can potentially be used to detect Medicare fraud. In a Healthcare Analytics report, researchers from Florida Atlantic University’s College of Engineering and Computer Science used Medicare Part B data in an attempt to automate fraud detection. When ultimately applied, the pioneering study could help recover anywhere from $19 billion to $65 billion in fraud losses annually.
There are also much broader uses of machine care in healthcare. Doctors can receive much more information such as risks for certain diseases and conditions. Machine learning has also been tapped to predict certain cancers.
It can learn to look at medical imaging results and identify possible abnormalities, making certain processes more accurate. In healthcare alone, machine learning holds enormous potential that research from McKinsey estimates that the technology can generate up to $100 billion a year.
Other applications include in agriculture, where machine learning can be used to observe, measure and respond to inter and intra-field variability in crops. The goal of this technology is to preserve resources like water, seedlings, fertilizer and the like, while maximizing output at the same time. In insurance, machine learning can be utilized to analyze insurance data from customers and give an automated premium estimate.
Needless to say, machine learning is poised to transform a lot of industries as the technology matures and develops through the years.
If Gartner’s prediction is to be believed, by 2020, as much as 85% of customers’ relationship with the enterprise will be managed without human interaction.
Machine learning can be used to take customer data and deliver more personalized services. It can also be used to deliver automated responses for certain issues, reducing the burden for a company’s manpower.
A lot of companies are already making use of the technology as they automate certain aspects of their interaction with customers. This, combined with the utilization of traditional customer experience delivery, allows for improved efficiency.
Customers with simple concerns do not need to jump through hoops to see their issue/s resolved, allowing those with more complex concerns to connect to a representative faster.
Uber is one of those companies that have utilized machine learning in an effort to improve their CX. It utilizes a tool called a Customer Obsession Ticket Assistant. This is basically machine learning technology that helps its agents resolve issues faster.
The goal of the tool, Uber says, is to “simplify, expedite, and standardize the ticket-resolution workflow.” At the moment, the company reports that so far, the technology has reduced resolution time by at least 10%. This while also helping increase customer satisfaction.
Machine learning is predicted to become a bigger part of CX delivery. This will allow companies to better make complex decisions, and make operations more streamlined and efficient through a mix of automation and the continued use of human talent.
Brands will be able also to utilize machine learning to better understand customers. This in turn will lead to more contextualized offers for and interactions with customers. All in all, machine learning is poised to be a win-win for the customer experience, as well as for the organizations delivering that experience.