Machine learning apps are actually more ubiquitous than many people think. The lack of awareness is not surprising since the study of machine learning, as it is, has been relegated to academia.
Even in school, not all students study the subject. Ask around and you will see that only those who specialize in computer science or other courses related to computing and even engineering will be required to do so.
But let’s try to change that with this article. Here are the specific machine learning apps used:
Chatbots, like the ones we saw in the examples in the first part of the article, are one of the most used machine learning apps in the customer service industry. In fact, since chatbots are everywhere, Gartner says that 67% of people already expect to see or use them when speaking with a company.
Machine learning lets chatbots precisely identify the correct tag for each conversation using natural language processing. The result is the chatbot “reading” and understanding what you say. Once it understands what you’re saying, it sends the appropriate response (see the “Sunshine” example above) or directs you to the right person who can care for your problem.
The more conversations the chatbot has, the more correct your answer will be. The feedback it receives from customers who say whether the markup is correct or incorrect also allows the chatbot to improve its performance.
Virtual assistants are often confused with chatbots. They, however, are not the same. Chatbots simulate an interaction with an agent, while virtual assistants focus on specific areas of the customer journey to assist the customer.
For example, if you’re using Microsoft, you can verbally ask Cortana when summer starts, and she’ll give you the information you need.
Using Natural Language Processing that mimics human speech patterns will tell you the answer in a tone that mimics human style, creating more intimate interactions. But how do virtual assistants like Cortana, Apple’s Siri, Google Assistant, and Amazon’s Alexa work exactly?
When you enable them, your request is sent to your device’s company-owned servers (so you must have a good internet signal). While this is done, your phone or smart speaker is trying to figure out if it can handle the command without the server information. When the request reaches the servers, an algorithm analyzes the words and tone of your request and matches it with an order that it thinks you asked for.
Email Verification Tools
Suppose you’ve ever embarked on an email marketing campaign or outreach email in your life. In that case, whenever you send your emails, I’m sure you’ve checked them by the email verification tool first . . You do this all the time because you know that when emails are sent to invalid email addresses, they bounce back—the higher the bounce rate, the lower the sender’s score. The result is that you can get caught by spam filters.
But have you ever wondered how exactly this email verification tool worked? Well, that’s machine learning at play. Simply put, sophisticated machine learning algorithms allow this email scanning tool to track down the most elusive disposable address providers and analyze whether or not an email exists.
Machine Learning In Customer Experience: Perspectives
There’s no denying that machine learning, as it is, already plays a vital role in the customer experience. And the prospects are even better, according to some experts.
According to Digital Information World, machine learning will ” explode” as business demand continues to grow. An article published in Towards Data Science states that machine learning tools will continue to “evolve” and provide an optimized customer experience. And this is far from improbable.
For example, sound, chatbots, and virtual assistants have not yet reached their full potential. Currently, some chatbot responses and virtual assistants are disabled. Sometimes they don’t answer specific questions or don’t understand particular questions. As machine learning continues to evolve, chatbots and virtual assistants can have a full range of answers in their database and a greater understanding of the data.