What do you think of when you hear the words “artificial intelligence” or “machine learning”? To many people, those phrases come off as cold or impersonal, but when it comes to customer service, they’re actually anything but.
Studies have shown machine learning can make customer service more effective and efficient, and can ultimately help customer service representatives connect with people more meaningfully. Here’s why it works.
1. Machine learning guards against data overload
If your eyes have ever blurred from looking at a spreadsheet too long, you know that human brains aren’t built to process constant, monotonous streams of data. Algorithms are. When deployed properly, artificial intelligence (AI) can parse data seamlessly to make smart, efficient decisions. That takes the processing load off of customer service representatives, which leaves them with more time, brainpower and empathy to offer customers.
2. Customers like self service
As a customer, which do you prefer when you first encounter an issue: a self-service option or a live representative? Studies show that 81% say self service, which means most people prefer to first address matters on their own if given a choice. If a proper base of knowledge has been used to create the AI they’re accessing (chatbots, virtual assistants, etc.) customers get a response — and results — faster. That means customer service representatives can focus on solving more complex problems while standard issues are handled by machine learning applications.
3. Machine learning cuts costs — for customers and companies
Harvard Business Review found that DIY transactions cost less than a dollar each. Interactions with live customer service representatives? At least 7 times as much.
Agent interactions are estimated to cost:
- $7 for a B2C company, and
- $13 for a B2B company.
With the proper investment in machine learning, companies can remove simpler issues from live agents, which allows them to cut costs. Those savings can then be used to improve services or lower prices, both of which help create happy customers.
4. Efficiency improves customer satisfaction
This one’s simple: people are happier when they get what they want faster. Machine learning helps route customers to the type of agent they need quickly, and that leads to more efficient solutions. Machine learning can also help a customer service representative do their job more effectively once they’re connected with a customer by:
- Recommending resolutions
- Offering scripts to address certain problems, and
- Providing agents with relevant customer history.
When data is both collected and applied, neither person starts from scratch. That leads to faster, more satisfying interactions.
5. Machine learning is always evolving
Business owners know that analytics are key in determining what customers want, but metrics only matter if you can learn from them. Today, machine learning applications help humans do that without thinking twice.
- Some ML apps are so advanced that they can use deep learning to continually improve their responses. That means AI-enabled services can improve on their own, while you and your team are interacting with customers in real time.
- Predictive customer service analytics can even use data from past interactions to catch things agents might miss, or to suggest related products or services that similar customers have enjoyed.
Whatever the situation, the important thing is that knowledge is being used — not just collected.
How Retention360 fits in
While machine learning often focuses on efficiency and cost-savings, at its core, it’s really about creating a better customer experience. Retention360 was designed with that goal in mind. Our one-on-one customer feedback platform collects, tracks and analyzes customer feedback in real time so you can address customer concerns directly and immediately. Contact us to learn how we can help you put machine learning in action today.