How to Use Machine Learning in Customer Service Contact Centers

Robot machine learning

Although machine learning sounds like an advanced scientific term, the idea behind it is relatively straightforward: machine learning leverages data to detect patterns and make decisions. But even though machine learning has many applications, many businesses aren’t sure how exactly to use it. According to a recent study, 49% of organizations are exploring or “just looking” into deploying machine learning, while 51% claimed to be early adopters (36%) or sophisticated users (15%). Many contact centers, for example, are looking for ways to use machine learning in customer service departments, but struggle to do so. 

While finding machine learning applications in customer service contact centers can seem like a challenge, there are many common use cases. For example, customer service contact centers that adopt business text messaging platforms can use built-in machine-learning features that route messages and collect message data. Additionally, because texts are short and the scope of their content is limited, training models are easier to create and reuse.

Want to learn about the top machine learning applications in customer service contact centers? Let’s dive in. 

Identify Inappropriate Messages

With machine learning, customer service contact centers don’t have to manually triage and review inappropriate messages. 

No matter the industry, modern contact centers with email, web chat, or texting capabilities are often sent spam or inappropriate communications from customers or spammers. People send messages that clutter up queues, reduce agent productivity, and slow response time to legitimate, well-meaning customers. 

In the past, these messages would have to be manually triaged, analyzed, tagged, and closed. Some contact center software added tagging features, so that agents could quickly triage these messages. But unfortunately, these messages still required some manual intervention.

Today, modern contact centers can use a machine learning training model that identifies keywords and determines whether or not they are inappropriate. For example, “money” may or may not be a word an inappropriate message. But with the appropriate machine learning software, messages that contain the inappropriate use of “credit” can automatically be archived and marked as spam, while appropriate messages with the word “credit” will remain in the queue and be routed to the appropriate agents. 

Inappropriate: Your credit card has been charged in Miami, Florida. Please call back to confirm or reject. 

Appropriate: I believe there is a credit in my account. Could you confirm?

Having these inappropriate messages immediately removed by machine learning software helps speed up response times. It’s similar to the new software that filters out spam from our email inboxes—with the spam filtered out already, it’s far easier to get your real work done.

Route Messages to Agents

With machine learning, customer service contact centers can also streamline message routing. Business person at computer

As we explored previously, machine learning is excellent at detecting certain keywords in a message. But this skill is useful for more than just separating inappropriate messages from legitimate requests. A software system with machine learning capabilities can identify the intent of messages and either send the appropriate auto reply or route the message to the appropriate agent. 

For example, the word “hours” might be used in a message like “How many hours will it take for your team to deliver and install the product?” or “What are you weekend hours?” A software system with machine learning capabilities can review the first message and route it to your business’s product team. It can also tell that the second message can be solved with an auto-reply, and send the appropriate, pre-written template automatically. 

This kind of machine-learning-based message routing will dramatically reduce the number of messages your agents have to manually reply to—and also improve customer response times.

Analyze Message Data 

For businesses interested in using machine learning for customer service, reports and analysis are an excellent place to focus. 

Contact centers collect a lot of valuable data, especially when they use business texting platforms. These advanced platforms allow businesses to receive and send messages from any messaging app, including native texting apps and over-the-top (OTT) apps, like WhatsApp or Facebook Messenger. They record both messages and message data, too.

A modern business text messaging platform should be able to export data based on a variety of factors, like date, queue or inbox, and agent. This information can be exported from the platform and then imported into a reporting tool that has machine-learning capabilities. 

For example, some software can determine customer sentiment. With the help of machine learning, the software can review a random selection of customer messages and look for certain words and phrases that reveal either positive or negative customer feelings. 

With the help of machine learning, contact center teams can confirm the validity or CSAT scores, or simply get an idea of how satisfied customers are with your customer service. 


Ready to capitalize on the top machine learning applications in customer service? Try our business text messaging platform for free today. 

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