Customer service: 4 machine learning mistakes

Customer service may not be the most glamorous function in a company, but it still touched by advances in modern technology. Machine learning is playing a prominent role in customer service in the 21st-century. Even the federal government is starting to use AI and machine learning in customer service capacities.

However, machine learning is only a viable solution for customer service if it is properly used. Many brands make mistakes that damage their relationships with customers, which can cause irreparable harm to their image.

Here are some of the biggest machine learning mistakes to avoid while developing a customer service strategy.

Attempting to automate the entire customer service process

Automation is one of the primary benefits of using machine learning for customer service. This is one of the reasons that there are nearly a quarter of a million openings for computer support jobs.

One of the primary benefits of machine learning is the ability to facilitate automation. By better understanding behavior of people using your site, you can develop technology to streamline the customer service process.

However, automation has its limits and ignoring them can have serious repercussions. Brands that use machine learning most effectively strike the right balance between automation and relying on trained customer service personnel. Before turning to machine learning, you identify functions that should be reserved for human employees.

According to research from the University of Pennsylvania, the biggest benefit of machine learning is going to come from educating employees. This should be your focus, rather than trying to replace them.

Being afraid to relinquish control

While having too much faith in automation can be a mistake, the other extreme can be almost as bad. AI can be a tremendous timesaver if you let it. However, many brands insist on handling every element manually. Lisa James of Digitalist Magazine has spoken with many brands that have been reluctant to take a leap of faith and trust their data.

Tasks like categorizing tickets, routing to skilled agents, recommending solutions, and recommending equipment for technicians (just to name a few), can take valuable minutes away from resolution times when done manually. With machine learning, your service solutions remove this responsibility from you. The more data your company has to work with, the more the machines learn, the smarter and more accurate the algorithms become, the fewer manual steps need to be done by agents. Machines do most of the thinking, and your agents can focus on more complex tasks. Let go of control.

Segment users to minimize noise

Many different groups of people use your website and other digital properties on a daily basis. Employees, customers, journalists, competitors and the general public will look at your site.

It is important to make sure that your machine learning algorithms can differentiate between different users. Otherwise, they may draw inaccurate assumptions by confusing the behavior of your employees, customers and other website visitors.

Here are some ways to ensure your AI systems are monitoring the right groups:

1. Tag customers that visit your website through PPC platforms. Your AI algorithms need to understand that people visiting your site through ads are usually customers. If you also run ads to promote jobs, you should create separate tags to distinguish customers from prospective employees.
2. Use different machine learning processes for different webpages. If a webpage is designed specifically to capture new leads, then you don’t want to rely on behavioral data from other parts of your website.
3. Make sure your algorithms are weighted towards user feedback. While user heat maps and other data are insightful, they don’t provide data with the same accuracy as user feedback. When people fill out forms, try to get as much identifying information as possible. Crossmatch this data against other information, so you can get a better understanding of your users’ behavior.

You don’t want to focus on maximizing Data volume. It is better to get highly accurate data on different user groups.

Creating your own machine learning tools from the ground up                                               

Developing your own machine learning tools from scratch doesn’t make much sense. There are many different APIs that you can use instead. This post from Tech Republic discusses the Google machine learning APIs that many brands are benefiting from.

Google has been increasingly investing in machine learning, opening up new tools for customers to use to improve efforts in customer service. Google customers can approach machine learning with pre-built APIs, or use TensorFlow and the Cloud Machine Learning platform to build a custom solution.