Learn how machine learning for customer support drives immediate and long term ROI

Today, machine learning is perceived as an optional feature — something that would be nice to have, but not integral to the customer support process. However, due to its utility for improving customer satisfaction and streamlining agent efficiency, machine learning will be intrinsic to any successful CXM in the next five years.

In addition to being an early adopter of a technology that will soon become the standard, there are several immediate and long term ROI opportunities offered by machine learning technology. The efficiency improvements that come with machine learning integration are far-reaching — improved ticket triage, augmenting agents with historically-proven information, and automating ticket responses — all drive ROI for customer support teams.

The goal of any effective CXM should be to resolve customer questions quickly and accurately — simple right? Well, without the proper tools many customer support teams lag behind and effectively inhibit company growth through outdated processes. Here are a few ways in which integrating machine learning into your CXM will pay off and keep your team ahead of the curve.

Promote word of mouth with exceptional CXM efficiency

Think back to a time when you received exceptional customer service. Weren’t you compelled to sing the company’s praises from a mountaintop? Now, remember a time you experienced horrible customer service. You were likely determined to run the company through the mud in front of everyone you know.

Don’t undersell the value of a good recommendation. In the infancy of a customer support system, you should aim to promote as much positive word of mouth as possible. The goal should be to provide excellent support in order to build a clientbase, one shining recommendation at a time.

On average, happy customers will recommend a company to nine friends. Conversely, unhappy customers will complain to around 16 friends. When it comes to customer support efficiency, you truly catch more bees with honey than vinegar, so isn’t it worth implementing as many tools as possible to improve customer satisfaction?

Machine learning promotes initial ROI by making your CXM faster. AI researches historically successful tickets that have been approved by agents and passes that knowledge on in a variety of ways (more on that later). Machine learning allows your agents to draw off past success and build CXM efficiencies.

In addition to making agents more efficient, historical data can also inform ticket triage. AI technology can study routing protocols, determine what worked and what did not, and alter triage based off this valuable information. Making sure the right tickets get to the right agents seems easy, but implementing machine learning makes the routing process far more efficient through automation.

Build reputation with great CSAT scores

Looking ahead, customer support managers should aim to further leverage machine learning technology to establish a reputation for providing a stellar customer experience. After reeling in new customers through word of mouth, you must sustain your good standing by continuing to offer great support and adapting to customer needs.

Long term CSAT scores are worth more than mere bragging rights: 86 percent of customers say they would pay more for better customer service. Factors that contribute to a positive reputation range from helpfulness to resolution speed, but there’s no denying that customers look for a level of agent compassion and empathy when they call in for support.

Forty percent of survey respondents said “human interaction” was the most important element of a customer support call. However, customer support is a highly demanding job — especially during the holidays — and overwhelmed agents can’t always optimally serve customer needs when contending with an over-capacity queue. That’s where machine learning comes in.

Augment agents with recommended responses, which derive from machine learning, to improve resolution accuracy and timeliness. Reduce response times by 30 - 40 percent with agent suggestions that are based on historically-proven ticket resolution templates. Agents with access to the “roadmap” of recommended responses are led to the quickest resolution possible, which makes for an more efficient ticket overall. With the aid of speedy and accurate responses generated by a machine learning system, agents can focus primarily on delivering empathy and connection to customers.

Keep up with demand through automation

You have provided excellent customer support up to this point, and your clientbase is growing like crazy. Congratulations! But, now you need a way to contend with the new strain brought onto your system by increased ticket volume.

Don’t take one step forward and two steps back — accommodate growing CXM demand by automating a percentage of processes to save time and money. Even automating a small portion of tickets will significantly relieve agents’ workload and make scaling to growing customer volume easy.

Let’s do the math: a company that gets 100,000 tickets a month can easily automate 20 percent of tickets with the help of AI technology, and this would result in 20,000 tickets that agents no longer have to handle, thereby saving 20 percent in labor costs.

When automating a percentage of tickets, agents are free to assist in more complicated customer calls and have more time to offer that necessary human touch to resolution. Remember, empathy is a key element of the customer support process, but is severely lacking due to overworked agents. By automating common responses, agents can refocus their energy from redundant tasks to offering a compassionate and relevant solution.

Don’t overlook the immediate and long term ROI offered by AI in customer support, and stay ahead of the curve in order to best serve growing customer demand. Contact AnswerIQ to learn more about how machine learning technology is changing the way customers interact with support and why AI is becoming the new standard for CXMs.