Interactions Blog

It is impossible to ignore chatbots if you are even remotely following the customer care industry. They seem to be everywhere. Some tech publications even called 2016 ‘the year of chatbots’ and 2017 ‘the year when chatbots will actually make customers more productive.

Whether we realize it or not, we encounter machine learning on a daily basis. Aside from in our day-to-day lives, industries from retail to government and more are depending on machine learning to get things done. Below is a short list of how different industries are utilizing machine learning.

Have you ever yelled at a customer service agent over the phone? How about an AI-powered virtual customer service agent? If you answered yes to the latter, then thanks, you’ve made a significant contribution to the evolution of artificial intelligence.

Most companies are aware that the importance of providing a good customer service experience cannot be understated. Increasingly, more and more consumers are making purchasing decisions based on a company’s reputation for providing good customer service.

The terms ‘artificial intelligence’ and ‘machine learning’ are often used in conjunction with one another to describe leading-edge technologies. But while the two are interconnected, machine learning and AI are different. It’s perhaps easiest to think of machine learning as one of the underlying technologies of AI.

Your customers are busy. So when they interact with your company, they want to do so quickly and effectively. But, too often, consumers are slowed down by the customer service tactics that companies actually put in place to help.

Implementing some kind of biometrics authentication to protect your customers’ identities used to be an innovation play, but with increasing incidents of frauds and breaches, it has become a hygiene issue in the past few years.

Customer effort — the amount of time or effort a customer has to put in to get an issue resolved — can be complicated. There are a lot of different obstacles that can drive up customer effort, and measuring it isn’t always very straightforward, either. But the benefits of reducing customer effort can be substantial.

When it comes to machine learning, one size does not fit all. Different algorithms, and different techniques within those algorithms, are used to build a model that is application appropriate. But how do you determine which technique is best? Because machine learning is not a concrete set of algorithms used across the board, it depends on what you are trying to achieve.

Artificial intelligence is already being used in businesses across industries – from retail to telecommunications to travel and hospitality. Yet as AI usage continues to expand, the misperceptions regarding AI’s role in the corporate world are impacting companies’ next steps in their industries. It’s time to leave those myths behind and instead focus on AI’s potential.

Currently, artificial intelligence (AI) technologies are having an increasing impact on many aspects of daily life. Artificial intelligence refers to the capability of a machine to mimic or approximate the capabilities of humans.

When companies compete on the basis of customer experience, the metric that is most often used to gauge performance is customer satisfaction (CSAT). And while CSAT is certainly an important measurement for any company to track, it’s not the only (and probably not the most complete) one.