Calculate how an Intelligent Virtual Assistant can save valuable call minutes.
Investing in Conversational AI technology and putting it at the forefront of the contact center can optimize labor and improve customer experience while providing a strong foundation for future business goals.
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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.
For businesses, social media comes with its own unique challenges that distinguish it from the rest of your customer care strategy. Which is why we’ve put together some best practices in social customer care to help you maximize the benefits of this channel.
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.
Customers are increasingly making purchase decisions not just based on product and price, but also based on a company’s reputation for providing superior 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.
