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.
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.
With a company-wide initiative to improve customer experience, Westar Energy wanted a customer care solution that would cut down on Customer Service Representative (CSR) handle times and improve self-service transactions.
As a company, it’s important to provide the best security for your customers. And with so many potential ways for strangers to hack into accounts and steal someone’s identity, a foolproof way to verify your customer’s identity is crucial – for your business and for your customers.