Lots of people have lots of things to say about AI, but there’s one type that we are hyped about above all else: Augmented AI.
It is a ton of work to rip out an existing BPO partner and replace it with another, but it's often the right move. Read our guide on the five best ways to structure the move.
AI is no longer an imaginary development in some far off future — Large Language Models and other generative AI advances are reshaping our lives right now. Here's how we think AI will factor into the future of customer experience.
The best customer service software depends on customers’ needs and the challenges your support team faces. Here are some software tools that can help.
In Part 1, we were introduced to the main data types and what you need to look out for in your dataset before you set out to clean it. Here, we will be taking a look at the actual cleaning steps required to get your data ready for service.
Data cleansing (or cleaning), is used to refer to the process of detecting and correcting inaccurate, corrupt or unusable data. It is an essential step before any data analysis project, since every step after it assumes the data is “clean” or, in other words, trustworthy and accurate.
Conducting your study while using the incorrect sample size comes with the risk of obtaining biased results, and therefore drawing incorrect conclusions. Here's a guide for you.
