This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Here we are in 2020 with much better data collection than we’ve ever had. There needs to be a new way to organize that behavior data (that’s largely click data), and marry it together with the qualitative data that might be sitting in your CRM or other tools. Alex contends that anyone can do the same.
Currently, there are two categories of speech analytics vendors in the market: a large group of competitors who sell basic applications that focus on identifying key words and phrases, and a smaller set of feature-rich solution providers who offer extensive business intelligence platforms that provide enterprise-level data.
According to Accenture , 85% of customer interactions will be managed with AI by 2020. The most important AI technologies relevant for analyzing customer feedback fall in the area of natural language processing (NLP) and machinelearning. Both groups of technologies can be utilized to make analytics more actionable.
According to Accenture , 85% of customer interactions will be managed with AI by 2020. The most important AI technologies relevant for analyzing customer feedback fall in the area of natural language processing (NLP) and machinelearning. Both groups of technologies can be utilized to make analytics more actionable.
We organize all of the trending information in your field so you don't have to. Join 20,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content