Learning Hub

Sentiment analysis identifies customer sentiment and intent in feedback by using text analytics. It analyzes comments for positive or negative opinions to provide an overall view of customer satisfaction.

At its most basic, text analytics uses AI and Natural Language Processing to extract meaning out of raw text. It’s more efficient and often more accurate than manual analysis, especially with large volumes of text.

Ticket analysis using machine learning allows brands to quickly analyze qualitative data from their help desk and improve efficiency for support ticket classification and management.

Customer experience (CX) refers to how a business interacts with customers at every point during their customer journey, ranging from marketing to customer service. It also plays a critical role in sentiment analysis.

Predictive analytics uses machine learning models to leverage existing data to predict an unknown future fact or event. Essentially, it leverages past data to predict future data.

Open-ended questions are an important element to build into any survey, but analyzing them can seem daunting. Learn how you can automate the analysis of your open-ended responses and build a survey results report that includes insights from qualitative data.

AI tools deliver actionable insights to help businesses manage feedback volume, identify customer satisfaction drivers, and ultimately improve customer experience. Learn how to implement AI for CX solutions at your organization.

Consumer feedback is readily available online to all brands, and with the right tools it can make a big difference to a company’s bottom line. Learn how to collect and leverage this powerful asset at your organization.