Process of using natural language processing and machine learning techniques to determine and
analyze the sentiment expressed in customer interactions across various channels. The goal is to
understand the emotions, opinions, and attitudes conveyed in customer messages, whether they come
from emails, chat messages, social media, or other communication channels within an omnichannel ticketing
system.
Monitoring your customers' feedback about your brand on social media platforms such as Facebook or Twitter is essential to prevent escalations. The social shout index can recognize and address negative experiences swiftly. As today's customers are very active on social media and can quickly turn their negative experiences into public opinions,iMedia enables your support team to actively track your customers' comments on Facebook and Twitter and provide support in real-time.
Real-time sentiment analysis of call centres is possible. This implies that sentiment analysis algorithms can process the data as soon as client interactions take place. This efficiency enables businesses to react quickly to the requirements or worries of their clients.
Direct customer reactions are included in call centre data analysis. It’s possible to accurately capture the true feelings, attitudes, and tones of your consumers by using voice recordings and written transcripts. This abundant source of information offers unedited and informal feedback in addition to organised surveys and evaluations.
A continuous feedback loop for continuous improvement is offered by call centre sentiment analysis. Organisations can determine regions that regularly generate favourable comments and those that attract negative feelings by analysing sentiment patterns over time. Strategic choices, including refining marketing tactics to suit client preferences or increasing customer service training or product quality, might be guided by this information.
