When we talk about customer care, customer satisfaction is essential. Sentiment Analysis, is an Artificial Intelligence-based technology that is revolutionizing the way call centers interact with users. In this article, we will explore how Sentiment Analysis can improve the efficiency and quality of service offered by customer services.
What is Sentiment Analysis and how does it work?
Sentiment Analysis is a natural language processing technology that allows you to identify, extract, and evaluate the opinions and emotions expressed in written text or a recorded interaction, such as a social media post, a product review, or a customer support message. This technology is based on natural language processing (NLP) and uses machine learning algorithms to identify keywords, expressions, and phrases that indicate positive, negative, or neutral emotions.
Applications of Sentiment Analysis in customer care
Sentiment Analysis is becoming increasingly used in contact center settings, as it allows monitoring and analyzing customer feedback in real-time.
By processing customer text or call data, using machine learning algorithms that identify emotions, tone, and sentiment, it then proceeds to quickly identify customer issues and dissatisfaction, improving their overall customer journey experience.
Sentiment Analysis can be applied in various areas of customer care, both technical and brand reputation, including:
Sentiment Analysis for Customer Support
One of the main areas of application of Sentiment Analysis is customer support. Companies can use this technology to monitor user messages on social media, product reviews, and support emails to understand how customers perceive their brand and their products. In this way, companies can quickly detect issues and complaints and promptly intervene to resolve them.
Online Reputation Monitoring
Another important application of Sentiment Analysis is online reputation monitoring. Through sentiment analysis, it is particularly effective to check what is being said on social media and online review sites about a company, the brand, and the products it markets. The brand is then monitored, and brand reputation can be easily improved with targeted interventions.
Sentiment Analysis for Offer Personalization
Sentiment Analysis can also be used to personalize the offer to meet customer needs. Companies can use this technology to detect user needs, desires, and preferences and offer them customized products and services.
Improving contact center performance
Sentiment Analysis can also help improve contact center performance. By analyzing data collected through this technology, call center managers can identify trends and areas for improvement, allowing them to make necessary changes to the call management process. This leads to greater efficiency, cost reduction, and improved performance.
XCALLY Motion and Sentiment Analysis tools
The XCALLY suite, omnichannel software for contact center management, includes, starting from version 2.5.0, new Artificial Intelligence Tools that allow determining customer sentiment even through a call. Thanks to the integration with Amazon AWS, for each call recorded in XCALLY, it will be possible to obtain customer sentiment.
The contact center supervisor can then use:
- Amazon Transcribe to create a transcription of the recorded call text.
- Amazon Comprehend to analyze this transcription and provide the resulting sentiment (positive, negative, neutral, and mixed) in the XCALLY Motion web GUI.
The future of sentiment analysis: trends and perspectives
In the near future, it is evident how this technology will increasingly integrate into very different areas, such as marketing, customer care, and, predictably, the daily life of each of us, through the integration of sentiment analysis into other systems and platforms, such as social networks and chatbot systems.
Thanks to this integration, it will be possible to improve the customer experience and provide increasingly personalized and targeted support.
Sentiment analysis algorithms will become more and more sophisticated and accurate, thanks to the use of machine learning techniques and artificial intelligence, expanding their ability to recognize non-verbal signals and languages, such as images and videos, as well as covering an ever-increasing linguistic diversity efficiently and credibly.
What we will be able to do through this technology is only to be imagined.