Description: Emotional analytics refers to the analysis of data related to human emotions, using data mining techniques to extract patterns and trends that can be useful in various applications. This approach is based on the premise that emotions influence human behavior and, therefore, purchasing decisions, brand loyalty, and customer satisfaction. Emotional analytics combines methods of natural language processing, sentiment analysis, and machine learning to interpret data from social media, surveys, customer feedback, and other sources. Through this technique, companies can gain a deeper understanding of how their customers feel about their products or services, allowing them to adjust their marketing strategies and improve customer experience. The relevance of emotional analytics lies in its ability to provide valuable insights that go beyond traditional quantitative data, enabling organizations to make more informed and customer-centric decisions.
History: Emotional analytics began to gain attention in the 2000s with the rise of social media and the increasing availability of user-generated data. As companies started to recognize the importance of emotions in decision-making, tools and techniques were developed to analyze this data. In 2010, the term ‘sentiment analysis’ became popular, leading to increased interest in emotional analytics as a field of study. Since then, it has evolved with advancements in artificial intelligence and machine learning, allowing for more sophisticated and accurate analysis of human emotions.
Uses: Emotional analytics is primarily used in marketing and customer service. In marketing, it allows companies to better understand consumer perceptions and feelings towards their products, helping to personalize advertising campaigns and improve market segmentation. In customer service, it is applied to analyze customer satisfaction and detect issues before they escalate into crises. It is also used in human resources to assess workplace climate and in product development to gather emotional feedback on new offerings.
Examples: An example of emotional analytics is the use of sentiment analysis tools on social media to assess public reaction to a product launch. Companies like Coca-Cola have used this technique to measure the effectiveness of their advertising campaigns. Another case is that of customer service platforms that analyze interactions with users to identify negative emotions and improve customer experience. Additionally, some human resources applications use emotional analytics to assess employee well-being through surveys and feedback.
