Alterna CX’s Emotion Analysis Tool

Emotion analysis software is a powerful tool to adjust today’s changing consumer behavior. “Human” signals, such as emotional text or social media comments, are difficult to analyze and decode, yet these often demand immediate and concentrated customer engagement. Feelings are involved; misinterpretations may occur; cultural barriers may hinder deep insights. Now, more than ever, with increasing screen time and prevailing interactive technologies, people express their feelings on various occasions across platforms. These reactions piling up as loads of complex CX signals become noise. It provides a quick understanding of the consumer’s reactions. It thoroughly examines positive and negative human emotions and sensitivity. This sensitivity highlights the differences in how people express their emotions. As a result, it is a vision and reveals people’s complex feelings and motivations. In short, by examining the motives and desires of customers and users, emotion analysis reveals valuable, accurate information.

Here are some real-world examples across industries and geographies where Alterna CX’s AI-driven emotional customer experience software recognizes the mood of the people from text.

Emotion analysis using natural language processing (NLP) reduces churn, measures performances of frontend and customer service agents, unlocks upselling and cross selling opportunities. In today’s traditional CX management approach, surveys often fail to reveal the root causes of customer emotional experience and sentiments. In fact, scores may vary based on many outside factors, including geographical bias and industry shocks. These variables make it difficult to perform reliable root-cause analysis using surveys alone. On the other hand, artificial intelligence and machine learning subsist on data. By using out-of-the-box models for text analytics, organizations are able to analyze survey or non-survey data so as not to merely register sentiments, but indeed to understand themes and trends—“emotions”—in customer feedback. Different data acquired this way are synthesized to make a relevant and comparable picture of discernible customer experience.

Here are three cases of Alterna CX’s emotion analysis tool for improving customer loyalty and CX quality:

1. Aligning business policies throughout the organization

Lacking implementation of a solid AI-driven experience management system, a lot of investment and time are necessary to get employees to behave in line with a company’s top goals. Each employee is expected to let previously-defined objectives guide her actions and execution in daily work life. In the long run, finally this discipline becomes the new normal embedded in company culture. For example, zero-tolerance policies against harassment, bullying and discrimination are very hard to adopt throughout the organization to the extent that they resonate in micro-moments. Furthermore, AI-based CX analytics solutions make it happen without delay between comprehension and action. Customer comments registering the feeling “disgust” and “employee behavior with discrimination” can be easily detected in real time with emotion analysis and pre-trained models. The AI-based CX system acts as a round-the-clock analyst, alerting relevant employees to react to particular customers’ negative moods.

2. Detection of personas with the strongest opinions

Emotion analysis can tell CX professionals which customers feel disgusted or angry. For example, this technology can promptly deduce an accurate cause-and-effect understanding of people who are disgusted by niche pricing. Likewise, emotion analysis can help marketers recognize which customers are feeling enough pride or trust to make them potential brand ambassadors.

3. Track customer feelings vs. time

Natural language processing techniques like sentiment analysis and emotion analysis automatically classify trends and achieve high accuracy for identifying the most likely causes. For example, you could see a rise in consumer feelings of “sadness and fear,” then trace those data back to their root cause with a subfield of natural language processing, emotion analysis in real-time.