Feature
The Gist
- CX enhancement. Sentiment analysis boosts customer satisfaction.
- AI integration. Chatbots utilize sentiment analysis for personalized interactions.
- Challenge tackling. AI advancements address sentiment analysis hurdles.
Sentiment analysis enables brands to more fully understand what their customers are saying, as well as the emotional state and meaning behind their words. When used by customer service agents, sentiment analysis can leverage the power of natural language understanding (NLU) and natural language processing (NLP) to understand the mood, tone and attitude of the customer, enabling them to route the call to the most appropriate agent. This article will look at the ways that sentiment analysis can improve the customer experience, how brands are using it, and the challenges that must be overcome.
What Is Sentiment Analysis, and What Does It Do?
Sentiment analysis is a subfield of NLP that involves determining the attitude, sentiments and emotions of a person — via text or voice conversations — based on the identification of subjective information within the conversation. Michael Allmond, VP and co-founder of Lover's Lane, a lingerie, apparel and products retailer, told CMSWire that sentiment analysis is an important part of what his company does, and that it enhances the customer experience in several ways. It also enabled them to analyze the sentiment in their marketing materials. "First, one of the major things we recognized via sentiment analysis was the overall tone and messaging we use in our marketing and advertising."
“When we were lumped in with other retailers in our niche, we were receiving a lot of negativity from customers,” said Allmond. “Many felt 'shame' or 'guilt' when entering our stores. This was early on in our journey, and thankfully we were able to pick up on this and completely rebrand. Our focus is on body positivity, romance enhancement, and inclusivity.” When the business began taking that tone in everything it put out, they were able to give customers a much more positive experience overall in its retail stores and on its website.
“Whether it is in the brand introduction phase, the in-store or online shopping experience, or the post-purchase journey, sentiment analysis is a key facet of our business that has helped us provide fantastic CX across all of our functions and channels,” said Allmond.
Related Article: 3 Ways Sentiment Analysis Can Improve the Customer Experience
How Is Sentiment Analysis Used in the Contact Center?
Sentiment analysis enables contact centers to understand and respond to customer emotions by rating the sentiments that come from the text or voice data. This allows agents to gain a better understanding of customer satisfaction levels and identify specific pain points that can lead to product or service improvements. Contact center software platforms often record customer service calls, and create text transcripts from the recordings that can then be used by the contact center team to monitor the performance of customer service agents, noting those in need of additional training or support.
Stanislav Khilobochenko, VP of customer services at Clario, a cybersecurity software development company, told CMSWire that sentiment analysis is an important process for his company, as there are a lot of strong customer feelings when it comes to cybersecurity. “In a call center scenario, I think having a lexicon handy in your employee handbook is great for new hires, but as they become more experienced they learn the nuances of customer sentiment,” said Khilobochenko. “Learning to de-escalate, interpret technical problems from a layperson's frustrated explanation, and of course how to respond to positive feedback are all essential skills employees must learn in order to support sentiment analysis.”
Khilobochenko’s company is using aspect-based sentiment analysis, which means they aren't just scanning for negative or positive feedback, but rather, they are interested in the aspects of their products and services that are evoking these sentiments in particular. “Not only does this help us redirect inquiries to appropriate representatives in the moment, but it also allows us to gather aspect-specific data to prioritize improvements to our product.”
Sentiment analysis also acts as an early warning system for emerging dissatisfaction trends, providing brands with proactive opportunities and preventing escalations. With a more complete understanding of a customer's sentiment, agents can personalize their approach to problem-solving and communication, enhancing the overall customer experience.
Anna Koval, co-founder and CMO at Tarotoo, an online tarot reading platform, told CMSWire that she has been leading the customer service team at Tarotoo for over a year and has been involved in the implementation and evaluation of sentiment analysis for her service. "I have seen how sentiment analysis has improved the customer experience and satisfaction, as well as the efficiency and productivity of our tarot readers and chatbot."
Many businesses are using AI-driven chatbots as part of their customer service offerings, as they allow the customer to control their own narrative and help live agents provide a more effective experience. “We use sentiment analysis to enhance the interaction and engagement that our clients have with our chatbot,” said Koval. “Our chatbot can detect the mood, tone, and attitude of our clients, and respond accordingly with appropriate language and emojis. It can also suggest relevant topics or offers based on the sentiment of our clients, and transfer them to a human agent if necessary.”
Related Article: Contact Center Technology and Strategies to Keep Customers Cool
Sentiment Analysis and Customer Feedback
Koval said she uses sentiment analysis to analyze the feedback and reviews her clients leave on the Tarotoo website and social media platforms. “This helps us to identify the strengths and weaknesses of our service, and to address any issues or complaints that our clients may have,” said Koval. “We also use it to measure the satisfaction and loyalty of our clients, and to tailor our marketing and communication strategies accordingly.”
Koval uses sentiment analysis to monitor the conversations that clients have with her tarot readers via chat or video call. “This helps us to understand the emotional state and needs of our clients, and to provide them with the most suitable and empathetic tarot reader for their situation.” Much like the use of sentiment analysis for monitoring customer service agents, Koval uses it to evaluate the performance and quality of her tarot readers, and to provide them with feedback and coaching.
Derek Flanzraich, CEO and founder of Ness, a venture-backed startup, told CMSWire that people often think of the customer experience as having to do with interactions that occur after the customer engages with a business. "In reality, it begins with their first impression and this is how using customer sentiment metrics can have a major impact through marketing," said Flanzraich. "Customers can first encounter a brand through many channels, whether it is an advertisement, a social media mention, or another customer’s review."
Sentiment analysis is an important tool for social listening, as well as understanding the feelings and emotions behind product reviews, feedback and customer interviews. "By studying these reactions through customer sentiment tracking, you can see general moods and opinions about your products, presentation or methodologies. This, in turn, can help you optimize future marketing efforts by utilizing your strengths, while addressing weaknesses and misperceptions,” said Flanzraich, who went on to suggest that by using sentiment analysis, you can make the appropriate pivots to optimize your marketing while bolstering your brand image.
The Challenges of Sentiment Analysis
Despite the clear benefits, the application of sentiment analysis in customer service and marketing still comes with challenges. Understanding sarcasm, idioms, language complexity, ambiguity, accents and local slang can be difficult for AI systems, and these nuances can lead to misinterpretations of sentiment. For example, the word “sick” could mean ill, as in not well, or it could mean that something is excellent, depending on the context in which it was used. Context is also used to determine the continuity of a discussion, i.e., “The box is rectangular” followed by “It is also blue” would indicate that the conversation is still about “the box,” which is both rectangular and blue.
Many contact center platforms are now integrated with AI solutions that enable call center agents to precisely understand customers' feelings and emotions — even those with accents or those who use colloquialisms. One contact center platform, Cogito, uses emotion AI (which enables the platform to identify, process and simulate human feelings and emotions) along with conversation AI to detect and measure honest signals or true feelings behind the customer’s voice.
Cogito’s co-founder and CEO, Josh Feast, said that emotion AI is able to detect a multitude of voice signals that reveal how speakers are feeling, while conversation AI examines the spoken words to add additional context to the conversation. Being able to interpret tones and emotional conversational elements enables a business to more fully understand the meaning behind the words, along with the customer's true sentiment. "Feelings like sarcasm or disappointment are important to capture to understand the entirety of the interaction," said Feast.
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There is also the substantial challenge of multilanguage and multidialect support for global companies. Technology advancements continue to address these issues, and improvements in language models are making sentiment analysis increasingly more effective for those whose native language is other than English.
Sentiment analysis is all about getting to know your customers better, empathizing with them, and helping them have a better experience. Khilobochenko suggested that humans experience the world subjectively, and with that comes an emotional response to every interaction. "To ignore this would be missing out on a great opportunity to not only improve your customer experience but connect with them on a human level. This is what creates repeat customers!"
Final Thoughts on Sentiment Analysis
Sentiment analysis allows brands to listen to their customers on a deeper level and understand their feelings, giving a unique and beneficial perspective that can drive improvements across the entire customer journey. Brands that employ sentiment analysis can more effectively build trust, foster loyalty, and improve customer satisfaction through the use of honest communications and interactions.
