Expectations are high when it comes to customer service and experience. That’s why brands are turning to automated sentiment analysis for help.
Automated sentiment analysis can provide actionable insights to brands, allowing them to better understand how customers feel when they encounter pain points along the customer journey or when they have positive, emotionally satisfying experiences.
How are brands today using sentiment analysis to improve the customer experience? What are the challenges of using sentiment analysis? What does the process of using sentiment analysis involve? What tools are available for sentiment analysis?
“Understanding and acting on customer sentiment is critical to acquiring and retaining customers,” said Dave Mingle, VP and head of CX at Reputation, an online reputation management platform. “Brands today are using sentiment analysis to identify areas of the customer experience that are either working well or need to improve, so they can make efficient, targeted changes that will help attract and retain customers.”
Using Sentiment Analysis With Artificial Intelligence
Sentiment analysis can automatically detect emotions and opinions by classifying the customer’s text as positive, negative or neutral through the use of artificial intelligence (AI), natural language processing (NLP) and machine learning (ML). Brands can use sentiment analysis to parse through and derive insights from customer feedback, reviews, social media posts and survey results.
One such tool, shown above, is MonkeyLearn. With it, brands can upload their data sets in the form of spreadsheets, or they can connect their data via native integrations, an SQL connection or the MonkeyLearn API.
Related Article: NLP and Text Analytics Enhance VoC Programs, Boost CX Engagement
Using Sentiment Analysis and Social Listening Together
Many brands use sentiment analysis alongside social listening (i.e., social sentiment analysis) to discover how their customers truly feel about them, their products and their services.
Social sentiment analysis looks at all of a brand’s mentions, feedback, reviews and comments in an effort to understand the emotions behind them. The end result is a much clearer picture of the positive, negative and neutral opinions of customers.
Like the majority of sentiment analysis tools, idiomatic’s online sentiment analysis uses AI to delve deeper into posts on social media, customer support tickets, app reviews and customer feedback to obtain genuine voice of the customer (VoC) insights. Other social listening platforms, such as Oktopost, also use AI along with social media monitoring and sentiment and context analysis to enable brands to stay on top of social conversations that may impact brand perception and trust.
Using NLP- and ML-Based Sentiment Analysis in Voice and Chatbots
Pieter Buteneers, director of engineering in ML and AI at Sinch, an SMS messaging, voice, video and verification API provider, spoke with CMSWire about the use of natural language programming (NLP) for sentiment analysis.
NLP is a branch of AI that allows computer applications to understand, write and speak languages similar to the way that humans do. It also facilitates a deeper understanding of customer sentiment. When NLP is incorporated into chatbots and voicebots it permits them to have seemingly human-like language proficiency and adjust their tones during conversations, Buteneers explained.
“NLP also enables chatbots to detect sentiment,” he added, “so if a customer is upset, for example, the bot can adjust its tone to diffuse the situation while moving along the conversation. This would be an intuitive shift for a human, but bots that aren’t equipped with NLP sentiment analysis could miss the subtle cues of human sentiment in the conversation, and risk damaging the customer relationship.”
According to Buteneers, breakthroughs in NLP are making an enormous difference in how AI understands input from humans. “For example, NLP can be used to perform textual sentiment analysis, which can decipher the polarity of sentiments in text.”
Sourabh Gupta, CEO and co-founder of Skit.ai, an augmented voice intelligence platform, told CMSWire that it’s highly challenging to capture a customer’s state of mind and emotions correctly. However, with emotion analysis and paralinguistics, Skit.ai’s automated voice AI platform can capture intent, emotions and urgency, as well as the environment from which a call is made. As such, brands can better understand customer pain points and further refine their products and offerings.
“Skit.ai’s Voice AI agent has been highly successful at automating a significant portion of total customer support calls and augmenting CX because of its advanced machine learning models that have been developed with an exceptional effort from the engineering team,” said Gupta. “Not only do ML models learn at an exceptional pace, but they are also evolving every day to process and capture emotions better.”
Along with voice and chatbots, sentiment analysis is being used in platforms such as Grammarly Business, a writing and editing tool that uses AI-driven sentiment analysis for tone detection and brand tone profiles to boost productivity, confidence and connection with customers and employees.
Related Article: 3 Ways AI Strengthens the Customer-Brand Relationship
The Challenges of Sentiment Analysis
Because of the difficulty of understanding the nuances of both the spoken and written word, it can be challenging for sentiment analysis to correctly determine when a customer is being serious or sarcastic. Consider the following statement:
“I just love it when an appointment takes four hours!”
It may be obvious to the reader that the statement above is sarcastic, since most people don’t enjoy waiting hours for an appointment. To a software program, however, it could be ambiguous.
Similarly, how does the tone of this statement feel?
“I particularly enjoy receiving a package that has obviously been crushed during shipping.”
Unless the receiver of the package actually enjoys the process of contacting customer service and potentially getting a replacement, it’s very likely that the customer was being sarcastic. Again, this type of statement is very difficult for a sentiment analysis platform to determine if the tone is positive or negative.
Carlos Garcia Jurado Suarez, applied AI engineering director at Outreach, a sales execution platform provider, spoke with CMSWire about the challenges of sentiment analysis. Suarez said that natural language is complex, nuanced and varied and that the language used for expressing sentiment varies significantly in formal and informal settings.
“Most of the work on sentiment analysis in industry has been focused on text, which lacks information carried in non-verbal mechanisms, such as facial expressions, posture and intonation,” said Suarez. “Certain language constructs, such as negation or sarcasm, can be hard to understand even by humans. There are further challenges around the differences across languages and cultures that influence human expression.”
The wide variety and amount of disparate and siloed data sources can also be an impediment to effectively analyzing customer sentiment. “Analyzing and applying sentiment data from customer feedback platforms can be daunting due to the large volumes of data many organizations deal with,” explained Mingle. “One challenge, for example, is that many companies collect and store feedback data in different spaces. Public feedback may live on one platform, while private feedback is stored elsewhere.”
It’s important that brands employ a software platform capable of unifying data from both public and private sources, along with data in siloed departments. “Customer feedback platforms like Reputation are able to conduct sentiment analysis from both public and private data sources,” said Mingle, “giving CX practitioners a 360-degree view of how their brand is perceived in the market.”
Consumer expectations have grown over the past few years, and customers expect brands to resolve any issues they have in a quick and painless manner. Still, many brands struggle to determine what customers like and dislike about their products and services.
By using sentiment analysis, along with AI, NLP and ML, brands can analyze customer sentiments from a variety of mediums — feedback, reviews, social media posts and surveys — and obtain actionable insights they can use to remove customer pain points while improving the customer experience.