Everyone is a customer to endless public and private entities. Finding out what consumers think, feel, and want in every interaction has always been the motivation for improving the customer experience (CX). The goal being to obtain their trust and allegiance to products or services. Customer sentiment analysis in 2021 is the best means of mining the mountains of customer speech and text data for the nuance of emotion and context within.
eCommerce growth during the pandemic has increased the importance of sentiment analytics. As a result, brands have increased their focus on identifying customer pain points to improve CX. As face-to-face customer communication wains, customer support, marketing, and sales work to define customer sentiment analysis meaning in a post-pandemic new normal.
Successful brands have always leveraged emotional intelligence to communicate with care, honesty, and empathy to build trust. While this goes to the heart of customer sentiment analysis, businesses still search for better ways to understand and use it for optimal customer communication in the digital age.
What is Customer Sentiment Analysis?
Customer sentiment analysis is essentially collecting, analyzing, and acting on customer feedback. It has become, “a powerful way to keep a pulse on customers and even the larger population to recognize changing needs and anxieties, and new moments of truth,” according to Bain & Company.
Sentiment analysis is the automated process to analyze text and speech to interpret the sentiments behind it. But delivering an accurate understanding of individual or group sentiment requires lots of real-world data.
Businesses can mine this data from customer text and/or voice interactions using machine learning (ML) algorithms for natural language processing (NLP), which are forms of artificial intelligence (AI). The sentiment analysis algorithm helps determine whether the data received expresses a positive, negative, or neutral tone.
Businesses must strictly define and adhere to data mining parameters. They should base these parameters on objectives, such as positive brand experiences or best resolution for customer support interactions. This sets the stage for understanding how sentiment analysis works.
How Does Sentiment Analysis work?
Sentiment analysis algorithms are trained to identify nouns, verbs, adjectives, and adverbs in text, or voice, and their associated positive or negative emotional indicators. This common form of sentiment analysis has definitive limitations when it comes to the ability to discern emotions in customer text or speech.
In customer support and brand building, the goal is to have maximum insight into a customer’s current emotional state. This enables businesses, brands, and customer service agents to tailor their communication and customer support to be engaging and helpful. They can then create positive customer experiences that build brand loyalty for services and products.
The most advanced customer sentiment analysis applications can parse the nuanced emotional sentiment of the customer, which is the key for improving human-to-human customer support outcomes. The most advanced sentiment analysis tools are based on augmented intelligence, referred to as intelligent automation (IA) with a focus on conversational intelligence.
Beyond AI to Augmented and Conversational Intelligence
While AI has the lofty goal of mimicking human intelligence and reasoning, the success of many AI-driven applications are limited to highly repetitive tasks in closed systems. IA-based sentiment analysis tools are based on human and machine collaboration. In this approach, human control leverages the sentiment analysis algorithm for better interactions and outcomes.
IA sentiment analysis still uses ML for NLP to gather the sentiments from text and conversational data directly. Conversation intelligence software is a form of sentiment analysis for analyzing customer and prospect conversations.
The analysis of that data yields customer sentiment metrics for real-time, actionable insights for marketing and customer support teams to deliver better customer service. The most advanced methods of sentiment analysis customer service yield countless benefits to businesses and customers.
Customer Sentiment Analysis Benefits
Today’s customer sentiment analysis tools must mine and define the emotion behind customer engagement, which requires interpreting tone, context, and feeling from customer interactions. This real-time analytics can reduce customer service caller handle time by 40 percent and boost service to sales conversions by 50 percent, according to McKinsey & Company.
By tapping into customer emotions during customer/call center interactions, for example, customer support agents get more qualitative insights and guide positive interactions to:
- Determine customer sentiment in real time
- Gauge the mood and emotions of customer attitudes to make better customer-centric CX decisions
- Ensure fast and positive customer pain point resolution
- Predict and prevent communication roadblocks
- Reduce agent and customer churn
- Increase customer loyalty, retention, and advocacy
- Improve products and services
- Personalize and enhance customer interactions
Training a sentiment analysis algorithm is challenging because of sarcasm, enthusiasm, frustration, anger, and other nuances and context of human emotion in speech. The key to success in identifying and differentiating this nuance and context of speech is to:
- Start training the ML algorithm with the largest possible data sets
- Design the sentiment analysis algorithm to learn from new and incoming data
This opens a much broader landscape of use opportunity for customer sentiment analysis that can affect different areas of the business that include:
- Customer Service via customer support and contact centers
- Guiding product and service marketing campaigns
- Identifying and solving product or service issues
- R&D and innovation
- Brand building
Identifying ideal customer interactions for reproduction across all human-to-human interactions can change the game for call, customer support, and service centers for every business in the public and private sector.
The resulting customer sentiment score breakdown reveals repeated phrases and interactions that anticipate positive and negative interaction trajectories. Customer support agents, marketers, and sales teams can then change or improve that trajectory in real time. This shows how consistently checking the customer pulse through sentiment analysis can lead to benefit for humans across the entire socio-economic landscape.
The Present and Future of Customer Sentiment Analysis
Not all types of sentiment analysis can accurately interpret the human emotions in speech and text. Voice assistants like Siri and Alexa, along with chatbots, are perfect examples of human-to-machine sentiment analysis that are improving, but fall short of true emotional understanding and empathy.
The COVID-19 pandemic has given rise to the broader use of chatbots, which rely on AI to increase their ability to resolve customer issues via text. But chatbots are still only trusted to handle the easy customer interactions, such as product info, small purchases, and simple requests. These are deemed to have minimal circumstantial weight on customer finance, health, or significant needs. This often comes down to chatbots lacking an ability to engender:
- Customer control
- Genuine responsive empathy
Most chatbots and forms of customer sentiment analysis that take humans out of the loop cannot parse the nuances of emotions in speech and text to respond empathetically and change the course of negative interactions. The latest forms of sentiment analysis applications driven by sophisticated sentiment analysis algorithms and augmented intelligence excel in this area.
These advanced sentiment analysis tools make the most of human control and very advanced algorithms that can parse emotional sentiment in speech to improve customer interactions. The software-based algorithms provide human agents in customer support with greater insights into sentiment at times when humans may miss the subtle changes due to fatigue, inexperience, or workload.
The algorithms can provide practical insights into differences in the nuanced and contextual tone and emotion of customer speech. They base these insights on defined interaction cues using sentiment analysis AI to extract common and unique identifiers.
Attaining actionable insights that drive successful benefits of customer sentiment analysis will always require gathering the right data. They must build this data gathering and cleansing on precise interaction parameters for text or voice in human-to-machine and human-to-human interactions.
This is the foundation of greater personalization that enables businesses and brands to achieve sustainable growth and customer loyalty. A focus on personalized messaging can help businesses achieve as much as a 16-percent increase in commercial outcomes over businesses that don’t, according to a Gartner study.
The pursuit of personalized messaging via sentiment analysis and augmented intelligence has led to a wide variety of customer sentiment analysis tools that:
- Aggregate mentions across social media
- Find sentiment in traditional media like print, online, broadcast, cable, and other sources via a multichannel approach
- Focus on brand management and performance by scanning for mentions and brand images like logos and other brand associated artwork across multiple media channels
- Use APIs with search engines, websites, and custom applications to grow database and continuously learn from user activity across a vast range of sources
The importance of sentiment analysis customer experience has never loomed larger than in the post-pandemic digital age. But businesses must have the next generation of sentiment analysis applications that can understand customer emotions in real-time interactions. This is the only way to ensure a positive CX that builds a foundation of brand loyalty and sector competitiveness.
Reaching that goal requires every business to leverage the most advanced forms of customer sentiment analysis to capture valuable, actionable customer insights. Only then can businesses improve customer communication, experience, and all aspects of the brand.