What Is Sentiment Analytics? 3 Examples for CX Teams

      by Sherod A. Williamson

In today's highly competitive marketplace, businesses must leverage all the data at their disposal to understand their customers and stay ahead of the competition. But understanding the customer is easier said than done. It requires a solution with pinpoint accuracy in detecting, analyzing, and interpreting customer sentiments from a wide range of data sources. While humans are better equipped to understand natural language, analyzing customers’ sentiments accurately from tons of structured and unstructured data is a challenge for manual processing and traditional data analytics.  

This is where sentiment analytics come in handy.  

What Is Sentiment Analytics?  

In theory, sentiment analysis is a computational study of customers’ opinions, attitudes, views, and emotions to measure their attitudes towards a company, product, or service. Traditional sentiment analysis leverages simple methods like dictionary referencing, keyboard processing, and rule-based algorithms.   

The reality of human interaction and linguistics means that these pre-trained models often fail to get close to human levels of understanding. The rigid rules of these models work means that scores may be off or important signals may be missed. These models also face linguistic hurdles like sarcasm, negations, and word ambiguity that can trip up even the most well-spoken humanoids. You’re not going to confuse people and AI when you don’t speak clearly. (Crystal clear, right?)  

On the other hand, sentiment analytics use natural language processing (NLP) to detect and analyze customer intent with much higher levels of accuracy. Advanced AI-powered sentiment analytics platforms like Stratifyd combine NLP with machine learning (ML), predictive analysis, topic modeling, and other methods to analyze structured and unstructured data from surveys, reviews, CRM, and other sources. It goes beyond traditional sentiment analysis by enhancing understanding of customers’ needs and provides actionable insights for companies to meet these needs.  

With sentiment analytics, companies can identify the most pressing issues and the best strategy. to fix or address the problem to meet customers’ expectations and improve their experience. Analyzing sentiment from all angles is important for companies to improve customer experience (CX).  

Why is Sentiment Analysis Important for CX?

In the world of CX, sentiment analysis is a key brand differentiator. Sentiment analysis brings all the pieces and puzzles together to create a complete CX masterpiece. It enhances understanding of customer intent and what they need or want from a brand, product, or service. To improve CX, companies have to invest in sentiment analysis. Here are two reasons why you should consider investing in sentiment analysis right now:  

  1. Improving NPS Metrics

Net Promoter Score (NPS) is a customer loyalty metric for measuring and evaluating the customer experience. It uses an index score of between -100 and 100 group customers into Detractors or Promoters based on likelihood to recommend the company’s products or services. While the NPS is straightforward, it provides limited insights into the true health of a customer’s experience.  

Many brands rely on NPS as a key indicator of the success of overall CX or particular touchpoints. However, NPS alone is ineffective for predicting customer loyalty in the future or propensity to purchase more from the company. Sentiment analysis can improve NPS metrics. It allows companies to analyze survey responses to understand why a customer is a Promoter or a Detractor. Sentiment analytics solutions like Stratifyd use topic modeling and can categorize responses to highlight popular themes. This provides actionable insights for the company. You can apply the most popular positive sentiment across your company’s operations to convert some detractors into promoters.  

  1. Going beyond Surveys  

Instead of analyzing NPS, CSAT, CES, social media posts, reviews, and other data independently, sentiment analytics combines structured and unstructured data from a wide range of sources. It goes beyond surveys to paint a clearer picture of the customer. Sophisticated sentiment analysis tools like text analytics leverage NLP, ML, and topic modeling to categorize customer feedback and to answer the why and the how of the CX equation. It uncovers trending themes and other actionable insights for you to prioritize issues and fix them sequentially to take your CX to the next level.  

How are CX Teams Using Sentiment Analysis?

With sentiment analysis, CX teams can improve customers' experiences and boost the performance of different departments across their organizations. Luckily, there is no lack of data to analyze and extract actionable insights. According to a survey by McKinsey, companies are using advanced contact center analytics to reduce average handle time by up to 40% and boost their conversion rate on service-to-sales calls by nearly 50%. The survey also found that organizations use internal and customer-facing analytics apps in their contact centers to generate actionable insights to help them improve CX. Here are three examples of how CX teams use sentiment analysis to extract actionable insights hidden in their company’s data.

      Example 1: Identifying Key Themes

CX teams use sentiment analysis to identify key themes in structured and unstructured feedback from data sources like forums, chats, contact center calls, surveys, reviews, and more. Then, they analyze recurring topics to extract key features to attain actionable insights. A CX team of a leading CPG company used this approach and uncovered a recurring topic about a new toilet paper brand. Using sentiment analytics, the team analyzed the recurring topic and identified dissatisfaction as the key theme. Customers were purchasing their toilet paper, but they did not know that it was tubeless. Most customers were dissatisfied and wouldn’t buy it again. The information allowed the company to cancel inventory orders for the tubeless toilet paper and avoid losses.  

      Example 2: Tone of Voice

Using speech analytics, CX teams can extract tone of voice from conversations between customers and representatives to determine their interest in specific products or services. A CX team of an insurance company used AI-powered speech analytics software to determine whether potential customers’ level of interest warranted a follow-up. Person-to-person sales agents followed up only on potential customers who were highly interested in the company’s insurance policies. The insights provided by speech analytics helped the agents improve sales by 50%.  

      Example 3: Uncovering the Most Popular Topics

A CX team of an auto manufacturer used AI-powered Sentiment Analytics with Natural Language Understanding (NLU) and topic modeling to analyze customer feedback. The software helped the company’s CX team to break down the most popular discussions in the feedback into topics. It uncovered “burning smell” as one of the most popular topics among some customers. The manufacturer investigated the issue and found that there was a production error. The CX team used sentiment analytics again to pinpoint the car model affected. Recalling the affected car model only saved the company millions.  

Challenges of Sentiment Analytics

While the process of understanding natural human language is effortless for most people, attaining 100% accuracy using sentiment analytics is still a challenge. Sentiment analysis is undermined by the five defined elements of natural language, including the object, attributes, and the opinion's holder, orientation, and strength. The object refers to products, services, individuals, etc., and their components and properties are the attributes. The opinion holder is the customer expressing the sentiment, and the opinion orientation is whether their opinion is positive, neutral, or negative. Lastly, the opinion strength is the level, scale, or intensity of the sentiment, such as ecstatic or contented. Sentiment analytics must identify each of the five defined elements individually, and then interpret them together to provide full context and actionable insights. Understanding what the other person is talking about is easier for you because you can interpret the implied by combining facial expressions, body language, context, irony, sarcasm, idioms, and other unique factors. These complexities of natural language make it extremely difficult for keyword processing and algorithms to detect sentiment.  

Another challenge is the complexity of training models used for algorithmic text categorization. If you have a working text categorization solution for the call center, you need to retrain each new dataset to analyze feedback from sources like NPS or customer surveys. The lack of transferability and inherent complexity of language make it difficult to guarantee accurate analysis of tone and context in sentiments using algorithms.  

Companies can avoid most of these issues by using AI-powered sentiment analytics.  

How Is Better AI Helping Overcome These Challenges?

In the digital age of social media, the complexity of analyzing natural language captured in tons of unstructured data is overwhelming. With AI-powered sentiment analytics tools like Stratifyd, you can exploit the powers of machine learning to process both structured and unstructured data to extract hidden insights. It is typically easier for AI models with machine learning capabilities to detect linguistic aspects from structured data sources like surveys and unstructured sources like social media listening tools.  

How does it work? Advanced sentiment analytics software uses built-in connectors to pull in data from feedback sources like surveys, reviews, CRM, and social listening tools. Once it ingests all that data, Natural Language Processing transforms human language in the text or recording into something that machines can understand. AI automatically applies syntactic and semantic techniques to understand the structure of the text and identify the meaning of the words in the text. It classifies the text as Positive, Negative, or Neutral making it easier for sentiment analytics to automatically detect emotions and opinions. Smart AI™ ensures business users can leverage the technology on their own and allows them to personalize insights at the speeds they need. It addresses the sentiment analysis challenges facing many companies.  

At Stratifyd, we are dedicated to providing companies with the tools they need to understand their customers better. You can rely on our AI-powered Experience Analytics Platform to streamline both text and speech feedback analysis processes and improve CX.  

One way our customers are using our Speech Analytics functionality is to build AI models that detect customers’ interest in their products or services based on the energy level. Your company can benefit from using Stratifyd’s sentiment analytics solutions, too. The right use case will depend on your unique needs, however. Get a demo today to see how!


About the Author

Sherod A. Williamson is a Sales Engineer at Stratifyd and serves as a critical link between our business and our clients, helping to discover business requirements and articulate technical solutions that meet our clients' and prospects' needs. When he's not doing that, he's practicing Jiu-Jitsu and building super computers for digital currency mining.

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