Top Customer Sentiment Analysis Best Practices

Traditional rotary phone-based customer service is a thing of the past. Today, customer support technologies have evolved from simply connecting humans and technology to using artificial intelligence (AI) to understand, decipher, and analyze customer sentiment in the span of the entire customer journey. 

And while sentiment analysis has been around for a long time, generative AI is now revolutionizing the field. With Gen AI, businesses configure the analysis of positive and negative customer sentiments in a manner that transcends basic numerical scores. This technology interprets the words, context, and underlying sentiments, offering a more accurate understanding of customer conversations. 

For organizations, this data is a golden opportunity. The customer is king, after all. So, how do you leverage generative AI in customer sentiment analysis to improve your business? Let’s find out.

What is sentiment analysis?

Customer sentiment analysis uses natural language processing (NLP), computational linguistics, and artificial intelligence to identify, extract, quantify, and study affective states and subjective information in customer feedback. This practice encompasses various data sources — customer calls, chats, tickets, reviews, and survey responses.

In fact, Bain & Company reports that sentiment analysis technology adoption is increasing, with its usage rising from 54% in 2020 to 80% in 2023. Sentiment analysis provides deep insights into customers' feelings about a product or service. This metric measures an important aspect of the customer experience (CX), and the emotions are broadly categorized as positive, negative, or neutral. 

The NTT Data 2023 Global Customer Experience Report reveals that 83% of organizations view CX as a weak point affecting their business. By enabling organizations to proactively identify and address negative sentiments, you enhance customer experience.

🙇Discover the top 6 CX misconceptions and how to avoid them.

Effective sentiment analysis best practices

Follow these seven customer sentiment analysis best practices for improved CX: 

1. Leverage Gen AI for advanced sentiment analysis

Generative AI models, built on complex neural networks and trained on vast datasets, are adept at processing diverse text sources. Converting this text data into structured tags and filtering out irrelevant information allows for precise sentiment categorization​​. 

Start with defining the problem scope, choosing the suitable base model, and deciding how to use it for specific data sets. This lifecycle includes prompt engineering, fine-tuning, aligning with human feedback, model evaluation, optimization, deployment, scaling, and application integration​​.

Consider a company that uses a generative AI tool to analyze customer conversations. Finely tuned to understand industry-specific jargon and nuances, the tool classifies sentiments in these conversations. Refining the model, the AI analyzes this customer data succinctly to grasp the overall sentiment.

🧑‍💻 Learn how generative AI transforms customer experience (CX).

2. Assess customer sentiment across every channel in real-time 

Real-time customer sentiment analysis allows businesses to capture the immediate reactions of customers, offering an up-to-the-minute understanding of their good and bad experiences and emotions. AI-powered customer sentiment analysis tools instantly process large volumes of data from various sources, providing a comprehensive and timely overview of customer satisfaction.

Harvard Business Review reports that only 15% of companies actively maintain a unified view of customer data and effectively leverage those insights. Embracing an omnichannel system keeps essential interaction data accessible to the customer success team. This approach enables the team to quickly identify and prioritize high-priority queries, ensuring that you address urgent or negative sentiments promptly.

Practical text sentiment analysis requires omnichannel capture and ensures you don't miss critical feedback and can respond swiftly to customer needs and trends. Implement a system that analyzes the sentiment of every customer interaction across all communication channels. By understanding sentiment in real-time, companies better engage with customers. 

3. Customize AI sentiment configurations for your business needs

One of the strengths of generative AI is its adaptability. Tailor your AI model to understand what positive, neutral, and negative sentiments mean for your business context. You can also configure it to focus on different parts of customer interactions, such as the initial complaint or the final feedback. Train the model on your specific business lexicon and customer interaction scenarios. 

Measuring changes in sentiment across individual interactions not only tracks the fluctuations in customer emotions but also forecasts behavior and needs, enabling proactive responses to issues. For instance, if a customer starts a conversation with frustration but ends with a positive sentiment, this transition helps to understand the effectiveness of the support provided.

Centralizing feedback from different channels into a single platform is another critical aspect. This centralization allows for a comprehensive view of customer sentiment, making identifying trends, patterns, and areas of concern easier. 

4. Further employ Gen AI to prioritize emerging issues

Beyond advanced sentiment analysis, Generative AI can also be used for summarizing the main drivers of both positive and negative sentiment using the context of each conversation. For instance, if a new product release leads to a surge in customer queries, generative AI quickly identifies and categorizes the specific bugs driving the most negative sentiment. If it detects a significant increase in negative sentiment, perhaps due to a widespread issue with the product, it alerts the support team to prioritize this issue.

This approach allows for prioritizing issues that could significantly impact customer satisfaction and loyalty. Regularly review the AI-generated sentiment reports to promptly identify and address these key issues.

5. Include sentiment analysis when measuring VoC

Traditional voice of customer (VoC) practices rely heavily on quantitative data like ratings or numerical feedback. However, this data alone doesn't fully capture the nuances of customer opinions and emotions, so incorporating text sentiment analysis into VoC metrics is vital. 

Leveraging Gen AI with customer sentiment analysis tools adds a qualitative layer, offering deeper insights into the "why" behind the scores and feedback, revealing what customers truly feel about your product or service​​. For instance, customers' responses about their likes and dislikes vary depending on the questions asked. Recognizing sarcasm and irony also requires a deep understanding of context, which AI-driven customer sentiment analysis tools can provide​​.

Whether refining a service offering or enhancing customer service approaches, use the nuanced understanding provided by sentiment analysis to make data-driven decisions that resonate with your customer base. Integrating sentiment analysis into VoC measurement empowers businesses to move beyond surface-level metrics. This integration enables a nuanced understanding of customer sentiment, leading to better customer experience and business decisions.

6. Leverage negative agent sentiment analysis for AutoQA

Beyond customer feedback, gen AI evaluates the sentiment in agent interactions. This analysis reveals stress, confusion, or dissatisfaction in agents' responses, which might indicate areas needing improvement or additional support. This approach provides insights into the overall quality of customer service and identifies areas where agents might need additional training or support. 

Implementing AI in this context means setting up a system to analyze agent-customer interactions' emotional tone and content. For instance, a call center might use AI to detect signs of agent stress or burnout, prompting timely interventions such as additional training, support, or changes in workflow. Integrate this sentiment analysis into your quality assurance processes to continuously improve the customer service experience.

7. Regularly update AI models to ensure data quality 

AI models, especially those used for sentiment analysis, are not static and require regular updates to maintain accuracy and relevance. This is because language, expressions, and how people communicate their sentiments evolve. In sentiment analysis, it's important to balance the quantitative data provided by AI models with qualitative insights to get a complete picture of customer experience. 

The maintenance of AI models for sentiment analysis is a continuous process that involves updating the data or the model itself and refining the approaches and techniques used to analyze and interpret the data. This ongoing process helps keep the AI aligned with the latest language use and customer interaction patterns, thereby ensuring the effectiveness and relevance of sentiment analysis in understanding and responding to customer feedback.

8. Perform audits to drive improvements in configuration

Businesses should regularly audit customer sentiment analysis processes to implement these practices effectively. It's essential to conduct regular audits of sentiment analysis tools. Unlike traditional machine learning models, Gen AI doesn't require continuous training with new data. Instead, AI prompting and configuration improvements can be driven through these audits. They help identify any emergent issues or opportunities for refining AI performance. 

While sentiment analysis is a powerful tool, it should be part of a broader strategy that includes other metrics and qualitative insights. It's important to balance quantitative sentiment data with qualitative feedback for a complete picture of customer experience. Educate your team on the role of sentiment analysis and how to integrate it with other forms of customer data to inform decision-making and strategy.

Understand customer sentiments better with Echo AI

As businesses increasingly recognize the value of understanding and responding to customer emotions, AI-driven conversation intelligence solutions like Echo AI provide an efficient way to gather and measure customer sentiments. 

Designed for business leaders to implement the voice of the customer best practices, Echo AI leverages AI-native technologies to provide quick, accurate, cost-effective solutions for monitoring customer behavior and feedback​​.

Our platform uses AI and automation to streamline service operations, enabling real-time collection and processing of customer feedback. This capability ensures effective customer sentiment analysis.