Using sentiment analysis to gauge customer feedback and measure customer satisfaction

Creating a positive customer experience (CX) has become a necessity for consumer-facing businesses, but how can organisations assess and manage their CX journeys?

One of the most effective methods for understanding – and responding to – customer needs is known as customer sentiment analysis, which uses sentiment analysis tools to unlock valuable customer insights and optimise customer experiences in order to drive business success.

What is sentiment analysis?

Sentiment analysis can determine the emotional tone of a piece of text by using artificial intelligence and machine learning algorithms such as natural language processing (NLP). It typically works by classifying the sentiment expressed in a piece of text as a positive, negative, or neutral sentiment. 

“Today, companies have large volumes of text data like emails, customer support chat transcripts, social media comments, and reviews,” explains Amazon Web Services (AWS) in an article about sentiment analysis. “Sentiment analysis tools can scan this text to automatically determine the author’s attitude towards a topic.”

Also known as opinion mining, sentiment analysis technology has diverse applications including uses in customer service, marketing campaigns, and social media monitoring.

What is customer sentiment analysis?

Customer sentiment analysis is a specialised branch of sentiment analysis that focuses on understanding and interpreting the feelings and opinions expressed specifically by an organisation’s customers. It delves into the wealth of textual data generated by customer interactions – such as online reviews, open-source forums, third-party product reviews and review sites, and feedback forms – in a process known as text mining, all of which allows organisations to better understand the overall sentiment of their customer base.

How is customer sentiment measured and analysed?

Customer sentiment can be measured with various techniques and tools, although arguably the most useful are the NLP algorithms that sift through massive datasets of customer feedback and customer reviews to extract sentiments and generate sentiment scores.

These tools use lexicons, dictionaries, and machine learning classifiers to determine the emotional tone of the text, and analysis can be performed in real time, allowing companies to promptly respond to customer concerns and feedback as they emerge. 

Scales and metrics used to measure customer satisfaction

Customer satisfaction has multiple angles, so it’s difficult to distil into a single metric, and many businesses will use a variety of scales to gauge customer satisfaction. Examples include: 

• Net promoter score (NPS). An NPS measures the likelihood of customers recommending a company’s products or services.
• Customer satisfaction (CSAT) score. CSAT scores directly assess the satisfaction level of customers with a specific interaction, service, or product.
• Churn rate. A churn rate indicates the percentage of customers who stop using a product or service over a given period. This score is often used to gauge customer dissatisfaction.
• Sentiment score. A sentiment score is a value assigned to reflect the sentiments expressed in customer data, such as customer feedback. This value might be a number ranging from 1–5, or even 1–100, but generally helps businesses to quantify their customers’ emotions.

The benefits of customer sentiment analysis

Implementing customer sentiment analysis offers businesses a number of advantages. For example, it enables:

• Proactive customer support. By identifying and addressing real-time negative sentiments, customer support teams can resolve issues before they escalate.
• Enhanced customer experiences. Understanding customers’ emotions and needs enables businesses to better tailor their products and services, creating a more personalised – and satisfying – customer experience.
• Improved brand reputation management. Monitoring customer sentiments allows companies to protect and enhance their brand reputations by showcasing and highlighting positive feedback – such as those demonstrating customer loyalty – as well as by promptly and publicly addressing concerns.
• Informed decision-making. Sentiment analysis provides valuable insights and market research that aids in strategic decision-making, such as when businesses are developing new products, refining their marketing strategies, or assessing their pricing models.

Challenges in customer sentiment analysis

Like any tool rooted in technology, customer sentiment analysis offers several benefits, but it can also present some challenges. For example, there can be be issues around:

• Ambiguity in language. Human language is often ambiguous, making it challenging to accurately interpret the subtle nuances of people’s speech or their opinions as customers during text analysis.
• Contextual understanding. Context can be difficult for machines to parse, but understanding context is crucial for accurate sentiment analysis as the same words may have different meanings based on their context.
• Multilingual analysis. Business providers catering to diverse markets may face challenges in accurately analysing sentiments expressed in different languages.
• Difficulties detecting irony and sarcasm. The use of irony and sarcasm in customer feedback can lead to misinterpretations, impacting the accuracy of sentiment analysis results.

The future of customer sentiment analysis

The evolution of sentiment analysis technology similarly expands the possibilities for customer sentiment analysis – and customer journeys.

There are the obvious examples, such as the benefit of ongoing advancements in artificial intelligence and machine learning that will in turn enhance the accuracy and efficiency of sentiment analysis algorithms.

There are also implications in areas such as:

• Integration with customer support. Seamless integration of sentiment analytics tools within wider customer support systems, as well as further automation in the technology, will enable better responses and issue resolution.
• Emotion recognition. Sentiment analysis tools are likely to increasingly incorporate emotion recognition capabilities, such as those offered through computer vision technology, to provide a deeper understanding of customers’ emotions.
• Augmented reality (AR) in customer feedback. AR technologies may be employed to gather real-time customer opinions through immersive customer experiences, providing richer data for sentiment analysis.

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