How Can Sentiment Analysis Be Used to Improve Customer Experience?

How can we really know what our customers want? Thinking beyond the basic rule of supply and demand and looking at the specifics, what is it that makes consumers choose one brand over another? How do we get to the bottom of what factors nudge a potential buyer away from one brand and towards another? What areas/stages of the business – customer relationship have the most impact on customer satisfaction?

The difficulty lies not in a lack of resources here – be it via online review platforms, Facebook, X, etc. - the consumer public benefits from easy access to a library of consumer experiences and testimonials and an ability to share their own experiences with huge audiences in the time it takes to make a cup of coffee.

It’s the abundance of information which poses the problem for businesses looking to learn from consumer feedback. Burning through hundreds of man-hours sifting through reviews, mentions, and customer service logs is a highly inefficient approach to tapping into the consumer mindset.

A shift in perception is necessary. What if we could instead refine the customer feedback into relevant data points that are easier to digest and map out the broad picture? This is where the concept of sentiment analysis steps in.

Sentiment analysis, less often referred to as opinion mining, is a data interpretation tool that uses AI and machine learning to draw actionable conclusions from large pools of information. Specifically, sentiment analysis aims to determine customers' feelings towards a brand in general, its products, and its various services. 

How can customer sentiment analysis be used to improve customer experience and your business prospects, and what are the ways in which your brand can get the most out of it? Let’s start with the first question that comes to mind.

• What Is Sentiment Analysis?
• Why Is Understanding Customer Sentiment So Important?
• Why Is Sentiment Analysis Crucial To The Customer Experience?
• Sentiment Analysis Techniques And Tools
• How Does Sentiment Analysis Assist Your Customer Support?
• Our Unique Offering In Sentiment Analysis

What Is Sentiment Analysis?

In layman’s terms, sentiment analysis refers to the process of scanning a source of data with the aim of categorizing the sentiment that underlines it into ‘positive’, ‘negative’, or ‘neutral’. The purpose here is to order seemingly ‘unstructured’ data from sources such as online reviews into useful indicators of consumer feeling.

Basic ‘lexical’ sentiment analysis uses existing rules such as dictionary definitions that identify predetermined ‘positive’ and ‘negative’ words and phrases. For example: “The service was excellent”, “Terrible experience, I will never shop here again”. 

In keeping with the broader developmental trend, AI and machine learning is at the cutting edge of this field. AI-powered sentiment analysis allows the process to be carried out at much greater efficiency and scale, using algorithms ‘trained’ on existing datasets of human dialogue to inform its decision-making. The subtleties and nuances of human language usage present the main limitation to its capacity.

Human expression is ambiguous and often full of implied, indirect meaning that is dependent on context and subjective interpretation. Identifying these complex intricacies of human speech is incredibly difficult without human annotation of the source text. To address this, a hybrid approach can be used to maintain accurate text classification and make more complex algorithmic approaches easier to interpret and use. 

Why Is Understanding Customer Sentiment So Important?

A clear picture of consumer sentiment is advantageous in several key areas of business. From actionable market insights gained from customer feedback analysis that can be applied to customer service and product innovation to real-time data that will guide reputation management. The carefully curated image that your brand presents to the world isn’t necessarily going to align with that actual consumer perception, so you need to take measures to understand the latter to avoid the risk of walking blindly into PR crises borne of ignorance about what your public actually wants.

What is customer sentiment analysis? More than just a way to keep your finger on the pulse of market trends and consumer perceptions, sifting large volumes of data will return insights into crucial areas for customer satisfaction, product performance, and brand perception. Many businesses put sentiment analysis tools to work as a means of streamlining and optimizing their operations based on actual consumer opinions and feedback.

Continuously monitoring consumer sentiment keeps a business in the loop. Your company will get the heads up on any issues that your customers are experiencing and thereby be well-positioned to take action to mitigate them before they grow into wider, less manageable problems. For example, if there's a sudden uptick in negative sentiment related to customer service waiting times, then it will be quite clear that a call center restructuring is needed to address the issue before unresponsiveness becomes synonymous with your brand name.

Why Is Sentiment Analysis Crucial To The Customer Experience?

Sentiment analysis is not just a reactive measure; it has a powerful constructive use as a strategic tool that informs customer experience initiatives that are based on reliable, grassroots data. This way of parsing vast amounts of customer dialogue to arrive at an understanding of customer emotions associated with your brand, its products, and how it interacts with its customers gives a significantly wider scope to your brand’s efforts to improve the customer experience, and through that, a major competitive advantage.

Customer satisfaction with your brand is intertwined with how well it listens to, understands, and addresses customer needs. In determining what these needs are, sentiment analysis equips your business with the capacity to accurately tailor responses to customer questions that reflect their emotional state, settling matters with less conflict and leading to many more positive interactions.

Reputation management

When a single social media post can go viral and the ensuing fallout have a ruinous impact on your brand’s reputation, sentiment analysis serves a vital function as an early warning system. By making you aware of patterns of poor sentiment in customer feedback, often in the areas you would least expect, your brand will have time to seal up the cracks before the problem spreads among your customer base.

Sentiment Analysis Techniques And Tools

Text-based sentiment analysis

Text analysis draws from, for example, online consumer reviews, Facebook posts, customer service logs, and consumer surveys to determine sentiment. The process is based on natural language processing (NLP) techniques that reduce the text to its constituent parts (words, phrases, sentences). Commonly, this method follows what’s referred to as a ‘lexicon’ based approach, in which words and phrases in the source texts are cross-referenced with a dictionary of words and phrases that have been classified as ‘positive’, ‘negative’, or ‘neutral’. 

It’s a rather simple concept – words such as ‘poor’, ‘rude’, and ‘unhelpful’ will fall into the negative category, whereas ‘friendly’, ‘excellent’ and so on will be processed as positive. 

AI-based tools such as IBM Watson, TextBlob, and Google Cloud Natural Language utilize machine learning to carry out sentiment analysis at a much greater scale. They can be quite accurate in identifying subtleties such as tone, context, and implied meaning, like sarcasm.

The speed at which algorithm-supported text-based analysis is its main advantage, and this advantage proves particularly useful for brands that seek to actively monitor their online mentions across a multitude of social media platforms. 

Speech-based sentiment analysis

Of course, not all customer interactions are text-based. Customer service call logs and video chats can also be analyzed for sentiment using speech recognition-based methods that apply NLP approaches and are trained on the non-verbal aspects of human emotion conveyed through speech, such as tone and emphasis. This is very useful in uncovering the real emotions underlying the words, the unspoken feeling which the words do not reveal. For example, a customer may say, “ok, that will do,” indicating a neutral or positive sentiment to a text-only-based analysis - whereas a speech-based technique could associate the tone of voice with a negative sentiment like disappointment or resignation.

Speech analytics tools, including Microsoft Azure Speech and Nuance, can provide immeasurable value to brands and their customer service teams as they vastly upgrade the ability of an automated customer service bot to recognize a caller’s emotional state, triage and decide when to pass the case to a human agent.

How Does Sentiment Analysis Assist Your Customer Support?

With the correct application, sentiment analysis can significantly enhance the capabilities of customer service teams. The conclusions informed by sentiment analysis can direct staff training so that it focuses on the most common complaints or scenarios that lead to customer dissatisfaction and resolves those issues.

As mentioned above, it also speeds up responses by allowing AI chatbots to automatically triage and quickly assign incoming calls based on the emotions being expressed by the customer, as identified by sentiment analysis.

Another boost to support teams is the ability that sentiment analysis has to identify the common issues and stumbling blocks customers face. Be they inefficiencies due to a poorly thought-out returns policy or difficulty in navigating your UI by collating data from across thousands of unique interactions? Sentiment analysis will bring these sticking points to the attention of your team, allowing them to tackle them in good time and in a systematic fashion.

Sentiment analysis in practice

Netflix employs sentiment analysis to evaluate viewer reviews and social media feedback related to its in-house productions. This enables Netflix to continuously monitor the popularity of and reception to its series and movies. 

If analysis suggests that a given genre or theme is enjoyed more by a particular audience or demographic, Netflix can then use this data to prioritize investment in similar content aimed at a certain group. Conversely, less well-received productions can be reworked to reflect the findings of sentiment analysis, or even cancelled if reception is particularly poor.

This AI-derived insight most likely plays a major role in guiding strategy decisions for future content productions.

Study platform Coursera utilizes sentiment analysis to sift reviews and comments on its courses to determine which are delivering the best outcomes and value to its users and those which are falling short.

The data gleaned from sentiment analysis is referred to update course content and guide the development of future study courses. For example, if an analysis finds that many users of a particular course find it prohibitively challenging or unclear in its task assignments, Coursera can make adjustments or overhaul the course content. 

Our Unique Offering In Sentiment Analysis

Here at PissedConsumer, we’re already putting the power of sentiment analysis to work in helping businesses uplift their customer experience. We’ve put together a range of services aimed specifically at helping your brand get the very best from this transformative technology.

Refined training datasets

“Where do I start?” is the common conundrum companies find themselves in when making the decision to implement sentiment analysis. Well, the first step is the data itself, and for the outcomes to be effective, you’re going to need a lot of it. We can provide your brand with the right datasets from our comprehensive library covering a huge range of industries and customer interaction settings. These datasets are also invaluable for companies that already employ sentiment analysis and need to expand or improve their current models.

Custom sentiment analysis reports

We assemble custom sentiment analysis reports that return in-depth insights into customer sentiment across a multitude of the largest and most relevant channels, including all major social media, PissedConsumer, and other leading review platforms. In these reports, you will find a comprehensive analysis of:

• Overall customer sentiment. Are your customers generally satisfied, neutral, or dissatisfied with the product or service?
• The most common themes and issues raised by customers. What recurring topics or problems do your customers mention most frequently?
• The root causes of customer dissatisfaction. What specific factors are driving negative feedback or complaints?
• Key strengths of the product or service. What positive aspects or features do your customers frequently praise?
• Customer service sentiment analysis and evaluation. What feedback do customers provide specifically about their interactions with your customer service?
• Areas for improvement. Based on the feedback, what are the most critical areas that need immediate attention?
• Recommendations. What specific steps should you take to address the issues identified in the report? 

We’re confident that our sentiment analysis reports will equip your brand with a unique and unparalleled understanding of your customer’s needs, perfectly positioning you to capitalize using a consumer-centric strategy. 

Sentiment Analysis And The Future Of Customer Experience

AI and machine learning tech are weaving their way into every aspect of commerce; hence, we can be sure that sentiment analysis will be an essential tool of customer experience management going forward. Brands that wield this technology will be better equipped to foresee market changes, better prepared to meet and exceed customer expectations, and have all the cards in place to maximize customer satisfaction.