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An essential step that must be taken while trying to build a great customer experience is to develop an understanding of customer sentiment. Only when you have a clear idea of how your customers feel about your brand can you go ahead and devise effective strategies to elevate CX.
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In today’s article, we will learn about an invaluable tool that can be leveraged by businesses to analyse cosmic amounts of customer feedback in real-time to understand customer sentiment and take measures to enhance customer experience.
Sentiment analysis, or opinion mining, involves the analysis of qualitative data to determine the underlying sentiment of a piece of text. It is a kind of technology that leverages natural language processing (NLP) and artificial intelligence (AI) to categorize the emotional tone of a piece of unstructured data.
Sentiment analysis is used by businesses to guage the social sentiment of their brand and to gather real time insights on trends and customer pain points. The insights gathered help companies create more effective marketing campaigns and also help improve the overall customer experience they provide.
A sentiment analysis tools refers to a kind of AI-driven software that has the ability to analyze large amounts of open text to identify the underlying emotion. This technology has become an invaluable tool for many successful businesses to analyze feedback and understand customer emotions.
Sentiment analysis tools utilize highly advanced technology to gather information from a range of different channels so that companies can receive insights on customer sentiment. Whether the feedback is written in response to a campaign, a specific touchpoint, or toward the brand as a whole, sentiment analysis tools are able to collect it and extract insights from it. It has the capability to detect the tone, emotion, and urgency of a piece of open text so that customer service agents know which customers to prioritize so as to avoid churn.
There are many different ways in which sentiment analysis tools can gather insights from customer data. Let’s take a look at some common types of sentiment analysis:
This is a branch of sentiment analysis that aims to identify the emotion reflected in a piece of text. It can go beyond categorizing ‘positive’ and ‘negative’ sentiments and can differentiate between emotions such as anger, frustration, and sadness.
Multilingual sentiment analysis tools can identify the sentiment within open text in a range of different languages (without the need for translation). This is a valuable tool that allows organizations to combat language barriers and extract valuable insights from their data regardless of which language it is in.
Aspect-based sentiment analysis, or ABSA, is a technique of text analysis that categorizes customer feedback by aspect (or attribute) and then identified the sentiment attached to each individual aspect. For instance, attributes of a mobile phone could include affordability and design. Sentiment analysis conducted on customer feedback in regard to the phone can then be categorized based on these different aspects as people may have varying opinions on the affordability of the product and its design.
Fine-grained sentiment analysis is used to identify the key topic of a sentiment. It is applied on a sub-sentence level by breaking a sentence down into multiple phrases before analyzing the connection of these different parts. This is a useful tool when trying to understand how customers evaluate your product, and also helps you identify which aspects or features of the product are most spoken about.
Most modern sentiment analysis tools are driven by artificial intelligence and machine learning. So how exactly are these machines able to decipher human emotion and sentiment? Let’s take a look.
When organizations receive customer feedback in the form of unstructured data, sentiment analysis tools leverage Natural Language Processing (NLP) to transform the text into a language that machines can understand and analyze. There are many different NLP techniques that are used to transform the data including stemming, lemmatization, and named-entity recognition.
Once the text is translated using these techniques, the machine can then leverage various machine learning algorithms to make classifications and find trends within the data. With the help of AI and ML, the machine is able to identify key patterns within the data and also create predictive models. AI-driven sentiment analysis tools use training data to create accurate machine learning algorithms rather than relying on programmed instructions that would need to be manually inputted.
As a result of the enhancements in AI and ML technology, sentiment analysis tools are now faster, more accurate, and have a wider range of applications. Certain sentiment analysis tools are even able to differentiate genuine customer feedback from sarcastic comments!
Businesses have colossal amounts of unstructured data just sitting idle because it is too complex or vast to be analysed. However, with the right technology, businesses can leverage all this customer data to their advantage.
Sentiment analysis has become a pivotal tool to many large and small corporations as it has enabled them to eliminate the need for manual open text analysis and to gather actionable insights from cosmic amounts of customer feedback in real-time.
Sentiment analysis tools provide companies with the means to efficiently identify sentiment so that they can get a deeper understanding of their customers’ emotions and sentiment as a result of their experiences with the company.
Sentiment analysis tools are advantageous to business in many ways, including:
Voxco Intelligence’s Sentiment Analytics Tool uses advanced AI-based sentiment analysis to analyze endless lines of customer feedback in real time.
Translate large amounts of unstructured data into relevant actionable insights that can be used to improve CX, reduce customer churn, and boost CLV.
Identify any existing customer pain points within the customer journey as soon as they occur using real time trends analysis. Take proactive action to eliminate issues and boost satisfaction.
Read more about our Powerful Sentiment Analytics Tool
Sentiment analysis refers to the contextual mining of data to identify the underlying sentiment in a piece of text. It uses NLP (natural language processing) and artificial intelligence to determine whether a piece of feedback is positive, negative, or neutral.
A sentiment analysis tool is a type of AI-driven software that can identify the sentiment reflected in a piece of open text in real-time. This type of technology is often used by businesses that want to gather actionable insights from unstructured customer data.
Sentiment analysis tools help organizations automate the process of analyzing survey responses and facilitate the analysis of vast amounts of open text in a matter of seconds.
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