- July 12, 2021
- Posted by: Suhith Kumar
- Category: Text Analytics
“People will forget what you said. People will forget what you did. But people will never forget how you made them feel.”
It is a famous quote of American author and poet Maya Angelou which applies to many aspects of our lives and, also, means a lot for businesses.
A McKinsey study illustrates what the quote means for businesses. A positive customer experience resulted in more than 85 percent of customers purchasing more from a brand. On the flip side, a negative experience resulted in more than 70 percent of them purchasing less.
In essence, brands cannot afford to get customer experience wrong.
But how exactly can businesses know what makes their customers recognize as excellent service?
The answer is sentiment analysis.
Thanks to social media platforms, ecommerce websites and online review portals, brands now have the opportunity to interact closely with their customers, understand customer sentiment and incorporate the feedback into their product and service offerings for better customer experience.
The internet has made it possible for customers to share their opinion, write a product review or discuss their recent online shopping experience on forums. All of which is valuable data for companies to tap into to understand their strengths, weaknesses, and market gaps.
What Is Sentiment Analysis?
It is the automated process of analyzing text to identify the expressed sentiment, which may be positive, negative or neutral.
Sentiment analysis tool is powered by machine learning (ML) and converts unstructured data—which comprises text, images and videos—into meaningful information.
Language deconstruction techniques such as part-of-speech tagging, tokenization, lemmatization and parsing help break down text to understand the meaning (or sentiment) conveyed.
Automated text analysis can be applied to any source of text, social media posts, live chats, survey responses and more. All of which is useful information for brands, helping them glean actionable insights and make data-driven business decisions.
Sentiment analysis-powered insights can aid in business transformation and accelerate growth. It can pertain to negative sentiment about product pricing, features, return policy and more. It also applies to areas of business where the customer sentiment is positive.
Let us take the below statement for example.
“It is irritating when the customer support keeps saying your request has been processed but I cannot see any progress with my request. This is poor customer support.”
The topic is “customer support”, with the sentiment clearly being negative based on words such as “poor”, “irritating”, et cetera.
Sentiment analysis solution mainly helps businesses in the following ways:
- Gain competitive advantage
- Attract and retain customers
- Increase sales
- Minimize customer servicing
- Improve marketing messaging and campaigns
Use Cases Of Sentiment Analysis
Sentiment analysis can be applied to various business functions. In this article, we will look at five major use cases.
Social media posts often reflect brand sentiment. The sentiment can pertain to products, services and even businesses themselves, as buyers today increasingly share their opinion with the world.
Every second, around 6000 tweets are being published, and a vast majority of them mention businesses. Regardless of their domain (finance, retail, tech, health and life sciences, or others), it is possible for businesses to receive plenty of feedback on their social channels.
Given the volume of all the social data, manually analyzing them is not only time-consuming but often inaccurate. ML-powered sentiment analysis software can help brands make sense of all their social data in minutes, analyzing all the emotions and public sentiment conveyed on Twitter, Facebook and other forums.
Another key aspect of sentiment analysis is that it helps track customer feedback 24/7 and in real-time. This enables businesses to quickly respond to any negative sentiment circulating on social media and boost brand image in case of positive mentions.
Along with brand monitoring, reputation management is one of the main use cases of sentiment analysis.
Sentiment analysis tools help brands stay on top of reviews, comments, and other types of online customer feedback and allow to monitor progress over time.
Common sources include news stories, forums, blog posts and social media comments about a brand. Using sentiment analysis tools, companies can convert the data into actionable information.
With the power of machine learning, brands can also keep track of trends, pre-empt outcomes and, thus, be proactive rather than reactive.
Voice of Customer (VoC)
Sentiment analysis enables brands to structure and categorize VoC data to detect patterns and uncover recurring topics and issues.
For example, by leveraging customer survey data, brands have the option to provide free promotions and services to entice unhappy or dissatisfied customers. On the other hand, happy customers could be offered rewards, so that they spread the word about the brand and its services and products.
Listening to customers and learning how best to communicate and connect with them will help brands to offer a personalized customer experience.
Sentiment analysis can help brands gauge public sentiment about a new product feature or a new product soon after it is launched. It also enables them to analyze historic feedback from buyers.
Keywords of specific product features such as UI, UX and more can be searched for and then sentiment analysis tools and models can be trained to uncover only the necessary information.
Whether a company wants to know its product is being perceived by the public or which elements of a product need to be enhanced, sentiment analysis helps gain better results than when it is done manually because artificial intelligence (AI) does not tinker with the results and is objective.
Competitor And Market Research
Market and competitor research is another prominent use case of sentiment analysis, enabling companies to identify those competitors who are trending and potentially analyze how each of their marketing efforts stack up. It is also possible to gain a granular view of every aspect of a brand and its competition’s customers and prospects.
This type of research provides the basis for identifying the strengths and weaknesses of a brand and how they compare with the market.
As the use cases indicate, sentiment analysis provides the foundation for brands to listen to and understand their customer’s pain points. That, in turn, helps businesses to improve their products and services to not only achieve customer satisfaction but also become a customer-centric brand.
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