How Customer Sentiment Analysis Can Influence Your Business
Let’s start this blog post with the powerful and very well known quote by the great Maya Angelou that perfectly explains why people feel so passionately positive about some brands and so intensely negative about others: “People will forget what you said, people will forget what you did, but people will never forget how you made them feel.”
Accordingly, whenever a customer engages with your company by leaving any customer feedback—whether leaving a positive review about your latest feature on your website or a brief complaint on your Facebook page—there’s always an underlying emotion, or sentiment, behind their sincere feedback.
With customer sentiment analysis, you can seize this personal information to understand your customers better. Which product features do they like the most? Why are they feeling so negative about your customer service? Who are those particular customers? What products are your audience most hyped about?
After all, businesses worldwide are trying hard to evoke positive emotions for a reason: positive feelings are strongly correlated with profits. Customer sentiment analysis can considerably impact your business by accurately interpreting the customer’s feelings, sentiments, and interests. Simply put, customer sentiment analysis is the process that companies utilize to evaluate what customers feel about their brand, products, and services and understand their expectations.
So if you want to take your business to the next level, relying on customer sentiment analysis is a surefire way to identify challenges and resolve them before they can become destructive. In this blog post, we’ll exclusively focus on explaining what customer sentiment analysis is and how it can positively influence your business if you play your cards right.
Defining Customer Sentiment Analysis
Customer sentiment analysis, also known as opinion mining or emotion AI, is the automated process of detecting customers’ feelings and emotions to uncover how customers feel about your product, brand, or service. This metric can empower businesses to spot positive, negative, and neutral emotions about their offerings accurately and take necessary measures to address those areas.
Through the millions of opinions and comments regularly posted on online surveys, social media chatter, videos, and whatnot, by utilizing customer sentiment analysis, companies can gain insights and know precisely what’s expected of them to act accordingly.
There are many ways to explore customer sentiment analysis, including data mining, which processes the results and extracts public opinions out of the online text. For instance, high engagement rates on social media platforms are good because likes and comments can drive the company’s popularity and reach a broader audience. Nevertheless, if the majority of comments or mentions are negative, that means something completely different.
By understanding your customers better, you can put your company into a better position to fix problems, provide the proper response and solutions to correct misunderstandings, solve particular issues, provide more features, and improve user engagement.
But, how does customer sentiment analysis work to analyze online product reviews, tweets, and social media posts, as a way to track customer opinions and ultimately improve customer service and experience? Below, we’ll look at the entire process and how sophisticated technologies make this automated method of processing information possible.
How Does Customer Sentiment Analysis Work?
Client sentiment analysis, or opinion mining, works thanks to two of the essential branches of artificial intelligence technology—natural language processing and machine learning. Basically, this information processing method uses NLP and ML algorithms to determine the emotional tone behind online conversations automatically.
Different algorithms can be implemented for your customer sentiment analysis depending on how much data you need to analyze and how precise you need the model to be. However, all sentiment analysis algorithms fall into one of the three main models:
- The rule-based model: these models automatically perform customer sentiment analysis based on a set of manually crafted rules.
- The automatic model: the model that solely relies on machine learning techniques to learn from data.
- The hybrid model: is the one that uses both rule-based and automated algorithm approaches.
Typically, a rule-based model utilizes a set of human-crafted rules to help identify polarity, subjectivity, or the subject of an opinion. These rules include numerous natural language processing techniques developed in computational linguistics including stemming, tokenization, part-of-speech tagging, parsing, and lexicons.
Here’s how a rule-based customer sentiment analysis model works. First, it defines two lists of polarized words (e.g. positive words like best, good, beautiful, etc., and negative ones like ugly, bad, worst, and so on). Afterward, the model counts the number of positive and negative words that appear in a given text. If the number of positive words is bigger than the number of negative ones then the system returns a positive sentiment and vice versa. And if the numbers are even, the model will return a neutral sentiment regarding the text.
Contrary to the rule-based model, the automatic one doesn’t rely on manually crafted rules but machine learning techniques. Each client sentiment analysis task is typically modeled as a classification problem, whereby a classifier is fed a text and returns a category, whether positive, negative, or neutral.
For instance, Y Meadows’ software utilizes customer sentiment analysis to classify customer queries and requests within a broad range of emotional magnitudes based on a scale of 0-100. Besides, it possesses the ability to classify messages across a range of intensities, so if a dissatisfied customer threatens that they’ll quit your service via email, the automated machine learning-powered sentiment analysis model will determine whether the client is angry, upset, or even outraged.
With the help of this customer sentiment analysis model, your customer service agents will be able to adequately handle all situations with both satisfied and disgruntled clients by having an accurate assessment of their feelings before successfully meeting their needs and desires.
The hybrid model combines the desirable elements of both rule-based and automatic techniques into one system.
How The Implementation Of Customer Sentiment Analysis Can Positively Influence Your Business?
As you can tell, with the help of machine learning and natural language processing, customer sentiment analysis can quickly turn unstructured customer data into meaningful information. With the aid of algorithms, such unstructured data gathered from social media platforms, reviews, chatbots, and survey responses can become potent reservoirs of intelligence that can guide focused and strategic change for the overall improvement of your business’s customer experience and value delivery. In other words, the insights gained from client sentiment analysis can help your company to bring real change and transformation in all critical areas.
These can be areas that generate the most negative sentiment like price, product features, customer service, or return policies, or company spheres that stakeholders are most interested in, like net banking, price match, and so on.
Nevertheless, here’s how the implementation of customer sentiment analysis can positively influence your business and why it’s so important.
It Empowers Better Customer Service
Top-level customer service is extremely important for the profitability of your business. After all, one in every three customers is likely to leave your business behind after just one bad customer experience.
For that reason, using customer service feedback for customer sentiment analysis can be exceptionally useful for your business. It offers valuable insights into why, when, and how customers have negative emotions when they encounter your company so that you can eliminate the factors that contribute to such experiences.
For example, by running sentiment analysis on survey responses, in which clients will rate your customer service, you can discover what makes customers feel content and what frustrates them regarding your service. You might find out that negative comments often relate to long response times, suggesting that you start automating response processes by utilizing Y Meadows’ AI-based platform to set up fully automated workflows and resolve routine customer support tickets without delay.
You’ll Put Yourself In The Position To Offer Improved Products And Services
Client sentiment analysis can be of assistance so that you gain insights about new markets, foresee industry trends, and most importantly, understand what didn’t go well with previous product releases to help you improve your existing and future products and services.
Additionally, performing customer sentiment analysis on social media posts, reviews, surveys, and more can shed light on issues like:
- Bugs that need to be fixed, as you might notice an unexpected flow in negative notes mentioning bugs.
- Scope for improvement, as clients might often say particular features of your product in a negative connotation.
- Specific customer needs, as you might receive negative comments about poor response times or missing features.
Gaining Winning Insights To Optimize Your Marketing Strategies
Businesses can leverage customer sentiment analysis to gain powerful insights to optimize their marketing strategies. For instance, the marketing department in your company can keep an eye on the latest industry trends by analyzing emotions towards new products or product features on social media.
Maybe your fierce competitor has released a new product recently, and you want to discern any weaknesses related to their release so that you can address these problems before releasing your own product. Conversely, you can also utilize customer sentiment analysis to determine your competitor’s strengths and target to emulate them in your marketing efforts.
Another utilization of this process of detecting customer’s feelings is to segment them successfully. By targeting clients based on how they feel about your business, you can create hyper-personalized experiences and leverage the gathered behavioral data to tailor products, services, and experiences.
Businesses can grow only when they genuinely understand the people buying their products and services. This is a momentous task because human experience comes with a broad spectrum of complicated emotions and interactions.
Nowadays, artificial intelligence gives us the capability to delve deep into gathering and segregate these emotions and create a threshold for using this emotional intelligence as a benchmark for maintaining company reputation, optimizing marketing campaigns, and fine-tuning your new product launches.
To conclude, customer sentiment analysis can entirely transform your business for the better by helping you get a grasp of the problems with your products or services on a granular level and help you resolve them one by one.
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