Human beings possess emotions, affecting every facet of our daily lives. This includes feedback on products and services as customers. Emotions play a crucial role in customer experience & drive purchase behavior. Customer sentiment analysis is a comprehensive tool that helps you understand the emotions of customers or people hidden behind the language.
This can either be in the form of an appreciation email, a grievance expressed on a social media platform, a testimonial, or any other product/service-related communication.
Understanding customer sentiments is critical for any brand looking to improve customer satisfaction (CSAT Score). But how do you execute customer sentiment analysis? Let’s find out.
Customer sentiment analysis is used to gauge customer emotions expressed via online/social communication. It will give you an idea of how your customers perceive your brand, products, and services. This data can help you take adequate measures to improve customer satisfaction.
The advent of technology, like Natural Language Processing (NLP), has made it easy to extract relevant insights from customer communication available in textual form. These tools use sentiment analysis models and algorithms to identify patterns. Once identified, the analysis system will automatically categorize sentiment data as positive, negative, or neutral.
Doing this task manually can lead to several issues. In addition to being tedious, it will also be prone to human errors. Furthermore, it could lead to incorrect tagging which may not solve your problem. NLP tools help you overcome all of these problems.
Customer sentiment analysis comprises of trained algorithms that allow you to:
Manually measuring these elements may not always be possible. Call centers and customer support apps also use customer sentiment analysis. Let’s now look at how the entire process works:
Marketing teams collect information as background data to understand and analyze customers’ sentiments better. The data can be in customer reviews, interaction on social media, or email communication. Social Media Sentiment analysis helps gauge the audience’s interest levels in a particular topic. Similarly, email communication data can be leveraged to identify answers from objective-style feedback or questionnaire-type communication.
Customers can leave product ratings and reviews on a variety of social platforms such as Google Play, Amazon, and others. These reviews provide an excellent source of information about product data, which can be used to analyze customer sentiment. Using the customer sentiment analysis model, you can analyze your competitor’s product reviews from various online platforms, run them, and determine what didn’t work.
Customer sentiment analysis helps organizations gauge consumers’ interest levels and craft targeted campaigns. Besides enabling the creation of targeted campaigns, audience insights also help marketers make necessary adjustments to their future product launches. They can do away with things that no longer work, avoiding loss of resources and business.
Successful marketing campaigns deliver high ROI. Customer sentiment analysis makes that possible by understanding customer experience. Data-driven insights help plan campaigns and increase the chances of success.
A successful brand needs to maintain a good reputation in the market. That becomes possible when you have insights into how your customers feel about your business. You can ultimately craft engaging communication material that resonates well with your customers.
Support teams can use customer sentiment analysis tools to check audience satisfaction levels. The data helps them tailor their responses to satisfy the customers.
Here are the various types of customer sentiment analysis and how they work:
Product reviews make for excellent customer communication. It helps organizations understand how customers feel about a product and its features. If they are dissatisfied, you can work to enhance their experience.
We built an Aspect-based Sentiment Analysis model at Gramener to help enterprises analyze client attitudes regarding the quality of a product.
It helps you understand the action that led to the review or feedback from the customer. It enables support teams to solve customer issues. For example, when they receive a query about finishing the KYC process on the platform, it can identify the source of the problem. The support team can then respond accordingly.
You can detect the emotions behind customer responses. These often are emotions of happiness, anger, or frustration. One area that you need to look for is certain words that can have more than one meaning. For example, a customer may use the word unbelievable or out of the world.
Now, both these words have negative and positive meanings. Emotion analytics is increasingly relying on emoticons, which are distinctive facial expressions that represent specific emotions.
A popular type of customer sentiment analysis, it understands the tone of customer communication. Most of the communication from customers can be subjective. You can eliminate subjectivity by categorizing the details and understanding customer sentiment.
For example, if a customer mentions, “I love how the XYZ feature works,” the sentiment is positive. “The software is tough to use and time-consuming” reflects negativity. It calls for an improvement in the product offering.
It involves a deeper analysis of customer communication to understand their sentiment. Whatever the feedback is, you can classify it into very positive, positive, neutral, negative, and very negative with the help of analysis tools.
Following is a step-by-step guide of how you can get started with customer sentiment analysis:
Data forms the backbone of the customer sentiment analysis process. You will need it to train the NLP model and to test it. Gathering data is easy as several sources are available.
You can conduct online surveys using Google Forms and get feedback about your products and services. It is easy to export responses in CSV and XSL files for analysis. Product reviews are the next bit of customer communication, which you can collect with the help of web scraper tools.
Social media websites are another crucial source of customer data. Twitter, Instagram, and Facebook are rich repositories of your brand reviews. You can go through mentions, hashtags, and other keyword searches to gather customer opinions about your product reviews. You can use related APIs to pull data for analysis.
Collected data must be cleaned & analyzed. Open-source tools like OpenRefine help you clean the data and prepare it for further analysis. It is a crucial step in the process as it will allow you to get accurate insights.
Once you organize the data and remove redundant information, the final step is the analysis. ML models allow you to divide data into various categories. These include positive, negative, and neutral sentiments. Positive sentiments are associated with company promoters, negative sentiments with company detractors, and neutral sentiments with passive customers. Let’s understand this process with an example.
To calculate the Net Promoter Score subtract the number of Detractors from the number of Promoters. To illustrate, if 50% of your respondents are Promoters and 30% are Detractors, your NPS is 50 – 30 = 20.
As sentiment analysis continues to evolve, new trends are reshaping the landscape, making it even more insightful and efficient. One of the most exciting trends is the integration of advanced AI models like ChatGPT into sentiment analysis workflows. ChatGPT, powered by cutting-edge natural language processing (NLP), can decipher complex emotions and subtle nuances in customer feedback. Its ability to engage in natural conversations allows businesses to gain deeper insights into customer sentiments.
Additionally, real-time analysis is becoming pivotal in understanding immediate customer reactions. Social media monitoring tools equipped with AI algorithms enable businesses to track customer sentiments in real-time, providing instant feedback for agile decision-making.
Moreover, the rise of emotion AI, a technology that detects and responds to human emotions, is transforming sentiment analysis. By employing facial recognition and voice analysis, emotion AI offers a deeper understanding of customer feelings, supplementing text-based analysis methods.
Incorporating these new trends, especially integrating AI models like ChatGPT, can revolutionize your approach to sentiment analysis. By staying updated with these advancements, businesses can enhance customer experiences and build stronger relationships with their audience.
Find out what Net Promoter Score (NPS) is and how to calculate it. In this guide, we share a Machine Learning Model that can automate text analysis and improve NPS Scores.
Here are the various advantages of customer sentiment analysis:
When you conduct customer sentiment analysis on social media posts, surveys, and other customer communication, it will help you identify flaws. There can be some bugs that you can fix to improve the customer experience. Furthermore, there could be suggestions from customers that you can implement to improve the user experience.
Offering top-notch customer support is critical to retaining your existing users. It does not take much for customers to switch to alternate service providers if customer support isn’t adequate. When your users rate your services, it will help you identify their satisfaction and dissatisfaction levels. If there are issues related to query response times, you can fix them to improve customer experience and reduce customer churn.
A successful marketing campaign involves targeting the right users and meeting their needs. By analyzing past customer communication, marketers can incorporate features that delight the users.
It will also help you segment your target audience into various categories for a more personalized experience. For example, certain users may like a feature more than anything else. You can specifically create a campaign targeting those users for improved chances of success.
You can track the brand mentions online in real-time to understand the audience sentiment. If there are issues with certain aspects of your service, you can introduce quick fixes to overcome the problem. Negative brand stories can harm your brand online, making it crucial for you to perform sentiment analysis regularly.
The audience’s perception of your brand may increase or decrease with time. For example, it could be at its peak when you launch your product. However, consistent negative user experience can lead to a dip in maybe a month or two. You can track this shift to understand what is wrong and take corrective steps.
The following is a list of tools you can use to perform customer sentiment analysis:
Gramex is a low-code platform used to build enterprise-grade solutions that are driven by AI and ML. It has the capability to build applications in days and not weeks by using over 200 microservice elements and configuration-driven functions.
Gramex can be integrated with any in-house enterprise software such as Power BI and more to extract valuable insights from data.
Find out more about Gramex and how it works to solve complex enterprise problems with Machine Learning and Advanced Analytics techniques.
MonkeyLearn comes with a range of text analysis tools to gauge customer sentiments. You can also integrate it with Google Sheets and Zendesk. The platform also offers an API that you can use to connect with other sentiment analysis tools.
The sentiment analysis model from IBM Watson comes with several advanced tools, including the Project Debater. It helps you understand idioms that often have multiple words with different meanings. It also offers custom options that hold better chances of delivering accurate results.
You can use Lexalytics sentiment analysis tool with the cloud. It is an ideal option for those looking for an on-premise solution. You can customize the NLP sentiment analysis system of the platform based on your needs. Besides text analysis, the platform also offers the scope for data visualization.
Amazon Comprehend is ideal for analyzing product reviews by customers. It uses deep learning algorithms that use attributes and scoring mechanisms for evaluation. It comes with a list of APIs that enable you to perform sentiment analysis from phrases, languages, and named entities. You can also conduct topic modeling with the help of this tool.
Aylien is ideal for conducting aspect-based sentiment analysis. You can understand the severity of any news related to your brand in real-time with this tool. You can create your models for analysis even without advanced knowledge of NLP or machine learning.
Google Cloud NLP uses machine learning to understand the structure and meaning of the text. We see its application already in Google’s search results. When you know what information Google considers relevant, you can create content for your marketing efforts to resonate with your audience.
You can perform multilingual sentiment analysis with the help of Meaning Cloud tool. It uses aspect-based sentiment analysis to categorize positive, negative, or neutral data. You can also create a dictionary of words that are relevant to your brand for better detection and analysis.
Here are various sources through which you can gather customer feedback data:
One of the best ways to train machine learning models to understand customer sentiments is to use data. You can also gather customer feedback after the product launch, resolve their queries, or finish the onboarding process. It is crucial to ask for reviews towards the end of the chat session as it will help you get accurate insights.
The feedback will help you segregate customers into categories like promoters, detractors, and passive users, among others. You can then focus on the promoter category that will eventually help you grow your business. Similarly, you can create a strategy to engage the other two groups of your audience.
Negative sentiments also help you identify the problematic areas faced by your customers. Once you fix those issues, you can gather feedback from your customers again to check if there is an increase in their satisfaction levels with using the product or service.
Platforms such as Facebook, Instagram, and Twitter are rich sources of brand-related feedback from customers. People are usually more vocal on such platforms to share their experiences with their audience.
You can use the related APIs offered by each social media platform to gather feedback data. Besides collecting real-time information, you can also collect past brand mentions and hashtags with the help of APIs.
You can create detailed surveys to gather extensive customer data based on their experience of using your product or service. Whether it is for new or long-time customers, surveys are ideal for everyone. They will help you gather varied feedback to understand what has worked effectively for you and what did not.
A negative experience about a particular feature is an opportunity for improvement. If something is working in the right direction, you can capitalize on that to attract new customers.
Multiple sources like Amazon, App Store, and Google Play Store allow you to collect customer reviews and ratings. The data is handy to understand the hidden sentiments behind those words. You can also collect review data of your competitor to check what worked for them and what did not.
After launching a new feature or product, make sure you gather feedback instantly. It will help you understand the customer sentiment and refine the product or service for a better customer experience.
Chatbots are ideal for freeing your resources from time-consuming and laborious tasks. They are of having intelligent conversations with customers. When combined with NLP and ML, chatbots can respond accordingly to negative or positive customer sentiments.
When integrated with your website, chatbots can help you gather data on customer sentiments. You can use this data to prepare your ML model for customer sentiment analysis. You can also use the data to train your chatbots for future communication.
Market research helps you unveil the latest trends and understand customer preferences. It is critical, especially when you are entering new markets. You can eliminate uncertainties and make data-driven decisions to overcome the possibilities of failure.
Furthermore, you can also collect competitor data and conduct sentiment analysis to understand their weak links. You can then capitalize on those points to acquire new customers by offering a better experience.
Customer interaction data is vital for any business looking to scale its operations. Whether your customer has just finished the onboarding process or has been around for a long time, asking for their feedback is crucial. It will help you understand what your customers think about you and why.
There are plenty of data sources for customer interactions – live chat data, email communication, product reviews, social media conversations, etc. All you need is the right customer sentiment analysis tool to get started.
Our proprietary platform, Gramex, can help you create tools to understand your customers better. We make the best use of all the advanced ML and NLP algorithms to ensure that you get the most relevant insights to grow your business rapidly.
Contact us for custom-built low-code data and AI solutions for your business challenges and check out unstructured data analytics solutions built for our clients, including Fortune 500 companies. Book a free demo right now.
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