Sentiment Analysis: Analyze Customer Sentiment in Real-Time

With the changing times, consumer needs and expectations have changed as well. Moving forward with the same tactics and strategies is the biggest vulnerability for businesses today.

In recent times, smart customer support teams have steered their approach in how they engage customers. With personalization as a critical priority, they have adopted new-age solutions like sentiment analysis software to map to the new consumer trends. 

If you and your team members are in the support department, customer sentiment analysis could possibly be the biggest growth driver for you.

So what is sentiment analysis, and why is it important?

In this guide, we’ll discuss all aspects of sentiment analytics and emotion analysis AI – and how it can help your team deliver an exceptional experience to every customer.

What is Sentiment Analysis?

Sentiment analysis is the process of analyzing customer opinions by automatically identifying and extracting subjective information from customer interactions, including calls. 

The system uses artificial intelligence and natural language processing to categorize information in order to determine whether a response is positive, negative, or neutral. 

AI sentiment analysis has been essential for businesses that need to evaluate how their customers feel about a product or service. 

It’s hard for humans to analyze millions of data points. However, an automated sentiment analysis engine helps you gain a deeper understanding of your customer’s wants and needs. You can use this information to improve your product, marketing, and after-sale service.

Sentiment analytics is an integral part of conversation intelligence tools that provides actionable insights from multiple customer feedback channels. A comprehensive solution will give you real-time data on how your customers feel about your product, service, or brand so that you can respond with relevant messaging.

Here is a sentiment analysis example.

If you’re receiving customer calls with complaints, you can analyze the context of their negative sentiment to identify the core problem.

Maybe they are not happy with the product? Maybe they need help in using the product? Maybe the delivery was late?

There could be countless problems. AI sentiment analysis can pinpoint the exact issue, thereby unearthing opportunities for reps to calm the customers, address their problems, and provide higher satisfaction.

Opinion mining through a robust sentiment analytics solution can also be an asset in training your reps. You can use the intel to enhance your team’s performance and productivity. This type of training, on the back of sentiment analysis artificial intelligence, can even be delivered in real time.

7 Benefits of Sentiment Analysis

Sentiment analysis benefits pervade across departments like product, customer, or marketing. Here is a look at the most significant benefits of sentiment analysis.

Improve Customer Experience

Sentiment analysis keeps track of customers’ opinions and thoughts about your brand. This way, you become aware of any problems or issues customers face with your brand and make necessary changes.

Showing that you care about customers with prompt responses goes a long way in keeping customers satisfied.

Improve Product and Service Quality

Sentiment analysis lets you see how customers discuss your products online and what improvements they suggest. It also helps you identify trends and customer preferences.

You can use such insights to improve your product and service quality and reduce customer churn.

Get Multi-lingual Insights

A great thing about sentiment analysis is that you can use it to analyze customers from any geography, ethnicity, or culture. The feature is beneficial for hospitality and e-commerce industries as they generally have a wide variety of customers.

It gives them a core insight into their product popularity and marketing strategy success across backgrounds.

Identify Current Trends

Sentiment analysis mines data from news websites, articles, magazines, video content, and online platforms to extract trends and anticipate market behavior.

Organizations can use the data to speculate, plan, and prepare for any situation. This can include making changes in the supply chain, altering product launches, and planning other marketing and operational function.

Better Crisis Management with Real-Time Sentiment Insights

Sentiment analysis also works in real time. You can use it during live video streams,  live shows, corporate events, seminars, etc., to analyze audience participation and satisfaction.

It also provides live social feeds from platforms such as Facebook, Instagram, or Twitter, which you can use to promptly address negative or wrongful mentions before they blow up and turn into a PR disaster.

Monitor Brand Reputation

Sentiment analysis gives you a peek into your customer brand experience and the general market sentiment regarding your brand.

These insights help you understand what customers expect from you, identify gaps, and get ahead of issues that might harm your brand reputation.

Generate More Leads and Boost Sales

All the above points, such as better customer experience, improved product quality, and data-driven marketing strategy, will help you attract and retain more customers.

Happy customers will recommend you to their friends and family. It will ultimately increase your leads and boost your sales.

9 Tools to Perform Sentiment Analysis

Here’s a look at the nine excellent customer sentiment analysis tools:

JustCall IQ

Just Call IQ is a conversation intelligence tool that comes with Just Call’s premium plan. The tool creates a call transcript, analyzes each call, and identifies the top moments of the conversation.

JustCall IQ tells you whether the customer’s sentiment was positive or negative during the call so that you can measure the success of your pitch. Further, it lets you coach and collaborate with your agent during calls.

Repustate

Repustate is another sentiment analysis tool that analyzes both customer and employee sentiment.

Unlike the above tools, Repustate doesn’t study a conversation in real time. Instead, it analyzes videos, surveys, social media posts, and other existing content to provide insights into customer sentiments about the product or brand.

MonkeyLearn

MonkeyLearn is ready to use customer sentiment analysis tool that integrates with other tools like Zendesk or Google Sheets. It uses a machine learning model to help you find what customers think about you.

It also lets you build a customized analysis model using your business data and criteria.

Lexalytics

Lexalytics is a customer sentiment analysis tool specially built for analyzing text posts and hashtags. It first deconstructs a text into syntax and semantics and then runs sentiment analysis to understand the intent behind the text.

What makes it different is that it also tells you why your customers feel the way they feel.

Social Mention

Social mention is a free social media analysis tool that tells you the kind of buzz you have generated among your audience.

You can search for any keyword on the tool, and it gives you a complete analysis of its performance on various social media platforms.

It will also tell you the overall sentiment associated with the keyword and the amount of positive or negative comments it has garnered.

Brandwatch

Brandwatch is a social listening platform renowned for its accuracy and precision in sentiment analysis capability.

The tool can help businesses monitor their mentions online, understand customer pulse, detect sentiment fluctuations, and measure brand visibility online.

It also has a unique ‘image insight’ feature that tracks how your logo is shared online.

Rosette

Rosette is a suitable customer sentiment analysis tool for global enterprises that want to analyze feedback in multiple languages.

This tool can identify up to 30 languages. This means you won’t have to translate conversations before uploading them for analysis. 

Rosette’s algorithm is mainly trained for tweets, making it an excellent Twitter sentiment analysis.

Sentiment Analyzer

Sentiment Analyzer lets you conduct a sentiment test on any text written in English.

To use this tool, navigate to its site and paste a text you want to analyze in the box. Once you hit analyze, the tool evaluates the text and gives it a sentiment score.

Companies can use this tool to decipher the tone and feeling behind tricky customer responses.

Talkwalker Quick Search

Quick Search from Talkwalker is another tool for analyzing sentiments from social media communications. It can perform real-time social listening and sentiment analysis in 25 languages.

You can use it to evaluate campaign outcomes, discover new trends and get fresh content ideas.

Sentiment Analysis Techniques

While there may be many approaches to sentiment analysis, the three most commonly used sentiment analysis techniques are:

Lexicon Based Approach

The lexicon method, also known as the rule-based approach, relies on manually set parameters for determining sentiment. It counts the number of positive or negative words in a given text to determine the sentiment strength and calculate a sentiment score.

The best feature of the lexicon-based approach is that you can customize its algorithm based on context. For instance, you can set rules to analyze expressions along with individual words.

Since this method does not require implementing advanced algorithms, it is less expensive and easily accessible as a publicly available resource.

However, a downside of this approach is that it usually fails to identify sarcasm, negation, misspellings, or irony.

Automated or Machine Learning Approach

This approach uses machine learning and AI to determine a sentence’s intent. This improves the accuracy of analysis as information is processed on many criteria by machine instead of just a few parameters set manually.

The algorithm for this approach is trained with many sample pieces until it can predict with accuracy. You can also trust this method to detect sarcasm, negation, and irony.

Machine learning methods are widely used because they enable sentiment analysis on a large scale. Moreover, technology is continually improving. Today, deep learning technology can learn patterns through multiple layers of complex unstructured data to perform sentiment analysis.

Hybrid Approach

As the name suggests, the hybrid approach is a combination of lexicon-based and automated sentiment analysis techniques.

 This approach provides the benefits of machine learning along with the flexibility of customization. Thus, it is one of the most efficient and modern approaches to sentiment analysis.

A lexicon-based approach alone may not always give you an accurate analysis. This is especially true in the case of social media sentiment analysis, where language features in platforms like Twitter and Instagram are continually evolving.

As such, adding machine learning capabilities to the lexicon approach could be beneficial for most companies as this hybrid method compensates for the flaws of the individual techniques.

How to Perform Customer Sentiment Analysis?

There are two broad ways to perform customer sentiment analysis: manual and auto (done through AI-powered sentiment analytics). If you’re planning to go the manual route, here’s how you can move ahead.

1. Choose Your Feedback Channels

The first step in customer sentiment analysis is to identify your sources of customer feedback. These can include public platforms such as social media as well as private platforms such as email, chat, and phone. 

You can perform sentiment analysis on textual data from all of these channels. (In our present context, your primary feedback channel would be phone calls. The response of customers calling your support team can be recorded for further analysis).

2. Centralize All the Feedback

Once you have identified the sources for collecting customer feedback, collate them in one place in a single format. 

This way, it’s easier to analyze the data and create reports. 

Now, in addition to data from phone interactions, you should also involve data from CRM.

When was the first time the customer came into contact with your business, when did they purchase the product, and other similar data points can be integrated into your collected feedback to beef up the database for a thorough analysis? Here, integrating your phone system with CRM can be very helpful and efficient.

3. Label the Feedback Based on Sentiments

To create reports and perform useful analysis, you need to categorize your feedback into positive and negative sentiments. 

If a customer says, “I loved your awesome service,” the sentiment here is ‘positive’. Similarly, if another customer says, “I didn’t like the way you handled my request”, this would be considered a negative sentiment. 

By labeling the feedback into positive and negative sentiments, you can understand what your customers are happy about and where are areas for improvement in your company or product.

4.  Create a Sentiment Analysis Report

Once your feedback has been labeled with the sentiment, you can create reports that summarize the results. These reports will give you an overall view of customer sentiment in a number of different areas. 

For example, a simple report might show what percentage of customers had positive experiences in each area. More comprehensive reports can break down customer satisfaction even further by looking at factors like age group and gender.

You can also map the sentiments on a 1-10 scale, with 1 being negative and 10 positive. 

The Problem with Manual Sentiment Analysis

Manual sentiment analysis is a resource-intensive and time-consuming process that requires significant human capital. It’s also very costly. 

The high number of people necessary to execute the tasks means you’ll be paying higher salaries and other overhead costs. This can be particularly detrimental if you’re working with a limited budget. 

The process of manually collecting customer feedback and analyzing sentiment is also not time-efficient.

And while people are excellent at understanding data that comes from customer interactions, they’re still susceptible to things like misreading cues, over-reliance on biases, fatigue, and distraction. 

All this means there’s room for error, which can have huge consequences for your business.

In the end, most importantly, the result of manually-analyzed customer sentiment won’t be comprehensive enough. It won’t give you a thorough picture of what customers really think about your products or services.

This is why it’s recommended to opt for a sentiment analysis solution that automatically records and analyzes your customer interactions.

5 Practical Use Cases of Sentiment Analysis

The following customer sentiment analysis example and use cases will help you understand how to deploy customer sentiment analysis tools for maximum benefit.

Customer Support Management

Managing customer support requests is challenging for any organization that receives a high volume of tickets for varied issues.

Companies can use a customer sentiment analysis tool to automatically process customer support tickets, chats, phone calls, and e-mails. They will read each request’s meaning, emotion, and tone and help prioritize urgent issues.

Product Analysis

Launched a new product? Find out the public reaction to your product with a sentiment analyzer. Discover how your target audience perceives your product.

 You can search keywords for each specific product feature and find out the general sentiment regarding them.

It will also help you identify the features that customers are not happy with so you can plan your product improvement accordingly.

Determining the Most Effective Communication Channel

The key to a successful marketing campaign is identifying which channel works best for your target audience. The sentiment analysis will help you with that.

It will show where you have received the most positive feedback and where the response is negative. You can use the valuable insights to make necessary changes and improve the way you interact with your customers.

Market and Competitor Research

Researching the market and competitors is another worthwhile use case of sentiment analysis.

Find out who is receiving the most positive response from your competitors and compare your marketing efforts.

Analyze what it is about their language that impresses the audience and try to weave it into your brand messaging.

Employee Analysis

Analyzing employee sentiment is as important as customer sentiment analysis. It will help you engage better with employees and increase their productivity.

Use sentiment analysis to evaluate online employee reviews, e-mails, slack messages, and more. Use the insight to discover your employees’ common concerns and find ways to keep them happy.

Pros and Cons of Using a Sentiment Analysis System

Using a sentiment analysis system has many pros, but like any other system, it also has its own challenges.

Pros

Removes Human Bias While Analysing Sentiment

Sentiments can be highly subjects. As humans, how we perceive written and spoken words depends on our experience and personal bias.

Using a sentiment analysis system can remove bias from the process. It will apply uniform criteria to generate more accurate insights than human analysis.

Processes Data At Scale

Sentiment analysis systems can process and analyze huge quantities of data.

For instance, say you want to review customer feedback. Instead of trawling through each feedback, you can feed the entire data into a sentiment analysis system.

It will classify the feedback according to sentiment, so you quickly understand how customers feel about your product or service.

Saves Time with Automation

With a sentiment analysis system, you can analyze hundreds of megabytes of data in a few minutes. It takes off manual labor from the analyzing process with automation.

With this, your team can spend more time on other essential tasks. For instance, you can study the insights from the system and strategize how to act on them.

Provides Real-Time Insights and Analysis

The sentiment analysis system is an automated AI-based system. This means it can give you real-time insight into customer sentiment.

The feature can be extremely helpful in addressing negative comments or mentions on social media. You can fix the issue on priority and prevent the negative perception from escalating.

Helps Increase Sales and Revenue

Empowered with customer sentiment insights, you can improve your product and provide better customer service. You can also formulate marketing messages as per your target audience’s sentiments.

All these will help you increase leads and conversions, ultimately improving your bottom line.

Cons

May Not be Useful for Small Scale Analysis

Sentiment Analysis systems are made for large and numerous data sets. You will get the best value out of this tool only if you need to analyze large quantities of data regularly.

May Not Analyze Contextually

Context is crucial when it comes to identifying sentiment. For instance, the word ‘disappointed’ is negative. However, when used in a sentence like “I was not at all disappointed,” it gives a positive connotation.

You may have to train your system with lots of data until it can capture the necessary context.

May Not Identify Irony & Sarcasm

Humor and sarcasm can be challenging for a machine-learning system to identify. A customer may sarcastically comment, “Thank you for the long waiting time,” which the system may take as positive. Such instances may require manual correction.

How Can You Analyze Customer Sentiment by Monitoring Different Channels?

Here are a few channels you can collect data from for customer sentiment analysis:

Live Chats

Gathering data through live chats can be very effective in customer sentiment analysis. You can identify scenarios that lead to positive sentiments and the pain points customers frequently face.

Ask for customer feedback on multiple touchpoints for accurate analysis.

Social Media

Customers tend to be vocal about products and services they like or dislike on social platforms such as Facebook, Twitter, or Instagram. They can be great channels to collect data on positive or negative mentions of your brand.

Online Surveys

If you’re looking to collect extensive customer data, online surveys could be a great place. They can give you an in-depth understanding of which features helped you gain customer loyalty and which led to increased churn rate.

Product Reviews and Ratings

When you launch a new product, customers will likely give feedback online through reviews and ratings. Collect these data for customer sentiment analysis to identify areas of improvement on your product.

Market Research

Businesses must continually monitor changes in the market and trends to create marketing messages that resonate with their customers. Hence, sentiment analysis must be done of market trends as well. It will help remove uncertainties, especially if you’re looking to expand.

Leverage AI to Automate Sentiment Analysis

Customer sentiment analysis AI is a new-age solution used by customer support teams at top contact centers around the world. 

Alongside providing deep analysis of customer interactions, these tools also ensure automation and other advanced capabilities that assure support reps of enhanced productivity and performance.

Often sentiment analytics come bundled with conversation intelligence, a software that uses AI, NLP, and machine learning to drive detailed insights from customer interactions.

Conversation intelligence solutions like JustCall IQ come packed with cutting-edge features, which include real-time transcription, AI scoring, moment analysis, coaching insights, and live agent assistance. 

From analyzing customer sentiments to providing real-time assistance to reps – conversation intelligence can add great value to your existing customer service efforts, directly impacting important metrics like CSAT and NPS.

Recommended Read: What is Conversation Intelligence? (Features, Benefits)

So, add to your tech stack the power of conversation intelligence – which also packs sentiment analysis on conversational texts and calls – and level up your customer support game. 

Artificial intelligence is redefining traditional practices and methodologies to pave the way for automation, optimization, and cost-saving. Make sure your customer support team is keeping up with this trend, successfully tapping on the limitless possibilities that AI brings through solutions like sentiment analytics.

Customer Sentiment Analysis Simplified With JustCall IQ

JustCall IQ is one of the most prominent conversation intelligence solutions that comes bundled in JustCall’s premium plan. JustCall is a popular cloud phone system that’s used by thousands of sales and customer support teams around the world.

JustCall IQ comes with AI sentiment analysis that ramps up not just the customer services strategies but also elevates internal training modules. With AI opinion mining, managers can guide their reps, eliminate their weaknesses and double down on their strengths.

Sign up for JustCall today, start using sentiment analytics and take confident strides to edge every customer service benchmark.