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4 Sentiment Analysis Examples to Improve Your Customer Experience
Your customer leaves a negative review on social media. But it gets overlooked and the customer starts to drift away from your brand, never to do business with you again. See, this is why sentiment analysis is important — it enables you to uncover important insights like this one in real time.
Recent studies have indicated that over two-thirds of consumers who have a positive experience with a brand on social media are likely to recommend it to others. So, catering to your audience on social media is important to keep them satisfied and engaged.
And to understand your customers better, you need to focus on sentiment analysis. In this article, we will share four real-life examples of sentiment analysis done right and key takeaways so that you can apply the learnings to level up your brand’s reputation.
What is sentiment analysis?
Sentiment analysis (or opinion mining) is a technique used to recognize and categorize the emotions behind texts. The process is a mix of machine learning and natural language processing (NLP) that analyzes words and patterns to determine the emotion — positive, negative or neutral.
Sentiment analysis makes listening to your customers' reactions online much easier and plays a major role in building your overall brand strategy framework.
With sentiment analysis, brands can identify trends and patterns, monitor online brand reputation, measure campaign effectiveness, improve customer engagement and gain insights into product/service development.
It goes without saying that sentiment analysis is a powerful tool in the age of social media customer service. It effectively bridges the gap between a brand and its customers by empowering brands to understand what customers feel about their existing line of products and services, what improvements they are looking for and how to best deliver them.
Simply put, sentiment analysis enables you to build a customer-centric brand.
Types of sentiment analysis
Sentiment analysis can help you understand what is working for your business and what needs improvement. Let’s dive deeper into the subject to understand how it works.
Sentiment analysis can be categorized into four broad categories:
1. Fine-grained
A customer visits your restaurant and leaves a review that says, “The aglio e olio here was amazing but the cocktail menu could have had more options.”
This review expresses a nuanced sentiment that is not entirely positive or entirely negative.
With fine-grained sentiment analysis, you can go beyond classic categorizations and label this review as a “slightly positive” sentiment. This helps identify more specific sentiments based on experience, emotions, intensity and current business goals.
This type of sentiment analysis is dominant in political campaigns, customer feedback, product reviews, and news and media analysis.
2. Emotion detection
You check your brand’s social media notifications and see that you have been tagged in a social media post stating, “I have been on a customer service call for an hour. It’s so frustrating!”
The emotion of this post is very clear — the customer is ranting about bad customer service.
Emotion detection sentiment analysis involves identifying the emotion behind a text-based message. It can identify emotions such as anger, happiness, fear and surprise, helping you better understand a person’s motivation or emotional state, and is mainly used in social media monitoring, speech analysis and customer feedback management.
3. Aspect-based
Let’s say a customer visits your beauty product store and leaves a review on social media, saying, "I loved the new range of products, but the check-out process was exhausting.”
This review expressed mixed sentiments about different aspects of your brand.
Aspect-based sentiment analysis focuses on identifying sentiment toward specific aspects or features of a product or service. It helps to understand which parts of a product or service are being praised or criticized.
In the above scenario, you can assign “positive” sentiment for the products and “negative” sentiment for on-ground customer experience.
Aspect-based sentiment analysis can help in building customer experiences that matter. This type of analysis is mostly used for retail, e-commerce, finance, healthcare and human resources operations, but its applications can be diverse.
4. Intent-based
A review that goes, “I am so disappointed by your service and I want a refund,” and tagged with your brand’s social media handle is both upsetting but straightforward.
The text indicates the customer's desire to get a refund due to a negative experience.
Intent-based sentiment analysis aims to determine the underlying intent behind the expressed sentiment. Brands can quickly respond to customers on social media by correctly identifying the intent, i.e., request, complaint or suggestion behind a post, comment or message. This technique is largely popular in the advertising and marketing industry.
Recommended Read: How to Perform a Social Media Sentiment Analysis
4 Sentiment analysis examples from leading brands
Here are four real-life examples of sentiment analysis to help build your use cases and improve your social media communications framework:
1. Microsoft
Microsoft has a dedicated team – Social Intelligence Practice (SIP) – that listens to customers on social media channels. The SIP team utilized Sprinklr’s social listening tool to provide actionable customer experience data to internal stakeholders. The information was used to help drive decisions around product and feature roadmaps, thought leadership, public relations and more.
In the first year of the SIP team's use, Sprinklr caught 8.6 billion listening mentions and enabled a 100% increase in the number of projects the SIP team executed for Microsoft stakeholders.
One such insight led to Microsoft becoming a leader in hybrid work-setting conversations. Using social listening and sentiment analysis, Microsoft’s various teams understood what people wanted in a hybrid workspace. They actively created social media content catering to the audience’s needs.
The result? Microsoft added new product features for people working in hybrid settings.
Read More: Sprinkr’s Microsoft case study
2. Chick-fil-A
In 2016, Chick-fil-A replaced its popular Original BBQ sauce with a new Smokehouse BBQ variety. The company leveraged Sprinklr's social listening tools and discovered a rise in negative comments and mentions on social media.
Between July and October 2016, Chick-fil-A experienced a 923% rise in weekly mentions of “BBQ sauce.” The hashtag #BringBackTheBBQ quickly gained traction, with fan sentiment dropping to 73% negative.
So, Chick-fil-A partnered with Sprinklr and creative agency Moxie to relaunch its original BBQ sauce. The team developed a hashtag – #BroughtBackTheBBQ – and used Sprinklr’s social media publishing platform to plan its campaign content.
After the campaign launch, the brand saw an 188X increase in Chick-fil-A and BBQ sauce mentions on the day the sauce returned to restaurants. Its teams responded to or reviewed over 5,000 messages in the first three days after the announcement.
The result? Fan sentiment changed from 73% negative to 92% positive.
💡Pro Tip: What do you do once you see a spike of negative comments and mentions on social media channels? Responding to each of them individually is not an option, at least not a realistic one.
Here’s what you should do — leverage Sprinklr’s Unified Customer Experience Management (Unified-CXM) platform. It consolidates listening and insights, social media management, campaign lifecycle management and customer service in one unified platform. It also:
💯 Empowers your marketing and customer service teams with generative AI technologies and tools
🏛️ Follows comprehensive security standards and robust data privacy measures to protect your brand
🎮 Integrates seamlessly with thrid-party software and systems for effective collaboration
Ready to empower your teams with an AI-first unified platform?
3. Netflix
Netflix is known for leveraging social media features effectively. It cashes in on trending memes and viral content and has a great influencer marketing strategy.
But did you know it also uses social listening and sentiment analysis to make product-related decisions?
Let’s take the example of “Bridgeton,” a popular Netflix show. During the second season's release in 2022, the brand noticed the show’s content becoming a trend. It quickly jumped on the bandwagon and used a popular meme from Bridgerton to promote another show, “The Recruit.” This kept the buzz going on social media for the brand.
💡Pro Tip: Trends on social media come and go.
Keeping track of all of them sounds daunting, right? But it doesn’t have to be that way.
Sprinklr’s AI-powered social listening tool makes it easy for enterprises to turn data into actionable insights. With this tool, you can:
🗣️ Capture relevant conversations that matter to your brand
🛡️ Protect your brand’s online reputation
🏁 Compare your brand’s performance against your competitors
Ready to leverage the power of social listening to stay ahead of trends in your industry?
4. Google
Google is the most popular search engine in the world, with a market share of 81.95% as of July 2023. In addition to being user-friendly, the brand continues to stay on top of its game by leveraging sentiment analysis for product innovation.
Google Chrome’s development team constantly monitors user feedback, whether it is through direct (social media) or indirect (open) sources. The team matches the sentiment with the relevant product aspect, whether it is scalability, UI or security, and recognizes its strengths and weaknesses. This contributes to more in-depth research on all its products, including the Google search engine and Pixel phone series.
Another case in point: After facing repeated complaints and negative feedback over spam content, Google began addressing the issue in 2022. By May 2024, it introduced new policies to enhance search quality and prevent users from encountering the same content.
How to use sentiment analysis for your business
Unlike traditional sentiment analysis tools, AI-powered tools can accurately handle vast amounts of data. Here is how AI can help implement sentiment analysis for your business:
1. Monitor sentiment on social media
Social media monitoring tools supported by AI, can significantly enhance your sentiment analysis capabilities through advanced features and automation. These tools leverage natural language processing (NLP) and machine learning algorithms to analyze vast amounts of data more efficiently and accurately than traditional methods.
With extensive social media reports that analyze data in real time, brands can now communicate with their customers much faster. The outcome? They can turn negative feedback into a positive experience.
2. Manage customer interactions
AI tools monitor and interpret customer feedback in real time, allowing businesses to proactively address issues, tailor responses and improve overall customer interactions.
For instance, a major energy company recently partnered with Sprinklr to manage customer interactions across platforms and stay on top of customer sentiment on social media.
The company was grappling with reports of power outages in certain regions. Extreme weather conditions had further complicated efforts to keep customers informed and satisfied. With Sprinklr Social and Sprinklr Insights, the energy provider understood customers’ challenges and quickly responded to emerging issues.
That’s not all. Pleased with the results, the company expanded its use of the solution suite by integrating Sprinklr Service for end-to-end communication management.
Full Case Study: Energy company electrifies the customer experience on social channels
3. Categorize topics for quicker analysis
Future-facing social media management tools use AI-powered sentiment analysis to understand customers’ needs. They can help convert unstructured data into insights by listening to online conversations in real time, allowing you to uncover actionable insights based on the share of voice (SoV) distribution of particular topics or industries.
4. Gain insights on emerging trends
AI-based social media analytics and reporting platform use sentiment analysis to identify emerging trends. This helps optimize your efforts to drive revenue. These tool stacks also provide insightful reports on your social media campaign performance and competitive benchmarking.
Final thoughts
Sentiment analysis has emerged as a critical tool for businesses seeking to understand customer perceptions, improve brand strategies and make data-driven decisions.
By analyzing customer feedback and online conversations, brands can gain valuable insights into customer satisfaction, identify areas for improvement and measure the effectiveness of their marketing efforts.
In this article, we have explored various examples of sentiment analysis, from monitoring sentiments for product development to sorting reviews for food delivery chains. These examples demonstrate the versatility and power of sentiment analysis in addressing a wide range of business challenges.
Ready to unlock the power of sentiment analysis for your brand with Sprinklr Insights?
