4 sentiment analysis examples
User sentiment analysis is the process of identifying the thoughts and feelings behind customer comments in unstructured data—like survey responses or help center interactions—and turning these subjective opinions into actionable and objective insights.
These insights help you improve the customer experience (CX) and boost customer satisfaction, loyalty, and retention.
We’ve chosen four sentiment analysis examples relating to the most common sentiment analysis use cases:
- Social media posts
- Customer support requests
- Customer feedback from surveys, forms, etc.
- Text from emails, customer reviews, sales call transcripts, etc.
These real-life case studies show you how to extract and analyze data from each source—and how to apply these insights to create a better product experience for your users.
Let’s take a look:
1. Social media sentiment analysis: Nike
Social media sentiment analysis looks at the meaning behind comments and posts on social media. It tells you what people are saying—and how they feel—about your product and brand online.
Social media sentiment analysis lets you take the pulse of public opinion and customer sentiment—before and after launching a marketing campaign—so you can monitor performance. You can also use it for brand monitoring to see what influencers are saying about your brand or your market.
All this helps you stay in line with customer expectations, build your product's reputation, and establish trust with your users. It also helps you stay informed of trends as society and your ideal customer profile evolve.
🔥How Nike used social media sentiment analysis
Sportswear retailer Nike used social media sentiment analysis in 2018 for reputation management when it backed NFL player Colin Kaepernick who ‘took the knee’ during the US national anthem. His actions generated controversy and criticism, including from former President Donald Trump. But Kaepernick also got a lot of support, which Nike capitalized on by sponsoring him to use the #justdoit hashtag in his tweets.
This was a risky move. Nike needed to keep tabs on public opinion to make sure its reputation wasn’t in danger. It used Sentieo to gauge reaction to its campaign by analyzing tweets and related news before and after the inclusion of the #justdoit hashtag.
Initially, Nike noticed more negative sentiment than positive. And certain customer segments included #boycottNike hashtags within their social posts. But, over time, this was replaced by overall positive sentiment from the general public.
Nike also discovered that purchase intent was positively affected, which was a win-win.
Tracking social media mentions let Nike monitor public opinion during its sponsorship of Colin Kaepernick. Source: Sentieo
Social media sentiment analysis tips:
- Gauge public opinion during a campaign, event, or new product launch: Install user sentiment analysis software like Talkwalker, Sentieo, and Critical Mention, which also alerts you to mentions of your brand in the news. Use these tools to benchmark reactions to your campaign in real-time.
- Understand customer segments’ opinions: Find out what different customer segments think of your product or new release. For example, a B2C app developer might monitor social media sentiment among early adopters—and make improvements before rolling it out to the wider community.
- Track emerging trends: An overall sentiment analysis can be a form of market research that lets you track new trends, so you can identify when you need to become a part of the conversation
- Monitor competitor feedback: Find out how you can improve your products by tracking what people think of competitors. For example, if you sell online courses and see users complaining that a competitor’s sign-up process is too complex, you’ll know to make yours that much simpler.
2. Customer support sentiment analysis: a mobile carrier
For many customers, the quality of their interactions with your brand is almost as important as the product itself. And many unhappy customers won’t bother complaining to customer support. They’ll just leave and you’ll never know why.
Analyzing customer support sentiment lets your customer-facing teams—like sales and customer support—improve their services to boost customer retention metrics and brand reputation.
Customer sentiment analysis helps marketing teams create content to address common customer concerns. For example, case studies can show how other, similar users complete their jobs to be done (JTBD) with your product. At the same time, product teams can act fast to fix bugs, user experience (UX) and user interface (UI) design issues, and remove barriers to conversion or adoption.
💡How a mobile carrier used customer support sentiment analysis
In this Repustate case study, a large mobile provider used customer support sentiment analysis to spot customers at risk of churning. First, it installed speech-to-text software to transcribe each call center interaction. Then, it used Repustate to analyze each call's user sentiment and mentions of specific products and services.
Finally, it produced an overall customer sentiment score for each customer. Any low scores—or ones that dipped below a certain threshold for too long—triggered an automatic message of an apology to the customer.
Call center operators were then able to access a summary of customer scores and previous interactions the next time specific customers called, which helped them offer them relevant solutions or promotions.