How do your users truly feel about your products or brand? It might seem easy enough to learn, thanks to customer feedback tools, such as surveys or online reviews. However, determining the emotions behind reviews, social media comments, and survey answers isn’t always so straightforward.
That’s where sentiment analysis comes in. This approach helps you truly understand the positive, negative, or neutral emotions behind the user feedback you receive. A well-designed feedback analysis process can help you understand how your customers truly feel about your brand. Read ahead to learn helpful tips for measuring user sentiment, so you have the vital information needed to improve your products and overall user experience.
For a video tutorial on sentiment analysis and experimentation, check out this guide from UX metrics researcher Milan Mijatovic (ex-PayPal, ex-E*TRADE).
Understanding user sentiment
User sentiment refers to the emotions and attitudes your users have toward your product and brand. Once you know how users feel, you can dig deeper to identify the factors behind those emotions and adapt your products or brand direction accordingly.
User sentiment analysis is the process of examining subjective data (such as survey results, social media comments, and online reviews) to draw conclusions about the user’s feelings and develop actionable insights. Generally, text analysis software employs computational linguistics, such as Natural Language Processing (NLP), to attempt to identify the emotions, tone, and intent behind text.
There are two basic approaches to consumer sentiment analysis: quantitative and qualitative. Quantitative analysis focuses on measurable metrics, such as numerical scores. For example, an NPS survey may be a good source of quantitative data that could be analyzed to determine the volume and intensity of positive and/or negative sentiment.
Qualitative analysis doesn’t focus on numerical metrics. Rather, its goal is to understand the deeper nuances and context behind user feedback. This type of analysis often requires a deep interpretation of text to identify patterns in user feedback and understand subtleties, such as sarcasm or mixed emotions.
Effective strategies for measuring user sentiment
There are several ways to collect data for user sentiment tracking and analysis. Gathering feedback from multiple sources allows you to capture a broad range of emotions and opinions. This data can then be analyzed for insight into the user's feelings and attitudes.
Surveys and feedback forms
Surveys are effective user feedback tools if designed correctly. Keep surveys short with clear questions. If possible, use a mix of closed-ended questions (e.g., asking the user to rate their experience on a numerical scale) and open-ended questions (e.g., asking the user to write a brief explanation for the rating they chose).
When should you survey users? It depends on the type of feedback you want. For instance, you might want to know how users feel about their interactions with your customer service team. Consider surveying users immediately after they complete a conversation with a customer service representative.
If you’re looking for information about a specific product, consider using in-product surveys. Sprig’s Surveys can be integrated into your product to capture user feedback in real-time, providing insight into their feelings as they use your app or software.
Social media monitoring
Social media sentiment analysis, or opinion mining, can be an excellent source of information about how users feel about your brand overall. People tend to be more honest in social media comments than they may be in a survey response. Additionally, analyzing customer behavior data (such as likes and shares) can provide insight into which aspects of your brand or reputation users like or dislike.
Social listening tools can find conversations about your brand online. You can then analyze those discussions to see how your company is being perceived and discussed. With that data, you can make changes to address issues that users dislike.
SoFi provides a great example of using social media analytics to serve users better. Through voice of the customer research and analytics, the fintech company realized that a large portion of its target audience had negative feelings about managing student loans and structuring their finances.
These sentiments weren’t directly about SoFi, but they presented a good opportunity for the brand to better serve its target audience. SoFi started publishing easy-to-understand educational content about student loans, providing value to its target audience and establishing itself as a trustworthy industry leader.
Sentiment analysis tools
Once all user sentiment data has been collected, it must be analyzed. Some brands opt for manual monitoring whereas others leverage automated sentiment analysis tools.
Both methods have pros and cons. Manual monitoring may provide more precise results because humans are generally better at recognizing and understanding sarcasm, backhanded compliments, double negatives, and other linguistic nuances. However, manual analysis is extremely time-consuming and may not be practical for large data sets.
Sentiment analysis tools, on the other hand, are far more efficient. They can filter and analyze data much faster than a human can. Spring’s AI Analysis tool analyzes user feedback quickly and at scale, providing key takeaways in a clear, actionable format.
Depending on the type of sentiment analysis you’re conducting, it may make sense to use a combination of manual and automated approaches.
Customer reviews and ratings
Online reviews and ratings can be excellent sources of information about user sentiment. AI analysis software can help separate reviews into positive, neutral, and negative categories, providing a big-picture perspective on the user base's overall feelings. Then, you can dive deeper to find recurring themes and keywords. This approach can give you more insight into features that users love and negative issues they consistently experience.
After conducting online review analysis, you can take action. Responding quickly to negative reviews can help prevent churn and build a reputation as a brand that cares about its customers. You can also take those negative reviews to the product development team so they know how to improve your products.
It’s also crucial to find ways to encourage positive reviews. For example, consider surveying users after they have just made a purchase or had a positive experience with customer service. Engaging with your users throughout their journey can improve their overall experience and encourage them to leave a positive review.
Leveraging user sentiment for business growth
Once you have the results from user sentiment analysis, you can apply those insights in several ways.
- Improving product and service offerings: Evolving your offers is the key to long-term success. Make changes based on the feelings and emotions you’ve identified in your users — fix issues that create a negative experience and build on the elements your users love. Sprig’s Replays tool helps you pinpoint the exact sources of negative sentiment by recording live clips of user behavior so you can see exactly where things go awry.
- Enhancing customer support: User sentiment analysis results show your customer service team where changes need to be made. Identify situations where users consistently portray negative emotions, and develop strategies to address them.
- Shaping marketing and communication strategies: Brand sentiment analysis gives you insight into how your target audience views your company. Utilize that data to adjust your messaging to effectively address common pain points, identify user knowledge gaps, and highlight positive aspects of your offers.
- Forecasting and trend analysis: Sentiment data helps you predict future trends and user needs. Continually monitoring social media discussions and online reviews allows you to identify features and functionality that users want. This enables you to create customer-centric products to meet those upcoming needs.
Challenges and considerations for measuring user sentiment
Sentiment analysis can be extremely powerful, but it’s not without challenges. For example, text analysis software can’t always understand context — irony, sarcasm, idioms, and emojis may be misinterpreted.
Additionally, the efficacy of leveraging AI analysis software depends on the data used to train it. Developers may unintentionally introduce bias into the system simply because of the data sets they select for training.
Many algorithms also struggle with nuances related to dialects and cultural differences. Software may not be able to accurately interpret the emotion behind sentiments expressed by people from different backgrounds and cultures.
User sentiment analysis presents potential ethical concerns as well, because consumer feedback data may be vulnerable to privacy breaches. It’s crucial to take necessary security measures and inform users how their personal data is collected, stored, and analyzed.
Use sentiment analysis to guide effective business strategies
The more you understand about your target audience and users, the better you can serve them. User sentiment analysis helps you identify the underlying emotions behind user reviews, social media mentions, and survey feedback.
Sprig’s tools simplify user sentiment analysis by making it easier to gather, filter, and analyze user feedback. From deploying in-app surveys and capturing real-time clips of user behavior to analyzing that data via powerful AI, Sprig gives you valuable insight into how users feel about your products, customer service, and overall brand. Start your Sprig free trial today.