It’s one thing to collect feedback about your product, but quite another to read between the lines and understand how users actually feel about your brand. Not to mention figuring out what your customers are thinking as they interact with your website or digital product.
That’s why user sentiment analysis is such a powerful technique to truly understand your customers.
User sentiment analysis uses tools and algorithms to process and interpret unstructured data—such as feedback forms, interviews, customer reviews, and social media comments.
This analysis helps you understand customer thoughts and feelings at various stages of the buyer journey and beyond. By segmenting users and addressing issues, businesses can enhance their products, boost customer loyalty, and increase overall satisfaction.
This guide walks you through the fundamentals of user sentiment analysis—what it is, why it matters, how to analyze sentiment, and the challenges that come with it—to improve your product and reach your business and customer goals.
Summary
User sentiment is the positive, negative, or neutral feelings people have for your brand or products
You can collect user sentiment through things like surveys, online review sites, social media platforms, customer support tickets, and more
Some benefits of user sentiment analysis include being able to uncover customer emotions and motivations, gauge users’ emotional connection to your product, and monitor your brand’s reputation
You can perform user sentiment analysis in five steps:
Gather your data
Sort, process, and clean your data
Analyze your data using user sentiment analysis software
Visualize and share your insights
Put your findings into practice
Common challenges of user sentiment analysis include developing hypotheses, collecting data, understanding context within your datasets, and achieving a large enough data sample size
What is user sentiment?
User sentiment is the positive, negative, or neutral thoughts and feelings customers have about your brand or product.
For example, if a user says, “Amazon Prime’s documentary selection is better than Netflix’s,” they’re expressing positive sentiment about Amazon Prime. Or if they say, “The interface on my new phone is hard to navigate,” you know they’re not happy with the user interface (UI) design.
However, it’s not clear—in either of these cases—exactly which emotion the user is feeling. That's why sentiment text analysis is key to gauging their level of emotional reaction and connection to your product—and improving the customer experience (CX).
Where to find user sentiment
These days, customers express their thoughts and feelings over a variety of channels:
Customer feedback: like online customer satisfaction surveys where users tell you exactly what they love and hate about your product and the customer experience
Chatbot or help center interactions: including call transcripts and emails to customer support teams. For example, if users are frustrated because they can’t find essential information, their messages are likely to express that emotion.
Sales calls: conversation analysis tools can reveal uncertainty in a potential customer’s words, even if their hesitation is too subtle for a human to detect from their tone
Product review sites: these are a goldmine of customer sentiment as people don’t hold back from expressing themselves
Social media platforms: through posts and comments on popular online platforms. For example, if you post about your latest update, a user might comment that they’re annoyed by the price hike.
Forums: industry-relevant forums can give you a glimpse into how people feel about your product and business
💡 Nike used social media sentiment analysis to monitor public opinion during its controversial sponsorship of NFL player Colin Kaepernick after he ‘took the knee’ before a game. Source: Sentieo.com
Benefits of user sentiment analysis
User sentiment analysis is the process of extracting objective, actionable insights from subjective, unstructured data. Analyzing user sentiment often involves using artificial intelligence, machine learning, and natural language processing (NLP)—speech recognition, natural-language understanding, and natural-language generation—to analyze datasets and recognize and understand emotions in speech and text.
Analyzing and acting on user sentiment is key to helping you achieve your business goals, improve CX, and boost customer satisfaction and loyalty.
Analyzing user sentiment lets you:
Uncover customer emotions: connect the dots on customer satisfaction scores and reveal what customers think and feel about your website, brand, and digital products. Then, respond with small changes that have a big impact. For example, analyzing feedback from an unsatisfied mobile user can pinpoint whether they’re confused, frustrated, or planning to switch to the competition. So you can prioritize changes and actions to address the issue.
Understand customer motivations: discover customer motivations, pain points, and challenges. For example, if you're using Contentsquare, watch session replays of users navigating your website to find out exactly where they drop off. What they won’t reveal is what users were thinking and feeling right before they abandoned the page. So, combine session recordings with insights from feedback widgets to put two and two together and make quick-win changes to reduce drop-offs.
Bring the voice of the customer (VoC) to your decision-making: align cross-functional teams, eliminate disagreement about ‘what the customer wants,’ and provide valuable insights to get buy-in from stakeholders. When you empathize with your customers in this way, you stop relying on guesswork and instead make decisions based on hard data—which puts you in a better position to achieve product-market fit and growth.
Gauge users’ level of emotional connection to your product: this is an indicator of loyalty, repeat purchases, and recommendations. Customers who love your product are more likely to renew and recommend it to others. Sentiment analysis lets you identify and target these customers with marketing campaigns and incentives to boost referrals. For example, give a discount for every three new customers that sign up using a referral link.
Spot patterns and trends: monitoring social media posts lets you gauge public opinion and determine if your marketing campaign is missing the mark, so you can adjust it before it ends
Improve and scale data processing: user sentiment analysis tools speed up the time-consuming process of categorizing and analyzing masses of data to give you more reliable insights, faster
Monitor brand reputation: use real-time sentiment analysis to determine how people feel about your brand during things like campaigns, product launches, and potential public relations crises
Perform market research: pinpoint how users feel about things like your competitors, the products in your market, and gauge their overall preferences. This deep knowledge can help you tailor your offerings and meet customer needs.
Brands are trying to understand how customers feel about the brand itself. Analyzing sentiment helps them discover possible emotional connections that tie customers to the products they use. And customers in turn benefit from products tailored to their needs.
Vaclav Vincalek, Founder, 555vCTO
How to perform user sentiment analysis in 5 steps
There are five basic steps in the user sentiment analysis process:
Gather your data: the sources and tools you use to gather data will depend on your objectives and customer base. For example, if you’re a B2C retailer looking to gauge public opinion after launching a new campaign, you may want to monitor social media. But if your B2B SaaS company wants to understand how customers feel about your onboarding process, place customer surveys at strategic points in your app.
Sort, process, and clean your data using dedicated tools: for example, if you’re a B2B sales organization wanting to improve customer interactions, you can use transcription software to transcribe calls with your customer-facing teams. Then, remove stopwords, incorrect punctuation, etc., to make your data easier to analyze.
Analyze your data using user sentiment analysis software: for example, AI-based software like Lumoa generates an overall customer sentiment score, which serves as a metric you can track. When the score drops below acceptable or benchmarked levels, read and look at individual customer feedback to find out why.
Visualize and share your insights: create actionable reports with visuals (graphs, charts, etc.), to see what’s happening in your product at a glance with all your meaningful insights on one page, to present your findings to team members and stakeholders.
Put your findings into practice: examples of user sentiment analysis in action include:
Improving product listing pages and removing barriers to conversion on your ecommerce website. For example: clarifying information that was confusing people or making returns policies more accessible at checkout.
Providing call center operators with a customer sentiment score and history of previous interactions. This helps route calls to the most appropriate operator and offer relevant solutions.
Enacting damage limitation measures if a prominent sponsor’s social media comments are poorly received by your audience
Running A/B testing on a SaaS website to see which versions of your pages and CTAs convert best
🔥Pro tip: connect Contentsquare’s experience insights tools to Omniconvert or Optimizely to monitor A/B testing.
What are the challenges of user sentiment analysis?
Like anything that involves human emotions, sentiment analysis comes with its challenges.
At the planning stage of sentiment analysis, challenges include:
Developing a hypothesis: coming up with a hypothesis and working out what data you need to test that hypothesis requires clear direction. Start out with broad research questions and narrow down those questions based on your team’s goals and what you hope to uncover through sentiment analysis.
Designing questions: the questions you ask depend on your hypothesis. These questions should always be open-ended so users can tell you what they think and feel in their own words.
Defining data collection methods: choose the best method for data collection that lines up with your budget and time restraints. These methods typically include things like customer surveys, interviews, call transcripts, etc. Combine multiple methods to capture a comprehensive view of customer sentiment.
Data collection comes with its own challenges:
Achieving a large enough sample size: it’s not always easy to persuade people to answer surveys, and customer interviews can be time-consuming and costly to run. To overcome this, use feedback widgets or NPS surveys that ask customers to provide a rating. Then, follow each rating request with a longer, open-ended question. This way, it feels like less effort for the customer to answer you questions.
Monitoring user sentiment: monitoring customer sentiment can be time-consuming. Lean on tools to streamline data processing and analysis.
And the challenges don’t end once you’ve collected data:
Managing multiple data sources: analyzing hundreds of data sources is time- and resource-intensive. To overcome this, invest in user sentiment analysis software to do the heavy lifting.
Understanding context: it’s difficult to detect irony or sarcasm from text alone. Curse words can also completely alter the meaning of a sentence. So can irony, sarcasm, and backhanded compliments. Consider incorporating a manual review process into your sentiment analysis. This helps you capture accurate sentiment.
Interpreting emojis: customers frequently use emojis on social media and, increasingly, other sources. NLP tools are trained to be language-specific, whereas emojis have their own language and their use changes all the time (as any confused parent of teenagers will tell you). Choose tools that you can train to understand emojis, or replace them with text that reflects their meaning.
Avoiding misinterpretation: results can be taken out of context and lead to the wrong conclusion. Review individual responses to truly understand how a customer feels about your brand and product experience. Observation bias and subjectivity also impact interpretation. Even with the best will in the world, the unconscious biases we all suffer from can distort data collection and interpretation.
Analyzing multilingual data: if you’re analyzing text in a language you don’t speak well, you may miss nuances. But, for many global brands, analyzing multilingual customer feedback is a must. Use multilingual NLP tools or involve native speakers to review sentiment in multiple languages.
Dig deep into the what and the why with user sentiment analysis
It’s not enough to collect feedback; you need to be able to understand your user's sentiment behind their words. Only then can you understand why customers do what they do when they interact with your website or product.
To perform user sentiment analysis, first, identify what data you need to collect, and from which customer segments and sources. Then, combine experience analytics tools and customer research with qualitative data analysis tools to better address customer issues and improve your product to boost loyalty and customer satisfaction.