Sentiment analysis reads between the lines of your users’ survey responses. It tells you a lot about how users feel about your product — beyond what you can learn from the already strong questions in your surveys.
If you’re wondering how to analyze customer feedback sentiment, using AI is the answer. It’s way faster than reading hundreds of responses, looking for patterns, and making assumptions. Plus, it minimizes bias, analyzes new customer feedback as it comes in, and identifies potential issues before they become big problems.
Understanding Sentiment Analysis
Sentiment analysis, sometimes called opinion mining, is a technique that helps you understand whether user feedback is positive, negative, or neutral. It helps you understand what your customers aren’t quite saying outright in their feedback while revealing deeper insights you can leverage for product improvements.
Benefits of Using AI for Sentiment Analysis
AI analysis detects what people might miss
At its core, an AI model’s main purpose is to see patterns in the data it’s trained on. AI sentiment analysis tools detect patterns in your survey responses that aren’t as obvious to humans, like sarcasm, cultural differences, or translation errors. AI sentiment analysis recognizes these patterns on a greater scale — and more effectively — than sentiment lexicons and other traditional methods.
AI analysis can work faster than humans
Combing through all that data takes time. AI sentiment analysis completes the heavy lifting more quickly, efficiently, and accurately than humans. This helps you get products to market faster and release updates on a truncated timeline.
AI analysis removes human bias from the process
Each person brings their own lens to text interpretation, so two product team members can draw wildly different conclusions from the same body of information. This gap in interpretation can skew results when your team is assessing sentiment in customer feedback. AI tools mostly remove that subjective point of view so you don’t gloss over important insights or hone in on something few users care about.
AI analysis frees up your team’s time
Data analysis takes significant time and effort. AI sentiment analysis gives your team time back by taking over the most laborious parts of analyzing customer sentiment. This way, your team can focus its energy on answering the questions posed by the user data.
Analyzing Sentiment with AI: A Step-by-step Guide
- Collecting Customer Feedback
High-quality feedback is the name of the game for sentiment analysis. The more complete your data set, the more accurate your AI model’s outcomes. This means your data collection practices should be robust and thorough to ensure best results when working with AI for sentiment analysis.
Sprig makes this process easy with in-product Surveys. These surveys gather feedback from users right within your product so their experience is fresh in their minds while providing their thoughts. The result is more direct and meaningful customer data that reaches you right away.
Sprig’s in-product Surveys pop up seamlessly within your UX and only take a few seconds to complete. Your users can share real-time, off-the-cuff thoughts, then go right back to getting their work done. You’ll get a higher response rate since you’re not chasing after customers in their inbox or over text. Plus, you’ll minimize recall bias that can skew results, yielding data that arrives in excellent shape — no cleaning or organizing needed.
- Conducting the Analysis
Without an AI tool like Sprig’s GPT-Powered AI Analysis, conducting analysis would be time-consuming. During your analysis, customer sentiment would be assigned a numerical scale, which is then translated into a quantitative measure that your team evaluates.
With Sprig AI, you no longer need to do this manual process. Sprig AI takes user feedback and turns it into themes that reflect the sentiment of users. Simply conduct user feedback with Surveys or Replays and then see themes and product recommendations.
- Update Product Roadmap
Once you have product recommendations and themes, it’s time to update your roadmap to reflect user needs. We recommend the following tips to update the roadmap properly:
- Present results to the right stakeholders. Make sure your product team is on the same page with research, sales and customer success.
- Update your product roadmap tooling (e.g. Notion) with the new features and timelines.
- Properly scope requirements and resources to make feature updates.
- Train customer success to be prepared to answer any questions on product changes.
Tap Into The Power of Sentiment Analysis With AI
Sentiment analysis is key for continual product improvement, but collecting and analyzing this important feedback isn’t a cakewalk. It takes significant time and effort to properly review the data and put it into action. With Sprig AI, you can automate the hardest parts so your team can focus on bringing solutions to life. Sign up for a demo and see how Sprig AI can simplify your data review processes.
