Understanding the voice of your employees is more important than ever. As organizations strive to improve their internal processes, the insights gathered from end-user experience surveys become crucial. These surveys typically combine both quantitative and qualitative results to paint a comprehensive picture of the employee experience. While quantitative data is invaluable for measuring progress, setting priorities, and tracking trends, it’s the qualitative data—the open comments—that truly capture the stories, struggles, and preferences of employees.
However, analyzing large volumes of open comments can be overwhelming. This is where the smart use of AI comes into play. By integrating AI tools into your comment analysis process, you can unlock deeper insights and make your data more actionable. But it’s not just about using AI—it’s about using it wisely.
The Challenge of Open Comments
Open comments provide rich, nuanced feedback that can be a goldmine of information for any organization. Employees often share their genuine feelings, ideas, and experiences in these comments, offering valuable context that numbers alone can’t provide. But there’s a catch: the sheer volume of comments can make it difficult to extract meaningful insights efficiently.
Without a strategic approach, you risk drowning in data, missing out on critical feedback, or worse—making decisions based on incomplete or unrepresentative information. This is where a well-thought-out AI strategy becomes indispensable.
The Smart AI Approach: A Three-Step Strategy
At Yorizon, we’ve developed a smart approach to integrating AI into the analysis of open comments, ensuring that you get the most out of your qualitative data without reinventing the wheel.
1. Filtering for Value
Not all comments are created equal. To avoid clutter and focus on actionable insights, we exclude comments with fewer than five words. While brief comments like “Great, keep on going!” are encouraging, they often lack the depth needed for strategic decision-making.
By focusing on lengthier comments, you tap into feedback that provides context and detail—elements that are crucial for understanding the underlying issues and opportunities.
2. Categorization and Prioritization
To make sense of the data, we categorize comments based on six key criteria:
- Sentiment (tone of voice)
- Clarity
- Specificity
- Actionability
- Relevance
- Quality
Each comment is then ranked on a scale from 1 to 5 for each criterion. This allows us to prioritize comments based on what’s most important to your organization. For example, a comment with low sentiment (negative tone), maximum relevance, and high actionability might be flagged for immediate attention.
Additionally, our AI tools can identify positive comments that are suitable for ‘Shoutout’ use—perfect for internal motivational purposes. This automated categorization not only saves time but also ensures that no valuable insight slips through the cracks.
3. Integrating Quantitative and Qualitative Insights
The real magic happens when we combine AI-summarized comments with quantitative results. This integration offers a holistic view that is both meaningful and rich. By aligning qualitative insights with numerical data, you gain a deeper understanding of employee sentiment and can make more informed decisions.
The Human Touch: Why Manual Review Still Matters
Despite the power of AI, we still advocate for manually reading open comments, especially when dealing with smaller datasets, such as feedback from a specific department, country, or building. Human review allows for a nuanced understanding of sentiment and context that AI alone may not fully capture.
However, for larger datasets and broader trends, the integration of AI and human analysis provides a powerful tool for extracting actionable insights without the need for extensive manual labor.
Conclusion: Why You Shouldn’t Do It Alone
While the use of AI in analyzing open comments may seem straightforward, the real value lies in how it’s implemented. At Yorizon, we’ve developed a system that combines the best of AI with human expertise, tailored to meet the specific needs of your organization. This means you can focus on what really matters—taking action on the insights gained—without the hassle of developing your own approach from scratch.
In the end, the smart use of AI isn’t just about analyzing data; it’s about turning that data into a powerful tool for driving positive change in your organization. By leveraging a well-thought-out approach like ours, you can ensure that you’re getting the most out of your qualitative feedback—efficiently, effectively, and meaningfully. So why start from square one when you can partner with experts who’ve already done the heavy lifting? With Yorizon, the tools are at your disposal, and the insights are within reach. All it takes is a smart approach!
