Creating interactive visualization tools for analyzing text-based customer feedback, such as aspect-based sentiment analysis (ABSA), can feel overwhelming. In this post, I’ll share practical insights from my experience as a Machine Learning Engineer at an e-commerce company, where I developed a visualization app for ABSA. Both our developer and UX teams needed to analyze customer feedback effectively, yet they had distinctly different experiences with the tool. You’ll learn valuable lessons for building analytics tools that clearly address user needs and simplify complex analysis tasks.

The Backstory: One App, Different Experiences

Initially, I created a visualization app to explore our ABSA pipeline, quickly gaining popularity among our developer team. They found it especially valuable for analyzing customer complaints, enabling them to rapidly identify pain points. When applied to new datasets, the app quickly became essential for their workflow.

On the other hand, the UX team faced different challenges. Despite having similar analytical needs, they struggled with the tool’s complexity, particularly due to jargon like “named entities” and unclear sections such as “random samples.” During user testing sessions, it became evident that screens lacking immediate visualizations were frequently ignored.

Essential Lessons Learned

Clearly Guide Users Through the Workflow

Avoid overwhelming users with too many options or unclear navigation. Instead, design your analytics tool around a clear and intuitive workflow, guiding users step-by-step through their analytical journey. Clearly defined interactions and workflows help users quickly and confidently accomplish their goals.

Focus on Solving a Clearly Defined Problem

Rather than attempting to address multiple analytical challenges simultaneously, pinpoint a specific problem your analytics tool aims to solve. For instance, our app initially focused solely on surfacing customer pain points from sentiment data. Having a clearly defined purpose improved adoption and effectiveness dramatically.

Seek Immediate, Actionable User Feedback

Quickly improve your analytics tool through direct user observation and short usability tests. Observe and document how real users interact with the tool, noting points of confusion or frustration. Regularly iterate based on user insights to continuously enhance your app’s usability and value.

Continuously Simplify Your Codebase

When reusing code for new projects, you’ll often find simpler and more efficient approaches than your initial implementations. Regularly refactoring and removing redundant or overly complex code can streamline your codebase, leading to improved performance, maintainability, and faster development cycles.

Special Considerations for Analytics Visualization Tools

Clearly Associate Controls and Visualizations

Each control (buttons, filters, toggles) should clearly correspond to a specific visualization or data view. Ambiguity can discourage users—especially those unfamiliar with your tool’s internal logic—so aim for intuitive, unmistakable connections.

Provide Immediate Visual Feedback

Ensure interactions immediately produce visible results. Users should clearly see how adjusting a control affects visualizations or data outputs. Immediate feedback encourages exploration and reassures users that their interactions are meaningful.

Leverage Intuitive Data Grids

Never underestimate the effectiveness of a well-designed data grid. Data grids provide users with a familiar and intuitive way to explore, sort, and analyze complex datasets. Incorporating intuitive grids significantly enhances analytical capabilities and user satisfaction.

Conclusion

Building analytics visualization tools that effectively address diverse user needs is challenging but achievable. By clearly guiding users through a streamlined workflow, focusing on solving specific analytical challenges, continuously simplifying your codebase, and iteratively refining through real-world feedback, you’ll create analytics tools users genuinely rely on and enjoy using.