Overview
Designed a data-driven feature for Persil’s companion app that used device and activity data to help users better understand their washing habits. The feature aimed to make the app more valuable in daily use by translating usage patterns into practical insights and efficiency tips. Instead of showing raw data, the concept focused on helping users recognize behavior over time and identify opportunities to save energy and water. The result was an Activity tab intended for implementation, combining product thinking, data visualization, and user-centered communication.
Role: Product designer (end-to end)
Job type: Feature design & optimization
Client: Persil
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How did we validate the idea?
The concept was tested with over 20 target users during a two-hour interview session. Users were first asked about their expectations for an Activity tab, then shown early mockups exploring how the feature could work.
The first concepts focused on basic summaries of habitual data. This helped validate interest in the topic, but also showed that showing data alone was not enough to make the feature feel useful or meaningful.
What did we learn from the analysis?
The analysis focused on identifying which parts of the available data could become valuable for users. I worked with app data, device-recorded data, and historical usage patterns to define which insights were most relevant, understandable, and worth surfacing.
A key part of this process was working within the limits of the available data. Some ideas were interesting, but not fully supported by the inputs we had. This led to the final concept: an Activity tab built around selected insights rather than raw reporting, helping users better understand their behavior and spot simple opportunities to improve.
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How was the final direction shaped?
Once the right insights were defined, the next challenge was how to present them clearly. I explored different chart types and visual directions to show multiple insights without making the Activity tab feel overloaded or too technical.
To validate the direction, I created different visualization options, tested them through A/B comparison, and used the results to define the strongest final approach.
Once the right insights were defined, the next challenge was how to present them clearly. I explored different chart types and visual directions to show multiple insights without making the Activity tab feel overloaded or too technical.
To validate the direction, I created different visualization options, tested them through A/B comparison, and used the results to define the strongest final approach.
Outcome
Clearer Purpose
The feature gave the app a more defined role in everyday use by turning tracked activity into something more meaningful.
Meaningful Insights
The most relevant patterns were translated into outputs users could understand more easily and connect to their own behavior.
Product Direction
Tested visual directions helped create a clearer foundation for implementation and gave users a stronger reason to return to the app over time.
Feature Overview
Designed a data-driven feature for Persil’s companion app that used device and activity data to help users better understand their washing habits. The feature aimed to make the app more valuable in daily use by translating usage patterns into practical insights and efficiency tips. Instead of showing raw data, the concept focused on helping users recognize behavior over time and identify opportunities to save energy and water. The result was an Activity tab intended for implementation, combining product thinking, data visualization, and user-centered communication.
Role: Product designer (end-to end)
Job type: Feature design & optimization
Client: Persil
How did we validate the idea?
The concept was tested with over 20 target users during a two-hour interview session. Users were first asked about their expectations for an Activity tab, then shown early mockups exploring how the feature could work.
The first concepts focused on basic summaries of habitual data. This helped validate interest in the topic, but also showed that showing data alone was not enough to make the feature feel useful or meaningful.
What did we learn from the analysis?
Users did not see enough value in raw data alone. What they wanted was a clearer understanding of their behavior and where they could improve efficiency. This shifted the feature direction from simple summaries to actionable insights.
I reframed the concept by identifying patterns in user activity and turning them into more meaningful outputs. One example was highlighting inefficient wash loads and suggesting ways to save water, energy, and detergent. This made the experience more relevant, easier to understand, and more engaging for everyday use.
Open questions & considerations
The final concept was an Activity tab focused on data-driven insights around washing behavior. Instead of showing raw activity alone, it translated usage into clearer patterns and practical opportunities to improve efficiency. This gave the app a more concrete purpose and created a stronger direction for implementation.
Clearer purpose
The feature gave the app a more defined role in everyday use
Meaningful insight
User habits and real-life conditions vary, making some edge cases difficult to predict in advance.
Product direction
The concept created a stronger and more actionable foundation for implementation.