Visualization of Public Stool-Health Data
Overview
The Danone App has accumulated check-in data from tens of thousands of users, raising a key question: Can we identify user habits from this data to support feature optimization and product promotion?
We observed that the previously overlooked “stool condition” check-in module was used much more frequently than expected. In response, we conducted a feature-level analysis of this stool condition check-in data, visualized our findings, and used these insights to redesign the stool condition check-in feature.
We observed that the previously overlooked “stool condition” check-in module was used much more frequently than expected. In response, we conducted a feature-level analysis of this stool condition check-in data, visualized our findings, and used these insights to redesign the stool condition check-in feature.
TeamHongyu Yue, Yutong Fu, Yilin Shen, Xinyue Zhang
DurationOct 2023 – Dec 2023 (2 months)
InstructorJing Cao (Tongji University), Danone Product Team
Based on stool check-in data, we conducted an analysis and visualized the results, revealing the following patterns:
- Users with worse stool conditions tend to check in less frequently.
- Users with more favorable stool conditions generally check in more frequently.
- Users with more favorable stool conditions also tend to complete more probiotic check-ins.
Redesign
Building on the insights uncovered from stool check-in data, we redesigned this feature in the Danone app and carried out iterative testing to increase check-in participation and probiotic sales.
Building on the insights uncovered from stool check-in data, we redesigned this feature in the Danone app and carried out iterative testing to increase check-in participation and probiotic sales.