Research Approach
To understand why users were disengaging, I combined:
- Qualitative feedback (user comments and themes)
- Quantitative data (exit surveys and usage patterns)
Methodology
- Synthesized qualitative insights to identify friction in the editing flow
- Analyzed churn and engagement data to pinpoint drop‑off moments
- Used rapid prototypes and usability checks to validate early assumptions
Key Qualitative Insights
Recurring themes from user feedback included:
- Too many ads disrupting creative flow
- Feature overload causing confusion
- Difficulty discovering advanced filters
- Concerns around app reliability and trust
Quantitative Findings
Exit survey analysis revealed:
- Younger users (18–24) cited feature confusion as a primary reason for leaving
- Older users (35+) reported ad fatigue and perceived lack of value
- A majority of churned users had never used filters, despite filters being a top engagement driver
Key Insight
The problem was not the lack of features, but the lack of clarity and discoverability.