To identify the "magic moment," I would start by analyzing user behavior data to detect patterns in engagement. Specifically, I’d look at what actions successful, long-term users took early in their journey, for example, how soon they completed their first reflection, scheduled a therapy session, or interacted with a piece of personalized content. Using tools like event tracking I’d compare the behavior of retained users vs. churned users to isolate moments that correlate with long-term engagement. I would also complement this withinterviews or surveys to understand how users describe the moment they felt that Robyn was genuinely helpful or impactful.

One retention experiment I worked on involved analyzing which customers were most likely to cancel their mobile service. The goal was to proactively approach these users to either offer better deals or understand their reasons for leaving. I used a combination of Python and R for data processing and regression analysis (including logistic regression and other predictive models). For visualization and reporting, I used Python (matplotlib, seaborn) and Power BI to identify trends and communicate insights effectively. This experiment helped segment the customer base more intelligently and suggested actionable strategies to improve retention

One Gen Z-focused app I really love is TikTok. It’s incredibly easy to use, and despite the huge number of creators, it’s surprisingly simple to identify what people like, what captures their interest, and how they consume content. What I find especially fascinating is the short-form video format — fast, straight to the point, and highly engaging. It's interesting to analyze how this structure impacts attention, communication, and even creativity.