To find Robyn’s “magic moment” — that turning point when a user goes from curious to emotionally connected — I’d take a hybrid approach that blends data science with emotional intuition. For me, it’s not just about tracking clicks or sessions. It’s about finding the moment someone feels seen. I’d start by mapping the full user journey using tools like PostHog or Mixpanel — identifying touchpoints where users shift from passive interaction to meaningful engagement. Maybe it’s when they journal something deeply personal for the first time, or when Robyn mirrors back something they’ve said with care. I’d define “magic” not as a spike in usage, but as a change in emotional trust. From there, I’d use Python-based sentiment analysis (with transformers, spaCy, etc.) to trace emotional arcs over time — especially transitions from guarded to open, or from confused to calm. I’d look at sequences where a user expresses vulnerability, then returns the next day — that’s a huge trust signal. To deepen the insight, I’d cluster users by emotional journey using unsupervised learning — grouping patterns not by feature use, but by tone, pace, and emotional depth. Then I’d layer in qualitative feedback: in-app surveys, open-text responses, even user interviews. The goal is to validate the magic moment not just with data, but with language — the kind that makes you pause. Like: “That’s when I felt Robyn really got me.” Ultimately, I’d present not just a metric, but a narrative — a story of how Robyn earns trust. Because if we understand what moves someone, we can build more moments that feel like emotional homecomings. That’s the kind of magic that scales — and sticks.

One growth and retention experiment I’d love to run builds directly on the technical foundation of my senior thesis, where I used Python to analyze a multi-source panel dataset of over 4,000 NCAA athletes and their NIL outcomes. My modeling pipeline included linear regression to identify baseline relationships, ElasticNet to isolate high-impact predictors, XGBoost to capture nonlinear interactions, and K-Means clustering to segment athletes into behavioral archetypes. Across models, social media engagement consistently emerged as the strongest driver of NIL valuation—especially when paired with team visibility and emotionally resonant narratives. Building on those findings, I’d design an experiment to test whether emotionally driven content increases user retention and engagement more effectively than performance-based messaging. I’d A/B test two content strategies with a cohort of mid-tier NCAA athletes: one focused on stats and highlights, the other on personal storytelling and team moments. I’d use SQL to structure and query campaign data by athlete, content type, and engagement tier, then analyze behavioral trends in Python using pandas and seaborn. Logistic regression would help model conversion likelihood, and I’d consider XGBoost if nonlinear patterns emerge. To surface insights, I’d build a Tableau dashboard that combines both exploratory and explanatory views. I’d use line charts to visualize engagement decay over time by content type, cohort retention curves to track repeat interactions across posting intervals, and stacked bar charts to compare conversion funnels between A/B groups. A heatmap matrixcould reveal which combinations of content theme and posting cadence yield the highest engagement lift. Tableau’s interactivity would allow stakeholders to filter by athlete segment or platform, making it easy to identify which emotional triggers resonate most with different audiences. This experiment would explore how authenticity fuels growth loops—an idea I’m excited to bring to Robyn AI, where retention is as much about emotional resonance as it is about reach.

One Gen Z-focused app I absolutely love is Notion. As a Princeton student-athlete managing academics, varsity softball, leadership roles, and community initiatives, my life felt like complete chaos without a system. Notion became my lifeline—not just as a productivity tool, but as a way to create emotional order in an overwhelming world. I used it to build my own ecosystem: academic calendars synchronized with athletic schedules, budget trackers for Saturn marketing campaigns, collaborative event planning pages for my team, habit trackers for personal growth, and even a system for managing travel forms and media day reminders. What made it powerful wasn't just the organization—it was how it let me design workflows that reflected how my brain actually works and what I value most. I think Notion resonates so deeply with Gen Z because we're the generation that refuses one-size-fits-all solutions. We want tools that adapt to our complex, multi-faceted lives rather than forcing us into rigid templates. Notion empowers us to build emotionally intelligent systems—ones that understand our need for both structure and flexibility, productivity and self-expression. What excites me about this for Robyn AI is how it demonstrates Gen Z's hunger for personalized, adaptive experiences that feel genuinely connected to who we are. We don't just want efficient tools; we want tools that help us understand ourselves better and build meaningful connections with others. That's exactly the kind of emotionally intelligent, user-centered approach I see Robyn AI pioneering—and why I'm so excited to help analyze what makes Gen Z tick when it comes to authentic digital connection.