Week 10: Zero Support Tickets, the Final Case Study, and the Finish Line

Ten weeks ago, I set out to answer a question that plagues almost every early-stage startup: How do you translate a mountain of noisy user feedback into actual, shipped product features when you barely have enough engineering resources to keep the lights on?

Welcome to the final entry of my senior project blog. This week, the coding officially stopped, and the data synthesis began. It was time to look at the analytics, format the academic case study, and see if the Creator Management System I shipped at SideShift actually solved the problem.

The Results
In product engineering, your code is only as good as the metrics it moves. I pulled the final PostHog analytics and Firebase data to compare our Week 2 baselines against our post-launch reality. The results absolutely blew my mind:
– Administrative Burden: Down from a self-reported 3+ hours a week to a measured average session length of just 42 seconds. Creators log in, see their money is processing, and log out.
– Approval Velocity: By embedding the “Approve” button directly inside the Whop Chat UI, the average time it takes a brand to approve content plummeted from 3.2 days to just 14 hours.
– User Satisfaction: We captured a 4.8/5.0 CSAT score from our active users.
– Support Ticket Volume: This is my favorite metric. In the two weeks post-launch, manual support tickets asking “Has this been paid yet?” dropped from an average of 14 per week to zero. We completely automated the anxiety out of the workflow.

The Replicable Framework
Because this, at its core, is a research project, my final deliverable had to be something other startups could actually use. Drawing from my successes (and my stressful pivots, like killing the Kanban board and scrapping Stripe for Whop), I finalized my academic Case Study document.

Within the paper, I formalized the Feedback-to-Feature Acceleration Model. It’s a blueprint for resource-constrained teams that includes:
1. Categorical Discovery: Using strict multiple-choice surveys to prevent qualitative data sprawl.
2. Dynamic RICE Prioritization: Using objective math to rank features, but keeping the “Effort” variable highly flexible if you discover a third-party SDK (like we did with Whop Chat).
3. Tech-Debt Auditing: Identifying infrastructure roadblocks before designing the UI.
4. Context-Embedded Actions: Placing state-change buttons exactly where the conversation happens.
5. The “Fast-Follow” Loop: Leaving a 10% engineering buffer post-launch to build quick, high-value tools requested by power users (like our PDF Invoice generator).

Preparing for the Symposium
For the upcoming Senior Project Symposium, I initially wanted to do a live coding demonstration. But as any developer knows, relying on live third-party APIs on a school Wi-Fi network is a recipe for disaster. Instead, I spent a day recording and editing a demo video showcasing all the features in the final software.

It has been an incredibly challenging, intense, and rewarding ten weeks. I’ve learned that the gap between academic theory (like the Lean Startup methodology) and actual startup practice is much narrower than people think; you just need the discipline to apply it.

A massive thank you to my internal advisor Mr. Daniel Ong, my external advisor Drew Levin, and the entire team at SideShift for their support in granting me access to the data and insights I needed to see this project through.

If you want to see the final app in action and read the full case study, come see my presentation at the Senior Project Symposium!