Case Study - Turn your dating data into insights

Overview

Dating apps generate massive amounts of data, but users rarely get meaningful insights from it. SwipeStats changes that by giving people a clear, honest view of their dating app behavior through comprehensive data analysis.

Users can upload their Tinder data export anonymously and receive detailed visualizations covering match rates, conversation patterns, response times, peak activity periods, and more. The analytics reveal patterns most people don't notice: which photos perform best, when they're most active, how their swipe behavior compares to others, and where conversations tend to drop off.

The platform launched with strong organic growth, driven by word-of-mouth and Reddit communities. As the user base grew, the business model proved itself: the product achieved profitability, attracted enterprise interest from dating app companies, and built an engaged community of users sharing insights.

The tech stack (Next.js for the frontend, TypeScript for type safety, and MongoDB for flexible data storage) enabled rapid feature development while handling sensitive personal data with appropriate privacy safeguards. All data processing happens with anonymization, and users maintain full control over their information.

Future development focuses on expanding to additional dating platforms beyond Tinder, deeper behavioral analytics, and enterprise features for dating app companies to better understand user behavior patterns.

What I built