Simulated Full-Funnel Analytics for a Startup Professional Women’s Hockey League
As part of my portfolio, I designed and executed a comprehensive mock analytics project for a fictional sports league — the Women’s Elite Hockey Federation (WEHF). This case study demonstrates how I would approach building an analytics ecosystem from scratch for a professional sports property.
🔧 What I Built
- SQL-Based Data Warehouse: Created realistic, relational tables for fans, ticketing, email campaigns, web traffic, and merchandise. All fan data used realistic geographic distribution to simulate real-world league markets.
- Power BI Dashboard Suite: Developed a 7-page report covering KPIs, ticketing funnels, fan segmentation, digital engagement, merchandise sales, and market expansion.
- Data Modeling & DAX: Built lifetime value metrics, renewal rate analysis, email campaign scoring, and web session flow logic using custom measures.
📊 Key Analyses
- Ticket Sales Funnel: Identified conversion drop-offs between ticket browsing and checkout, and modeled differences by weekday vs. weekend, game quality, and fan type.
- Email Campaign Performance: Analyzed open/click behavior, conversion rates, and timing impacts. Highlighted oversaturation issues and underperforming creative.
- Web Behavior Sankey: Mapped session flows across the site, flagged high-bounce pages, and recommended mobile UX improvements.
- Fan Segmentation: Built cohorts by market, engagement level, and purchase behavior. Identified key markets with strong digital signals and low activation costs.
- Merchandising & Revenue: Revealed underperformance outside core teams and suggested localized and performance-themed product ideas.
- Market Expansion: Used early fan signals in non-core cities to identify low-cost testing opportunities for league growth.
🧠 Why This Project Matters
This mock case study reflects how I think and operate in a Business Analytics role, balancing technical execution with strategic impact. I leaned into realistic fan behavior, imperfect data, and market variability, just like you’d expect in a startup league environment.
I treated this project as if I was leading analytics for a real league, not just building dashboards, but generating insights that drive business decisions across departments: marketing, sales, digital, and ops.
