distinctlymktgllc / fanflux_v2

Fanflux: Understanding and Engaging Sports Fans

Fanflux is a powerful analytics platform designed to provide in-depth insights into sports fandom across various leagues and demographics. This project leverages advanced data visualization and interactive filters to help brands, teams, and marketers better understand their audience and tailor their strategies accordingly.

Key Features Comprehensive Data Analysis Integrates data from multiple sports leagues (MLB, NBA, NFL, NHL). Utilizes survey data and Google Trends to quantify current and potential fans.

Interactive Filters Allows users to filter data by fandom level, race, league, team, and income level. Real-time updates to visualizations and scorecards based on user selections. Visual Insights Maps showing geographical distribution of fans. Scorecards summarizing fan intensity and economic potential.

How It Works

Data Sources Survey Data: Provides detailed insights into fan demographics and behaviors. Google Trends: Offers additional context by analyzing search patterns related to sports teams.

Key Metrics Current Fans: Individuals who actively follow and engage with a team. Potential Fans: Individuals who have the propensity to become fans based on demographic and geographical factors. Monetized Fans: Fans who contribute to team revenue through direct and indirect purchases.

Analytical Approach Current and Potential Fans by DMA/City and Demographic: Combines survey data and Google Trends to estimate the size and economic impact of both current and potential fan bases. Fan Worth Methodology: Calculates the economic value of converting potential fans into monetized fans.

User Guide

Sidebar Menu The sidebar allows you to navigate between different sections:

Home: Overview of Fanflux and its objectives. Leagues Analysis: Detailed analysis across different leagues. Chatbot: Interact with an AI-powered assistant for additional insights. Filters Use the filters on the left side to narrow down the data:

Fandom Level: Choose between Avid, Casual, and Convertible fans. Race: Filter by racial demographics. League: Select the league (MLB, NBA, NFL, NHL). Team: Focus on specific teams within the selected league. Income Level: Analyze fan engagement across different income brackets.

Visualizations Maps: Show the geographical distribution of fans based on your filter selections. Scorecards: Display total counts of Avid, Casual, and Convertible fans. These numbers always represent the total fans based on the selected filters, not just the sampled data points.

Important Notes Sampling To ensure fast performance, the map visualization defaults to showing a sample of 1,000 data points. However, the scorecards always reflect the total number of fans based on the current filters, providing a comprehensive view of the data. This approach balances performance with accuracy, ensuring users can interact with the data quickly without compromising on insights.

Fan Engagement Levels Based on insights from the R/GA report, fan engagement has evolved:

On-Demand Fans: Engage with sports through broader cultural and social contexts, not just live games. Avid Fans: Traditional, deeply engaged fans who follow games and statistics closely. Casual Fans: Less engaged, but still contribute to the overall fanbase and potential monetization.

Conclusion Fanflux is designed to bridge the gap between data and actionable insights, helping sports organizations and brands to better understand and engage their audience. By leveraging advanced data analytics and interactive visualizations, Fanflux provides a comprehensive toolset for maximizing fan engagement and economic potential.