Purchase Behaviors Of U.S. Sports Fans - Sample

Sports Innovation Lab uses multiple licensed data sets to compile a picture of how sports fans in the United States behave. The core data is seven years of daily credit card and debit card purchase data on over 20 million anonymized individual fans.

Our proprietary models allow customers to see how specific sets of fans (e.g. NY Yankees Fans or NFL Fans in Ohio) spend on different categories, retailers, and brands. The data can be used to identify the behaviors and unique attributes of high value fans, new fans, and competitive fan communities, as well as:

- Uncover loyalty, churn, and wallet share of key sport communities including OTT subscribers and sports bettors
-Reveal the value of newly acquired fans vs. retained fans
-Identify life stage, lifestyle, and sports fandom habits and preferences of fans
-Track spending trends over time

Sample tables in this listing include:
- OTT Network Win/Loss Analysis of video streamers
- Change in Spend by WNBA Fans on CarMax Following Sponsorship in 2020 vs. General Population
- Racing Fan Community Composition By Characteristic
- Retained vs. New Fan Contribution to Revenue For NY Yankees
- Geographic Distribution of MLS Fans in the U.S.

体育创新实验室（Sports Innovation Lab）依托多份授权数据集，全面勾勒出美国体育球迷的行为图景。其核心数据集涵盖超2000万名匿名个体球迷长达7年的每日信用卡及借记卡消费数据。
本机构的专有模型可支持客户精准查询特定球迷群体（例如纽约洋基队球迷、俄亥俄州美国国家橄榄球联盟（National Football League, NFL）球迷）在不同消费品类、零售商及品牌间的支出情况。该数据集可用于识别高价值球迷、新晋球迷及具有竞争力的球迷社群的行为模式与独特属性，同时可实现以下应用：
- 挖掘包括过顶流媒体（Over The Top, OTT）订阅用户、体育投注者在内的核心体育社群的用户忠诚度、流失率及钱包份额
- 对比分析新晋获客球迷与留存球迷的价值差异
- 识别球迷的人生阶段、生活方式及体育观赛习惯与偏好
- 追踪随时间推移的消费趋势变化

本次展示的示例数据表包括：
- 流媒体视频创作者的过顶流媒体（OTT）平台网络盈亏分析
- 2020年赞助活动后，美国女子职业篮球联赛（Women's National Basketball Association, WNBA）球迷与普通群体在卡迈斯（CarMax）的消费变化对比
- 按特征划分的赛车球迷社群构成
- 纽约洋基队留存球迷与新晋球迷对营收的贡献对比
- 美国职业足球大联盟（Major League Soccer, MLS）球迷的地域分布情况

搜集汇总

背景与挑战

背景概述

该数据集整合了2000万美国体育迷7年间的匿名消费记录，通过专有模型分析不同粉丝群体（如特定球队或地区粉丝）的消费模式和偏好，包含OTT订阅、赞助效果等多个维度的分析示例表。