解锁高价值的足球迷:用于客户细分和终身价值的无监督机器学习
Karim Chouaten1,2, Cristian Rodriguez Rivero3,4, Frank Nack1
1Faculty of Science, University of Amsterdam, Amsterdam, Netherlands.
Frontiers in sports and active living
|September 9, 2024
概括
本研究引入了一个加权的近期,频率和货币 (RFM) 模型,使用分析层次过程 (AHP) 和机器学习来按客户终身价值 (CLV) 分类足球迷. 它确定了针对性营销和提高利能力的关键粉丝群体.
科学领域:
- 运动营销运动营销
- 客户关系管理 (CRM)
- 数据分析数据分析数据分析.
背景情况:
- 足球俱乐部越来越多地使用数据来获得商业优势和保持球迷.
- 客户细分对于营销至关重要,但RFM在足球中的应用有限.
- 识别和保留高价值粉丝对于利能力至关重要.
研究的目的:
- 解决将RFM分析应用于足球球迷细分的差距.
- 开发一个增强的RFM模型,将AHP和机器学习纳入客户终身价值 (CLV) 分段.
- 为足球俱乐部的营销策略提供可操作的见解.
主要方法:
- 采用一种新的加权的近期,频率和货币 (RFM) 方法.
- 使用分析层次过程 (AHP) 量化RFM组件的意义.
- 利用无监督机器学习进行基于加权的RFM值和估计的客户终身价值 (CLV) 的风扇细分.
主要成果:
- 确定了八个不同的粉丝群,包括"金色粉丝" (高价值) 和"有前途"部分.
- 衍生的特定权重:货币 (0.409),频率 (0.343) 和近期 (0.248).
- 强调需要针对不同的细分市场制定有针对性的战略,例如"需要关注"",新粉丝"和"退出/低价值".
结论:
- 拟议的加权RFM-AHP-ML方法有效地对足球球迷进行CRM细分.
- 可操作的见解使高价值细分市场的优先级和量身定制的营销策略成为可能.
- 该框架支持增强球迷参与度,利能力和足球的长期商业成功.
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