购物中心的客户上下文分析:一种结合语义行为和智能手机的室内定位的方法
Ye Tian1, Yanlei Gu2, Qianwen Lu1
1Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究引入了一种用于购物中心线下客户上下文分析 (CCA) 的新系统. 它整合了客户行为和位置数据,以增强零售商的营销决策.
科学领域:
- 营销 营销 分析 分析
- 行为科学 行为科学
- 计算机科学 计算机科学
背景情况:
- 线下客户背景分析 (CCA) 对于零售营销决策至关重要.
- 在实体店了解客户的兴趣和购买行为是一项挑战.
研究的目的:
- 为线下客户环境分析提出一个综合系统.
- 为了增强实体零售商的营销决策.
主要方法:
- 使用时间频域分析进行客户行为建模,区分运动相关行为 (MB) 和语义相关行为 (SB).
- 基于深度学习的行人死亡计算 (DPDR) 用于商店级自主定位,辅助行为识别和节点地图.
主要成果:
- 该系统有效地整合了客户行为和位置数据,以实现全面的CCA.
- 与运动相关的行为有助于本地化,而定位则完善语义行为识别,使准确的CCA生成成为可能.
结论:
- 拟议的系统为线下客户上下文分析提供了强大的解决方案.
- 这种方法为零售商提供了宝贵的见解,以优化营销策略和客户参与.
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