Recently, there is a growing number of off-line stores that are willing to conduct customer behavior analysis. In particular, predicting revisit intention is of prime importance, because converting first-time visitors to loyal customers is very profitable. Thanks to noninvasive monitoring, shopping behaviors and revisit statistics become available from a large proportion of customers who turn on their mobile devices. In this paper, we propose a systematic framework to predict the revisit intention of customers using Wi-Fi signals captured by in-store sensors. Using data collected from seven flagship stores in downtown Seoul, we achieved 67–80% prediction accuracy for all customers and 64–72% prediction accuracy for first-time visitors. The performance improvement by considering customer mobility was 4.7–24.3%. Furthermore, we provide an in-depth analysis regarding the effect of data collection period as well as visit frequency on the prediction performance and present the robustness of our model on missing customers. We released some tutorials and benchmark datasets for revisit prediction at https://github.com/kaist-dmlab/revisit.

中文翻译：

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利用店内传感器预测客户回头率的系统框架

最近，越来越多的离线商店愿意进行客户行为分析。尤其重要的是，预测重新访问的意图非常重要，因为将首次访问者转化为忠实客户是非常有利的。由于采用了非侵入式监控，因此打开了移动设备的大部分客户都可以获得购物行为和重新访问统计信息。在本文中，我们提出了一个系统框架，可使用店内传感器捕获的Wi-Fi信号来预测客户的重访意愿。使用从首尔市中心的7家旗舰店收集的数据，我们为所有客户实现了67-80％的预测准确性，为首次访问者实现了64-72％的预测准确性。考虑到客户移动性，性能提升为4.7–24.3％。此外，我们提供了有关数据收集时间以及访问频率对预测效果的影响的深入分析，并提供了我们模型对缺失客户的鲁棒性。我们在https://github.com/kaist-dmlab/revisit上发布了一些用于重新访问预测的教程和基准数据集。