用视频分析顾客行为，挖掘购物偏好以提升销售。
Customer Analytics using Surveillance Video
- 融合年龄性别与停留时长的加权聚类算法识别兴趣商品
- 通过表情和动作特征关联顾客与目标服装，准确率显著提升
- 适合零售业做用户画像和精准营销策略制定
销售数据分析是制定有效营销策略的关键步骤。本文提出一种新方法，通过分析顾客购物行为以识别其购买模式。采用改进的多簇重叠k均值扩展（MCOKE）算法结合加权k均值算法，将顾客映射至感兴趣的服装类别。利用顾客的年龄、性别、停留时间以及选购时的表情特征，建立顾客或群体与目标服装之间的关联。对零售企业客户群体的研究有助于推断消费者偏好，支持制定有效商业策略，从而提升客户满意度、忠诚度、销售额与利润。
原文摘要 · Abstract (English)
The analysis of sales information, is a vital step in designing an effective marketing strategy. This work proposes a novel approach to analyse the shopping behaviour of customers to identify their purchase patterns. An extended version of the Multi-Cluster Overlapping k-Means Extension (MCOKE) algorithm with weighted k-Means algorithm is utilized to map customers to the garments of interest. The age & gender traits of the customer; the time spent and the expressions exhibited while selecting garments for purchase, are utilized to associate a customer or a group of customers to a garments they are interested in. Such study on the customer base of a retail business, may help in inferring the products of interest of their consumers, and enable them in developing effective business strategies, thus ensuring customer satisfaction, loyalty, increased sales and profits.
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