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基于敬业度的电子商务客户终身价值体系

An Engagement-Based Customer Lifetime Value System for E-commerce
课程网址: http://videolectures.net/kdd2016_vanderveld_value_system/  
主讲教师: Ali Vanderveld
开课单位: Groupon股份有限公司
开课时间: 2016-09-22
课程语种: 英语
中文简介:
全面了解客户个人价值对于任何成功的客户关系管理策略都是至关重要的。这也是打造长期价值回报产品的关键。然而,由于用户级行为的噪声性质和潜在的庞大客户基础,对客户终身价值(CLTV)建模可能充满技术困难。本文介绍了一种解决这些问题的新型CLTV系统。这是在一家大型全球电子商务公司Groupon建立起来的。在Groupon,面对本地商业的独特挑战,意味着要快速迭代新产品和优化库存,以吸引广泛而多样化的受众。考虑到当前的购买频率,我们需要一种更快的方法来确定单个客户的健康状况,考虑到有限的资源,我们需要知道将精力集中在哪里。 我们的CLTV系统采用随机森林模型,以单个用户为基础预测未来价值,该模型包含了几乎所有客户与我们平台关系的特性。这个功能集包括那些通过电子邮件和我们的移动应用程序量化用户粘性的功能,这使我们能够比仅仅基于购买行为的模型更快地预测价值变化。我们进一步分别建模不同的客户类型,例如一次性购买者和高级用户,以便考虑不同的特征权重,并增强结果的可解释性。此外,我们开发了一个经济的评分框架,其中当任何触发事件发生时,我们重新评分用户,否则应用衰减函数,以支持使用复杂模型频繁评分大型客户基础。我们部署了这个系统,每天预测数亿用户的价值,并在我们的产品和业务计划中积极使用。
课程简介: A comprehensive understanding of individual customer value is crucial to any successful customer relationship management strategy. It is also the key to building products for long-term value returns. Modeling customer lifetime value (CLTV) can be fraught with technical difficulties, however, due to both the noisy nature of user-level behavior and the potentially large customer base. Here we describe a new CLTV system that solves these problems. This was built at Groupon, a large global e-commerce company, where confronting the unique challenges of local commerce means quickly iterating on new products and the optimal inventory to appeal to a wide and diverse audience. Given current purchaser frequency we need a faster way to determine the health of individual customers, and given finite resources we need to know where to focus our energy. Our CLTV system predicts future value on an individual user basis with a random forest model which includes features that account for nearly all aspects of each customer’s relationship with our platform. This feature set includes those quantifying engagement via email and our mobile app, which give us the ability to predict changes in value far more quickly than models based solely on purchase behavior. We further model different customer types, such as one-time buyers and power users, separately so as to allow for different feature weights and to enhance the interpretability of our results. Additionally, we developed an economical scoring framework wherein we re-score a user when any trigger events occur and apply a decay function otherwise, to enable frequent scoring of a large customer base with a complex model. This system is deployed, predicting the value of hundreds of millions of users on a daily cadence, and is actively being used across our products and business initiatives.
关 键 词: 管理策略; 快速迭代; 随机森林模型
课程来源: 视频讲座网
数据采集: 2022-12-12:chenjy
最后编审: 2022-12-12:chenjy
阅读次数: 37