论文标题
基于直接和持久效应预测的个性化晋升决策
Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions
论文作者
论文摘要
促销活动一直在电子商务市场上趋于发展，以建立客户关系，并指导客户采取所需的行动。由于激励措施可有效吸引客户，并且客户对不同类型的激励措施有不同的偏好，因此随着时间的推移，对个性化促销决策的需求正在增加。 但是，对促销决策的研究专门针对促销期（直接效应）的购买转换，同时通常会忽略促销后的持久效果。为了获得更好的提升投资回报率（LIFT ROI）对促销的持久效果并提高客户的保留和忠诚度，我们建议通过对每个客户的直接和持久响应进行建模来实现多种治疗促进决策的框架。首先，我们提出了一个直接和持久效果（CDEE）模型，该模型可以预测客户的直接和持久响应。在CDEE的预测的帮助下，我们将激励分配个性化，以优化持久效果，同时将成本保持在预算之下。为了估计决策的效果，我们使用随机对照试验（RCT）数据采用公正的业务指标评估方法。我们使用Mercari中的两个促销活动将方法与基准进行比较，并取得明显更好的结果。
Promotions have been trending in the e-commerce marketplace to build up customer relationships and guide customers towards the desired actions. Since incentives are effective to engage customers and customers have different preferences for different types of incentives, the demand for personalized promotion decision making is increasing over time. However, research on promotion decision making has focused specifically on purchase conversion during the promotion period (the direct effect), while generally disregarding the enduring effect in the post promotion period. To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the predictions of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget. To estimate the effect of decision making, we apply an unbiased evaluation approach of business metrics with randomized control trial (RCT) data. We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results.
