As 98 percent of shoppers do not make a purchase on the first visit, we study the problem of predicting whether they would come back for a purchase later (i.e., conversion prediction). This problem is important for strategizing “retargeting”, for example, by sending coupons for customers who are likely to convert. For this goal, we study the following two problems, prediction of market and predictability of customer. First, prediction of market aims at identifying a conversion rate for a given product and its customer behavior modeling, which is an important analytics metric for retargeting process. Compared to existing approaches using either of customer or product-level conversion pattern, we propose a joint modeling of both patterns based on the well-studied buying decision process. Second, we can observe customer-specific behaviors after showing retargeting ads, to predict whether this specific customer follows the market model (high predictability) or not (low predictability). For the former, we apply the market model, and for the latter, we propose a new customer-specific prediction based on dynamic ad behavior features. To evaluate the effectiveness of our methods, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs and retargeting campaign logs. The evaluation results show that conversion predictions and predictability by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment.

中文翻译：

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来自点击流的转化预测：建模市场预测和客户可预测性

由于 98% 的购物者不会在第一次访问时进行购买，因此我们研究了预测他们以后是否会回来购买的问题（即转换预测）。这个问题对于制定“重新定位”的策略很重要，例如，为可能转换的客户发送优惠券。为此，我们研究了以下两个问题，市场预测和客户可预测性。首先，市场预测旨在确定给定产品的转化率及其客户行为模型，这是重新定位过程的重要分析指标。与使用客户或产品级转换模式的现有方法相比，我们提出了基于充分研究的购买决策过程的两种模式的联合建模。第二，我们可以在展示重定向广告后观察特定客户的行为，以预测该特定客户是否遵循市场模型（高可预测性）或不（低可预测性）。对于前者，我们应用市场模型，对于后者，我们提出了一种基于动态广告行为特征的新的特定于客户的预测。为了评估我们方法的有效性，我们对基于一组真实网络日志和重定向活动日志生成的模拟数据集进行了大量实验。评估结果表明，与动态市场环境中的现有基线相比，我们方法的转换预测和可预测性始终更准确和稳健。对于前者，我们应用市场模型，对于后者，我们提出了一种基于动态广告行为特征的新的特定于客户的预测。为了评估我们方法的有效性，我们对基于一组真实网络日志和重定向活动日志生成的模拟数据集进行了大量实验。评估结果表明，与动态市场环境中的现有基线相比，我们方法的转换预测和可预测性始终更准确和稳健。对于前者，我们应用市场模型，对于后者，我们提出了基于动态广告行为特征的新的特定于客户的预测。为了评估我们方法的有效性，我们对基于一组真实网络日志和重定向活动日志生成的模拟数据集进行了大量实验。评估结果表明，与动态市场环境中的现有基线相比，我们方法的转换预测和可预测性始终更准确和稳健。