基于细粒度混杂平衡的营销效果评估方法

Marketing-Effect Estimation Based on Fine-grained Confounder Balancing

营销效果评估是精准化市场营销的重要依据。虽然因果效应估计为营销效果评估提供了有效的研究框架,但现有因果效应估计方法主要针对群体因果效应评估问题而设计,在基于细粒度个体的因果效应评估时往往面临着用户时序特征描述困难、时序非时序混合特征混杂因子选择稳定性差等问题。针对上述问题,提出了细粒度混杂平衡的营销效果评估方法。首先,引入长短记忆神经网络对用户时序特征进行建模;然后,采用稀疏多层神经网络从时序和非时序属性等混杂因子中学习样本权重;最后,采用上阶段学习到的样本权重对营销效果进行独立评估。实验结果验证了本文提出的时序特征建模和近邻匹配思想可降低效果评估的偏差、提升稳定性,对于营销策略优化具有参考意义。

The marketing effect estimation is an important issue for precision marketing.Although the causal effect estimation has provided the research framework for this problem,the existing method mainly focuses on the coarsegrained causal effect estimation on a group of users.When dealing with the fine-grained evaluation problem on an individual user,the existing methods usually fail to solve the challenges raised by the user temporal feature modeling and high dimensional confounder selection.To address the above challenges,a fine-grained deep confounder balancing marketing effect estimation method is proposed.Firstly,a long and short memory network is introduced to model the users’temporal features.Secondly,a neural network with multi-sparse-connected layers is devised to construct the confounders balancing weight network for both temporal and non-temporal features.Finally,the marketing effect is evaluated using the balancing weight learned in the previous stage.It shows that the time feature modeling and nearest neighbor matching ideas proposed in this paper can reduce the deviation of the effect estimation and improve the stability,it has reference significance for the optimization of marketing strategy.