Predicting incremental sales in personalised marketing campaigns with Netlift science

Abstract:

Heterogenous treatment and its effects have a verity of applications in diverse fields such as medicine, psychology, and marketing. In marketing it is generally used to identify the target customers who will respond to a campaign if they get the exposure. In this paper, we are solving a different problem which is to predict the incremental sales generated by the customers who get the treatment. This problem is tougher to solve since conversion value won’t be available before the intervention so usual supervised machine learning algorithms where we attempt to predict an outcome directly from given observations cannot be applied here. In order to solve this problem, we are using R-meta-learner. This technique estimates marginal effects and treatment propensities to form an objective function which isolates the causal component of signal. We further develop a framework which generalizes this meta learner for non-linear functions. We have implemented this approach using gradient boosting and neural networks and the results show significant improvement from baseline.