A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling

Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature. Many algorithms have been proposed for uplift modeling and some have generated promising results on various data sets. Yet little is known about the theoretical properties of these algorithms. In this paper, we propose a new tree-based ensemble algorithm for uplift modeling. Experiments show that our algorithm can achieve competitive results on both synthetic and industry-provided data. In addition, by properly tuning the "node size" parameter, our algorithm is proved to be consistent under mild regularity conditions. This is the first consistent algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin

Affordable Uplift: Supervised Randomization in Controlled Experiments

Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment assignment is costly, since random targeting deviates from an established targeting policy. To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization. Supervised randomization is a novel approach that integrates existing scoring models into randomized trials to target relevant customers, while ensuring consistent estimates of treatment effects through correction for active sample selection. An empirical Monte Carlo study shows that data collection under supervised randomization is cost-efficient, while downstream uplift models perform competitively

Uplift Modeling in Direct Marketing, Journal of Telecommunications and Information Technology, 2012, nr 2

Marketing campaigns directed to randomly selected customers often generate huge costs and a weak response. Moreover, such campaigns tend to unnecessarily annoy customers and make them less likely to answer to future communications. Precise targeting of marketing actions can potentially results in a greater return on investment. Usually, response models are used to select good targets. They aim at achieving high prediction accuracy for the probability of purchase based on a sample of customers, to whom a pilot campaign has been sent. However, to separate the impact of the action from other stimuli and spontaneous purchases we should model not the response probabilities themselves, but instead, the change in those probabilities caused by the action. The problem of predicting this change is known as uplift modeling, differential response analysis, or true lift modeling. In this work, tree-based classifiers designed for uplift modeling are applied to real marketing data and compared with traditional response models, and other uplift modeling techniques described in literature. The experiments show that the proposed approaches outperform existing uplift modeling algorithms and demonstrate significant advantages of uplift modeling over traditional, response based targeting

Dynamic Uplift Modeling

In this thesis, a new approach to Uplift modeling which considers time dependent behavior of the customers is analyzed. Uplift modeling attempts to measure the impact of a treatment on an entity in a controlled experiment. While the overall incremental effect can be measured indirectly (i.e., the average performance of a treatment group over a statistically equivalent control group), the entity-specific performance cannot be determined. It has applications in business, insurance, banking, personalized medicine, and other fields. Direct marketing, a multi-billion dollar field in the US alone, is a key area in which uplift modeling is studied and can have a significant financial impact. In direct marketing, the entities studied are customers and the treatments are various direct-to-consumer promotions delivered through mail, email, social media, etc. Simulated customer and campaign datasets which reflects the naturally observed trends are used to analyze the effectiveness of various modelling approaches. Research on Uplift modeling specific to above mentioned fields started in the beginning of 21st century even though the idea of Uplift is present before that. Researchers have introduced a wide range of uplift modeling approaches. These approaches broadly include two model approach, additive model approach and unified modeling approach. But all of the research until now has considered this as a static problem, modeled at a single instance of time. The method introduced in this work considers modeling uplift in a dynamic environment and simulates the periodic purchasing behavior of the customer. In contrast to static uplift models, the uplift in the purchase probability of the customers considered in this problem is dependent on time as well as customer’s previous purchases and offers received. In addition, the model will not have direct access to all the parameters effecting customer actions, but it has to learn them with time. The effectiveness of various modeling approaches, two model approach, additive model approach and unified modeling approach is analyzed in this work for dynamic uplift modeling. Appropriate modifications are made to these methods for adapting them to the longitudinal paradigm. The results obtained from these models are compared to the model with zero treatment and random treatment. This study demonstrates significant potential for both researches and retail companies for thinking about the problem of uplift longitudinally. Retail companies can use the methodology used for data generation for matching the customer purchase data available with them. The model built from there can be used both to design direct marketing campaigns as well as to predict future purchases