Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

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

2019 SDSU Data Science Symposium Program

https://openprairie.sdstate.edu/ds_symposium_programs/1001/thumbnail.jp

Uncertainty representation and risk management for direct segmented marketing

Mining for truly responsive customers has become an integral part of customer portfolio management, and, combined with operational tactics to reach these customers, requires an integrated approach to meeting customer needs that often involves the application of concepts from traditionally distinct fields: marketing, statistics, and operations research. This article brings such concepts together to address customer value and revenue maximization as well as risk minimization for direct marketing decision making problems under uncertainty. We focus on customer lift optimization given the uncertainty associated with lift estimation models, and develop risk management and operational tools for the multiple treatment (recommendation) problem using stochastic and robust optimization techniques. Results from numerical experiments are presented to illustrate the effect of incorporating uncertainty on the performance of recommendation models