In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%).
Uplift modeling, also known as incremental modeling, true lift modeling, or net-lift modeling is a predictive modeling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behavior. Uplift modeling has applications in customer relationship management for up-sell, cross-sell and retention modeling. It has also been applied to personalized medicine. Unlike the related Differential Prediction concept in psychology, Uplift modeling assumes an active agent. All of your marketing effort are about Return on Investment (ROI), ultimately, unless you are a non-profit.
This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action organically. Our approach draws on strengths of causal inference, uplift modeling, and multi-armed bandits. It optimizes on causal treatment effects rather than pure outcome, and incorporates counterfactual generation within data collection. Following uplift modeling results, we optimize over the incremental business metric. Multi-armed bandit methods allow us to scale to multiple treatments and to perform off-policy policy evaluation on logged data. The Thompson sampling strategy in particular enables exploration of treatments on similar customer contexts and materialization of counterfactual outcomes. Preliminary offline experiments on a retail Fashion marketing dataset show merits of our proposal.
Uplift models support decision-making in marketing campaign planning. Estimating the causal effect of a marketing treatment, an uplift model facilitates targeting communication to responsive customers and efficient allocation of marketing budgets. Research into uplift models focuses on conversion models to maximize incremental sales. The paper introduces uplift modeling strategies for maximizing incremental revenues. If customers differ in their spending behavior, revenue maximization is a more plausible business objective compared to maximizing conversions. The proposed methodology entails a transformation of the prediction target, customer-level revenues, that facilitates implementing a causal uplift model using standard machine learning algorithms. The distribution of campaign revenues is typically zero-inflated because of many non-buyers. Remedies to this modeling challenge are incorporated in the proposed revenue uplift strategies in the form of two-stage models. Empirical experiments using real-world e-commerce data confirm the merits of the proposed revenue uplift strategy over relevant alternatives including uplift models for conver-sion and recently developed causal machine learning algorithms. To quantify the degree to which improved targeting decisions raise return on marketing, the paper develops a decomposition of campaign profit. Applying the decomposition to a digital coupon targeting campaign, the paper provides evidence that revenue uplift modeling, as well as causal machine learning, can improve cam-paign profit substantially.
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. It has been widely and successfully used in healthcare analytics and business operations, where one tries to measure the net effect of a new medicine on patients or to understand the impact of a marketing campaign on company revenue. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). This new formulation allows us to handle the lack of explicit labels, to deal with any number of actions (in comparison to the normal two action uplift modeling), and to apply it to applications with responses of general types, which is a challenging task for previous methods. Furthermore, we also design an unbiased metric for more accurate offline evaluation of uplift effects, set up a better reward function for the policy gradient method to solve the problem and adopt some action-based baselines to reduce variance. We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that our method can achieve significant improvement over previous methods.