Response transformation and profit decomposition for revenue uplift modeling

Abstract

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 an 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 confirms the merits of the proposed revenue uplift strategy over relevant alternatives including uplift models for conversion 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 campaign profit substantially.

Profit uplift modeling for direct marketing campaigns: approaches and applications for online shops

“…Second, profit is more difficult to model since this outcome is only observable in a few cases but more closely related to the main objective than website visit, purchase, or revenue. The proposed new approaches in this paper extend findings from the field of binary and revenue uplift modeling (e.g., Surry 1999, 2011;Kane et al 2014;Rudaś and Jaroszewicz 2018;Gubela et al 2020) and from the field of two-stage estimation via sample selection (see, e.g., Heckman 1979) and zero-inflated regression (see, e.g., Lambert 1992;Ridout et al 2001) as well as one-stage parameter estimation via ordinary regression and random forest. We show that the new approaches are well suited to select "best" customers as targets for direct marketing campaigns and improve profit.…”

“…Here, logistic regression or decision trees can be applied to estimate model parameters. However, more recently, also revenue uplift modeling approaches have become popular (Gubela et al 2020;Rudaś and Jaroszewicz 2018). The main idea behind this new development is that the revenue uplift more closely relates to economic objectives than a website visit or purchase uplift.…”

“…Another interesting aspect in Table 1 is that most recent uplift modeling approaches rely on transformed outcomes for parameter estimation. This transformation was introduced for binary outcomes in a seminal paper (Lai 2006) and later extended to continuous outcomes (Gubela et al 2020;Rudaś and Jaroszewicz 2018). The main idea behind it is to transfer as much information as possible from the observed responses in the two groups into the dependent variable and so being able to directly estimate the uplift model parameters (a so-called direct model).…”

“…As already discussed, most uplift modeling approaches reflect binary outcomes. Only recently, continuous outcomes have received more interest, e.g., in the papers by Rudaś and Jaroszewicz (2018) as well as Gubela et al (2020). This is surprising since, from the beginning of the development of uplift modeling approaches, also datasets with continuous outcomes have been made available.…”

Changing perspectives: Using graph metrics to predict purchase probabilities

“…However, profitable targeting must consider the effect of treating a customer in relation to the cost of treatment. Prior research tends to neglect application-specific profit and cost as decision variables and instead assume an external restriction on the number of customers to target (Ascarza, 2018;Gubela et al, 2020). While there exists work that explicitly develops targeting policies that optimize the profit of the marketing campaign , these policies are restricted to settings in which the cost of the treatment is known at the time of the targeting decision, e.g.…”