Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods

Kane, Kathleen; Lo, Victor S. Y.; Zheng, Jane

doi:10.1057/jma.2014.18

Cited by 53 publications

(62 citation statements)

References 10 publications

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“…We formally introduce this approach in Section 3. Lai et al (2006) propose an alternative strategy, Lai's weighted uplift method (LWUM), which was further refined by Kane et al (2014). The approach involves a modification of the target variable.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The continuous prediction target prohibits the use of existing uplift modeling approaches for conversion, where the target variable is binary. A first contribution of the paper is the extension of the binary target variable transformation (Lai et al, 2006), a competitive method to develop conversion uplift models (Devriendt et al, 2018;Kane et al, 2014), to accommodate a continuous target variable. We propose two transformations that facilitate developing a revenue uplift model using SML algorithms for regression or classification.…”

Section: Introductionmentioning

confidence: 99%

“…However, a model that gives biased estimates of the CATE -or foregoes its estimation altogether -can still be a valuable uplift model if the relative order of customers in the ranked list is accurate. Uplift modeling strategies such as the class variable transformation, which display appealing results in several benchmarks (Devriendt et al, 2018;Gubela et al, 2019;Kane et al, 2014), and their extensions as proposed in this paper, belong to the latter category. In view of recent advancements in the literature on treatment effects and the development of several highly recognized methods such as causal forests , causal boosting (Powers et al, 2018), causal BART , or the X-learner of Künzel et al (2019), it is interesting to examine whether these CATE estimators are a suitable vehicle for revenue uplift modeling.…”

Section: Introductionmentioning

confidence: 99%

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Response transformation and profit decomposition for revenue uplift modeling

Gubela

Lessmann

Jaroszewicz

2020

European Journal of Operational Research

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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.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Response transformation and profit decomposition for revenue uplift modeling

Gubela

Lessmann

Jaroszewicz

2020

European Journal of Operational Research

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“…Several works investigate combining such trees into ensembles (Guelman et al 2012;Sołtys et al 2014). Work on linear uplift models includes approaches based on class variable transformation (Lai 2006;Jaśkowski and Jaroszewicz 2012;Kane et al 2014;Pechyony et al 2013) used with logistic regression and approaches based on Support Vector Machines (Kuusisto et al 2014;Jaroszewicz 2013, Oct 2017). Those works only address the problem of classification and do not provide theoretical analyses which would clearly demonstrate the merits of each approach.…”

Section: Literature Overviewmentioning

confidence: 99%

“…The idea is quite simple: the class in the control training set is reversed, both training sets are concatenated (possibly with some weighting of cases) and a single classifier is built on such a combined dataset. The approach was first mentioned by Lai (2006), rediscovered and formally justified in Jaśkowski and Jaroszewicz (2012), some additional analyses were provided in Kane et al (2014).…”

Section: The Uplift Regression Estimatormentioning

confidence: 99%

Linear regression for uplift modeling

Rudaś

Jaroszewicz

2018

Data Min Knowl Disc

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The purpose of statistical modeling is to select targets for some action, such as a medical treatment or a marketing campaign. Unfortunately, classical machine learning algorithms are not well suited to this task since they predict the results after the action, and not its causal impact. The answer to this problem is uplift modeling, which, in addition to the usual training set containing objects on which the action was taken, uses an additional control group of objects not subjected to it. The predicted true effect of the action on a given individual is modeled as the difference between responses in both groups. This paper analyzes two uplift modeling approaches to linear regression, one based on the use of two separate models and the other based on target variable transformation. Adapting the second estimator to the problem of regression is one of the contributions of the paper. We identify the situations when each model performs best and, contrary to several claims in the literature, show that the double model approach has favorable theoretical properties and often performs well in practice. Finally, based on our analysis we propose a third model which combines the benefits of both approaches and seems to be the model of choice for uplift linear regression. Experimental analysis confirms our theoretical results on both simulated and real data, clearly demonstrating good performance of the double model and the advantages of the proposed approach.

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Uplift Modeling

2017

Profit‐Driven Business Analytics

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Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods

Cited by 53 publications

References 10 publications

Response transformation and profit decomposition for revenue uplift modeling

Response transformation and profit decomposition for revenue uplift modeling

Linear regression for uplift modeling

Uplift Modeling

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