Direct Marketing When There Are Voluntary Buyers

Lai, Yi‐Ting; Wang, Ke; Ling, Daphne; Shi, Hua Yu; Zhang, J.

doi:10.1109/icdm.2006.54

Cited by 12 publications

(13 citation statements)

References 7 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%

“…Given that none of the tree-based techniques has been employed for modeling revenue uplift in e-commerce, the evaluation also broadens the scope of empirical results for causal machine learning methods. Causal Forest Cai et al (2011) Two-Step Estimation Procedure Chickering and Heckerman (2000) Uplift Tree with Post-Processing Procedure Diemert et al (2018) x - Guelman et al (2015a) Causal Conditional Inference Tree/Forest Guelman et al (2015b) Uplift Random Forests Gutierrez and Gérardy (2017) -x Hahn et al (2019) Causal Bayesian Regression Trees Hansen and Bowers (2008) x - Hansotia and Rukstales (2002a) Incremental Response Tree Hansotia and Rukstales (2002b) Uplift Tree with the ∆∆ splitting criterion Causal BART Imai and Ratkovic (2013) Uplift Support Vector Machine Jaroszewicz and Rzepakowski (2014) Uplift k-Nearest Neighbors Kane et al (2014) x - Kuusisto et al (2014) Uplift Support Vector Machine Künzel et al (2019) X-Learner Lai et al (2006) x - Lechner (2019) Modified Causal Forests Lo (2002) x - Lo and Pachamanova (2015) Multiple Treatments Logistic Regression Nassif et al (2013) x Oprescu et al (2018) Orthogonal Causal Random Forest Powers et al (2018) Causal boosting Radcliffe and Surry (1999) Uplift Trees Radcliffe and Surry (2011) -x Rzepakowski and Jaroszewicz (2012a) Multiple Treatments Uplift Trees Rzepakowski and Jaroszewicz (2012b) Information Theory-Based Uplift Trees Rudaś and Jaroszewicz (2018) x - Shaar et al (2016) Pessimistic Uplift Shalit et al (2017) Causal Artificial Neural Network Sołtys et al (2015) Uplift Ensemble Methods Su et al (2012) Uplift k-Nearest Neighbors Taddy et al (2016) Causal Bayesian Forests Tian et al (2014) x - Yamane et al (2018) Separate Label Uplift Modeling This study…”

Section: Background and Related Workmentioning

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: Background and Related Workmentioning

confidence: 99%

Response transformation and profit decomposition for revenue uplift modeling

Gubela

Lessmann

Jaroszewicz

2020

European Journal of Operational Research

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show abstract

“…Uplift models for conversion also predict a binary response variable but alter the group definition to model incremental conversions. The underlying learning methods are the same as those used in response Lai's weighted uplift method (LWUM) (Lai et al, 2006). The paper focuses on the latter approach because recent benchmarking results indicate that it often outperforms alternative techniques .…”

Section: Uplift Taxonomymentioning

confidence: 99%

“…The response z i,c equals one for treatment group customers who convert and control group customers who do not convert. Both states represent a success (Lai et al, 2006). In all other cases, z i,c is set to zero.…”

Section: Conversion Response Transformationmentioning

confidence: 99%

Changing perspectives: Using graph metrics to predict purchase probabilities

Baumann

Haupt

Gebert³

et al. 2018

Expert Systems with Applications

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I wish to express my deepest gratitude to my supervisor, Prof. Stefan Lessmann, whose excellent teaching inspired me to start a PhD in machine learning. His support has given me the chance to pursue ideas that still fascinate me and his input and guidance have given me the means to turn these ideas into research. I want to express my gratitude to my second supervisor, Prof. Daniel Guhl, and to Prof. Dr. Daniel Klapper, who have introduced me to the field of quantitative marketing. I also thank Prof. Dr. Bart Baesens for inviting me to work with his group at KU Leuven.

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“…In addition, offline solutions might be biased towards historical data and require dynamic calibration [28] according to long-term changes and seasonality trends, which are particularly common in the travel industry [2] and in promotional campaigns [12]. Dynamic adaptive strategy adjustments is a common practice in cases of budget constraints and were found effective in ads management [17], influence maximization [7,24], marketing budget allocation [1,15,26] and discount personalization [6].…”

Section: Introductionmentioning

confidence: 99%

Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints

Goldenberg¹,

Albert²,

Bernardi³

et al. 2020

Fourteenth ACM Conference on Recommender Systems

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Promotions and discounts have become key components of modern e-commerce platforms. For online travel platforms (OTPs), popular promotions include room upgrades, free meals and transportation services. By offering these promotions, customers can get more value for their money, while both the OTP and its travel partners may grow their loyal customer base. However, the promotions usually incur a cost that, if uncontrolled, can become unsustainable. Consequently, for a promotion to be viable, its associated costs must be balanced by incremental revenue within set financial constraints. Personalized treatment assignment can be used to satisfy such constraints. This paper introduces a novel uplift modeling technique, relying on the Knapsack Problem formulation, that dynamically optimizes the incremental treatment outcome subject to the required Return on Investment (ROI) constraints. The technique leverages Retrospective Estimation, a modeling approach that relies solely on data from positive outcome examples. The method also addresses training data bias, long term effects, and seasonality challenges via online-dynamic calibration. This approach was tested via offline experiments and online randomized controlled trials at Booking.coma leading OTP with millions of customers worldwide, resulting in a significant increase in the target outcome while staying within the required financial constraints and outperforming other approaches. CCS CONCEPTS • Computing methodologies → Learning from implicit feedback; • Information systems → Personalization.

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Direct Marketing When There Are Voluntary Buyers

Cited by 12 publications

References 7 publications

Response transformation and profit decomposition for revenue uplift modeling

Response transformation and profit decomposition for revenue uplift modeling

Changing perspectives: Using graph metrics to predict purchase probabilities

Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraints

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