A decision support framework to implement optimal personalized marketing interventions

Guelman, Leo; Guillén, Montserrat; Pérez-Marín, Ana M.

doi:10.1016/j.dss.2015.01.010

Cited by 34 publications

(24 citation statements)

References 37 publications

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“…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%

“…Aiming at comparing customers, an uplift model emphasizes individual-level effects, which correspond to the finest level of conditioning. Since the fundamental problem of causal inference (Holland, 1986) renders individualized effects unobservable, a more common term is that of on an individualized average treatment effect; sometimes also called personalized treatment effect (Guelman et al, 2015a). In principle, any approach for CATE estimation facilitates 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%

Response transformation and profit decomposition for revenue uplift modeling

Gubela

Lessmann

Jaroszewicz

2020

European Journal of Operational Research

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“…Current design type of research presents new artefacts (frameworks, models, methods etc.) focused on personalized marketing support, among others in the following areas: mobile marketing (Tang et al, 2013), marketing service in an interactive exhibition space (Bae et al, 2012), constructing a behavioural perspective model as a conceptual system for managerial decision making in e-mail marketing (Sigurdsson et al, 2016), or proposing a new method that can be used for selecting the best targets for cross-selling products (Guelman et al, 2015). The main output of this article also belongs to the design type of research.…”

Section: Personalization Of Marketing Activitiesmentioning

confidence: 99%

Business Rules Mining Using GUHA Method for the Personalization of Commercial Offers

Vojíř¹,

Smutný²

2017

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A shift from mass to personalized marketing, and thus also to personalized offers, brings firms an advantage for building customer relationships. The article introduces an artefact in the form of a method for creating personalized offers, focused on companies engaged in e-commerce. This method is based on mining association rules using the GUHA method. The association rules are converted into business rules and then combined with other manually defined business rules. The application of the method in practice is illustrated on customers' data from the e-shop of a company doing business in navigation systems, for the purpose of selecting personalized offers for an e-mailing campaign. The practical illustration uses a combination of the data mining system LISp-Miner and the business rule system JBoss Drools, which are freely available tools. The advantages and limitations of the proposed method, in connection with creating personalized marketing strategies in e-commerce, are discussed. The purpose of this practically oriented article is to address marketing and account managers and the issues connected with the personalization of marketing activities.

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“…Uplift modeling can be implemented in different domains. However, the most common applications are found in the fields of marketing (Lo 2002;Hansotia and Rukstales 2002;Guelman et al 2012Guelman et al , 2014aKane et al 2014;Guelman et al 2015;Gross and Tibshirani 2016;Michel et al 2017;Gubela et al 2017) and personalized medicine (Alemi et al 2009;Jaskowski and Jaroszewicz 2012). Particularly, uplift modeling has helped marketers to increase the return on marketing investment by segmenting the customer base into four categories according to the recommendations of the model.…”

Section: Introductionmentioning

confidence: 99%

A survey and benchmarking study of multitreatment uplift modeling

Olaya

Coussement

Verbeke

2020

Data Min Knowl Disc

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Uplift modeling is an instrument used to estimate the change in outcome due to a treatment at the individual entity level. Uplift models assist decision-makers in optimally allocating scarce resources. This allows the selection of the subset of entities for which the effect of a treatment will be largest and, as such, the maximization of the overall returns. The literature on uplift modeling mostly focuses on queries concerning the effect of a single treatment and rarely considers situations where more than one treatment alternative is utilized. This article surveys the current literature on multitreatment uplift modeling and proposes two novel techniques: the naive uplift approach and the multitreatment modified outcome approach. Moreover, a benchmarking experiment is performed to contrast the performances of different multitreatment uplift modeling techniques across eight data sets from various domains. We verify and, if needed, correct the imbalance among the pretreatment characteristics of the treatment groups by means of optimal propensity score matching, which ensures a correct interpretation of the estimated uplift. Conventional and recently proposed evaluation metrics are adapted to the multitreatment scenario to assess performance. None of the evaluated techniques consistently outperforms other techniques. Hence, it is concluded that performance largely depends on the context and problem characteristics. The newly proposed techniques are found to offer similar performances compared to state-of-the-art approaches.

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A decision support framework to implement optimal personalized marketing interventions

Cited by 34 publications

References 37 publications

Response transformation and profit decomposition for revenue uplift modeling

Response transformation and profit decomposition for revenue uplift modeling

Business Rules Mining Using GUHA Method for the Personalization of Commercial Offers

A survey and benchmarking study of multitreatment uplift modeling

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