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Uplift modelling, also known as incremental modelling, true lift modelling, or net modelling is a predictive modelling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behaviour.
Uplift modelling has applications in customer relationship management for up-sell, cross-sell and retention modelling. It has also been applied to political election and personalised medicine. Unlike the related Differential Prediction concept in psychology, Uplift Modelling assumes an active agent.
However, many marketers define lift (rather than uplift) as the difference in response rate between treatment and control, so uplift modeling can be defined as improving (upping) lift through predictive modeling.
The table below shows the details of a campaign showing the number of responses and calculated response rate for a hypothetical marketing campaign. This campaign would be defined as having a response rate uplift of 5%. It has created 50,000 incremental responses (100,000 - 50,000).
| Treated | 1,000,000 | 100,000 | 10% |
| Control | 1,000,000 | 50,000 | 5% |
This model would only use the treated customers to build the model.
In contrast uplift modeling uses both the treated and control customers to build a predictive model that focuses on the incremental response. To understand this type of model it is proposed that there is a fundamental segmentation that separates customers into the following groups (their names were suggested by N. Radcliffe and explained in N. Radcliffe (2007). Identifying who can be saved and who will be driven away by retention activity. Stochastic Solution Limited)
The only segment that provides true incremental responses is the Persuadables.
Uplift modelling provides a scoring technique that can separate customers into the groups described above.
Traditional response modelling often targets the Sure Things being unable to distinguish them from the Persuadables.
Victor Lo also published on this topic in The True Lift Model (2002),Lo, V. S. Y. (2002); The True Lift Model, ACM SIGKDD Explorations Newsletter, Vol. 4, No. 2, 78–86, available at http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=4FD247B4987CBF2E29186DACE0D40C3D?doi=10.1.1.99.7064&rep=rep1&type=pdf and later Radcliffe again with Using Control Groups to Target on Predicted Lift: Building and Assessing Uplift Models (2007).Radcliffe, N. J. (2007); Using Control Groups to Target on Predicted Lift: Building and Assessing Uplift Models, Direct Marketing Analytics Journal, Direct Marketing Association
Radcliffe also provides a very useful frequently asked questions (FAQ) section on his web site, Scientific Marketer. The Scientific Marketer FAQ on Uplift Modelling Lo (2008) provides a more general framework, from program design to predictive modeling to optimization, along with future research areas.Lo, V. S.Y. (2008) “New Opportunities in Marketing Data Mining.” In Encyclopedia of Data Warehousing and Mining, 2nd edition, edited by Wang (2008), Idea Group Publishing.
Independently uplift modelling has been studied by Piotr Rzepakowski. Together with Szymon Jaroszewicz he adapted information theory to build multi-class uplift decision trees and published the paper in 2010. (2025). 9781424491315 ISBN 9781424491315 And later in 2011 they extended the algorithm to multiple treatment case.
Similar approaches have been explored in personalised medicine.Cai, T.; Tian, L.; Wong, P. H.; and Wei, L. J. (2009); Analysis of Randomized Comparative Clinical Trial Data for Personalized Treatment Selections, Harvard University Biostatistics Working Paper Series, Paper 97 (2025). 9783642387081 ISBN 9783642387081 Szymon Jaroszewicz and Piotr Rzepakowski (2014) designed uplift methodology for survival analysis and applied it to randomized controlled trial analysis. Yong (2015) combined a mathematical optimization algorithm via dynamic programming with machine learning methods to optimally stratify patients.Yong, F.H. (2015), "Quantitative Methods for Stratified Medicine," PhD Dissertation, Department of Biostatistics, Harvard T.H. Chan School of Public Health, http://dash.harvard.edu/bitstream/handle/1/17463130/YONG-DISSERTATION-2015.pdf?sequence=1 .
Uplift modelling is a special case of the older psychology concept of Differential Prediction. (2025). 9783642334597 ISBN 9783642334597 In contrast to differential prediction, uplift modelling assumes an active agent, and uses the uplift measure as an optimization metric.
Uplift modeling has been recently extended and incorporated into diverse machine learning algorithms, like Inductive Logic Programming, Bayesian Network, Statistical relational learning, Support Vector Machines,
Even though uplift modeling is widely applied in marketing practice (along with political elections), it has rarely appeared in marketing literature. Kane, Lo and Zheng (2014) published a thorough analysis of three data sets using multiple methods in a marketing journal and provided evidence that a newer approach (known as the Four Quadrant Method) worked quite well in practice. Lo and Pachamanova (2015) extended uplift modeling to prescriptive analytics for multiple treatment situations and proposed algorithms to solve large deterministic optimization problems and complex stochastic optimization problems where estimates are not exact.
Recent research analyses the performance of various state-of-the-art uplift models in benchmark studies using large data amounts.
A detailed description of uplift modeling, its history, the way uplift models are built, differences to classical model building as well as uplift-specific evaluation techniques, a comparison of various software solutions and an explanation of different economical scenarios can be found here.R. Michel, I. Schnakenburg, T. von Martens (2019). „Targeting Uplift“. Springer,
(2025). 9783662448502 ISBN 9783662448502 Survival Analysis and Ensemble learning.
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