[1]https://en.m.wikipedia.org/wiki/Uplift_modelling
   Implementations include 5 python libraries.  I found UpliftML in a 2021
   review of recommended python libraries.

Uplift modelling

   Uplift modelling, also known as incremental modelling, true lift
   modelling, or net modelling is a [2]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 [3]customer relationship
   management for up-sell, cross-sell and retention modelling. It has also
   been applied to political election and [4]personalised medicine. Unlike
   the related Differential Prediction concept in psychology, Uplift
   Modelling assumes an active agent.

Contents

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   Introduction[5]Edit

   Uplift modelling uses a randomised [6]scientific control to not only
   measure the effectiveness of an action but also to build a predictive
   model that predicts the incremental response to the action. The
   response could be a binary variable (for example, a website
   visit)^[7][1] or a continuous variable (for example, customer
   revenue).^[8][2] Uplift modelling is a [9]data mining technique that
   has been applied predominantly in the financial services,
   telecommunications and retail direct marketing industries
   to [10]up-sell, [11]cross-sell, [12]churn and [13]retention activities.

   Measuring uplift[14]Edit

   The uplift of a marketing campaign is usually defined as the difference
   in response rate between a treated group and a
   randomized control group. This allows a marketing team to isolate the
   effect of a marketing action and measure the effectiveness or otherwise
   of that individual marketing action. Honest marketing teams will only
   take credit for the incremental effect of their campaign.

   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).
    Group  Number of Customers Responses Response Rate
   Treated           1,000,000   100,000           10%
   Control           1,000,000    50,000            5%

   Traditional response modelling[15]Edit

   Traditional response modelling typically takes a group
   of treated customers and attempts to build a predictive model that
   separates the likely responders from the non-responders through the use
   of one of a number of [16]predictive modelling techniques. Typically
   this would use [17]decision trees or [18]regression analysis.

   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 ^[19][3])
     * The Persuadables : customers who only respond to the marketing
       action because they were targeted
     * The Sure Things  : customers who would have responded whether they
       were targeted or not
     * The Lost Causes  : customers who will not respond irrespective of
       whether or not they are targeted
     * The Do Not Disturbs or Sleeping Dogs : customers who are less
       likely to respond because they were targeted

   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.

   Return on investment[20]Edit

   Because uplift modelling focuses on incremental responses only, it
   provides very strong return on investment cases when applied to
   traditional demand generation and retention activities. For example, by
   only targeting the persuadable customers in an [21]outbound
   marketing campaign, the contact costs and hence the return per unit
   spend can be dramatically improved.

   Removal of negative effects[22]Edit

   One of the most effective uses of uplift modelling is in the removal of
   negative effects from retention campaigns. Both in the
   telecommunications and financial services industries often retention
   campaigns can trigger customers to cancel a contract or policy. Uplift
   modelling allows these customers, the Do Not Disturbs, to be removed
   from the campaign.

   Application to A/B and multivariate testing[23]Edit

   It is rarely the case that there is a single treatment and control
   group. Often the "treatment" can be a variety of simple variations of a
   message or a multi-stage contact strategy that is classed as a single
   treatment. In the case of [24]A/B or [25]multivariate testing, uplift
   modelling can help in understanding whether the variations in tests
   provide any significant uplift compared to other targeting criteria
   such as behavioural or demographic indicators.

   History of uplift modelling[26]Edit

   The first appearance of true response modelling appears to be in the
   work of Radcliffe and Surry.^[27][4]

   Victor Lo also published on this topic in The True Lift
   Model (2002),^[28][5] and later Radcliffe again with Using Control
   Groups to Target on Predicted Lift: Building and Assessing Uplift
   Models (2007).^[29][6]

   Radcliffe also provides a very useful frequently asked questions (FAQ)
   section on his web site, Scientific Marketer.^[30][7] Lo (2008)
   provides a more general framework, from program design to predictive
   modeling to optimization, along with future research areas.^[31][8]

   Independently uplift modelling has been studied by Piotr Rzepakowski.
   Together with Szymon Jaroszewicz he adapted [32]information theory to
   build multi class uplift [33]decision trees and published the paper in
   2010.^[34][9] And later in 2011 they extended the algorithm to multiple
   treatment case.^[35][10]

   Similar approaches have been explored in [36]personalised
   medicine.^[37][11]^[38][12] Szymon Jaroszewicz and Piotr Rzepakowski
   (2014) designed uplift methodology for [39]survival analysis and
   applied it to randomized controlled trial analysis.^[40][13] Yong
   (2015) combined a mathematical optimization algorithm via dynamic
   programming with machine learning methods to optimally stratify
   patients.^[41][14]

   Uplift modelling is a special case of the older psychology concept of
   Differential Prediction.^[42][15] 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 [43]machine learning algorithms, like [44]Inductive Logic
   Programming,^[45][15] [46]Bayesian Network,^[47][16] [48]Statistical
   relational learning,^[49][12] [50]Support Vector
   Machines,^[51][17]^[52][18] [53]Survival
   Analysis^[54][13] and [55]Ensemble learning.^[56][19]

   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.^[57][20] 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.^[58][21]

   Recent research analyses the performance of various state-of-the-art
   uplift models in benchmark studies using large data
   amounts.^[59][22]^[60][1]

   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.^[61][23]

   Implementations[62]Edit

In Python[63]Edit

     * [64]CausalML
     * [65]EconML
     * [66]UpliftML
     * [67]PyLift
     * [68]scikit-uplift

Other languages[69]Edit

     * [70]uplift package for [71]R
     * [72]JMP by SAS
     * Portrait Uplift by [73]Pitney Bowes
     * Uplift node for [74]KNIME by Dymatrix
     * Uplift Modelling in [75]Miró by [76]Stochastic Solutions

   Datasets[77]Edit
     * [78]Hillstrom Email Marketing dataset
     * [79]Criteo Uplift Prediction dataset
     * [80]Lenta Uplift Modeling Dataset
     * [81]X5 RetailHero Uplift Modeling Dataset
     * [82]MegaFon Uplift Competition Dataset

   Notes and references[83]Edit
    1. ^ [84]^a [85]^b Devriendt, Floris; Moldovan, Darie; Verbeke, Wouter
       (2018). "A literature survey and experimental evaluation of the
       state-of-the-art in uplift modeling: A stepping stone toward the
       development of prescriptive analytics". Big Data. 6 (1):
       13–41. [86]doi:[87]10.1089/big.2017.0104.
    2. [88]^ Gubela, Robin M.; Lessmann, Stefan; Jaroszewicz, Szymon
       (2020). "Response transformation and profit decomposition for
       revenue uplift modeling". European Journal of Operational
       Research. 283 (2): 647–661. [89]doi:[90]10.1016/j.ejor.2019.11.030.
    3. [91]^ N. Radcliffe (2007). Identifying who can be saved and who
       will be driven away by retention activity. Stochastic Solution
       Limited
    4. [92]^ Radcliffe, N. J.; and Surry, P. D. (1999); Differential
       response analysis: Modelling true response by isolating the effect
       of a single action, in Proceedings of Credit Scoring and Credit
       Control VI, Credit Research Centre, University of Edinburgh
       Management School
    5. [93]^ Lo, V. S. Y. (2002); The True Lift Model, ACM SIGKDD
       Explorations Newsletter, Vol. 4, No. 2, 78–86, available
       at [94]http://www.sigkdd.org/sites/default/files/issues/4-2-2002-12
       /lo.pdf [95]Archived 2015-01-20 at the [96]Wayback Machine
    6. [97]^ Radcliffe, N. J. (2007); Using Control Groups to Target on
       Predicted Lift: Building and Assessing Uplift Models, Direct
       Marketing Analytics Journal, Direct Marketing Association
    7. [98]^ [99]The Scientific Marketer FAQ on Uplift Modelling
    8. [100]^ 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.
    9. [101]^ Rzepakowski, Piotr; Jaroszewicz, Szymon (2010). Decision
       trees for uplift modeling. In Proceedings of the 10th IEEE
       International Conference on Data Mining (ICDM'10). Sydney,
       Australia.
       pp. 441–450. [102]doi:[103]10.1109/ICDM.2010.62. [104]ISBN [105]978
       -1-4244-9131-5.
   10. [106]^ Rzepakowski, Piotr; Jaroszewicz, Szymon
       (2011). [107]"Decision trees for uplift modeling with single and
       multiple treatments". Knowledge and Information Systems. 32 (2):
       303–327. [108]doi:[109]10.1007/s10115-011-0434-0.
   11. [110]^ 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
   12. ^ [111]^a [112]^b Nassif, Houssam; Kuusisto, Finn; Burnside,
       Elizabeth S; Page, David; Shavlik, Jude; Santos Costa, Vitor
       (2013). Score As You Lift (SAYL): A Statistical Relational Learning
       Approach to Uplift Modeling. European Conference on Machine
       Learning (ECML'13). Lecture Notes in Computer Science. 8190.
       Prague.
       pp. 595–611. [113]doi:[114]10.1007/978-3-642-40994-3_38. [115]ISBN
       [116]978-3-642-38708-1. [117]PMC [118]4492311. [119]PMID [120]26158
       122.
   13. ^ [121]^a [122]^b Jaroszewicz, Szymon; Rzepakowski, Piotr
       (2014). [123]"Uplift modeling with survival data" (PDF). ACM SIGKDD
       Workshop on Health Informatics (HI KDD'14). New York, USA.
   14. [124]^ Yong, F.H. (2015), "Quantitative Methods for Stratified
       Medicine," PhD Dissertation, Department of Biostatistics, Harvard
       T.H. Chan School of Public
       Health, [125]http://dash.harvard.edu/bitstream/handle/1/17463130/YO
       NG-DISSERTATION-2015.pdf?sequence=1 .
   15. ^ [126]^a [127]^b Nassif, Houssam; Santos Costa, Vitor; Burnside,
       Elizabeth S; Page, David (2012). Relational Differential
       Prediction. European Conference on Machine Learning (ECML'12).
       Lecture Notes in Computer Science. 7523. Bristol, UK.
       pp. 617–632. [128]doi:[129]10.1007/978-3-642-33460-3_45. [130]ISBN
       [131]978-3-642-33459-7.
   16. [132]^ Nassif, Houssam; Wu, Yirong; Page, David; Burnside,
       Elizabeth (2012). [133]"Logical Differential Prediction Bayes Net,
       Improving Breast Cancer Diagnosis for Older Women". American
       Medical Informatics Association Symposium (AMIA'12). 2012:
       1330–1339. [134]PMC [135]3540455. [136]PMID [137]23304412.
   17. [138]^ Kuusisto, Finn; Santos Costa, Vitor; Nassif, Houssam;
       Burnside, Elizabeth; Page, David; Shavlik, Jude (2014). Support
       Vector Machines for Differential Prediction. European Conference on
       Machine Learning (ECML'14). Lecture Notes in Computer
       Science. 8725. Nancy, France.
       pp. 50–65. [139]doi:[140]10.1007/978-3-662-44851-9_4. [141]ISBN [14
       2]978-3-662-44850-2. [143]PMC [144]4492338. [145]PMID [146]26158123
       .
   18. [147]^ Zaniewicz, Lukasz; Jaroszewicz, Szymon (2013). "Support
       Vector Machines for Uplift Modeling". The First IEEE ICDM Workshop
       on Causal Discovery. Dallas, Texas.
   19. [148]^ Sołtys, Michał; Jaroszewicz, Szymon; Rzepakowski, Piotr
       (2015). [149]"Ensemble methods for uplift modeling". Data Mining
       and Knowledge Discovery. 29 (6):
       1531–1559. [150]doi:[151]10.1007/s10618-014-0383-9.
   20. [152]^ Kane, K.; Lo, V.S.Y.; Zheng, J. (2014). "Mining for the
       Truly Responsive Customers and Prospects Using True-Lift Modeling:
       Comparison of New and Existing Methods". Journal of Marketing
       Analytics. 2 (4): 218–238. [153]doi:[154]10.1057/jma.2014.18.
   21. [155]^ Lo, V.S.Y.; Pachamanova, D. (2015). "From Predictive Uplift
       Modeling to Prescriptive Uplift Analytics: A Practical Approach to
       Treatment Optimization While Accounting for Estimation
       Risk". Journal of Marketing Analytics. 3 (2):
       79–95. [156]doi:[157]10.1057/jma.2015.5.
   22. [158]^ Gubela, Robin M.; Bequé, Artem; Lessmann, Stefan; Gebert,
       Fabian (2019). "Conversion Uplift in E-Commerce: A Systematic
       Benchmark of Modeling Strategies". International Journal of
       Information Technology & Decision Making. 18 (3):
       747-791. [159]doi:[160]10.1142/S0219622019500172.
   23. [161]^ R. Michel, I. Schnakenburg, T. von Martens (2019).
       „Targeting Uplift“. Springer, [162]ISBN [163]978-3-030-22625-1

   See also[164]Edit
     * [165]Lift (data mining)

   External links[166]Edit
     * [167]Abby Johnson explains how it works in this video broadcast
     * [168]Introductory white paper with full references
     * [169]Eric Siegel: Uplift Modeling
     * [170]User guide for uplift modelling on uplift-modeling.com

   [171]Last edited 19 days ago by [172]WikiCleanerBot
   Wikipedia
     * Content is available under [173]CC BY-SA 3.0 unless otherwise
       noted.

     * [174]Privacy policy
     *
     * [175]Terms of Use
     * [176]Desktop

References

   Visible links
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