IDEAS home Printed from https://ideas.repec.org/a/pal/jmarka/v11y2023i4d10.1057_s41270-022-00186-3.html
   My bibliography  Save this article

Increasing the robustness of uplift modeling using additional splits and diversified leaf select

Author

Listed:
  • Frank Oechsle

    (Karlsruhe Institute of Technology (KIT))

Abstract

While the COVID-19 pandemic negatively affects the world economy in general, the crisis accelerates concurrently the rapidly growing subscription business and online purchases. This provokes a steadily increasing demand of reliable measures to prevent customer churn which unchanged is not covered. The research analyses how preventive uplift modeling approaches based on decision trees can be modified. Thereby, it aims to reduce the risk of churn increases in scenarios with systematically occurring local estimation errors. Additionally, it compares several novel spatial distance and churn likelihood respecting selection methods applied on a real-world dataset. In conclusion, it is a procedure with incorporated additional and engineered decision tree splits that dominates the results of an appropriate Monte Carlo simulation. This newly introduced method lowers probability and negative impacts of counterproductive churn prevention campaigns without substantial loss of expected churn likelihood reduction effected by those same campaigns.

Suggested Citation

  • Frank Oechsle, 2023. "Increasing the robustness of uplift modeling using additional splits and diversified leaf select," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 738-746, December.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:4:d:10.1057_s41270-022-00186-3
    DOI: 10.1057/s41270-022-00186-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41270-022-00186-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41270-022-00186-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mirjana Pejić Bach & Jasmina Pivar & Božidar Jaković, 2021. "Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees," JRFM, MDPI, vol. 14(11), pages 1-25, November.
    2. Atef Shaar & Talel Abdessalem & Olivier Segard, 2016. "Pessimistic uplift modeling," Post-Print hal-02376023, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ken Nishimatsu & Akiya Inoue, 2023. "User Intent-Based Segmentation Analysis for Internet Access Services," International Journal of Strategic Decision Sciences (IJSDS), IGI Global, vol. 14(1), pages 1-21, January.
    2. Tianyuan Zhang & Sérgio Moro & Ricardo F. Ramos, 2022. "A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation," Future Internet, MDPI, vol. 14(3), pages 1-19, March.
    3. Mydyti Hyrmet & Kadriu Arbana & Pejic Bach Mirjana, 2023. "Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development," Organizacija, Sciendo, vol. 56(2), pages 138-154, May.
    4. Londhe Sanket Tanaji & Palwe Sushila, 2022. "Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach," Business Systems Research, Sciendo, vol. 13(1), pages 35-45, June.
    5. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jmarka:v:11:y:2023:i:4:d:10.1057_s41270-022-00186-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.