IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v3y2016i4d10.1007_s40745-016-0090-z.html
   My bibliography  Save this article

Targeted Marketing Using Balance Optimization Subset Selection

Author

Listed:
  • Shouvik Dutta

    (University of Illinois at Urbana-Champaign)

  • Jason Sauppe

    (University of Wisconsin-La Crosse)

  • Sheldon Jacobson

    (University of Illinois at Urbana-Champaign)

Abstract

Customers today are faced with a plethora of choices of products to buy and consume. The sheer volume of choices can be daunting, and customers forced to sift through the products are likely to become dissatisfied. Retailers have the ability to solve this problem by providing customers with recommendations of products that are likely to be of interest to each specific customer. This can be done by profiling each customer and identifying products that similar customers like. This paper presents a balance optimization approach, where customers are characterized and matched as groups. By identifying and analyzing a group of customers who have shown positive reactions to a specific product, we propose a technique to find a comparable group who we hypothesize will show a similar positive reaction. This allows for the creation of targeted advertisements, mailing lists, and other material to recommend products to customers. The methodology is tested using a Netflix dataset, where we are able to show a statistically significant improvement on the mean rating of selected users over random selection of 0.384 when the ratings are on a scale of 0–5.

Suggested Citation

  • Shouvik Dutta & Jason Sauppe & Sheldon Jacobson, 2016. "Targeted Marketing Using Balance Optimization Subset Selection," Annals of Data Science, Springer, vol. 3(4), pages 423-444, December.
  • Handle: RePEc:spr:aodasc:v:3:y:2016:i:4:d:10.1007_s40745-016-0090-z
    DOI: 10.1007/s40745-016-0090-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-016-0090-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40745-016-0090-z?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. Alexander G. Nikolaev & Sheldon H. Jacobson & Wendy K. Tam Cho & Jason J. Sauppe & Edward C. Sewell, 2013. "Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data," Operations Research, INFORMS, vol. 61(2), pages 398-412, April.
    2. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
    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. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
    2. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.
    3. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
    4. Hochbaum, Dorit S. & Rao, Xu & Sauppe, Jason, 2022. "Network flow methods for the minimum covariate imbalance problem," European Journal of Operational Research, Elsevier, vol. 300(3), pages 827-836.
    5. Hee Youn Kwon & Jason J. Sauppe & Sheldon H. Jacobson, 2019. "Treatment Effect Decomposition and Bootstrap Hypothesis Testing in Observational Studies," Annals of Data Science, Springer, vol. 6(3), pages 491-511, September.
    6. Dutta Shouvik & Jacobson Sheldon H. & Sauppe Jason J., 2017. "Identifying NCAA tournament upsets using Balance Optimization Subset Selection," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(2), pages 79-93, June.
    7. Md Saiful Islam & Md Sarowar Morshed & Md. Noor-E-Alam, 2022. "A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3023-3041, November.
    8. Md Saiful Islam & Md Sarowar Morshed & Gary J Young & Md Noor-E-Alam, 2019. "Robust policy evaluation from large-scale observational studies," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
    9. Jason J. Sauppe & Sheldon H. Jacobson & Edward C. Sewell, 2014. "Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 547-566, August.
    10. Martin Cousineau & Vedat Verter & Susan A. Murphy & Joelle Pineau, 2022. "Estimating causal effects with optimization-based methods: A review and empirical comparison," Papers 2203.00097, arXiv.org.

    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:spr:aodasc:v:3:y:2016:i:4:d:10.1007_s40745-016-0090-z. 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.springer.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.