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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
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    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.
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