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Retargeting Customers Using Uplift Modeling

Author

Listed:
  • Todor Krastevich

Abstract

“Traditional” digital marketing campaigns are based primarily on a priori geotargeting, augmented with profiling of potential consumers based on language, sociodemographics, interests and preferences. A step ahead is when experimental results from A/B testing are used for more precise retargeting, in order to prove in a statistically significant way the direction and size of the effect of a potential communication marketing impact. Through the application of uplift modelling, it is possible to complement the experimental data from A/B testing by identifying the effect of specific marketing treatments (e.g. a specific message, alternative display ad design, web page layout and/or change in price offer) on specific individuals as opposed to an overall increase or decrease in conversion rates caused by the impact. This technique can help evaluate and predict their responses through supervised machine-learning classification algorithms. This nuanced analysis allows for personalized targeting of marketing communication to only leads who are likely to respond positively to an impact. This paper proposes and demonstrates a prototype model for optimal retargeting of customers based on machine learning algorithms and open-source programming.

Suggested Citation

  • Todor Krastevich, 2023. "Retargeting Customers Using Uplift Modeling," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 78-99.
    • Handle: RePEc:bas:econst:y:2023:i:2:p:78-99
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    File URL: https://www.iki.bas.bg/Journals/EconomicStudies/2023/2023-2/05_Todor-Krastevich.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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