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Deriving target selection rules from endogenously selected samples

Author

Listed:
  • Bas Donkers
  • Richard Paap
  • Jedid‐Jah Jonker
  • Philip Hans Franses
Abstract
The selection of the most profitable customers in a customer database for targeted activities is often done based on observed behaviour in the past. Consequently, databases arising from the responses to, for example, direct mailings in the past are not random samples. When not all heterogeneity across customers is observed, target selection will be based on unobserved heterogeneity and hence it is endogenous. We develop a method to adjust the likelihood function of latent class models to correct for this endogenous sampling process. We apply this technique to the selection of mail targets for a Dutch charity. Based on a joint model for the response rate and the amount donated, we create a target selection rule that maximizes expected revenues. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    • Handle: RePEc:wly:japmet:v:21:y:2006:i:5:p:549-562
    DOI: 10.1002/jae.858
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    1. Schröder, Nadine & Hruschka, Harald, 2016. "Investigating the effects of mailing variables and endogeneity on mailing decisions," European Journal of Operational Research, Elsevier, vol. 250(2), pages 579-589.
    2. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    3. Thomas, Suman Ann & Feng, Shanfei & Krishnan, Trichy V., 2015. "To retain? To upgrade? The effects of direct mail on regular donation behavior," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 48-63.
    4. Marc Fischer, 2019. "Practice Prize Paper–Managing Advertising Campaigns for New Product Launches: An Application at Mercedes-Benz," Marketing Science, INFORMS, vol. 38(2), pages 343-359, March.
    5. Sarkar, Mainak & De Bruyn, Arnaud, 2021. "LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 80-95.
    6. Park, Chang Hee & Agarwal, Manoj K., 2018. "The order effect of advertisers on consumer search behavior in sponsored search markets," Journal of Business Research, Elsevier, vol. 84(C), pages 24-33.
    7. van Diepen, Merel & Donkers, Bas & Franses, Philip Hans, 2009. "Does irritation induced by charitable direct mailings reduce donations?," International Journal of Research in Marketing, Elsevier, vol. 26(3), pages 180-188.
    8. Haupt, Johannes & Lessmann, Stefan, 2022. "Targeting customers under response-dependent costs," European Journal of Operational Research, Elsevier, vol. 297(1), pages 369-379.
    9. Rust, Roland T. & Kumar, V. & Venkatesan, Rajkumar, 2011. "Will the frog change into a prince? Predicting future customer profitability," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 281-294.
    10. Donkers, Bas & van Diepen, Merel & Franses, Philip Hans, 2017. "Do charities get more when they ask more often? Evidence from a unique field experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 66(C), pages 58-65.
    11. Haupt, Johannes & Lessmann, Stefan, 2020. "Targeting Cutsomers Under Response-Dependent Costs," IRTG 1792 Discussion Papers 2020-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Piersma, Nanda & Jonker, Jedid-Jah, 2004. "Determining the optimal direct mailing frequency," European Journal of Operational Research, Elsevier, vol. 158(1), pages 173-182, October.
    13. Gázquez-Abad, Juan Carlos & Canniére, Marie Hélène De & Martínez-López, Francisco J., 2011. "Dynamics of Customer Response to Promotional and Relational Direct Mailings from an Apparel Retailer: The Moderating Role of Relationship Strength," Journal of Retailing, Elsevier, vol. 87(2), pages 166-181.
    14. Hruschka, Harald, 2010. "Considering endogeneity for optimal catalog allocation in direct marketing," European Journal of Operational Research, Elsevier, vol. 206(1), pages 239-247, October.
    15. Feld, Sebastian & Frenzen, Heiko & Krafft, Manfred & Peters, Kay & Verhoef, Peter C., 2013. "The effects of mailing design characteristics on direct mail campaign performance," International Journal of Research in Marketing, Elsevier, vol. 30(2), pages 143-159.
    16. Johannes Haupt & Stefan Lessmann, 2020. "Targeting customers under response-dependent costs," Papers 2003.06271, arXiv.org, revised Aug 2021.
    17. Jonker, J.-J. & Piersma, N. & Van den Poel, D., 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Research Papers EI 2002-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Lemmens, A. & Croux, C., 2006. "Bagging and boosting classification trees to predict churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.

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    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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