IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v227y2013i3p538-551.html
   My bibliography  Save this article

Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station

Author

Listed:
  • Durango-Cohen, Elizabeth J.

Abstract

Funding pressures have forced many not-for-profit organizations to reduce their reliance on mass-marketing efforts, e.g., pledge drives, and increase the volume and sophistication of their direct marketing activities. The efficiency of direct marketing, however, is linked to an organization’s ability to target population segments effectively, which, in turn, has motivated the development of methodological approaches for market segmentation.

Suggested Citation

  • Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    • Handle: RePEc:eee:ejores:v:227:y:2013:i:3:p:538-551
    DOI: 10.1016/j.ejor.2013.01.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221713000350
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2013.01.008?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. Duncan I. Simester & Peng Sun & John N. Tsitsiklis, 2006. "Dynamic Catalog Mailing Policies," Management Science, INFORMS, vol. 52(5), pages 683-696, May.
    2. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    3. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 371-404.
    4. Ulf Böckenholt & Rolf Langeheine, 1996. "Latent change in recurrent choice data," Psychometrika, Springer;The Psychometric Society, vol. 61(2), pages 285-301, June.
    5. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
    6. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
    7. Füsun F. Gönül & Frenkel Ter Hofstede, 2006. "How to Compute Optimal Catalog Mailing Decisions," Marketing Science, INFORMS, vol. 25(1), pages 65-74, 01-02.
    8. Füsun Gönül & Meng Ze Shi, 1998. "Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models," Management Science, INFORMS, vol. 44(9), pages 1249-1262, September.
    9. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    10. Ronald A. Howard, 2002. "Comments on the Origin and Application of Markov Decision Processes," Operations Research, INFORMS, vol. 50(1), pages 100-102, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Ülengin, Burç, 2014. "Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model," European Journal of Operational Research, Elsevier, vol. 237(1), pages 278-288.
    2. Durango-Cohen, Elizabeth J. & Torres, Ramón L. & Durango-Cohen, Pablo L., 2013. "Donor Segmentation: When Summary Statistics Don't Tell the Whole Story," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 172-184.

    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. Durango-Cohen, Elizabeth J. & Torres, Ramón L. & Durango-Cohen, Pablo L., 2013. "Donor Segmentation: When Summary Statistics Don't Tell the Whole Story," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 172-184.
    2. 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.
    3. Verhoef, Peter C. & Venkatesan, Rajkumar & McAlister, Leigh & Malthouse, Edward C. & Krafft, Manfred & Ganesan, Shankar, 2010. "CRM in Data-Rich Multichannel Retailing Environments: A Review and Future Research Directions," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 121-137.
    4. George, Morris & Kumar, V. & Grewal, Dhruv, 2013. "Maximizing Profits for a Multi-Category Catalog Retailer," Journal of Retailing, Elsevier, vol. 89(4), pages 374-396.
    5. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    6. Duncan I. Simester & Peng Sun & John N. Tsitsiklis, 2006. "Dynamic Catalog Mailing Policies," Management Science, INFORMS, vol. 52(5), pages 683-696, May.
    7. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    8. Dimitris Bertsimas & Adam J. Mersereau, 2007. "A Learning Approach for Interactive Marketing to a Customer Segment," Operations Research, INFORMS, vol. 55(6), pages 1120-1135, December.
    9. 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.
    10. Holtrop, Niels & Wieringa, Jaap E., 2023. "Timing customer reactivation initiatives," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 570-589.
    11. Shie Mannor & Duncan Simester & Peng Sun & John N. Tsitsiklis, 2007. "Bias and Variance Approximation in Value Function Estimates," Management Science, INFORMS, vol. 53(2), pages 308-322, February.
    12. Giuliano Tirenni & Abderrahim Labbi & Cesar Berrospi & André Elisseeff & Timir Bhose & Kari Pauro & Seppo Pöyhönen, 2007. "—Customer Equity and Lifetime Management (CELM) Finnair Case Study," Marketing Science, INFORMS, vol. 26(4), pages 553-565, 07-08.
    13. Romana Khan & Michael Lewis & Vishal Singh, 2009. "Dynamic Customer Management and the Value of One-to-One Marketing," Marketing Science, INFORMS, vol. 28(6), pages 1063-1079, 11-12.
    14. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    15. Blattberg, Robert C. & Malthouse, Edward C. & Neslin, Scott A., 2009. "Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 157-168.
    16. 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.
    17. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    18. De Cannière, Marie Hélène & De Pelsmacker, Patrick & Geuens, Maggie, 2009. "Relationship Quality and the Theory of Planned Behavior models of behavioral intentions and purchase behavior," Journal of Business Research, Elsevier, vol. 62(1), pages 82-92, January.
    19. 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.
    20. Drèze, Xavier & Bonfrer, André, 2008. "An empirical investigation of the impact of communication timing on customer equity," Journal of Interactive Marketing, Elsevier, vol. 22(1), pages 36-50.

    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:eee:ejores:v:227:y:2013:i:3:p:538-551. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.