IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v30y2011i5p924-940.html
   My bibliography  Save this article

Assessing the Effect of Marketing Investments in a Business Marketing Context

Author

Listed:
  • V. Kumar

    (J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia 30303)

  • S. Sriram

    (Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

  • Anita Luo

    (J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia 30303)

  • Pradeep K. Chintagunta

    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

Recent research has empirically characterized the buyer-seller relationship as dynamically evolving from one discrete state to another. Conventional wisdom would suggest that a customer in a higher relationship state that has a higher transaction value would also have greater lifetime value to the firm. However, recent evidence suggests that higher relationship states can be ephemeral. Hence, the link between transaction value and lifetime value is not obvious. In this study, we seek to understand, within a specific empirical context, (i) the relationship between a customer's transaction value and that customer's lifetime value and (ii) the relationship between the lifetime value of a customer and the optimal level of marketing activity that needs to be directed at that customer. To this end, we develop a trivariate Tobit hidden Markov model that allows for (a) transitions among relationship states, (b) possible synergies between the various products that the supplier firm offers, (c) endogeneity in marketing activity, (d) heterogeneity in model parameters, and (e) the presence of the no-purchase option. Our results reinforce recent findings by Schweidel et al. [Schweidel, D. A., E. T. Bradlow, P. S. Fader. 2011. Portfolio dynamics for customers of a multiservice provider. Management Sci. 57 (3) 471-486] that higher relationship states can be short-lived. Importantly for the supplier firm, a customer in the highest relationship state in a given period does not yield the highest lifetime value to the firm. Hence, the relationship between transaction value (i.e., relationship state) and lifetime value can be nonmonotonic. At the same time, we also find a nonmonotonic relationship between the optimal expenditures that should be directed at a customer and that customer's lifetime value; i.e., the optimal level of marketing contacts is not the highest for customers with the highest lifetime value. Furthermore, we find that the optimal marketing expenditures for myopic agents are 14%-33% lower than the corresponding values for forward-looking agents. Therefore, not accounting for the long-term effects of marketing contacts would lead to suboptimal marketing budgets. Moreover, a comparison with the current marketing expenditures suggests that the current practice is closer to the myopic policy than to the forward-looking one.

Suggested Citation

  • V. Kumar & S. Sriram & Anita Luo & Pradeep K. Chintagunta, 2011. "Assessing the Effect of Marketing Investments in a Business Marketing Context," Marketing Science, INFORMS, vol. 30(5), pages 924-940, September.
    • Handle: RePEc:inm:ormksc:v:30:y:2011:i:5:p:924-940
    DOI: 10.1287/mksc.1110.0661
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1110.0661
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.1110.0661?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
    ---><---

    References listed on IDEAS

    as
    1. Berger, Paul D. & Bechwati, Nada Nasr, 2001. "The allocation of promotion budget to maximize customer equity," Omega, Elsevier, vol. 29(1), pages 49-61, February.
    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. Sanjog Misra & Harikesh Nair, 2011. "A structural model of sales-force compensation dynamics: Estimation and field implementation," Quantitative Marketing and Economics (QME), Springer, vol. 9(3), pages 211-257, September.
    4. John Liechty & Rik Pieters & Michel Wedel, 2003. "Global and local covert visual attention: Evidence from a bayesian hidden markov model," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 519-541, December.
    5. Harikesh Nair, 2007. "Intertemporal price discrimination with forward-looking consumers: Application to the US market for console video-games," Quantitative Marketing and Economics (QME), Springer, vol. 5(3), pages 239-292, September.
    6. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    7. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    8. Pradeep Chintagunta & Tülin Erdem & Peter E. Rossi & Michel Wedel, 2006. "Structural Modeling in Marketing: Review and Assessment," Marketing Science, INFORMS, vol. 25(6), pages 604-616, 11-12.
    9. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    10. Andrew Ainslie & Peter E. Rossi, 1998. "Similarities in Choice Behavior Across Product Categories," Marketing Science, INFORMS, vol. 17(2), pages 91-106.
    11. Swinyard, William R, 1993. "The Effects of Mood, Involvement, and Quality of Store Experience on Shopping Intentions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(2), pages 271-280, September.
    12. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
    13. S. Sriram & Pradeep K. Chintagunta & Manoj K. Agarwal, 2010. "Investigating Consumer Purchase Behavior in Related Technology Product Categories," Marketing Science, INFORMS, vol. 29(2), pages 291-314, 03-04.
    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. Paul Valentin Ngobo, 2017. "The trajectory of customer loyalty: an empirical test of Dick and Basu’s loyalty framework," Journal of the Academy of Marketing Science, Springer, vol. 45(2), pages 229-250, March.
    2. Timsit, Jean-Philippe & Castiaux, Annick & Truong, Yann & Athaide, Gerard A. & Klink, Richard R., 2015. "The effect of market-pull vs. resource-push orientation on performance when entering new markets," Journal of Business Research, Elsevier, vol. 68(9), pages 2005-2014.
    3. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    4. Hongju Liu & Qiang Liu & Pradeep K. Chintagunta, 2017. "Promotion Spillovers: Drug Detailing in Combination Therapy," Marketing Science, INFORMS, vol. 36(3), pages 382-401, May.
    5. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    6. Yingjie Zhang & Beibei Li & Xueming Luo & Xiaoyi Wang, 2019. "Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 787-804, September.
    7. Jean-Pierre Dubé & Sanjog Misra, 2017. "Personalized Pricing and Consumer Welfare," NBER Working Papers 23775, National Bureau of Economic Research, Inc.
    8. Kohsuke Matsuoka, 2020. "Exploring the interface between management accounting and marketing: a literature review of customer accounting," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(3), pages 157-208, September.
    9. Arora, Anshu Saxena & Sivakumar, K. & Pavlou, Paul A., 2021. "Social capacitance: Leveraging absorptive capacity in the age of social media," Journal of Business Research, Elsevier, vol. 124(C), pages 342-356.

    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. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    2. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
    3. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    4. Christof Naumzik & Stefan Feuerriegel & Markus Weinmann, 2022. "I Will Survive: Predicting Business Failures from Customer Ratings," Marketing Science, INFORMS, vol. 41(1), pages 188-207, January.
    5. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    6. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    7. Pancras, Joseph & Gauri, Dinesh K. & Talukdar, Debabrata, 2013. "Loss leaders and cross-category retailer pass-through: A Bayesian multilevel analysis," Journal of Retailing, Elsevier, vol. 89(2), pages 140-157.
    8. Clarence Lee & Elie Ofek & Thomas J. Steenburgh, 2018. "Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged," Management Science, INFORMS, vol. 64(6), pages 2473-2495, June.
    9. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
    10. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    11. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    12. Xiaojing Dong & Pradeep Chintagunta & Puneet Manchanda, 2011. "A new multivariate count data model to study multi-category physician prescription behavior," Quantitative Marketing and Economics (QME), Springer, vol. 9(3), pages 301-337, September.
    13. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
    14. Ma, Yu & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "Modeling Dependencies in Brand Choice Outcomes Across Complementary Categories," Journal of Retailing, Elsevier, vol. 88(1), pages 47-62.
    15. Chang, Chun-Wei & Zhang, Jonathan Z., 2016. "The Effects of Channel Experiences and Direct Marketing on Customer Retention in Multichannel Settings," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 77-90.
    16. Lemmens, A. & Croux, C. & Stremersch, S., 2012. "Dynamics in international market segmentation of new product growth," Other publications TiSEM 306086bd-670f-48d2-97d1-3, Tilburg University, School of Economics and Management.
    17. Lemmens, Aurélie & Croux, Christophe & Stremersch, Stefan, 2012. "Dynamics in the international market segmentation of new product growth," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 81-92.
    18. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    19. Peter Ebbes & Oded Netzer, 2022. "Using Social Network Activity Data to Identify and Target Job Seekers," Management Science, INFORMS, vol. 68(4), pages 3026-3046, April.
    20. Amy Wenxuan Ding & Shibo Li & Patrali Chatterjee, 2015. "Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation," Information Systems Research, INFORMS, vol. 26(2), pages 339-359, June.

    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:inm:ormksc:v:30:y:2011:i:5:p:924-940. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.