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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
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    References listed on IDEAS

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    3. 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.
    4. 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.
    5. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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