Abstract
Inertia might secure consumers’ continued patronage, but it also can stunt potential expansion. By examining the psychology underlying inertia, this research informs managers about whether to engage inertial consumers proactively. In the proposed conceptual model, an inertia mindset orients a customer toward status quo consumption. This mindset emerges from dual sources, and each source consists of a behavioral and a psychological component. Specifically, the behavioral consistency of prior consumption activates an inertia mindset by prompting a psychological inclination to minimize thinking; the magnitude of prior consumption leads to inertia by evoking an inclination to minimize regret. Complementary survey and field studies offer support for the proposed model and reveal that a proactive loyalty reward can reinforce inertia based on regret minimization but disrupt inertia based on thinking minimization. Even well-intentioned marketing initiatives thus might be ineffective or detrimental, depending on the source and strength of inertia already present in the customer.
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Notes
To increase confidence that the likelihood of answering the phone among the treatment group did not bias our estimates, we undertook several steps that control for possible differences in customers’ likelihood to receive this proactive loyalty reward if the company called them (see Web Appendices B and C). The results are consistent with the presented findings.
The equation to estimate the logit model is Probability(treatment group = 1)i = β1Consistencyi + β2Mag_Legnthi + β3Mag_Breadthi + β4Customer agei + β5Billingsi + β6Lifestylei + β7Regioni + β8Billing cyclei + β9Total productsi + β10Outbound marketingi + β11Fixed line callsi + εi. See Web Appendix B for model results and reduction of potential differences across predictors between treatment and matched control group.
For robustness, we use various matching techniques (e.g., genetic matching, exact match), which generate alternative control groups but yield consistent results for our studied effects (see Web Appendix C).
Billing for fixed line telephones by this service provider in the region served is based on both usage levels and type of call. Drastic changes would stem from new usage behavior and grab customers’ attention when the bill arrives.
In support of our analysis, we considered possible issues arising from assessing relatively rare events; only 6.58% of customers defected, and 3.84% expanded. These small percentages have substantial firm performance implications, but their low frequency qualifies them as rare events, which could lead to biased coefficients. According to rare event guidelines though, this risk is minimal, at less than 1% (King and Zeng 2001). Nevertheless, we estimate the multinomial model with a smaller sample, dropping 524 random status quo cases, so that even expansion represents at least 5% of all outcomes. Despite the smaller sample, the signs and significance of the effects remain.
To conduct these analyses, we selected five meaningful combinations of prior consumption consistency and magnitude: (1) low (bottom 5% level of all customers) consistency and low (bottom 5% level) magnitude (length and breadth), (2) high (top 5% level) consistency and high (top 5% level) magnitude, (3) low consistency and high magnitude, (4) high consistency and low magnitude, and (5) moderate consistency and moderate magnitude (median).
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Acknowledgments
The authors thank the MSI working paper series (MSI 14-121; 2016 Robert D. Buzzell MSI Best Paper Award) for helpful feedback and recognition on an early version of this research. The authors would also like to thank the review team as well as Hari Sridhar, Joshua Beck, and Ju-Yeon Lee for helpful suggestions on previous drafts of this article.
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Appendices
Appendix 1
Appendix 2
Study 2 multinomial logistic model specification
Customers likely change their existing account subscription if they perceive greater potential utility from the change. Customer i’s perceived utility for defection (UiD) and expansion (UiE) are:
and
The estimated parameters correspond to the constants (β0D and β0E), the undisturbed effects of each characteristic of prior consumption (β1–3D and β1–3E), the treatment effects of the proactive loyalty reward (β4D and β4E), the altered effects of each characteristic of prior consumption conditional on receiving the proactive loyalty reward (β5–7D and β5–7E), and the effects of the controls (β8–15D and β8–15E) for defection and expansion, respectively. Finally, εiD and εiE are extreme-value distributed error terms, such that the multinomial logit form of the probability that each customer i will end the observation period with account status y (y = D, E) for defection or expansion can be represented as:
\( {P}_i^y=\frac{\exp \left({V}_i^y\right)}{\exp \left({V}_i^D\right)+\exp \left({V}_i^E\right)+1} \),
and the probability of no changes, such that account retains its status quo (SQ), is given by.\( {P}_i^{SQ}=\frac{1}{\exp \left({V}_i^D\right)+\exp \left({V}_i^E\right)+1} \).
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Henderson, C.M., Steinhoff, L., Harmeling, C.M. et al. Customer inertia marketing. J. of the Acad. Mark. Sci. 49, 350–373 (2021). https://doi.org/10.1007/s11747-020-00744-0
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DOI: https://doi.org/10.1007/s11747-020-00744-0