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Erschienen in: Journal of the Academy of Marketing Science 3/2021

28.01.2021 | Original Empirical Research

A customer portfolio management model that relates company’s marketing to its long-term survival

verfasst von: Leigh McAlister, Shameek Sinha

Erschienen in: Journal of the Academy of Marketing Science | Ausgabe 3/2021

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Abstract

A typical customer relationship management model is designed to increase the value of a company’s existing customers in the next period. While useful in the short term, such a process, followed blindly period after period, would drive the company out of business when those existing customers all eventually died. In reality, no company would do this. Instead, these short-term models are nested within a long-term view of customer management, and it is long-term customer management that the proposed model addresses. The model assumes that a company has identified a set of customer types across which it needs balance in order to remain viable in the long-term (e.g., a company might wish to maintain a supply of “entry-level customers” in order to eventually replenish its collection of more profitable “loyal customers”). Though the model is applicable in any industry, we illustrate it for automobiles. Results reveal the strengths with which each marketing intervention causes General Motors to attract each of their desired customer types. The model is extended to also reveal differences in the ways that marketing interventions by Ford, Toyota, and Honda change the strengths with which those automakers attract customers.

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Fußnoten
1
Johnson and Selnes talk about “customer segments” rather than about “customer-types.” However, Terho and Halinen (2007) point out that using the term “segment” in customer portfolio management may cause confusion. A segment is a distinct subset of the market as a whole, while customer portfolio management is concerned with distinct subsets of the company’s current customers.
 
2
Netzer et al. (2008) identify three forces that influence whether a customer will change from one type to another in a particular year: whether the year in question is a reunion year for the alum, whether the alum has participated in a reunion since graduation, and whether the alum has volunteered for a university role after graduation. These customer mix changing forces are alum’s actions. The university’s marketing could have triggered these actions, but interactions with other alums (Zhang 2019) or some other, unidentified force could also have triggered these changes.
 
3
Note that marketing mix models estimate the influence of a marketing intervention on overall sales. What they cannot do is report the kinds of customers to whom those sales were made. Consequently, marketing mix models cannot identify unintended consequences of a seemingly “successful” marketing intervention. For example, an extrinsic motivation communication might increase sales for Seventh Generation, but that sales increase would come with the unanticipated consequence of alienating “high involved with sustainable consumption” consumers, the group on which Seventh Generation’s brand equity rests.
 
4
Because a given customer purchases a durable only infrequently, durable companies cannot accumulate the many transactions per customer needed to estimate transaction-based customer relationship management models, Netzer et al. (2008) or Homburg et al. (2009). A durable company can, though, observe its marketing and its customer mix each period, and with a suitable number of such observations, estimate the parameters of the proposed customer portfolio management model.
 
5
400 West Wilson Bridge, Suite 200 Worthington, Ohio 43,085.
 
6
The concept of attraction as a latent variable with a company’s customer mix being a normalized function of that latent variable is consistent with many models of natural phenomena in marketing. In fact, McFadden (1974) began by asserting equation (1)‘s functional form because of the intuitive appeal of Luce’s (1959) choice axiom (which is defined by this functional form). Given the functional form desired for his model, McFadden (1974) reports that he worked backward to determine that this function form would result if one assumed that the random component has a double exponential (Gumble Type II extreme value) distribution. Hence he proposed that distributional assumption, knowing that it would yield the functional form in equation (1) which he desired for his model.
 
7
The structure of the proposed customer portfolio management model is parallel to the structure of the more familiar brand choice model. This parallel might make one wonder whether it would not be possible to achieve equivalent customer mix insights using a brand choice approach. Web Appendix D shows that customer mix insights drawn from brand choice models are inferior to those derived directly from the proposed customer portfolio management model.
 
8
Netzer et al. (2008) refer to customer types as “states” and refer to customer type–specific intercepts as “state dependent intercepts.”
 
9
Our data stretch from the beginning of 2008, before the 2008 financial-institution-failure-induced recession, until 2011, well after that recession ended. Because an economic recession effects some customer types more powerfully than others, we expect the recession to influence the strength with which General Motors is able to attract different customer types. Following the National Bureau of Economic Research definition, the recession began September 2008 when GDP fell and Unemployment Rate rose dramatically and ended after December 2009 when the economy returned to conventional levels of GDP and Unemployment.
 
10
The authors thank the AE for suggesting this question.
 
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Metadaten
Titel
A customer portfolio management model that relates company’s marketing to its long-term survival
verfasst von
Leigh McAlister
Shameek Sinha
Publikationsdatum
28.01.2021
Verlag
Springer US
Erschienen in
Journal of the Academy of Marketing Science / Ausgabe 3/2021
Print ISSN: 0092-0703
Elektronische ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-020-00765-9

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