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2017 | OriginalPaper | Chapter

4. Models for the Financial-Performance Effects of Marketing

Authors : Dominique M. Hanssens, Marnik G. Dekimpe

Published in: Handbook of Marketing Decision Models

Publisher: Springer International Publishing

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Abstract

We consider marketing-mix models that explicitly include financial performance criteria. These financial metrics are not only comparable across the marketing mix, they also relate well to investors’ evaluation of the firm. To that extent, we treat marketing as an investment in customer value creation and communication that ultimately leads to shareholder value.

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previous chapter Innovation and New Products Research: A State-of-the-Art Review, Models for Managerial Decision Making, and Future Research Directions

next chapter Loyalty Programs: Current Insights, Research Challenges, and Emerging Trends

Higher cash flows are, for example, obtained when marketing helps to acquire additional customers, or convinces consumer to spend more. However, the role of marketing could also be to prevent a decline in sales or cash flows through appropriate defensive actions (see, for example, Gatignon et al. 1997; Hauser and Shugan 1983; Roberts 2005 or Roberts et al. 2005 for discussions on defensive marketing strategies).

In later models, we will also allow for a direct impact of marketing support on financial metric (the “direct route” in the terminology of Joshi and Hanssens 2010).

By accounting definition, “free” or “net” cash flow is operating profit minus investment. Investment is the net change in the firm’s capital. However, “marketing induced capital” such as brand equity or customer equity is currently not recognized on the firm’s balance sheet. For example, a $20 million investment in a plant or equipment is recognized as an asset, whereas a $20 million advertising campaign for a brand is not.

An extensive literature exists whether it is reasonable to assume that the investor reaction mechanism is always accurate and complete (given that many of them are not marketing experts, or because investors may be influenced by persuasive, but potentially misleading, communications by company executives and/or other mediating factors). An in-depth coverage of this debate is beyond the scope of the current chapter. We refer to Srinivasan and Hanssens (2009) for a more extensive coverage.

This limited-volatility criterion has received less attention in the marketing literature. A notable exception is Fischer et al. (2016), who consider various drivers of (potentially sub-optimal) revenue and cash-flow volatility.

We opt for the multiplicative, rather than the linear, model as our base model as (i) it allows for diminishing returns to scale, (ii) offers direct elasticity estimates, and (iii) automatically allows for interaction effects.

See in this respect Hanssens (2015).

We refer to Hanssens et al. (2001) for a review of other functional specifications that have been regularly used in the literature (pp. 94–115), and for a more in-depth discussion on the use of the lag operator (p. 181).

Apart from brand and customer equity, one could also consider a third source of market-based assets: channel equity emerging from collaborative channel relationships (see, for example, Srivastava et al. 2006 for a discussion).

Cross-sectional comparisons of brand equity cannot monitor the formation of brand strength, only the equilibrium result of the branding process. By contrast, longitudinal data, possibly across several brands or markets, allow us to infer how marketing spending builds brands over time.

Dekimpe and Hanssens (1999) also discuss the case where temporary (rather than sustained) spending has persistent brand-building effects, a situation they call hysteresis. Hanssens et al. (2016), in turn, identify windows of opportunities for opportunistic “brand-building” expenditures.

An excellent review on the link between perceptual marketing metrics and financial performance is given in Gupta and Zeithaml (2006; see e.g. their Table 1).

Apart from stock returns, marketing flow and stock metrics have also been linked to two other components of shareholder value, systematic risk and idiosyncratic risk (see, among others, Tuli and Bharadwaj 2009 or Osinga et al. 2011).

As before, also non-linear specifications could be considered, which may be more appealing from a (subsequent) optimization point of view.

Other studies on the stock-market reaction to firms’ innovation activities include, among others, Sorescu et al. (2007) and Sorescu and Spanjol (2008).

Through the feedback loops, VAR models also capture the role that (past) stock-price variations play in managerial decision making. It would be useful to extend these models to also incorporate anticipated stock-price variations (foresight) into dynamic marketing models.

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Title: Models for the Financial-Performance Effects of Marketing
Authors: Dominique M. Hanssens
Marnik G. Dekimpe
Publisher: Springer International Publishing
Book: Handbook of Marketing Decision Models
Print ISBN: 978-3-319-56939-0

Electronic ISBN: 978-3-319-56941-3

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-56941-3_4

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