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2008 | OriginalPaper | Buchkapitel

5. Sales Promotion Models

verfasst von : Harald J. van Heerde, Scott A. Neslin

Erschienen in: Handbook of Marketing Decision Models

Verlag: Springer US

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Firms spend a significant part of their marketing budgets on sales promotions. The Trade Promotion report (2005) indicates that during 1997–2004, promotion accounted for roughly 75% of marketing expenditures for US packaged goods manufacturers; the other 25% was for advertising. In 2004, 59% of the budget was spent on promotion to the trade (i.e., from manufacturers to retailers), and 16% on manufacturer promotions to consumers. Since the impact of promotions on sales is usually immediate and strong (Blattberg et al. 1995), promotions are attractive to results-oriented managers seeking to increase sales in the short term (Neslin 2002). In a recent meta-analysis, Bijmolt et al. (2005) report that the average short-term sales promotion elasticity is –3.63, which implies that a 20% temporary price cut+ leads to a 73% rise in sales.1 There are few, if any, other marketing instruments that are equally effective. Because of this, coupled with the availability of scanner data, marketing researchers have been very active in developing models for analyzing sales promotions. …

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Fußnoten
1
This figure holds for temporary price cuts without feature or display support. A feature or display may increase the sales effect up to a factor 9 (Narasimhan et al. 1996).
 
2
The derivation of CV and its place in the purchase incidence models can be derived from a “nested” logit framework (e.g., see Bucklin et al. 1998).
 
3
Multinomial probit is an alternative to the logit (e.g., see Jedidi et al. (1999). The advantage of the probit model is that it avoids the independence of irrelevant alternatives (IIA) assumption of logit models (see Guadagni and Little 1983). However, it does not produce a closed form for the probability of consumer choice.
 
4
The full expressions for the elasticities are not shown due to space restrictions; they require taking the derivatives of Equations (5.2), (5.4), and (5.6) (see Gupta 1988).
 
5
It is noteworthy that there is some evidence (Abramson et al. 2000; Chiang et al. 1999) that not including choice set heterogeneity significantly distorts parameters. However, perhaps because there have not been too many studies, choice set heterogeneity is usually not included.
 
6
Another important method is “PromotionScan” (Abraham and Lodish 1993). PromotionScan uses a time series approach to estimating the short-term promotion bump. It is based on the “Promoter” methodology that we discuss in Section 6.8.
 
7
We note that the final version of this paper (Steenkamp et al. 2005) does not contain these results for anymore, since the journal requested the authors to focus on competitive reactions.
 
8
The 22% cannot directly be calculated from Table 4 in Macé and Neslin (2004) because the elasticities reported in that table are point elasticities and therefore do not exactly correspond to the 20% price cut effects calculated in that table. However, calculations using the detailed results summarized in Macé and Neslin’s Table 4, reveal that on average, 66.2% of the combined pre and post effect is due to post effects. Since the combined effect reported in Macé and Neslin’s Table 4 is 33.3% of the bump (1–.667 from the last column in the table), the percentage due to postpromotion dips is .662*.333=.2204 = 22.0%.
 
9
In this same handbook, the chapter by Leeflang (7) provides an in-depth discussion on models for competitive reactions, including structural models.
 
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Metadaten
Titel
Sales Promotion Models
verfasst von
Harald J. van Heerde
Scott A. Neslin
Copyright-Jahr
2008
Verlag
Springer US
DOI
https://doi.org/10.1007/978-0-387-78213-3_5