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Sales Promotion Models

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Handbook of Marketing Decision Models

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 254))

Abstract

Firms spend a significant part of their marketing budgets on sales promotions. Since the impact of promotions on sales is usually immediate and strong, promotions are attractive to results-oriented managers seeking to increase sales in the short term. This chapter discusses models for measuring sales promotion effects. Part I focuses on descriptive models, i.e. models that describe, analyze, and explain sales promotion phenomena. We start by discussing models for analyzing the immediate impact of promotions and decomposing the resulting sales promotion bump into a variety of sources. Next we examine what happens after the immediate bump, and describes models for measuring feedback effects, reference price effects, learning effects, permanent effects, and competitive reactions. Next we turn to descriptive models for promotions aimed at retailers (“trade promotions”), and discuss forward buying and pass-through. In Part II we discuss normative models, i.e. models that tell the decision maker what is the best (profit maximizing) decision on sales promotion activities. We cover models for planning promotions to consumers as well as decision models on trade promotions for manufacturers, and normative retailer models for optimal forward buying and pass-through. Part III concludes this chapter with a summary, practical model guidelines, and directions for future research.

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Notes

  1. 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. 2.

    Multinomial probit is an alternative to the logit (e.g., 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.

  3. 3.

    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.

  4. 4.

    This working paper was reprinted as Chap. 12 of Wieringa et al. (2011).

  5. 5.

    Another important method is “PromotionScan” (Abraham and Lodish 1993). PromotionScan is a time series approach to estimating the short-term promotion bump. It is based on the “Promoter” method (See Sect. 2.8).

  6. 6.

    Note that the final version of this paper (Steenkamp et al. 2005) does not contain these results anymore, since the journal requested the authors to focus on competitive reactions.

  7. 7.

    The 22% cannot directly be calculated from Table 2.4 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 2.4, 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 2.4 is 33.3% of the bump (1 − 0.667 from the last column in the table), the percentage due to postpromotion dips is 0.662 × 0.333 = 0.2204 = 22.0%.

  8. 8.

    Leeflang (2008) provides an in-depth discussion on models for competitive reactions, including structural models.

  9. 9.

    “Factory Shipments” are shipments from the manufacturer to the retailer and reflect retailer orders or manufacturer sales. Blattberg and Levin (1987) use factory shipments data. Abraham and Lodish (1987) note their method can be applied to factory shipment data as well as other data such as warehouse withdrawals.

  10. 10.

    We use the formula provided on page 129 of Besanko et al. (2005): AAC(t) = [(Retailer Purchases in t) × Wholesale price in period t) + [(Inventory at end of t − 1) − (Retail sales in t)] × AAC(t − 1)] × (Inventory at end of t)−1.

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Authors and Affiliations

  1. Massey University, Auckland, 0630, New Zealand

    Harald J. van Heerde

  2. Tuck School of Business, Dartmouth College, Hanover, USA

    Scott A. Neslin

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  1. Rotterdam School of Management, Erasmus University, Rotterdam, South Holland, The Netherlands

    Berend Wierenga

  2. Department of Marketing, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Ralf van der Lans

Appendix: Variable Definition for the Decomposition in Sect. 2.4.3

Appendix: Variable Definition for the Decomposition in Sect. 2.4.3

Van Heerde et al. (2004) obtain decomposition (2.11) for price index variables with four types of support (with/without feature, with/without display). To achieve this, they transform the original four promotion variables (PI, FEATONLY, DISPONLY, FEAT&DISP) from the Scan*Pro model into seven new variables: price index with feature-support (PF), price index with display-only support (PD), price index with feature and display support (PFD), price index without support (PWO), plus FWO (Feature without price cut), DWO (Display without price cut), and FDWO (Feature&Display without price cut). Regular price is indicated by a price index with value “1”. A 20% discount would be indicated by 0.8. The PWO, PF, PD, and PFD variables are defaulted to “1” if there is no price discount, but change depending on whether there is a discount and if so how it is supported. The FWO, DWO, and FDWO variables default to “0” and can equal “1” only if there is a feature, display, or both, without a price cut.

To illustrate the transformation, Table 2.11 contains the four original and seven new variables. In case # 1 there is no promotion, and the original price index (PI) equals 1 while the FEATONLY, DISPONLY, FEAT&DISP are zero, as defined in the Scan*Pro model. Since there is no (supported or unsupported) price discount in case #1, the four new price index variables (PWO, PF, PD, PFD) are all at their nonpromotional value of 1. The FWO, DWO, and FDWO variables are zero since there is no feature or display without a price cut. In case # 2 there is a twenty percent price discount without any support, which shows up in the original variables as a price index of 0.8 while FEATONLY, DISPONLY, FEAT&DISP remain zero. Since this is a price cut without support, among the new price indices only the price index without support (PWO) variable is decreased to 0.8. The other three price indices PF, PD, and PFD stay at their nonpromotional level of 1, while the FWO, DWO, and FDWO variables stay at their default value of 0. Case # 3 represents a twenty percent price cut with a feature-only, and hence price index with feature-only support (PF) is lowered to 0.8 (and again, all other variables are at their nonpromotional levels). Analogously, in cases # 4 and 5 the variables PD and PFD become 0.8, in turn. In case # 6 there is a feature without a price cut, which can be seen from the original variables since FEATONLY becomes 1 while PI remains 1. Consequently, among the new variables, FWO becomes 1, while DWO and FDWO remain 0, and PWO, PF, PD, and PFD stay 1 since there is no price cut. Cases #7 and 8 show how DWO and FDWO are defined.

Table 2.11 Transforming the four Scan*Pro variables into seven new variables
Full size table

Since price cuts tend to be communicated with feature and or display, the four Scan*Pro Variables tend to be highly correlated. The seven new variables, in contrast, describe seven mutually exclusive promotion situations, and they tend to be much less correlated. While the seven new variables are larger in number than the four Scan*Pro variables, a few of them typically do not vary and can therefore be excluded from models (especially the FDWO variable). Researchers who are concerned about multicollinearity in their (store-level) model may consider using the new set of seven variables proposed in this appendix.

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van Heerde, H.J., Neslin, S.A. (2017). Sales Promotion Models. In: Wierenga, B., van der Lans, R. (eds) Handbook of Marketing Decision Models. International Series in Operations Research & Management Science, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-56941-3_2

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