The Proper Interpretation of Sales Promotion Effects: Supplement Elasticities with Absolute Sales Effects

Transcription

1 The Proper Interpretation of ales Promotion Effects: upplement Elasticities with Absolute ales Effects Contribution to the special issue of Applied tochastic Models in Business and Industry, Bridging the gap between academic research and practitioners concerns Harald J. van Heerde 1 August 6, Harald J. van Heerde is Professor of Marketing, Faculty of Economics and Business Administration, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands. He can be contacted by telephone ( ), fax ( ), or by (heerde@uvt.nl). The author thanks the Netherlands Organization for cientific Research for its research support, AC Nielsen for providing the data, and the two guest editors, four reviewers, Tammo Bijmolt, Valentina Melnyk, and Dick Wittink for useful suggestions.

2 The Proper Interpretation of ales Promotion Effects: upplement Elasticities with Absolute ales Effects, Abstract ales promotions such as temporary price reductions are frequently used by managers to stimulate sales in the short run. Marketing academics and practitioners tend to rely on price elasticities to summarize sales promotion effects. Although elasticities have some attractive benefits such as the invariance to measurement units, they have led to three misinterpretations in the marketing literature, as described in this paper. The proper theoretical and managerial interpretation of sales promotion effects is obtained by expressing effects in terms of absolute sales. Key Words: sales promotions, elasticities, sales effects 1

3 1. Introduction ales promotions are frequently used by managers to stimulate sales in the short run. The sales effects tend to be huge [1]. Over the past decades, a substantial part of the marketing literature has been devoted to sales promotion effect measurement [2]. The claim in this paper is that despite this vast literature, our understanding of sales promotion effects is incomplete, and that this has been caused by misinterpretations of price elasticities. We define price elasticities as the percentage change in a criterion variable as the result of a 1% temporary price cut. 2 Elasticities have attractive properties, such as their invariance to measurement units, and their comparability across brands, categories, and markets if they are based on the same dependent and independent variables. However, if elasticities refer to different dependent and independent variables, the comparison of elasticities can be misleading, as we demonstrate in this paper. To enhance the comparability, we propose to use absolute sales effects. Absolute sales effects are defined as the change in unit sales for a criterion variable as a result of a sales promotion. Unit sales effects are preferable since sales promotions are by definition instruments to promote sales. Therefore, any measurement tool or marketing model that is intended to measure the effectiveness of sales promotions should ultimately report the results in terms of absolute sales effects. In addition, absolute sales effects are more tightly connected to profits than elasticities are [3]. That is, incremental profit calculations require the combination of absolute sales effects with margin and cost data. Elasticities have to be transformed to absolute sales effects before profits can be computed [3]. Among marketing academics, the use of elasticities has led to three misunderstandings. One goal of this paper is to briefly summarize these misunderstandings (sections 2, 3, 4). A second goal is to translate their resolutions into managerial implications 2 From now on, we use the term elasticities instead of price elasticities. 2

4 (section 5). Third, we present directions for future research (section 6), and we end with some concluding remarks (section 7). 2. Asymmetry in brand switching In a seminal paper in sales promotion research [4], the authors find that the cross-price elasticity of high price, high quality brands on low-price, low quality brands is much stronger than the reverse cross-price elasticity. Thus, consumer switching is asymmetric, in the sense that higher-quality brands attract more switchers if these are promoted than low-quality brands. Various rationales for the asymmetric switching effect have been provided by [4], [5], [6], [7], and [8]. everal empirical studies have validated the effect (e.g. [9]). Despite the apparently strong theoretical and empirical support for asymmetric brand switching effects, [10] show that the asymmetries tend to favor the higher priced brand simply because of scaling effects. They demonstrate that the asymmetries largely vanish if one uses absolute cross-price effects. Based on 1060 cross-price effects between 280 brands from 19 different grocery product categories, they conclude that a 10 cents price cut of a high-tier brand takes away as much market share points of a low-tier brand as is the case in the reverse situation. The study by [3] goes one step further by showing a reversal of the commonly assumed asymmetry. That is, they show that low market share brands gain more share at promotions than high-share brands when absolute cross-price effects are considered. A similar phenomenon is shown in [11]. The cross-price elasticity of Amazon.com on BarnesandNobles.com (3.5%) is much larger than the reverse cross-price elasticity (0.2%). However, the asymmetry is strongly reduced if one accounts for the fact that the sales of Amazon.com are much higher. The conclusion from [3], [10], and [11] is that in order to assess cross-price effects, one should use absolute effects rather than elasticities, since scaling effects can distort the 3

5 conclusions from elasticities. These scaling effects occur both in the independent variable (price) and in the dependent variable (market share, sales). 3. Magnitude of brand switching A major breakthrough in sales promotion research was the application of the multinomial logit model for brand choice based on household-level purchase data obtained by scanning in supermarkets [1]. During the 80s this model gained importance in the marketing literature and it became the dominant model in the 90s. In his seminal paper in sales promotions research, [12] supplements the brand choice model with purchase incidence and purchase quantity models. He demonstrates that the sales elasticity due to promotions for coffee can be decomposed into a brand choice elasticity (84%), a purchase incidence elasticity (14%), and a purchase quantity elasticity (2%). [13] analyze 173 brands from 13 categories with household-level panel data and conclude that on average the brand choice elasticity is 74%, the purchase incidence elasticity 11%, and purchase quantity elasticity is 15%. They also demonstrate that these percentages are dependent upon category characteristics such as the stockpileability of products. In the marketing literature, these findings have been interpreted to mean that the main source contributing to the sales promotion effect (about 75%) is brand switching (see Table 1 in [14]). Thus, if the promoted brand gains 100 units, the other brands in the category lose 75 units. However, [14] show that this interpretation is incorrect. They transform the elasticity result into unit sales effects, and conclude that in the promotional week, the other brands only lose on average about 33 units, and that the category as a whole gains 67 units. This conclusion is based on the same set of 173 brands as in [13] plus 8 newly analysed brands using household panel data. In this case elasticities are incomparable because they refer to different dependent variables: purchase incidence probability, brand choice probability, and purchase quantity. Other research [15] shows that store-level data confirm that cross-brand 4

6 effects in unit sales are about 1/3 of the own effect. Thus, this result seems independent of the data source (household or store). Recent research using time-series models also demonstrates the importance of short-term (one week) category expansion effects of sales promotions [16]. Long-term effects of sales promotions are nearly absent [16, 17]. Relatively strong category demand effects are consistent with theories that explain why retailers use promotions in the first place. The theory by [18] says that promotions shift holding costs from the retailer to the consumer, whereas [19] considers sales promotions as price discrimination tools between high- and low-holding cost consumers. Both theories only consider temporary category demand effects of promotions and no brand switching effects. 4. Modeling market shares A common way of modeling at the aggregate level (store, chain) is to write brand sales as the product of market share and category sales: = M * C. It is easy to demonstrate that the sales elasticity can be decomposed into a market share elasticity and a category sales elasticity: P M P C P η = = + = η M + ηc. P P M P C Perhaps one of the consequences of seemingly strong (yet overstated) cross-brand effects is that many studies in marketing at the aggregate data level (store, chain) use market share models without category sales models. In contrast, we suggest that even if market share elasticities dominate category sales elasticities, using market share models without category sales models leads to incomplete conclusions. To illustrate this point, let us consider the example in Table 1. We specify constant elasticity models for sales, market shares, and category sales for four brands in the tuna category. The log of these three variables are used as dependent variables. These are 5

7 separately explained by a set of independent variables: log price indices (actual over regular price), dummies for feature-only, display-only, feature and display, store and week. 3 [Insert Table 1 about here] In Table 1, we show the OL results based on store-level scanner data from 28 stores and 104 weeks. The average η = 2. 29, η 2.03, andη =. 26. ince the market M = C share elasticity is 89% of the sales elasticity, this expression could suggest that cross-brand effects are dominant. However, the calculations in Table 1 show this conclusion is highly erroneous. We compute absolute sales effects of a 20% temporary price cut (one price index going from 1.00 to 0.80 during one week). All independent variables stay at their nonpromotional values, which is 1.00 for price index variables and 0 for feature, display dummies. 4 The calculations show an average own brand sales increase of 74 units, which is for 40% attributable to a cross-brand sales loss and for 60% to an increase in category sales. For brand 3 we find a particularly strong change. Whereas its strong market share elasticity ( 2. 86, 93%) might suggest that its competitive impact is strong, actually competing brands do not lose sales if brand 3 is promoted. Thus, a strong market share elasticity does not imply that cross-brand effects are strong. 5. Managerial implications There are important managerial implications from the assertions that brand switching is primarily symmetric and that the primary source of the sales promotion bump is not brand switching. For retailers symmetric switching means better news than asymmetric switching to high-tier brands, since their private labels are typically in low tiers. Conversely, for manufacturers of national brands symmetry is worse than asymmetric switching favouring high-tier brands. 3 Using the same set of independent variables and functional form for all three dependent variables ensures that the elasticities add up exactly. An attraction model is more common for market shares, but that model does not ensure exact additivity. 4 One may study interaction effects by simulating sales effects while manipulating two or more variables at the same time. 6

8 For retailers, the decomposition result also is good news. That is, promotions do not just redistribute purchases in the category, but can really make categories grow temporarily. For manufacturers, temporary category growth is also better than brand switching, since it implies less strong brand competition and also more opportunities to convince retailers to participate in sales promotions. However, one important caveat applies here, since part of the temporary category growth may be borrowed from other time periods due to e.g. stockpiling effects. [15] finds that the borrowing effect is half of the temporary category growth. 6. Future Research Future research on sales promotion effects could focus on the following question: if a promoted brand gains X units, where do these units come from? ome possible sources are brand switching, store switching, stockpiling, and increased consumption. To provide such a complete decomposition an elaborate household-level should be developed that includes all consumer responses corresponding to these sources. By simulating consumer behavior with the model in the presence and absence of a sales promotion for one brand, it should be possible to quantify the magnitude (in unit sales) of all sources. Hitherto, no such model is available in the literature. Future research could also focus on the question whether household and store-level data yield similar conclusions on the magnitude of the decomposition effects in unit sales. In particular, it seems that cross-brand effects are about 1/3 in both cases [14, 15], but it is unclear to what extent equality holds for other decomposition effects. One would expect that store-level data give correct estimates of the magnitude of these effects, but that householdlevel data gives additional insights into consumer responses driving them. Another fruitful avenue for future research is on what really happens with buyers of non-promoted brands. The elasticity decomposition suggest that buyers of non-promoted brands are drawn to the category by the promotion of the competing brand, and decrease their 7

9 conditional choice probability of non-promoted brands to a large extent. An alternative scenario is that customers of the non-promoted brands are not drawn to the category due to the competing brand s promotion. For managers, the first scenario suggests that buyers of non-promoted brands do consider the promoted brand, whereas in the second scenario they (almost) don t. Future research should show what scenario is more correct. 7. Concluding remarks Importantly, we do not argue against computing elasticities or using constant-elasticity models. If they refer to similar dependent and independent variables, they can enhance our understanding of price (promotion) effects. One excellent example is a meta-analysis of own price elasticities across many studies [20], or within one study [13]. However, for a proper interpretation of sales promotion effects, it is inappropriate to compare elasticities across cases referring to different dependent and independent variables. One such example is the comparison of cross-brand elasticities from high-tier and low-tier brands. This comparison has led to the asymmetric brand switching result that has dominated the marketing literature for at least a decade, until [10] proved it was untrue in absolute terms. A second example is the comparison of purchase incidence, brand choice, and purchase quantity elasticise. This has yielded the result that cross-brand effects are the primary source contributing to the sales promotion effect, until [14] proved it was untrue in absolute sales effects. The third example in this paper has shown that if market share elasticities are much larger than category sales elasticities, we still can encounter huge temporary category expansion effects. Therefore, we propose to supplement elasticities with absolute sales effects, not only to bridge the gap between academics and practitioners, but also to avoid misinterpretations among academics. We recommend to do this irrespective what model (sales, market share, choice, incidence, etc.) or data (store level, household level, etc.) are being used. 8

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11 Table 1: Illustration of sales promotion effect measurement (tuna data) Brand ales 1 Market share 1 Category sales 1 Elasticity-based fraction crossbrand effects = 90. 8P M 1 = 0. 12P = 750.0P = P M 2 = 0. 28P = 750.0P = 39. 9P M 3 = 0. 05P = 750.0P = P M 4 = 0. 20P = 750.0P4 RZQ brand sales 2 FURVVbrand sales 2 category sales 2 Unit sales based fraction cross-brand effects C C C C Average The model shows only the focal brand s price index variable. All other marketing mix instruments are put equal to their non-promotional levels, and the intercept represents an average store and week. All parameter estimates are significant (p<0.05). tandard errors and p-values are available on request. 2 The column shows the effect of a 20% price cut for the focal brand. 3 The number represents the average price elasticity.