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Erschienen in: Journal of the Academy of Marketing Science 6/2023

25.10.2022 | Original Empirical Research

An empirical study of scarcity marketing strategies: Limited-time products with umbrella branding in the beer market

verfasst von: Masakazu Ishihara, Minjung Kwon, Makoto Mizuno

Erschienen in: Journal of the Academy of Marketing Science | Ausgabe 6/2023

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Abstract

We analyze empirical aspects of the limited-time product strategy—introduction of a new product that is available only for a limited time—that involves the use of umbrella branding. Despite the popularity of such a marketing strategy in many consumer packaged goods categories, little empirical work has studied how consumers react to this scarcity marketing in their actual choices. We use individual-level transaction data from the beer market and model consumers’ beer purchases using a multiple discrete-continuous choice model. The proposed model captures the patterns of consumer choices in response to the limited-time product and the asymmetric sales spillover effects between a parent product and a limited-time subproduct. Using the model estimates, we quantify the effects of incorporating a product’s limited-time nature and adopting an umbrella brand for a limited-time product. Our analyses indicate that (1) the product’s limited-time nature is associated with a rapid jump in demand in the launching period; (2) the return from store coverage for limited-time products decreases over time; and (3) umbrella branding for limited-time products increases the brand-level demand despite the cannibalization of the sales of a parent product. Managerial implications about the nature of perceived scarcity associated with the limited-time product and the effective targeting and distribution strategy are discussed.

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Fußnoten
1
Note that an LT product, a distinct type of new product, is distinguished from limited-time promotion deals for existing products.
 
2
US$ 1 \(\simeq\) ¥100.
 
3
GRP quantifies TV advertising impressions during the sample period, measured by the average advertising frequency per 100 target audience.
 
4
We aggregate the purchase observations at the weekly level. Detailed procedures used to construct the sample for model estimation can be found in Web Appendix B.
 
5
The periods after the week with minimal sales volume—i.e., below ten liters per week—are not counted for the lifetime.
 
6
In the left panel of Fig. 2, to control for the impact of product availability, we also plot adjusted sales, which we obtain by dividing the original weekly sales by the store coverage rate for the corresponding week. The result looks very similar to the pattern of unadjusted sales.
 
7
We report the positive sales spillover effects for umbrella brands obtained by pooling the purchases of parent and child LT products in Web Appendix D.
 
8
In other words, we model consumers’ beer product and quantity choices conditional on purchasing beer.
 
9
Updating \({\lambda }_{ijt}\) based on Eq. (3) guarantees that \({\sum }_{j}{\lambda }_{ijt}=1\) for any \(\left(i,t\right)\).
 
10
We discuss our estimation strategy in detail in Web Appendix E and report the model fit in Web Appendix F.
 
11
We consider two additional unobserved factors that could contribute to the front-loaded nature of LT products. The first factor is in-store promotion activity right around the release week. We interviewed industry experts and were told that any new products (regardless of whether they are LT or non-LT) receive in-store promotion in the release week only. To control for this, we included a dummy for the release week of new products. The second factor is an unobserved demand shock for LT products; i.e., LT products are released when the demand for them is high. To control for this, we included product-month fixed effects. The results of this model are reported in Table A9 of Web Appendix G. We find that the results are qualitatively the same as in the baseline model and that LT products exhibit a steeper aging effect than non-LT new products (similar to Table 4). Thus, the front-loaded nature of LT products is not driven purely by in-store promotion activity or unobserved demand shocks.
 
12
We notice that the estimated coefficient for advertising goodwill is very small in magnitude. We will validate this in the related analysis of advertising elasticity in the next section.
 
13
Other parameters are similar to those of the baseline model. We report them in Table A11 of Web Appendix G.
 
14
In our data, we do not observe product availability at the store level. However, consumers usually shop for alcohol products at multiple stores (supermarkets, convenience stores, discount stores, etc.), so changes in store coverage should capture the change in product availability perceived by consumers.
 
15
Once again, other parameters are similar to those of Model 2 and are reported in Tables A12 and A13 of Web Appendix G.
 
16
We compute the elasticity as follows. For each brand and each week, we compute the baseline aggregate sales using 1000 draws for the extreme value errors. We also simulate the baseline aggregate sales for each brand and each week by increasing the price or advertising of one of the brands, using the same 1000 draws for the extreme value errors. We then compute the quantity-weighted average elasticity across weeks for each draw and take the average value (Dubé, 2004). This results in a 40-by-40 elasticity matrix for each of price and advertising.
 
17
Chūhai are fruit-flavored carbonated alcoholic drinks made of Shōchū, a distilled beverage, and soda.
 
18
To examine the results in an equilibrium framework, we need a supply-side model, as well as the cost information. Unfortunately, we have no cost information. Also, developing a supply-side model is beyond the scope of this paper.
 
19
Operationally, we set the LT-product dummy and its aging effect for one of the LT products to zero and simulate the sales over the entire sample period using 1,000 draws of the extreme value errors. We then repeat this for all 15 LT products.
 
20
We obtain these results by setting the LT product dummy to zero either only in the baseline marginal utility or only in the satiation parameter.
 
21
To assess the joint outcome, the percentage change in joint profits would be a more relevant metric for brewers. However, we do not have the cost information, so we use the joint revenue as an approximation.
 
22
We keep the dummy for umbrella brands for the parent products, as it is part of the parent products’ brand value.
 
23
The market shares based on revenues show similar patterns.
 
24
An example of the product information disclosed at a major firm’s website can be found in Web Appendix I.
 
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Metadaten
Titel
An empirical study of scarcity marketing strategies: Limited-time products with umbrella branding in the beer market
verfasst von
Masakazu Ishihara
Minjung Kwon
Makoto Mizuno
Publikationsdatum
25.10.2022
Verlag
Springer US
Erschienen in
Journal of the Academy of Marketing Science / Ausgabe 6/2023
Print ISSN: 0092-0703
Elektronische ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-022-00899-y

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