How Do Different Brand Equity Measures Affect Long-Term Promotional Effectiveness?

Yuying Shi

doi:10.1515/roms-2022-0003

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How Do Different Brand Equity Measures Affect Long-Term Promotional Effectiveness?

Yuying Shi

Published/Copyright: July 18, 2022

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From the journal Review of Marketing Science Volume 20 Issue 1

Abstract

Studies on the relationship between brand equity and long-term promotional effectiveness have many issues. Brand equity measures can be classified as either consumer-based (CB) or firm-based (FB) measures. Previous literature has mostly focused on one or the other type of measure, mostly due to computational difficulties or data limitations, making it difficult to determine whether the results can be generalized to all types of measure. In addition, long-term promotional effectiveness remains a debatable issue due to complicated long-term effects. Our study is the first to include both CB and FB brand equity measures in a long-term promotional effectiveness study. Using weekly scanner data in the breakfast category covering a three-year period, we are able to compute a variety of popular operationalizations of brand equity measures by employing the aggregate demand model and capture total long-term effects via the persistence model. Our major finding is that the general consensus that higher brand equity generates higher promotional effectiveness applies only to typical FB measures—not CB measures. Moreover, we show a positive accumulated long-term effectiveness and an absence of permanent effects for all our brands. These results offer new insights into the differentiation of major brand equity measures and long-term promotional effectiveness.

Keywords: brand equity; long-term promotional effectiveness; aggregate demand model; persistence model; choice model

Corresponding author: Yuying Shi, Department of Marketing and Business Analytics, Texas A&M University-Commerce, 2200 Campbell St., Commerce, TX 75428, USA, E-mail: yuying.shi@tamuc.edu

Appendix

Table A1:

BLP model results for three years.

		Year 1	Year 2	Year 3
	Parameter	Coefficient	Coefficient	Coefficient
	Constant	−5.666 (1.37)^c	−9.539 (1.22)^c	−2.738(1.376)^a
	Price	−79.298(32.199)^b	−50.469(18.85)^c	−46.258(13.859)^c
	Advertising	0.01(0.00)^c	0.057(0.00)^c	0.086(0.011)^c
	Sugar	0.718(0.774)	−0.005(0.039)	−0.296(0.342)
	Fat	−3.36(0.001)^c	−4.84(0.723)^c	−0.260(1.021)
	Fiber	−0.544(0.632)	−0.684(0.743)	−1.081(0.579)
	Calories	0.82(0.046)^c	0.675(0.096)^c	0.143(0.108)
	All family	0.73(0.150)^c	0.923(0.207)^c	0.586(0.192)^c
	Kids	−0.256(0.23)	0.134(0.099)	0.173(0.302)
	Health	4.87(0.522)^c	2.173(0.228)^c	1.037(0.304)^c
Standard deviations	Constant	0.008(0.369)	0.001(0.594)	0.01(0.601)
	Price	0.128(3.257)	0.036(3.183)	0.087(3.174)
	Sugar	0.099(0.062)	0.048(0.097)	0.042(0.095)
	Fat	0.032(3.594)	0.003(3.851)	0.030(3.794)
	Fiber	0.013(4.58)	0.011(4.491)	0.112(4.438)
	Calories	0.0001(0.066)	0.005(0.049)	0.000(0.047)
	All family	0.005(2.372)	0.002(2.051)	0.223(2.205)
	Kids	0.001(1.030)	0.02(2.078)	0.020(2.076)
	Adults	0.001(5.810)	0.003(3.961)	0.101(4.080)
Interactions	Price * Child	0.329(7.132)	0.115(9.874)	0.115(10.149)
	Price * Income	0.230(67.069)	0.203(6.172)	0.207 (6.258)

^a p < 0.05; ^b p < 0.01; ^c p < 0.001. Numbers in the parentheses are standard errors.

Table A-1 presents the estimated model results. The price and advertising variables are the two major variables. As expected, price has a negative impact on individual demand, while advertising has a positive impact on an individual’s demand. The standard deviations of all the estimates are insignificant (as expected), suggesting that the heterogeneity in the coefficients has been mostly explained by the included demographics variables (Nevo 2000).

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Received: 2022-01-17

Accepted: 2022-06-25

Published Online: 2022-07-18

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https://doi.org/10.1515/roms-2022-0003

Keywords for this article

brand equity; long-term promotional effectiveness; aggregate demand model; persistence model; choice model