Analysis of Sales Promotion Effects on Household Purchase Behavior

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Table 7.23: Summary <strong>of</strong> studies relating promotion response across categories Sign Relationship Study + Bawa and Shoemaker (1987) Wierenga (1974) Seetharaman et al. (1999) Blattberg et al. (1976, 1978) Ainslie and Rossi (1998) 0 Bell et al. (1999) Cunningham (1956) Massy et al. (1968) Wind and Frank (1969) Manchanda et al. (1999) Narasimhan et al. (1996) Hypothesis: Deal proneness does not exist. Several studies mention category characteristics that are related to promotion response. These are: (1) the number <strong>of</strong> brands within a category, (2) the average price level within a category, (3) the average interpurchase time <strong>of</strong> a category, (4) storability, (5) perishability, (6) impulse sensitivity, and (7) category promotion frequency. Bolton (1989) found that the effects <strong>of</strong> category-display and feature activity on promotional elasticities are much larger than the effects <strong>of</strong> brand-prices, display, and feature activity. Raju (1992) concluded that there is more promotional response for categories with deeper, infrequent dealings and good storability. Narasimhan et al. (1996) studied the relationship between product category characteristics and promotional elasticity for 108 product categories. They reported that promotions get the highest response in easily stockpiled, high penetration categories with short purchase cycles. Bell et al. (1999) concluded that storability and share <strong>of</strong> budget are two category characteristics that play a large role. Based on the fact that category characteristics such as storability, perishability, promotion frequency, number <strong>of</strong> different brands, etc. differ across categories and influence promotion response, we expect that promotion response differs across product 155
categories. But, this has to be empirically tested to answer the question whether consistencies exist or not, and, if so whether these consistency can be mainly explained by demographic, socio-economic, and purchase process characteristics, or if they should be attributed to a deal proneness trait. We will do so in the next sub-section. 7.6.1 Across Product Category Dependence In contrast to most prior research (Ainslie and Rossi 1988 being a positive exception), we do not try to answer the question about the existence <strong>of</strong> a deal proneness trait directly from household purchase behavior (or attitudinal household statements regarding their purchase behavior). Deal proneness is not isomorphic with promotion utilization. Consistencies in promotion response across different categories can be caused by household characteristics. Based on the extensive research overview, we believe that the most important household variables are incorporated in this study. We therefore believe that we can use the error terms to make a specific statement about the existence <strong>of</strong> a deal proneness trait. An errorterm in a regression model represents (among other things) the influence <strong>of</strong> omitted variables on the dependent variables. In the analyses, the error terms represent the promotion response observed, corrected for several household characteristics. If the term deal proneness is justified, then not incorporating deal proneness (or an indicator for deal proneness) into the category models should lead to errors at the household level that are correlated across product categories. For each category, the error terms are deduced including the same set <strong>of</strong> explanatory variables as used throughout the binary logistic regression analyses described before. The analyses were carried out in the corresponding subset <strong>of</strong> the data, only those records which dealt with each specific product category. Table 7.24 contains the correlation coefficients found between the error terms <strong>of</strong> each product category. The error terms are computed by taking the average error term per household per product category. 156
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LINDA H. TEUNTER Analysis</
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Analysis o
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Voorwoord Soms valt de appel echt n
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Contents 1 INTRODUCTION AND OVERVIE
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5.4.2.3.1 Inter Purchase Time .....
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1 INTRODUCTION AND OVERVIEW 1.1 Int
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generally generated from a smaller
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switching, purchase acceleration, c
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strategy change (decrease in promot
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of promotions, or
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characteristics are important in ex
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Chapter 5 are applied to study the
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2 THEORIES OF CONSUMER BUYING BEHAV
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systematically explored the decisio
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the part of the co
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As mentioned before, Figure 2.3 is
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product use coupled with the import
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2.3.2 Stimulus-Response Model Appli
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2.3.2.2 Operant Conditioning In ope
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X is very popular that it will brin
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But, in contrast, economic theory c
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compared to a perceived benchmark -
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High need-for-cognition (NFC) indiv
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2.3.4.2 Literature Overview Our rev
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The previously described research h
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has its deficiencies. Any purchase
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Chandon et al. 2000). But deal pron
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2.4 Concluding Remarks Regarding th
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stimulus-organism-response model) c
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Shoemaker 1987). Several factors ha
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such as display promotions. Althoug
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3.2.3 Type of resi
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3.2.5 Education Education links to
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these advantages therefore could be
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less likely to seek variety. More e
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3.3.1 Store Loyalty Store loyalty s
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more often (probab
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Table 3.12 continued Hypothesis H9a
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4.2.1 Brand Switching Brand switchi
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involvement purchases for most cons
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Neslin (1998), there is a great nee
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are called “attributions.” Attr
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and less than 2 percent by stockpil
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into a unit sales decomposition and
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4.3.4 Storability/Perishability Sto
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Economic theory ignores mental deci
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seeking (cf. Assael 1987, Desphande
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there another factor that is relate
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Pr(event) 90 = 1+ 1 z e − Here Z
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5.4.2 Sales <stron
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It represents the fraction
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96 We measure possible time acceler
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promotional post-promotional purcha
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( 12) CategoryExpansion effect : 10
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PART II EMPIRICAL ANALYSIS 103
Page 117 and 118: more persons living together and di
Page 119 and 120: 6.4 Linking Household and Store Dat
Page 121 and 122: Table 6.1 contains the results <str
Page 123 and 124: The age of the sho
Page 125 and 126: Table 6.4: Frequency distribution <
Page 128 and 129: 7 EMPIRICAL ANALYSIS ONE: DRIVERS O
Page 130 and 131: Table 7.1: Operationalization <stro
Page 132 and 133: during 435 shopping trips more than
Page 134 and 135: The variables size, age, and cycle
Page 136 and 137: 7.5 Results Drivers of</str
Page 138 and 139: esponsive the households within tha
Page 140 and 141: Table 7.3: Relationship promotion r
Page 142 and 143: indication that decreasing marginal
Page 148 and 149: Table 7.10: Relationship promotion
Page 154 and 155: 7.5.2.8 Variety Seeking Two intrins
Page 156 and 157: anymore. Therefore it is concluded
Page 158 and 159: Thus, store loyalty and promotion r
Page 160 and 161: dummies add up to one. This implies
Page 162 and 163: Table 7.22: Overview empirical test
Page 164 and 165: enacting-on-impulse hypothesis and
Page 168: Table 7.24: Correlation error coeff
Page 171 and 172: another category? Or do households
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Page 175 and 176: 164 In general, the average quantit
Page 177 and 178: Purchase frequency is defined as th
Page 179 and 180: Table 8.3: Product category rating
Page 181 and 182: Table 8.4: Pearson correlation betw
Page 183 and 184: We hypothesized that within impulse
Page 185 and 186: measure (PU). Referring to Figure A
Page 187 and 188: Table 8.6: Significant within categ
Page 189 and 190: looked at this promotional bump dec
Page 191 and 192: Table 8.8: Differences in promotion
Page 193 and 194: approach (about one third) are much
Page 195 and 196: promotions lead to large purchase t
Page 197 and 198: studies. Promotion
Page 199 and 200: Promotional extra
Page 201 and 202: categories revealed that the strong
Page 203 and 204: degree. When households switch bran
Page 205 and 206: specific sales promotion reaction m
Page 207 and 208: increasing (Inman and Winer 1998),
Page 209 and 210: indirectly from promotional purchas
Page 211 and 212: quantity downward for potato chips.
Page 213 and 214: 9.2.2.3 Deal Proneness With respect
Page 215 and 216: 204 related to promotion response.
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influenced by out-of</stron
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Andrews, K.Z. (1997), “Do Marketi
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Blattberg, R.C., Eppen, G.D. and J.
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Chintagunta, P. (1993), “Investig
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East, R. and K. Hammond (1996), “
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Gupta, Sachin and P.K. Chintagunta
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Kahn, B.E., Morrison, D.G.and G.P.
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Lattin, J.M. and R.E. Bucklin (1989
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McAlister, L. and E. Pessemier (198
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Petty, R.E. and J.T. Cacioppo (1981
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Shimp, T.A. (1990). Promoti
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Van Trijp, H.C.M. and J.B.E.M. Stee
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Winer, R.S., Bucklin, R.E., Deighto
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APPENDIX A4: Research Overview Tabl
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Table A4.1 continued Article Promot
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� Size of munici
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Table A7.1 continued Symbol Descrip
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Table A7.2: Data legitimacy check (
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Table A7.3: Results binary logistic
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Table A7.4: Results interacting wit
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Table A7.5: Results age and size no
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Table A7.5 continued Variable B S.E
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Table A8.1 continued Impulse BS TA
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Table A8.2 continued Number <strong
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Table A8.3: Relative occurrence and
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Figure A8.2: Decomposing total prom
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interessante aanbieding is. Combina
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aantalmerken in een categorie en im
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ERASMUS RESEARCH INSTITUTE OF MANAG
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Title: Beyond Generics;A closer loo
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Title: Planning and Control Concept
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