Analysis of Sales Promotion Effects on Household Purchase Behavior

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7.5 Results Drivers <strong>of</strong> <strong>Promotion</strong> Response In this section, the hypotheses as derived in Section 3.2 and 3.3 will be empirically tested using 17,144 promotional purchase records from 156 households for the period (1996(II) – 1997(IV)). The structure <strong>of</strong> this section is as follows. The results using the singlepromotion data, the decomposed multiple-promotion data and the combined data are compared in sub-section 7.5.1. Subsequently, the obtained results are dealt with in section 7.5.2 in detail. Each hypothesis is tabulated along with the estimated coefficients regarding the variable tested. Finally, the implications <strong>of</strong> the findings are discussed in Section 7.5.3. 7.5.1 Data Legitimacy Check Before discussing the findings in detail, first the validity <strong>of</strong> the findings is checked by comparing the estimates for the combined-record data format (where all promotion shopping trips are decomposed in such a way that each data record deals with only one type <strong>of</strong> promotion) with the single-promotion data (where only those records are selected where only one type <strong>of</strong> promotion is present) and the decomposed multiple-promotion data (where those records are selected where more than one different type <strong>of</strong> promotion was present during a specific shopping trip, these shopping trips are decomposed such that every record deals with only one type <strong>of</strong> promotion) (see Section 7.3 for details about these three data formats). The results <strong>of</strong> the binary logistic regression analyses for these three different data inputs can be found in Table A7.2. The significance levels reported for the categorical variables indicate whether the variables itself are significant or not. Overall, the parameter estimates for the three groups are reasonably in agreement. When we look at the parameter estimates for the possible drivers <strong>of</strong> promotion response, the overall picture that emerges <strong>of</strong> drivers <strong>of</strong> promotion response is also consistent (most significant estimates have the same sign and about the same size). Although some differences are present, the relative ranking <strong>of</strong> the parameter estimates for the different categories <strong>of</strong> a variable lead to the same findings. 125
126 Similar results are found regarding the influence <strong>of</strong> price-cut combined promotions for all three data sets: feature combined price cuts are used more than display combined price cuts, price cuts supported by both display and feature have the biggest impact from the different promotion types, and promotions for the favorite brand have the biggest impact on promotion response. Therefore, regarding the effect <strong>of</strong> the different types <strong>of</strong> sales promotions on promotion response, the results seem to validate the legitimacy <strong>of</strong> the multiple-promotion decomposition. But there are some exceptions. The parameter estimates for the different non-price cut promotion types (XD, XDF) do vary between the three groups <strong>of</strong> parameter estimates. For these promotions, the effects are estimated to be greater for the combined data and the decomposed multiple-promotion data. But, the effect <strong>of</strong> such a promotion is reduced due to the presence <strong>of</strong> other types <strong>of</strong> promotion. It can therefore be concluded that the presence <strong>of</strong> other types <strong>of</strong> promotions is especially detrimental for non-price cut sales promotions (although this only partly explains the differences found). Overall, enough evidence for consistency in findings is obtained, leading to the conclusion that the results are providing us with valid and reliable results regarding the drivers <strong>of</strong> promotions response. In the subsequent sub-sections, the results using the combined data will be dealt with in more detail. 7.5.2 Results Hypotheses Testing Household (Purchase) Characteristics Related to <strong>Promotion</strong> Response The results <strong>of</strong> the binary logistic regression analysis can be found in Table A7.3. In the next sub-sections, each hypothesis from Section 3.2 and Section 3.3 will be tested. As mentioned before, in each table we will zoom in on each specific variable that is hypothesized to be related with promotion response. Each sub-section deals with the outcomes <strong>of</strong> the relationship between promotion response and a specific driver <strong>of</strong> this promotion response. Each possible categorical (nominal) driver <strong>of</strong> promotion response is incorporated in the analysis as a deviation variable. The resulting estimated logistic regression coefficients for these categorical variables tell us how much more or less
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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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Page 87 and 88: are called “attributions.” Attr
Page 89 and 90: and less than 2 percent by stockpil
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Page 93 and 94: 4.3.4 Storability/Perishability Sto
Page 95 and 96: Economic theory ignores mental deci
Page 97 and 98: seeking (cf. Assael 1987, Desphande
Page 99 and 100: there another factor that is relate
Page 101 and 102: Pr(event) 90 = 1+ 1 z e − Here Z
Page 103 and 104: 5.4.2 Sales <stron
Page 105 and 106: It represents the fraction
Page 107 and 108: 96 We measure possible time acceler
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Page 111 and 112: ( 12) CategoryExpansion effect : 10
Page 114: 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 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 166 and 167: Table 7.23: Summary of</str
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
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Table 8.6: Significant within categ
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looked at this promotional bump dec
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Table 8.8: Differences in promotion
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approach (about one third) are much
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promotions lead to large purchase t
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studies. Promotion
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Promotional extra
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categories revealed that the strong
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degree. When households switch bran
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specific sales promotion reaction m
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increasing (Inman and Winer 1998),
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indirectly from promotional purchas
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quantity downward for potato chips.
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9.2.2.3 Deal Proneness With respect
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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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