Analysis of Sales Promotion Effects on Household Purchase Behavior

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Summarizing, binary logistic regression models are used to estimate the influence <strong>of</strong> household characteristics, characteristics <strong>of</strong> the purchasing process, and types <strong>of</strong> sales promotion on promotion response. One possible limitation <strong>of</strong> the empirical approach chosen is that we assume that all households in the dataset come from one distribution. Mixture models have found widespread application in marketing (Andrews et al. 2002). It is based on the assumption that the data arise from a mixture <strong>of</strong> distributions, and it estimates the probability that objects belong to each class. The purpose <strong>of</strong> mixture models is to “unmix” the sample, that is to identify groups or segments, and to estimate the parameters <strong>of</strong> the density function underlying the observed data within each group (Wedel and Kamakura 2000). The underlying assumption <strong>of</strong> mixture models is that because <strong>of</strong> one or more an additional variables, not incorporated in the model, consumers may come from different segments. The purpose <strong>of</strong> the current analysis is to get insights in drivers <strong>of</strong> sales promotions response, where sales promotion response, the dependent variable, is measured as a single binary variable. A large number <strong>of</strong> independent variables are incorporated in the model. According to Wedel and Kamakura (2000), a large number <strong>of</strong> independent variables decreases parameter recovery <strong>of</strong> mixture regression models. Furthermore, mixture modeling has an additional problem, related to the independent variables. As there are fewer observations for estimation the regression model in each segment, collinearity among the independent variables could lead to severe identification problems (Wedel and Kamakure 2000). Based on the large number <strong>of</strong> independent variables, the limited number <strong>of</strong> households, and the possible presence <strong>of</strong> collinearity, it was chosen to identify the drivers <strong>of</strong> sales promotion response by incorporating the dependent and independent into a traditional, non-mixture model. Still, the outcomes <strong>of</strong> this non-mixture binary logistic regression model can a posteriori be used to identify segments based on different levels <strong>of</strong> relevant, significant independent variables. We will come back to the topic <strong>of</strong> mixture models in Section 9.4 when dealing with limitations and interesting topics for future research. 91
5.4.2 <strong>Sales</strong> <strong>Promotion</strong> Reaction Mechanism Measures The work discussed in Chapter 4 (prior research done on sales promotion reaction mechanisms) uses mainly the individual brand as the unit <strong>of</strong> analysis. Additional sales <strong>of</strong> a specific brand as a consequence <strong>of</strong> sales promotions are decomposed into extra sales resulting from brand switching, purchase time acceleration, and purchase quantity acceleration. However, in our research, we are not interested in the promotional sales decomposition for a specific brand. We are interested in the promotional sales decomposition for a specific household. How is household purchase behavior influenced by sales promotions? Can we decompose the promotional bump into brand switching, purchase time acceleration, and purchase quantity acceleration? We emphasize the household as the unit <strong>of</strong> analysis, not the individual brand. As households encounter different brands, the household promotional bump decomposition is therefore possibly based on promotions for different brands. This promotional bump decomposition (irrespective <strong>of</strong> the unit <strong>of</strong> analysis used) deals only with the effects during the promotional shopping trip itself. Intertemporal dynamics are not incorporated. As we remarked before, however, pre- and post-promotional effects influence the pr<strong>of</strong>itability <strong>of</strong> a sales promotion and hence <strong>of</strong> great importance. As our unit <strong>of</strong> analysis differs from the work discussed above, new measures have to be developed for the effects <strong>of</strong> sales promotions. Furthermore, measures developed by other researchers reflect how consumers respond to price changes at given points in time, but do not indicate how long it takes before these consumers return to the market. Our measures are based on an intertemporal analysis and do capture consumption dynamics. Following Bell et al. (1999), our measures compare promotional and non-promotional averages. As described throughout this thesis and in detail in Chapter 4, promotions can affect consumer purchase behavior in various ways. The primary approach in this section is to develop a specific measure for each sales promotion reaction mechanism. These measures can be estimated for a single household, a single product class and a specific type <strong>of</strong> promotional activity, but they can also be used to measure the effect <strong>of</strong> promotions on more than one (possibly all) households, across different types <strong>of</strong> promotion, or across 92
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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
Page 52 and 53: The previously described research h
Page 54 and 55: has its deficiencies. Any purchase
Page 56 and 57: Chandon et al. 2000). But deal pron
Page 58 and 59: 2.4 Concluding Remarks Regarding th
Page 60: stimulus-organism-response model) c
Page 63 and 64: Shoemaker 1987). Several factors ha
Page 65 and 66: such as display promotions. Althoug
Page 67 and 68: 3.2.3 Type of resi
Page 69 and 70: 3.2.5 Education Education links to
Page 71 and 72: these advantages therefore could be
Page 73 and 74: less likely to seek variety. More e
Page 75 and 76: 3.3.1 Store Loyalty Store loyalty s
Page 77 and 78: more often (probab
Page 79 and 80: Table 3.12 continued Hypothesis H9a
Page 81 and 82: 4.2.1 Brand Switching Brand switchi
Page 83 and 84: involvement purchases for most cons
Page 85 and 86: Neslin (1998), there is a great nee
Page 87 and 88: are called “attributions.” Attr
Page 89 and 90: and less than 2 percent by stockpil
Page 91 and 92: into a unit sales decomposition and
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: Pr(event) 90 = 1+ 1 z e − Here Z
Page 105 and 106: It represents the fraction
Page 107 and 108: 96 We measure possible time acceler
Page 109 and 110: promotional post-promotional purcha
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 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 150 and 151: Table 7.12: Relationship promotion
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Table 7.13: Relationship promotion
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7.5.2.8 Variety Seeking Two intrins
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anymore. Therefore it is concluded
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Thus, store loyalty and promotion r
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dummies add up to one. This implies
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Table 7.22: Overview empirical test
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enacting-on-impulse hypothesis and
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Table 7.23: Summary of</str
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Table 7.24: Correlation error coeff
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another category? Or do households
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and unweighted. With respect to the
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164 In general, the average quantit
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Purchase frequency is defined as th
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Table 8.3: Product category rating
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Table 8.4: Pearson correlation betw
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We hypothesized that within impulse
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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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