Analysis of Sales Promotion Effects on Household Purchase Behavior

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6 DATA DESCRIPTION 6.1 Introduction To study the effects <strong>of</strong> sales promotions on household purchase behavior, continuous household-level scanner data on product items is combined with retail outlet data regarding their promotional activity (the so-called causal data). The household data comes from GfK ConsumerScan and the retail data comes from IRI/GfK InfoScan. Both data sets cover the period from the last quarter <strong>of</strong> 1995 (IV 1995) until the last quarter <strong>of</strong> 1997 (IV 1997), 9 quarters in total. GfK ConsumerScan is the market leader with respect to household panel data in The Netherlands and is part <strong>of</strong> the pan-European market research agency GfK. For this study, data <strong>of</strong> various frequently-purchased packaged consumer goods is used, covering a variety <strong>of</strong> food/beverage products. We use only a subset <strong>of</strong> the available data (c<strong>of</strong>fee, s<strong>of</strong>t drinks, fruit juice, potato chips, candy bars, and pasta). These categories are chosen because <strong>of</strong> their frequent purchase and promotion activity character. Of the 2.25 years covered, we use the first 13 weeks (the last quarter <strong>of</strong> 1995) to initialize some model variables. We use the remaining 104 weeks <strong>of</strong> the period (1996 and 1997) for model calibration. The two different levels <strong>of</strong> data used (household panel and retail panel) are elaborated upon in the subsequent sections (Section 6.2 and Section 6.3). Section 6.4 describes linking these two different data sets in more detail. Section 6.5 deals with the representativeness <strong>of</strong> the resulting household analysis data set used in this research to answer the research questions. We conclude this chapter by providing some general descriptive statistics <strong>of</strong> the analysis data set. 6.2 Household Level Scanner Data Household scanner panel data <strong>of</strong>fers certain unique opportunities for understanding consumer behavior and deriving implications for marketing actions (Gupta et al. 1996). To decompose purchase behavior into the choice (brand, store), purchase timing, and purchase quantity effects, analysis must be conducted at this disaggregate (household) level (Bucklin et al. 1998). The GfK household scanner panel comprises 4060 households. This household panel represents the Dutch population <strong>of</strong> households. A household is defined as one or 105
more persons living together and dining at home at least four times a week. The sample is a stratified sample, using ‘size <strong>of</strong> the household’, and ‘age <strong>of</strong> the housewife/househusband’ as stratification variables, applying a Neyman-allocation (drawing relatively more/less households from strata with large/small dispersion regarding the stratification variables (Luijten 1993). Because <strong>of</strong> non-response (caused by all kinds <strong>of</strong> reasons, such as vacation, illness, and real non-response), social class, size <strong>of</strong> the municipality, and district are used as weights within each stratum. Appendix A6 provides an overview <strong>of</strong> the variables involved in the sample procedure. Although household panel data <strong>of</strong>fers several important advantages over store level data, it is <strong>of</strong>ten feared that this data is not representative. Russel and Kamakura (1994) did not find evidence that household panel data is nonrepresentative <strong>of</strong> store data. Tellis and Zufryden (1995) and Gupta et al. (1996) demonstrated that panel data provides unbiased estimators for general behavioral indicators, but more specific estimators are biased. Luijten and Hulsebos (1997) investigated the representativeness <strong>of</strong> the GfK ConsumerScan panel <strong>of</strong> the Dutch population. They concluded that the panel members are as price sensitive as non-panel members, and have the same attitudes towards media behavior, store choice, and store evaluation. Price knowledge differs for some product categories, where, in those cases, panel members were better informed about the exact prices. Panel members also turned out to have more interest in leaflets, brochures, etc. Most households in the dataset are not single-person households (81.2%), which could lead to the problem that purchases from the same household may reflect the choices <strong>of</strong> different consumers on different purchase occasions (Mayhew and Winer, 1992). If the shopper within the household changes from one purchase occasion to the next, a possible brand switch does not have to be caused by, for example, promotional activity or variety seeking behavior at the individual consumer level. Conclusions are therefore drawn at the household level, not at the individual consumer level. For each <strong>of</strong> these 4060 households, we have information available for the six product categories mentioned above. More specifically, we have information on the specific item bought, the retail chain, the amount bought, the price paid, and the day and time <strong>of</strong> the purchase (morning, afternoon, or evening). 106
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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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Page 67 and 68: 3.2.3 Type of resi
Page 69 and 70: 3.2.5 Education Education links to
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Page 73 and 74: less likely to seek variety. More e
Page 75 and 76: 3.3.1 Store Loyalty Store loyalty s
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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 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 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
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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
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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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