Analysis of Sales Promotion Effects on Household Purchase Behavior

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With only purchase-record information, one cannot infer the promotional environment during each shopping trip. The price variable in the data set does indicate whether purchased items where promoted using a price cut. But there is no information on other promotional activities for purchased items, such as display activity. Furthermore, there is no information at all for items that were not purchased. So, purchase-record information can be used to observe brand switching-, store switching-, purchase acceleration-, category expansion-, or repeat purchasing effects, but the causes <strong>of</strong> these possible effects remain unknown. Causal data from the retail outlet visited by a household is used to derive the missing promotional information. This is dealt with in the next section. 6.3 Store Level <strong>Promotion</strong>al Data The IRI/GfK retail panel consists <strong>of</strong> about 10% <strong>of</strong> the total population <strong>of</strong> retail outlets in The Netherlands. For each <strong>of</strong> these stores, weekly, SKU (Stock Keeping Unit)-level information about promotional activity is registered for display and feature promotions, together with the action price. This information can, first <strong>of</strong> all, be used to infer the promotional environment during each household shopping trip. Second, this information can be used to infer regular and special prices. The regular price is assumed to be the last non-special price for a brand in a store (cf. Mayhew and Winer 1992). When this price differs greatly from historically paid non-special prices, we apply an extra check on the validity <strong>of</strong> the price data. If each retail outlet within a retail chain would apply the same promotional strategy, we could generalize the available retail chain-level information for all households from the household panel. Unfortunately, it turned out that promotional activity is not uniform across retail outlets within the same retail chain (using data from the last quarter <strong>of</strong> 1995). This was found for all six product categories included in this research. The estimated uniformity rate, defined as the percentage <strong>of</strong> sample store outlets having the same promotional activity in the same week, is typically less than 50%. This quite astonishing result (even outlets who pr<strong>of</strong>ile themselves as being nationally organized retail chains showed low uniformity rates) forced us to think <strong>of</strong> another way to combine the household level data and the store. This linking process is dealt with in the next section. 107
6.4 Linking Household and Store Data Since the individual household serves as the level <strong>of</strong> analysis in this research, the promotional activity data has to be disaggregated towards the same level. First <strong>of</strong> all, a specific retail outlet has to be assigned to each household that is a member <strong>of</strong> the household panel. For this purpose, we have determined the primary retail chain for each household, which is defined as the supermarket where the households spent at least 50 % <strong>of</strong> their grocery expenses for the last quarter <strong>of</strong> 1995. This assignment process resulted in 1770 (43.6%) households with a primary store. Next, we assigned a specific primary retail outlet to each household, based on postal code information (assuming that a household shops at the nearest by retail outlet belonging to their primary retail chain). This assumption is not very restrictive. Most prior research on store choice concluded that store location and travel distance are the most important determinants <strong>of</strong> store choice (e.g., Brown 1989, Craig, Ghosh, and McLafferty 1984, Huff 1964, Bell et al. 1998). Figure 6.1 represents the household derivation process, starting with the household panel and resulting finally in the households remaining in the analysis. The household panel consists <strong>of</strong> 4060 households <strong>of</strong> which 1770 households can be assigned to a primary retail chain. Subsequently, these remaining 1770 households are assigned to a specific retail outlet from this primary chain, based on shortest distance. Then, it is possible to check whether each specific retail outlet is a member <strong>of</strong> the retail panel (indicated by r), which leads to an analysis set <strong>of</strong> 239 households for which both the household and the promotional activity data are at our disposal (indicated by h). Unfortunately, 39 households were not a member anymore <strong>of</strong> the household panel in 1996 or 1997. The final household analysis set therefore consists <strong>of</strong> 200 households. Only purchases from the primary store are incorporated in the analyses, as for these purchases both the household data and the promotional data are available. 108
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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
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 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
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: more persons living together and di
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 166 and 167: 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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