Analysis of Sales Promotion Effects on Household Purchase Behavior

More documents

Recommendations

Info

during 435 shopping trips more than three different promotion types were present within the same product category. Therefore, during one shopping trip, several different types <strong>of</strong> promotion for different brands can be present within the same product category. A household can decide to use one <strong>of</strong> the available promotions, more than one, or all <strong>of</strong> them. The household therefore has to decide for each available promotion whether to use it or not. That is the reason for decomposing the multiple-promotion data such that each case deals with only one available type <strong>of</strong> promotion. The dependent variable only indicates whether or not a household made use <strong>of</strong> at least one <strong>of</strong> the available promotions during a shopping trip. It does not indicate which (<strong>of</strong> the) promoted SKU’s were purchased. However, we do have that information available and want to incorporate it into the analysis. As mentioned before, we therefore change the data format by multiplying a single record in the multiple-promotion data set, corresponding to a shopping trip with n, n=2,3,…, promotions, to n records with 1 promotion in the new data set. We first describe this method <strong>of</strong> formatting the data using an example, and then discuss its legitimacy. We remark that the new combined-record data set has 17,144 records (the 4,886 single-promotion records plus the decomposed records from the multiple promotion data (12,258 records). Suppose there is display activity for one <strong>of</strong> the favorite SKU’s and feature activity for another, non-favorite SKU during the same shopping trip, both <strong>of</strong> them combined with a price cut. The household uses the display activity (in the sense that the household purchases the product that was on display), but not the feature activity. The original multiple-promotion-record (Y=1, Xdp=1, Xfp=1) decomposes into two single-promotion records, namely a two-record data format with only one type <strong>of</strong> promotion present in each specific record (Y=1, Xdp=1, Xfp=0), (Y=0, Xdp=0, Xfp=1). Then, two extra explanatory binary variables X other and X *other are added, indicating whether (1) or not (0) there were promotions for other SKU’s and for other favorite SKU’s respectively. So we end up with the following records: (Y=1, Xdp=1, Xfp=0, X other =1, X *other =0) and (Y=0, Xdp=0, Xfp=1, X other =1, X *other =1). Is the decomposition <strong>of</strong> an n-promotion type record into n one-promotion type records legitimate? A household that encounters n, n=2,3,…, promotions actually has to 121
make n purchase decisions. But those purchase decisions probably depend on each other. Indeed, that is why the explanatory variables X other and X *other are added to the multiplied records. We expect that the information provided by those two additional variables is sufficient to remove most <strong>of</strong> the bias. This will be validated in Section 7.5.1 by comparing the results obtained using only the single-promotion data with the results obtained from the combined record data. Why should we even bother about the multiple-promotion data at all? Why not only take the single-promotion data into account? This research is aimed at acquiring knowledge about the drivers <strong>of</strong> sales promotion response. If we would only take singlepromotion data into account, we would not obtain insights in the impact <strong>of</strong> the presence <strong>of</strong> other types <strong>of</strong> promotions on sales promotion response. This could lead to an unrealistic description <strong>of</strong> the drivers <strong>of</strong> sales promotion response. The presence <strong>of</strong> other types <strong>of</strong> promotions for other SKU’s within the same category will intuitively have a detrimental effect on the impact <strong>of</strong> a promotion. Before presenting the results <strong>of</strong> the data validity check and the results <strong>of</strong> the empirical hypotheses testing, variable screening is performed to circumvent possible multicollinearity problems. 7.4 Variable Screening All variables that could possibly be related to promotion response (Table A7.1) are candidates to be incorporated in the binary logistic regression analysis. But, the presence <strong>of</strong> multicollinearity could lead to large standard errors, which in turn could lead to unjustified non-significance <strong>of</strong> possible drivers <strong>of</strong> promotion response. In this section we discuss which variables are excluded from the analysis and for what reason. If a correlation coefficient is provided in the text, we will also mention its 2-sided significance (p-value), and the number <strong>of</strong> observations it is based on. These three numbers will be put between parentheses. For example (0.65, 0.03, 156) indicates that, based on 156 observations, a correlation coefficient <strong>of</strong> 0.65 was found and that the associated p-value is 0.03. This pvalue <strong>of</strong> less then 0.05 implies that the correlation coefficient found statistically differs significant from zero (two-sided) using a significance level <strong>of</strong> 0.05. 122
Page 1 and 2:
LINDA H. TEUNTER Analysis</
Page 4 and 5:
Analysis o
Page 6 and 7:
Voorwoord Soms valt de appel echt n
Page 8 and 9:
Contents 1 INTRODUCTION AND OVERVIE
Page 10 and 11:
5.4.2.3.1 Inter Purchase Time .....
Page 12 and 13:
1 INTRODUCTION AND OVERVIEW 1.1 Int
Page 14 and 15:
generally generated from a smaller
Page 16 and 17:
switching, purchase acceleration, c
Page 18 and 19:
strategy change (decrease in promot
Page 20 and 21:
of promotions, or
Page 22 and 23:
characteristics are important in ex
Page 24:
Chapter 5 are applied to study the
Page 28 and 29:
2 THEORIES OF CONSUMER BUYING BEHAV
Page 30 and 31:
systematically explored the decisio
Page 32 and 33:
the part of the co
Page 34 and 35:
As mentioned before, Figure 2.3 is
Page 36 and 37:
product use coupled with the import
Page 38 and 39:
2.3.2 Stimulus-Response Model Appli
Page 40 and 41:
2.3.2.2 Operant Conditioning In ope
Page 42 and 43:
X is very popular that it will brin
Page 44 and 45:
But, in contrast, economic theory c
Page 46 and 47:
compared to a perceived benchmark -
Page 48 and 49:
High need-for-cognition (NFC) indiv
Page 50 and 51:
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 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 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 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
Page 168: Table 7.24: Correlation error coeff
Page 171 and 172: another category? Or do households
Page 173 and 174: and unweighted. With respect to the
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.
Page 217 and 218:
influenced by out-of</stron
Page 219 and 220:
Andrews, K.Z. (1997), “Do Marketi
Page 221 and 222:
Blattberg, R.C., Eppen, G.D. and J.
Page 223 and 224:
Chintagunta, P. (1993), “Investig
Page 225 and 226:
East, R. and K. Hammond (1996), “
Page 227 and 228:
Gupta, Sachin and P.K. Chintagunta
Page 229 and 230:
Kahn, B.E., Morrison, D.G.and G.P.
Page 231 and 232:
Lattin, J.M. and R.E. Bucklin (1989
Page 233 and 234:
McAlister, L. and E. Pessemier (198
Page 235 and 236:
Petty, R.E. and J.T. Cacioppo (1981
Page 237 and 238:
Shimp, T.A. (1990). Promoti
Page 239 and 240:
Van Trijp, H.C.M. and J.B.E.M. Stee
Page 241 and 242:
Winer, R.S., Bucklin, R.E., Deighto
Page 244 and 245:
APPENDIX A4: Research Overview Tabl
Page 246:
Table A4.1 continued Article Promot
Page 249 and 250:
� Size of munici
Page 251 and 252:
Table A7.1 continued Symbol Descrip
Page 253 and 254:
Table A7.2: Data legitimacy check (
Page 255 and 256:
Table A7.3: Results binary logistic
Page 257 and 258:
Table A7.4: Results interacting wit
Page 259 and 260:
Table A7.5: Results age and size no
Page 261 and 262:
Table A7.5 continued Variable B S.E
Page 263 and 264:
Table A8.1 continued Impulse BS TA
Page 265 and 266:
Table A8.2 continued Number <strong
Page 267 and 268:
Table A8.3: Relative occurrence and
Page 269 and 270:
Figure A8.2: Decomposing total prom
Page 271 and 272:
interessante aanbieding is. Combina
Page 273 and 274:
aantalmerken in een categorie en im
Page 276 and 277:
ERASMUS RESEARCH INSTITUTE OF MANAG
Page 278 and 279:
Title: Beyond Generics;A closer loo
Page 280 and 281:
Title: Planning and Control Concept
show all

Analysis of Sales Promotion Effects on Household Purchase Behavior

Create successful ePaper yourself

Delete template?

Save as template?