Analysis of Sales Promotion Effects on Household Purchase Behavior

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
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