Analysis of Sales Promotion Effects on Household Purchase Behavior

temporarily attracts a disproportionate number <strong>of</strong> households with low purchase probabilities.
When the repeat rates <strong>of</strong> these households are averaged with the repeat rates <strong>of</strong> those that
would have bought the brand even without a promotion, the average rate after a promotion
purchase is lower.
The weight <strong>of</strong> research evidence suggests that promotions do not induce a positive
repeat purchase effect (e.g., Dodson et al. 1978, Ehrenberg et al. 1994, Neslin and Shoemaker
1983, 1989, Scott 1976; Bawa and Shoemaker 1987, Nijs et al. 2001). However, Guadagni
and Little (1983) showed that it is possible for promotion to induce a net positive increase in
repeat purchase probability. So the book is not closed on this topic.
4.2.6 Integrating the Mechanisms
In real-world marketplaces, it is likely that all the sales promotion reaction mechanisms as
described above occur simultaneously. Therefore, several researchers have tried to investigate
the joint effects <strong>of</strong> some or all <strong>of</strong> these mechanisms. Neslin and Shoemaker (1983)
constructed a simulation model that included brand choice, repeat purchasing, and
acceleration effects. Vilcassim and Jain (1991) analyzed brand switching and purchase timing
decisions <strong>of</strong> households in a single framework. They concluded that price and promotion had
a greater impact on the rate <strong>of</strong> brand switching than on the rate <strong>of</strong> repeat purchase. Bucklin
and Gupta (1992) developed an approach to market segmentation based on consumer
response to marketing variables in both brand choice and purchase timing. The results
suggested that many households that switch brands on the basis <strong>of</strong> price and promotion do not
also accelerate their category purchases and that many households that accelerate their
category purchases do not switch brands.
Gupta (1988) modeled brand choice, purchase time, and purchase quantity to
decompose the promotional purchase bump for c<strong>of</strong>fee using scanner panel data. The data set
contains records <strong>of</strong> the complete purchase history <strong>of</strong> each household in the panel. In addition,
a store file records weekly information on prices and promotions for all the c<strong>of</strong>fee brands
available in the stores in the market. It was found that more than 84 percent <strong>of</strong> the total sales
increase is accounted for by brand switching, 14 percent or less by purchase time acceleration,
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