Analysis of Sales Promotion Effects on Household Purchase Behavior

and less than 2 percent by stockpiling. Gupta (1988) remarked that this decomposition could
be different for other product categories, based on for example storage constraints.
Narasimhan et al. (1996) studied the relationships between product category
characteristics and the average increase in brand sales resulting from promotions within the
product category. They considered four mechanisms: brand switching, store switching,
category expansion, and purchase acceleration. They used store-level data to measure the
effect <strong>of</strong> multiple types <strong>of</strong> promotions (price, price-feature, and price-display). Product
category characteristics that were found to be <strong>of</strong> importance in explaining variety in
promotional elasticities across product categories were: (1) category penetration (positive
relation between category penetration and promotional elasticity, especially for featured price
cuts); (2) interpurchase time (longer interpurchase times are associated with lower
promotional elasticities); (3) price (higher price levels are associated with higher promotional
elasticity for pure price cuts); (4) number <strong>of</strong> brands (negative relationship with promotional
elasticity); (5) ability to stockpile (associated with higher promotional elasticity).
Bucklin et al. (1998) developed a joint estimation approach to segment households
based on their response to price and promotion in brand choice, purchase timing, and
purchase quantity decisions. This work extends the work <strong>of</strong> Gupta (1988) by incorporating
segmentation in response to marketing activity. The model was fitted and estimated using
household panel data from the yogurt category, which lead to five segments. Subsequently, the
overall sales elasticity was decomposed into the response due to choice, timing, and quantity
decisions. Aggregate-level results decompose response for all households into the choice
(58%), time (20%), and quantity (22%) components. But, the intersegment variation turned
out to be substantive. The impact <strong>of</strong> choice decisions ranged from 38% to 64%, the impact <strong>of</strong>
purchase timing ranged from 10% to 29%, and the impact <strong>of</strong> quantity ranged from 11% to
52%.
Bell et al. (1999) extended the work <strong>of</strong> Gupta (1988) by decomposing the sales
increase for a brand on promotion into brand switch, purchase time, and purchase quantity
elasticities. This was done for 173 brands across 13 different categories. Two goals were
defined: (1) investigate the decomposition across product categories, and (2) analyze more
formally the variability <strong>of</strong> this decomposition. The brand-level is the unit <strong>of</strong> analysis. The
authors examined the extent to which variance in brand choice, purchase time, and quantity
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