Estimating the Impact of Consumer Expectations of Coupons on Purchase Behavior: A Dynamic Structural Model

Abstract

We examine the basic premise that consumers may anticipate future promotions and adjust their purchase behavior accordingly. We develop a structural model of households who make purchase decisions to minimize their expenditure over a finite period. The model allows for future expectations of promotions to enter the purchase decision. Households with adequate inventory of the product may face a trade-off of buying in the current period with a coupon or defer the purchase until next period, given their expectations of future promotions. Thus, we provide a framework for examining the impact of consumer expectations on choice behavior. The target audiences for our paper are (a) empirical researchers who intend to make structural models part of their applied research agenda; and (b) managers who value and seek to understand the impact of consumers' coupon expectations on current purchase behavior. Our research objective is to provide an empirical framework to examine whether and to what extent consumers anticipate future coupon promotions and adjust purchase behavior. The central premise of our approach is that a rational consumer minimizes the present discounted value of the cost of a purchase where cost in a single period consists of purchase price, inventory holding cost, gains from coupons, and potential stockout cost. We aim to test whether our hypotheses regarding the various elements of the cost structure are supported and that whether consumers take into account future discounted cost when making current purchase decisions. The research methodology we adopt is relatively new in econometrics and known as the estimable stochastic structural dynamic programming method. The methodology amounts to incorporating a maximum likelihood routine embedded in a dynamic programming problem. The dynamic programming problem is solved several times within a maximum likelihood iteration for each value of the state space elements and for each value of the parameters in the parameter set. The state space in our model consists of purchase and nonpurchase alternatives in each time period, coupon availability and no coupon availability in each time period, level of inventory in each time period for each household, and consumption rate of each household. We use scanner panel data on purchases in the disposable diaper product category and promotions. We estimate the inventory holding and stockout costs, brand-specific value of coupons, and consumers' expectations of future coupons. The key insights and lessons learned can be summarized as follows: (1) Our results are consistent with the notion that consumers hold beliefs about future coupons, and that such beliefs affect the purchase decision. We find that the dynamic optimization model performs significantly better than a single-period optimization model and a naive benchmark model. (2) We find a high and significant stockout cost, consistent with the essential nature of the product category. Our estimate of the holding cost yields a reasonable annualized percentage value when conver...

A Dynamic Utility Maximization Model for Product Category Consumption

“…So, promotion anticipation does not only occur in purchase behavior (Gönül and Srinivasan 1996), but there is also some evidence that it occurs in consumption behavior.…”

“…The proportionality factor contains a scaling parameter and it may depend on marketing variables M i,t in order to allow for promotion anticipation effects. For example, Gönül and Srinivasan (1996) show that coupon anticipation can be relevant and they find that the time period until the next coupon promotion is expected to be longer when a coupon is available at the last purchase occasion. Such expectations provide anticipating households with an incentive to stockpile after a coupon promotion, so that they can comfortably wait for the next coupon promotion without having to incur stock-out costs.…”

“…A low inventory level increases the risk of incurring stock-out costs. For example, Gönül and Srinivasan (1996) demonstrate that these stock-out costs can be substantial.…”

Internet Auction Features as Quality Signals

“…To account for this heterogeneity (Allenby and Rossi 1999;Gönül and Srinivasan 1996), we write the coefficients in Equation 1 as functions of the participating bidders' experiences. Let π π i = (Φ i , β i )′.…”

A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models

“…A common framework to capture consumers' or firms' forward-looking behavior is the discrete choice dynamic programming (DDP) model. This framework has been applied to study a manager's decision to replace old equipment (e.g., Rust 1987), career decision choice (e.g., Keane and Wolpin 1997;Diermeier, Merlo and Keane 2005), choice to commit crimes (e.g., Imai and Krishna 2004), dynamic brand choice (e.g., Erdem and Keane 1996;Gönül and Srinivasan 1996;Crawford and Shum 2005), dynamic quantity choice with stockpiling behavior (e.g., Erdem, Imai and Keane 2003;Sun 2005;Hendel and Nevo 2006), new product/technology adoption decisions (e.g., Ackerberg 2003;Song and Chintagunta 2003;Yang and Ching 2010), new product introduction decisions (e.g., Hitsch 2006), dynamic pricing decisions (e.g., Ching 2010), etc. Although the framework provides a theoretically tractable way to model forwardlooking incentives, and this literature has been growing, it remains small relative to the literature that models choice using a static reduced form framework.…”