تاثیر ناهمگونی سطح خانواده در اثرات قیمت مرجع بر سیاست های قیمت گذاری بهینه خرده فروش

The field of marketing has witnessed substantial improvement in modeling household level heterogeneity. However, relatively little has been written about how modeling household heterogeneity translates into better marketing decisions. In this paper, we study the impact of household level heterogeneity in reference price effects on a retailer's pricing policy. Reference prices are certain anchors or standards that households use to compare the observed purchase price of a product against. If the observed price is greater than the reference price it is perceived as a “loss” and if it is smaller than the reference price it is perceived as a “gain”. In order to study the impact of heterogeneity in reference price effects on retail pricing, we test a nested logit model under two alternative reference price (memory and stimulus based) and heterogeneity (finite mixture and hierarchical Bayes) specifications. In the empirical analysis, we find that households are quite heterogeneous in terms of their gain and loss effects. For some households a gain has higher impact than a corresponding loss, while the opposite is true for others. Using individual level estimates we then develop a normative pricing policy for a retailer maximizing category profit. Our results indicate that the optimal pricing policy derived from the heterogeneous case is qualitatively different, and more profitable, than the case when heterogeneity is ignored. We show that for an important marketing problem pertaining to a retailer, the optimal pricing decisions for various brands in a category are inextricably related to household heterogeneity in reference effects and brand preference.

Over the last two decades, the field of marketing has witnessed substantial improvement in modeling household level heterogeneity. The progression from aggregate (e.g., Guadagni and Little, 1983 and Basu et al., 2007) to latent class (Kamakura and Russell 1989) to hierarchical Bayes (Rossi, Allenby, and McCulloch 2005) models reflects the inroads we have made in characterizing household differences. While these methodological advances are commendable, relatively little has been written about the impact of modeling household heterogeneity on marketing decisions (Basu and Vitharana, 2009 and Rossi et al., 1996). In this paper, we demonstrate the pricing and category profit implications of incorporating heterogeneity in reference price effects for a retailer (Brown and Dant, 2009, Grewal et al., 1998a and Grewal et al., 1998b). We show that for an important marketing problem pertaining to a retailer, the optimal pricing decisions for various brands in a category are inextricably related to household heterogeneity in reference effects and brand preference. A considerable amount of research exists on reference prices (Han et al., 2001, Kalyanaram and Winer, 1995, Krishnan et al., 2006, Mazumdar and Papatla, 2000, Moon et al., 2006, Mazumdar et al., 2005, Popkowski Leszczyc et al., 2009 and Yadav and Seiders, 1998). Reference prices are certain anchors or standards that households use to compare the observed purchase price of a product against. If the observed price is greater than the reference price it is perceived as a “loss” and if it is smaller than the reference price it is perceived as a “gain”. Empirical evidence regarding the relative impact of perceived gains and losses on household choice has been quite mixed. For example, Putler (1992) found that, consistent with prospect theory (Kahneman and Tversky 1979), the effect of a loss on demand is greater than that of an equal gain. Greenleaf (1995), on the other hand, finds that the effect of a gain is greater than that of a loss. While investigating the effects of reference price on household utility is of considerable theoretical interest, an equally interesting issue is the normative impact of these effects on a retailer's pricing policy. Some papers (Greenleaf, 1995 and Kopalle et al., 1996) in marketing have studied the normative implications of reference price effects from the standpoint of a retailer. Kopalle et al.’s (1996) results suggest that when the impact of a price gain is greater than that of a loss, “hi/lo” prices are optimal; on the other hand, when the impact of a loss is greater than that of a gain, constant prices are optimal. This result is consistent with Greenleaf's (1995) monopoly analysis. A limitation of the above normative models is that they do not take into consideration household level heterogeneity in reference price effects. As noted above, current normative research on reference prices suggests that at an aggregate level, when the impact of a gain is greater than that of a loss, a retailer should promote. However, uncovering heterogeneity is likely to reveal that not all households are the same with respect to the reference price effects; for some, the impact of a loss may be larger than that of a gain and for others, the reverse may hold. Further, the magnitude of the gain and loss effects may also vary across households. Under such circumstances, it is not clear whether a retailer should price promote, and if so, which brands to promote. Therefore normative policies based on a model that does not account for heterogeneity could potentially result in a pricing policy that does not maximize retailer profits. Also, from a methodological standpoint, empirical research suggests that it is important to formally consider heterogeneity in reference effects because aggregate models tend to overstate the magnitude of the model estimates. For example, Chang, Siddarth, and Weinberg (1999) use a hierarchical Bayes model to show that upon accounting for the price response heterogeneity, the reference price effect (“sticker shock”) gets diminished. Similarly, Bell and Lattin (2000) use a latent class model to show that once the price response heterogeneity is taken into consideration, the impact of reference prices on choices that consumers make is reduced. The primary focus of this paper is to study the impact of household heterogeneity in reference price effects on normative pricing policies for a retailer. We use a nested logit model with two different heterogeneity specifications (finite mixture and hierarchical Bayes) and develop a normative pricing policy for a retailer maximizing category profit. We then test the robustness of our results to (i) an alternative, stimulus based reference price formation process and (ii) another product category. For different product categories and reference price specifications we find that there is significant heterogeneity in the gain and loss parameters across households. Based on household level estimates we develop normative pricing policies for a retailer maximizing category profit by simultaneously optimizing prices of various brands in the category. Our results indicate that the optimal pricing policy derived from the heterogeneous case is qualitatively different, and more profitable, than the case when heterogeneity is ignored. The remainder of the paper is organized as follows. In the next section, we provide the conceptual background for our study and describe the relevant literature. This is followed by a description of the model and variables. We then present the empirical results. Next, optimal pricing policy implications for a retailer carrying multiple brands in a category are discussed, followed by additional analyses. Finally, we provide a summary and discussion of our analysis.

Our goal in this paper is to demonstrate how recognizing household heterogeneity can potentially translate into more profitable marketing decisions (Grewal et al. 2011). We accomplish this goal by showing how a retailer's pricing policy is inextricably related to household heterogeneity in reference effects, price response, and brand preference. We show that the normative pricing policies based upon individual level estimates of reference price effects and brand preference are quite different from those that are based on aggregate estimates that ignore household heterogeneity. In the empirical results reported, even when at the aggregate level households exhibit loss aversion, the impact of gain is greater than loss for a number of households. Our results show that incorporating household heterogeneity results in a pricing policy that is noticeably different and more profitable than in the case where there is no household heterogeneity. We develop normative pricing implications from the perspective of a retailer managing multiple brands. We find that the optimal pricing policy is closely related to the interdependencies between key model parameters such as brand preference, price sensitivity and gain/loss sensitivity. These interdependencies have been largely ignored in previous research that studies price optimization. Our results across two categories and two reference price formation processes suggest the following. First, retailer pricing policies are qualitatively different when heterogeneity in reference prices is taken into consideration relative to when such heterogeneity is ignored. Further, there is a significant increase in profitability under the heterogeneity case. Second, when the gain-seeking segment is an overwhelming majority, it is optimal to promote all brands. Third, if the households are mainly loss-averse, mostly constant pricing policy for all brands is optimal. Finally, when some households are loss-averse and others are “gain-seeking”, that is, the segment sizes are more evenly matched, it is optimal for a retailer to promote high preference brands. It is instructive to contrast heterogeneity in reference dependent effects studied in this paper with heterogeneity in well-known deal proneness effects (e.g., Lichtenstein, Burton, & Netemeyer, 1997). While “gain-seeking” tendency and deal proneness are somewhat similar, the heterogeneity in reference price effects are more nuanced than the heterogeneity in deal proneness. For example, while heterogeneity in deal proneness may capture some of the heterogeneity in consumer gain-seeking tendencies it does not take into account the relative impact of gain-seeking versus loss-aversion effects for a consumer. As we observe from our results, even if a consumer has a large gain-seeking tendency, if his/her loss-averse tendency is larger, our model would tend not to suggest a pulsing pricing policy for a brand the consumer prefers highly, whereas considering only deal proneness may lead a retailer to over-promote. Thus, focusing on the heterogeneity in both gain-seeking and loss-averse parameters and taking into account their relative magnitudes enables us to go beyond the deal proneness concept in deciding on promotional policy. The paper opens several avenues for future research. First, it may be worthwhile to develop a demographic and psychographic profile of the “gain-seeking” households in order to target them with physical/electronic coupons, mail-in rebates, and so forth (Erdem, Mayhew, and Sun 2001). Second, the proposed model is conditional on store choice. This condition could be relaxed to incorporate store competition (Kopalle et al. 2009). Third, the retailer objective is assumed to maximize category profits. The objective function could be extended to multiple categories or a basket of goods. The two datasets used by us in this paper are for different panelists over different time horizons. This data limitation did not allow us to investigate how household reference dependence effects vary by category. This aspect of household behavior certainly deserves further investigation. Third, future research could modify the demand function to incorporate price as an indicator of quality (Ding, Ross, and Rao 2010). Fourth, household estimates are imprecise. In this paper we ignore this imprecision or posterior standard deviation of household estimates because price optimization over the entire posterior distribution of each household is extremely computer intensive. Future research could suggest ways in which the optimization could be carried out more efficiently. Finally, in this paper we do not incorporate the role of brand manufacturers who are also likely to impact retailer pricing. A more comprehensive model (e.g., Kopalle et al. 1999) could be developed to capture the retailer–manufacturer dynamics. This paper takes a first step by examining the influence of household level heterogeneity in reference price effects, brand preference, and price response on optimal retailer pricing policies. Further research on characterizing such heterogeneity as well as incorporating competitive elements is clearly warranted.