How do price range shoppers differ from reference price point shoppers?

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Journal of Business Research 62 (2009) 31 – 38

How do price range shoppers differ from reference price point shoppers? ☆ Sangkil Moon a,⁎, Glenn Voss b,1 a

College of Management, North Carolina State University, United States Cox School of Business, Southern Methodist University, United States

b

Received 19 December 2006; accepted 12 January 2008

Abstract Existing research demonstrates that reference price models can explain a significant amount of the variation in customers' price perceptions and purchase behaviors. This study extends the reference price literature by introducing the price range model, which proposes that price judgments are based on a comparison of the market price to the entire range of currently available prices. Our results demonstrate that the fit of a structural heterogeneity finite mixture model improves when the price range model is included along with internal and external reference price models and that the price range model explains a substantial proportion of customers' purchase histories in the toilet tissue category. Profile analysis indicates that internal reference price shoppers switch brands much less frequently than the other two segments and respond to feature promotions for their preferred brand(s). External reference price shoppers have an intermediate level of brand preference and respond significantly less than the other two segments to feature and display promotions. Price range shoppers have the lowest brand loyalty and respond most strongly to both feature and display promotions. © 2008 Elsevier Inc. All rights reserved. Keywords: Price range model; Reference price; Price perception; Customer segmentation and profiling

The reference price literature explains purchase behavior on the basis of reference price point formation and customer heterogeneity (Kalyanaram and Winer 1995, Mazumdar, Raj, and Sinha 2005). Reference price point formation refers to the process whereby some customers compare the current price of each brand to an internal reference price (IRP) that is formed on the basis of past prices for the brand (Winer 1986), whereas other customers—who are unlikely to remember past prices— form an external reference price (ERP) on the basis of the currently observed price of a focal brand (Hardie, Johnson, and Fader 1993). Customer heterogeneity indicates that customers ☆ The authors thank Peter Rossi for providing access to the data for empirical analysis. The authors also thank Associate Editor Abhijit Biswas, two anonymous reviewers, Wagner Kamakura and Gary Russell for their invaluable comments. The authors also thank those who attended the session that included the presentation of this paper at the 2006 Marketing Science Conference at the University of Pittsburgh. ⁎ Corresponding author. Tel.: +1 919 515 1802; fax: +1 919 515 6943. E-mail addresses: [email protected] (S. Moon), [email protected] (G. Voss). 1 Tel.: +1 214 768 2236; fax: +1 214 768 4099.

0148-2963/$ - see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2008.01.017

use different reference price formation strategies (e.g., IPR and ERP) to judge the attractiveness of an offered price (Mazumdar and Papatla 2000). The reference price literature draws on Adaptation-Level Theory (Helson 1964) to conceptualize reference prices as explicit price points. However, Janiszewski and Lichtenstein (1999) use behavioral experiments to validate a price perception model that is consistent with Range Theory (Volkmann 1951), which proposes that customers use the full range of available prices to evaluate the attractiveness of individual market prices. Niedrich, Sharma, and Wedell (2001) use behavioral experiments to demonstrate that experimental conditions determine which of three distinct price perception theories—AdaptationLevel Theory, Range Theory, and Range-Frequency Theory— best explains behavior. The price perception literature is enriched by behavioral experiments that support price range conceptualizations but additional empirical research is required to fully validate their implications. We contribute to this stream of research in two ways. First, we develop an empirical Price Range (PR) model

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S. Moon, G. Voss / Journal of Business Research 62 (2009) 31–38

based on the notion that customers evaluate market prices by comparing each price to the current range of market prices. We use panel data to empirically test a structural heterogeneity finite mixture model (Kamakura, Kim, and Lee 1996) that includes a PR model in conjunction with IRP and ERP models. The results demonstrate that the fit of the structural heterogeneity model improves when the PR model is included along with the IRP and ERP models and that the PR model explains a substantial portion (26%) of customers' price perception and shopping behaviors. Second, we conduct additional analyses to examine the relationships between price response models and five customer characteristics—(1) brand switching, (2) purchase on display, (3) purchase on feature, (4) shopping basket size, and (5) residence status. This profile analysis indicates that internal reference price shoppers switch brands much less frequently than the other two segments and respond to feature promotions for their preferred brand(s). External reference price shoppers have an intermediate level of brand preference and respond significantly less than the other two segments to both feature and display promotions. Price range shoppers have the lowest brand loyalty and respond most strongly to both feature and display promotions. In the following section, we review the IRP and ERP models that have dominated empirical price perception research and introduce a PR model in the context of brand choice. We then integrate all three price-perception models into a structural heterogeneity model for customer segmentation, which we empirically test using the toilet tissue product category. We present the profile analysis and finish with a discussion of the results. 1. Developing reference price and price range models Reference price research draws on Adaptation-Level Theory (Helson 1964) and Prospect Theory (Kahneman and Tversky 1979) to model customers' choice responses to price information. According to Adaptation-Level Theory, customers develop prototypical reference prices that act as the adaptation level for current price assessments (e.g., Mazumdar, Raj, and Sinha 2005). Prospect Theory enriches the reference price conceptualization by distinguishing between losses, which occur when the market price is higher than the customer's reference price, and gains, which occur when the focal price is lower than the reference price. Considerable research has focused on the distinction between internal and external reference price formation. Conceptually, IRP represents the internalization of observed prices over time. For practical purposes, IRP is modeled as a brand-specific phenomenon so that each brand has a different IRP (Briesch et al., 1997, Mazumdar and Papatla 2000). ERP is contextually determined at the point of purchase and is modeled as the current price of the most-recently purchased brand so that it is common across all brands (Hardie, Johnson, and Fader 1993). Early empirical research (e.g., Rajendran and Tellis 1994, Briesch et al., 1997) examined whether the internal or external reference price model explained purchase behavior better under different conditions. Subsequent research incorporated customer heterogeneity into models that allowed custo-

mers to use a mixture of internal and external reference prices (Mazumdar and Papatla 2000) or used a structural heterogeneity finite mixture model to separate customers into segments that follow distinct price response models (Moon, Russell, and Duvvuri 2006). Empirical studies to date have focused on reference price effects and have ignored the effects of the price range or extreme price values. This focus belies early price perception research (Monroe, Della Bitta, and Downey 1977), which proposed that price structure is defined by three factors— the reference price level, the range of prices, and extreme price values—and recent behavioral studies (Janiszewski and Lichtenstein 1999, Niedrich, Sharma, and Wedell 2001) that support a price perception process consistent with Range Theory (Volkmann 1951). These exemplar models posit that reference price is represented by a distribution of prices. We address this gap in the literature by using a finite mixture model that separates customers into segments that follow the IRP model, the ERP model, or the PR model. Compared to the prototypical IRP and ERP models, the PR model is based on the range or, more broadly, the variability of prices for a given choice alternative. Thus, we refer to IRP and ERP as reference price point models and PR as a price variability model. We formally develop each model in the following sections. 1.1. The internal reference price (IRP) point model Empirical reference price research has focused considerable attention on how to represent IRP (Kalyanaram and Winer 1995, Briesch et al., 1997). Following the majority of earlier work, we define IRP as the exponentially weighted average of past prices of the same brand. IRPhjt ¼ kIRPhjðt1Þ þ ð1  kÞPhjðt1Þ ;

ð1Þ

where λ(0 ≤ λ ≤ 1) is a smoothing parameter that determines the weight of each past price that influences the current IRP. Since λ in Eq. (1) is a non-linear parameter in the multinomial logit model described in Eq. (2) below, we apply the estimation method developed by Fader, Lattin, and Little (1992). The utility function captures gains and losses as follows: 
 Uhjt ¼ aj þ bY LOYhj þ bP Phjt þ bL Lhjt T Phjt  IRPhjt 
 þ bG Ghjt T IRPhjt  Phjt þ bc PChjt þ bF Fhjt þ bD Dhjt þ ehjt ; ð2Þ where U = utility, LOY = loyalty, P = price, PC = price control (to correct for price endogeneity as explained below), F = feature, D = display, and ε = disturbance term. The subscript h indicates household (i.e., customer), subscript j brand, and subscript t category purchase occasion. We expect price to have a negative effect on utility so the βP coefficient should be negative. L and G indicate loss and gain functions, respectively. If P N IRP (loss), L = 1 and G = 0, and the size of the loss is (P − IRP). If P b IRP (gain), L = 0 and G = 1, and the size of the gain is (IRP − P). Thus, Eq. (2) captures the magnitude of a gain or a loss as a nonnegative value. Because a loss should decrease the utility, βL should be

S. Moon, G. Voss / Journal of Business Research 62 (2009) 31–38

negative (βL b 0). Similarly, because a gain should increase the utility, βG should be positive (βG N 0). The loss aversion phenomenon in Prospect Theory further predicts that the βL coefficient will be larger than the βG coefficient. The PC term in Eq. (2) alleviates the price endogeneity concern, which has received considerable attention in marketing science (Villas-Boas and Winer 1999, Moon, Russell, and Duvvuri 2006). PC is relevant in the IRP model because price is defined as the shelf price net of coupon for the chosen brand only. This common practice in analyzing grocery panel data ignores the possible presence of coupons for competitor brands. It creates a price endogeneity concern because coupon information is provided for chosen brands only, which can inflate the price effect. Blundell and Powell (2004) maintain that the coefficient for the potentially endogenous price variable will be statistically consistent given the presence of the PC variable, which is computed based on residuals of the time-series regression of price at time t-1 on price at time t, for each combination of brand and segment. In summary, the PC term attenuates the price endogeneity problem and improves the consistency of the price term estimate. 1.2. The external reference price (ERP) point model In contrast to the IRP model in which customers recall past prices from memory, ERP customers use prices observed in the current choice environment to develop a reference price. Once the ERP value is established, ERP customers also evaluate price according to the loss–gain mechanism. Accordingly, we define the utility of an ERP customer as 
 Uhjt ¼ aj þ bY LOY
hj þ bL Lhjt TPhjt  ERPht þ bG Ghjt T ERPht  Phjt þ bc PChjt þ bF Fhjt þ bD Dhjt þ ehjt : ð3Þ The ERP model also includes the PC term to correct for potential price endogeneity and separate loss (βL) and gain (βG) effects. Unlike IRP, ERP is independent of brand j so that the ERP value for household h at purchase occasion t is uniformly applied to all brands. This results in an identification problem that prevents the inclusion of current market price P in the utility model (Briesch et al., 1997). Hardie, Johnson, and Fader (1993) argue that market price is not needed because customers draw psychological meaning by comparing P to ERP. Following previous studies (Hardie, Johnson, and Fader 1993, Briesch et al., 1997, Moon, Russell, and Duvvuri 2006), we define ERP as the current price of the brand purchased by household h on the previous purchase occasion (i.e., t-1). Instead of remembering past prices, customers construct ERP at the point of purchase by examining the current price for the most-recently purchased brand (i.e., the reference brand), which is easier to remember than past prices (Hardie, Johnson, and Fader 1993).

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prototypical reference price, some customers may evaluate market prices in the context of the entire range of currently offered prices. Following Janiszewski and Lichtenstein (1999), we define the measure of price range (PR) to be
 ð4Þ PRhjt ¼ Phjt  LPht =ðHPht  LPht Þ; where LP denotes the lowest brand price and HP denotes the highest price in a given product category. LP and HP depend on household h and time t, but are independent of brand j because they are common across brands on the same purchase occasion. PR captures the percentile value (between 0 and 1) of the focal market price within the range of prices, so that a lower value reflects a more attractive price evaluation. The psychologically transformed PR value in Eq. (4) is included in the PR utility function as follows: Uhjt ¼ aj þ bY LOYhj þ bR PRhjt þ bc PChjt þ BF Fhjt þ bD Dhjt þ ehjt :

ð5Þ

We assume that some customers compare the PR value of the focal brand with the PR values for other brands in the product category. The current market price P does not enter into Eq. (5) as a separate term for conceptual and methodological reasons. Conceptually, Range Theory maintains that PR provides a better representation of customers' price judgment than P because customers draw psychological meaning by comparing P within the context of the price range, which yields a price perception arising from PR rather than P. Methodologically, adding P to Eq. (5) would create a multicollinearity problem because PR and P are highly correlated. 2. Empirical analysis 2.1. Customer segmentation Following the approach used by Moon, Russell, and Duvvuri (2006), we use a finite mixture model that integrates all three price response models—IRP, ERP, and PR—and we assume that each customer uses one type of price response model. Unlike a common finite mixture model used for customer segmentation (Kamakura and Russell 1989), this approach assumes a priori that three price perception segments account for the observed structural heterogeneity (Kamakura, Kim, and Lee 1996). The finite mixture model computes the probability that household h belonging to segment s will select brand j at purchase occasion t as   X   ðsÞ ðsÞ Prhjt ¼ exp Uhjt = exp Uhjs Vt : ð6Þ jV

Superscript (s) indicates a segment of customers who follow each price perception model. For each customer, we create the customer segment log likelihood expression as follows:

1.3. The price range (PR) model

InLhs ¼ Lðh;Yh segment sÞ;

Janiszewski and Lichtenstein (1999) and Niedrich, Sharma, and Wedell (2001) demonstrate that, rather than relying on

where θ = parameters and Yh = observed information for household h, which includes multiple purchase occasions. Next,

ð7Þ

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S. Moon, G. Voss / Journal of Business Research 62 (2009) 31–38

lnLhs is summed over all households in the dataset to obtain the following overall log likelihood Lc: ln Lc ¼

H X h¼1

ln

3 X

½ps Lhs ;

ð8Þ

s¼1

where πs = size of segment s. Because one objective for this study is to develop a profile for each segment, we assume that each household belongs to only one of the three segments. By constraining each segment to follow a single price perception model, we require that households be placed into one of the three segments, each of which is characterized by a different approach to interpreting price information. In the structural segmentation model, each segment has a distinct, non-nested model structure, which implies a multi-modal parameter distribution in price response. This increases the likelihood that the segmentation model will recover the true heterogeneity in the customer price perception mechanism (Andrews, Ainslie and Currim 2002). The structural heterogeneity model does not capture unobserved preference heterogeneity related to individual differences in brand preferences and responses to the marketing mix variables. However, heterogeneity in logit model brand intercepts across brands and customers produces heterogeneity in own-price elasticities across brands and customers, even in the absence of an explicit model for heterogeneity in market response parameters across customers within each segment (Bucklin, Russell and Srinivasan 1998). Moreover, customer heterogeneity is explicitly captured by the loyalty variable that appears in the utility Eqs. (2), (3) and (5), which controls for brand preference heterogeneity and allows us to focus on heterogeneity in price perception structure and process. A key advantage to this approach is that it generates a deeper understanding of differences in customer characteristics across the three segments.2 We also tested a segmentation model that allows for multiple sub-segments in each price response model. More specifically, we decomposed each of the three price response segments into multiple sub-segments, each of which had different parameter values with the same price response structure (parameter heterogeneity). Even though these expanded models captured additional customer heterogeneity, they did not change the results with respect to segment profiling. Logically, differences among sub-segments in the same segment (parameter heterogeneity in the same segment) will be much smaller than differences across the three structurally distinct model segments (structural heterogeneity across segments). This test is tantamount to the mixed logit specification proposed by Klapper, Ebling, and Temme (2005). Unlike the mixed logit specification, however, our test is based on the assumption that house-

2 Other approaches to incorporating customer heterogeneity include Mazumdar and Papatla (2000), who use a hybrid model that allows customers to use a mixture of IRP or ERP and Erdem, Mayhew and Sun (2001), who incorporate heterogeneity into the parameters governing consumer reactions to reference price. Neither approach can be used when the objective is to compare three structurally distinct segment models.

Table 1 Toilet tissue brand summary Brand

Market share (%)

Price

Feature

Display

Northern Charmin Cottonelle White Cloud Family Scott

33.4 30.5 19.9 8.5 7.7

1.12 1.15 1.13 1.23 .98

.23 .23 .32 .19 .25

.14 .22 .17 .41 .19

Notes: Price is expressed in dollars per 4000 sheets, which is an alternative volume unit for 92% of brand alternatives in the category. The feature and display indices are averages over dummy variables representing each brand's promotional activities.

hold parameter heterogeneity in each segment is discrete rather than continuous. 2.2. Sampling and variable definitions We used a data set collected by the ERIM marketing testing service in Sioux Falls, South Dakota, which is representative of the U.S. population as a whole. Sioux Falls had a population of 81,340, an average household size of 2.6, with 64% reporting no children in the household. Magnetic ID cards issued to 2500 households were presented at checkout when shopping at participating stores. The dataset covers 91 weeks. In analyzing the data, two sub-periods were used—a 39 week initialization period and a 52 week estimation period. The initialization period was used to (a) estimate the initial IRP value (Eq. (1)), (b) estimate the loyalty value (Eq. (9) below), and (c) compute customer characteristic indexes (Eq. (10) below). We used the toilet tissue product category and analyzed the top five brands—Northern, Charmin, Cottonelle, White Cloud, and Family Scott.3 Each of these brands had more than 5% market share (see Table 1) and collectively accounted for 83% of total purchases. During the entire period, 119,613 purchase occasions occurred in the product category. We excluded families that made fewer than 7 purchases during the 91 weeks because the small number of purchases implies serious purchase recording omissions. Systematic sampling of the remaining households generated a final sample of 341 families. When estimating the model, we limited to ten the number of purchase occasions for each household to prevent heavy purchasers of the category from dominating the results. We did examine the robustness of our results by replicating the analyses using all observations without the ten-purchase restriction, which generated results similar to the results reported in Tables 3 and 4. Most variables in the model are defined in a manner that is common in scanner data brand choice models. Price is operationalized as price net of coupon when the brand is chosen and as shelf price when the brand is not chosen. Feature and display indices are binary (0–1) variables indicating the presence or absence of feature and display conditions at the

3 We replicated all analyses using the peanut butter category, which produced very similar results.

S. Moon, G. Voss / Journal of Business Research 62 (2009) 31–38 Table 2 Price perception segmentation model comparison Segmentation model

Sample fit Log likelihood BIC

Fit of entropy IRP

Segment size (%) ERP PR

IRP only ERP only PR only IRP + ERP IRP + PR ERP + PR IRP + ERP + PR

− 2904.75 − 2765.18 − 2938.36 − 2698.58 − 2799.05 − 2695.34 − 2639.74

N/A N/A N/A .59 .64 .59 .62

N/A N/A N/A .56 N/A .60 .38

5904.79 5609.78 5948.20 5571.86 5764.85 5549.50 5533.60

N/A N/A N/A .44 .45 N/A .36

N/A N/A N/A N/A .55 .40 .26

Notes: IRP = Internal Reference Price, ERP = External Reference Price, and PR = Price Range. The table displays the log-likelihood and the Bayesian Information Criterion (BIC) for each model. The best model (IRP + ERP + PR) has the smallest BIC value. The Fit of Entropy of the best model (.62) shows that most households have a relatively clear membership between the three segments in the model. The Fit of Entropy can range from 0 to 1, with higher values indicating a clearer segmentation of households.

purchase occasion. The loyalty variable for household h and brand j is defined as follows: "  # 1 B nh þ LOYhj ¼ ln nhj þ ; ð9Þ 2 2

=

where nhj is the number of purchases of brand j by household h during the 39 week initialization period, nh is the total number of purchase occasions of household h during the initialization period, and B is the number of brands in the category. The loyalty values are obtained from the initialization period and are used as parameter estimates during the estimation period (Bucklin and Lattin 1991; Krishnamurthi and Raj 1988; Russell and Petersen 2000). This stable long-term loyalty measure yields consistent estimates of logit model parameters (Feinberg and Russell 2003). The loyalty measure is also preferable to a dynamic loyalty measure (Guadagni and Little 1983) because the dynamic loyalty measure and the IRP measure in Eq. (1) both are based on the same values, that is, the alternative brand's past prices. Due to the common structure of using a

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smoothing parameter, the two measures are likely to lead to an undesirable confounding effect. The stable loyalty measure in Eq. (9) is free from this problem. 2.3. Comparison of price perception models In segmenting customers based on their structural heterogeneity in price perception, we assume a priori the existence of three distinct price response processes that are captured by the IRP, ERP, and PR models. As shown in Table 2, the complete (IRP + ERP + PR) model is compared with single segment models (IRP only, ERP only, and PR only) and the three two-segment models (IRP + ERP, IRP + PR, and ERP + PR). The IRP + ERP + PR segmentation model provides the best fit because it has the smallest BIC value. The fit of entropy measures the standardized degree of fuzziness in segment memberships using posterior probabilities; 0 indicates maximum fuzziness and 1 indicates perfect membership assignment. Although there is no minimum cut-off value for entropy, a value of 0.62 for the IRP + ERP + PR model removes concerns about excessive fuzziness of posterior probabilities. Table 2 also shows the size of each segment. For the IRP + ERP + PR segmentation model, 36% of households follow the IRP model, 38% follow the ERP model, and 26% follow the PR model. Although the two traditional reference price models— IRP and ERP—account for most households (74%), the PR model also accounts for a substantial portion (26%). 2.4. Estimates of the best segmentation model (the IRP + ERP + PR model) Table 3 presents the parameter estimates of the IRP + ERP + PR model. Supporting the validity of the structural heterogeneity model and the household segment classifications, all coefficients measuring price and promotion responses are consistent with theory. Specifically, the coefficients for the shelf price term in the IRP segment and the price range term in the PR segment are both negative. In particular, the negative

Table 3 Price perception segmentation model estimates: the IRP + ERP + PR model Segment Brand preferences

Price effects

Promotion effects Segment proportion (%)

Charmin Northern Cottonelle White Cloud Family Scott Loyalty Shelf price Price range Loss Gain IRP smoothing parameter Price control Feature Display

IRP

ERP

PR

– −1.0905⁎⁎ (0.1239) −1.6914⁎⁎ (0.1479) −0.7057⁎⁎ (0.1777) −4.9923⁎⁎ (0.3260) 1.2171⁎⁎ (0.0642) −14.4469⁎⁎ (1.0694) – −3.5936⁎ (1.4784) 1.0240+ (0.6396) 0.7901⁎⁎ (0.0119) 2.3767⁎ (1.1658) 0.5915⁎ (0.2668) 2.3524⁎⁎ (0.2564) 36

– 0.1010 (0.1085) 0.7733⁎⁎ (0.1011) 0.2469+ (0.1543) − 0.7889⁎⁎ (0.1829) 0.5792⁎⁎ (0.0499) – – − 16.2051⁎⁎ (1.1128) 0.0910 (0.8172) – 0.4940 (0.9608) 0.8194⁎⁎ (0.2094) 1.4794⁎⁎ (0.2005) 38

– − 0.6279⁎⁎ (0.1376) − 0.8211⁎⁎ (0.1511) − 1.3292⁎⁎ (0.2263) − 2.8277⁎⁎ (0.2812) 0.3076⁎⁎ (0.0764) – − 1.9305⁎⁎ (0.2630) – – – − 1.7662 (1.3805) 4.8465⁎⁎ (0.4609) 4.6836⁎⁎ (0.4095) 26

Notes: IRP = Internal Reference Price, ERP = External Reference Price, and PR = Price Range. Brand constants for Charmin are set to zero for model identification. Numbers in parentheses are standard errors. Parameters that are significantly different from zero are denoted (+) for .10 level, (⁎) for .05 level and (⁎⁎) for .01 level.

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coefficient of PR demonstrates that PR shoppers react to relatively high prices negatively even after they interpret the shelf price in the context of the price range. For the IRP and ERP segments, the loss coefficients are significantly negative and the gain coefficients are positive but relatively weak, which may be due to the presence of the price control variable, which can weaken or eliminate reference price effects (Chang, Siddarth, and Weinberg 1999; Bell and Lattin 2000). Our results confirm that there are significant reference price effects even after controlling for price endogeneity typical in grocery panel data. The results also support the loss aversion phenomenon (Kahneman and Tversky 1979), which implies that retailers need to be more concerned with minimizing perceptions of loss than with creating perceptions of gains. The IRP smoothing parameter is significant and quite large (λ = 0.79), which indicates that the IRP value does not vary much over time, a result that is consistent with a mature, stable product category. The coefficients for loyalty, feature, and display are significant and have the expected positive sign. 3. Segment profiles The price perception segmentation model establishes the existence of a segment of households that follow a PR shopping strategy, but a direct comparison of parameter estimate values across segments is not meaningful due to differences in the pricing mechanism among segments. To generate additional insights regarding segment characteristics, we examined correlates of segment membership and indexes of purchase activities. More specifically, we ran a normal regression model for each segment separately with the household posterior probability of falling into the segment as the dependent variable as follows (see Table 4). PPhs ¼ b0 þ b1 x1 þ b2 x2 þ b3 x3 þ b4 x4 þ b5 x5 ;

ð10Þ

where PPhs is household h's posterior probability (ranging from 0 to 1) of being in segment s. Although the structural heterogeneity model (Eqs. (6)–(8)) does not explicitly allow for joint segment membership, PPhs in Eq. (10) does allow households to

Table 4 Relating segment membership to household characteristics Household characteristic Regression estimate IRP

ERP

PR

Intercept .483⁎⁎⁎ (.095) .487⁎⁎⁎ (.100) .030 (.085) Brand switching − .326⁎⁎⁎ (.077) .082 (.080) .245⁎⁎⁎ (.068) index (%) Purchase on feature (%) .178⁎⁎ (.095) − .390⁎⁎⁎ (.100) .212⁎⁎ (.085) Purchase on display (%) − .089 (.115) − .243⁎⁎ (.121) .332⁎⁎⁎ (.103) Shopping basket size ($) − .001 (.002) − .002 (.002) .003⁎⁎ (.001) Residence status − .041 (.051) − .036 (.054) .077⁎ (.046) Notes: IRP = Internal Reference Price, ERP = External Reference Price, and PR = Price Range. Each column represents a separate regression with the posterior probability of the particular segment as the dependent variable. Numbers in parentheses are standard errors. Parameters that are significantly different from zero are denoted (⁎) for .10 level, (⁎⁎) for .05 level, and (⁎⁎⁎) for .01 level. Residence Status is a dummy variable (1 = rented and 2 = owned).

have joint memberships and offers one approach to measuring the strength of segment membership. Three indexes provide insights with respect to promotion responsiveness: the brand switching index (x1) captures the proportion of times when the brand purchased at time t was different from the brand purchased at time t-1 and two promotional indexes capture the proportion of times that the purchased brand was on feature (x2) or display (x3) (Biswas and Blair 1991). The shopping basket size index (x4), which is the average of the total dollar amount the household spent for all purchases at each shopping occasion, offers insights into customers' general shopping behaviors beyond the product category. Residence status (x5) captures whether the household owned or rented its residence and offers insight into socioeconomic and household storage conditions. The analysis does not confound the estimation of the structural heterogeneity model with the measurement of household purchase activity because (1) the first four variables (x1–x4) are constructed using only initialization period data and (2) the residence status measure does not vary over time. In interpreting Table 4, note that the dependent variables in the three regression are perfectly correlated because the sum of the three segments' posterior probabilities should equal 1. Accordingly, the signs of the estimates indicate relative segment responses, not a direct positive or negative impact. In other words, a significant negative/positive sign in Table 4 indicates that the segment is significantly less/more likely to exhibit the associated characteristic. For instance, the coefficients for Purchase on Feature indicate that ERP shoppers (with a significantly negative coefficient) are less likely to react to feature than are the other two segments (positive coefficients), but it does not indicate that ERP shoppers react negatively to feature. Such a test result can be confirmed using Table 3 from our segmentation model estimation. We use the regression results in Table 4 to profile each customer segment on the basis of relative levels of brand loyalty and promotion responsiveness, and we speculate regarding the cognitive processing heuristics underlying choice behavior. The results indicate that the IRP segment is less likely to switch brands and more likely to purchase on feature, which suggests that IRP shoppers exhibit stronger brand preference than the other segments. They appear to remember past price information for their preferred brand(s), and armed with this explicit price memory, they evaluate and respond to feature advertisements for the preferred brand(s). We speculate that IRP shoppers are more likely to follow weekly advertisements and note when and where their favorite brand is featured and plan their purchases accordingly. If the price of the preferred brand is judged acceptable, IRP shoppers may engage in little or no point-of-purchase price comparison. Compared to the other two segments, the ERP segment exhibits an intermediate level of brand switching and is less responsive to both feature and display promotions, which suggests that they conduct relatively little price search, either before or during the shopping visit. Wernerfelt (1991) refers to this type of behavior as inertial brand loyalty, which results from the belief that effortful search offers little reward in terms of price or quality. These customers appear to apply a simple

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decision heuristic wherein the price of the reference brand is coded as acceptable or unacceptable, with additional search and processing occurring only if the reference brand price is perceived as a loss (see also the nonsignificant coefficient for Gain in Table 3). The PR segment has the highest brand switching index and the strongest response to both featured and displayed promotion. Compared to IRP and ERP shoppers who rely on brand preferences to direct their price search and purchase decisions, PR shoppers appear to focus primarily on price. These results are consistent with the idea that PR shoppers allocate less cognitive effort to brand or quality assessments and more cognitive effort to comparing the full range of current prices in the store. Consistent with Becker's (1965) theory of household production, this segment appears to take advantage of storage capabilities in their owned home to stock up on featured and displayed items, which results in basket sizes that are larger than the other two segments. 4. Discussion Our study makes two contributions to the existing literature. First, although Range Theory has been applied to price perception studies in laboratory settings (Janiszewski and Lichtenstein 1999, Niedrich, Sharma, and Wedell 2001), our study is the first to empirically test the theory using scanner panel data. The results indicate that a substantial portion (26%) of customers' purchase behaviors followed the price range model, supporting Janiszewski and Lichtenstein's (1999) contention that price perception research should incorporate price range effects. We hope our results will expand future research beyond the two common reference price models that have dominated previous empirical price perception research. Second, the profile analysis enriches our understanding of each segment's shopping behaviors and underscores the conceptual and practical insights that emerge from considering the three reference price models. IRP shoppers appear to use a branddirected heuristic that first establishes a preferred reference brand, perhaps on the basis of a quality assessment, with subsequent price evaluations comparing current prices to past prices for the preferred reference brand. At the other extreme, PR shoppers appear to use a price-directed heuristic that considers prices for the entire category with little consideration given to brand or quality differences. ERP shoppers appear to use an effort-minimizing heuristic that allocates modest importance to brand or price promotion. We now consider the implications of our results. 4.1. Managerial and theoretical implications Our results support prior research suggesting that IRP shoppers purchase fewer brands and respond more to coupon features and less to in-store displays (Rajendran and Tellis 1994; Mazumdar and Papatla 2000). Because these results suggest a decision heuristic that establishes the preferred brand(s) first followed by price-shopping behavior, retailers could influence retail patronage by targeting IRP shoppers (e.g., through direct mail) with coupons for their preferred brands. By rotating

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coupons for preferred brands over time, retailers may be able to build store loyalty among IRP shoppers, given their predisposition to stable purchase behavior. Additional research is needed to confirm whether IRP shoppers are loyal to a single or multiple brands, whether IRP shoppers use relational behavior to reduce the cognitive effort required to remember past prices for one or two brands rather than for many, and whether IRP shoppers would be more (or less) responsive to targeted coupons. Our results are inconsistent with prior research suggesting that ERP shoppers should be more responsive to in-store displays. This result may be related to our inclusion of a PR segment, which creates two distinct segments that use contextual price information to create a reference price point (ERP shoppers) or range (PR shoppers). Although ERP shoppers were the least responsive to both feature and display promotion, PR shoppers were the most responsive. We suspect that ERP shoppers may devote less cognitive effort to processing price information, both during and between shopping trips. This segment appears to be the least price-sensitive, which suggests that price promotions targeting this segment would yield smaller returns than would price promotions targeting the other two segments. Additional research is necessary to confirm whether ERP shoppers are more (or less) price sensitive and whether they would be more (or less) responsive to price promotions. Our results with respect to PR shoppers are consistent with previous research indicating that deal-prone shoppers are responsive to the range of available prices (Kumar, Karande, and Reinartz 1998). PR shoppers exhibit little brand loyalty and respond to both feature and display promotion, which suggests that they may be pure price- or value-seeking shoppers. Although this segment should respond positively to any form of price promotion, it seems unlikely that targeted promotions would ultimately build loyalty or enhance customer profitability. Additional research is needed to confirm these initial findings with respect to the PR segment.4 4.2. Limitations and future research The limitations of our study also suggest directions for future research. The structural heterogeneity model requires a priori specification of segment models based on prior knowledge and does not allow customers to change models across purchase occasions. Although developing a model that can accommodate temporal changes in segment memberships would require a long purchase history and be very challenging, it could be illuminating. We believe that customers may use different price perception mechanisms to adjust to the purchase environment. For example, IRP customers may implement the PR model when they go to a less familiar store because they 4 Modeling the PR segment improves upon results reported by Moon, Russell, and Duvvuri (2006), who profiled three segments—IRP, ERP, and no reference price (NRP). They found that only 9% of households fell into the NRP segment. By comparison, 26% of households in our study fall into the PR segment. They found that the NRP segment fell in between the other two segments in terms of price responsiveness.

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question the relevance of past price observations at a different store. It also would be worthwhile to investigate price response heterogeneity in the context of a category purchase incidence model (Bell and Bucklin 1999). This line of research could investigate which customer characteristics influence the price response model in the context of the category purchase incidence model along with managerial implications. It also would be instructive to link segment behaviors, especially promotion responsiveness, to individual segment and overall store profitability. As suggested above, targeted multiperiod promotions could have a positive effect on the profitability of IRP customers if they increased store loyalty, but they could have a negative effect if the frequent promotions decreased the IRP, ultimately producing a negative impact on current price perceptions. From another perspective, IRP should have greater implications for HiLo stores, where frequent promotions increase price variabilities over time, which will generate a situation where IRP loss and gain effects are pronounced. We expect that promotion has a positive impact on ERP customers irrespective of promotion frequency, which partially explains why frequently promoted brands still attract customers in spite of the widely-accepted negative long-term effect of frequent promotions. And finally, we expect that PR shoppers offer the smallest margins across product categories, as they switch brands in search of the best available price. References Andrews RL, Ainslie A, Currim IS. An empirical comparison of logit choice models with discrete versus continuous representations of heterogeneity. J Mark Res 2002;39:479–87 (November). Becker GS. A theory of the allocation of time. Econ J 1965;75:493–517 (September). Bell DR, Bucklin RE. The role of internal reference points in the category purchase decision. J Consum Res 1999;26:128–43 (September). Bell DR, Lattin JM. Looking for loss aversion in scanner panel data: the confounding effect of price response heterogeneity. Mar Sci 2000;19:185–200 (Spring). Biswas A, Blair EA. Contextual effects of reference prices in retail advertisements. J Mark 1991;55:1–12 (July). Briesch RA, Krishnamurthi L, Mazumdar T, Raj SP. A comparative analysis of reference price models. J Consum Res 1997;24:202–14 (September). Blundell RW, Powell JL. Endogeneity in semiparametric binary response models. Rev Econ Stud 2004;71:655–79. Bucklin RE, Lattin JM. A two-state model of purchase incidence and brand choice. Mark Sci 1991;10(1):24–39. Bucklin RE, Russell GJ, Srinivasan V. A relationship between price elasticities and brand switching probabilities. J Mark Res 1998;35:99–113 (February). Chang K, Siddarth S, Weinberg CB. The impact of heterogeneity in purchase timing and price responsiveness on estimates of sticker shock effects. Mark Sci 1999;18(2):178–92.

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