variability of brand price elasticities across retail stores: ethnic, income, and brand determinants

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Variability of Brand Price Elasticities across Retail Stores: Ethnic, Income, and Brand Determinants FRANCIS J. MULHERN Northwestern University JEROME D. WILLIAMS Pennsylvania State University ROBERT P. LEONE The Ohio State University Retail pricing decisions are often made with respect to the price sensitivity of shoppers in a retail store or market area. This paper explores how certain brand and consumer charac- teristics relate to the variability of brand price elasticities across stores belonging to the same chain. The authors measure the price sensitivity of 14 liquor brands sold in 35 store locations. Since the stores operate in a monopoly environment, the analysis is free from the confounds caused by competitive pricing or promotional activity that often hamper retail pricing studies. Also, the monopoly situation provides data that represent a census of brand sales in the categories studied. Results of a two-stage econometric analysis show that brand price elasticity is higher for brands that are promoted more frequently and for brands that have higher market shares. The magnitude of brand price elasticity is also found to be directly related to the household income in a market area and inversely related to the pro- portion of residents in a market area who are African-American. INTRODUCTION Among the retail marketing variables, price and price-related promotions have the most dramatic impact on short-term consumer purchase behavior. Accordingly, retailers are Francis J. Mulbem, Department of Integrated Marketing Communications,Northwestern University, 1908 Sheri- dan Rd., Evanston, IL 60208, t][email protected]. Jerome D. Williams, Pennsylvania State University, Department of Marketing, University Park, PA 16802, [email protected]. Robert P. Leone, Department of Marketing, The Ohio State University, Columbus, OH 43210, [email protected]. Journal of Retailing, Volume 74(3), pp. 427-446, ISSN: 0022-4359 Copyright © 1998 by New York University. All rights of reproduction in any form reserved. 427

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Page 1: Variability of brand price elasticities across retail stores: Ethnic, income, and brand determinants

Variability of Brand Price Elasticities across Retail Stores: Ethnic, Income, and Brand Determinants

FRANCIS J. MULHERN Northwestern University

JEROME D. WILLIAMS Pennsylvania State University

ROBERT P. LEONE The Ohio State University

Retail pricing decisions are often made with respect to the price sensitivity of shoppers in a retail store or market area. This paper explores how certain brand and consumer charac- teristics relate to the variability of brand price elasticities across stores belonging to the same chain. The authors measure the price sensitivity of 14 liquor brands sold in 35 store locations. Since the stores operate in a monopoly environment, the analysis is free from the confounds caused by competitive pricing or promotional activity that often hamper retail pricing studies. Also, the monopoly situation provides data that represent a census of brand sales in the categories studied. Results of a two-stage econometric analysis show that brand price elasticity is higher for brands that are promoted more frequently and for brands that have higher market shares. The magnitude of brand price elasticity is also found to be directly related to the household income in a market area and inversely related to the pro- portion of residents in a market area who are African-American.

INTRODUCTION

Among the retail marketing variables, price and price-related promotions have the most dramatic impact on short-term consumer purchase behavior. Accordingly, retailers are

Francis J. Mulbem, Department of Integrated Marketing Communications, Northwestern University, 1908 Sheri- dan Rd., Evanston, IL 60208, t][email protected]. Jerome D. Williams, Pennsylvania State University, Department of Marketing, University Park, PA 16802, [email protected]. Robert P. Leone, Department of Marketing, The Ohio State University, Columbus, OH 43210, [email protected].

Journal of Retailing, Volume 74(3), pp. 427-446, ISSN: 0022-4359 Copyright © 1998 by New York University. All rights of reproduction in any form reserved.

427

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428 Journal of Retailing Vol. 74, No. 3 1998

very interested in knowing how sensitive consumers are to price. The most prevalent mea- sure of consumer price sensitivity is the price elasticity of demand, which represents the percentage change in quantity sold for a given percentage change in price. Because of the paramount importance of price sensitivity, an understanding of what factors influence the magnitude of price elasticity of demand is an important area of research in economics and marketing.

Economists have identified several factors that influence product (commodity) elastici- ties such as whether a product is a necessity or luxury, the availability of substitutes, and consumer purchasing power. Marketers have explored determinants of elasticities that relate to product, brand, and consumer characteristics, as well as merchandising variables such as pricing policies and promotional activity (Bolton, 1989; Ghosh, Neslin, and Shoe- maker, 1983, Hoch et al., 1995; Shankar and Krishnamurthi, 1996). In this paper we explore the factors that explain the variability of brand price elasticity across retail stores.

Knowledge of what factors determine price sensitivity can help retailers in several deci- sion-making contexts. Since retailers make decisions on price levels for both regular and promotion prices on an ongoing basis, knowledge of price sensitivity facilitates many retail pricing decisions including identifying which items can withstand regular price increases, selecting items that should have price discounts, setting price discount levels, and manag- ing the relative price structure within a product category. In addition, an understanding of the determinants of price sensitivity can help retailers implement micro-marketing strate- gies with different prices in different stores or retail market areas. Such differential pricing better matches prices to consumer reservation prices, and can increase the retailer's overall profitability. The use of differential pricing across stores has become more prevalent because data from scanner systems allow a retailer to precisely calibrate price responsive- ness at the store level.

Our central objective is to identify factors that explain the variability of price responsive- ness at the brand level across stores in the same retail chain. We describe an empirical anal- ysis that attempts to determine how brand and consumer characteristics influence the magnitude of price elasticities of demand for several liquor brands sold in a total of 35 retail locations.

LITERATURE

Price responsiveness has been measured through consumer surveys, controlled experi- ments, and econometric analysis of sales data. Prior to the availability of large databases on unit sales, several studies attempted to isolate determinants of price responsiveness through consumer self-report surveys. Those studies found little if any relationship (e.g., Webster, 1965). Studies using controlled experiments (e.g., Huber, Holbrook, and Kahn, 1986) allow for strong causal inference but can be cumbersome to administer and may lack exter- nal validity. Recently, analysis of secondary data provided by store scanner systems has permitted the precise estimation of price elasticities and the evaluation of factors relating to the magnitude of elasticities.

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Variability of Brand Price Elasticities across Retail Stores 429

Bolton (1989) investigates how price and promotional activities influence individual brand promotional price elasticities. This study evaluates store level scanner data for 12 brands in 12 supermarket locations. No information on customer characteristics is included in this analysis. The results indicate that elasticities are positively related to category fea- ture activity and negatively related to category display activity. Price elasticities are not found to be significantly related to most of the brand related promotion factors investigated such as frequency of brand promotions and depth of promotion discounts.

Hoch et al. (1995) explore how consumer and competitive factors affect price elasticity at the category level. This paper analyzes store level scanner data for 18 categories in 83 supermarket locations and finds that consumer demographic characteristics dominate com- petitive characteristics in influencing price responsiveness. They also find that price responsiveness is higher for less educated consumers, minority consumers, and consumers from larger families. This study was done at the category level and deals with retail pricing decisions for overall categories; no individual brand (within category) analysis is included.

More recently, Shankar and Krishnamurthi (1996) investigate how retail pricing prac- tices and promotion activity relate to brand level, regular price elasticities. They study store level scanner data for a single brand in 38 store locations. The primary focus of the study is how overall pricing policy (every day low pricing versus high/low pricing) and feature and display activity affect brand price elasticity. The study finds that price elasticity is higher in stores offering everyday low pricing relative to stores offering high/low pricing. However, no consumer characteristics are included in the analysis of the one brand studied.

All three studies previously described use a two-stage modeling process: first, price elas- ticities are estimated with an econometric model, then, separately, a second econometric model is used to regress the estimated elasticities on several determining factors. We employ the same two-stage approach in this paper. However, our study differs from the existing research on several dimensions.

First, we analyze data from a chain of liquor stores. All three studies mentioned, as well as the preponderance of the studies that have estimated price elasticities in the literature, have investigated grocery products. An analysis of price responsiveness using liquor brands provides some balance to the retail pricing literature. In addition, the analysis of liquor data provides several advantages over grocery products. Most liquor categories (e.g., scotch, gin, and rum) have fewer SKUs than grocery categories. Hence, model spec- ification and estimation is less cumbersome. Also, relative to grocery items, liquor brands have larger absolute prices, longer interpurchase times, and less stockpiling by consumers. These characteristics of liquor brands should facilitate the isolation of the effects of prices on purchase behavior. Finally, liquor categories feature many strong brands with substan- tial brand equity.

A second way this study differs from previous research is that we include consumer characteristics in our analysis. While the Hoch et al. (1995) study also includes consumer characteristics, it estimates category level elasticities. We complement that study by eval- uating how consumer characteristics relate to brand level price elasticities. While our study uses market area demographic information to represent the characteristics of shoppers in a store as done by Hoch et al. (1995), we supplement that data with a survey of store manag- ers to ascertain the characteristics of shoppers in each of the stores studied.

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Third, our study differs from the others with regard to competitive factors. The Bolton (1989) and Shankar and Krishnamurthi (1996) papers exclude competitive effects entirely and are potentially misspecified. Hoch et al. (1995) incorporate competitive store informa- tion into their model. In our analysis, we study store level scanner data in a monopoly mar- ket established by state regulations. This provides a more controlled environment for measuring the effects of price on purchase behavior than other studies that typically take place in competitive markets, but have little or no data on competitive pricing or sales. In addition, since we have data from the only retailer in the market, the data that represent a census of liquor purchases (although we expect there is some, albeit minor, leakage across state lines). Census level information circumvents problems of generating representative samples.

HYPOTHSES

We explore two types of variables that may influence price responsiveness----descriptive characteristics relating to the brands, and consumer characteristics. In this section we develop hypotheses pertaining to how brand and consumer characteristics influence price responsiveness. Based on the literature and theories of consumer behavior, we develop five hypotheses, which we empirically test.

Brand Factors

We first consider the relationship between the frequency of price discounts and the mag- nitude of price responsiveness. It is possible that consumer response to price promotions is influenced by the past promotional activity. Bolton (1989) and Shanker and Krishnamurthi (1996) explore how feature and display activity affect price responsiveness, but do not incorporate a measure of the frequency of price discounts. Frequent promotions are likely to generate certain habituation behavior over time. Consumers who respond to promotions over a period of time may become conditioned to respond to promotions in an automatic fashion as suggested by classical conditioning theory (Sahakain, 1976). In fact, Inman, McAlister, and Hoyer (1990) find the presence of a habitual response to in-store promo- tions such that consumers react to the mere presence of a promotion shelf tag (without a price cut) as a signal, and adjust their behavior accordingly.

When consumers become conditioned to buy products on promotion, they may be less likely to make purchases during nonpromotion periods. This is a serious concern in today's promotional environment where discounts have become so frequent. Research on brand purchase behavior over time has shown that some consumers become promotion loyal, meaning they wait for a promotion to occur to make a purchase (Guadagni and Little, 1983; Jones and Zufryden, 1982). The mechanism for this effect may be consumer formation of reference prices (Winer, 1986). More frequent promotions can lead to the formation of a lower reference price, leading consumers to concentrate more purchases in promotion peri-

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Variability of Brand Price Elasticities across Retail Stores 431

ods. Thus the more frequently promotions are offered, the more likely consumers are to become conditioned to make purchases during deal periods and not during regular price periods. In addition, more frequent promotions may influence the type of customer that purchases a brand if frequently promoted brands attract more price sensitive shoppers. Mela, Gupta and Lehmann (1997) find that over time, frequent price promotions for a con- sumer packaged good are associated with increased price sensitivity of consumers. Since a trip to a specialty liquor store typically involves the purchase of only a few items, con- sumer learning about retailer promotion practices may become more accentuated than for grocery products. This would lead to an even greater likelihood that frequent promotions for liquor brands lead to greater price sensitivity. Accordingly, we hypothesize that:

HI: The more (less)frequently an item is promoted, the greater (lower) the price sensitivity.

Next we consider the relationship between brand market share and price responsiveness. Bolton (1989) suggests that high share brand are operating on the flat portion of the sales response curve reflecting the market power of these brands. Hence, price responsiveness would be less for these brands than for low share brands. Bolton (1989) and to a limited extent Ghosh, Neslin and Shoemaker (1983) find support for this relationship. Since the liquor categories studied here feature a substantial dispersion of market shares across brands, we expect this relationship will also be found. Based on the empirical conclusions of past research, we hypothesize that:

H2: Brands with higher (lower) market share have lower (higher) levels of price sensitivity.

Consumer Characteristics

The characteristics of consumers in a retail market area can dramatically affect purchase behavior. Here we consider how brand price responsiveness relates to the demographic characteristics of residents living in the market area surrounding each store. We explore two important consumer characteristics that have been shown to influence price sensitiv- ity---ethnicity and income. Other market area characteristics such as age, education, and home ownership have been shown to have little relationship to price sensitivity (e.g., Hoch et al., 1995) and are not included in our analysis.

Ethnicity is particularly important to retailers from both managerial and public policy perspectives. Ethnic groups represent large and rapidly growing consumer segments. For example, between 1990 and 1997, the share of consumer spending attributable to Hispanics increased from 5.2% to 6.1% while the share of consumer spending attributable to African- Americans rose from 7.5% to 8.2% (Wall Street Journal, 1997). Since many retailers have store locations in market areas with high concentrations of ethnic groups, ethnic aspects of price sensitivity should be included in decisions concerning market area, price zone, or individual store pricing.

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Few studies have utilized actual purchase data to explore the price sensitivity of ethnic groups. Mulhern and Williams (1994) use scanner data to evaluate the price sensitivity of Hispanics relative to non-Hispanics. Hoch et al. (1995) incorporate ethnicity into their analysis, but do so by aggregating African-Americans and Hispanics into a single compos- ite ethnic group, thereby eliminating the ability to evaluate the price responsiveness of spe- cific ethnic populations. We separately evaluate how price responsiveness relates to the portion of residents in a market area that are African-American and Hispanic.

Much of the marketing literature on ethnicity maintains that African-Americans and His- panics are more price sensitive than other consumers. Marketing and consumer behavior textbooks and the business press often asset this as well. However, empirical evidence is scant and equivocal. A number of characteristics have been associated with African-Amer- ican shopping orientation and store attribute preference profiles suggest that African- Americans should be more price sensitive when compared to the general population. For example, Wilkes and Valencia (1985) find that African-Americans indicate a greater ten- dency to embrace bargaining as part of their shopping lifestyle than Whites. A study by Deloitte and Touche (1990) finds that African-American women are primarily motivated by price and selection. Price sensitivity is related to the tendency to buy generic products which are generally less expensive than national brands. Wilkes and Valencia (1985) find that African-Americans spend relatively more money on generic grocery purchases than Anglos. For liquor brands, high price sensitivity of African-Americans can also be expected since the absolute price levels are relatively high. Based on past research we develop the following hypothesis:

1-I3: The higher (lower) the proportion of residents in a retail market area that are African-American, the higher (lower) the price sensitivity.

Price sensitivity has been studied more extensively for Hispanics than for African-Amer- icans. Several studies have found that Hispanic consumers claim to be very price sensitive. Gillette and Scott (1974) find that Hispanics, in response to direct questioning, place greater importance on price and promotion than non-Hispanics. Similarly, Saegert, Hoover, and Hilger (1985) report that, relative to non-Hispanics, Hispanics rate price as more important in shopping decisions. Hoyer and Deshpande (1982) find that Hispanics are more likely than non-Hispanics to say they buy the lowest priced brand in a category. Saegert and Yokum (1986) find that the reported price paid for several items is lower among Hispanic shoppers. Similarly, Mulhem and Williams (1994) find price sensitivity for grocery products is greater in stores located in Hispanic market areas than in those located in non-Hispanic areas. Overall, these findings suggest that price responsiveness will be greater in stores located in Hispanic market areas. Again, this relationship can also be expected for liquor brands, which have prices that are higher than most grocery items. Based on past research we test the following hypothesis:

1-14: The higher (lower) the proportion of residents in a retail market area that are Hispanic, the higher (lower) the price sensitivity.

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One of the difficulties in studying ethnic group purchase behavior is the interaction between ethnicity and other demographic characteristics, particularly income, which varies dramatically across ethnic groups. Both African-American and Hispanic households, on average, tend to have lower socioeconomic status when compared to Anglo households. For example, in the early 1990s, the median income of Hispanic families ($23,895) was about 63 percent of the median income of white families ($37,783; Garcia and Montgom- ery, 1991). As a result, the price sensitivity often attributed to African-Americans and His- panics could be a function of income rather than ethnicity. In an attempt to isolate the effect of income on price sensitivity, we include in our analysis retail stores located in market areas with a variety of income levels for different ethnic populations. This provides a wider degree of variability on the independent variables and offers an opportunity to isolate the effects of income and ethnicity.

We investigate the effect of income on price sensitivity by examining the purchase behavior of shoppers in retail stores with respect to the income demographics surrounding each store. In a similar analysis at the category level, Hoch et al. (1995) find no relationship between income and price sensitivity. However, an income effect may be more likely at the brand level because several close substitutes are available. Higher income consumers can better afford higher price brands, and therefore may be less sensitive to price. On the other hand, lower income consumers constrained by a smaller budget may be more inclined to be thrifty and more price sensitive. Based on this information, we develop the following hypothesis:

H5: The higher (lower) the income of residents in a retail market area, the lower (higher) the price sensitivity.

DATA AND METHOD

To explain variation in price elasticities across stores locations, we employ a two step mod- eling process. As stated earlier, this method of using a two-step process--first estimating price elasticities, and then separately assessing how responsiveness relates to determining factors has been used in several previous studies (Bolton, 1989; Hoch et al., 1995; Shan- kar and Krishnamurthi, 1996). The first step involves estimating sales response models to generate brand price elasticities for several brands in various store locations. The second step involves a regression model that predicts the elasticity of a single brand in a single store as a function of the brand and consumer factors specified in the hypotheses. We use brand elasticities as a measure of price responsiveness since this measure is clearly the most prevalent measure of price responsiveness in the marketing and economics literature.

Data

We use store level scanner data from a chain of specialty liquor stores in a single state. The data represents brand sales at the UPC level in several product categories from July

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1990 to June 1994. The majority of recent studies that measure brand price elasticities have also used store level scanner data (e.g., Blattberg and Wisniewski, 1989; Mulhern and Leone, 1991; Hoch et al., 1995; Waiters, 1991). While scanner panel data is often preferred for studies addressing brand choice, measures of direct price responsiveness can be esti- mated more precisely from store level data because of the availability of data in many prod- uct categories and many store locations and the large number of observations available.

The data were collected in four-week increments, yielding a total of 52 time-series obser- vations for each brand. For each item in a category, the dataset contains information on unit sales, actual price paid, and an indicator variable identifying time periods when a promo- tional price was in place. For this study we utilize data in four product categories: scotch, vodka, gin, and rum. These categories were selected because they are primary liquor con- sumption categories, and these categories feature several prominent brands and active pro- motional scenarios. We evaluate the price responsiveness for the major selling brands in these four categories. These brands feature a substantial amount of price discounting dur- ing the time period studied. Promotional discounts usually ranged from ten to 20%, and typically were in place for one week, two weeks, or four weeks.

We limit the analysis to the 750 ml bottle size. Limiting an econometric analysis to the primary package size is a widely used practice in the literature because doing so allows for precise model estimation without overburdening statistical models with too many terms (Blattberg and Wisniewski, 1989; Shankar and Krishnamurthi, 1996). The 750 ml size is the leading package size for scotch (55.6% of unit volume), gin (43.3%), and rum (60.1%) and the second leading size for vodka (25.8%). The only other package size with a suffi- ciently large volume for statistical analysis is the 1.75 liter bottle. However, in many of the stores we have studied, the unit volumes for that size are extremely low (often in the single digits) and quite erratic. In addition, most of the promotional activity features the 750 ml bottle size. The other sizes also have such little variability in prices that price response coefficients cannot easily be estimated.

The retail chain we study is in the unique situation of being the monopoly seller in the market. Hence, we have an environment to study price responsiveness that is free of the potential effects of competitive store's pricing activities, which can confound price respon- siveness studies that use supermarket scanner data. In our econometric analysis, we cir- cumvent the omitted variables problem common in other studies where competitors do exist in the marketplace, but data on competitive store prices and sales are not included in the analysis. Also, by having data from the only liquor retailer in the area, we have a com- plete dataset reflecting all liquor purchases by residents. Finally, since state law bans out-of-store price advertising, we have a unique opportunity to isolate the effects of in-store price information on purchase behavior.

We evaluate the sales of the selected brands in 35 different retail stores belonging to the same chain. The 35 stores are located in and around a major metropolitan area. The stores compete in a wide variety of market areas that feature substantial variability in demo- graphic and ethnic composition. In particular, some stores were selected to obtain some variety in the ethnicity and income levels to permit the isolation of those effects analyti- cally. For example, consider stores 10, 11, and 22 in Table 1. In all three market areas, over 90% of the residents are African-American, yet the median incomes are very different ($27,812, $18,761, and $36,266).

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Variability of Brand Price Elasticities across Retail Stores

TABLE 1

Descriptive Information on Market Areas for 35 Stores

435

S t o r e African-American % Hispanic % Median Income $

1 76.9 2.3 29,783 2 1.4 2.3 34,746 3 67.6 1.5 25,905 4 4.2 1.7 38,439 5 66.9 2.2 25,986 6 2.7 2.4 42,939 7 3.9 2.4 43,477 8 2.4 2.9 33,18~ 9 81.4 1.8 26,771

10 92.8 1.7 27,812 11 96.0 1.4 18,761 12 0.4 2.2 35,521 13 1.7 2.6 39,067 14 0.5 2.9 33,314 15 77.9 18.7 17,449 16 1.9 2.7 29,510 17 16.0 29.3 21,740 18 5.3 1.8 37,677 19 1.1 1.9 34,549 20 8.2 18.7 26,838 21 79.5 3.5 21,659 22 93.1 1.5 36,266 23 0.6 3.1 33,858 24 79.5 10.8 16,602 25 1.3 2.8 34,463 26 4.5 2.2 43,574 27 0.2 2.8 33,759 28 40.0 1.3 26,238 29 3.5 1.2 45,429 30 0.7 0.5 53,713 31 11.9 0.8 35,626 32 11.1 1.4 35,872 33 3.0 2.4 60,684 34 20.6 7.7 61,439 35 3.2 7.7 34,612

The data on income and ethnicity was obtained from a geodemographic system that uses data from the 1990 U.S. Census to forecast demographic statistics for subsequent years. The system can compute demographic statistics for nonstandard market areas delineated by a fixed radius around a store location. We draw demographic data for each store from areas with radii of one-half mile, one mile, and two miles from each store location. Since demographic composition did not vary significantly between different size market areas, we use a one-mile radius in this analysis. Table 1 provides descriptive information on the market areas for each of the 35 stores.

One potential complication with using market area data to measure consumer character- istics of shoppers is that some of the shoppers visiting a store may not reside in the imme-

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diate area. To address this potential problem, we administered a mail survey to all store managers. The survey asked managers at every retail location to provide information about the demographic characteristics of the shoppers at that store. In addition, managers were asked to estimate the extent to which the shoppers in the store represented the same profile as residents in the immediate area. The survey was sent from the main office of the retail chain. Over 80% of the store managers for the entire chain returned a completed survey. Stores for which completed surveys were not obtained were excluded from the study. In nearly all cases, the manager's estimates of demographic composition closely matched the demographic information drawn from census data. While this is not as precise a method of assessing characteristics of shoppers as direct questioning of the shoppers themselves, it is a reasonable proxy that provides a validation of the market area data. Two stores were excluded from the analysis because the manager's estimates did not match the demo- graphic profile of the market area. Based on the survey information, we are confident that the demographic profile of shoppers matches the demographic profile of shoppers in the immediate area for each of the 35 stores.

Sales Response Model

The first step in the analysis involves estimating the direct price elasticities for each brand in each store. We specify a sales response model with unit sales as the dependent variable and the prices of all the major brands in the category as the independent variables. Because liquor sales are seasonal, we include an indicator variable to represent the holiday period in December.

We use a negative exponential functional form for the sales response model. This form of a sales response model is widely used in the literature (e.g., Blattberg and Wisniewski, 1989; Mulhem and Leone, 1991).

We estimated the following model for each brand in each store:

Qit = exp + TIPit + ~-, ~JjPjt + 7H°lidaYt + ~it j = l

(1)

where Qit = the unit sales of brand i in time period t, Pit = the price of brand i in time period t, ejt = the price of competing brandj in time period t,

Holiday = 1 during peak selling season, 0 otherwise, q = direct price coefficient, ~j = cross price coefficients, and

~it = F-'t - - P E t t o represent an AR(1) process.

Since the same independent variables are contained in each model, there are no gains in estimation efficiency to be achieved by a simultaneous equation procedure. We do include

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Variability of Brand Price Elasticities across Retail Stores 437

an AR(1) coefficient to represent first order autocorrelation. Preliminary analysis of the data with more elaborate time-series formulations found that an AR(1) process captures autocorrelation quite well. The model was linearized by taking the natural logarithm of the dependent variable. This transformation also serves to make the distribution of the depen- dent variable, and hence the error terms, more normal.

With a negative exponential model, the elasticities are proportional to prices. The elas- ticity for a brand at a given price level is found by multiplying the price coefficient by that price level. To generate a single elasticity estimate for each brand in each store, we multi- ply the price coefficient by the average price charged for that brand throughout the entire time series. One possible complication is that since prices rose over time, average price might not be an appropriate base for converting a price coefficient to an elasticity. To investigate whether this was a problem, we also computed the elasticities for the brands using the beginning price in the time series, and separately, using the ending price. The derived elasticities for the three different conversion factors were then used in the subse- quent analysis (equation 2 below). Results of the analysis were compared across three ver- sions of the model (one each for elasticities computed with beginning, mean and ending price) with a series of pairwise F tests. In no cases was there a parameter estimate that sta- tisticaUy differed across equations. Hence we are confident that using mean price to con- vert price response coefficients to elasticities does not create a problem.

Elasticity Model

The second step of the analysis involves explaining the estimated elasticities as a func- tion of the descriptive characteristics of the brands and consumers in the market area for each store. The dependent variable is the price elasticity at the average price level for each brand in each of the 35 stores. There are a total of 490 observations (35 stores x 14 brands) in the model. The independent variables are the characteristics of the brand and the con- sumers who reside in the market area surrounding each store. We include in the model an indicator variable for price tier to control for possible effects on price responsiveness of overall price/quality tier (Blattberg and Wizniewski, 1989). We estimate the following lin- ear regression model:

E l a s t i c i t Y i k = ct + ~ , l S h a r e i + ~ , 2 P c t D e a l i + ~ , 3 P r i c e T i e r i + ~ , 4 P c t B l a c k k

+ ~ , 5 P c t H i s P k + ~ , 6 l n c o m e k + E r r o r i k

Elastici tYik =

Share i =

P c t D e a l i =

P r i c e T i e r i =

P c t B l a c k k =

Pc tHi spk =

I n c o m e k = median household income for one mile radius of store k (thousand $), ~,'s are parameters to be estimated.

(2)

the price elasticity for brand i in store k estimated in Equation 1, the market share for brand i for the entire period studied, percentage of time periods a price promotion was in place for brand i, 1 if brand i is a premium brand; 0 otherwise, percentage of population in one mile radius of store k that is African-Amer- ican, percentage of population in one mile radius of store k that is Hispanic,

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Equation 2 is estimated across all stores and brands. We explored the possibility that parameter estimates for the model may vary for brands in different price tiers. However, separate estimation of the model for each tier produced unstable parameter estimates that prevented testing for model differences between the two tiers.

ANALYTIC RESULTS

Table 2 contains the mean price elasticities for each brand at the average price level. The price elasticities estimated with equation 1 range from -2.00 to -5.36. In general, these elasticities appear to be larger (i.e., more negative) than for grocery products. Summary studies in the literature on grocery brand elasticities have found average elasticities of -1.76 (Tellis, 1988) and -2.60 (Ehrenberg, 1995). The larger (more negative elastici- ties) for liquor may relate to the overall price differences between this category and gro- cery products. In particular, since liquor purchases involve a higher absolute dollar amount than grocery purchases, the dollar size of price discounts is relatively large. Hence, consumers may be more responsive to these price discounts in order to accrue the relatively large benefit. Another possible explanation for the large elasticities is that shopping trips to liquor stores usually involve the purchase of only a few items while trips to grocery stores often involve many items. With so few items being purchased,

TABLE 2

Mean Brand Price Elasticities across 35 Stores

Relative Market Mean Mean Elasticity Standard Deviation Brand Share (%) Price $ Across Stores of Elasticities

Scotch Dewars 52.5 16.24* -3.11 1.40 J&B 27.0 15.77* -4.82 1.56 JW Red 20.8 16.00* -4.09 1.23

Gin Tanqueray 16.4 14.58" -3.38 1.27 Beefeaters 7.2 14.41 * -2.38 1.88 Seagrams 26.6 8.08 -2.00 1.63 Gordons 37.4 7.99 -4.38 1.49 Gilbeys 10.8 7.40 -5.36 1.55

Vodka Smimoff 40.2 9.22 -4.12 1.56 Absolute 21.6 14.31 * -2.55 1.40 Gordons 26.5 8.85 -4.38 1.62 Stolichnaya 11.7 14.09" -3.11 1.37

Rum Bacardi 78.8 9.20 -3.17 2.12 Myers 21.2 12.89* -2.45 1.21

Note: * Premium price tier brands

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TABLE 3

Parameter Estimates for Determinants of Brand Price Elasticities

Variable Parameter t value

Intercept -1.786 2.68" Share -0.023 2.84* PctDeal -0.044 5.30* PriceTier -0.370 1.75 PctBlack 0.012 2.91 * PctHisp 0.044 0.74 Income -0.036 2.65*

Adj. R-square 22.5%

Note: * Significant at .05 level.

shoppers may be more prone to process price information and make purchase decisions with respect to price.

To evaluate the variability of elasticities across stores, we report on the standard devia- tions of the elasticities. As shown on the Table 2, these are quite high. The relatively high price elasticities and the substantial amount of variation in elasticities across brands and stores provide an excellent opportunity for the evaluation of how the elasticities relate to descriptive characteristics.

Table 3 provides the parameter estimates for the regression model that predicts the elas- ticity levels (equation 2). The adjusted R 2 for the model is 22.5% which compares favor- ably to the 15% reported in a similar analysis at the brand level by Bolton (1989). Given that the dependent variable is a parameter estimate from another model, we explored the possibility that measurement error could influence the results. We hypothetically set the measurement error for the dependent variable in equation 2 at levels of 5%, 10% and 15%. We also explore one other measurement error simulation in which the dependent variable as well as the three demographic independent variables (PctBlack, PctHisp and Income) each had measurement error of 10%. Results of these measurement error simulations show that the parameter for price tier was the only estimate to experience a change in statistical significance as measurement error was introduced. We feel that this does not detract from the study because we make no substantive conclusions about price tier effects, and that variable was used only as a covariate in the analysis. Importantly, the parameter estimated for the significant coefficients found in the error-free model (Share, PctDeal, PctBlack and Income) all remained consistent for all error simulations studied.

Brand Factors

Hypothesis 1 addresses the relationship between promotional frequency and price responsiveness. The significant parameter estimate of -0.044 for percentage of periods a promotion was offered indicates that elasticities are more negative for more heavily pro-

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moted brands. A more negative elasticity implies greater price responsiveness. The magni- tude of this effect can be illustrated with a simple example. Assume a brand that promoted 20% of the time and has an elasticity of -3.00. If that brand's promotional frequency in increased to 30% of the periods, the elasticity increases to -3.44. This is quite a large increase in responsiveness. Therefore hypothesis 1 is supported; price sensitivity increases as brands are promoted more frequently. This result is consistent with the finding of Mela, Gupta and Lehmann (1997) that greater use of promotion leads to higher levels of con- sumer price sensitivity.

The other brand characteristic we consider is market share. Hypothesis 2 asserts that price responsiveness will be higher for higher share brands. The parameter estimate for market share in Table 3 is -0.023, and is significant. Higher share brands have more nega- tive elasticities, indicating a positive relationship between market share and price sensitiv- ity. Hypothesis 2 is supported.

Consumer Characteristics

Hypotheses 3 and 4 maintain that price sensitivity is positively related to the proportion of residents in the market area that are African-American (H3) and Hispanic (H4). From Table 3 we see that the coefficient for percentage African-American is 0.012 and is signif- icant. This indicates that price sensitivity is lower in market areas with higher concentra- tions of African-Americans. For example, assume a population in a market area is 50% African-American and a brand has a price elasticity o f -3 .0 . The parameter estimate of -0.012 implies that if the percentage of the population that is African-American increased to 60%, the elasticity for this brand would only be -2.88. This finding does not support the hypotheses and contradicts some existing research. It should be noted, however, that much of the existing research on the price sensitivity of African-Americans has not analyzed actual purchase behavior as we have done in this study. Also, much of that literature has not looked specifically at African-American purchase behavior with data that exhibits as much variability in African-American composition as exists in the market areas we inves- tigate. In fact, a frequent problem with existing research is that the subsample sizes that pertain to one particular ethnic group are quite small. Thus a case can be made that our con- clusion may rest on stronger empirical evidence than other studies appearing in the litera- ture.

The coefficient for percentage Hispanic is not significant. Hypothesis 4 is not supported. From Table 1, we can see that some of the market areas studied do feature relatively high levels of Hispanic concentrations. Thus we cannot dismiss the insignificant finding as being an artifact of a low amount of variability in Hispanic concentration--a problem that sometimes occurs with ethnic oriented studies. The lack of a relationship between Hispanic concentration and price sensitivity casts some doubt on the common assertion that Hispan- ics are more price sensitive than non-Hispanics. As noted previously, that assertion is based largely on consumer self-reports.

Finally we consider how income relates to price sensitivity. From Table 2, we see that the income coefficient of -0.036 is significant. Since this coefficient is negative, we con-

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clude that as income goes up, price sensitivity is greater (i.e., the elasticity is more nega- tive). This result does not support the hypotheses we developed based on past research. However there is a plausible argument for a positive relationship between income and price sensitivity. Given the high price for the brands studied here, higher income consumers may be more able to take advantage of price discounts because they have the financial resources to stock-up on discounted brands. In addition, there is some evidence that high-income consumers may be more likely to evaluate price information in shopping situations.

DISCUSSION

In this paper we have explored how managerial and consumer characteristics relate to the magnitude of direct price elasticities at the brand level using 14 brands of liquor. This study differs from other research in this area in that the data represent a census of brand sales in the market studied, and the market is free of competitive pricing or competitive promo- tional effects. Such a setting has many of the characteristics of a field experiment, and allows for very strong causal inferences.

Overall, we find the elasticities for the liquor brands studied are quite large, indicating a high level of price sensitivity. We find that price responsiveness is greater for brands that are promoted more frequently and for higher market share brands. The question of how promotion frequency affects consumer price sensitivity is an important issue for retailers. Many retailers (e.g., Wal-Mart) engage in value pricing with limited in-store promotions because they believe consumers who would buy the product at regular price (or a new, slightly reduced regular price) often wait for deals and then stock up on the product. Our finding that price sensitivity is higher for more frequently promoted brands is consistent with the notion that consumers can be trained to purchase on price and, if exposed to a high level of promotional activity, will concentrate their purchases during those periods. There- fore, retailers should be cautioned against the overuse of price promotions until they inves- tigate and understand whether a product or a product category might exhibit this same effect.

Our finding that elasticity is greater for higher share brands does not agree with the find- ing reported by Bolton (1989). This could be due to the differences in the product catego- ries studied. Unlike consumer packaged goods which feature a great deal of switching among comparably priced items, liquor categories feature some very prominent brands that have high market shares and high prices. When the higher share brands are promoted they may draw sales away from the lower priced brands which often have lower market shares. Unfortunately, we were unable to test for this effect because the most of the low tier brands had too few sales and too little price movement to warrant inclusion in the empirical anal- ysis. This finding does suggests that when making pricing decisions within product catego- ries retailers should carefully investigate whether price discounts on a higher share brand produces the greatest sales response.

With respect to consumer characteristics, we find that price elasticity decreases as the proportion of residents in the market area who are African-American increases. This find- ing conflicts with some self-report studies that have described African-Americans as more

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price sensitive than other shoppers (e.g., Wilkes and Valencia, 1985). There are several possible explanations for this finding. First, it is important to note that consumer self-reports do not always reveal as precise a measure of price responsiveness as analysis of actual purchase behavior. Since we have studied the actual purchase behavior for several inner city market areas with very high concentrations of African-American populations, our findings may have more merit that self-report data collected with surveys. Second, our findings could be unique to the product category we investigated, an issue discussed later under public policy considerations. Third, Alwitt, Quails and Williams (1996) suggest a number of factors (e.g., geographic limitations in shopping travel, limited financial resources, less tenacity for shopping) that suggest African-Americans might be less responsive to promotional discounts.

We also find that price sensitivity is higher in higher income market areas. At first glance, this may appear to be an unexpected finding. However, given the overall high prices for liquor brands relative to consumer packed goods, greater price sensitivity among higher income consumers is reasonable. Higher income consumers have a greater ability to take advantage of price deals when they are offered and stock up on brands or make pur- chases across multiple liquor categories to take advantage of price deals. Alternatively, since lower income consumers have less financial buying power, they often must pay what- ever price is offered at the time of their purchase.

We should note that the primary contribution of this paper may not be so much the exact findings we report on liquor brands as the evidence we provide that specific questions should be raised and investigated by retailers when making pricing and promotion deci- sions. For example, our findings lead to the following specific questions for any multi-store retail setting:

1. Should a retailer charge the same price in all stores or vary prices according to some specified geographic price zones?

2. Are there economic or psychological advantages to having a differential pricing pol- icy at the store level with prices matched to the consumers who shop at a given store?

3. Should pricing practices vary for different brands in a category based on the charac- teristics of the brands and the buyers of individual brands?

Our findings suggest that in some cases there are advantages o f having store specific pricing. Specifically, a retailer may be able to set prices in a more profit-maximizing man- ner by matching prices to the customers who patronize a given store. Evidence that micro level pricing can generate greater profits for a retail chain is provided by Montgomery (1997). Our findings also support those of Hoch et al. (1995) who suggest that retailers can use information on price responsiveness not only for pricing decisions, but for direct mail or localized advertising programs catering to the demographic mix in a specific market area. In addition, a retailer could offer more frequent price promotions in markets exhibit- ing higher levels of price responsiveness. In fact, the results of our analysis along with results of similar studies could be combined into a store level price planning model that would predict sales at a given price level as a function of the characteristics of the brand as well as the consumers in the market area. Such a model would be critical to any category

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management system since it would provide optimal pricing analysis for each of the brands within a category.

Our results must be interpreted in light of the limitations of the study. We have only explored one type of product, liquor, in a single retail chain. Other products or chains may experience different behaviors. The retailer we have studied has the unique situation of being a monopolist. While this strengthens our ability to measure price responsiveness and its determinants in this situation, our results could be partly a function of the monopoly market and might be different under competitive market conditions. For example, price responsiveness might be greater in markets with competing retailers because of the ability of promotions to attract shoppers from competing stores. Empirically, we can not eliminate the possibility that some of the shoppers patronized stores outside the market area where they reside. However, as noted, we attempted to minimize this problem by surveying store managers about shopper characteristics.

There are several avenues for future research in this area. From a retailer perspective, future research should address the economics of trading off the added costs of conducting market area or store level marketing against the increased profits such practices can gen- erate (Montgomery, 1993). Similarly, research could address the combination of con- sumer characteristics in trading areas and relate them to store level marketing decisions. From a public policy perspective, more empirical research is needed utilizing data on actual purchases to validate our finding that African-Americans are less price sensitive than other consumers.

PUBLIC POLICY CONSIDERATION

The marketing of liquor involves public policy considerations relating to the controver- sial area of target marketing to minorities. Although target marketing is generally associ- ated with advertising and promotion, it actually can include any of the other marketing mix strategies--including pricing strategies. Our findings present an awkward dilemma for public policy makers in that target marketing has both positive and negative connota- tions (Moore, Williams, and Quails, 1996). If pricing strategies for socially desirable products are implemented, generally such efforts are praised. For example, housing pro- grams based on subsidies have been targeted to specific racial/ethnic groups (Goodman, 1990). On the other hand, ethnic minority target marketing strategies for alcohol products are often viewed as suspect, and are sometimes considered unethical. For example, this was the case when H. G. Heileman Brewing Co. attempted to introduce the brand Power- Master, a new higher alcohol content malt liquor targeted toward African-Americans. The firm was forced to withdraw its product from the market as a result of the furor raised by community groups and the concern expressed by public policy makers (Wall Street Jour- ha/, 1991).

Our results suggest that marketers of liquor may have to execute a very delicate balanc- ing act in developing differential pricing strategies for specific marketing areas with a high concentration of ethnic minority consumers. They must avoid raising the ire of policy mak- ers concerned about the exploitation of target marketing strategies. We have suggested ear-

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lier that retailers could set prices in a more profit-maximizing manner by using store level planning models. However, they must trade off the potential gain in sales with the potential detrimental social effects. The responsiveness of consumers to price and other merchandis- ing variables, especially among minority populations, will continue to come under the watchful eye of policy makers, who may implement legislation designed to curb target marketing practices based on pricing.

SUMMARY

Our empirical analysis of 14 liquor brands in 35 stores has shown that brand price elasticity is higher for:

1. More frequently promoted brands, 2. Higher market share brands, 3. Market areas with lower concentrations of African-American consumers, 4. Market areas with higher incomes.

This information contributes to the ongoing stream of academic research that attempts to ascertain the determinants of price sensitivity. In particular, this study is unique in that it shows how consumer characteristics relate to brand level price elasticity. In addition to fur- thering our understanding of the determinants of price elasticity, this study provides useful information to assist retailers in implementing store level pricing policies.

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