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The Role of Retail Competition, Demographics and Account Retail Strategy asDrivers of Promotional Sensitivity

by

Peter BoatwrightCarnegie Mellon University

Graduate School of Industrial Administration

Sanjay Dhar and Peter RossiUniversity of Chicago

Graduate School of Business1101 E. 58th StreetChicago, IL 60637

July, 2000

Revised, July 2002

.

AcknowledgementsPeter Boatwright is Assistant Professor of Marketing, Sanjay Dhar is Professor of Marketing, andPeter Rossi is Joseph T. Lewis Professor of Marketing and Statistics. Support from the Kilts Centerfor Marketing, Graduate School of Business, University of Chicago is gratefully acknowledged.

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The Role of Retail Competition, Demographics and Account Retail Strategy asDrivers of Promotional Sensitivity

July, 2000revised, July 2002

AbstractWe study the determinants of sensitivity to the promotional activities of temporary price reductions,displays, and feature advertisements. Both the theoretical and empirical literatures on pricepromotions suggest that retailer competition and the demographic composition of the shoppingpopulation should be linked to response to temporary price cuts. However, datasets that spandifferent market areas have not been used to study the role of retail competition in determining pricesensitivity. Moreover, little is known about the determinants of display and feature response. Verylittle attention has been focused on retailer strategic decisions such as price format (EDLP vs. Hi-Lo)or size of stores. We assemble a unique dataset with all U.S. markets and all major retail grocerychains represented in order to investigate the role of retail competition, account retail strategy, anddemographics in determining promotional response. Previous work has not simultaneously modeledresponse to price, display, and feature promotions, which we do in a Bayesian Hierarchical model.

We also allow for retailers in the same market to have correlated sales response equations through avariance component specification. Our results indicate that retail strategic variables such as priceformat are the most important determinants of promotional response, followed by demographicvariables. Surprisingly, we find that variables measuring the extent of retail competition are notimportant in explaining promotional response.

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Introduction

Both the theoretical and the empirical literatures on the response to promotional activities

have focused primarily on response to temporary price reductions. The theoretical literature on

price promotions (c.f. Varian (1980), Narasimhan (1988), Lal, Little, and Villas-Boas (1996), Kim and

Staelin (1999)) emphasizes competition between retailers as the fundamental driver of short-term

price changes. On the other hand, in the empirical literature (e.g., Bolton (1989), Hoch et al (1995),

Shankar and Krishnamurthi (1996)) demographic factors have long been thought to be important

determinants of price sensitivity via various search-theoretic explanations in which the value of time

plays an important role in determining the extent of price awareness. No work has been done on the

importance of account retail strategy variables in determining price elasticities.

This study examines the relative impact of the role of retail competition, demographic

factors and account retail strategy variables in determining not only price elasticities but also display

and feature response. This is particularly important given that little is known about the determinants

of display and feature response. Using scanner level tracking data, manufacturers often observe that

there are enormous differences between accounts and across markets in response to promotional

activities. (Boatwright, McCulloch, and Rossi (1999) advance a new methodology for measuring these

differences.) Due to insufficient data, many studies (such as Hoch et al (1995)) omit display and

feature measurements and focus only on price response. Since price reductions are often correlated

with display and feature response, this may produce an omitted variable problem. Ainslie and Rossi

(1998) document the relationship between demographic variables and display and feature but only

for one retailer in one market area.

Furthermore, the extant empirical literature on price sensitivity has focused on only a limited

number of market areas (at most two as in the Bolton study). In one or two market areas, there can

be no variation in the basic structure of competition as indicated by the number and relative size of

retailers. Hoch et al use store-specific measures of retail competition, but the variation in the

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competitive climate within one or two market areas may be quite limited.1 Wittink (1977) studied

data from multiple markets, considering how price sensitivity is affected by different advertising

content.

In order to sort out the importance of retail competition, account retail strategy, and

demographic information, we assemble a unique dataset which spans all major U.S. market areas and

all major key accounts (a key account is a retailer-market combination, e.g., Safeway Denver). We

have complete display and feature ad information along with measures of retail competition, account

format, and demographics. We use a comprehensive sales response or demand model that

incorporates the effect of own price, display, feature, competing brand prices, and price at competing

retailers. This model is calibrated to weekly sales data at the key account level. Account aggregation

biases are reduced to the smallest possible extent using a method suggested by Christen et al (1997).

In addition, we provide a formal argument for why aggregation biases cannot affect the relative

magnitudes of our explanatory variable effects.

Our goal of estimating promotion sensitivities and the relationship of these sensitivities to

account level competition in a market, account retail strategy, and demographic variables presents a

formidable estimation problem. There are about 100 key accounts and five basic promotion

variables, yielding some 500 or so response coefficients. A nave strategy of estimating each account

model separately and then using the estimated coefficients in a second stage model does not properly

account for estimation error. If there is substantial estimation error in the account level response

coefficients, this sort of two-step approach may provide misleading views about the importance of

the explanatory variables. That is, the R-squared of the second stage regression can be low simply

due to estimation error rather than that the true coefficients are unrelated to these variables. In

addition, a joint estimation method will provide gains in statistical efficiency.

We use a hierarchical approach in which each account level regression coefficient vector is

viewed as a draw from a random effects distribution that incorporates the account and market level

1Bolton (1987) and Hoch et.al (1995) use data from multiple categories.

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competition, strategy, and demographic variables. We explicitly incorporate geographical markets in

our model by allowing retail accounts in a given geographical market to have correlated demand

shocks. We are the first to measure the common variance component in the demand shocks across

accounts. Contrary to previous speculation, we find these common components to be small.

Our relatively small set of explanatory variables measuring competition, account retail

strategy, and demographics accounts for around 30 per cent of the variation in response.

Surprisingly, we find that the retail competition variables are the least important determinants of the

responsiveness to promotional activities. Account retail strategy variables such as price format and

chain size are important determinants of both price and display/ feature responsiveness.

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A Framework for Selection of Explanatory Variables

Our goal in this paper is to relate promotion sensitivity in an account (retailer-market

combination) to the salient characteristics of the retail environment. In order to impose some

discipline on the process of selection of variables, it is useful to develop a framework for thinking

about response to promotions. Fundamentally, the promotions observed in retail scanner data are

various forms of temporary price reductions accompanied, in some cases, by in-store advertising

(displays) or out-of-store print ads (features). This advertising is designed to provide price

information and remind the consumer of the existence of the product. This is not image or

attribute-focused advertising. In our view, price sensitivity is influenced by consumer budget

constraints and wealth, by availability of competitive alternatives, and by consumer search activity.

The variables included in our analysis therefore reflect an understanding what sorts of consumers and

retail environment conditions will promote price sensitivity.

Conceptually, it is possible to organize our discussion by categorizing the variables that may

affect consumer response to temporary shelf-price reductions, display and feature activity into three

groups: consumer characteristics, account retail strategy, and retail competition.

Consumer Characteristics

Income or Wealth: Becker (1965) proposed that systematic differences in price sensitivity

could arise from the opportunity costs of time associated with demographic characteristics (Blattberg

et al. 1978). Higher income consumers (who also tend to live in higher home value neighborhoods)

have higher opportunity costs of time and are likely to be less price sensitive. Consequently, they are

likely to have a lower response to temporary shelf price reductions. However, these very consumers

are time constrained and consequently use feature advertisements and in-store displays to reduce

their search costs and are therefore more likely to respond to non-price retail promotional activity.

Age: Older consumers are likely to have lower opportunity costs of time and therefore more

likely to engage in more search for lower prices and therefore more likely to respond to temporary

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Previous research has shown significant cross-store effects at the brand level for a few product

categories (e.g., Kumar and Leone 1988; Walters 1991). Similarly, in competitive markets, feature

advertisements may be used to compete for customers (as in Lal and Matutes, 1994), and for this

reason features may be more effective in stimulating sales in the more competitive markets. Displays

facilitate in-store decision making of what to purchase and hence may benefit retail chains in more

competitive retail markets.

For ease of reference, we summarize the effects of the different variables in Table 1 below.

Note that price sensitivity is negative, so a '+' in the table indicates increased price sensitivity, which

will result in a negative impact on the price elasticity coefficient.

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Model and Datasets

In order to study the relationship between account retail strategy, competition, and

demographic variables and promotion sensitivities, we need sales and promotional data across

different retailers and different market areas. We have to combine scanner data on the sales volume

and price with demographic data and data on market and account (retailer-market combination)

characteristics. There are no academic or industry sources of data panels which are comprehensive

enough to include sufficient variation in market and chain characteristics (note: the national panels of

households maintained by IRI and Nielsen do not include audit information on store displays or

feature ads it is impossible to accurately measure what display and feature activity was faced by the

household on any given purchase occasion). There are national samples of stores maintained both

by IRI and Nielsen (INFO SCAN and SCANTRACK). These samples also include audit information

on displays and feature ads. It is the policy of both IRI and Nielsen not to release store level

information (note: the MSI/ Nielsen data sets do not include retailer identifiers and only include a

small number of markets). For this reason, we are limited to what Nielsen and IRI term account

level data. An account is a particular market-retailer combination, e.g. Safeway in Denver. The

information from the Nielsen sample of stores is aggregated to the account level. As discussed

below, we use a method suggested by Christen et al to reduce potential aggregation bias.

Through the intercession of a major Nielsen client, we were able to obtain account level data

for 97 major US retail accounts across 35 Nielsen SCANTRACK markets for the ground coffee

product. For example, this means that SafewayDenver is distinct from SafewaySalt Lake City. We

focus on the two major national brands, Folgers and Maxwell House, which account for 59% of the

category total sales. Other secondary national brands have very spotty distribution, so that it would

be difficult to use all 97 accounts. Our SCANTRACK data contain weekly sales, price, display, and

feature variables for each of 120 weeks.

Our 97 accounts are located in 35 different geographic markets. It has often been

speculated that there are market-wide shocks that affect each of the accounts in a given market.

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Television advertising and manufacturer coupon drops are often cited as possible examples. If there

were large and regular market-wide shocks, then the accounts within a market would have correlated

sales response equation errors terms. To measure the size of these market-wide shocks, we employ a

variance component model. We postulate a sales response model of the form:

( ) = + = = =K K K'amt amt a amt mln y x a 1, , A m 1, ,35 t 1, ,120 (1)

Equation (1) reflects the structure of our data in which each of the 97 accounts are located in 35

different geographic markets (e.g. Winn-Dixie in Raleigh-Durham or Publix in Miami). Each market

(indexed by m) has up to Amaccounts in it. The error term, , has a variance component structure

( )

( )

( )

= +

=

amt mt amt

mt amt

2

amt am

2

mt m

w v

cov w ,v 0

v ~ N 0,

w ~ N 0,

This variance component structure allows for a positive covariance between accounts in the same

market as they all share the same market-wide error term.

( ) ( ) = + + = 2amt a'mt mt amt mt a'mt mcov , cov w v ,w v

Since consumer promotions are planned on the level of the account (Boatwright, McCulloch, and

Rossi 1999), we assume correlation across accounts within a chain to be negligible, although this and

other types of correlations would be feasible to model.

The explanatory variables in the xamt vector include:

1. intercept

2. ln(price of brand)

3. display without feature

4. feature without display

5. feature and display

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6. ln(price of competing brand at the retail account)

7. ln(min(price at competing accounts in the market area))

8. ln(lag(sales))

Thus, our sales response model allows for account level sensitivity to all of the marketing mix as well

as competitive brand and competitive account prices. Our model includes lagged sales to allow for

autocorrelated errors. In addition, we investigated other model specifications which included base

(regular) price, multiple variables to capture seasonal effects, alternative specifications of prices of

competing brands in the same account, and alternative specifications of prices at competing

accounts. Our final model contains those variables that were consistently important in the many

specifications that we investigated. Also our final model contains one own price variable instead of a

regular price and discount measure. Base price and actual price are highly correlated (r=0.94),

preventing us from estimating separate short term and long term price effects. In order to investigate

the relationship between the account-level promotion sensitivities (a) and retail competition,

account retail strategy, and demographics variables, we employ a hierarchical model structure with a

second level that allows the promotion sensitivities to be related to both our observable variables and

a random component,

= +am am amz u (2)

where z is the vector of retail and demographic variables and is a matrix of regression coefficients.

u represents the unobservable component of promotion sensitivity.

In previous work (Hoch et al (1995) and Shankar and Krishnamurthi (1996)), the sales

response model in (1) is estimated first and the estimated coefficients are then regressed on

explanatory variables in a second regression. It is well known that this is an inefficient procedure.

Furthermore, the goodness of fit in the second regression is not an unbiased estimate of the extent to

which the explanatory variables can explain variation in promotion sensitivity. Since there is

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measurement error in the estimates of account level promotion sensitivity, the R-squared from the

second step equation will underestimate the goodness of fit. For these reasons, we do not use a two-

step estimation procedure, but, instead, we estimate the system (1-2) simultaneously in a

Bayesian hierarchical model approach. That is, we regard (2) as a random coefficient model and

complete the model with priors on the hyperparameters of the model.

We first specify the distribution of the error terms in (1) and (2)

( )

( )

2at a

a

~ N 0,

u ~ N 0,V

Note that we allow for correlation between the components of the vector by allowing a non-

diagonal matrix V . Priors on the key hyperparameters complete the model.

( )

( ) ( )

2a ~ IG ,

vec ~ N , V

In any hierarchical model, the prior on V plays a critical role. Adaptive shrinkage requires a

relatively diffuse prior in order for the data to dictate the amount of shrinkage or partial pooling

that takes place across accounts. It is well known that the standard inverted-Wishart prior has a

number of defects. It can be difficult to assess a relatively diffuse prior, and the inverted-Wishart

prior cannot be used in cases where one wants diagonal elements of V to be relatively large or small

for different elements of the regression vector. For these reasons, we adopt the approach of

Barnard, McCulloch, and Meng (2000). In the Barnard et al approach, V is decomposed into the

correlation matrix, R, and the diagonal matrix of standard deviations.

( ) ( )

=

i i i

correl

V diag(s)Rdiag(s)s ~ logn( , )

p R 1 I R

That is, on the correlations we use a flat or uniform prior over the space of positive definite matrices,

and we use log-normal priors on the standard deviations.

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E x planatory V ariables: A ccount R etail S trategy, Competitive Characteristics, and D emographics

The second level of the hierarchical model (eqn (2) above) relates each of the seven response

coefficients to account retail strategy, retail competition and consumer demographic variables

identified earlier. These data were obtained from several sources. A syndicated data service provided

detailed demographic distributional information on demographic variables for each account. Two

published secondary data sources, Market Scope, and Marketing Guidebook, provided additional

information on the characteristics of the retail chains and the markets served. Both publications are

well-known sources published annually by Trade Dimensions, a unit of the Progressive Grocer Data

Center. We also surveyed each of the retail accounts in the database for additional information.

The variables constructed to measure account retail strategy include 1). Chain size the

number of stores each chain operates in a particular market; 2).EDLP/ Hi-Lo (EDLP) from a

survey of accounts (see Dhar and Hoch (1997)); 3)Floor Space per Capita (FLOOR.SP) average

square footage / population size for an account. This serves as a proxy for the breadth and depth of

assortment carried.

Retail competition is measure by a Herfindahl (HRFNDL) index,

2

aas , where s is the

market share of account a. The Herfindahl index is lower, the higher number of accounts in a

market and the more equal-sized the share of those accounts are. Thus, lower Herfindahl values are

associated with higher levels of retail competition.

Consumer characteristics are measured by three variables. Income/ wealth is proxied by

home value (HOMEVAL) the fraction of the total number of households for a retail accounts

customer base owning homes with a value higher than $250,000. This variable is highly correlated

with income level. Age is measured by the fraction of an accounts customer base that is older than

55 years (ELD). Finally, we use Private Label Share (PVT.LBL as a proxy for the size of the price

sensitive consumer segement (see Dhar and Hoch (1997))

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All of the above measures are computed at the account level. There are seven explanatory

variables as well as an intercept, so z a will be dimension 1 x 8. The matrix is therefore 8 x 7.

Table 2 provides summary statistics for the distribution of these explanatory variables. The wide

variability of each of these measures across accounts and markets is striking. For instance, the range

of the Herfindahl index, 0.04 to 0.36, indicates large variation in the extent of retail competition

across the markets. Similarly, the percent of elderly in the shopping populations for these accounts

ranges from around 12% to around 34%. As for EDLP (not shown in Table 2), 34% of the 97

accounts consider themselves to follow an EDLP pricing strategy. It should be emphasized that

even in the empirical I/ O literature it is rare to see studies that consider such a wide range of

competitive conditions.

A ggregation Issues and A djustments

Our account level data are at a lower level of aggregation than typically used in the empirical

I/ O literature (where data are usually aggregated to the market or higher (see Berry, Levinsohn and

Pakes (1995) or Nevo (2001)). However, the nonlinear nature of the basic sales response model in

(1) creates the possibility of aggregation biases. As Allenby and Rossi (1991) point out, aggregation

biases occur under the condition that consumers in the aggregation unit are not uniformly exposed to

the same marketing action. In our data, the display variable is the major problem. Display activity is

a store-specific phenomenon. Thus, without any correction, the display coefficients can be biased

upwards as Christen et al (1997) document. The possible solutions to the aggregation problem

include obtaining store level data or making some of the adjustments that are advocated by Christen

et al. Both IRI and Nielsen have a policy of not releasing store-level data across many markets with

account identifiers. For example, the MSI/ Nielsen store level datasets do not include retail chain

identifiers so that we could not merge our market and retail competition and strategy dataset with the

sales data.

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Given the lack of store level data, we have chosen to adjust our account level data in a

manner suggested by Christen et al. While the account level data do not offer the detail of store level

data, we are able to obtain a decomposition of unit sales under various merchandizing conditions.

For example, we have not only total unit sales but also unit sales for five groupings of stores: stores

with 1. displays without feature, 2. feature without display, 3. display and feature, 4. price discount

and 5. no promotion. We follow the Christen et al procedure of expanding each account-week

observation into five disaggregate observations corresponding to the five groups above. Christen et

al demonstrate that this method removes a great deal of the aggregation bias.

Since the stores accounting for the sales volume vary over time within an account, i.e.

different collections of stores are grouped over time, the average sales of the groups varies over time.

In order to make the groupings comparable, and thus calculate response statistics across partitions

and time, we normalize sales of a store group and partition lagged sales of the account by "baseline

dollars" of the brand for the group. Calculated from store level data by ACN, baseline dollars

measures the non-promoted sales of the partition, i.e. the typical level of sales of that particular

collection of stores in periods when those stores do not offer any promotions. Our dependent

measure of sales yagt, then, is the log of the ratio of coffee unit sales (pounds) and baseline dollars

sales for account a=1 A , group g=1 5, in week t=1 T and our independent variables are the

corresponding price and merchandizing measures for each of the accounts and each of the five

promotion groups.

It should also be pointed out that we are primarily after the relationship between promotion

sensitivity and various account/ consumer variables. Level shifts due to aggregation bias will not

distort these relationships. That is, the relative importance of variables in explaining variation in

promotion sensitivity is not affected by aggregation bias. For our analyses, aggregation biases can

only cause a serious problem if the magnitude of aggregation bias varies by account andif the extent

of the aggregation bias is correlated to the variables used in our analysis.

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Results

Figure 1 shows the distributions of each of the promotion response coefficients across the

97 accounts. Price elasticities are negative; most of the mass of the distributions is in the range

between -1.06 and -1.82. For ease of interpretation, we have exponentiated the promotion sensitivity

coefficients so that they report percentage increase in sales due to the promotion (sometimes referred

to as lift). On average, features increase sales by 56%; most of the mass of the effect of features

ranges between an 18% increase and a 112% increase. The effect of a display ranges from 10% to

65%; on average it increases sales by 34%. On average, simultaneous features and displays increase

sales by 100%. The average sensitivity to price at other accounts is 0.10, and the average sensitivity to

price of competing brand is 0.36.2

We now turn to a discussion of how these promotion sensitivities relate to account retail

strategy, retail competition, and consumer demographic variables. We summarize these relationships

by considering the posterior distributions of the elements of , which are regression coefficients

from (2). Each row of the matrix contains a vector of regression coefficients.

=

'intercept

'price

'display

'feature

'display&feature

'comp account price

'compbrandprice

For example, the second row of (2) is the price elasticity regression.

= +'price,a price a price,az u

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Price Response

The boxplots in figure 2 show the posterior distributions of the elements of that relate to

the own price elasticity.3 Each of the continuous explanatory variables has been standardized with a

mean of zero and standard deviation of 1 so as to facilitate comparisons between coefficients on the

same scale. Figure 2 clearly shows that the EDLP format and private label shares are the most

important determinants of own price elasticity. Because EDLP is a discrete variable, the magnitude

of its coefficient cannot be directly compared with the remaining coefficients. Even so, the mass of

the posterior is positive for both brands. Consumers who shop at accounts with an EDLP pricing

strategy are less sensitive to short term price changes than consumers at non-EDLP accounts. This

is entirely consistent with an interpretation of EDLP as a signal of lower mean price and lower

variance (see Hoch, Drze, and Purk (1994)). Consumers at an EDLP account can be assured of

lower average prices and do not have as much incentive to track deals and switch stores as

consumers in a non-EDLP, Hi-Lo account. Bell and Lattin (1998) report the finding that large

market basket shoppers are less responsive to prices in individual categories and tend to shop at

EDLP chains.

Our results are somewhat different than those of Shankar and Krishnamurthi (1996) who

found that shoppers in EDLP chains have higher regular or long-run price sensitivities, using a much

more limited sample of only 2 accounts. There is not necessarily a contradiction between our results

on short-term promotional price response and the results of Shankar and Krishnamurthi. As noted

above, we do not include regular price terms in the sales response model due to insufficient variation

in regular prices. It could well be that EDLP shoppers do not respond to deals but are sensitive to

longer-run price changes.

2The averages given here are medians, while the ranges given are the 10% and 90% quantiles. The statistics

and boxplots for thecoefficients give results only for Folgers.3In the boxplots, the box indicates the range from the 10% to the 90% quantiles, while the whiskers show the

full range of the posterior.

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response. This is consistent with a search cost view of display and feature advertising. Displays

reduce in-store search costs while features reduce out of store search costs. The wealthy have a high

opportunity cost of time and, thus, the reduction in search costs is more important in the sense that

more search behavior can result from a given reduction in search costs. Similarly, the elderly

consumers are more display responsive and feature responsive, which again is consistent with the

search cost view of promotions, since the elderly may have greater cognitive and/ or physical

constraints.

Our findings resolve a number of puzzles in the literature on price sensitivity (at least in the

context of the coffee category). Hoch et al find that the elderly are often more price sensitive. Since

display activity was not measured in the Hoch et al data and displays and price cuts are correlated, the

findings of Hoch et al can be explained by an omitted variable bias. The proper interpretation of the

data is that the Elderly are more promotion rather than price sensitive. Since promotions and price

cuts are often correlated, the Elderly will appear more price sensitive in models without these

variables. Dhar and Hoch (1997) hypothesized that if the elderly were more price sensitive, they

would be more likely to purchase private labels. However, Dhar and Hoch did not find the elderly to

be more likely to purchase private labels. If in fact elderly are promotion sensitive rather than price

sensitive, as our results suggest, we would expect the elderly to appear price sensitive with respect to

the heavily promoted national brands but not particularly prone to purchase the less promoted and

lower priced private labels.

Finally, we find that private label share is positively correlated with display sensitivity and

with feature sensitivity, suggesting that private label buyers are more promotion sensitive than other

buyers. The coffee category is unusual in that private labels, at a national level, command 9.3%

market share, which is 3rd place, ahead of all brands except for Folgers and Maxwell House. Those

accounts with stronger private label coffees may be category leaders in their market, i.e. the strength

of the private label share could in part reflect strategic decisions of a retail account in the coffee

category

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Figure 5 provides the results for feature sensitivity and display and feature sensitivity. These

results are similar to display activity and to feature activity, with the elderly showing a greater

response to promotions and response is lower in the EDLP format account.

Also, the feature sensitivities are negatively related to chain size. This supports our earlier

discussion that the higher the number of stores that an account has in a market, the closer they are

located to consumers. In this situation, displays are less likely to be effective, as consumers are

already familiar with their neighborhood stores layout and hence do not reduce in-search costs.

Furthermore, the proximity that the accounts stores have to consumers in the market also reduces

the potential number of consumers that can switch leading to feature advertising being less effective

in reducing out-of-store search costs.

R esponse to Competing A ccount Price

In order to measure the effect of prices at other competing accounts in an accounts market

area, we included a measure of the price of the item at competing accounts (defined as the minimum

of price in that week at other accounts in the market). There are large variations in both the degree

of response to this variable (Figure 1) as well as in the competitive conditions across market areas

(Table 2). This is the only sensitivity for which we find an effect of variation in retail competition.

As might be expected, in market areas with greater competition as defined by the Herfindahl index,

we find greater sensitivity to competing account price.

R esponse to Competing Brand Price

In order to capture brand-switching effects within the same account, we also included the

price of the major competing brand (Folgers in the Maxwell sales model and Maxwell in the Folgers

model). The EDLP format variable is important in explaining account variation in this cross-

elasticity. As might be expected from the discussion of own price elasticities, customers at EDLP

format stores are less sensitive to competing brand prices. The chain size variable also explains some

of the account variation in this elasticity, indicating that consumers at accounts with more stores per

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capita are less sensitive to price changes of competing brands. This result is similar to the findings of

Ho, Tang, and Bell (1998), where in this case the lower travel costs associated with their trip to a

close store may more than compensate for the higher prices that they may pay.

Relative Contributions of E x planatory V ariable T ypes

We have related three groups of variables to promotion sensitivity: i. Retail competition, ii.

Account retail strategy, and iii. Consumer demographics. In order to assess the relative importance of

each group, we compute an R-squared like measure of the contribution of each group to the

explanation of the variability of the promotion sensitivities. To do this, we partition the matrix Z

into Z1, Z2, and Z3 that correspond to the three groupings of variables, fitting the models where Z is

replaced by Z1 and by [Z1 Z2]. Z1 contains the intercept, so all models were fit with an intercept. We

calculate the R-squared measure for each model using the formula (Model SS)/ (Total SS), where

Total SS refers to the sums of squares of the estimates of the sensitivities, and Model SS refers to the

sums of squares of the predicted sensitivities, which is Z for the full model. We model the two

brands, and we report price, display, feature, feature & display, store competitor price, and brand

competitor price results for each brand, giving 12 R-squared measures for each model. Also, we can

calculate R-squared for each draw, meaning that we get 12 distributions of R-squared measures for

each model. We report the medians of these distributions in Table 3.

The explanatory factors account for around 30% of the variation in almost all of the

consumer sensitivities. For instance, the explanatory factors explain 31% and 26% of the variance in

price sensitivity for Maxwell House and Folgers, respectively. Retail competition accounts for very

little of the variation in price sensitivities (3% and 4%). Account retail strategy accounts for more of

the variation in price sensitivities (11% and 12%), and demographics explain close to the same

amount of variation as account retail strategy (17% and 10%).

As for response to display and feature activity, demographics and account retail strategy each

explain roughly equal portions of the variance (around 12%), and retail competition explains about

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6% of the variance in consumer response. The explanatory factors also account for a large percent

of the variation in the cross-price sensitivities, close to 40% for Maxwell House for both the brand

competition and the account competition.

M ark et and A ccount-specific V ariance Components

Our sales response model includes a variance components error specification. This

specification breaks the regression error into a market wide component, which is common to all

accounts in the same geographic market, as well as an account-specific component. In some recent

work in marketing (c.f. Villas-Boas and Winer (1999)), this common component has been identified

as a possible source of endogeneity (if retailers can predict this common component and change

prices as a function of the component). We can provide some direct evidence on the size of this

component by exploring the relative size of the variances of the market-wide and account variances.

Figure 8 provides a histogram of the posterior median of ( ) + 2 2 2m m a for each of our 97

accounts for the Maxwell House regression errors. For the bulk of the accounts, the common

market-wide error component is much smaller than the account component.

Specification Searches

We approached the choice of explanatory variables using the framework outlined above.

Only variables that could be justified on a priori grounds were included as measures of consumer

demographics, retail competition or account retail strategy. In some cases, variables were collected

but found to have insufficient independent variation to be included in the model. In this section, we

briefly discuss some variables that were not included and explain why.

In our model, we used home value as a proxy for household wealth. Other possible proxies

include income and education. Income is often found to be a poor proxy for wealth, as it does not

include flows from physical, financial, and human capital assets. In addition, census measures of

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income have been found to be subject to large measurement error. Education is highly but not

perfectly correlated with home value.

Some have included ethnicity as an explanatory variable. We have elected not to include

ethnicity in our results reported above as there is no real theory that can account for effects of

ethnicity once wealth and household composition is controlled for. In specifications not reported

here, we found that Hispanic and black consumers are less price responsive.

We employ the Herfindahl index as a measure of degree of retail competition. It captures the

effect of both the number of chains in a market and the distribution of shares across these chains.

We also tried to include the retail market share that an account has as a measure of clout they have

in a market. However, this measure was correlated with the Herfindahl index and could not be used.

Finally, the presence/ absence of private label coffee was not included as a account retail

strategy variable, as only one of the 97 accounts in our sample did not have a private label coffee

brand.

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Conclusion

From an empirical point of view, little is known about the determinants of differential

response to promotion activities. Earlier work has focused only on price elasticities in a limited

number of market areas. The use of one or two market areas means that account retail strategy and

competition variables cannot be investigated. With the exception of Ainslie and Rossi (1998),

virtually no work has been done on the determinants of response to display and feature advertising.

We assemble a unique data set that spans all major US market areas and includes account

retail strategy, retail competition, and demographic variables. We also have access to display and

feature advertising information that allows computation of display/ feature sensitivities for each

major retailer-market combination. We conduct our analysis at the key account level. This is the

relevant unit of analysis for retail competition and account retail strategy. We adjust our analysis for

possible aggregation bias, following the suggestions of Christen et al. Two stage estimation methods

used in past studies are less efficient than our simultaneous Bayesian hierarchical method. This

means we bring maximum statistical power to bear on the problem. 4

Collectively, account retail strategy, retail competition, and demographic variables can

explain about 30 per cent of the variation in promotion sensitivity. Most surprising is our finding

that retail competition variables are the least important in explaining response to promotional

activities. The only detectible relationship is between retail competition and the cross-elasticity of

prices across accounts in the same market area. On the other hand, account retail strategy variables

such as price format and store size are quite important in explaining both price elasticity as well as

display/ feature advertising response. Since our analysis includes advertising variables, we are able to

better understand some of the puzzling results of earlier work relating demographics to price

4In order to address potential endogeneity concerns and to see if instrumental variables approaches would

affect our results, we projected prices on market-level wholesale price data (specifically collected for this studyfrom Leemis) and used the fitted values from these regressions in our hierarchical model (the regressions alsoincluded account dummies and interactions of account dummies with the market-level wholesale prices).Unfortunately, we find that with instruments in our hierarchical model removes all account level time seriesinformation in the data and there is insufficient information to measure the relationship between the

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response. In fact, it turns out that certain demographic groups (such as the Elderly) are not more

price elasticper sebut are more advertising responsive. Taken as a whole, our results call attention to

the importance of account retail strategy variables and to the non-trivial interaction between price

and other promotional variables.

promotional sensitivities and our explanatory variables.Finally, we want to argue that endogeneity biases, likeaggregation biases, are unlikely to affect the relative importance of the explanatory variables.

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References

Ainslie, Andrew and Peter E. Rossi (1998) Similarities in Choice Behavior Across ProductCategories. M arketing Science17, 2, 91-106.

Allenby, Greg M. and Peter E. Rossi (1991) There is No Aggregation Bias: Why Macro LogitModels Work, Journal of Business and E conomic Statistics, 9, 1 (Jan), 2-14.

Berry, S., J. Levinsohn, and A. Pakes (1995), Automobile Prices In Equilibrium, E conometrica 63,841-890.

Barnard, J., McCulloch, R., and Meng, X. (2000), "Modeling Covariance Matrices in Terms ofStandard Deviations and Correlations, with Application to Shrinkage," Statistica Sinica, 10, 4(Oct), 1281-1311.

Becker, Gary (1965), A Theory of the Allocation of Time, E conomic Journal, 75, 493-517.

Bell, David R. and James M. Lattin (1998) "Shopping Behavior and Consumer Preference for StoreFormat: Why 'Large Basket' Shoppers Prefer EDLP," M arketing Science17, 1, 66-88.

Blattberg, Robert C., Gary D. Eppen, and Joshua Lieberman (1978), A Theoretical and EmpiricalEvaluation of Price Deals for Consumer Nondurables, Journal of Mark eting, 45, 1, 116-129.

Boatwright, Peter, Robert McCulloch, and Peter E. Rossi (1999) "Account Level Modeling for TradePromotion: An Application of a Constrained Parameter Hierarchical Model," Journal of the A merican Statistical A ssociation, 94, 448, 1063-1073.

Bolton, Ruth (1989) "The Relationship Between Market Characteristics and Promotional Price

Elasticities,"M arketing Science, 8, 2, 153-169.

Chevalier, Judith A., Anil K. Kashyap, and Peter E. Rossi (2001), Why Don't Prices Rise DuringPeriods of Peak Demand? Evidence from Scanner Data, University of Chicago GraduateSchool of Business Working Paper.

Christen, Markus, Sachin Gupta, John C. Porter, Richard Staelin, and Dick R. Wittink (1997), UsingMarket-Level Data to Understand Promotional Effects in a Nonlinear Model, Journal ofM ark eting R esearch, 34, 3 (August), 322-334.

Dhar, Sanjay K. and Stephen J. Hoch (1997) "Why Store Brand Penetration Varies by Retailer,"M arketing Science16,3, 208-227.

Hausman, J. A. (1996), Valuations of New Goods Under Perfect and Imperfect Competition, in T.Bresnahan and R. Gordon, Eds, T he E conomics of N ew Goods, Chicago: University of ChicagoPress.

Ho, Teck-Hua, Christopher S. Tang, David R. Bell (1998) "Rational Shopping Behavior and theOption Value of Variable Pricing,"M anagement Science44, 12, S145-S160.

8/3/2019 SSRN-id331541

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- 27 -

Hoch, Stephen J., Xavier Drze and Mary E. Purk (1994) "EDLP, Hi-Lo, and Margin Arithmetic,"Journal of Mark eting, 58, 4 (Oct.). 16-27.

Hoch, Stephen J., Byung-Do Kim, Alan L. Montgomery, and Peter E. Rossi (1995) "Determinants ofStore-Level Price Elasticity," Journal of Mark eting Research32,1 (Feb) 17-29.

Kim, S.Y., and R. Staelin (1999) "Manufacturer Allowances and Retailer Pass-Through Rates in aCompetitive Environment," M arketing Science18, 1, 59-76.

Kumar, V. and Robert P. Leone (1988), Measuring the Effect of Retail Store Promotions on Brandand Store Substitution, Journal of Mark eting Research, 25 (May), 178-185.

Lal, Rajiv, John D.C. Little, and J. Miguel Villas-Boas (1996) A Theory of Forward Buying,Merchandising, and Trade Deals, M ark eting Science, 15, 1, 21-37.

Lal, Rajiv; and Carmen Matutes (1994) Retail Pricing and Advertising Strategies, T he Journal ofBusiness; 67, 3, 345-370.

Lal, Rajiv, and Ram Rao (1997) Supermarket Competition: The Case of Every Day Low Pricing,M arketing Science, 16, 1, 60-80.

Narasimhan, C. (1988), Competitive Promotional Strategies, Journal of Business, 61, 4 (October),427-449.

Nevo, A. (2001), Measuring Market Power in the Ready-to-Eat Cereal Industry, E conometrica,forthcoming.

Raju, J. S. (1992), The Effects of Promotions on Variability in Category Sales, M ark eting Science 11,207-220.

Shankar, Venkatesh and Lakshman Krishnamurthi (1996) "Relating Price Sensitivity to RetailerPromotional Variables and Pricing Policy: An Empirical Analysis," Journal of R etailing, 72, 3,249-272.

Tanner, M. A. (1993), T ools for Statistical Inference: Methods for the E x ploration of Posterior D istributions andL ik elihood Functions (2nd ed.), New York: Springer-Verlag.

Varian, Hal R. (1980) "A Model of Sales," The A merican E conomic Review70, 4 (Sept.), 651-659.

Villas-Boas, M. and R. Winer (1999), "Endogeneity in Brand Choice Models," M anagement Science, 45,

1324-1338.

Walters, Rockney G. (1991), Assessing the Impact of Retail Price Promotions on ProductSubstitution, Complementary Purchase, and Interstore Sales Displacement, Journal ofMark eting, 55 (April), 17-28.

Wittink, Dick R. (1977) "Exploring Territorial Differences in the Relationship Between MarketingVariables," Journal of Mark eting Research14, 2 (May) 145-55.

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Table 1

Anticipated Signs for Explanatory Variables Effects

Price Sensitivity Display Sensitivity Feature Sensitivity

Income/ Wealth - + +Age + + +Private Label Share + + +EDLP Format - - -

Number of Stores/ Assortment - - -Retail Competition + + +

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Table 2

Account Retail Strategy, Retail Competition and Consumer Characteristics:

Descriptive Statistics

Median St Dev Min Max

Elderly 0.1964 0.0375 0.1139 0.3430House Value 0.1731 0.2741 0.0551 0.8754Private Label Share 0.0639 0.0578 0.0000 0.2560Floor Space/ Capita 4.3664 1.4889 2.1198 9.3488Herfindahl 0.1182 0.0628 0.0402 0.3554Chain Size 60 41.5805 15 192

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Table 3

Relative Importance of Account Retail Strategy, Retail Competition and Demographics in Explaining

Promotion Sensitivity

Note: Figures in parentheses are standard deviations of posterior distributions of the R2 measure

Model Price Display FeatureFeat &Disp

AccountPriceComp

Brand PriceComp

Full Model Maxwell 31% (10%) 33% (11%) 24% (9%) 23% (9%) 41% (14%) 43% (13%)

Folgers 26% (10%) 22% (9%) 28% (10%) 31% (10%) 35% (14%) 24% (11%)

Retail Strategy & Maxwell 14% (8%) 17% (9%) 17% (8%) 15% (8%) 11% (8%) 18% (9%)

Competition Folgers 16% (8%) 8% (6%) 15% (7%) 14% (7%) 29% (14%) 15% (9%)

Retail Maxwell 3% (3%) 7% (6%) 6% (5%) 9% (7%) 4% (5%) 3% (4%)

Competition Folgers 4% (4%) 3% (4%) 3% (3%) 6% (5%) 26% (14%) 4% (5%)

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PriceResponse

-3.0

-2.0

-1.0

All Accts Selected Accounts

Price

DisplayLift

0

1

2

3

All Accts Selected Accounts

Display

FeatureLift

0

2

4

6

8

All Accts Selected Accounts

Feature

F&D

Lift

0

5

10

15

20

All Accts Selected Accounts

Feature and Display

ResponsetoCompet(Account)

-0.6

0.0

0.6

All Accts Selected Accounts

Price at Competing Account

ResponsetoCom

pet(Brand)

-1.0

0.0

1.0

All Accts Selected Accounts

Price of Competing Brand

Figure 1

Distribution of Promotion Sensitivities

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Figure 2

Relationship between Price Response and Account Retail Strategy, Competition and

Consumer Demographic Variables

-0.2

0.0

0.2

0.4

Price Response

Eld

Homeval

Pvt.Lbl

EDLP

ChainSize

Floor.Sp

Hrfndl

M F M F M F M F M F M F M F

M MaxwellF Folgers

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Figure 3

Relationship between Display Response and Account Retail Strategy, Competition and Consumer

Demographic Variables

-0.4

-0.3

-0.2

-0.1

0

.0

0.1

0.2

Display Response

Eld

Homeval

Pvt.Lbl

EDLP

ChainSize

Floor.Sp

Hrfndl

M F M F M F M F M F M F M F

M MaxwellF Folgers

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Figure 4

Relationship between Feature Response and Account Retail Strategy, Competition and Consumer

Demographic Variables

-0.6

-0.4

-0.2

0.0

0.2

Feature Response

Eld

Homeval

Pvt.Lbl

EDLP

ChainSize

Floor.Sp

Hrfndl

M F M F M F M F M F M F M F

M MaxwellF Folgers

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Figure 5

Relationship between Feature and Display Response and Account Retail Strategy, Competition and

Consumer Demographic Variables

-0.6

-0.4

-0.2

0.0

0.2

Feature & Display Response

Eld

Homeval

Pvt.Lbl

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ChainSize

Floor.Sp

Hrfndl

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M MaxwellF Folgers

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Figure 6

Relationship between Response to Competing Account Price and Retail Strategy, Competition and

Consumer Demographic Variables

-0.2

-0.1

0.0

0.1

0.2

Response to Competing Account Price

Eld

Homeval

Pvt.Lbl

EDLP

ChainSize

Floor.Sp

Hrfndl

M F M F M F M F M F M F M F

M MaxwellF Folgers

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Figure 7

Relationship between Response to Competing Brand Price and Account Retail Strategy, Competition

and Consumer Demographic Variables

-

0.4

-0.2

0.0

0.2

Response to Competing Brand Price

Eld

Homeval

Pvt.Lbl

EDLP

ChainSize

Floor.Sp

Hrfndl

M F M F M F M F M F M F M F

M MaxwellF Folgers

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0.1 0.2 0.3 0.4

0

5

10

15

Medians of Mkt Variance/(Mkt Variance + Acct Variance)

Figure 8

Posterior Median of Relative Variances Maxwell House Regression Errors