1

The Role of Strategic Pricing by Retailers in the Success of Store Brands

Sergio Meza * Rotman School of Management

University of Toronto Toronto, ON M5S 3E6, Canada

E-Mail: sergio.meza@rotman.utoronto.ca Phone: 905-569-4962 Fax: 905-569 -4302

K. Sudhir Yale School of Management

135 Prospect St, PO Box 208200 New Haven, CT 06520

Email: k.sudhir@yale.edu Phone: 203-432-3289 Fax: 203-432-3003

December 2005

* The work described in this paper is part of the first author’s dissertation at New York University. We thank Joel Steckel, Yuxin Chen, Peter Golder and Pinelopi Goldberg for their comments and suggestions on this research. We thank the seminar participants at Boston University, HEC, IESE, New York University, Rutgers, Santa Clara University, SUNY Buffalo, Universidade Catolica Portuguesa, University of Texas at Austin, University of Central Florida, University of British Columbia, University of Miami, University of Toronto and University of Washington. We also thank the participants at the Albert Haring Doctoral Consortium at Indiana University, the Cornell University Pricing Conference and the 2003 Summer Institute in Competitive Strategy at UC Berkeley for their comments.

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The Role of Strategic Pricing by Retailers in the Success of Store Brands

Abstract

A number of papers have evaluated demand and cost based explanations for the rapid

growth and success of store brands. However there has been little empirical investigation of the

strategic role of the retailer in facilitating the success of store brands. In this paper, we examine

the pricing behavior of a retailer in the ready to eat (RTE) breakfast cereal category in

investigating whether and how a retailer strategically favors store brands.

Our key result is as follows: After introducing the store brand, the retailer disfavors

national brands that it imitates (and is therefore in greater competition) by charging higher

margins. In contrast, it treats the national brands that it did not imitate (and therefore in less

competition) more favorably by charging lower margins. Thus somewhat counter intuitively,

some national brands actually benefit from the store brand introduction.

However such strategic behavior happens only in market segments that are “attractive” to

the retailer in terms of “market size” and the opportunity to steal share from the leading brand.

There is no difference in the treatment of imitated and non-imitated brands in the non-attractive

segments. We show that a naïve approach to infer retailer behavior without considering such

segment level differences can lead to wrong and misleading inferences about the retailer’s

strategic pricing. We discuss the managerial implications of our results for pricing and product

positioning of national brands.

1

1. Introduction

The Success of Store Brands

Store brands have enjoyed tremendous success over the last two decades in gaining

market shares at the expense of national brands. In an analysis of over 225 categories during the

period 1987 to 1994, Hoch et al (2000) found the average annual share of sales for store brands

increased by 1.12%, while the average shares for the top three national brands in each category

fell by 0.20%. Mathews (1996) reports that in 1996, private sales in food stores increased 6.3%

compared to 1.3% for national manufacturers.

This success of store brands has drawn the attention of both retailers and manufacturers.

Progressive Grocer’s Annual Reports for 1999 and 2000 state that “Stress Private Labels” was

rated as the first likely action to be taken by retailers during these years. Manufacturers have in

turn responded vigorously to store brand introductions. For example, Cotterill et all (2000) report

that in response to the success of store brands, Post and Nabisco recently cut their prices and

consequently increased their market share from 16% to over 20% while decreasing private label

shares. In response Kellogg’s announced a 20% across the board price cut.

The growth of store brands has spawned an academic literature investigating the factors

that facilitate its success (Hoch and Banerji, 1993; Starzynsky 1993, Raju et al, 1995; Hoch,

1996; Narasimhan and Wilcox, 1997; Dhar and Hoch, 1997; Chintagunta et al, 2002, Cotterill et

al 2000; Hoch et al 2000, Sethuraman 2000). The factors that have been considered in previous

research may be classified into three groups: (1) those associated with consumer characteristics

(demographics) and consumer preferences (brand loyalty, price sensitivity) (2) those associated

with the inherent costs and benefits of store brands (e.g. low cost of store brand, quality

differential between store and national brands) and (3) those associated with competitive

conditions of the category (e.g. number of competing brands, advertising levels).

This stream of literature however does not consider the retailer’s role as a strategic player

that can facilitate the success of store brands. As Hoch et al (2000) point out, the sustained

growth of the store brand over the last ten years could be because of the fact that the retailer has

control over not only the marketing mix of the store brand but also the marketing mix variables

of its competing national brands. Several papers have acknowledged or modeled theoretically the

strategic role of the retailer in the success of store brands. They have focused on two areas: (1) A

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number of papers (Hoch 1996, Hoch et al 2000, Sayman, et al 2001, Scott Morton and

Zettelmeyer 2001) argue that store brands have an inherent advantage in that the retailer can

flexibly choose the positioning of store brands conditional on the positioning of national brands.

(2) Other papers have discussed the retailer’s control over several marketing mix variables such

as price, shelf space position and promotion (Hoch and Banerji 1993, Raju et al 1995; Hoch

1996; Narasimhan and Wilcox 1998; Dhar and Hoch 1997). But these papers do not empirically

analyze whether and how a strategic retailer contributes to the success of the store brand by

exercising its power to set the retail marketing mix for both the store brand and competing

national brands.

Research Questions and Empirical Strategy

Our goal in this paper is to investigate the strategic role played by the retailer in enabling

the success of store brands. Specifically, we seek answers to the following questions: First, does

the retailer change its pricing rule for national brands after a store brand is introduced in order to

favor store brands? Second, if the retailer exercises its pricing power to favor store brands, do all

national brands “suffer”? Finally, if there is heterogeneity in how the retailer prices different

national brands after introducing the store brands, what are some of the market factors that

determine the differences in retailer pricing of national brands?

The introduction of a store brand affects demand facing national brands in potentially

complicated ways. Typically, the elasticities will increase for national brands, if the store brand

choose a similar positioning as the national brand, but it may fall if only price sensitive

customers systematically shift to the store brand. Chintagunta et al (2002) find significant

changes in elasticities. Such changes in demand elasticity due to the introduction of the store

brand will cause the retailer to change retail prices. Further, manufacturers may change their

wholesale prices to reflect the new market reality. The change in marginal costs for the retailer

(due to changes in wholesale prices) also causes the retailer to change retail prices. These

changes in retail prices are a natural outcome of the changed demand curves and marginal costs

faced by the retailer and not due to a strategic decision by the retailer to favor store brands.

Therefore, it is not possible to simply look at the differences in retail prices before and after store

brand introduction to see if the retailer is changing its pricing strategy towards manufacturers.

The empirical strategy needs to control for changes in demand and cost conditions before and

3

after the store brand introduction in order to identify whether the retailer strategically favors

store brands.

How we control for demand and cost conditions in the market is tied to the nature of the

data that we use to investigate the strategic behavior of the retailer. We now explain the choice of

data that we use for exploring the research questions discussed above. We use the Dominicks

data made available by the University of Chicago for our investigation, because Dominicks

introduced store brands in multiple categories during the period for which the data are available.

In order to empirically investigate the research questions about heterogeneity in strategic retailer

behavior as a function of market characteristics, one possible strategy would be to analyze data

on how retailer strategic behavior varied after the introduction of store brands in multiple product

categories with different market characteristics. However, a problem with this strategy is that the

“players” (manufacturers) in the different categories would be different and it would be hard to

isolate whether the differences in retailer strategic behavior is due to the inherent differences in

the relationships with the different manufacturers or due to differences in the market

characteristics. To avoid this problem, an alternative strategy would be to use a category where

store brands are introduced in multiple sub-categories with different characteristics (such as size

or concentration), but where the manufacturers in all of the subcategories are the same. Our

exploration of the Dominicks data revealed that only the breakfast cereal category satisfied this

criterion. Six store brands were introduced in different sub-categories of the breakfast cereal

category and the manufacturers in all of these categories were the same. Further ready to eat

breakfast cereal is one of the most important product categories for a retailer and there would be

significant incentives for the retailer to indulge in strategic behavior. We therefore use the

breakfast cereal category for this research. We discuss more details about the data in a later

section.

The Dominicks data set has information on wholesale prices, which allows us to easily

control for retailer cost differentials before and after store brand introduction. However,

controlling for demand effects is more difficult. We need to estimate a demand model that allows

us to flexibly characterize the demand facing the retailer before and after the store brand

introduction. From an estimation point of view, it is difficult to estimate a demand model for the

breakfast cereal category. This is because using just aggregate store level data, we have to

characterize cross-elasticities across a large number of brands. However, we use recent advances

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in the econometrics literature (Berry 1994; Berry, Levinsohn and Pakes 1995; Nevo 2000) to

estimate a flexible random coefficients logit model accounting for both observed heterogeneity

(demographics) as well as unobserved heterogeneity in the preferences for product attributes. See

Dube et al. (2002) for a discussion of the flexibility of the random coefficients logit model when

using aggregate data.

Related Research

This paper is closely related to recent research in the “New” Empirical Industrial

Organization (NEIO) framework that has focused on the detailed analysis of the strategic

behavior of the retailer within a category. Kadiyali et al. (2000) investigates the nature of power

in manufacturer-retailer interactions in their analysis of the analgesics market. Sudhir (2001b)

tests for three alternative types of behavior by the retailer: constant markup, brand profit

maximization and category profit maximization. He finds support for category profit maximizing

behavior for both the peanut butter as well as yogurt categories. Berto-Villas Boas (2001)

extends the analysis in Sudhir (2001b) using a random coefficients logit model. Villas-Boas and

Zhao (2001) perform a similar analysis of the ketchup market using individual level data.

Recently, researchers have paid specific attention to the retailer’s pricing behavior after

introducing store brands. Chintagunta (2002) finds that the retailer deviates from category profit

maximizing behavior in order to favor the store brand in the analgesics category. Chintagunta et

al. (2002) find that after a store brand is introduced, retail margin for Quaker Oats (the only

major incumbent in the oatmeal category they analyze) increases indicating that the retailer

gained power. The results of the above studies suggest that national brand manufacturers suffer

when store brands are introduced, as retailers will use their power to set the marketing mix for

both their store brands and national brands in such a way as to favor the store brand.1 The above

two studies do not differentiate the strategic behavior of the retailer towards different national

brands. In contrast we investigate and document differences in behavior towards imitated and

non-imitated national brands and in different segments of the category. As we will see later, a

key finding in our paper is that not all brands suffer due to the store brand introduction. In fact,

the retailer may favor some national brands after the introduction of store brands.

1 Chintagunta et al. (2002) also analyze the pasta category where they find that one of the national brands raises its

wholesale prices, but they attribute it to demand advantages gained by this manufacturer due to expansion of the

product line at the same time the store brand was introduced.

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The rest of the paper is structured as follows: Section 2 develops the expectations about

retailer behavior. Section 3 introduces the model and Section 4 discusses the estimation

methodology. Section 6 describes the data. In section 6 we present the results and its managerial

implications. Section 7 concludes.

2. Expectations about Retailer Behavior

A retailer has strong incentives to ensure the success of its store brands and therefore

strategically take actions to support the store brands. Chintagunta (2002) finds that a retailer

supports store brands by reducing the prices of store brands. In contrast, we suggest that the

retailer could not only reduce the prices of store brands, but also the raise prices of national

brands in order to favor store brands. However, the retailer may incur some costs when they

choose to favor the store brands. Therefore, the retailer needs to tradeoff the benefits from

favoring store brands to potential costs. We discuss these tradeoffs below.

Tradeoffs Facing the Retailer When Introducing Store Brands

We begin with the potential benefits of supporting store brands. First, store brands tend to

typically provide greater margins for the retailer (Hoch and Banerji 1993, Sayman et al 2000,

Narasimhan and Wilcox 1998, Ailawadi & Harlam 2002, Pauwels and Srinivasan, 2002),

because the wholesale prices for the store brands tend to be smaller than for national brands

(Narasimhan and Wilcox, 1998, Raju, Sethuraman, and Dhar, 1995, McMaster 1987). A strong

store brand also improves the retailer’s bargaining power with national manufacturers (Giblen

1993, Chintagunta et al 2002 and Kadiyali et al 2000). The improved bargaining power is due to:

(1) a high cross price sensitivity between the store brand and the national brands (Sayman, et al

2000) or (2) the existence of a considerable group of consumers willing to switch to the store

brand (Narasimhan and Wilcox, 1998). The improved bargaining power can translate into: better

trade deals (Giblen 1993), deeper and more frequent trade deals (Lal 1990), lower wholesale

prices (Narasimhan and Wilcox 1998, Sayman, et al 2001) and higher percent margins in

national brands (Ailawadi & Harlam 2002) for the retailer.

Corstjens and Lal (2000) provide a store loyalty based explanation as to why a retailer

might wish to have strong store brands. According to their model, a retailer would provide strong

incentives to favor the store brand initially after its introduction. Given a high quality store

brand, a reasonable fraction of consumers who try the store brand will become loyal to the store

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brand and the resulting store differentiation will improve the long run profitability of the retailer.

Thus a retailer wishing to favor the store brand might use its power to set retail prices to raise the

relative prices of national brands.

We now consider the potential costs of favoring store brands: A retailer who changes the

relative prices of national brands from the short-run profit maximizing price loses profits in the

short-run in the hope of potential “higher” long-run benefits from a strong store brand. However

this higher profit cannot be taken for granted. Second, disfavoring national brands by raising

prices, can lead to short-run losses if consumers switch to competing stores in order to purchase

their favorite national brands at lower prices. Finally national brand manufacturers could retaliate

by substantially lowering advertising and promotional support for the retailer (Hoch and Banerji

1993). The retailer, who needs the advertising and promotional support of manufacturers to grow

the overall demand for the product category in the long run, will tradeoff this cost with gains

obtained by favoring the store brand and disfavoring the national brand.2

In choosing an appropriate strategy of favoring certain national brands and disfavoring

others, in order to support the store brands, the positioning of the store brand needs to be

considered.

Store Brand Positioning

When introducing store brands, retailers may use either a differentiation strategy or an

imitation strategy in positioning the store brands. Examples of a high quality differentiation

strategy where retailers introduce high quality differentiated brands that differentiate them from

the national brands include “President’s Choice” from Loblaw’s in Canada, “World Classics”

from Topco, and “Sam’s Choice” from Wal-Mart. Alternatively, the retailer may differentiate by

offering a white-label generic or a low quality store brand (e.g., A&P’s “ Savings Plus line)

targeted to low quality oriented customers (Hoch 1996).

2 Even though the Robinson Patman act precludes manufacturers from discriminating between retailers, informal

conversations with sales people of consumer goods manufacturers suggests that manufacturers overcome this

problem by offering a menu of trade promotion deals to manufacturers, but given any particular retailer’s

requirements, business practices and cost structure they will choose the deal that the manufacturers want them to

choose.

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The more common strategy however is an imitation strategy, where a retailer introduces a

store brand as a me-too product relative to a popular national brand. (Hoch et al 2000, Hoch

1996, Schmalensee 1978, Scott Morton and Zettelmeyer 2001). This strategy accounts for more

than 50% of the store brand introductions in the grocery industry (Scott Morton, and Zettelmeyer

2001). In our dataset, the retailer Dominicks used the imitation strategy: the national brands it

imitated are sales leaders in their respective market segments and among the largest brands in the

overall cereal market: Cheerios (#1 in sales), Frosted Flakes (# 2 in sales), Rice Krispies (#3 in

sales), Corn Flakes (#5 in sales), Raisin Bran (#6 in sales), and Froot Loops (#10 in sales). Our

focus will therefore be on retailer behavior after they have adopted the “imitation strategy”.3

Retailer behavior towards Imitated and Non-imitated brands

The retailer introducing a store brand can “create room” for the store brand in the market

in several ways: (1) it can provide the best location in the aisles (in the center of the aisle or

besides the best selling brand in the category (Scott-Morton and Zettelmeyer 2001); (2) it can

reduce the promotional activity of national brands and (3) It can decrease the price of the store

brand (Chintagunta, 2002) or (4) it can raise the relative prices of national brands with respect to

the store brand. In this paper, we will focus only on how the retailer uses prices to favor the store

brand.

When retailers imitate national brands with their store brands, the cross price elasticity of

the imitated national brands with respect to the store brands should be very high. However

imitated national brands pull away much more sales from the corresponding store brand with a

price cut rather than vice versa. Given this asymmetry, an effective strategy would be to

“disfavor” (by means of increasing their prices) the national brands that are being imitated by the

3 We use the names of the store brands to identify which national brand it imitated. In some cases, Dominicks used

the same names as the national brands i.e., “Corn Flakes”, “Raisin Bran” and “Frosted Flakes”. For others, it used

different but suggestive names indicating the national brands that it imitates. We treat “Crispy Rice” as an imitator

of “Rice Krispies”. “Fruit Rings” as an imitator of “Froot Loops” and “Tasteeo’s as an imitator of “Cheerios”. In our

empirical analysis, we verify whether our interpretation of these as “imitation brands” is appropriate. We indeed find

that the cross-elasticity between the store brand and the appropriate imitated brand is much larger than with respect

to other brands (see Table 6. Further, imitated national brands pull away much more sales from the corresponding

store brand with a price cut rather than vice versa. This asymmetry in cross-elasticity is consistent with Blattberg

and Wisnewski (1989) and Sethuraman (1995).

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store brands. This idea is consistent with Sayman, Hoch and Raju (2001) who show that when

introducing a store brand, retailers need to target the leading brand in order to maximize profits.

We therefore expect the retailer to disfavor national brands that are being imitated, relative to the

pre-store brand introduction period.

Also as we discussed earlier, the retailer should trade off the benefits from favoring the

store brand with the costs. The long-term losses that can happen due to retaliation from national

brand manufacturers who may withdraw promotional and advertising support, which are

essential to the development of the category itself (Hoch and Banerjee 1993). Such support helps

the whole category because it builds awareness and drives traffic to the store. Loss of such

support can thus hurt the growth of the category at the retailer. Also it would hurt the retailer

relative to other retailers who keep the manufacturer support. In resolving this tradeoff, the

retailer has two options: (1) it may choose not to disfavor the brand that it imitates and continue

to treat it as before the store brand introduction; (2) it may disfavor the store brand, but favor

other national brands that do not directly compete with the store brands. Thus the national brand

manufacturers may “forgive” the retailer for intruding on the imitated brand because of the

benefits to the other national brands. However, given that the imitated brand tends to be the most

popular brand in the category, the effectiveness of the second strategy in avoiding retaliation

may not be very high. Manufacturers may not tolerate even a small loss to their popular brands.

We therefore treat the issue of whether the retailer will disfavor national brands that are being

imitated or favor national brands that are not being imitated as an empirical question.

Retailer pricing behavior towards brands on promotion (featured/displayed)

As we have argued previously, it is possible that in order to facilitate the success of the

store brands, the imitated national brands could be disfavored, less often promoted through

feature and display advertising. Further, even when promoted they may not be offered as large a

discount that accompanied such promotions prior to the store brand introductions. However, as

before, if the threat of manufacturer retaliation is large, we may expect that the retailer may not

disfavor the national brand. However, the retailer may assuage the losses of the manufacturers by

favoring their non-imitated brands (while still disfavoring the imitated brands to support store

brands) in order to reduce the threat of retaliation. Also, if one of the costs that a retailer expects

to incur by supporting store brands is the loss of trade promotions, the retailers may provide

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greater support for trade promoted brands to encourage manufacturers to continue trade

promotions. Our analysis will help to shed light on which of these strategies the retailer will

adopt.

Differences in Retailer Behavior in Different Market Segments

In the previous sub-sections, we discuss the motivation that a retailer has in behaving

strategically and disfavoring certain brands, while favoring others. However, will retailer

behavior be different across different segments or “sub-categories” of products? We believe that

the retailer is likely to engage in strategic behavior in segments or “sub-categories” of products

where the strategic behavior can lead to large payoffs. For example, the retailer may be induced

to behave strategically in larger segments due to the larger payoffs involved. Further, when

pursuing an imitation strategy, a market with higher concentration (or more specifically, where

the leader has a very high share) is potentially more attractive so that it might be relatively easy

to steal share from such a national brand. Also, a retailer might not also find it attractive to price

strategically in segments that are not very price sensitive. We therefore treat large segments,

highly concentrated segments and relatively price-sensitive segments as “attractive segments” for

the retailer to engage in strategic behavior. We expect retailer behavior to change with respect to

national brands in the attractive segments. In contrast, in unattractive segments, one may not

expect the retailer to behave very strategically, because the gains from such strategic behavior

may be minimal. We therefore expect that there will not be changes in the retailer behavior after

the introduction of the store brands in unattractive segments.

3. The Model

As we discussed in the introduction, our goal is to understand how retailers strategically

change the prices of national brands in order to favor the store brand. This requires that we

control for demand and cost effects when analyzing retailer pricing behavior. Accordingly, we

present a model of retailer pricing conditional on an estimated demand model and wholesale

prices. We explain the details of the demand and retailer pricing models below.

Demand Model

Given the large number of products in the breakfast cereal category (over 40), we cannot

model demand using simple specifications such as linear or loglinear models since the number of

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parameters necessary to model cross price effects will be enormous (see discussion in BLP 1995;

Sudhir, 2001a). We use a logit model where utility for a product is modeled as a function of

attributes and heterogeneity in consumer preferences for attributes is modeled using random

coefficients. The specicfication is both flexible and parsimonious and is ideal for our purposes.

Since different stores cater to different demographics and the intrinsic preferences for products

and price sensitivity are a function of demographics, we allow the preferences for product

attributes (distribution of the random coefficients associated with attributes) to be a function of

the empirical distribution of customers in the store’s trading area. Past research has shown

clearly that demographic characteristics of the area served by the store can be associated with the

success of the store brand (Hoch and Banerji, 1993; Raju et al (1995)). This modeling framework

allowing the random coefficients distribution to be a function of demographics for demand is

similar to Nevo (2001). We thus allow for observable heterogeneity as well as unobservable

heterogeneity into our specification of demand.

Since our data is observed at the level of each store, we specify a demand model at the

store level. We observe the data for each store s = 1,..., S of the chain for t = 1,..., T periods of

time. The conditional indirect utility of consumer i for brand j at store s at period t is then given

by:

* * (1)ijst j i i jst j ijstu x pβ α ξ ε= − + +

where xj is the k-vector of observable characteristics, pjst is the price of j at store s at time t, ξj is

the chain-level mean of brand specific valuation of j, εijst is a mean zero error term, and ( )* *i iα β

are k+1 individual-specific coefficients.

We also allow the consumer not to choose any of the J brands; i.e., we treat this non-

purchase option as the outside good whose average utility across individuals is normalized to

zero. Thus we can allow consumers to choose out of the category, if they find the prices are too

high.

Define ( )* * * i i iθ α β= as a k+1 column vector containing the individual-specific

coefficients. We decompose the individual specific coefficients into an observable and

unobservable component as follows: *

1 1 + , ~ (0, ), (2)i i i i kD N Iθ θ ν ν += Π + Σ

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Here θ1 contains the parameters ( ) α β , Di is a vector of demographic variables, Π is a

matrix that measures how the tastes for characteristics vary with observable demographics, Σ is

a scaling matrix, and νi represents the additional unobserved characteristics not explained by the

observed demographics.

Assuming vector ( ) ( )( )2 vec , vecθ = Π Σ and combining equations (1) and (2), we

have:

1 2 ( , , ; ) ( , , , ; ) (3)

, [ , ]'( )ijst jst j jst j ijst j jst i i ijst

ijst j jst j ijst jst j i

u x p x p D

x p p x D

δ ξ θ µ ν θ ε

δ β α ξ µ ν

= + +

= − + = Π + Σ

δjst represent the mean utility from brand j at store s at time t, that does not vary by individual,

while µijst represents the individual level utility that varies across individuals.

Given this utility specification, a utility maximizing consumer i will purchase one unit of

j if for all k ≠ j, if uij > uik. So the probability of an individual i choosing brand j from store s at

time t is given by

However we do not observe individual level purchases, but only aggregate store level

shares. Hence for matching to the observed data, we need to integrate out these individual level

probabilities over the population distribution of the observed (demographics) and unobserved

heterogeneity. Given the population distribution functions of Ds and ν denoted by P*(.) and

assuming independence among these distributions, the market share of j in store s at time t is

given by:

. . 2( , , ; ) *( ) *( ) (5)s

ijst sjst st stD

s x p P dP D dPν

δ θ ν= ∫ ∫

The demand qjst for each store is obtained by multiplying the market share in (4) by the

total potential market Mst of each store. The demand qjst for product j at time t at the store level is

given by:

. . 2 ( , , ; ) (6)jst st jst st stq M s x p δ θ=

Retailer Pricing Equations

exp( ) (4)

1 exp( )jst ijt

ijst

lst iltl

Pδ µ

δ µ+

=+ +∑

12

Dominicks, the chain whose data we analyze, uses zone pricing. Instead of setting prices

for each store separately, they group all their stores into three different pricing zones and select

for each week a unique price for each SKU in all the stores of a given zone. We therefore define

our supply equations at the zone level (denoted by z) rather than the store level.4

A retailer who maximizes category profits for zone z at time t, will maximize the

following objective function.

1

( ) (7)zt jzt jzt jzt ztj

p w s M=

Π = −∑

where j indexes the brands sold, p indicates prices, w indicates wholesale prices, s indicates

shares and M indicates total potential market size in the category.

Suppose this retailer wants to favor or disfavor national brands in order to support the

store brand. We can capture these effects by looking for deviations in prices relative to the

category profit maximizing prices. We operationalize such deviations by augmenting the

category profit maximization objective to have additional weights on the shares of each brand as

follows:

Consider the following “as if” objective function for the retailer at time t, for zone z:

1

[( ) ] (8)zt jzt jzt jzt jzt jzt ztj

p w s s Mφ=

Π = − +∑

where j indexes the brands sold, p indicates prices, w indicates wholesale prices, s indicates

shares and M indicates total potential market size in the category.

The first part of the objective ( ) jzt jzt jzt ztp w s M− is brand profit as before in equation (7).

By summing it over all brands, we capture the category profit. The second term jzt jzt zts Mφ ,

allows the retailer to aid (or suppress) the share of a given brand by placing a weight on that

brand's share φjzt (which needs to be estimated). This interpretation suggests that the retailer

deviates from the single period category profit maximizing price in order to favor or disfavor the

shares of the concerned brands depending on the sign of φjzt. This specification is similar to

4 It is inappropriate to estimate the supply model at the store level when the prices are set at the zone level because

it will make it appear that we have far more degrees of freedom than is warranted in the data. This inflates the

significance of the supply side coefficients by a factor of ZonesStores

##

. Given that we have 90 stores and 3 price

zones, we will inflate significance by a factor of 5.47.

13

Chintagunta (2002) that incorporates an additional weight on the share of the store brand in the

objective function. Our specification is more flexible in that it allows the retailer to strategically

favor/disfavor all brands.

Rewriting the first order conditions with respect to zone prices, the retailer’s price

equation for the brands has the following matrix form:

1 t 2 t t1 1

1 t 1 t 1 t

2 21 t

2 t

1 t

tRetail Price Wholesale Price

. . . .

. . . . . . . .

.

. .

. .

z z Jzzt zt

z z z

zt ztz

z

z

Jzt JztJz

s s sp p psp

sp

p w

p w

p w

∂ ∂ ∂ ∂ ∂ ∂

∂ ∂− ∂ ∂

=

123 123

1

11

2 2

2 t t

t tFavor/Disfavor EffectCategory profit maximizing margin

. . . .

. .

. .

ztzt

zt zt

z Jz

Jzt JztJz Jz

s sp p

s

s

s

φ

φ

φ

− − ∂ ∂ ∂ ∂ 144444424444443

(9)

From equation (9) it is clear that a positive value of φjzt implies prices will be set lower

than the category profit maximizing price and thus “favors” that brand in terms of improving its

market share. A negative φjzt would imply a higher price relative to the category profit

maximizing price in order to “disfavor” the brand.

The Favor/Disfavor Effect

Our primary objective is to understand the factors that affect the favor/disfavor effect

(φjzt). Specifically we look at how favor/disfavor effects are different for imitated brands versus

non-imitated brands and how these differences may vary across the different market segments

before and after store brand introductions. To do this, we include appropriate variables in the φjzt.

For example, if we are interested in understanding how imitated brands will be priced differently

from non-imitated brands, we introduce an imitation dummy. The imitation dummy is set to 1 for

all national brands that are imitated by store brands. To understand the effect of introduction of

store brand on the prices, we introduce a store brand dummy variable. It takes the value of 0

prior to the introduction of the store brand, and the value of 1 after the introduction of the store

brand. To understand how the prices for an imitated brand differ before and after the store brand

was introduced, we introduce an interaction variable: Imitation X Store brand. To identify these

estimates at the segment level, we estimate the different parameters discussed above for each

market segment. Additionally, we use other control variables in φjzt to account for relevant

14

differences before and after store brand introduction One control variable we used was

manufacturer dummies to account for any differences in manufacturer power with respect to the

retailer. We also test for how the retailer responded to promotions by including the promotion

variable. We define zjzt , as a vector of variables related to brand j at pricing zone z, and time t

that explain the favor/disfavor effect.

The effect φjzt is parameterized as:

2 (0, ) (10)jzt jzt jzt jztz Nφ θ ω ω σ= +

The estimates of θ allow us to test our hypotheses about the retailer’s strategic pricing behavior.

Revisiting equation (9), we have data on wholesale prices and retail prices. The category

profit maximizing margin can be computed given the demand elasticities estimated in the

demand equation. Thus the supply side equation enables us to estimate the θ parameters that

describe the retailer’s strategic pricing behavior. It is important to note that we are able to

estimate the θ parameters because we have data on wholesale prices.5

4. Estimation

In contrast to the random coefficients logit models that have been widely estimated using

individual data, our estimation will be using aggregate store level data. To estimate a random

coefficients logit model using aggregate data, we use a Generalized Method of Moments (GMM)

estimation procedure. This procedure was outlined in Berry (1994) and implemented by BLP

(1995) and extended by Nevo (2000).

A key issue in estimating the demand model is the endogeneity problem due to the

correlation between observed prices and the error term ξjzt in the demand model. This is because

any unobservable (to the researcher) characteristics captured in the demand side error term (ξjzt)

that attract or detract customers to the brand will be observed by both the retailer and the

consumer This necessitates the use of instrumental variables (IV) for estimation. Since the error

term ξjzt enters the demand equation non-linearly, we need to transform the equation in such a

way that the error term enters the estimation equation linearly to use IV methods. The

5 This is in contrast to Sudhir (2001b) and Berto Villas-Boas (2002), where the wholesale prices are inferred. In such

models, we will not be able to separately identify retailer strategic behavior as well as wholesale prices.

15

linearization is performed using the contracting mapping procedure outlined in Berry (1994), by

solving for jztδ .

( )1. . 2 = + ln ( ) - ln ( , , ; ) (11)h h

jzt jzt zt ztjzt jzt S s x pδ δ δ θ+

To avoid computing logarithms, we can follow the transformation suggested by Nevo

(2000). wjzt = exp (δjzt). In this way, equation 13 can be rewritten as:

1. . 2 / ( , , ; ) (12)h h

jt jzt zt ztjzt jztw w S s x p δ θ+ =

We then, iterate this equation until it converges. Then, we compute the errors in the

demand side in the form:

2 1 ( ) - (13)jzt jzt jztxξ δ θ θ=

For the supply side, we can compute the margin conditional on the observed share

derivatives (which are a function of demand parameters) *( , , ) jzt jzt jzt tmg w s s

where, st* represents the partial derivatives of shares as a function of prices.

Therefore the supply side error is given by the difference between observed margins and

predicted margins and the “deviations”: * - ( , , ) - (14)jzt jzt jzt jzt jzt jzttMg mg w s sω φ=

Given these error terms we use Generalized Method of Moments (GMM) to estimate the

model. Using the instruments z, which are assumed to be exogenous, and independent of the

error term; Therefore E(zξ) = 0 and E(zω) = 0 are the moment equations. Let ζ = (ξ, ω). and let θ

be the set of parameters to be estimated. Then, the GMM estimator, given the moment

conditions, is defined as.

-1 min ' ( ) ' (15)z z z zθ ζ ζΩ

Where Ω is the standard weighting matrix defined by E(ζζ’).

This algorithm is circular because the weighting matrix Ω is a function of the estimated

parameters θ, and the estimated parameters θ are a function of Ω. We therefore use an iterative

procedure in which we assume some initial values for θ, then we compute Ω. With this value of

Ω, we minimize the objective function (equation 15), and repeat the procedure until convergence

of θ.

16

5. Data

As stated before, we use the cereal category in the Dominicks Finer Foods (DFF)

Database at the University of Chicago for our empirical application. The DFF database is

particularly useful for our research as it has information on the weekly retail margins for the

products. The DFF database consists of data from several stores with different demographic

characteristics that are classified into different price zones that have different retail prices. This

enables us to estimate the demand model with a greater level of richness, because the sensitivity

of prices to the different demographic characteristics can be estimated.

The cereal category from Dominicks is particularly appealing for our purposes for the

following reasons: (1) Store brands are introduced into several segments of this market midway

through the period of our data, thus allowing us to exploit the data for analyzing the impact of

store brand introduction on retailer pricing behavior. Our study covers a period of 52 weeks, and

store brands were introduced during weeks 23-26 of this period. (2) The cereal category consists

of a set of well-defined segments based on previous research (Nevo 2001): (i) Family, (ii) Kids,

(iii) Health and Nutrition and (iv) Taste Enhanced, so it enables us to assess differences in

retailer behavior across segments. By analyzing differences and similarities in the retailer’s

behavior across segments, we can better understand the motivations underlying the strategic

behavior of the retailer (3) Finally, the cereal category is a very important category to the retailer.

It is the second largest category in terms of dollar sales and therefore is a very likely candidate

for strategic pricing behavior by the retailer.

As we discussed in the section on store brand positioning, Dominicks used an imitation

strategy for introducing the store brand. The introduced store brands typically imitated popular

national brands in their respective segments. The six store brands introduced imitated six of the

top ten brands in terms of chain sales. The imitated brands are: Cheerios (#1), Frosted Flakes

(#2), Rice Krispies (#3), Corn Flakes (#5), Raisin Bran (#6), and Froot Loops (#10). To reduce

the computational burden, we only use the top 40 brands (accounting for 67% of the sales of the

category) for our estimation.

Share of Outside Good

While we observe the sales of all the competing brands in the category, we do not

directly see the number of consumers who do not purchase within the category. Using an

17

estimate of the potential market size, we therefore compute the number of consumers exercising

the no-purchase option. We estimate potential market size as follows: We assumed that each

household member can potentially consume one serving per day on 33% of the days. One

serving is estimated as 30 grams (as defined in the Cheerios box). The potential market (in

servings) is obtained by multiplying the number of customer visits in a given week times the

average number of household members for each store, times the percentage of days the consumer

consumes RTE cereal.6

The share of the outside good is given by Quantity Purchased1Market Potential

− .

Instrumental Variables

As discussed earlier, we need to use instrumental variables estimation to estimate the

price coefficient without bias. For instruments, we need to find variables that are correlated with

the price shocks, but are independent of the error term. BLP (1995) consider the average of

product characteristics of competing products as instruments. Sudhir (2001a) uses a similar

average but computes them for each segment. Nevo (2000) uses the average prices of other

regions as instruments for a region’s price, since he used data from multiple markets.

Chintagunta (2001) uses the wholesale price. We use the spirit of the instruments used in the

above papers by using (a) the average price in other price zones, (b) the average price of all

competing products in each segment and (c) the wholesale prices.

6. Results

Descriptive Results

We graph the average retail prices, wholesale prices and margins over all brands during

the period of analysis in Figure 1. As can be seen in this graph, the retail prices and retail

margins tend to increase after the store brand introduction. Does this mean that retailers have

gained more power due to the introduction of the store brand? We cannot conclusively answer

this question without a structural model that separates out the demand, cost and strategic effects

6 We did the analysis with alternative assumptions of 28%, 30%, 40% and 60%. Our results are robust to these

assumptions.

18

as we do in this paper. Nevetheless, we look at more descriptive results to gain additional

insights about the market.

A more detailed segment-wise characterization of the data is provided in Table 1. Here

and in subsequent tables we split the data into three periods: (1) a 22 week pre-store brand

introductory period, (2) a 4 week transition period during which the six store brands were

introduced gradually into different stores of the chain. By this 4 week period, the introduced six

store brands had achieved penetration in 90% of the stores and (3) a 26 week post-store brand

introductory period. We exclude from our estimation the four-week transition period where store

brands are gradually being introduced throughout the chain.

It is evident from Table 1 that there is a sizable increase in retail margins after the store

brands were introduced in all of the segments except the Health/Nutrition segment. However, the

higher retail margin is not just due to an increase in retail prices. In the Family segment, both

retail and wholesale prices fell, but the reduction in wholesale prices was higher than the than the

reduction in retail prices causing retail margins to rise. Thus prima facie, it appears that the

introduction of store brands tend to have given the retailer the ability to increase its retail

margins.

We explore this issue further by separating out the effects by imitated and non-imitated

brands in Table 2. We expected the imitated brands to be treated more unfavorably than the non-

imitated brands and therefore retail margins to increase more for the imitated brands. As

expected, the retail margins increased for the imitated national brands in all segments.

Surprisingly, the retail margins also increased for the non-imitated brands except in the

Health/Nutrition segment. But the percentage changes in markup for the retailer is considerably

higher for the imitated brands, indicating these are being treated more unfavorably than the non-

imitated brands.

The overall increase in retail prices did not lead to a decline in sales in all of the

segments. In fact, the national brand sales increased (as seen in Table 1) overall indicating that

the cereal category was growing during this period. This growing demand should have also

contributed to the overall increase in retail prices.

From Table 1, we see that there are only insignificant changes in the level of promotion

(feature/display) induced sales in the family, health and taste enhanced segments, but there is a

doubling of promotional activity in the post-store brand introduction period in the kids segment.

19

It is interesting from Table 2 that there is a significant reduction in promotions for imitated

brands in the family and kids segments, but a significant increase in promotions for the non-

imitated brands in these segments. Thus it appears there is a big shift in promotions to the non-

imitated brands in these segments.

Insert Tables 1 and 2 here.

It is also instructive to see how the effects of store brand introduction affected each of the

manufacturers. In Table 3, we report the average wholesale prices, retail prices, retail margins

and level of promotion-induced sales by manufacturer. Manufacturers differ in their response to

the store brand introduction. Kellogg’s aggressively reduced the wholesale price of its brands.

This should perhaps be expected considering that five of the six imitated brands are from

Kellogg’s. General Mills, the manufacturer of Cheerios, the other imitated brand, however raised

its wholesale price. All other firms except Quaker also raised its wholesale prices. However

except in the case of Nabisco, the retailer raised the retail prices. This must be especially galling

for Kellogg’s since it had reduced wholesale prices in response to the retailer’s imitation of 5 of

its leading brands.

Further, Kellogg’s and General Mills, the firms that were imitated have a smaller

proportion of their sales when promoted but all the other firms that were not imitated find that

the proportion of their sales when promoted increased after store brand introduction. It is not

easy to separate out why this occurred: Did the manufacturers react by reducing promotional

allowances? Or did the retailer not accept national brands promotions as often as it did before

store brand introduction? Nevertheless it is clear that the store brand introduction reduced the

proportion of the brands on promotion for the largest national brand manufacturers: Kellogg’s

and General Mills. Curiously, they also are the manufacturers who were most imitated by store

brands.

Insert Table 3 here

Demand Side Estimates

Our demand side estimates shown in Table 4 have considerable face validity. The mean

coefficient of price is negative as expected and the standard deviation of the price coefficient is

significant indicating that there is heterogeneity in the price sensitivity of customers. Income

reduces price sensitivity, though this coefficient is insignificant. Fiber has a mean positive

20

coefficient, indicating that the population on average values the health benefits of a fibrous diet

that cereal is touted to be. However there is heterogeneity in the valuations of fiber in cereal. Not

surprisingly, people with higher incomes value a diet with high fiber, but kids do not value this.

The surprising result is the Education Variable; college educated consumers seem to not value

the fiber attribute. On average, the presence of sugar reduces the valuation for the product. Kids

consumed more of the sugary cereals. High income and college-educated consumers also sought

sugary cereals. Perhaps it is these consumers who buy the more expensive sugary cereals for

their kids rather than add sugar at home to reduce inconvenience. As expected we obtained a

positive coefficient on promotion, indicating that consumers value promotions. In particular,

seniors value them highly. The coefficient for the interaction term between price and promotions

is negative. Its magnitude reflects that price sensitivity increased around 33% in the presence of

promotions, a result that is consistent with other research (Van Heerde, Leeflang and Wittink,

2001, Sudhir 2001b).

Prior research (Hoch and Banerji 1993, Dhar and Hoch 1997, Starzynsky 1993, and Hoch

1996) identified several demographic characteristics of the population to be correlated with the

success of store brands. By incorporating these demographic variables and accounting for their

effects on demand, we can now be more certain that our inferences about the strategic behavior

of the retailer are not contaminated by unobserved demand side factors.

Like Chintagunta et al. (2002), we also find that there were no significant differences in

demand parameters before and after store brand introduction. This is not surprising because the

demand parameters we estimate are individual level characteristics, which should not be affected

by store brand introduction. We would not however expect the parameters to be the same if we

had estimated a reduced form linear or log-linear model. However the elasticities do increase on

average. We show the average self-price elasticities of the brands in Table 5 classified by

segments before and after store brand introduction. The average elasticity after the store brand

has been introduced is greater (based on a paired t-test of the difference in estimated elasticities

for each brand, p<0.001), indicating that the introduction of the store brand increased the

elasticity of the category itself. See the histogram of the brandwise price elasticity differences in

Figure 2 indicating that there is an average increase in elasticity after the store brand

introduction. However, what is also particularly interesting is that the elasticities of all brands do

not increase. In fact, for some brands the elasticities decline. This could be due to the fact that

21

some of the price sensitive customers who purchased national brands now completely switch to

the store brand and the national brands are now left with only the less price sensitive customers.

This highlights the flexible nature of the random coefficients logit model that accounts for

consumer heterogeneity. Even though consumer parameters and therefore the logit model

estimates do not change after the introduction of the store brand, there is a fairly rich change in

the pattern of changes in the elasticities.

Insert Table 5 here

We also check the nature of the estimated elasticities in two other ways to assess the face

validity of our estimates. First in Table 6, we check whether the cross-elasticity between

Dominicks store brands and its corresponding imitated brands are higher than its cross-elasticity

with respect to other brands. In fact, for all of the brands except for Corn Flakes, this pattern

holds. For example, the cross-elasticity of Dominicks Cheerios with respect to General Mills

Cheerios is 1.786, considerably higher than the cross-elasticity with respect to the other brands.

However, the cross-elasticity of General Mill Cheerios with respect to Dominicks Cheerios is

considerably lower indicating a considerable asymmetry in the nature of the elasticity. This

asymmetric elasticity between store brands and national brands is well documented in the

literature (e.g., Blattberg and Wisnewski 1989, Sethuraman 1995) and indeed our random

coefficients logit model is able to capture this asymmetry well. The cross-elasticity between

Kellogg’s Corn Flakes and Dominicks Corn Flakes however is not higher than with respect to

other brands. This suggests that Cornflakes is a staple cereal in the consideration set of most

household and therefore it has high cross-elasticity with respect to all other types of cereals, a

finding that is not very surprising with hindsight.

Insert Table 6 here

We also verify whether the general definition of segments that we define have face

validity based on the estimated demand elasticities. Table 7 shows the average cross price

elasticity within and across the segments we define. In general the higher values in the diagonal

provide support for the segment classification used. The only exception is that Health/Nutrition

demand is affected fairly highly by the changes in prices in the family segment. This is primarily

due to the substantial cross-elasticity between Cheerios (which is a relatively healthy cereal and

promoted as a heart-friendly product in the Family segment) and the brands in the

22

Health/Nutrition segment. Removing the impact of Cheerios from the average reduces the

elasticity to .22.

Insert Table 7 here

Estimates of Strategic Behavior

Pricing of Imitated Versus Non-Imitated Brands: No Control for Segment Differences in Behavior

In Table 8, we report our preliminary estimates about the retailer’s strategic behavior

without controlling for segment-level differences in retailer behavior. We control for

manufacturer effects and promotion effects. The promotion effect indicates that retailer prices

are lower when the brand is featured or displayed. This is consistent with the fact that the price

sensitivity of consumers are higher in the presence of features and displays, thus making it

sensible for the retailer to reduce margins when a brand is being displayed or featured.

Our results are as follows: Prior to the introduction of the store brand, imitated brands are

favored and prices are lower than for non-imitated brands as indicated by the negative coefficient

on imitation. This is not particularly surprising if we recognize that the imitated brands are the

most popular brands in the category and low prices on these brands are consistent with a pull

strategy of the retailer.

After the store brands are introduced, the non-imitated brands are disfavored as indicated

by the positive coefficient on the Store Brand. The Store Brand coefficient is only marginally

significant at the 10% level. However the Store Brand X Imitated interaction variable is

insignificant, indicating that in contrast to our expectation that imitated brands will be disfavored

in order to favor store brands, we find no significant effect.

Our hypothesis that retailers will favor store brands by raising the prices of national

brands (especially the imitated national brand) also does not appear to have support in this data.

Thus our results seem to be inconsistent with the result found in Chintagunta et al. (2001) that

store brands will be favored by the retailer, relative to national brand. In fact, what is particularly

surprising is that there is no significant “disfavor” effect on the imitated national brands. The

marginally significant “disfavor” effect we find is on the non-imitated national brands, which are

not in direct competition with the retailer’s store brands.

23

As we had suggested earlier in developing our expectations about retailer behavior, we

expected that the retailer’s behavior might be different across different segments based on the

attractiveness of the segment to the retailer. Specifically, we had suggested that behavior in the

more attractive segments of the market might be more consistent with the strategic support of

store brands, because this is where the strategic behavior might be most rewarding to the retailer,

while the effect may not be significant in the less attractive segments. We now proceed to test

this by investigating if and how the strategic behavior of the retailer is different in different

segments of the market.

Insert Table 8 here

Pricing of Imitated and Non-Imitated Brands: Allowing for Segment Differences in Behavior

To help us assess the segment-wise effects, we report some characteristics of the different

segments in Table 9. We find that the Family and Kids’ segments have the highest share of sales

volume and also the highest concentration. These two segments have 50% more sales and overall

will be more attractive to the retailer. The two segments contribute to 73.7% of the Herfindahl

index of concentration. It is easier to steal share by imitating a popular brand and using an

“against” positioning strategy towards a true market leader. Thus disfavoring the market leader

to make the store brand attractive can work better. Also the second highest share brand can also

be better favored by the retailer in order to assuage the manufacturers and still get promotional

support. These segments are therefore more “attractive” to the retailer in terms of the economic

potential for store brands as well as for strategic behavior by the retailer.

Insert Table 9 here

The coefficients for strategic behavior of the retailer based on our segment-wise analysis

are reported in Table 10. Note that we do not report our demand estimates as these are both

statistically not different and numerically very close to those reported in Table 4. Our estimates

of the coefficients of retailer strategic behavior indicate the following.

First as before, we find that imitated brands are priced lower than non-imitated brands

prior to the introduction of the store brands in all segments except the Kids segment. The

exception in the Kids segment is not surprising when we looked back at what was the imitated

brand in the Kids segment. The imitated brand in the Kids segment was Froot Loops, a premium

24

brand relative to the other Kids brands. This is also indicated by the relatively high price of the

imitated brand in Table 2.

Regarding the effect of store brand introduction on the imitated national brands, we find

that the effects are different across segments. In the Family and Kids’s segments, which have the

largest share of sales and have the highest concentration, the positive coefficient (which is highly

significant) on the Store Brand X Imitated indicates that these imitated brands are disfavored. In

contrast, the imitated brands are neither favored nor disfavored in the non-attractive segments.

This result is consistent with our hypothesis that strategic behavior is more likely to happen only

in the more attractive segments of the market, because the retailer may not pay much attention to

the less attractive segments. The deviation effects are graphically described in Figure 3.

Insert Table 10 and Figure 3 here

We next look at the effect of store brand introduction on the non-imitated national

brands. While the coefficient is not significant in any of the segments, the sign of the coefficient

differs in a significant way across the different segments. In the Family and Kids segments, that

are more attractive for strategic behavior, the negative coefficient on the non-imitated brand

indicates that these brands are relatively favored. In the other segments with smaller sales and

more evenly distributed sales across the brands in the segments, the general effect tends to more

positive but much less significant. At least directionally, this is consistent with our expectation,

that in order to assuage the losses of national brand manufacturers who may retaliate against the

retailer by cutting advertising and other promotional support, the retailer may favor such imitated

brands. However this result is insignificant.

We suspected that the insignificance of some results might be due to the large number of

parameters that we estimate in the segment-level estimation. Given, that we could not increase

the number of observations, we needed to reduce the number of parameters estimated in order to

improve the power of our tests. We therefore decided to estimate a model where we group

segments based on the relative attractiveness to the retailer. Based on our measures of

concentration and market size, we group the Family and Kids segments as Attractive segments

and the Health and Nutrition and Taste Enhanced segments as Non-attractive segments based on

a median split of the segments. These estimates are reported in Table 11.

Our results indicate the following: The pooling of the segments for the purposes of

inferring strategic behavior helped in improving the significance of the coefficients. It is clear

25

now that imitated brands are treated more favorably prior to the introduction of the store brands

in the attractive segments, but less favorably so after the introduction of store brands. In contrast,

the non-imitated brands are treated more favorably after the introduction of store brands in the

attractive segments. This is a really surprising result, because general intuition would have

suggested that the entry of store brands would cause harm to national brands. What we find is

that some national brands will be more supported by the retailer after the introduction of the store

brands. In contrast, there were no significant differences due to store brand introduction on either

the imitated or non-imitated brands in the non-attractive segments. These results are well

summarized in the effects we see in Figure 4.

Insert Table 11 here

The Impact of Promotions on Retailer Pricing Behavior

In Table 2, we see that after the introduction of the store brand there is a significant

reduction in promotions for imitated brands in the family and kids segments, but a significant

increase in promotions for the non-imitated brands in these segments. This by itself could imply

that the retailer is helping the introduction of the store brand by shifting promotions efforts

toward the non-imitated brand. Given that we see such systematic differences in patterns of

promotions after the store brand introduction, we wanted to analyze how the retailer’s pricing

behavior changes when accompanied by promotions. We did this by allowing for the effect of

promotions to vary before and after store brand introduction

We report these results in Table 12. The results indicate that a retailer treats brands that

are promoted more favorably but only after the introduction of the store brand (the negative

coefficient on Promoted X Store Brand). Imitated brands continue to be disfavored in the

attractive segments, indicating that these national brands are being disfavored not only through a

lower frequency of promotions but also through increased prices. However we notice that the

favoring effect for non-imitated brands in the attractive segment (store brand coefficient)

becomes insignificant though it still has the right sign. This is because since imitated brands are

less promoted and non-imitated brands are more promoted, the favoring effect on non-imitated

brands is now captured by the Promotion X Store Brand coefficient. Thus the non-imitated

brands are being favored by means of more frequent promotions accompanied with deeper

discounts. Overall it appears that the retailers provide extra support for promotions after

introducing store brands.

26

The positive and significant coefficient for non-imitated brands in the non-attractive

segments indicates that controlling for promotions these brands are disfavored. However, since

these non-imitated brands are promoted more, and promoted brands are more favored, this

disfavoring effect is neutralized by the benefits from more frequent promotions and overall there

is no significant differences between imitated and non-imitated brands in the non-attractive

segments.

Insert Table 12 here

Robustness Checks

We perform robustness checks to see if the results discussed so far are sensitive to

changes in assumptions and specifications of the model. We tested for an alternative definition of

market potential. Rather than the definition we used in the results discussed, where we assumed

that consumers eat cereals 33% of the time, we tested other percentages such as 28%, 30%, 40%

and 60%. Our results are robust to these alternative assumptions that defined the potential market

size.

Since Kellogg’s had five leading brands imitated, we tested for a change in the impact of

the Kellogg’s effect after store brand introduction. We did not find any significant effect for this

change variable. Thus any change in pricing behavior by the retailer is explained by the store

brand introduction and not due to fundamental changes in the interactions between Kellogg’s and

the retailer.

Chintagunta (2001) suggests the use of store traffic as a proxy for retail competition. If

indeed store traffic proxied for retail competition, then this would imply that on the supply side

retail prices would increase when store traffic increased. Instead our results indicated that retail

prices decline with increase in store traffic. Hence we do not believe that retail competition can

be proxied by store traffic. It is perhaps possible that store traffic is a measure of aggregate

demand and recent literature (e.g. Chevalier et al, 2000) has suggested that increases in aggregate

demand will lead to declines in retail prices. Nevertheless our substantive results about strategic

retailer behavior continue to be the same.

27

7. Conclusion

Summary

In this paper we investigated the strategic behavior of the retailer in enabling the success

of the store brand. Specifically, we investigated how a retailer changes its pricing behavior after

it introduced store brands. We appropriately control for changes in demand and cost conditions

due to the store brand introduction and still identify strategic pricing by the retailer to favor store

brands. Specifically, we adapt the methods used in Berry, Levinsohn and Pakes (1995) and

Nevo (2001) but relax the assumptions of Bertrand behavior used in these papers in order to

investigate the deviations from the category profit maximizing price.

To summarize our results, we found that a naïve aggregate approach that does not

account for segment level differences when inferring the strategic behavior of the retailer can

lead to counter intuitive as well as misleading interpretation of retailer behavior. By carefully

accounting for the effects of how a segment’s attractiveness will impact retailer behavior, we are

able to offer a richer description of how the introduction of a store brand can affect national

brands. Our key results are as follows:

Before introduction of the store brands, the retailer favors the popular brands that are

imitated later by acknowledging the “pull” power of these leading brands in most segments (the

kid’s segment was an exception, since the imitated brand was a premium brand). But after

introduction of the store brand, the retailer disfavors these imitated national brands (relative to

pre-store brand introduction period) by charging higher margins in order to support the store

brands. However the retailer favors non-imitated brands compared to before store brand

introduction. This result is surprising in that we might have expected all national brands to suffer

due to store brand introduction. However this differential behavior occurs in only the “attractive”

segments of the market where such strategic behavior has a greater impact on the profitability of

store brands. In the non-attractive segments, there is no statistically significant differential

treatment of either the imitated or non-imitated national brands before and after the store brand

introduction. We also find that the retailer promotes the imitated national brand less often after

introducing a store brand and further reduces margins when a brand is promoted. Thus the

retailer also disfavors imitated national brands by reducing the extent of promotions (features

28

and displays). The differential is further exacerbated by the fact that promoted brands (which

now tend to be the non-imitated brands) are more favored than non-promoted brands.

Managerial Implications

Our results should be of considerable interest to both researchers and practitioners as

well. Researchers interested in retailing in general and store brands in particular should find

several findings in this paper novel and insightful.

Our analysis indicates that retailers deviate from short-run profit maximizing behavior

both before and after the store brands are introduced. But the deviations are systematically

different before and after the store brand introductions. These results indicate that empirical

research on retailer pricing behavior needs to account for such deviations rather than make the

assumption of single period category profit maximization. Further, researchers studying

passthrough should recognize that extent of passthrough vary systematically for the imitated

brands versus the non-imitated brands. The gains in pass through for non-imitated national

brands in segments where store brands have been introduced are greater than those for the

imitated brands.

These findings have clear implications for practitioners. In the absence of a store brand, a

manufacturer with the most popular brand will have great clout due to the “pull” power it exerts

to get retailers into the store. However when a competing store brand is introduced to imitate

these popular brands, these brands are disfavored. and the non-imitated brands which were

previously not favored are favored. This implies that after a store brand is introduced, the

popular brand (which is imitated) manufacturer may have lower clout and may need to reduce

prices more in order to maintain market share. This is quite the case of Kellogg’s, which reduced

its wholesale prices very substantively to maintain share. However, non-imitated brands may be

able to improve their share without even reducing prices. Our results that non-imitated brands get

lower prices when they are promoted further implies that manufacturers should probably shift

their attention to brands that are not in direct competition to the store brand when promoting

brands.

However while this recommendation is for the short run, the results suggest being more

creative in the long run in terms of its product positioning. Our results indicate that retailers can

use the threat of store brands very successfully against manufacturers when there are strong

29

national brands with large market share from which share can be stolen. With such a threat, it

might be a useful strategy to create a number of variants around the core brand, so that the

retailer would have more difficulty imitating any particular brand and stealing share from such

brands. It would however not be as cost effective for the retailer in terms of shelf space or

manufacturing costs to attack a number of national brand variants. Thus our results provide one

justification for national brand manufacturers to create branded variants.

Coughlan and Choi (2001) have developed a theoretical model of competition between

national brands and private labels. Given the asymmetry in cross-elasticity between national

brands and private labels, they show that national brands will find it useful to invest in brand

asymmetries so as to reduce the comparability of national brands and private labels. Our

empirical results also lead us to recommend that national brands should invest in brand

asymmetry with private labels on the grounds that the retailer strategically disfavors brands that

are comparable to its private label brands.

Limitations and Future Research

We have limited our study to price as the strategic variable for the retailer. The retailer

has control over not only the retail price, but also other marketing mix variables such as shelf

space and position, features, displays and promotions. It could be expected that the retailer may

change its strategy with respect to these variables also so as to facilitate the long-term

penetration of the store brand. Future research needs to address issues related to other strategic

variables. For example, it would be interesting to study how the shelf space is relocated from the

national to the store brands. For example, Hoch, 1996 suggests that because 90 percent of people

are right-handed, the retailer invariably places the store brand to the immediate right of the

leading national brand it is imitating. It would be also important to study how national brands are

compensated by the loss of the shelf space that is taken by the store brands, so that retailers don’t

lose advertising and promotional support for the category

Another limitation of our study is that our research focused on one category. We chose

the RTE cereal category for our study because the presence of several segments allowed us to

test for heterogeneous strategic retailer behavior across multiple product segments. It would be

of interest to see if our hypotheses continue hold in other categories where multiple store brands

are introduced in different sub-segments.

30

Future research also needs to consider what other dimensions other than the imitated-non

imitated and attractive-non attractive dimensions are appropriate in explaining the retailer’s

strategic behavior in setting prices. For example, there are cases where the store brand does not

imitate a national brand. For example “President’s choice” from Loblaw’s in Canada, “World

Classics” from Topco, and “Sam’s choice” from Wal-Mart are not imitations of national brands,

but they are introduced as a high quality differentiated product with respect to national brands. In

other cases (as mentioned by Hoch 1996) the strategy for the low-quality tier is to offer either a

white-label generic or a second store brand (e.g., A&P’s “ Savings Plus line). Understanding

how the retailer’s strategic pricing behavior accompanies the introduction of these brands could

be of interest for both researchers and practitioners.

Summarizing, our paper takes an important step in studying the strategic role of the

retailer in enabling the success of store brands. We tested for conditions under which national

brands will be “disfavored” or “favored” after a store brand is introduced. We find broad support

for our hypotheses, indicating that the long term success of store brands and long term need for

promotional support drive the strategic behavior of the retailer.

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33

Table 1

Descriptive Statistics of the Data by Segments

Segments PeriodWholesale Price ($) Margin ($) Price ($)

Proportion of Brands on Promotion

Weekly Sales ($)

Weekly Sales (Oz)

Family Before SB 0.1393 0.0219 0.1612 12.9% 182,447 1,131,950SB Intro 0.1405 0.0257 0.1661 12.5% 165,048 993,405After SB 0.1295 0.0265 0.1560 12.1% 188,036 1,205,341

Kids Before SB 0.1687 0.0272 0.1959 5.7% 89,794 458,266SB Intro 0.1661 0.0257 0.1918 21.1% 106,156 553,377After SB 0.1689 0.0284 0.1973 11.8% 117,176 593,920

Health Before SB 0.1455 0.0242 0.1697 3.6% 62,155 366,362 & Nutrition SB Intro 0.1544 0.0216 0.1760 7.9% 61,538 349,649

After SB 0.1530 0.0237 0.1767 3.4% 79,132 447,832Taste Before SB 0.1429 0.0207 0.1636 9.1% 43,215 264,135

Enhanced SB Intro 0.1413 0.0261 0.1674 31.2% 54,863 327,736After SB 0.1392 0.0251 0.1643 10.8% 56,839 346,044

34

Table 2

Descriptive Statistics of the Data by Segments and Imitated vs Non-imitated

Segment PeriodWholesale Price ($) Margin ($) Price ($)

Proportion of Brands on Promotion

Weekly Sales ($)

Weekly Sales (Oz)

Family Before SB 0.1490 0.0264 0.1754 7.48% 65,670 374,416

(Non Imitated) SB Intro 0.1499 0.0312 0.1811 21.66% 84,317 465,632

After SB 0.1534 0.0284 0.1818 11.46% 83,154 457,370

Kids Before SB 0.1678 0.0271 0.1949 1.51% 76,326 391,716

(Non Imitated) SB Intro 0.1649 0.0257 0.1906 22.76% 98,190 515,111

After SB 0.1681 0.0280 0.1961 11.85% 106,505 543,139

Health & Before SB 0.1438 0.0242 0.1680 3.85% 55,732 331,701

Nutrition SB Intro 0.1533 0.0212 0.1745 8.74% 55,299 316,838

(Non Imitated) After SB 0.1515 0.0230 0.1745 3.87% 69,655 399,073

Taste Before SB 0.1538 0.0234 0.1773 11.60% 27,751 156,552

Enhanced SB Intro 0.1467 0.0295 0.1762 42.30% 40,466 229,720 (Non Imitated) After SB 0.1510 0.0284 0.1794 8.16% 38,166 212,763

Family Before SB 0.1345 0.0197 0.1542 16.34% 116,776 757,534

(Imitated) SB Intro 0.1322 0.0208 0.1530 5.10% 80,731 527,774

After SB 0.1148 0.0254 0.1402 8.70% 104,882 747,971

Kids Before SB 0.1740 0.0283 0.2024 29.75% 13,468 66,551

(Imitated) SB Intro 0.1820 0.0261 0.2082 0.00% 7,965 38,266

After SB 0.1784 0.0317 0.2101 11.66% 10,671 50,781

Health & Before SB 0.1613 0.0239 0.1853 1.06% 6,422 34,661

Nutrition SB Intro 0.1649 0.0253 0.1902 0.00% 6,240 32,812

(Imitated) After SB 0.1653 0.0291 0.1944 0.00% 9,477 48,759

Taste Before SB 0.1270 0.0167 0.1437 4.70% 15,463 107,583

Enhanced SB Intro 0.1286 0.0183 0.1469 0.00% 14,397 98,016 (Imitated) After SB 0.1203 0.0198 0.1401 16.31% 18,673 133,281

35

Table 3

Descriptive Statistics of the Data by Manufacturers

Manufacturer Period Wholesale Price ($) Margin ($) Price ($)

Proportion of Brands on Promotion

Weekly Sales ($)

Weekly Sales (Oz)

Kellog's Before SB 0.1380 0.0191 0.1571 9.3% 166,218 1,057,937

SB Intro 0.1403 0.0188 0.1592 6.6% 133,706 840,026

After SB 0.1274 0.0228 0.1502 7.9% 166,574 1,108,757

General Mills Before SB 0.1650 0.0281 0.1931 10.4% 158,386 820,254

SB Intro 0.1688 0.0282 0.1970 21.5% 177,432 900,767

After SB 0.1670 0.0304 0.1974 7.4% 191,937 972,452

Post Before SB 0.1322 0.0209 0.1531 7.3% 17,543 114,561

SB Intro 0.1381 0.0298 0.1679 0.0% 22,711 135,290

After SB 0.1446 0.0265 0.1711 17.6% 33,733 197,141

Quaker Before SB 0.1250 0.0234 0.1484 5.5% 25,258 170,192

SB Intro 0.1197 0.0309 0.1507 43.1% 45,738 303,569

After SB 0.1213 0.0253 0.1466 15.2% 35,093 239,388

Ralston Before SB 0.1616 0.0422 0.2038 13.0% 4,414 21,661

SB Intro 0.1825 0.0340 0.2165 0.0% 2,880 13,302

After SB 0.1742 0.0296 0.2038 34.4% 6,812 33,427

Nabisco Before SB 0.1322 0.0282 0.1604 2.5% 5,791 36,109

SB Intro 0.1405 0.0242 0.1646 0.0% 5,139 31,213After SB 0.1435 0.0241 0.1676 7.2% 7,034 41,972

36

Table 4

Demand Parameters

Mean Sigma Income Senior Education (College)

Children (<9)

Constant 0.7332 (0.0329)

-0.4129 (0.6054)

-8.2529 (0.1539)

5.8923 (0.4713)

5.0584 (0.2548)

-

Price -63.6998 (0. 9561)

-1.6965 (1.1296)

1.8722 (1.1446)

- - -

Fiber 0.1327 (0.0020)

0.1062 (0.0393)

0.2765 (0.0058)

-2.8359 (0.5678)

-4.7172 (0.0505)

0.2462 (0.0577)

Sugar -0.1739 (0.0032)

- 0.6429 (0.0130)

- 2.0085 (0.0325)

0.0850 (0.0385)

Promotion 2.3012 (0.9608)

1.2578 (0.6906)

- 7.6352 (0.6080)

- -

Price x Promotion

-23.7340 (5.9455)

- - - - -

37

Table 5

Self-Price Elasticities

Segments Imitated / non imitated

Before SB After SB Average

Family Non Imitated -10.62 -11.34 -11.01

Imitated -8.80 -8.95 -8.88

Kids Non Imitated -11.85 -12.14 -12.01

Imitated -11.70 -12.33 -12.04

Health & Nutrition Non Imitated -10.85 -11.45 -11.18

Imitated -9.14 -10.08 -9.65

Taste enhanced Non Imitated -9.93 -10.26 -10.11

Imitated -7.09 -6.84 -6.95

38

Table 6

Self & Cross-Price Elasticities Store Brands and Imitated National Brands

Table shows % changes in market share of brands in columns due to % changes in prices of brands in rows. D

omin

icks

Ta

stee

o's

Dom

inic

ks C

orn

Flks

Dom

inic

ks

Cris

py R

ice

Dom

inic

ks F

rost

Fl

k

Dom

inic

ks F

ruit

Rin

gs

Dom

inic

ks R

ais

Bra

n

Che

erio

s (G

M)

Cor

n Fl

akes

(K

ello

gg's)

Ric

e K

rispi

es

(Kel

logg

's)

Fros

ted

Flak

es

(Kel

logg

's)

Froo

t Loo

ps

(Kel

logg

's)

Rai

sin

Bra

n (K

ell,

Pst,

GM

)

Dominicks Tasteeo's -8.172 0.061 0.059 0.043 0.000 0.000 0.118 0.069 0.072 0.063 0.000 0.000Dominicks Corn Flakes 0.053 -4.730 0.119 0.176 0.000 0.000 0.046 0.091 0.086 0.072 0.000 0.000Dominicks Crispy Rice 0.053 0.114 -7.538 0.118 0.000 0.000 0.049 0.095 0.091 0.079 0.000 0.000

Dominicks Frosted Flakes 0.050 0.295 0.245 -7.089 0.000 0.000 0.042 0.104 0.103 0.132 0.000 0.000Dominicks Fruit Rings 0.000 0.000 0.000 0.000 -9.104 0.000 0.000 0.000 0.000 0.000 0.338 0.000Dominicks Raisin Bran 0.000 0.000 0.000 0.000 0.000 -5.534 0.000 0.000 0.000 0.000 0.000 0.592Cheerios (GM) 1.786 0.923 0.908 0.676 0.000 0.001 -10.90 0.948 1.024 0.885 0.000 0.000Corn Flakes (Kellogg's) 0.530 0.918 0.833 0.612 0.000 0.000 0.498 -6.248 0.891 0.805 0.000 0.000Rice Crispies (Kellogg's) 0.617 0.962 0.890 0.684 0.000 0.000 0.590 0.930 -10.38 0.808 0.000 0.000

Frosted Flakes (Kellogg's) 0.700 1.016 0.964 0.822 0.000 0.000 0.671 1.137 1.035 -9.325 0.000 0.000Froot Loops (Kellogg's) 0.000 0.000 0.000 0.000 0.961 0.000 0.000 0.000 0.000 0.000 -12.17 0.000Raisin Bran (Kell, Post, GM) 0.000 0.000 0.000 0.000 0.000 1.347 0.000 0.000 0.000 0.000 0.000 -7.850

39

Table 7

Average Cross-Price Elasticities by Segment

Table 8

Strategic Pricing Behavior with respect to imitated/non-imitated brands

GMM objective = 0.001082

Family Kids Health & Nutrition Taste enhanced

Family 0.4624 0.1174 0.3198 0.0405

Kids 0.0771 0.3646 0.0170 0.0031

Health & Nutrition

0.1401 0.0228 0.2904 0.0973

Taste enhanced 0.0110 0.0029 0.1308 0.6071

Variable Coef SD T-stat

Kellogg's -0.032 0.0013 -24.8288

GM -0.0264 0.0011 -23.3684

Post -0.0459 0.0024 -19.2774

Quaker -0.0172 0.0017 -10.1119

Ralston -0.0284 0.0044 -6.4203

Nabisco -0.0313 0.0015 -20.8525

Promotion -0.0377 0.0089 -4.2269

Imitated National Brand -0.0201 0.0024 -8.2497

Store Brand Introduced 0.002 0.0012 1.6775

S B Introduced x Imitated 0.0027 0.0033 0.8086

Main Effect + Control

40

Table 9

Segment Attractiveness

Segments Share of

Segment in Category

Margin (US $) Number of Brands

Contribution to Herfindahl

Index Family 35.4% 0.0246 17 54.6%Kids 25.1% 0.0277 20 19.1%Health/Nutrition 19.7% 0.0237 17 15.3%Taste Enhanced 19.9% 0.0233 25 10.9%Attractive Segments 60.42% 0.0258 37 73.7%Non-attractive Segments

39.58% 0.0235 42 26.3%

41

Table 10

Strategic Pricing by Segments

Coef SD T-stat

Control Kellogg's 0.004 0.0017 2.29

GM 0.0166 0.0018 9.13

Post 0.0032 0.003 1.09

Quaker 0.027 0.0026 10.41

Ralston 0.0089 0.0052 1.72

Promotion -0.0462 0.0106 -4.37

Intercept -0.0322 0.0024 -13.19

Family Imitated National Brand -0.0008 0.0021 -0.38

Store Brand Introduced -0.0014 0.0025 -0.57

S B Introduced x Imitated 0.0053 0.003 1.75

Intercept -0.0429 0.0019 -22.96

Kids Imitated National Brand 0.0118 0.0039 3.01

Store Brand Introduced -0.0037 0.0024 -1.53

S B Introduced x Imitated 0.0162 0.0052 3.14

Intercept -0.041 0.0026 -15.98

Health Imitated National Brand -0.0493 0.0039 -12.78

Store Brand Introduced 0.0023 0.0027 0.86

S B Introduced x Imitated 0.0012 0.0055 0.22

Intercept -0.0322 0.0016 -20.04

Taste enhanced Imitated National Brand -0.0519 0.0057 -9.13

Store Brand Introduced 0.0008 0.0016 0.48

S B Introduced x Imitated 0.0016 0.0077 0.20

By Segments + Control

42

Table 11

Strategic Pricing by Segment Attractiveness

Coef SD T-stat

Control Kellogg's -0.0009 0.0016 -0.57

GM 0.0137 0.0016 8.63

Post -0.0012 0.0027 -0.42

Quaker 0.0221 0.0026 8.61

Ralston 0.0124 0.0059 2.08

Promotion -0.0467 0.0117 -3.99

Intercept -0.0301 0.0017 -17.70

Attractive Imitated National Brand -0.0004 0.0018 -0.20

Segments Store Brand Introduced -0.0055 0.0017 -3.20

S B Introduced x Imitated 0.0113 0.0025 4.52

Intercept -0.0322 0.0015 -22.14

Non-Attractive Imitated National Brand -0.0529 0.0032 -16.48

Segments Store Brand Introduced 0.0015 0.0014 1.06

S B Introduced x Imitated 0.0026 0.0045 0.58

Segments Attractivness + Control

43

Table 12

Strategic Pricing by Segment Attractiveness

(Controlling for Promotional Effectiveness after Store Brand Introduction)

Coef SD T-stat

Control Kellogg's -0.0023 0.0018 -1.25

GM 0.0121 0.0017 7.07

Post -0.002 0.0029 -0.71

Quaker 0.022 0.0027 8.10

Ralston 0.0108 0.0061 1.77

Promotion 0.0046 0.003 1.49

Promot x SB -0.0809 0.0182 -4.45

Intercept -0.0318 0.0018 -17.31

Attractive Imitated National Brand 0.001 0.0017 0.58

Segments Store Brand Introduced -0.0015 0.0012 -1.19

S B Introduced x Imitated 0.0114 0.0025 4.50

Intercept -0.0329 0.0015 -21.79

Non-Attractive Imitated National Brand -0.0548 0.0032 -17.07

Segments Store Brand Introduced 0.0041 0.0015 2.64

S B Introduced x Imitated 0.0033 0.0046 0.71

Segments Attractiveness + Control

44

Figure 1

Average Retail Prices, Wholesale Prices and Margins during Period of Analysis

0 10 20 30 40 50 600.15

0.16

0.17

0.18

0.19

0.2

0.21

0 10 20 30 40 50 600.023

0.024

0.025

0.026

0.027

0.028

0.029

0.03

Retail Price

Wholesale Price

Margin

Weeks Store Brand Introduction

45

Figure 2

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

0

4

8

Differences

Freq

uenc

y

Histogram of Differences in Before and After Elasticities(with Ho and 95% t-confidence interval for the mean)

[ ]X_

Ho

46

Figure 3

Pricing behavior before and after the Store Brand Introduction by Segments (cents/Oz)

Attractive segments

Non-attractive segments

Health and Nutrition

-10

-8

-6

-4

Before SB After SB

imitated non-imitated

Family

-3.5

-3

-2.5

Before SB After SB

imitated non-imitated

Kids

-5

-4

-3

-2

-1

Before SB After SB

imitated non-imitated

Taste enhanced

-10

-8

-6

-4

-2

Before SB After SB

imitated non-imitated

47

Figure 4

Pricing behavior before and after the Store Brand Introduction by Segment Attractiveness (cents/Oz)

Attractive Segment

-4

-3.5

-3

-2.5

-2

Before SB After SB

imitated non-imitated

Non-Attractive Segment

-10

-8

-6

-4

-2

Before SB After SB

imitated non-imitated