Positioning of Store Brands

Serdar Sayman Stephen J. Hoch

Jagmohan S. Raju

April 2001

_______________________________________________________________________________

Serdar Sayman is Assistant Professor of Marketing College of Adm. Sciences, Koç University Rumeli Feneri Yolu, Sariyer Istanbul, 80910, Turkey Tel: 90– 212– 338 1616, Fax: 90– 212– 338 1653 E-mail: ssayman@ku.edu.tr Stephen J. Hoch is John J. Pomerantz Professor of Marketing The Wharton School, University of Pennsylvania, Suite 1400, Steinberg Hall - Dietrich Hall, Philadelphia, PA, 19104 - 6371. Tel: 215− 898 0233, Fax: 215− 898 2534 E-mail: hochs@wharton.upenn.edu

Jagmohan S. Raju is Professor of Marketing The Wharton School, University of Pennsylvania, Suite 1400, Steinberg Hall - Dietrich Hall, Philadelphia, PA, 19104 - 6371. Tel: 215− 898 1114, Fax: 215− 898 2534 E-mail: rajuj@wharton.upenn.edu

Abstract

We consider the retailer’s store brand (SB) positioning problem in a market with two national brand (NB)

competitors. In the context of our model, two brands are assumed to be positioned close to each other if the

“perceptual” distance between the two brands is small, and positioning affects the degree of price competition.

Our game-theoretic model indicates that the optimal strategy for the retailer is to position the store brand as

close as possible to the leading national brand (brand with the highest base level of demand) unless the cost of

doing so is beyond a critical level. Even when facing two equally strong brands, the retailer is better off

targeting one of the national brands rather than adopting a mid-point positioning where the store brand

competes “equally” with the two national brands. Positioning the store brand closer to the leading national

brand increases the cross price sensitivity between the two, and leads to (a) lower wholesale prices from both

the leading (NB1) and secondary (NB2) national brands; (b) higher margins for the retailer; and (c) increased

category demand --- all of which adds up to increased category profit relative to other positioning strategies.

We test the implications of our analysis in three empirical studies. In a field study in two U.S. supermarket

chains, observational data (labeling, package design, color, shelf placement, etc.) showed that the probability

of a national brand being targeted by the store brand is an increasing function of its relative market share. In a

second study we estimated cross price elasticity in 19 categories as a means of assessing inter-brand

competition. In categories with high quality SB’s, the cross-price effects do suggest that the SB and NB1

compete more intensely with each other than with NB2. In categories with low quality store brands, however,

the cross-effects are more inline with the asymmetries in cross-price elasticity reported in previous

price/quality tier research. In a third product perception study, we found that although explicit targeting by

store brands influenced consumer perceptions of physical similarity, it had no influence on consumers’

perceptions of overall or product quality similarity. In fact, the SB was rated as more similar to the lower share

national brands. And so while it appears that retailers do follow a positioning strategy consistent with our

model, it meets with more limited success in changing consumer perceptions and demand side behavior.

1

1. Positioning of Store Brands

Store brands (SB’s) or private labels are created and controlled by retailers. In aggregate

they constitute about 20% of unit sales (IRI 1998) and are among the top three brands in 70%

of supermarket product categories (Quelch and Harding 1996). As is true for any brand,

positioning of the store brand can have an important influence on its performance. Unlike the

manufacturers of the national brands (NB’s), however, the downstream retailer has a different

objective function. Whereas national brand manufacturers position their products to maximize

the profits from their own products, the retailer focuses on maximizing profits from the entire

product category, including profit from store and national brands (Hoch and Lodish 1998). We

model how the retailer should position the store brand to maximize category profits within the

context of a category with two national brands, one of which is stronger. Positioning is

operationalized as the perceptual distance between two brands. Brands positioned closer exhibit

a higher cross-price elasticity. We focus on: (a) whether the store brand should target a specific

national brand or follow an “in the middle” positioning and compete to a lesser degree with

both NB’s; and (b) if targeting is better, which NB should be targeted. Although we take the

retailer’s perspective, a better understanding of store brand positioning strategy is also

important to NB manufacturers who must coexist with store brands.

Schmalensee (1978) noted that store brands often imitate the category leader,

presumably to signal comparable quality at a lower price. Although the demand for the store

brand may increase, the downside is that the demand for the targeted leading national brand

may also decrease. Since the retailer also makes money by selling the national brands, it may

not be optimal to have the store brand specifically compete against the national brand with the

largest customer base and higher margins (Corstjens and Lal 2000). Instead of targeting a

2

national brand that generates substantial profit, adopting a mid-point position where the store

brand competes to a lesser extent with both NB’s may be better. Yet, we often observe retailers

targeting leading national brands. We believe that the answer to this puzzle lies in a better

understanding of how the retailer’s objectives drives the store brand positioning decision.

The product positioning literature usually ignores the retailer. To the best of our

knowledge, there is only one study incorporating the difference in the objectives of the retailer

and the national brand manufacturers into the positioning problem. Tyagi and Raju (1998)

examine the pre-emptive positioning strategies of national brands when there is a national

brand versus a store brand entrant. We focus on the store brand’s positioning problem and

attempt to find the exact optimal location in both the symmetric case, where both NB’s are

equally strong, and the asymmetric case, where one NB is stronger than the other. We adopt a

game theoretic approach and examine a market with two incumbent national brands one of

which is stronger, and a store brand entrant. SB positioning essentially involves choosing the

appropriate perceptual distance between the SB and the NB’s. This distance in turn determines

the degree of price competition between the store brand and each of the national brands. As

such, positioning the store brand closer to one national brand results in a higher cross price

sensitivity between the two.

Our analysis suggests that the retailer should position the store brand close to the

stronger national brand unless the cost of doing so is beyond a critical level. Further, compared

to other prospective strategies, this strategy is more profitable in categories where the leading

national brand is stronger. Our results also reveal that SB targeting of the leading national

brand leads to: (a) lower wholesale prices from both the leading national brand (NB1) and to a

lesser extent the secondary brand (NB2); (b) higher margins for the retailer on national brands;

3

(c) higher profits from the store brand; and (d) increased category demand --- all of which adds

up to increased category profit relative to other positioning strategies.

We test the implications of our analysis in three empirical studies. In a field study in

two U.S. supermarket chains, we gathered observational data (labeling, package design and

color, shelf placement, etc.) regarding the targeting strategies of store brands in various

categories. We found that if the store brand follows a targeting strategy, the category leader

invariably is the target. Further, the probability of a national brand being targeted by the store

brand is an increasing function of its market share relative to its competitors. In a second study

we used store-level data from A.C. Nielsen to examine demand−price relationships in 19

categories and estimate cross-price effects as a means of assessing inter-brand competition. In

categories with high quality SB’s, the cross-price effects do suggest that the SB and NB1

compete more intensely with each other than with NB2 (Bronnenberg and Wathieu 1996). In

categories with low quality store brands, however, the cross-effects are more in line with the

asymmetries observed in price/quality tier research (Blattberg and Wisniewski 1989). In a third

study we collected product perception data. We found that consumers could detect when store

brands targeted a national brand; consumers rated the physical similarity of the store brand and

the national brand to be much higher when the targeting was explicit rather than ambiguous.

However, explicit targeting had no influence on consumers’ perceptions of the overall

similarity or product quality similarity of the SB and NB1. In fact, the store brand was rated as

more similar to the lower share national brands (NB2 and NB3). And so while it appears that

retailers do follow a positioning strategy consistent with our model, it meets with more limited

success in changing consumer perceptions and demand side behavior.

4

2. The Model

We consider a market consisting of two NB’s, each offering one brand sold through a

common retailer. The retailer can introduce a store brand if it results in higher total category

profits for the retailer. Our model extends previous work in this area by allowing the retailer to

also decide how the store brand is positioned relative to the two national brands. For example,

the retailer may choose to position the store brand "in-between" the two national brands, or

may decide to target a particular national brand.

Before describing the model in detail, we discuss why we have chosen this particular

modelling approach. Previous studies of brand competition where there is heterogeneity in

consumer preferences has, by and large, used two distinct modelling approaches.

1. Models such as DEFENDER (Hauser and Shugan 1983) allow for horizontal

differentiation. No brand is uniformly better than the other brands; different consumers

buy different brands because of differences in tastes. Difference in tastes are often

modelled by allowing for different consumer ideal points in a Hotelling type framework

(Hotelling,1929). Competition amongst NB’s can be construed as competition among

horizontally differentiated brands.

2. Moorthy (1985) studies differences among vertically differentiated brands. In this

context, if prices are the same, all consumers prefer the brand with higher quality.

Competition between a SB and a NB is more like competition between vertically

differentiated brands.

Our positioning problem requires us to simultaneously model the competition among NB’s, as

well as the competition between a SB and NB’s. The problem is further complicated by the fact

that the competitive parties reside at two different levels of the distribution channel, with each

5

party maximizing its respective profits. To tackle this complex problem, we utilize a reduced

form modelling approach where heterogeneity in tastes and differences among brands are

captured through a parsimonious demand model well grounded in utility theory.

2.1. Demand Structure without the Store Brand.

The demand for national brand i, denoted by qi, i = 1, 2, is assumed to be as follows:

[ ])(11211

211 pppa

aaq −+−

+= θ (1)

[ ])(12122

212 pppa

aaq −+−

+= θ (2)

where pi is the price of national brand i, ai ∈ (0, 1) is the base level of demand of national brand

i, and θ ∈ (0, 1) is the cross-price sensitivity representing the degree of price competition

between the two national brands. The proposed linear demand function is consistent with utility

maximizing consumers with quadratic utility functions (see Shubik and Levitan 1980). A

different utility function could lead to another demand function; however, market data seems to

be consistent with linear demand (e.g., Brodie and de Kluvyer 1984, Bolton 1989).

The demand structure outlined in (1) and (2) generalizes the demand model used in

Raju, Sethuraman, and Dhar (1995) as it allows the base level of demand of the two national

brands to be different. Overall category demand equals 1 when p1 = p2 = 0 implying that there

is a bound on how much consumers will buy. We also assume that the marginal cost of NB’s to

the manufacturers is 0; so, prices are additional to the marginal cost.

2.2. Demand Structure with the Store Brand.

In addition to the two national brands, we now include the store brand denoted by the

subscript s in (3)-(5).

6

{ }

−+−+−++

= )()(211

11121121

1 pppppaaaa

q ss

δθ (3)

{ }

−+−+−++

= )()(211

22212221

2 pppppaaaa

q ss

δθ (4)

{ }

−+−+−++

= )()(211

221121

sssss

s pppppaaaa

q δδ (5)

where pS is the price of the store brand and aS ∈ (0, 1) is the base level of demand of the store

brand. As in (1) and (2), θ is the cross-price sensitivity between the two national brands. In

addition, δi ∈ (0, 1), the price sensitivity between the store brand and national brand i, captures

the extent to which the store brand competes with national brand i. As we shall discuss later in

more detail, the δi 's are affected by the positioning of the store brand. As in the case with only

two NB’s, overall category demand equals 1 when pl = p2 = pS = 0. However, we will show that

in equilibrium the introduction of a store brand leads to an increase in category volume due to a

lowering of the average price in the category.

Note that (3)-(5) have two price difference terms where as (1)-(2) contain only one price

difference term. The 1/2 outside the weighted sum of the price difference terms in (3)-(5) is a

normalization constant to ensure that the mere addition of another brand does not result in a

higher demand. This normalization also results in a structure where demand is affected by own

price and the difference between own price and the (weighted) average price of the competing

brands in the product category.

To keep the model tractable, we use the same parameter δl in (3) as well as (5), and δ2

in (4) as well as (5) implying that cross-price sensitivities are symmetric. In other words, a unit

price difference between NB1 and the SB has the same effect on NB1 demand as it has on SB

demand. This is consistent with the findings in Sethuraman, Srinivasan and Kim (1999), where

7

cross-price effects, when measured in absolute terms, are by and large equal. We are not,

however, assuming that the elasticities are symmetric because elasticities also depend on the

base level of demand. The linear demand functions impose a particular structure on the effect

of changes in wholesale prices on retail prices as noted in Tyagi (1999). We acknowledge that

other functional forms may lead to different outcomes, a limitation that we hope will be taken

care of in future research.

2.3. Modeling the Store Brand Positioning Decision.

Recall that the parameters δ1 and δ2 in (3)-(5) capture the extent to which the store

brand competes with the two national brands. In our framework, positioning corresponds to

choosing δ1 and δ2 so as to maximize the retailer’s category profits. δ1 and δ2 are determined by

the perceptual distance between the SB and the two NB’s, respectively.

In order to formally model the positioning decision, we assume that brands are located

in an n-dimensional perceptual space. Let f(d) map the distance between the two brands into

cross-price sensitivity. We assume that f(d) has the following characteristics.

1. f(d) should be a non-increasing function of d: This property implies that as the distance between the two brands increases, the cross-price sensitivity decreases. That is, d is inversely related to the δi’s.

2. As d tends to ∞∞∞∞, f(d) should approach 0: This property implies that if the two brands are

positioned very far apart, they do not compete with one another. 3. As d tends to 0, f(d) should approach 1: This puts an upper bound on δi. Recall that the

upper bound on θ is also 1. 4. The same change in d should lead to a greater change in f(d) when d is small: This

property assumes that a unit change in the store brand's position will have a greater impact on cross-price sensitivity when the store brand is closer to a national brand than when it is farther away. For example, imagine a product category characterized by a single perceptual dimension, and a brand is located at the origin. It seems reasonable to expect that a move by a second brand from 5 units of distance away to 4 units of distance would have a smaller impact on price competition than a move from 2 units away to 1 unit.

8

In addition to these four properties, if we restrict f(d) to be monotonic and continuous, then it

follows that f(d) must be a strictly convex function of d. The assumption that f(d) is convex is

easy to justify in situations where NB’s are positioned around clusters of consumer ideal points

and Lee and Staelin (2000) also show that f(d) is convex even when consumer ideal points are

distributed uniformly.1

2.4. Effect of SB Positioning on the Marginal Cost of the Store Brand.

The marginal cost of the store brand can be affected by its intrinsic quality (the

ingredients) and also by packaging, labelling, and the like. While these differences are

important, we combine the two into one marginal cost parameter in our model. Recall that our

model assumes that the marginal cost of the national brands equals zero. For parsimony, we

assume that when the store brand targets a national brand, the marginal cost of the store brand

to the retailer also equals zero. However, when it is positioned in the “middle”, we assume that

the store brand has a cost advantage over the national brands. More specifically, we assume

that the marginal cost of the store brand equals -km when the SB is positioned in the middle.

We also study a case where the cost of targeting depends on which national brand is being

targeted.

2.5. Additional Model Features.

In our model formulation positioning affects cross-price elasticity but is not assumed to

affect the base level of demand. One could argue that as the SB is positioned closer to a NB, its

base level of demand should increase as compared to when it is positioned in the middle

because the NB’s are probably positioned closer to consumer ideal points. On the other hand,

9

Lee and Staelin (2000) point out that the base level of demand may in fact decrease in these

circumstances. We decouple positioning from changes in base level of demand partly because

the effects are not clear, but mainly because we want to partial out the effect of positioning on

base demand and focus primarily on the effect of positioning on cross-price sensitivity and the

resultant effects on wholesale prices, demands, and profits.

Our model ignores store competition. Store competition may lower margins on leading

national brands as stores price these brands competitively to attract new shoppers. Hence, the

additional mileage that a retailer may gain by positioning the SB optimally may be lower as the

retailer margins on NB will be lower due to store competition. On the other hand, store

competition has the potential to increase the power of the NB manufacturers vis-à-vis a

particular retailer. Therefore, the importance of SB positioning as a means to discipline the NB

manufacturer may increase once one takes into account store competition. Overall, store

competition is an important issue but we do not account for it in our model.

2.6. Sequence of Decisions.

The assumed sequence of decisions is as follows.

• Stage 1: The retailer positions the store brand (δ1 and δ2 are determined).

• Stage 2: National brand manufacturers choose their respective wholesale prices w1 and w2 to maximize their respective profits.

• Stage 3: The retailer chooses retail prices p1, p2 and ps to maximize category profits. We assume that the national brand positions are fixed. Although NB’s may prefer to reposition,

it does not happen often even over the long run (Halstead and Ward, 1995). We also do not

account for the effect of other marketing variables such as advertising or personal selling.

1 Assume f(d) is linear in d ∈ (0, ∞). In this case the slope of f(d) has to be negative and finite to satisfy the above

conditions. But then it has to intersect with the d axis. Similarly any strictly concave function has to intersect with the d axis, given the above conditions. Only a strictly convex function can satisfy the four conditions above.

10

3. The Analysis

The analysis consists of two parts. First we consider the symmetric case where we

assume that the base level of demand for all three brands (NB1, NB2, and SB) are all equal

(i.e., we set a1 = a2 = aS = 1). The main purpose of the first part is to study how positioning of

the store brand affects overall retailer profits as well as its components so that one can get a

deeper insight into why positioning strategy is different when adopting a retailer versus

manufacturer perspective. In the second part of our analysis, we consider the asymmetric case

where one of the national brands is stronger than the other. More specifically, we consider the

case when a1 > a2 , aS.

3. 1. Symmetric Brands (a1 = a2 = aS = 1)

To focus only on demand side issues, we first begin by analyzing a scenario where the

retailer does not incur any additional cost when it is positioned to target a national brand. In

other words, we begin by assuming that km =0.

Lemma 1 The optimal store brand position lies on a line segment connecting the two national brands. Proof: See Technical Appendix A.

While the proof is in the Appendix, it may be worthwhile here to outline how this result

is derived. Let Πr denote the retailer's profit. We show in the Appendix that 01

>∂∏∂δ

r , and

02

>∂∏∂δ

r . Any point that is not on the line-segment joining the two national brands is

dominated by a point on the line-segment that joins the two national brands because by moving

to the line segment we can increase at least one of the δi's without reducing the other.

11

We believe that Lemma 1 has important and non-obvious implications. The fact that

retailer profits increase with δ1 and δ2 implies that the retailer gains by increasing the

competition between the store brand and the national brands. While it is commonly believed

that one should position a brand to minimize competition, it does not hold when we are talking

about the retailer. By appropriate positioning of the store brand, the retailer increases the

competition at the retail level. More competition at the retail level lowers NB – which is good

for the retailer. As we shall see later, this basic intuition can also help us further understand the

precise positioning strategy of the SB.

Proposition 1 When the brands are symmetric (a1 = a2 = aS = 1), it is optimal for the store brand to target either one of the national brands than to be positioned elsewhere on the line segment joining the two national brands, as long as f(d) is reasonably convex. Proof: See Technical Appendix A.

The intuition is as follows. The retailer's profits increase as the δi’s increase. However,

from Lemma 1, the optimal position is on the line segment joining the two national brands. If

we were to move the SB on the line segment joining the two national brands, δ1 and δ2 cannot

increase simultaneously. Therefore, the optimal positioning essentially boils down to

determining what is the best combination of δ1 and δ2. Since Πr is symmetric with respect to δ1

and δ2, the solution is either targeting or mid-point positioning. As f(d) is convex, it is best to

increase one of the δi’s to the maximum and the other to its lowest level by targeting one of the

national brands. Note that Proposition 1 depends on the convexity of the distance function. If

f(d) is linear, the optimal position is in the middle – which corresponds to δ1 = δ2 = (1+θ )/ 2.

How much convexity is needed? The answer is that while linear f(d) is not good

enough, only a small degree of convexity produces the results (see proof of Proposition 1 in

Technical Appendix A). The optimal positioning of the store brand is not a continuous function

12

of the convexity of f(d). As soon as the convexity of f(d) increases beyond a critical level, and

this critical level is not that high, it is optimal for the SB to target a NB rather than position at

any other point on the line segment joining the two brands.

The retail and wholesale prices of both national brands decrease with the introduction of

the store brand but the targeted national brand experiences a greater decrease.2 This is

consistent with Halstead and Ward (1995) who report that the most common response of NB’s

to the increasing SB threat is to decrease their prices. The introduction of the SB leads to

increased retailer margins on the NB’s. Furthermore, this increase is larger on the targeted

brand. The profits of both NB’s decline after store brand entry; and the decrease is larger for

the targeted NB. Finally, the equilibrium category demand increases, and demand for both

NB’s decreases with the introduction of the SB because the overall category demand is

relatively inelastic. However, the targeted NB does not lose as much demand as the non-

targeted NB because of its lower equilibrium price. Overall, targeting a NB results in better

terms of trade for the retailer and a more desirable allocation of demand across the three brands

that leads to higher profits – higher than the profits that would be obtained by positioning the

SB anywhere else.

We now allow for SB marginal cost to depend on its positioning strategy. Recall that

the marginal cost of the NB’s is assumed to equal 0. To keep the number of parameters to a

minimum, we capture cost of positioning by assuming that store brand marginal cost is equal to

the marginal cost of the national brands (i.e., 0) when it targets a NB, but it is –km when it is

positioned in the middle. The main result is reported in Proposition 2.

2 The equilibrium expressions are reported in Table A1 in Technical Appendix A.

13

Proposition 2: When the brands are symmetric (a1 = a2 = aS = 1), it is optimal for the store brand to target either one of the national brands, as long as f(d) is sufficiently convex and km is small. Proof: See Technical Appendix A.

If f(d) is more convex, km can be higher, and yet it is optimal for the SB to target a NB. The

maximum value of km (defined as km*) that can be tolerated before it becomes better to position

in the middle, increases as f(d) becomes more convex.

3.2. Asymmetric Brands (a1 > a2 , aS)

Analysis of the symmetric case results in the following key insights:

• The optimal position of the store brand is on the line segment joining the two national brands.

• As long as the distance function is reasonably convex, and the cost of targeting a NB is below a threshold, it is best to target one of the national brands. More convex the distance function, higher is the cost threshold.

As the two national brands were assumed to be symmetric, targeting one is equivalent to

targeting the other. Once we allow one national brand to be stronger than the other as well as

the SB (a1 > a2, aS), we can resolve the issue of whether it is better to target the strong or the

weak national brand. The main result is summarized in Proposition 3. Note that comparison of

profits from targeting NB1 or NB2 is independent of the cost advantage of positioning at the

midpoint.

Proposition 3 If a1 > a2, aS, the retailer's profit is higher if the store brand targets NB1 than when it targets NB2. Proof: See Appendix.

Proposition 3 is consistent with Schmalensee (1978). To understand the intuition, we consider a

specific case where a1 > a2 = aS. We show in the Appendix that targeting the stronger brand

results in lower total national brand demand than targeting the weaker national brand.

14

Furthermore, targeting the stronger national brand results in a lower average wholesale price.

These two effects combined result in lower total manufacturer profits on national brands when

the SB targets NB1 as opposed to NB2. Hence, targeting NB1 leads to a greater profit pressure

on the manufacturers, allowing the retailer to capture some of what is given up by the

manufacturers. Therefore, it is not surprising that we find that, as long as the SB is not too

weak, the retailer’s combined profits from the two NB’s are higher when the strong brand is

targeted as opposed to the weak brand. Furthermore, retailer’s profit from the SB is higher

when it is targeted at the strong brand as opposed to the weak brand.

Table 1 Equilibria in a Category with Asymmetric Brands

Without the Store Brand With the Store Brand

NB1 wholesale price (w1*) 2 2

4 8 32

2

+ +

+ +

θ θθ θ

a

2 4 4

24 32 72

2

( )+ +

+ +

θ θθ θ

a

NB2 wholesale price (w2*) 2 2

4 8 32 2

2

a a+ +

+ +

θ θθ θ

2 6 2

24 32 72 2

2

( )a a+ +

+ +

θ θθ θ

NB1 retail price (p1*) w a1 2

2

1

2 4

*+

+ +

+

θ θθ

)3148(2

428262

*2

22

2221

θθθθθθθθ

+++++++++

+aaaaaw SSS

NB2 retail price (p2*) w a a2 2 2

2 2 4

*+

+ +

+

θ θθ

)3148(2

64482

*2

22

22222

θθθθθθθθ

++++++++

+aaaaaw SS

SB retail price (pS*) )3148(2

482622

22

222

θθθθθθθθ

+++++++++ aaaaa SSS

NB1 demand (q1*) ( )( )

( )( )

1 2 2

1 8 16 62

22

+ + +

+ + +

θ θ θθ θ

aa

)73224)(1(2

)44)(3(2

2

2

θθθθθ++++

+++

Saaa

NB2 demand (q2*) ( )( )

( )( )

1 2 2

1 8 16 62 2

22

+ + +

+ + +

θ θ θθ θ

a aa

)73224)(1(2

)26)(22(2

2

22

θθθθθ

+++++++

Saaaa

SB demand (qS*) )73224)(1(2

2744)1(2 2

2

22

22

2 θθθθθθ

++++++++

+++ SS

S

aaaa

aaa

Retailer’s profit (Πro*, Πr*) q1*(p1*−w1*) + q2*(p2*−w2*) q1* (p1* − w1*) + q2* (p2* − w2*) + qS* pS*

15

Table 1 summarizes the equilibrium expressions for the asymmetric case with the SB

targeting the stronger national brand (note that a1 is set to 1, without any loss of generality). So

far, we have assumed that SB cost is the same whether it targets NB1 or NB2. This is consistent

with our basic assumption that the marginal production cost of NB’s is equal. Yet what happens

if the marginal cost of SB is lower when it targets NB2? Assume that the SB cost decreases to –

k2 < 0 when it targets NB2. What we find is that targeting NB1 is more profitable for the

retailer than targeting NB2, as long as k2 is small. The intuition is straightforward.

It is also of interest to understand how the advantage of targeting NB1 is affected by the

relative strength of the two NB’s. This result is useful from an empirical perspective. Define

∏ sr to be the retailer’s profit when the SB targets NB1, ∏ w

r to be the profit when it targets

NB2, and Π rm is the profit if the SB is positioned at the mid-point between the NB’s.

Proposition 4 The profit advantage of targeting the strong national brand is greater compared to other prospective strategies, if the second national brand is weaker. More specifically:

(i) [ ] .0

2

<∂

∏ ∏−∂a

wr

sr (ii)

∂∂

[ ]Π Πrs

rm

a−

<2

0

Proof: See Appendix.

Proposition 4 states that in categories where NB1 is stronger relative to NB2, targeting the NB1

is more advantageous compared to either targeting NB2 or the mid-point positioning. We note

that we resorted to numerical analysis to prove (ii), since Π rm depends on f(d) (or δm = δ1 = δ2 ).

We varied a2, aS, θ, and δm in relevant ranges and checked the sign of the derivative. Our

numerical analyses also showed that Proposition 4 holds when the cost of SB is lower at the

mid-point or when it targets NB2.

16

In the symmetric case, we were able to show that targeting one of the NB’s is better

than being anywhere else on the line (Proposition 1). In the asymmetric case, while we have

demonstrated analytically that targeting NB1 leads to higher profits than targeting NB2

(Proposition 3), we were not able to show analytically whether positioning the store brand on

the line segment results in lower profits than targeting NB1. Retailer profits from targeting

NB1 or NB2 do not depend on the precise distance function. However, profits from other

positioning choices on the line joining the two national brands do depend on the assumed

distance function, and comparison of mid-point positioning versus targeting is not sufficient.

Unlike the symmetric case, we were not able to derive a general result analytically for the

asymmetric case. Therefore, for the asymmetric case, assuming a number of distance functions

(e.g., f(d) = –exp(d), and f(d) = 1 / (1 + d) satisfy the conditions above), we conducted

numerical analysis, and in each of these cases, it turned out that targeting NB1 results in higher

profits than positioning anywhere else on the line segment joining the two NB’s. Even for the

linear distance function, which corresponds to the limiting case for convexity, targeting NB1 is

optimal unless NB2 is sufficiently strong. This implies that the convexity condition on the

distance function weakens when NB’s are asymmetric.

4. Empirical Studies

In this section we present three empirical studies that test implications of our analysis.

4.1. Study 1: Observational Data on Store Brand Positioning

A key finding in the theoretical analysis is that the store brand should locate next to the

leading national brand (Proposition 2) unless the cost of targeting is beyond a critical level.

Because of differences in cost of targeting, we may not observe this strategy in all product

categories. But we do not have access to marginal cost (and cost of targeting) data. However,

17

Proposition 4 suggests that other things being equal, the relative profitability of targeting NB1

is an increasing function of asymmetries in national brand strength, hence the probability that a

store brand targets the leading national brand should be higher in categories where the leader is

stronger.

4.1.1. Data and Methodology. We collected observational data. In two leading US

grocery chains, two observers collected data regarding the positioning / targeting strategies of

the store brands. To maintain consistency, the product categorization scheme provided in the

IRI Marketing Fact Book (1998) was utilized. 75 categories were randomly selected ranging

from dry grocery to frozen/refrigerated foods and health and beauty aids. Data were eventually

collected for 64 of these 75 categories in one chain (call this Store A) and for 56 categories in

the other (Store B). For the remaining categories, there was no store brand alternative

available. 53 of these categories were common to both stores.

Store brand products are easily identified by their brand names. Observers evaluated the

available extrinsic cues, and judged the positioning strategy of the store brand products. The

specific extrinsic dimensions used in the evaluation were: (i) package design; (ii) labeling /

color; (iii) shelf placement; and (iv) shelf talkers (“Compare and Save” signage) − if any. Prior

research has found that consumers evaluate store brands based on such extrinsic cues

(Richardson, Dick, and Jain, 1994). Each observer independently made a judgment of whether

SB was targeting one particular NB based upon a close match on all four extrinsic dimensions

listed above.3 Observers agreed 85% of the time. When both observers agreed that the store

brand was trying to compete with a specific national brand, the brand name of the targeted

3 The two observers were not aware to the market shares of the national brands and also unaware as to which of

the national brands was the leading national brand in a product category when they collected these data.

18

product was recorded.4 Examples of clear targeting and ambiguous targeting (or not targeting)

are shown in Figure 1.

Figure 1: Examples of Categories with Ambiguous and Clear Targeting

Ambiguous Targeting Clear NB Targeting

4.1.2.Results. In 25 of the 64 (39%) categories in Store A and 18 of 56 (32%)

categories in Store B, the store brand followed a targeting strategy. In the remaining categories,

the store brand either did not follow a targeting strategy or targeted multiple NB’s. Although at

first glance the overall level of targeting seems fairly low, it should be pointed out that the

observers concluded that a SB was targeting a particular NB only if all four criteria were

satisfied. Also, one needs to recognize that it is not that easy for the SB to differentially target

only one NB in cases where the two NB look quite similar.

We identified that in 21 out of 25 product categories of Store A, and in 15 out of 18

categories of Store B, the target was the category leader (identified from the Fact Book). This

finding is consistent with our first prediction that store brands generally target the category

leader if and when they follow a targeting strategy.

To test our second prediction (Proposition 4), we estimated separate logit models for

4 We also repeated the analysis only on the categories where both observers agreed a-priori. The results were

qualitatively similar.

19

Stores A and B based on the following variables:

Targeting Strategy of the Store Brand (TS). We use a dichotomous variable to represent whether the store brand in the category targets the leading national brand or not. Hence, for Store A, TS = 1 for 21 categories, and TS = 0 for 43 categories. The respective numbers for Store B are 15 and 41.

Base Level of Demand for the Major National Brands: We use national market shares as surrogates for base level of demands.5 In the context our model, NB with the higher base level of demand also, by and large, has the higher equilibrium market share. We identified the top two NB’s based on unit volume shares from the Marketing Fact Book for each of the product categories employed. The underlying assumption is that the effect of other/weaker NB on the store brand positioning is limited. MS1 and MS2 represent the unit shares of the highest and second highest share national brands respectively. Number of National Brand in the Category (#NB) and Category Size (M). We include these covariates to take into account other plausible explanations of the retailer’s store brand strategy. One can argue that targeting a specific national brand may not be the optimal if there is a large number of national brands in the category. The number of national brands in the category (#NB) is obtained from the Fact Book by identifying the number of distinct brand names. We do not have a strong prior regarding the effect of the category size. However, incremental profits obtained by the targeting strategy are higher in a large category (although the relative profitability does not depend on the category size). Therefore, in large categories the retailer may formulate the positioning strategy more carefully, and the store brand is more likely to target the leading national brand. Category size (M) is obtained by multiplying the category volume and the average price in the category.

We estimated binary logit models for both Stores A and B. In both specifications, the

log-odds ratio pertains to TS which represents whether the store brand targets the leading

national brand (TS = 1) or not (TS = 0). In the first model we use MS1 as a predictor variable.

However, when there are more than two national brands, relative base strength of the leading

national brand with respect to the secondary brand may be a more realistic measure. MS1/MS2 is

5 Note that the market shares for a particular retailer may be different than the national level market shares. To

make sure this was not a serious issue, we computed correlation in ranks (based on market share) computed at the local level and at the national level for 19 different product categories (see more on these data in Study 2). The correlation across all was 0.5. We then repeated the analysis for only the top five brands and the correlation was XXX. We also had access to data specific to area where one of the two stores used in Study 1 is located, though the data are not from the same time period during which we conducted Study 1. The leading national brands in this market is the same as the leading national brand nationally in XX% of the categories. While shares differ

20

used as a predictor variable in the second model. There is a positive correlation (+0.42 and

+0.73 for Stores A and B respectively) between MS1 and MS1/MS2. M and #NB serve as

covariates. Table 2 shows the results.

Table 2 Results of Study 1

Chain A Chain B

Model 1 Model 2 Model 1 Model 2

Variable Coefficient (p value)

Coefficient (p value) Coefficient

(p value) Coefficient

(p value)

Intercept −2.8797 (0.047)

−2.4232 (0.036)

−4.1662 (0.006)

−2.8421 (0.010)

M: Category size 0.0000 (0.867)

0.0001 (0.299)

−0.0001 (0.389)

−0.0000 (0.733)

#NB: Number of NB’s −0.0692 (0.440)

−0.1526 (0.095)

0.1160 (0.159)

0.0634 (0.401)

MS1: Share of the leading NB 7.9858 (0.012)

7.2619 (0.026)

MS1 / MS2: Relative market share 1.3209 (0.006)

0.6652 (0.052)

In the first model MS1 is the only significant variable for both data sets, and its effect is

in the hypothesized direction. In categories where the market share of the leading NB is higher,

the probability of observing a store brand that targets the leader increases. The effect of

MS1/MS2 is significant in the second model, although the significance is smaller for Store B.

This implies that if the leading national brand is stronger relative to the underdog, it is more

likely to be a target for the store brand. The effect of number of national brands and category

across stores, using national level data on shares in place of store level data in this particular case does not seem to introduce all that much error.

21

size have little impact on store brand positioning.

Estimating separate models for each chain may not be ideal as a number of omitted

variables relevant to both stores may result in correlated errors. We estimated this correlation

and it was 0.32 for the first model and 0.35 for the second model. We therefore estimated a

probit model that allows correlated errors across the two stores. This was done only for the 53

product categories that were common to both stores. We allowed store intercepts and the β

coefficients to be different for the two stores. But it turned out that we could not reject the

hypothesis that the β coefficients are the same across stores and so we estimated a probit with

pooled data model that allows for different store intercepts and correlated errors. The results are

very similar to the ones reported in Table 2.

4.1.3. Discussion. Study 1 provides evidence that if the store brand follows a targeting

strategy, the target is, indeed, the leading national brand. We also found that SB targeting

strategy depends on the (relative) market share of the leading national brand. A limitation of

this first study is that it considered only two grocery chains and may not generalize to other

retailers. More importantly, however, the data do not provide any direct evidence on whether

the targeting strategy has the intended influence on either consumers’ perceptions of store

brands or their buying behavior. A positioning strategy is a means for the retailer, and whether

or not a store brand is successful in competing with the leading brand is still an empirical

question. The next two studies address these issues.

4.2. Study 2: Inferring SB-NB Competitive Relationships from Secondary Data

Our model predicts that it is optimal for the store brand to choose a position closer to

NB1 thereby resulting in greater competition between NB1 and the SB than between NB2 and

the SB or NB1 and NB2.

22

4.2.1.Predicted Pattern of Cross-Price Effects: The three demand equations outlined

in (3)-(5) can be rewritten as follows:

++++−++

= ss

pppaaaa

q22

)22

1(1 121

11

211

δθδθ (6)

+++−+++

= ss

pppaaaa

q2

)22

1(2

1 22

212

212

δδθθ (7)

++−++++

= sss

s pppaaaa

q )22

1(22

1 212

21

1

21

δδδδ (8)

Let us define βij to be the effect of price of Brand i on the demand of Brand j. For example, β1

s

represents the effect of NB1’s price on SB demand. Recall that when the store brand targets

the leading national brand, it follows that δ1=1, δ2=θ. Furthermore, θ is less than 1 and so

δ1 > δ2 = θ. Keeping these in mind, (6)-(8) suggest the inequality relationships summarized in

Column 2 of Table 4.

Table 3 Predicted Pattern of Cross-Price Effects

Our Model

Price/Quality Tier Research

βs1 vs βs

2 > < βs

1 vs β2s > <

βs1 vs β2

1 > < βs

1 vs β12 > <

β1s vs β2

s > > β1

s vs βs2 > >

β1s vs β1

2 > > β1

s vs β21 > >

It is worthwhile to contrast the predictions of our model with the predictions from price/quality

tier research (e.g., Blattberg and Wisniewski 1989; Allenby and Rossi 1991). Assuming that

NB1 is in the top tier, NB2 is in the next quality tier, and the store brand is in the lowest quality

23

tier, price/quality tier research would make the predictions shown in Column 3. In 4 out of 8

cases, our model makes the same prediction as the price/quality tier research and in the other 4

cases the predictions are different. If we assume that NB1 and NB2 are in the same

price/quality tier, then two of the inequalities change to equalities, specifically βs1=βs

2 and

β1s=β2

s.

4.2.2. Data and Methodology: We utilized syndicated sales data from A.C. Nielsen for

19 product categories and 122 retailers operating in the top 50 US markets. In each of the

categories, a store brand was sold by more than 50% of the retailers. For each retailer, the data

base includes brand level information (unit sales, prices, and promotions) on a four week basis

for each category over 30 periods (February 1993 through May 1995). As in Study 1, we

assume that smaller share brands have limited influence on the store brand strategy. To take

into account the effects of other national brands, we combined them into an omnibus “third”

national brand.

We assume that the store brand positioning strategy is determined at the retailer level

and not modified over the 30 four week periods. Therefore we identified the category leader,

the secondary brand, and other national brands separately for each retailer based on unit sales

over the 30 periods. Hence, what we are measuring does not pertain to specific national

brands, but to the leader and the secondary national brand for each retailer. For each of the 19

categories, we estimated demand functions for the store brand, and the leading and secondary

national brands using a linear specification.6 The demand of a product is a function of the retail

prices and sales promotions.

We use the following notation. Let i = 1, 2, and 3 refer to national brands, and S refers

24

to the store brand. The subscript r = 1, ..., 122 refers to the 122 different retailers in the data.

Finally, the subscript t = 1, ..., 30 represents each of the four-week periods over which we have

the data. Our key measures are defined next.

Demand for National Brand i (Qirt). We use equivalent units as the basis for demands. Hence, the demand for a national brand is the pound (or ounce) sales. i = 1 designates the leading national brand based on the sales in retailer r, and i = 2 is the secondary national brand. The demand for national brand i = 3 is the sum of the demands for the other national brand products in the category.

Demand for the Store Brand (QSrt). Likewise, we use the equivalent unit sales of the store brand in the analysis. In the 19 categories employed here, store brands are present in 75% of the retailer-category combinations. Conditional on the presence, average store brand share across categories and retailers is 28%.

Retail Prices (Pirt, PSrt). Retail prices are also based on equivalent units. Equivalent unit prices are obtained by dividing the dollar sales by the number of equivalent units. As is standard when computing prices for brands made up of numerous individual sku’s, the prices are defined as the geometric share average or the Divisia price index of all the UPCs that make up that brand. P3rt is the weighted average price of the other national brands. The prices are effective prices net of promotions rather than regular prices.

Indicator for Sales Promotions (Dirt, DSrt). These dummy variables represent whether the corresponding brand was promoted in the particular retailer and period. Because sales promotions are often accompanied by a price reduction, there is some degree of negative correlation between the sales promotion indicator variables and the corresponding price. However, these simple correlations are less than 0.2 in magnitude. We note that D3rt is the percentage of the promoted brands in the “other brands” basket, since i = 3 consists of multiple products.

Indicator for Retailers (Rr). We included these dummy variables to account for the variation in demand across retailers. Basically Rr = 1 if the data point comes from retailer r, and 0 otherwise. More detailed discussion will be presented below.

We estimate the following models for each of the 19 product categories:

rtrtrtSrtSrtrtrtSrtSr

rrrt DDDDPPPPRQ 3132

121

11

13

132

121

11

111 ααααββββλ ++++++++= ∑ (9)

rtrtrtSrtSrtrtrtSrtSr

rrrt DDDDPPPPRQ 3232

221

21

23

232

221

21

222 ααααββββλ ++++++++=∑ (10)

6 Results using a double-log specification produced similar results.

25

rtS

rtS

rtS

SrtSSrt

Srt

Srt

SSrt

SS

rr

SrSrt DDDDPPPPRQ 332211332211 ααααββββλ ++++++++=∑ (11)

where Q, P and D represent demand, price and availability of sales promotions respectively.

Equations (9), (10), and (11) are estimated separately, and they designate the demands for the

leading national brand, the secondary brand, and the store brand. In the empirical model, the

demand and price terms are normalized with respect to their averages within respective

retailers. This allows us to interpret the coefficients as elasticity estimates. Retailer indicator

variables capture the differences in base levels of demand due to possible differences in

consumer demographics, retail competition etc.

Prior research (Bronnenberg and Wathieu 1996; Dhar and Hoch 1997) has shown that

the quality of the store brand is a key determinant of performance. Therefore we a-priori

divided the categories into high (n = 10) and low quality (n = 9) groups using data from Hoch

and Banerji (1993).7 Our main interest here is the jiβ terms (j = 1, 2, S, and i = 1, 2, 3, S). These

represent the effect of brand is price on the demand for brand j. In order to summarize the

results for the high and low store brand quality groups, we combined the estimates for each

group by weighting each coefficient according to its precision (the inverse of its standard error

squared). In all, we obtained a total of twelve jiβ ’s for each group. To avoid potential

econometric difficulties that could arise due to aggregating across heterogeneous retailers, we

also estimated models at the individual retailer. Due to sparse data (30 observations/retail

account), we identified the five largest multi-market retailers that operated in a minimum of

five different markets (30 periods x 5+ markets=150+ observations/retailer). We estimated the

models in Eq. 9-11 separately for each retailer and included market level dummies.

26

4.2.3. Results: The average adjusted R2 from the regression models is 0.72. Note that

the dependent variables in Equations (9)-(11) are ratios to the average demands. We also

calculated the estimated demands by multiplying the estimations from the above models with

the corresponding averages. The average simple correlation between the actual demands and

the estimated demands is 0.94. The models explain the data quite well thereby providing

support for our assumed demand structure. The price and promotion variables together explain

around 0.25 of the variation.

Table 4: Average Estimated Cross-Price Elasticities in Study 2

Aggregate Results Top 5 Multi-Market Retailers Our

Model Price/Quality Tier Research

High Quality Store Brands

Low Quality Store Brands

High Quality Store Brands

Low Quality Store Brands

βs1 vs βs

2 > < .165=.157 -.024<.090 .237=.226 .068<.110

βs1 vs β2

s > < .165>.117 -.024<.126 .237>.053 .068<.107

βs1 vs β2

1 > < .165>.035 -.024<.035 .237>.023 .068<.073

βs1 vs β1

2 > < .165>.065 -.024<.017 .237>.123 .068<.259

β1s vs β2

s > > .146>.117 .225>.126 .206>.053 .186>.107

β1s vs βs

2 > > .146=.157 .225>.090 .206<.226 .186>.110

β1s vs β1

2 > > .146>.065 .225>.017 .206>.123 .186<.259

β1s vs β2

1 > > .146>.035 .225>.035 .206>.023 .186<.216

The estimated cross-price effects are compared with the main predictions of our model

in Table 4 and as can be seen the pattern depends crucially on store brand quality. All pairs of

coefficients are significantly different from each other (p<.05) except when there is an = sign.

First, let us focus on the cross-price effects for categories with higher quality store brands

(columns 4 and 6 of Table 4). For both the aggregate model and the models for the top 5

individual retailers, the results are reasonably consistent with our model. The only sign

7 Hoch and Banerji (1993)’s measure of store brand quality is based on a survey of retail experts (quality assurance

managers) from 50 leading chains and wholesalers.

27

reversal occurs for the individual models, where β1s < βs

2. The most striking result is that the

effect of SB price on NB1 demand (βs1) is more than both the effect of NB2 price on NB1

demand (β21) and NB1 price on NB2 demand (β1

2), i.e., {row 3, column 4 and 6} and {row 4,

column 4 and 6}. This is consistent with our conjecture that the SB should be positioned closer

to NB1 and is counter to the prediction of the price/quality tier theory. Overall, the effects for

high quality store brands are by and large consistent with the predictions of our model.

It is equally striking that the results for the categories with low quality store brands do

not support our model, and in fact are in line with previous price/quality tier research.

Specifically, prices of both NB’s have a greater impact on SB demand than vice versa.

Moreover, the SB appears to compete more with NB2 than with NB1.

4.2.4. Discussion. Overall, Study 2 offers some limited support for our model. In

categories with higher quality store brands, it does appear that the SB and NB1 compete with

each other to a greater extent than they do with NB2. Such is not the case for the low quality

store brand categories. What might explain the difference? One possibility is that retailers

pursue different positioning strategies depending on the quality of the store brand that they can

procure. When they can buy a store brand that is comparable to national brand quality, they

follow the predictions of our model and position against the leading national brand. When

store brand quality cannot match that offered by the NB’s, the retailer treats the SB as an

inferior good and positions it against the weaker NB’s. Alternatively, let us assume that the

retailer follows the dictates of our model irrespective of store brand quality, always positioning

against the leading NB. The observed results for low quality categories could also arise if the

consumer simply does not accept the position that the retailer stakes out for their SB. In this

case consumers may readily perceive the retailer’s intent to position the SB against NB1 based

28

on extrinsic characteristics but still not accept that the SB offers a similar level of intrinsic

product quality. We address these issues in Study 3.

4.3. Study 3: Store Brand Positioning and Consumer Perceptions

4.3.1. Method. The task required consumers to judge the similarity between the top 3

national brands and the store brand in 8 different product categories. Respondents were 102

primary shoppers in households recruited through a local PTA. The categories were yogurt,

tomato sauce, toilet paper, canned peaches, canned tuna, chocolate syrup, peanut butter, and

skin lotion. The store brands came from two local supermarket chains; 90% of respondents

indicated that they had shopped at both stores within the past year. For each category

respondents saw color pictures of the brands lined up four across the page (see Figure 1) and

then made one of three similarity ratings for each of the {4 choose 2}=6 pairs: (1) “How similar

overall are the following pairs of brands?”; (2) “How similar are the following pairs of brands

in terms of product quality?”; and (3) “How similar are the following pairs of brands in terms

of physical appearance?”. Similarity rating were assessed on a 1=very similar to 7=very

dissimilar scale; type of rating task was manipulated between subjects.

There also were three within subjects manipulations. The first variable was whether or

not the SB targeted one specific NB. Using data from Study 1 we selected four categories

where the store brand clearly targeted one of the NB’s, in all cases here NB1; in the other four

categories, there was no clear target. The second variable was the location of the SB relative to

NB1 in the stimuli . Either the SB was put adjacent to NB1 (NB2, NB1, SB, NB3) or it was

separated (NB1, NB2, NB3, SB). Although this manipulation had absolutely no impact, we

thought a priori that adjacency might increase similarity. Finally, we manipulated the price

differential between NB1 and the SB; either the SB sold at a 15% or 30% discount to NB1.

29

Again, although this variable also had no impact on similarity ratings, we thought that

consumers would be more likely to believe that SB quality was comparable to that of NB1

when the price differential was smaller. To summarize, the overall design was an 3 rating task

x 6 brand pairs x 2 level of targeting x 2 level of location x 2 level of price mixed design where

the four categories for each level of targeting were rotated across the 2 levels of location and

price according to a Latin square. Rating task was a between subjects variable and level of

targeting, price differential, and location were within subjects. We also collected supplemental

information about store brand familiarity, usage and attitudes.

4.3.2. Results. Although the design is complicated, the results are robust, simple, and

therefore easy to interpret. The results are displayed graphically in Figure 2. The six pairwise

similarity judgments are broken down by type of rating task: overall similarity, product quality

similarity, and physical appearance similarity. For purposes of comparing individual means,

the critical range is 0.35. As mentioned previously, there are no significant main effects or

interactions effects due to the price or location manipulations. The key effect is a significant

interaction between rating task, brand pair, and the targeting variable, F10,4794=5.64, p<.0001

The pattern of similarity ratings differ systematically depending on the rating task and whether

SB targeting is explicit or ambiguous. Both the overall similarity and product quality similarity

ratings produce comparable results and do not differ systematically depending on the targeting

variable. Specifically, all of the NB’s are seen as fairly similar (mean of 3.0 for overall and 2.7

for quality similarity). In contrast, the SB is viewed as less similar to each of the NB’s

(mean=4.2), especially NB1 (mean=4.5). The results for overall and product quality similarity

do not differ much whether the SB explicitly targeted NB1 or targeting was ambiguous.

30

Figure 2: Similarity Ratings Depending on Explicit Targeting of NB1 from Study 3

It is only in the case of physical appearance that explicit targeting has any impact.

Specifically, when the SB purposefully targets NB1, consumers readily detect the similarity in

physical appearance (3.0 vs 5.1). In addition, targeting reduces perceived similarity between

the SB and both NB2 (4.9 vs 5.2) and NB3 (4.1 vs 4.7). Finally, because perception data are

influenced by relative context, targeting also reduces perceptions of similarity between NB1

NB NB NB SB3.7

4.0

4.2

1 2 3 3.3

3.5

2.8NB NB NB SB

4.

4.5

4.7

1 2 3 2.9

2.9

2.6

NB NB NB SB3.8

4.2

4.5

1 2 3 3.3

3.1

2.5NB NB NB SB

4.1

4.1

4.7

1 2 3 2.6

2.7

2.1

Explicit Targeting Ambiguous Targeting

NB NB NB SB4.1

4.9

3.0

1 2 3 3.7

4.2

4.3NB NB NB SB

4.7

5.2

5.1

1 2 3 3.5

3.9

3.1

Overall Similarity

Product Quality

Physical Appearance

31

and both NB2 (3.1 vs 4.3) and NB3 (3.7 vs 4.2). Table 5 shows a simplified view of the data

focusing on similarity ratings of SB and NB1 and the average of the similarity ratings of the SB

with NB2 and NB3. Again the 3-way interaction between similarity type, brand pair, and

targeting is significant, F2,1530=6.92, p<.001. Explicit targeting of NB1 by the SB has two

effects: respondents see the SB as more similar to NB1 and less similar to NB2 and NB3, but

only in the case of physical appearance. In contrast, explicit targeting does not produce the

intended effect on perception of overall similarity or product quality, as the SB is viewed as

slightly more similar to the secondary brands than to NB1. This is also the case when the

targeting is ambiguous.

Table 5: Similarity Ratings of the SB with NB1 and NB2/NB3

Explicit NB1 Targeting Ambiguous Targeting Type of Similarity NB1 vs SB NB2 & NB3 vs SB NB1 vs SB NB2 & NB3 vs SB

Overall 4.7 4.3 4.2 3.9 Product Quality 4.8 4.2 4.5 4.0

Physical Appearance 3.0 4.5 5.1 5.0 Note: low numbers indicate greater perceived similarity

4.3.4. Discussion. The overall picture that emerges from Study 3 is as follows. When

the retailer specifically targets the leading national brand, such attempts do succeed to the

extent that consumers easily perceive the proximal position of the SB relative to NB1 in

physical similarity space. Moreover, this positioning tactic also tends to distance both the SB

and NB1 from the other NB’s. However, it appears that consumers interpret this positioning in

a very literal and narrow manner. And so what does not happen is that there is any discernible

carry over from physical appearance space to perceptions of either product quality similarity or

overall similarity. Explicit targeting had no influence on consumers’ perceptions of the SB and

NB1 in terms of overall similarity or product quality similarity. In fact, the SB was rated as

32

more similar to the lower share NB2 and NB3.

5. Conclusions

Retailers are, or at least should be, interested in category profits rather than the profit

from any specific brand. In this paper, we examined how the retailer’s objective function

reveals itself in the optimal positioning strategy of the store brands. Our contribution is

twofold. On the theoretical side, we address the store brand positioning problem. On the

empirical side, we provide evidence that store brands in fact aspire to compete with the

category leader; although there is mixed evidence that they are successful in doing so.

We frame the retailer’s positioning decision as choosing the degree of competition

between the store brand(s) and each of the national brands in the product category. Assuming a

category with two national brands, we find that the store brand should be positioned closer to

the stronger national brand. Targeting is relatively more profitable in categories where the

leading national brand is stronger. We tested the implications of the analysis with three

empirical studies. We provide evidence that store brands indeed target the leading national

brand in the category (Study 1). We also analyze demand-price relationships in 19 categories

and find support for our conjecture that differential positioning leads to greater competition

between the store brand and the leading national brand but only for categories with high quality

SB alternatives (Study 2). We find that even though consumers can readily detect retailers’

efforts to use extrinsic cues to position against the leading national brand, this does not

necessarily translate into consumer perceptions that the SB offers comparable intrinsic quality

(Study 3).

Our analysis indicates that the retailer prefers to have a store brand which competes

heavily with the national brands. The basic premise here is that it can not increase both cross

33

price sensitivities δ1 and δ2 at the same time. The conceptual space in which the positioning

game takes place allows us to represent the tradeoff between δ1 and δ2. In the presence of this

tradeoff, it is better to have a high δ1 than a high δ2. Hence, there is a rationale for the tendency

of store brands to imitate the category leader.

There is empirical evidence that store brands do particularly well in categories with

high concentration (Dhar and Hoch, 1997). Rubel (1995) suggests that store brands do better

because it is easier for consumers to compare the store brand when there is a distinct category

leader. Dhar and Hoch (1997) argue that store brand can pursue a focused positioning strategy

in a concentrated market characterized by less heterogeneity in tastes, and offer an attractive

alternative with a lower price. Our analysis is in line with Dhar and Hoch (1997), and we claim

that the focus of that positioning strategy should be the leading national brand.

One can devise scenarios in which targeting the category leader may not be the optimal

strategy. For example, if the secondary national brand provides a much lower margin than the

leader, the retailer may be better of by diverting the sales of the secondary national brand to the

store brand. Alternatively, in some categories targeting strategy may lead to negative

inferences; consumers may prefer to buy the “real thing” rather than the “lower quality

copycat”. In this case the retailer may elect to make its brand as distinct as possible. It is also

possible that being closer to the customer may help the retailer identify the unfulfilled needs or

a niche market, thus leading to a differentiated product strategy.

34

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