Journal of Business Research 66 (2013) 60–66

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Journal of Business Research

Analyzing the intensity of private label competition across retailers☆

John Dawes ⁎, Magda Nenycz-ThielEhrenberg-Bass Institute for Marketing Science, University of South Australia, GPO Box 2471 Adelaide SA 5001, Australia

☆ The authors thank three anonymous reviewers whothe paper.⁎ Corresponding author. Tel.: +61 8 8302 0592; fax:

E-mail addresses: John.Dawes@marketingscience.infMagda.Nenycz-Thiel@MarketingScience.info (M. Nenyc

0148-2963/$ – see front matter © 2011 Elsevier Inc. Aldoi:10.1016/j.jbusres.2011.07.023

a b s t r a c t

a r t i c l e i n f o

Article history:Received 1 June 2010Received in revised form 1 November 2010Accepted 1 February 2011Available online 26 August 2011

Keywords:Private labelOwn labelNational brandCompetitionDuplication of purchasePrice promotions

Examining how buyers of one private label (PL) in a product category also cross-purchase the private labels ofcompeting retailers in the same category is the focus of this study. Understanding consumer cross-purchasing ofPLs is important to retailers, who use PLs as one tactic to differentiate from other retailers; and important tomanufacturers, who compete against PLs. A higher level of PL cross-purchasing indicates heightened competitiveintensity among the PLs of rival retailers. Results across 27 categories indicate that PLs compete against nationalbrands (NBs) within-store, but also compete against the PLs of other retailers across stores. Heightenedcompetition among the PLs of different retailers occurs in categories with higher purchase frequency; in whichthe average PL price is well below the average NB price; and in categories with higher levels of manufacturerbrand price promotions.

se comments greatly improved

+61 8 83020442.o (J. Dawes),z-Thiel).

l rights reserved.

© 2011 Elsevier Inc. All rights reserved.

1. Introduction

Companies generally brand their products with a national brandlabel or a private label. The owner of a national brand (NB) is generallya producer. Retailers, wholesalers, or distributors own private-label(PL) brands, which are also known as home brands, store brands orown label brands (Bushman, 1993; De Wulf, Odekerken-Schröder,Goedertier, & Van Ossel, 2005). Manufacturers, whom often alsoproduce the national brands that the PLs compete against, producethe PLs. From a marketing mix point of view, the main differencesbetween the two types of brands are advertising support, distributionand price. NBs tend to obtain more advertising support at the nationallevel than do PLs. While retailers who own PLs do advertiseextensively, the advertising support is spread over all the productsin the store, rather than being for the retailer's own specific PL. PLstend to have restricted distribution compared to NB's because theysell in one retail chain (Chen, Narasimhan, & Dhar, 2010). By contrast,NBs sell in multiple retail chains. While some retailers such as Tesco inthe UK have expanded their retail presence such that the availabilityof their PL is arguably comparable to NBs, in general PLs are lesswidely available than NBs. Finally, themajority of PLs are cheaper thanNBs. Both brand types appear next to each other on retail shelves andtherefore they compete for consumers’ choice.

PL brands have witnessed significant growth in the past twodecades, far outpacing the growth of NBs (Baltas & Argouslidis, 2007;Lincoln & Thomassen, 2008). PL growth is particularly strong in Europe(Euromonitor, 2007). In the UK, the focus of this study, grocery marketshare of PLs grew from 39% of sales in 2008 to 41% in 2010 (Marian,2010). PL share is also growing fast in the US (Loechner, 2010).

Retailers usually carry several tiers of PLs to cover the spectrum ofconsumer needs: from value products that compete mostly on price,to premium products that offer the highest quality and unique lines atprices equal or higher than NBs (Kumar & Steenkamp, 2007). All PLscompete against NBs and, given that people shop at different stores,they compete against the PL brands of other retailers. Therefore,research into how PLs compete is important for NBmanagers, but alsomanagers of PL brands and retailers in general.

In order to understand the full picture of PL competition, the studyexamines competition between PLs and NBs within a store, as well asthe competitionbetween thePLsof competing stores. The reason for thisdual focus is that consumers distribute their purchases across differentstores over a time period such as six months or a year (Uncles &Hammond, 1995). The present study analyzes consumer purchaserecords to see how, in a given category, PLs and NBs share consumerswith each other. Specifically, the study examines which brands thebuyers of one retailer's PL buy, when they re-purchase from the samecategory (either at the same retailer, or at another retailer). The studyexamines the extent towhich buyers of one retailer's PL switch to NBs ifthey visit another store, or stick to PLs at the rival chain. The analysisuses multiple categories to identify category characteristics thatheighten or lessen the tendency to cross-purchase PL brands.

This study provides important contributions to the marketingliterature. Even though PL competition has been an area of research

Table 1Overview of major findings concerning PL / NB competition.

Main findings Source

High price substitutability between NBsand PLs leads to higher private labelbrand shares and retailer profits.

Raju, Sethuraman, and Dhar (1995),Morton and Zettelmeyer (2004)

Categories where PLs are most likely tosucceed are those with many NBs, as anintroduction of a PL does not have largenegative impacts on retailer profits fromNBs.

Raju et al. (1995), Morton andZettelmeyer (2004)

Consumers cross-buy NB's and PL'sapproximately in-line with theirrespective market shares

Uncles and Ellis (1989), Ellis andUncles (1991), Bound and Ehrenberg(1997)

PLs compete most effectively when theytarget the leading NB.

Sayman, Hoch, and Raju (2002),Sethuraman and Srinivasan (2002),Morton and Zettelmeyer (2004),

Second tier NBs suffer the most in thePL / NB competition

Pauwels and Srinivasan (2004), Kumarand Steenkamp (2007)

PL share is lower in categories with highNB advertising expenditure

Hoch and Banerji (1993), Dhar andHoch(1997), Morton and Zettelmeyer (2004)

Price cuts on NBs hurt PLs more than PLprice cuts hurt NBs

Blattberg and Wisniewski (1989),Sethuraman (1996), Cotterill and Putsis(2000)

61J. Dawes, M. Nenycz-Thiel / Journal of Business Research 66 (2013) 60–66

for many years, the majority of studies focus on shopper behavior atjust one retail chain. A single-chain focus hampers the ability to detectthe full extent of competition between all NBs and all PLs. The presentstudy considers shopping behavior across many retail chains, whichallows a fuller picture of competition between PLs and NBs. Theimplications from this research are important for marketers of PLs andNBs. First, retailers stock PLs to create a point of differentiation fromother retailers (Ailawadi, Neslin, & Gedenk, 2001; Corstjens & Lal,2000) and build customer loyalty. If consumers engage in cross-retailer PL buying—buying the PLs of multiple retailers in the samecategory in a time period—this suggests retailers are less successful intheir differentiation strategy. In addition, identifying NB-PL competitivemarket structurewill showwhether PL brands take their sales primarilyfrom NBs, or from other PLs. The analysis can therefore clarify the realthreat PLs pose to NB manufacturers in a selected category.

Organization of the paper is as follows. The next section discussesthe literature on PL competition and segmentation. Description of thedata and analysis follows the literature review. Results and discussioncome after the analysis. Implications, limitations and areas for futureresearch conclude the paper.

2. Background

2.1. How do private labels compete?

Consider a consumer in a retailer's store making a purchase. Theconsumer can usually choose among a range of NBs and PLs offered bythat specific retailer. Competition within the store is thereforeprincipally NBs versus PLs. Furthermore, PL brands face restrictedavailability. For example, Tesco private label cola is not available inSainsbury, but NBs such as Coke and Pepsi are available in almost everyretailer—all major chains as well as independents and conveniencestores. Therefore, restrictedavailability should constrain the tendency ofthe PL buyers of one retailer to also buy the PL brands of other retailers.Indeed, examinations of the competitive environment for PLs generallyfocus on the competition betweenPLs andNBs, not PLs against other PLs(e.g., Parker & Kim, 1997; Quelch & Harding, 1996; Steiner, 2004).Table 1 summarizes prominent studies of PL competition.

While PL brands face restricted availability, evidence shows thatshoppers in frequently bought categories shop at multiple stores in aspecified timeperiod (Ellis &Uncles, 1991;Uncles&Ellis, 1989;Uncles&Hammond, 1995). Therefore, consumers encounter the PLs of differentstores at different times. The result is that consumers buy multiple PLbrands (of various retailers) just as they do for NBs. Three studies(Bound & Ehrenberg, 1997; Ellis & Uncles, 1991; Uncles & Ellis, 1989)examined the buying patterns of PL and NB consumers, and found thatPLs share customers with NBs and also with other PLs, approximatelyin-line with their respective market shares. However, there are severalreasonswhy further investigation could enhance the state of knowledgeabout PL-NB competition. First, studies such as Bound and Ehrenberg(1997) reported aggregated results for PL brands—such aggregationmight mask a tendency for PLs to compete more intensely in somecategories and less so in others. A category-by-category analysis couldtherefore reveal specific categories in which PLs compete especiallyintensely. Second, PLs have continued to grow and evolve over the pastten years (Kumar & Steenkamp, 2007), therefore the extent to whichconsumers cross-buy various PL labelsmay have changed. Third, retailerconcentration is now very high in markets such as the UK, with thefive leading retailers accounting for over 50% of all grocery sales(Euromonitor, 2007). Higher retail concentration could lead to moreprivate-label proneness because larger, consolidated retailers can investin PLsmore thanwould occurwith a fragmented retail sectorwithmanysmaller operators. Finally, the rise of retailer loyalty programs(Liu, 2007; Meyer-Waarden & Benavent, 2006; Uncles, Dowling, &Hammond, 2003) could result in more shoppers being private-labelprone because retailers can use those programs to specifically promote

their own PLs (Nies & Natter, 2010). The paper now briefly discussesprivate-label proneness and contextualises research on that topic inrelation to the present study.

2.2. The private-label prone shopper

As the term suggests, a private-label prone shopper buys PL brandsto a greater extent than would be expected given the market share ofthose PL brands. Identifying the characteristics of the PL-prone shopperis one of the oldest research topics in the PL literature, with 26 studiespublished between 1965 and 2004 (Sethuraman, 2006). A rationale foracademic interest in this topic is that since PLs tend to be lowpriced, thepeople who buy them comprise a price sensitive segment. As Baltas(1997, p. 315) states, “the most obvious benefit to consumers affordedby own brands is lower prices”. Indeed, in the majority of the studies inthis area, consumers who buy PLs exhibit higher price sensitivity (e.g.,Ailawadi et al., 2001; Baltas, 1997). However, there is also strongevidence that thosewho buy PLs are equally quality sensitive (e.g., Batra& Sinha, 2000). Several studies (Coe, 1971; Fitzell, 1982) posit that lowhousehold income is a likely indicator of PL proneness. However,empirical results show the counterintuitive opposite—that lowerincome customers buy fewer private-label brands. The reason statedfor this result is that consumers with lower income usually have lowereducation levels and stronger price-quality associations, leading togreater trust in national brands and more receptivity to national brandadvertising. Sethuraman and Cole (1999) find that those with middleincome are most likely to buy PLs. The mixed results about PLsegmentation lead to an opinion in the literature that the direct effectof demographics and psychographics on PL usage is relatively weak(Ailawadi et al., 2001; Baltas & Argouslidis, 2007).

The private-label prone shopper concept implies heightenedcompetition between the PLs of rival retailers. However, the conceptdoes not distinguish between propensity to buy many private-labelproducts from the same retailer, versus propensity to buy the PLs ofdifferent retailers in a time period, such as a year. Specific investigationof shopper propensity to buy the PLs of different retailers will assist inunderstanding the broader idea of private-label proneness, aswell as PLcompetition among retailers.

Therefore, the present study builds on these following points:

■ Some consumers at least, may be private-label prone;■ PL proneness may manifest not only in a heightened tendency to

buy PLs across different categories from one retailer, but to undulypurchase PLs from any retailer the shopper visits;

62 J. Dawes, M. Nenycz-Thiel / Journal of Business Research 66 (2013) 60–66

■ PLpronenessmaybemoreprevalent in certainproduct categories thanin others, thus there is a need for category-by-category examination.

These points lead to two research questions:

RQ1. Do PL buyers at retailer A have a heightened tendency to alsobuy the PL brands of retailers B, C, etc. in a time period such as a year?

RQ2. What category characteristics influence the intensity of cross-retailer PL competition?

3. Method

Themethod is to analyze the cross-purchasing of PLs and NBs over aspecified time period such as a year. The cross-purchase analysisidentifies if buying one PL increases the tendency to buy a differentretailer's PL in the same category. While the focus of the study is PLbrands, the analysis includes NBs as a comparison. The overall approachis purchase duplication analysis: the extent to which buyers of onebrand also appear as buyers of another brand—i.e., are duplicated in theother brand's customer base—in a time period.

Calculated first is the proportion of consumers who buy a brand ina time period (i.e., the brand's penetration). Consumers buy multiplebrands in a time period (Ehrenberg & Goodhardt, 1970), therefore aproportion of the buyers of brand A also buy brand B, C, D and so on.The proportion of A buyers who also buy brands B, C, D is the purchaseduplication for these respective pairs of brands. A widespreadempirical generalization is that the proportion of A buyers who alsobuy brands B, C, D generally falls in-line with the brand size of B, C, D.That is, a brand will share more of its customers with bigger brandsthan with smaller brands (Ehrenberg, Uncles, & Goodhardt, 2004).Likewise, the average proportion of any brand's buyers who also buyany particular brand such as B should be similar.

The duplication analysis highlights exceptions to the generalpurchase-sharing pattern, identifying groups of brands that competemore or less intensely against each other than they do with the rest ofthe market. A partition is the term given to such groups (e.g., Kalwani& Morrison, 1977). Recognizing partitions is important, because salesgains by one brand in a partition will come unduly at the expense ofother brands in the partition. The present study utilizes the purchaseduplication approach to examine how buyers of PLs and NBs also buyother PLs and NBs in the same category, and to identify if PL brandsform a competitive partition.

Table 2Purchase duplication table for colas. TNS data, UK, one-year period.

% Who also bought

NB

Pen D P CC

NB Dt Coke (D) 35 48 44Pepsi (P) 33 52 47Coca Cola (CC) 34 45 45Coke Zero (CZ) 14 70 61 51Ave. duplication 44.9Ave. penetration 29.0D-coefficient 1.5 (DNB–NB

PL Tesco (T) 9 48 50 40Asda (A) 6 48 55 42Sainsbury (S) 4 48 49 41Morrisons (M) 3 45 59 42Freeway–Lidl (F) 3 39 54 41Tesco value (TV) 2 41 49 34Ave. D. 39.5Ave. pen. 29.0D-coefficient 1.4 (DPL–NB

Pen. = Penetration, D = Average Duplication/Average Penetration.a The PL–PL result does not include duplications between Tesco and Tesco value.

A statistic called the duplication coefficient (D-coefficient) identifiesthe extent of competitive intensity. The duplication coefficient iscalculated as average brand duplication / average brand penetration. Aduplication coefficient of 2, for example, estimates the expectedproportion of A buyers who also buy B, is 2 times the penetration ofbrand B. To take the example further, consider four brands A, B, C, andD.The first two brands (A, B) are PLs of competing retailers. Brands C andDrepresent NBs. Next, consider for illustration that the D-coefficient forthe pair of PL brands (% ofA buyerswhobuy B, and vice versa) is 4.0. TheD-coefficients for PL brand buyers who also buy NBs (% of A buyers whobuy C, & D; the % of B buyers who buy C, & D; and vice versa) is 2.0. TheD-coefficient within the group of PLs is twice that of the figure for PLbuyers who also buy NBs. The higher coefficient for PL indicatescompetition between the group of PLs is two times stronger than thecompetition between the PLs and the NBs—given the market shares ofall the respective brands. Note that if a retailer offers multiple PLs in theform of sub-brands (such as Tesco, Tesco Finest and Tesco Value), theduplications between the parent PL and its sub-brands are not includedin the analysis here. The reason for not including the PL–PL sub-brandduplications is that they would inflate the estimate of PL cross-buying.The focus of the study is buying PL across retailers, not within a singleretailer.

4. Data, analysis and results

The analysis covers 27 categories over a 52-week time period. Thecategories are diverse covering food, cleaning, pet care, over thecounter pharmaceuticals, and personal care. The categories also varyconsiderably in terms of purchase frequency, ranging from 50occasions per year for bread, to three occasions per year for batteries,pepper and cough liquid. Diversity in buying frequency addsgeneralizability to the results. TNS provided the data from its UKSuperpanel—a 15,000-member panel in which consumers electroni-cally record their purchases. The panel is geographically anddemographically representative of the UK shopper population. All27 categories in the analysis containmultiple NBs, as well as the PLs ofdifferent retail chains. Using data comprising purchases acrossmultiple retailers is a major advantage over other data sets used inprivate-label research, which usually cover only one retailer (e.g.Hansen, Singh, & Chintagunta, 2006). Some previous studies focus onspecific geographic areas to enable comparison across retailers withlimited geographic presence (e.g., Ellis & Uncles, 1991). This studyanalyzes retailers with national distribution coverage.

PL

CZ T A S M F TV

28 12 9 5 3 3 326 14 11 5 5 5 421 10 8 4 3 4 2

14 9 6 4 4 46.34.5

) 1.4 (DNB–PL)22 22 14 10 8 a

20 32 10 12 12 424 36 18 9 8 419 33 28 12 12 818 25 25 10 11 822 a 10 6 9 10

14a

4.5) 3.1 (DPL–PL)

63J. Dawes, M. Nenycz-Thiel / Journal of Business Research 66 (2013) 60–66

Preparing a duplication table is the first step of analysis. Table 2 isan example. Splitting the brands into two groups, NBs and PLs, is thenext step. The purchase duplications within and across each of thetwo groups form four quadrants: NB buyers who also buy other NBs;NB buyers who also buy PLs; PL buyers who also buy other PLs; and PLbuyers who also buy NBs. Brands are ordered by market share withineach group. Using this tabular method identifies the competitiveinterplay between and within the two brand groups, PL and NB.

To clarify themeaningof the table, considerDiet Coke, thebiggestNBin the cola category. Reading across the row for Diet Coke, the tableshows that over a one-year period 35% of consumers bought Diet Coke.Of those Diet Coke buyers, 48% also bought Pepsi, 44% also bought CocaCola, down to 3% also buying Freeway and 3% also buying Tesco Value.The figures in that row represent the purchase duplications for DietCoke, and they follow a pattern called the Duplication of Purchase Law(Ehrenberg et al., 2004), namely that brands share their customerswithcompeting brands in-line with the size (penetration) of thosecompeting brands.

Turning to the PL brands more specifically, of the consumers whobought Tesco cola, 48% also bought Diet Coke, 50% bought Pepsi, andsmaller proportions bought the other national brands. Tesco cola buyersalso bought other PL brands, for example 22% also bought Asda, 14%bought Sainsbury and so on. The key point is that the buyer of a focalbrand has an expected probability of buying any other particular brand,with the size (penetration) of the other brand largely dictating thatpurchase probability (Ehrenberg, 2000). However, are shoppers whobuy the PL of one retailer more likely to buy another retailer's PL thanwould be expected, given the penetrations of those other PLs? Thepurchase duplication analysis outlined below answers that question.

Duplication (D) coefficients for the PL and NB brand groups identifywhether buyers of one PL unduly buy the PLs of another retailer. Table 1indicates these D-coefficients in bold. There are two D-coefficients forthe purchase duplication within each brand group—one for PL (DPL–PL),and one for NB (DNB–NB). There are also two D-coefficients showing thepurchaseduplicationbetween thebrandgroups (DPL–NB,DNB–PL). For the

Table 3Private label competition in 27 categories. TNS data, UK, one-year period.

Productcategory

PL market sharein this category

Extent of PL partitioning(higher=more intense PLpartitions across retailer)

Cp

1 Porridge 32 2.7a

2 Colas 14 2.2a 33 Nappies 30 1.7a 14 Tea bags 21 1.6a

5 Toothpaste 16 1.6a

6 Baked Beans 47 1.6a 17 Whisky 41 1.5a 18 Yoghurt 36 1.4a 29 L'dy Detergent 21 1.4a 110 Bread 53 1.4a 411 Deodorant 15 1.4a

12 RTE cereal 28 1.3a 213 Margarine 24 1.3a 114 Soup 25 1.2a

15 Prem. ice cream 36 1.116 Fromage 33 1.1 117 Cat food 42 1.1 218 Mineral water 58 1.0 119 Analgesics 63 1.0 120 Toilet tissue 45 1.0 121 Lollies/treats 15 1.022 Frozen pizza 40 1.023 Liquid bleach 60 .9a

24 Pepper 63 .9a

25 Herbs 66 .8a

26 Batteries 30 .8a

27 Cough liquid 30 .8a

Average 37 1.3 1

a Indicates statistically significantly higher (if N1) or lower (if b1) cross-purchasing at the p

Cola category, the D-coefficient for PLs is 3.1. The figure of 3.1 indicatesthat the proportion of PL brand buyerswhobuy another PL in a differentstore is 3.1 times the average penetration for the group of PLs sold atdifferent stores. In contrast, the D-coefficient for PL buyers to also buyNBs is 1.4, in other words 1.4 times the penetration of NBs. The higherD-coefficient for PLs indicates stronger competition between the groupof PL brands than should occur—the rate at which PL buyers buy otherPLs is 2.2 times (1.4×2.2=3.1) higher than expected, given therespective penetrations of the PLs and the NBs. The study uses apermutationmethod to calculate the statistical significance of partitionsas outlined in Appendix A.

Results across the 27 categories (see Table 3) indicate thatheightened competition between the PL brands is common. In someother categories the PL brands exhibit lessened competition—that is, abuyer of one retailer's PL is less likely to buy the PL of another retailerthan would be expected given the overall market share of the PLs in thecategory. These findings suggest there could be category characteristicsthat help explain the intensity of competition seen among the privatelabels from different stores.

5. Explaining the variation in PL competitive intensity

Examined next are three factors that could account for some of thevarying intensity of PL competition across retailers. The three factorsare category purchase frequency, the price relativity between PLs andNBs in the respective category, and the extent of manufacturer brandpromotions.

Firstly, categories with higher purchase frequency are more likely toshow PL partitioning. Examples of more heavily bought categories arecolas and cereal, comparedwith less frequently bought products such astoothpaste or mustard. A higher rate of category purchasing corre-sponds to greater cross-shopping across retailers (e.g., Narasimhan &Wilcox, 1998) because frequent purchasing affords opportunity to shopat multiple stores. The second explanatory factor relates to referenceprice. The traditional appeal of PL brands is their lower price compared

ategoryurchase frequency

Price ratio (PL price asproportion of NB price)1=parity

Proportion of manufacturerbrand volume sold onpromotion

7.0 .38 .357.0 .33 .517.0 .80 .377.0 .60 .476.0 .47 .469.0 .60 .211.0 .86 .405.0 .80 na1.0 .70 .504.0 .88 .119.0 .40 .394.0 .70 .314.0 .66 .236.0 1.0 .425.0 1.0 .502.0 .77 .367.0 .40 .283.0 1.0 .261.0 .50 na5.0 .95 .537.0 1.0 .238.9 .80 .597.0 .70 .143.0 .50 .093.6 .50 .103.0 .75 na2.7 .52 .173 .70 .33

b .05 level.

64 J. Dawes, M. Nenycz-Thiel / Journal of Business Research 66 (2013) 60–66

to NBs (Baltas, 1997). Past prices encountered by the shopper influencereference price (e.g., Hardie, Johnson, & Fader, 1993) and causeresistance to paying prices higher than the referent. If the PL brandstend to sell at a price ratio that is small compared to the average NB inthe category (for example, if the PL price is 60% of the price of theaverage NB), then the shopper who buys PLs at one chain will have alower reference price for the category. Therefore, when that shopperbuys fromthe same category at a rival chain, theywill seek a brand in theacceptable range around their reference price. If the PL of the rivalretailer is in the same range as the shopper's reference level, the rivalretailer's PL has a higher chance of purchase. In summary, categories inwhich PL prices are well below NBs will show heightened cross-retailerPL purchasing. The PL price ratio calculation is:

PL Price Ratio = Average PL price= Average NB priceð Þ

A price ratio of 1.0 indicates parity between the PLs and NBs. Aprice ratio of .7, for example, indicates the average PL price is 70% ofthe average NB price.

Finally, the extent of NB price promotions could heightenprivate-label partitioning. If NBs are frequently price-promoted,consumers become more price sensitive (Kaul & Wittink, 1995) andengage in more deal-to-deal buying (Mela, Gupta, & Lehmann, 1997).Higher price sensitivity could reinforce preference for PLs. The reasonis that prices for PLs are still generally belowNBs, evenwhen the latterare temporarily reduced. Likewise, more deal-to-deal buying couldresult in shoppers choosing among the various NBs for a promoteditem. The outcome is higher rates of purchasing within the group ofPLs and within the group of NBs, but less purchasing across them.

OLS regression tested the explanatory power of these threevariables, namely category purchase frequency, the PL price ratioand manufacturer brand promotions. Table 4 shows the results of theregression.

The regression analysis explains 31% of the variance in PLpartitioning. The three independent variables are all statisticallysignificant at p≤ .05. The results show that PL brands compete especiallyintensely in categories with higher purchase frequency. PL brands alsocompete more intensely in categories where the average PL sells at asmaller proportion of the average NB price. The negative coefficient forPL price ratio indicates a lower PL ratio equates to higher levels of PLcompetition. Third, categories with higher incidence of manufacturerbrand promotions showheightened intensity of competition amongPLsof different chains. By contrast, in product categories with lowerpurchase frequency,where the PL brand's prices aremore in accordancewith NBs, or where there are fewer NB price promotions, the PLscompete more against NBs and not as intensely against each other.

6. Summary and implications

This paper examines the competition between PLs and NBs in 27consumer goods categories in the UK. The findings indicate there are

Table 4Second-stage regression.

B S.E t-ratio p

Dependent variable:PL partition strength

Independent variables:Intercept 1.28 .29 4.5 .00Purchase frequency(average purchase occasions in 12 months)

.014 .007 2.1 .05

Price ratio of PL to NB −.82 .34 −2.4 .02% of NB volume sold on promotion 1.17 .48 2.4 .02Adjusted R2 .31

categories in which many buyers of the PL of one retailer are as likely,or more likely, to also buy the PL of another retailer in the samecategory—than would be expected given the overall popularity of thatother PL. The fact that a shopper must buy the other PL in a differentretailer does not necessarily dampen the probability theywill buy thatPL in the course of a year. In categories that are purchasedmore often,where the PL brands are typically well below the price of the NB's, andinwhich there are frequent NB promotions, competition is heightenedbetween the PLs of different retailers.

The results provide important managerial and academic implica-tions. First, the findings imply that competition between PLs and NBsoccurs across multiple stores the shopper buys from in a one-yearperiod. Therefore, researchers who conduct future studies on PLcompetition or PL image should obtain data across stores, and not justwithin a store. Looking at competition narrowly by focussing on oneretailer hampers the ability to detect the full extent of competitionbetween all NBs and all PLs. Yet, the majority of retailers still rely onnarrow one retailer information when they mine their loyalty programdatabases. A better comprehension of market structure and buyerbehavior based on PL competition across stores would help managersconsider appropriate competitors while mapping out their ownstrategy.

Another implication is that since PLs compete more intensely withother PLs than with NBs in some categories, PL growth may sometimeshurt other PLsmore so thanNBs. This result highlights the importance ofPL marketers looking at competition between the two types of brandsacross stores, and regarding PLs from other retailers as close compet-itors. Likewise, for the manager of a NB worried about the growth of aparticular PL, the results indicate those category characteristics inwhichother PLs could bear the brunt of the particular PL's growth, rather thanNBs.A further implication forNBowners concerns thefinding thatmanyconsumers confine their purchases to PL brands in categories wherethere are frequent NB promotions. Therefore, frequent promotionsmaynot be a good way for NBs to recover market share from PLs.

The fact that inmany of the studied categories, consumerswill buy aPL regardless of the store the PL belongs to implies many PLs do notcreate exceptional store loyalty. This finding therefore supports pastliterature (e.g., Ailawadi, Pauwels, & Steenkamp, 2008; Kumar &Steenkamp, 2007) and extends that literature by explaining whichcategory characteristics impact on PLs’ ability to differentiate retailers.PLs in categorieswith lowerpurchase frequency,with a largerprice ratiorelative to national brands, andwith lower levels of NBpromotions havehigher potential to differentiate a store from competitors. Therefore,retailers who want to use PLs as a way to increase their store loyaltyshould invest in good quality, higher priced PLs, in less frequentlybought categories such as laundry detergent or shampoo. PLs that aremuch cheaper than NBs, especially in frequently bought categories, canattract value-driven, cherry-picking customers, whowill buy a cheap PLanywhere they can find it. As Corstjens and Corstjens (1999, p. 204)note, shoppers are indifferent between low-price PLs in frequentlybought, commodity-like categories. PLs in categories that requirepurchases several times a week, such as bread, milk, vegetables ormeat are not likely to build store loyalty because consumers usually buythose goods on fill-in shopping trips, which are likely to occur in severalstores. However, having PLs in such categories helps to create storetraffic, which is another important retailer objective.

Thefinding that PLsmay fail to create high store loyalty alsoprovidesa recommendation for the PL branding strategy best suited to increasethe link between a PL and its retailer: use the retailer's nameexplicitly inthe PL brand name. Even though using a store name in the name of a PLmay be risky, such a strategy allows differentiation of the PLs of oneretailer from another. The retailer's name also provides a link for theconsumer to associate a PL to a particular retailer or store (as per Kumar& Steenkamp, 2007). Furthermore, any advertising activity for theretailer / storemay transfer to the PLs if the retailer uses the same brandname in its PL name.

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7. Limitations and future research

The study used a sizable and diverse set of 27 product categories,however the categories are all fast-moving consumer goods. The limitsof this sample mean the study is not generalizable to broader marketcontexts in which PLs are common, for example clothing, prescriptionmedicine or home-ware. Another limitation is that the data all comefrom one geographic market, namely the UK. Replication across othersectors and countrieswill establish soundgeneralizations.Multi-retailershopping similar to the UK is found in China (Uncles & Kwok, 2009) andJapan (Keng, Uncles, Ehrenberg, & Barnard, 1998) indicating general-izable results about PL competition are possible.

Another limitation is that the study did not examine partitioningbetween the very cheap value ranges of PLs such as Tesco value, butrather focused on the mid-tier PL brands. Given the currentthree-tiered PL price/quality strategy in the majority of retailers(Geyskens, Gielens, & Gijsbrechts, 2010), future research couldinvestigate if price-quality tiers affect PL partitioning across stores.

Next, analysis of additional categories and variables such asadvertising intensity or quality differences between PL and NB couldaccount for more of the variation in PL partitioning. Finally, the effectof complementary purchases on cross-retailer PL buying is anintriguing avenue for future research. For example, are there shoppercharacteristics such as heavy-basket or value-conscious buying thatcorrespond to higher cross-buying of PLs across retailers? Insightsinto shopper or basket characteristics would add to research byAilawadi et al. (2008) and Hansen and Singh (2008) that find heavystore brand buyers (thosewho buy private labels for many categories)are not store loyal. Knowing the composition of shopping basketswould help identify individual-level factors contributing to highercross-buying of PLs across stores.

Appendix A. Statistical testing

Consumers buy multiple NBs and PLs in a time period so thepurchase record data used in the study are multinomial. While thepurchase duplication tables resemble a contingency table, usuallyanalyzed using log-linear methods, the multinomial characteristicmakes the data unsuitable for log-linear models (e.g., Agresti, 2002).Therefore, the statistical significance of partitions is assessed using apermutationmethod, building on anapproachproposedby Loughin andScherer (1998). Specifically, a visual basic program repeatedly drawsrandomsamples fromanextremely largepopulation. Eachdraw is of thesame sample size, number of brands, and brand penetrations as theproduct categories used in the present study. Purchase duplicationsbetween the PL andNBbrands are set as equal to test the null hypothesisof no difference between the groups of PL and NB brands. The programrecords the purchase duplication between PL and NB brands for eachsample. Over a series of 10,000 iterations, the procedure reveals theprobability of observing differences in the purchase duplicationcoefficients of a particular magnitude. Where the difference in theduplication coefficients is less than 5 percent likely to occur due torandom sampling variation, the difference is statistically significant.

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