A decision tree approachto modeling the private label

apparel consumerMarguerite Moore

Department of Textile and Apparel Technology Management,College of Textiles, North Carolina State University,

Raleigh, North Carolina, USA, and

Jason M. CarpenterDepartment of Retailing, University of South Carolina,

Columbia, South Carolina, USA

Abstract

Purpose – The purpose of this paper is to profile the private label apparel consumer usingdemographic and behavioral predictors. The paper also aims to examine cross-shopping behaviorsamong purchasers of private label apparel across the five top US private label apparel retailers.

Design/methodology/approach – Decision tree analysis is used to model the impacts ofdemographics and behaviors on private label purchasing. A secondary database (n ¼ 1,289) of USprivate label purchasers provides data for the analysis.

Findings – Findings indicate demographic predictors as important drivers of private label apparelpurchase among retailers positioned as providers of value, while behavioral drivers are more commonamong patrons of retailers that are differentiated on service or brand. Cross-shopping is more commonamong the retailers positioned on value.

Research limitations/implications – The research design provides a profile of the private labelconsumer but does not explain why this consumer chooses private labels over national brands. Thedata-mining approach provides an innovative tool for identifying the drivers of private labelconsumption. Future research should investigate these drivers more deeply, to establish a fullerunderstanding of this consumer. The sample is limited to US consumers.

Practical implications – Findings suggest that retailers positioned on value/low price need todifferentiate private labels to deter cross-shopping. Likewise, comparatively upscale retailers need tocontinue to be sensitive to the behavioral demands of their respective target market.

Originality/value – Results provide a profile of the private label consumer and offer insight intoprivate label cross-shopping using an innovative modeling approach that facilitates examination ofactual purchase data.

Keywords Decision trees, Labelling, Consumer behaviour, United States of America

Paper type Research paper

IntroductionGiven the global economic downturn of 2008-2009, already tough retail markets havebecome increasingly challenging for incumbent firms that must fight for decreasingconsumer demand. Prior to this economic contraction, the US retail industry wasexperiencing growth in private label sales across product categories as diverse asgrocery, apparel and footwear and home furnishings. In 2006, private label accountedfor 45 percent of total US apparel sales. During the same year, two US companieslead global growth of private label sales: Wal-Mart with $126 billion and Target with

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Marketing Intelligence & PlanningVol. 28 No. 1, 2010

pp. 59-69q Emerald Group Publishing Limited

0263-4503DOI 10.1108/02634501011014615

$17 billion (Lincoln and Thomassen, 2008). The strategic benefits of private labelintegration have been noted by both practitioners and empirical researchers (Ailawadiet al., 2008; Tarzijan, 2004). Academic research into private label adoption byconsumers focuses primarily upon the grocery industry in markets outside of the USA.Despite the significant resources that US retailers have committed to private labelprograms, we know very little about the contemporary profile of the private labelconsumer. The purpose of this study is to provide a demographic and behavioralprofile of the private label consumer and to investigate their cross-shopping behaviorsusing actual purchase data. The study is designed to provide retailers with realistic,actionable directions to effectively target the private label consumer within theirstrategic approach.

LiteratureThe private label consumerLincoln and Thomassen (2008, p. 6) define private label simply as, “brands owned andsold by the retailer and distributed by the retailer.” As the practice of private labeldevelopment increased over the years, academics began to investigate different aspects ofits effectiveness from supply-chain, firm and consumer perspectives. Within this body ofwork, consumer research focused primarily upon the interplay between store loyalty andprivate label choice (Ailawadi et al., 2008; Corstjens and Lal, 2000; Sudhir and Talukdar,2004; Richardson et al., 1996; Steenkamp and Dekimpe, 1997) and the consideration ofnational versus private label brands (Ailawadi et al., 2001; Batra and Sinha, 2000).

Findings within this stream of research tend to be mixed and do not offer aconsensus on the drivers of consumers’ private label choice decision, thoughresearchers consistently suggest that store loyalty likely plays a role in private labelpurchasing (Richardson et al., 1996). Ailawadi et al. (2008) noted in a recent study thatthe relationship between store loyalty and private label choice remains in question.They undertook a study of Dutch grocers to address this gap and found that greaterprivate label sales for a retailer drive greater sales overall, however private labelcustomers are often more loyal to low prices than to a particular retailer. The literaturetends to focus more heavily on private label in the grocery industry and culturesoutside of the USA (Shannon and Mandhachitara, 2005).

Cross-shopping behaviorBased in part upon Ailawadi et al. (2008) findings, we also examine cross-shoppingbehaviors among the five formats to investigate whether consumers are exclusive toone retailer when buying private label or split their purchases among multiple privatelabel providers. For the purposes of this study, cross-shopping refers to the sameconsumer shopping multiple outlets for substitutable private label products. Thecross-shopping literature is very limited with only a few examples of empirical inquiryinto apparel cross-shopping (Cassill and Williamson, 1994; Carpenter and Moore, 2009).To date, there is no empirical work in the area of cross-shopping for either nationalbrands or private label.

MethodologyThe most effective tool for examining the impact of both behavioral and demographicvariables on private label purchasing, in a simultaneous manner, is the relatively new

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approach to data analysis that employs classification trees, referred to as decision treemodeling. Decision tree analysis evolved in the field of data mining and is most usefulfor approaching problems that can be addressed through extensive and diverse data(Garver, 2002). The method allows the researcher to predict a dependent target variableby incorporating numerous and diversely measured independent variables into themodel. In many cases, the flexibility and reduced operational constraints of decisiontree modeling deem it preferable to logistic regression.

Data for the study are provided by the retail forward shopperscape database whichuses an online consumer panel to survey shopping behavior across an exhaustivevariety of retail formats and product categories. The panel includes approximately onemillion households across the USA. Participants are recruited through differentchannels including web portals, web communities, web aggregators, and internetadvertising firms. Demographic data are weighted to reflect the US census. Allrespondents designate themselves as the primary household shopper. Data arecollected on a monthly basis with an average response rate of 46 percent (www.retailforward.com). The data for this study were collected during July 2007 and focusedupon consumers’ private label purchasing and shopping behavior across five distinctretailers: Wal-Mart, Target, Kohl’s, JC Penney, and Macy’s.

The five retailers selected for the study are recognized as leaders in the US privatelabel apparel market. The retailers vary somewhat in terms of format and/or strategicapproach. Wal-Mart and Target are both considered discount formats, thoughWal-Mart is recognized for low prices and Target for more fashionable apparel brands.Kohl’s is considered a value department store that is highly promotional and also notedfor low prices. JC Penney and Macys represent the traditional department store formatbut offer distinct retail mixes, with JC Penney positioned as a value retailer and Macy’spositioned as more upscale, offering comparatively higher prices. Based upondemonstrated success over time, specific private label brands were selected torepresent each retailer to model purchase behavior: Faded Glory in Wal-Mart, Meronain Target, Sonoma in Kohl’s stores, Arizona in JC Penney and Charter Club in Macy’s.

The dependent/target variable captures actual purchase behavior and isrepresented by a binomial measure for private label purchase within each of the fiveretailers (1 – yes, 0 – no). Again, each purchase variable is associated with a specificprivate label brand: Faded Glory in Wal-Mart, Merona in Target, Sonoma in Kohl’sstores, Arizona in JC Penney and Charter Club in Macy’s. In order to model actualpurchase behavior, the dependent variables were associated with specific purchases ofthe identified private label products.

The predictors of private label purchasing include both demographic variables andshopping behaviors. Demographic variables include: age, income, education level,household size, ethnicity, and marital status. Shopping behavior variables includeshopping frequency on a three-point scale from never-frequent (mall, discount center,online) and propensity to bargain shop or to splurge (1 – yes, 0 – no). Additionaldecision trees are performed on the target variable for each retail format usingpurchase and shopping behaviors from the remaining four formats as the predictors todetermine cross-shopping behavior. All variables are measured on ordinal or nominalscales.

Chi-square Automatic Interaction Detector (CHAID) is used to model the predictorsof private label purchase behavior due to the nominal nature of the target variables.

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CHAID uses x 2 and F-tests to select variables and split them into groups referred to asnodes. CHAID is capable of splitting significant variables into multiple nodes. A prorimodel settings include: a maximum of five levels for the tree, a minimum of 60 casesper parent node and 25 cases per child node. Pearson x 2 are used to detect differencesin the cases with alpha designated at 0.01 for both splitting and merging. Of the sample(n ¼ 860), 65 percent is used for the initial model, commonly termed as the trainingmodel while the remaining 35 percent (n ¼ 429) of the sample is used as the testing orvalidation model. In cases that sample sizes are limited, the training model is usuallytested on a larger proportion of the data (SPSS, 2002).

ResultsCHAID generated five distinct models associated with private label purchasing in eachof the retailers (Figures 1-5). Three additional models that examined cross-shoppingamong the retail formats were also generated (Figures 6-8). All eight models exhibitedidentical structure and similar risk estimates between respective training and testingsamples (Table I), which suggests that each model generalizes well across the data.Based upon the level of agreement between the training and test models, interpretationof the decision trees (training models) proceeded.

The five trees designed to examine the demographic and behavioral dimensions of theprivate label consumer indicate somewhat distinct profiles among the retailers examined.Within the decision tree model, the order of variable splits from top to bottom indicates themagnitude of effects in terms of influence on the target variable. Two of the five modelsexclusively generated demographic predictors (Target and Kohl’s) while the remainingthree models generated significant demographic and behavioral predictors.

Figure 1.Wal-Mart private labelpurchasers demographicsand behavior

Y = 28%

N = 72%

Y = 28%

N = 72%

$49K >

Not married

Y = 33%

N = 67%

3-4, 6 members

Split 1: Household sizeX2 = 22.65, p< 0.003

5 members

1-2,7members

Y = 22%

N = 78%

Y = 48%

N = 52%

< $49KSplit 2: IncomeX2 = 8.39, p< 0.011

Y = 45%

N = 55%

Married

Split 3: Marital statusX2 = 6.73, p< 0.028

Y = 31%

N = 69%

Y = 9%

N = 91%

Note: n = 860

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Household size (x 2 ¼ 22.65, p , 0.003) was the most influential variable for predictingprivate label purchase among Wal-Mart consumers, followed by income (x 2 ¼ 8.39,p , 0.011) and marital status (x 2 ¼ 6.73, p , 0.028) (Figure 1). For the most part,purchases of private label increased as household size increased. The second splitindicated that among medium-sized households (three to four, six members) private labelpurchasers tend to belong to a lower income group ($25,000-49,999). Among the higherincome group ($49,999 . ), purchasers were much more likely to be married than single.

The second model which investigated private label purchasing in Target generatedthree levels of significant predictors (Figure 2). The propensity to bargain shop(x 2 ¼ 6.56, p , 0.010) generated the first split followed by age (x 2 ¼ 9.66, p , 0.009)and frequency of discounter shopping (x 2 ¼ 6.36, p , 0.035). A greater proportion ofprivate label purchasers at Target tended not to bargain shop. The smaller group ofpurchasers that indicated a propensity to bargain shop tended to be younger (,45) andfrequent shoppers of discount formats.

The third model examined the drivers of private label purchase among the Kohl’sgroup (Figure 3). The model indicated two significant predictors: income and age.

Figure 2.Target private label

purchasers demographicsand behavior

Y = 11%N = 89%

Split 1: Bargain shopperX2 = 6.56, p<, 0.01 Yes No

Y = 11%N = 89%

Y = 9%N = 91%

Y = 14%N = 86%

Split 2: AgeX2 = 9.661, p< 0.009< 45 > 45

Y = 3%N = 97%

Occasional/infrequent

Frequent Split 3: Discount shopping frequencyX2 = 6.36, p< 0.035Y = 19%

N = 81%Y = 9%N = 91%

Note: n = 860

Figure 3.Kohl’s private label

purchasers demographicsand behavior

Y = 17%N = 83%

Y = 9%N = 91%

$25K-$99K

Split 1: IncomeX2 = 16.85, p< 0.0007

< $25K $99K >

Y = 16%N = 84%

Y = 26%N = 74%

Split 2: AgeX2 = 9.949, p< 0.0145

No43 >

Split 3: AgeX2 = 7.65, p< 0.0283

Y = 16%N = 84%

< 44

Y = 3%N = 97%

Y = 13%N = 87%

< 4344 >

Y = 23%N = 77%

Note: n = 860

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Figure 4.JC Penney private labelpurchasers demographicsand behavior

Y = 21%N = 79%

Y = 45%N = 55%

Y = 14%N = 86% Split 2: Education

X2 = 19.40, p< 0.001

Y = 10%N = 90%

55 >43-55

Y = 18%N = 82%

Occasional

Y = 14%N = 86%

Split 5: Mallshopping frequencyX2 = 10.68, p< 0.003

Y = 27%N = 73%

3-5 members 6-7 members

Frequent/neverOccasional/never Frequent

Split 1: Household sizeX2 = 34.43, p = 0.000

1-2 members

Split 3: AgeX2 = 16.54, p< 0.009

< 43 < Some college< HS grad Some college >

Y = 9%N = 91%

Y = 24%N = 76%

Y = 42%N = 58%

Y = 21%N = 79%

Somecollege >< Some college

Y = 4%N = 96%

Y = 23%N = 77%

Y = 36%N = 64%

Y = 9%N = 91%

Y = 30%N = 70%

Split 6: Online shopping frequencyX2 = 7.56, p< 0.017

Split 4: EducationX2 = 11.63, p< 0.005

Note: n = 860

Figure 5.Macy’s private labelpurchasers demographicsand behavior

Y = 6%N = 94%

Split 1: Shopping frequency-mallX2 = 13.34, p< 0.0008

Y = 12%N = 88%

Y = 5%N = 95%

Y = 4%N = 96%

Caucasian,AfricanAmerican

Split 2: RaceX2 = 13.43, p< 0.0007

< $50-$99K

Y = 3%N = 97%

$99K >

Never

Occasional/never Frequent

Hispanic,Asian, otherY = 16%

N = 84%

Split 3: IncomeX2 = 8.62, p< 0.009

Y = 8%N = 92%

Split 4: Shoppingfrequency-mallX2 = 4.47, p< 0.035

Occasional

Y = 4%N = 96%

Y = 0%N = 100%

Note: n = 860

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Income (x 2 ¼ 16.85, p , 0.0007) indicated the greatest influence on private labelpurchasing followed by age which split twice beneath separate income groups. Thegreatest proportion of private label purchasers were from moderate ($25,000-99,999)and high ($99,999 . ) income groups. The model indicated the second split with the

Figure 6.Wal-Mart private label

purchasers cross-shopping

Y = 28%N = 72%

Yes No

Y = 21%N = 79%

Y = 55%N = 45%

Split 1: Purchased JCPenneyX2 = 78.35, p = 0.000

Split 2: Purchased Kohl’sX2 = 12.64, p< 0.0004

Yes

Y = 81%N = 19%

No

Y = 34%N = 66%

Note: n = 860

Figure 8.JC Penney private label

purchasers cross-shopping

Y = 21%N = 79%

Split 1: Purchased Wal-MartX2 = 78.35, p = 0.000

Yes

Y = 13%N = 87%

Y = 40%N = 60%

No

Split 2: Purchased targetX2 = 7.91, p< 0.004

Y = 36%N = 64%

NoYes

Y = 60%N = 40%

Note: n = 860

Figure 7.Kohl’s private label

purchasers cross-shopping

Y = 17%N = 83%

Y = 69%N = 31%

Split 1: Purchased Wal-MartX2 = 9.49, p< 0.002 Yes No

Y = 88%N = 12%

Note: n = 680

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high-income group in terms of age (x 2 ¼ 9.94, p , 0.014). A greater proportion of themoderate income buyers were younger (,43). The third split, again associated withage (x 2 ¼ 7.65, p , 0.0283), suggested that the larger group of low income privatelabel purchasers at Kohl’s were slightly older (44 . ).

The fourth model indicated six significant predictors for private label purchase amongthe JC Penney group: household size (x 2 ¼ 34.43, p ¼ 0.000), education (x 2 ¼ 19.40,

p , 0.001), age (x 2 ¼ 16.54, p , 0.009), education (x 2 ¼ 11.63, p , 0.005), mall shopping

frequency (x 2 ¼ 10.68, p , 0.003), and online shopping frequency (x 2 ¼ 7.56, p , 0.017)(Figure 4). The strongest predictor, household size, increased proportionately with theoccurrence of private label purchasing at JC Penney. Households with three to five and sixto seven members were more likely to purchase private label than smaller households (oneto two members). Purchasers among the three to five member household group variedsignificantly in terms of education. The greatest proportion of this group was moderatelyeducated with a high school degree/some college. A smaller group of college educatedpurchasers indicated occasional online shopping behavior. Among the smallerhouseholds, middle aged (43-55) respondents tended to purchase more private label atJC Penney compared to younger (,43) and older (55. ) groups. This group of purchasersalso indicated some college as the highest level of education. Younger private labelpurchasers at JC Penney tended to be frequent mall shoppers.

The model for the Macy’s purchasers indicated three significant predictors: mallshopping frequency, race (x 2 ¼ 13.43, p , 0.0007) and income (x 2 ¼ 8.62, p , 0.009)(Figure 5). Mall shopping frequency indicated effects at the first (x 2 ¼ 13.34,p , 0.0008) and fourth (x 2 ¼ 4.47, p , 0.035) level of the tree. A larger proportion ofMacy’s private label purchasers reported that they were frequent mall shoppers.Among the smaller group who reported that they only occasionally shopped in malls,the majority of customers had incomes above $99,999 per annum.

An additional series of decision trees were also generated to identify whether theprivate label customer was cross-shopping within retailers in the sample (Figures 6-8).Each original target variable for each retailer (purchased private label apparel (1 – yes,0 – no)) was modeled using the private label purchase variables from the remainingfour retailers as predictors to determine cross-shopping behaviors. In addition, four

Risk estimateModel Traininga Testingb

1. Wal-Mart: demographics and behavior 0.2813 0.30062. Target: demographics and behavior 0.1116 0.10723. Kohl’s: demographics and behavior 0.1697 0.19114. JC Penney: demographics and behavior 0.2058 0.21445. Macy’s: demographics and behavior 0.0593 0.07696. Wal-Mart: cross-shopping 0.2616 0.28677. Kohl’s: cross-shopping 0.0534 0.04428. JC Penney: cross-shopping 0.1965 0.2074

Notes: an ¼ 860; bn ¼ 429

Table I.Risk estimates fordecision trees

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variables that simply asked whether consumers had shopped in each retailer forprivate labels (1 – yes, 0 – no) were also examined as predictors within these models.

Three of the five decision trees indicated significant cross-shopping effects: Wal-Mart,Kohl’s, and JC Penney. The Target and Macy’s private label purchasers did not indicateshopping or purchasing private label apparel from the other retailers. Both Kohl’s andJC Penney purchasers indicated significant private label purchases from Wal-Mart.Vice-versa, the Wal-Mart model indicated the same effects with JC Penney accounting forheavier private label cross-shopping followed by Kohl’s. The model for JC Penney alsoindicated that their private label purchasers also purchase private label from Target.

Conclusions, implications, and future researchThe decision trees yielded varying models to describe the private label consumer.Demographic indicators were more prominent than behavioral characteristics whenpredicting private label purchase across the five retailers. In particular, household sizeand income represented the primary predictor for private label purchasing in formatsthat are positioned on price (Wal-Mart, Kohl’s, and JC Penney). Both Target and Macy’sprivate label purchasers indicated behavioral predictors as significant drivers ofchoice. Target private label patrons indicated low levels of bargain shopping whileMacy’s patrons were frequent mall shoppers.

Findings suggest that private label purchasers are more different than similar interms of demographics and behaviors according to the selected retail format, withseveral exceptions between the Wal-Mart, Kohl’s and JC Penney formats which appearto share competitive space. The cross-shopping trees indicated that Wal-Mart andJC Penney private label consumers engage in purchasing comparable private labels inboth chains. Wal-Mart and Kohl’s also share private label consumers for comparableprivate label brands. Further, JC Penney private label purchasers patronize Target forprivate label apparel. The higher incidence of cross-shopping among Wal-Mart, Kohl’s,and JC Penney could be attributed to the positioning strategies of this group ashigh-value/low-price providers within their respective channels of distribution.Respondents did not indicate significant levels of cross-shopping for private labelamong the Target or Macy’s group, suggesting that these retailers may have highercompetitive barriers for their private label brands compared to the other retailers in thestudy. In conclusion, the findings reinforce the logic that retailers should positionprivate label programs to accurately reflect the core value proposition within theirrespective marketing strategies. Further, the findings suggest that retailers facingtough experiencing higher incidences of private label cross-shopping in the US market(Wal-Mart, Kohl’s, and JC Penney) could benefit from differentiating their privatelabels within their offering, thereby encouraging trading up behaviors, potentiallyhigher margins and ultimately greater loyalty.

This study provides a superficial examination of the characteristics of the privatelabel consumer. The design is focused upon what rather than why in explaining theprivate label purchase choice. The study sacrifices deep understanding for a rigorous,comprehensive overview of the practical drivers of consumer behavior in private labelpurchasing. Additional behavioral variables including a more comprehensiveconception of shopper orientation may have resulted in more robust models.Further, we have no knowledge whether our subjects recognize private labels asdistinct from other types of brands, or their degree of loyalty to the retailer of choice.

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From a methodological standpoint, larger datasets with longitudinal data wouldhave strengthened the credibility of the trees by cross-validating the models over timeand different consumer samples. However, the variables considered in our models arecommonly used by retailers to segment markets and position brands due to theiraccessibility and utility in representing homogeneous groups of consumers.

In keeping with directions already suggested in the extant literature, future researchwithin this stream should consider the interaction of private label brands with nationalbrands to more fully understand the drivers of brand choice in the retail context andultimately inform merchandising and marketing strategy. Further, findings suggestthat private label consumer profiles were fairly well aligned with the target consumersof each retailer, which suggests that these consumers may exhibit loyalty towards theretailer and purchase its private labels as a result of that loyalty. Again, this questionremains unanswered in the extant literature and provides a potentially importantdirection for understanding the private label consumer within the apparel context.

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About the authorsMarguerite Moore is an Associate Professor at North Carolina State University. Her researchand teaching is primarily in the areas of retail strategy, brand management and consumerbehavior in textiles. Marguerite Moore is the corresponding author and can be contacted at:Marguerite_Moore@ncsu.edu

Jason M. Carpenter, PhD, is an Assistant Professor of Retailing at The University ofSouth Carolina. His work has been published in the Journal of Retailing & Consumer Services,International Journal of Retail & Distribution Management, and the Journal of Product & BrandManagement.

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