The_role_of_geodemographic_segmentaion_in_retail_location

The role of geodemographicsegmentation in retail location strategy

Óscar González-Benito and Javier González-BenitoUniversidad de Salamanca

This paper studies the role of geodemographic segmentation as an analytic toolin retail location strategy. The most relevant factors that should determine retaillocation selection are revised, and the potential contribution of geodemographicsegmentation to the assessment of such factors is examined. The empiricalapplication provides evidence on the differences between store networks ofleading Spanish supermarket chains in relation to the geodemographic profile oftheir market areas. This result confirms the potential of geodemographicsegmentation for the spatial delimitation of retail chains’ target markets.

Introduction

Location is a transcendental decision to ensure the viability of retail stores.Not for nothing is it usual to claim three key success factors in retailmanagement: location, location and location (Jones & Simmons 1987).The retail site determines the market area – that is to say determines theset of consumers willing to travel to the store for shopping. The quantityof consumers that make up this market area, as well as their affinity withthe store in terms of shopping needs and habits, are key questions to reacha profitable sales level.

Although population density is an important attribute for the selectionof spatial markets, the spatial heterogeneity of consumers is alsofundamental. While the former is related to the quantity of consumers, thelatter is related to the quality of consumers. Geodemographic segmenta-tion seeks to capture the spatial heterogeneity of the market by classifyingintra-urban areas in terms of the characteristics of their residents. Thepotential of geodemographic segmentation for indicating the shoppingneeds and habits of different geographic areas makes it a useful tool in theselection of optimal locations for retail stores.

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This paper aims to develop a theoretical and empirical analysis of therelevance of geodemographic segmentation as an analytical tool foroptimising retail location strategy. First, an explanatory model of thefactors and key information that determine retail site selection is proposed.This theoretical framework allows us to assess the scope of geodemo-graphic segmentation; in other words, it allows us to analyse which relevantinformation could be reflected by this segmentation approach, and whichrelevant information should be drawn from other sources. Second, anempirical analysis provides evidence for the explanatory potential ofgeodemographic segmentation. Specifically, leading Spanish supermarketchains are compared in terms of the geodemographic profile of theirmarket areas. The existence of geodemographic differences between marketareas of retail chains implies that geodemographic segmentation explainsto some extent their site selection criteria. Therefore, geodemographicsegmentation could be a useful tool to support retail location strategy.

The main contents of the paper are divided into two sections that tacklethe theoretical and empirical developments, respectively. The conclusions,limitations and suggestions for further research are summarised in a finalsection.

Theoretical framework: location strategy andgeodemographic segmentation

The affinity between the needs of the target market and productpositioning is crucial to success. The same is applicable to the retailcontext. The viability of a retail store depends to a great extent on itscapability to satisfy the expectations of the consumers that make up itsmarket area. Therefore, the spatial heterogeneity of consumers should beborne in mind when developing the location strategy. Only those sitessurrounded by consumers akin to the positioning of the store should beselected. The spatial heterogeneity of the market may also be taken intoaccount once the store has been located by: (i) developing marketingactions adapted to the market area – the approach consisting of developingdecentralised marketing actions adapted to the market area of each storein a retail chain has been referred to as micromarketing (Hoch et al. 1995;Montgomery 1997; Ziliani 2000) – and (ii) distinguishing geographicsegments with different shopping needs and habits within the market area.

Geodemographic segmentation tries to capture the spatial heterogeneityof the market. González-Benito and González-Benito (2004) deal with therole of geodemographic segmentation in the development of retail

The role of geodemographic segmentation in retail location strategy

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micromarketing strategies, specifically the development of retail pricediscrimination strategies. In the same way, geodemographic segmentationis used to make other post-location decisions, such as those related tocommunication campaigns or the proposal of new service offerings. Theseapplications of geodemographic segmentation are especially relevant whenthe market is saturated and retail chains have reached a mature stage intheir coverage strategy. The interest of this study is the role of geodemo-graphic segmentation in the development of an optimal retail locationstrategy. This application of geodemographic segmentation is especiallyrelevant in a former stage of the life cycle: the growth and expansion ofretail chains. First, an explanatory model of the relevant factors to takeinto account in retail site selection is proposed. Then, the discussionfocuses on the extent to which the spatial heterogeneity captured bygeodemographic segmentation facilitates the analysis of these factors.

Key factors in retail location strategy

Retail location decisions can be managed at different levels of geographicaggregation: choice of region, metropolitan area, intra-urban area, specificsite, etc. In any case, the factors to bear in mind in the assessment ofpossible locations can be classified into two broad types: on the one hand,market factors, relating to the potential of the location for attractingconsumers and enhancing sales; on the other hand, operative factors,relating to the effort involved in opening and operating the store. Theformer refer to the capability to generate income, while the latter refer tothe costs involved. Both market and operative factors determine theprofitability of the store. This approach is summarised in Figure 1.


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Figure 1 Location strategy: key factors

MARKET FACTORSImplications for store attraction

OPERATIVE FACTORSCosts involved in operating a location

LOCATION RELATING TO CONSUMERS

LOCATION RELATING TO OTHER FACILITIES LOCATION STRATEGY

LOCATION RELATING TO COMPETITORS


Location is a key factor in the retail attraction exerted by a retail store.Cliquet (1992, p. 73) defines the concept of retail attraction as thecapability to make consumers come to the store by overcoming bothphysical barriers and competition influence. This meaning is especiallyappropriate from the perspective of location because it points out thespatial component of shopping behaviour. Shopping activity impliestravelling and, consequently, an additional cost in terms of time, money,effort and opportunity. The spatial accessibility of the store, which isdetermined by location, constitutes an important attribute in theperception of utility by consumers. The further the distance to the store,the higher the costs involved, the lower the utility perceived by consumers,and the lower their predisposition to visit the store. This conception ofdistance as a factor dissuading the predisposition to shop is the basicprinciple of spatial interaction theory (Fotheringham & O’Kelly 1989).The underlying notion is a descending spatial demand curve based on theinverse relationship between demand and price (Jones & Simmons 1990,pp. 38–45).

Therefore, location implies the spatial delimitation of the marketapproached by the store – that is to say, the choice of geographic market.On the one hand, this circumstance suggests the choice of sitescharacterised by a high population density in the surrounding area. On theother hand, it requires a geographic segmentation of the market thatallows managers to select those residential areas whose shopping needsand habits match the positioning of the retail store or chain. In summary,location selection should consider both the quantity and the quality of theconsumers within the market area.

These arguments focus on the location of the store in relation to theresidence of consumers, and on the role of the distance to consumers asdeterminant of the attraction exerted by the store. In addition, the locationof the store should also be assessed in relation to the location of othercomplementary facilities. There is growing evidence of shopping trips inwhich consumers combine different destinations and purposes (Dellaert etal. 1998; Popkowski Leszczyc et al. 2004). Therefore, the location inrelation to complementary retail stores, recreational facilities orworkplaces, may enhance retail attraction by fostering multi-purpose andmulti-destination shopping trips and by taking advantage of thepopulation flows generated by these complementary facilities. Moreover,some shopping needs arise when consumers are far from their residence,during leisure time or at the workplace, the location in relation to theseareas being determinant. As a final argument in this respect, there may be


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both positive and negative effects of the location in relation tocomplementary facilities based on image transference (Burns 1992; Finn &Louviere 1996).

The effect of location on retail attraction also depends on the locationof competitors. Location close to competitors implies the overlapping ofmarket areas and, consequently, the sharing out of the demand (Ingene &Lusch 1981). Therefore, the selection of market areas with low competitorpresence may be a key to success.

Nevertheless, a possible positive effect on retail attraction of theproximity to competitors has also been recognised in the literature (e.g.Fotheringham 1986, 1988). This positive effect may compensate thenegative effect derived from the overlapping of market areas. Theexplanation is based on economic arguments. The proximity of competingstores facilitates the reduction of risk perceived by consumers because theycan search and compare between stores without increasing to a greatextent the travel costs involved. This reasoning is more appropriate forthose retail sectors in which the search and comparison are moreimportant – that is to say, for high implication shopping. In addition, therole of variety-seeking in the repetitive shopping for some products andservices should be taken into account. The spatial concentration ofcompetitors becomes more attractive because it facilitates shopping indifferent stores during the same trip.

The opposite effect has also been recognised in the literature(Fotheringham 1986, 1988; Haynes & Fotheringham 1990; Lo 1990,1991a, 1991b) – that is, the positive effect of the location close tocompetitors can be neutralised or even inverted. The explanation is basedon psychological arguments. Sometimes consumers tend to choose ashopping area first and then the specific store in which to shop. When thevolume of stores located in a shopping area is high, the location of newcompetitors may not be appreciated by consumers – that is to say,consumers might not distinguish between shopping areas when the size oftheir retail offer goes beyond a certain threshold. As a consequence, thelocation of new competitors does not attract more consumers and existingdemand must be distributed among a higher number of stores. This trendto underestimate shopping opportunities when the size of a spatialconcentration of competing retail stores increases is based on apsychophysical principle (Stevens 1957).

Although location determines to a great extent the capability of theretail store to attract consumers and generate sales, it also influences thecosts involved in the opening and maintenance of the retail store. In fact,


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the high costs involved in the opening of a store make location a strategicdecision with a top priority. The failing of a location might have importantfinancial consequences for the retail chain. The operating costs are notnegligible either, because they determine the profitability of the store. Theland value, which differs considerably across intra-urban areas, logisticfactors, relating to the supply and stock of merchandise, or the provisionof services to overcome accessibility barriers such as parking or freetransport, are key characteristics of alternative locations that should beassessed in the selection process. The underlying idea is that the emphasison attracting the demand that should characterise the location strategyshould be weighed against the costs involved in satisfying such demandappropriately.

The role of geodemographic segmentation

Geodemographic segmentation refers to the classification of consumers bythe type of residential area in which they live. It is therefore based on thedifferentiation of residential areas according to the demographic, socio-economic or even psychographic characteristics of their residents. The firstunderlying principle is that similar residential areas have similar shoppingneeds and habits and, consequently, similar response patterns to marketingstimuli (Batey & Brown 1995; Birkin 1995). The second underlying ideais that individuals with similar characteristics tend to reside in the sameareas and share the same environments – that is to say, residential areastend to be internally homogeneous so that their residents do not differsignificantly from a mean profile.

The potential utility of geodemographic segmentation has led to thecommercialisation of several standard geodemographic classifications thatembrace almost the entire urban geography of developed countries. Widelyknown examples are ACORN from CACI, or MOSAIC from CCNSystems. As a pioneer firm in Spain, MOSAIC Iberia S.A. launched thefirst geodemographic typology of the Spanish urban geography. The lastversion of this taxonomy is now commercialised by EXPERIAN.

The importance of the geodemographic characterisation of thecustomers of retail stores has repeatedly been pointed out in the literature(Hoch et al. 1995; Putler et al. 1996; Kumar & Karande 2000; González-Benito 2002; Inman et al. 2004). Many studies have dealt with bothcurrent and potential applications of geodemographic segmentation (see,for example, Beaumont & Inglis 1989; Flowerdew & Goldstein 1989;Journal of the Market Research Society, 31, 1, 1989; Mitchell & McGoldrick


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1994; O’Malley et al. 1995, 1997). Most of them have emphasised theselection of geographic markets and the location of retail stores asespecially relevant applications. Geodemographic segmentation allowsretailers to measure the spatial heterogeneity of the market, distinguishingthe intra-urban zones that are more attractive for each type of store.

Therefore, the utility of geodemographic segmentation as an analytictool in retail site selection is based on its capacity to differentiategeographic markets in terms of the quality of the consumers. The idea isto select possible locations not only in terms of the quantity of consumersbut also the fit between these consumers and the projected store. Themarket area should include those consumers whose shopping needs andhabits match the assortment and services provided by the store.Geodemographic segmentation facilitates a precise selection of intra-urbanareas in this respect. In summary, geodemographic segmentation facilitatesretail location strategy as an indicator of the market factors mentionedabove.

Since the focus of geodemographic segmentation is on the classificationof consumers according to their residence, the information provided bythese classification schemes concerns site assessment in regard to thelocation relating to consumers. Nevertheless, geodemographic typologiesmay also reflect the retail and services infrastructure, business activity orcomplementary facilities located in a market area. Therefore, geodemo-graphic segmentation may indirectly assist retail site assessment in regardto the location relating to complementary facilities. Also indirectly,geodemographic typologies may be indicators of the type of competitionlocated in a market area. Site selection based on the characteristics ofmarket areas derives necessarily in significant correlation between geo-demographic profiles and retail formats and chains. Therefore,geodemographic segmentation may facilitate retail site assessment inregard to the location relating to competitors. In any case, the geographicinformation systems that support standard geodemographic classificationsusually record detailed information in relation to industrial andcommercial activities across intra-urban zones, and this information maydirectly facilitate retail site selection relating to complementary facilitiesand competition.

The relationship between geodemographic segmentation and theoperative factors mentioned above is also indirect, but not negligible.Residential zones differ in terms of land value, logistic facilities orrequisites for spatial accessibility such as transport, parking, safety, etc.,and these circumstances are sometimes related to the profile of the


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residents. Consequently, the relationship between geodemographicsegmentation and the cost involved in opening and maintaining the retailstore should not be dismissed.

Empirical analysis

The empirical contents of this study seek to provide evidence for theusefulness of geodemographic segmentation in retail site selection.Specifically, the relationship between supermarket chains and thegeodemographic profile of their market areas is analysed. The supermarketis a mature format in Spanish retailing, although in constant evolutiontowards new variants, such as the discount or the hard discount.Therefore, well-established supermarket chains comprise a suitablescenario for the a-posteriori appraisal of the relationship between locationpatterns and geodemographics. The confirmation of the relationshipimplies that geodemographic segmentation explains part of the criteriafollowed by retailers in the selection of spatial markets, regardless ofwhether retailers have used geodemographic segmentation to make thedecision. Presumably, these criteria would be those related to the shoppingneeds and habits of the residents in the market area.

Data

The empirical analysis focuses on supermarket-type chains operating inSpain. Detailed information about each supermarket operating in thewhole Spanish geography was obtained from the Censo de Supermercados(Supermarket Census) of Publicaciones Alimarket in November 2001. Thedata include chain, group, address, size of sales area and retail format. Inall, the census includes 12,769 supermarkets.

The geodemographic classification MOSAIC, commercialised byEXPERIAN, was used to characterise each of the supermarkets included inthe census. MOSAIC divides the Spanish urban geography into 506,329areas classified into 14 groups and 48 typologies (see Table 1). It isimportant to point out that the relevance of some groups and typologiesvaries across towns and regions. For example, some geodemographictypologies are only present in large metropolitan areas such as Madrid orBarcelona, and some typologies are inherent to highly industrialisedregions.

Each store was assigned the geodemographic group and type associatedwith its address, although the subsequent analysis focuses exclusively on


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Table 1 MOSAIC groups and typologies

Group % population Typology % population

A. Elite 5.4 A01 Classic elite 2.5A02 Urban elite 1.8A03 Residential elite 1.1

B. Urban well-off 4.0 B04 Settled well-off 1.4B05 Consummate well-off 1.2B06 Pre-retirement well-off 1.3

C. Provincial well-off 7.1 C07 Well-off in tourist area 1.4C08 Provincial well-off 2.6C09 Industrial well-off 2.0C10 Well-off in mixed areas 1.1

D. Qualified professionals 7.0 D11 Newly settled professionals 1.7D12 New emerging professionals 1.4D13 Professionals from the 80s 2.3D14 Professionals from the 70s 1.6

E. Mid-level professionals 9.6 E15 Satisfied immigrants 1.7E16 Autochthonous residents 2.8E17 Apparent white collar 2.4E18 Provincial white collar 2.7

F. Consolidated 10.6 F19 Stable employees 1.1F20 Apparent employees 2.4F21 Traditional employee 1.2F22 Mid-level employee 2.7F23 Modest employee 3.2

G. Tourist 3.3 G24 Summer resorts 1.5G25 Tourist areas 1.8

H. Industrial 14.3 H26 Older workers 3.2H27 Modern workers 1.6H28 Workers in SME 2.9H29 Classic workers 2.7H30 Traditional workers 1.2H31 Modest workers 2.7

I. Non-qualified 5.4 I32 Unskilled stable workers 1.6I33 Unskilled large households 2.3I34 Unskilled modest workers 1.5

J. Sectorial mix 7.5 J35 Local business 2.7J36 Territorial services centre 2.6J37 Small mixed city 2.2

K. Diversified rural 5.3 K38 Rural in expansion 1.3K39 Older rural 1.8K40 Rural border 2.2

L. Agricultural 10.5 L41 Young farmers 2.5L42 Traditional farmers 4.5L43 Mature agricultural workers 3.5

M. Passive areas 9.1 M44 Retired urban professionals 2.1M45 Older people on their own 0.6M46 Unskilled retired 0.6M47 Rural aged 5.8

N. Security and defence 0.9 N48 Security and defence 0.9

Source: MOSAIC (EXPERIAN Marketing Services)


the group. Since some addresses were not precise enough, thegeodemographic characterisation was not possible for 1274 stores.Therefore, the subsequent analysis obviates these observations. A priori,there are no reasons to expect that missing observations bias therelationship between retail chain and geodemographic profile.

MOSAIC classification is based on a cluster analysis with sevengeodemographic factors: professional activity, habitat, tourism andcommerce, families, employment, type of household, and business. Table 2summarises the interpretation of these factors, which are measured on ascale between 0 and 10. Mean values of the factors within each group andtype allowed us to characterise the supermarkets, too, according to thesegeodemographic dimensions.

The analysis focuses exclusively on ten leading supermarket chains:Caprabo, Champion, Charter, Consum, Día, El Arbol, Lidl, Mercadona,Plus Superdescuento and Supersol. The selection of these chains was basedon the following criteria.

• Size of the stores. The geodemographic profile assigned corresponds toa geographic area that is presumably smaller than the store’s marketarea. Consequently, the reliability of the geodemographiccharacterisation increases as the size of the market area diminishes.For this reason, the analysis obviated retail chains operating bigsupermarkets (hypermarkets). This circumstance does not mean thatgeodemographic segmentation is not useful to assess the market areaof large stores. However, the consideration of these large storesrequires a characterisation of the market area that is more complexand sophisticated than the simple one used in this study. The mean andstandard deviation of the size of the stores within each retail chain areshown in Table 3.


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Table 2 MOSAIC factors

Factor Minimum Maximum

Professional activity Primary sector/building Services sectorHabitat Intensive urban development Extensive urban developmentTourism and commerce Low linking with tourism and commerce High linking with tourism and commerceFamilies Older families Young familiesEmployment Active economies UnemploymentType of household Households in transition Settled householdsBusinesses Low economic activity High economic activity

Source: MOSAIC (EXPERIAN Marketing Services)


• Size of chains. The interest in identifying possible relationshipsbetween retail chains and the geodemographic profile of their marketareas requires that the analysis should focus on those chains with ahigh number of supermarkets. This circumstance also implies that allthe selected chains are widely known, although the store networkoperated by some of them covers only some Spanish regions. Thenumber of stores within each chain is also shown in Table 3.

• Property structure. Some chains were selected because they belong tothe same business group. Therefore, it is possible to assess possiblesimilarities or differences in the geodemographic profile of their storenetworks. The relationship between chains and business groups isshown in Table 4.


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Table 3 Retail chains studied: number of stores and size

Alimarket census After geodemographic characterisation

Retail chain Number of stores Mean size S.D. size Number of stores Mean size S.D. size

Caprabo 350 889.29 776.55 315 849.80 760.86Champion 153 1696.60 714.36 131 1692.79 739.85Charter 171 286.15 137.36 159 286.43 141.29Consum 715 715.50 342.39 645 705.40 333.07Día 2316 258.29 169.88 2154 249.63 160.60El Arbol 618 531.73 257.40 572 527.89 254.96Lidl 323 784.37 129.99 256 786.49 128.35Mercadona 558 1058.58 380.56 500 1054.20 372.15Plus Superdescuento 172 720.03 80.01 140 719.90 80.48Supersol 467 740.00 499.98 403 712.55 490.65All retail chains 5843 577.03 478.24 5275 558.48 470.50

Date: November 2001

Table 4 Retail chains studied: retail group and format

Retail chain

Retail group Supermarket Discount store

Caprabo CapraboEl Arbol Dist. y Supermercado El ArbolAhold SupersolMercadona MercadonaEroski Charter, ConsumCarrefour Champion DíaLidl Supermercados LidlTengelmann España Plus Superdescuento

Date: November 2001


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Table 5 Geodemographic characterisation of retail chains

Retail chain

Geodemographic group Caprabo Champion Charter Consum Día El Arbol Lidl Mercadona Plus Superdescuento Supersol

A. Elite 15.2 ↑↑ 13.0 ↑↑ 3.8 8.5 5.0 5.2 3.9 6.8 4.3 8.4B. Urban well-off 9.2 ↑↑ 4.6 3.1 3.3 5.6 1.6 4.3 1.8 0.7 ↓↓ 2.7C. Provincial well-off 7.9 9.2 3.8 ↓↓ 12.6 ↑↑ 7.1 10.7 ↑↑ 5.9 7.6 8.6 7.4D. Qualified professionals 6.0 ↑↑ 4.6 6.3 ↑↑ 5.1 5.0 4.4 3.1 ↓↓ 3.4 ↓↓ 5.7 5.7E. Mid-level professionals 3.5 ↓↓ 7.6 13.2 ↑↑ 10.9 10.3 10.5 10.2 8.6 7.1 9.2F. Consolidated 5.1 4.6 ↓↓ 3.8 ↓↓ 7.1 8.5 9.4 5.5 8.8 8.6 13.2 ↑↑G. Tourist 10.2 ↑↑ 8.4 ↑↑ 3.8 9.1 ↑↑ 2.0 ↓↓ 1.7 ↓↓ 4.7 6.0 3.6 5.5H. Industrial 20.0 16.0 24.5 ↑↑ 16.0 15.1 6.3 ↓↓ 12.9 17.8 13.6 4.5 ↓↓I. Non-qualified 0.6 ↓↓ 3.8 0.0 ↓↓ 2.2 2.3 3.8 5.9 5.4 4.3 8.9 ↑↑J. Sectorial mix 8.6 4.6 18.9 ↑↑ 9.9 10.9 19.1 ↑↑ 6.3 8.0 2.9 ↓↓ 5.2K. Diversified rural 2.2 ↓↓ 7.6 6.3 4.3 5.8 3.7 7.8 5.6 11.4 ↑↑ 4.5L. Agricultural 0.3 ↓↓ 1.5 ↓↓ 5.7 2.8 9.1 10.8 ↑↑ 5.9 8.6 4.3 8.4M. Passive areas 2.9 2.3 4.4 2.8 7.1 ↑↑ 5.6 2.3 1.8 2.9 2.2N. Security and defence 0.3 0.8 0.0 ↓↓ 0.5 0.6 1.0 0.8 1.0 1.4 3.0 ↑↑Non-residential area 7.9 11.5 2.5 ↓↓ 5.0 5.6 6.1 20.7 ↑↑ 8.8 20.7 ↑↑ 11.2All geodemographic groups 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Pearson’s χ2 = 838.226 (sign. 0.000)Column percentages

↑ denotes percentage at least one standard deviation above the average percentage across chains↓ denotes percentage at least one standard deviation below the average percentage across chains

Date: November 2001


• Store format. Chains were selected because they operate withindifferent retail formats. In this way, it is possible to make comparisonsbetween them. Specifically, traditional supermarkets and discountstores were distinguished. The relationship between chains andformats is also shown in Table 4.

Analysis and results

Since the main purpose was to depict and compare the geodemographicprofile of each retail chain included in the sample, the contingency tablefor both dimensions was calculated. Each cell in the table quantifies thenumber of supermarkets within the chain in question (see Tables 3 and 4)whose market area is within the MOSAIC geodemographic group inquestion (see Table 1). In order to facilitate comparison across chains,Table 5 shows the percentages within each chain derived from thecontingency table. Figure 2 illustrates graphically the same content.

The χ2 test included in Table 5 allows us to reject the hypothesis ofindependence between retail chains and geodemographic groups.Consequently, a significant relationship between both dimensions isconfirmed. The location strategy of retail chains implies the selection ofspatial markets with specific geodemographic profiles. And, reciprocally,the geodemographic group indicates to some extent which retail chains arelocated in an intra-urban zone. Such a relationship could be partiallyexplained by the relationship between the Spanish regions and thepresence of specific retail chains and geodemographic groups.

In order to analyse specific differences between retail chains, χ2 testswere obtained for each pair of retail chains. The results are summarised inTable 6. In almost all cases, the hypothesis of equality in geodemographicprofiles is rejected. This implies that, in most of the cases, chains differfrom each other in terms of the type of markets selected to locate theirstore networks.

It is interesting to point out that there are no significant differencesbetween Lidl and Plus Superdescuento. Both chains operate within thediscount format and, more specifically, within a format that could bedubbed ‘discount with parking’. They are stores that have about 800square metres of sales area, with facilities for access with vehicles, andpractise a low pricing policy. The results also reflect that thegeodemographic profile of Champion has an intermediate position. Theconfidence level for the differences between Champion and PlusSuperdescuento, Lidl, Consum, Mercadona and Caprabo is substantially


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Figure 2 Geodemographic characterisation of retail chains

%

0

20

40

60

80

100Non-residential area

N. Security and defence

M. Passive areas

L. Agricultural

K. Diversified rural

J. Sectoral mix

I. Non-qualified

H. Industrial

G. Tourist

F. Consolidated

E. Mid-level professionals

D. Qualified professionals

C. Provincial well-off

B. Urban well-off

A. Elite

SupersolPlusSuperdescuento

MercadonaLidlEl ArbolDíaConsumCharterChampionCaprabo


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Table 6 Differences between pairs of retail chains

Retail chain

Retail chain Caprabo Champion Charter Consum Día El Arbol Lidl Mercadona Plus Superdescuento

Champion 26.726 **(0.021)

Charter 77.780 *** 51.324 ***(0.000) (0.000)

Consum 60.700 *** 21.799 * 45.768 ***(0.000) (0.083) (0.000)

Día 182.546 *** 66.141 *** 39.568 *** 149.019 ***(0.000) (0.000) (0.000) (0.000)

El Arbol 196.222 *** 80.638 *** 74.493 *** 124.381 *** 87.387 ***(0.000) (0.000) (0.000) (0.000) (0.000)

Lidl 101.899 *** 24.495 ** 62.603 *** 89.914 *** 114.371 *** 100.877 ***(0.000) (0.040) (0.000) (0.000) (0.000) (0.000)

Mercadona 100.078 *** 23.689 * 53.428 *** 55.203 *** 87.473 *** 89.905 *** 35.912 ***(0.000) (0.050) (0.000) (0.000) (0.000) (0.000) (0.001)

Plus Superdescuento 85.889 *** 22.000 * 66.465 *** 71.837 *** 81.251 *** 77.772 *** 13.549 33.121 ***(0.000) (0.079) (0.000) (0.000) (0.000) (0.000) (0.484) (0.003)

Supersol 145.651 *** 44.466 *** 119.204 *** 120.237 *** 173.702 *** 87.903 *** 52.328 *** 55.210 *** 42.928 ***(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Pearson’s χ2 (significance in parentheses)* p < 0.10; ** p < 0.05; *** p < 0.01Date: November 2001


lower. The differences observed for the rest of the paired comparisons arehighly significant. As might be expected, the geodemographic profiles ofchains included within the same business group differ significantly (seealso Table 4). This result is consistent with the interest in avoidingcannibalisation effects. There are also significant differences betweenchains that operate within supermarket and discount formats (see Table 4).However, it is logical to expect stronger differences across formats thanwithin formats. In this respect, it should be taken into account that thedistinction between supermarkets and discount stores is not a very preciseclassification of retail formats, and there is still high heterogeneity withinthem. The similarities detected for ‘discounters with parking’, and betweensome supermarket chains, could be the consequence of the overlapping ofsegments targeted by retail chains within formats.

An in-depth analysis of the geodemographic profile of each retail chainis beyond the scope of this research. Nevertheless, it is interesting tomention the peculiarities of the geodemographic profile of each chain. Inthis respect, the geodemographic groups that are more typical of eachchain were identified by considering those whose percentage within thechain is one standard deviation above the average percentage acrosschains. Analogously, the geodemographic groups that are less typical ofeach chain were identified by considering those whose percentage withinthe chain is one standard deviation below the average percentage acrosschains. The result is also shown in Table 5, and is summarised in thefollowing associations.

• Caprabo. Typical groups: elite, urban well-off, qualified professionalsand tourist. Atypical groups: mid-level professionals, non-qualified,diversified rural and agricultural.

• Champion. Typical groups: elite and tourist. Atypical groups:consolidated and agricultural.

• Charter. Typical groups: qualified professionals, mid-levelprofessionals, industrial and sectorial mix. Atypical groups: provincialwell-off, consolidated, non-qualified, security and defence, and non-residential areas.

• Consum. Typical groups: provincial well-off and tourist.• Día. Typical groups: passive areas. Atypical groups: tourist.• El Arbol. Typical groups: provincial well-off, sectorial mix and

agricultural. Atypical groups: tourist and industrial.• Lidl. Typical groups: non-residential areas. Atypical groups: qualified

professionals.


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• Mercadona. Typical groups: qualified professionals.• Plus Superdescuento. Typical groups: diversified rural and non-

residential areas. Atypical groups: urban well-off and sectorial mix.• Supersol. Typical groups: consolidated, non-qualified, and security

and defence. Atypical groups: industrial.

To go deeper into the geodemographic profile of each retail chainrequires analysis of the different variables and dimensions that define theMOSAIC groups used in this research. In this respect, the retail chainswere characterised according to the geodemographic factors thatdetermine MOSAIC groups and typologies (see Table 2). The mean valuesof each factor within each group were the raw data used for this purpose.Then a factor score for each chain was obtained by weighting these meanvalues with the percentages of the groups within the chain. The results aresummarised in Table 7, and illustrated graphically in Figure 3.

Analogously, the main associations between chains and geodemographicfactors were pointed out by distinguishing factors with a high or low scoreacross chains. Specifically, a factor score for one chain was consideredrelatively high if it was one standard deviation above the average factorscore across chains. In the same way, a factor score for one chain wasconsidered relatively low if it was one standard deviation below theaverage factor score across chains. The results are shown in Table 7, andare summarised in the following associations.

• Caprabo. Services sector as professional activity, high linking with tourismand commerce, and active economies with low unemployment.

• Champion. Services sector as professional activity, and high linkingwith tourism and commerce.

• Charter. Primary sector/building as professional activity, intensiveurban development, low linking with tourism and commerce,households in transition and low economic activity.

• Consum. High linking with tourism and commerce.• Día. Low linking with tourism and commerce, older families and low

economic activity.• El Arbol. Extensive urban development, older families and high

unemployment.• Lidl. High economic activity.• Mercadona. Young families.• Plus Superdescuento. Young families.• Supersol. Extensive urban development, young families, high

unemployment and settled households.


311


The ro

le of g

eod

emo

grap

hic seg

men

tation

in retail lo

cation

strategy

312

Table 7 Characterisation of retail chains with geodemographic factors

Retail chain

Geodemographic factor Caprabo Champion Charter Consum Día El Arbol Lidl Mercadona Plus Superdescuento Supersol

Professional activity 5.97 ↑↑ 5.74 ↑↑ 5.03 ↓↓ 5.60 5.36 5.13 5.26 5.19 5.25 5.65Habitat 5.42 5.54 5.15 ↓↓ 5.34 5.56 5.74 ↑↑ 5.56 5.38 5.48 5.72 ↑↑Tourism and commerce 5.96 ↑↑ 5.95 ↑↑ 5.44 ↓↓ 6.00 ↑↑ 5.45 ↓↓ 5.59 5.75 5.73 5.68 5.91Families 5.45 5.55 5.42 5.50 5.35 ↓↓ 5.38 ↓↓ 5.50 5.71 ↑↑ 5.63 ↑↑ 5.69 ↑↑Employment 4.64 ↓↓ 5.03 5.08 5.09 5.40 5.63 ↑↑ 5.44 5.41 5.48 5.81 ↑↑Type of household 5.37 5.45 5.08 ↓↓ 5.40 5.32 5.38 5.36 5.55 5.56 5.76 ↑↑Businesses 6.30 6.28 5.75 ↓↓ 6.03 5.81 ↓↓ 6.15 6.39 ↑↑ 6.07 6.29 6.16

Score from 0 to 10↑ denotes score at least one standard deviation above the average score across chains↓ denotes score at least one standard deviation below the average score across chains

Date: November 2001


Intern

ation

al Jou

rnal o

f Market R

esearch V

ol. 47 Q

uarter 3

313 Figure 3 Characterisation of retail chains with geodemographic factors

4.6

4.8

5.0

5.2

5.4

5.6

5.8

6.0

6.2

6.4 Supersol

Plus Superdescuento

Mercadona

Lidl

El Arbol

Día

Consum

Charter

Champion

Caprabo

BusinessesType ofhousehold

EmploymentFamiliesTourismand commerce

HabitatProfessionalactivity


Conclusions

The growth of retailers is directly related to the definition of a locationstrategy to configure their store networks, and the need to segmentgeographic markets is implicit in the development and implementation ofsuch a strategy. Only those consumers that match the desired competitivepositioning should be considered. Since geodemographic segmentationrepresents the spatial approach to market segmentation – that is to say, itclassifies intra-urban zones according to the characteristics of theirresidents, it constitutes a potentially useful analytical tool for under-standing and selecting geographic markets. Therefore, geodemographicsegmentation may facilitate retail site selection in the growth andexpansion of retailers.

This research has tackled the role of geodemographic segmentation inreducing uncertainty in location decisions. The proposal of an explanatorymodel of the factors involved in retail site selection has allowed us to inferthat the main contribution of geodemographic segmentation is its potentialfor assessing the quality of consumers within a market area. The demo-graphic, socio-economic and psychographic characteristics of the residentsin an intra-urban zone indicate their shopping needs and habits and, con-sequently, their affinity with the store to be located. The geodemographicprofile can also be an indirect indicator of the presence of complementaryfacilities that allow retailers to generate and capture flows of possiblecustomers, or the presence of specific types of competitor that weaken orstrengthen the expected attraction for the new store. Finally, the geodemo-graphic profile can also be an indirect indicator of the costs involved in theopening and maintenance of the store in each possible location.

An empirical analysis of some leading supermarket chains operating inSpain has allowed us to prove that their store networks differ in terms ofthe geodemographic profile of their market areas. This lends support tothe potential of geodemographics to target specific publics in the locationstrategy. The commercialisation of standard geodemographic classifica-tions covering the urban geography of most of the developed countriesallows retailers to develop a detailed analysis of the spatial heterogeneityof the market and identify the residential zones that are more adequate forthe opening of new stores. The utility of these standard classificationsbecomes reinforced when they are complemented with additionalinformation recorded in the geographic information systems that supportthem.

In any case, the empirical analysis is only proof of the possibilities ofgeodemographic segmentation. The difficulties found in relation to the


314


geodemographic characterisations of supermarkets’ market areas shouldbe borne in mind. It was not possible to assign a geodemographic profileto some of them because of the incompatibility between data sources. Inaddition, the profile refers only to the close residential area that surroundsthe store. Although these limitations can be overcome, they require a moretechnical effort than the one required for this research. On the other hand,it should be taken into account that once the utility of geodemographicsegmentation has been proved, the interest lies in identifying the relation-ships between geodemographic dimensions, consumers’ shopping needsand habits, and the attributes and qualities of the stores that satisfy them.

Acknowledgements

The authors are grateful for the generous collaboration of EXPERIAN andPublicaciones Alimarket, which provided the data used in this study(MOSAIC information and Censo de Supermercados, respectively). Thisresearch was financed by the Regional Ministry of Economy andEmployment of Castile and Leon, Spain.

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