The impact of location-specific factors on

attractiveness and performance of

product categories

K.Campo1

E.Gijsbrechts2

T.Goossens3 A.Verhetsel4

1 Postdoctoral Fellow of the Fund for Scientific Research - Flanders; and UFSIA

2 Professor of Marketing at UFSIA and FUCAM 3 Research assistant at UFSIA

4 Professor of Economic Geography at UFSIA Acknowledgments The authors thank Mike Hanssens, Lee Cooper, and Randy Bucklin for their useful suggestions and comments.

The impact of location-specific factors on

attractiveness and performance of

product categories

I. Introduction

The topic of geo-marketing has received increasing attention from academics as well as

practitioners. The use of geographic information in marketing has many potential applications,

and research in the area is rapidly evolving (see e.g. Longley and Clarke 1995). Interesting papers

by Hoch et al. (1995), Montgomery (1997), Kalyanam and Putler (1997) and Mulhern et al.

(1998) demonstrate that geo-demographic profiles of a store’s trading area may strongly affect

SKU-item market shares or sales, and open up opportunities for highly profitable store-specific

marketing actions. Geo-marketing has also found its way to practice. Examples of companies that

incorporate geographical technology in their marketing strategy are Levi Strauss and IKEA. Levi

Strauss starts from spatial data to determine the combination of products to be offered in

individual stores, while IKEA uses a geographical information system to optimise the distribution

of its catalogue (Kotler et al. 1996, Longley and Clarke 1996).

In this paper, we are interested in the impact of location characteristics on the attractiveness and

performance of different product categories. Today, many retail chains offer a broad assortment

of products at various locations. It is clear that the overall performance of each outlet depends on

location characteristics like the socio-economic profile of local inhabitants, and competitive

conditions in the area. Less obvious, however, is the potentially differential impact of these

1

location characteristics on the various product categories in the retailer’s assortment. Depending

on the type of location, some product departments or categories within the store may be more or

less attractive, and hence contribute more or less strongly to overall outlet performance.

Insights into the relationship between relative category attractiveness and location characteristics

may be crucial to chain managers for many reasons. First, location differences in category

attractiveness may provide a basis to develop more efficient assortment strategies. During the last

two decades, competition in the retail sector has grown substantially (see e.g. Corstjens and

Corstjens 1995). In order to maintain their competitive position, retailers need to manage costs

carefully, and in many cases are forced to reduce their assortments. Yet, the increasing demand

for variety by a heterogeneous and variety seeking customer base implies that assortment

reductions entail high risks of dissatisfying and losing customers (see e.g. Johnson 1997, Kahn

1998). Adjusting assortment composition to location characteristics could provide a means to cut

costs down while limiting the risk of negative customer reactions. Second, retail companies face

the problem of allocating scarce resources available at each outlet to the different categories.

Examples of such resources are floor space, promotional budgets, or local personnel. Efficient

allocation of these resources requires insight into category attractiveness. To the extent that

category appeal varies with the trading area’s geo-demographic profile, adjusting for these

differences may allow for more efficient allocation decisions. Moreover, the problem of location-

specific assortment and allocation strategies is not only relevant to retail chains. Service

companies (like financial institutions) and not-for-profit institutions (like hospitals or cultural

organisations) could also benefit from the insights of this type of analysis. Yet, to our knowledge,

little systematic research has been conducted on the impact of location characteristics on category

attractiveness. This study attempts to shed light on the issue.

2

This research has three major objectives. First, we want to investigate the impact of location-

specific factors on the relative attractiveness of various categories within the store. A different

way of putting this question is: do location factors significantly affect the ‘mix’ of a store’s

business. In a second stage, we study implications for overall store performance. More

specifically, we analyze whether differential effects of location features on performance of

various product categories cancel out, or produce a net overall effect on store returns or store

margins. Third, we examine whether location-specific differences in category attractiveness

provide a rationale for micro-marketing strategies, that is, marketing assortment strategies at the

store level as opposed to chain wide assortment strategies.

3

Previous research has paid some attention to micro-marketing implications of demographic and

competitive environmental store variables (Hoch et al. 1995, Montgomery 1997, Kalyanam and

Putler 1997, Mulhern et al. 1998). While these studies emphasize the importance of micro-

marketing, and provide interesting basic insights for our study, they differ from the present

analysis in several ways. The previous studies concentrate on price and promotion issues, and

systematically link price responsiveness observed in different stores to geo-demographic data.

Price sensitivity is assessed at the level of product items or SKU’s, for a selected set of items

offered by the store. Even though these studies measure price response in several categories (2

categories in Montgomery 1997, and Kalyanam and Putler 1997, 18 categories in Hoch et

al.1995, 4 categories in Mulhern et al. 1998), their focus is on price response differences between

stores. Our analysis takes a different perspective. We primarily seek to explain differences in

intrinsic attractiveness of product categories or departments, and to analyze all categories offered

by the store. Our ultimate interest is in implications of these differences in intrinsic category

appeal for decisions at the store level, like allocation of marketing efforts and resources to each of

the departments in which the store is active. Naturally, given this focus, our models should

account for the role that these categories play in producing store level results, by allowing for

flexible category interactions, and distinguishing between within store shifts and store expansion

effects. Besides this difference in scope and resulting model structure, our analysis is somewhat

different from previous research in the category and location factors that it includes. While price

and promotion do not come into play, space allocations across categories are included. Also, the

models incorporate additional competitive indicators, and account for the potential business from

people not living in the trading zone. This leads to additional insights on location effects and

micro-marketing opportunities.

The remainder of the discussion is organized as follows. Section II sheds light on the

methodology adopted to study location effects on the composition and overall level of store

results. Section III describes the data sets, variables and estimation results of category and store

performance models. Implications for micro-marketing strategies are discussed in section IV.

Section V, finally, provides conclusions and indicates areas for future research.

II. Methodology

As outlined in the introduction, we are interested in the impact of location factors on store

performance. Previous research has demonstrated that overall store sales increase with local

market potential and buying power, and decrease with level of local competition. In turn, local

market potential and buying power can be related to population characteristics like family size,

age distribution, income level, and ethnicity (see e.g. Johnson 1997). The effects mentioned so far

are typically studied and obtained at the store level, based on associations between overall store

performance measures on the one hand, and characteristics of the trading area on the other hand.

4

As such, they reflect an ‘average’ influence of location characteristics for the different store

activities, or a location impact ‘common’ to the store’s different sources of business or categories.

We refer to these effects as ‘direct’ effects of location factors on store performance: they are

represented by the bold arrows in figure 1.

In addition to these direct effects, location factors may affect store results indirectly through their

impact on the relative attractiveness of various categories in the store. Differently stated, location

characteristics may determine the composition of store sales. This effect is represented by the

dotted arrow in figure 1, and is referred to as the ‘indirect’ impact of location characteristics on

store sales. Even if overall store sales remain unchanged, the share of return captured by various

categories may have a significant influence on overall store margins, given that different margins

apply to different categories within the store. In this paper, we intend to shed more light on (i) the

differential effects of location factors on category performance, and (ii) on how these effects

indirectly influence store sales and margins. In turn, we discuss the category performance model,

the store performance model and the variables affecting category and store performance.

Store Sales

return share 1

return share n

Location Factors

Store characteristics

surfaceshare 1

other var cat 1

surfaceshare n

other var cat n

Direct

(Return) ... ...IndirectFigure 1: Direct and indirect effects of location factors on store performance

5

A. The category performance model

To assess the differential effect of location characteristics on various product categories offered

by the store, we estimate a model linking the relative attractiveness of a category in a store, to

factors characterising that store’s location. Relative category attractiveness is measured as the

category’s share in total outlet return. By concentrating on the share that each category represents

in the outlet’s total sales value, we are able to detect any differential effects of location features

on intrinsic category attractiveness1. If location variables have no significant impact on

categories’ ‘share of return’, this indicates that all categories undergo a similar influence of these

location variables, which can be completely captured at the level of the outlet as a whole. If

location factors do affect category share of return, this points to differential effects of location

features on the attractiveness of various product categories. As stated before, these location

effects may prove crucial for efficient resource allocation across categories at the store level, as a

function of the store’s location type.

In addition to location factors, a category’s share of the store’s return may depend on category-

specific variables and store characteristics (e.g. marketing variables). These factors constitute the

explanatory variables in the return share models.

6

To model category share of return, we use an asymmetric MCI attraction structure suggested by

Carpenter, Cooper, Hanssens and Midgley (1988). Besides being robust, this model allows for

flexible interrelationships between categories. Category sales within a store are not mutually

independent, but may exhibit important complementary and substitution relationships, which

cannot be captured by symmetric attraction models. Ignoring category interrelationships may lead

to biased estimates. Using a flexible model like the one developed by Carpenter et al. (1988),

allows to avoid these biases. In our application, Carpenter et al.’s model takes the following form:

P = Cross

L * S * P * = Att

Cross * Att

Cross * Att = SR

rj,ir

mj,i,

tj,t

sj,s

rj,i,r

i0,ji,

mj,c,Cm

jc,c

mj,i,Cm

ji,

ji,

rm,i,

3,i,ts2,i,r1,i,

c

i

,

)1(

γ

βββ

ε

ε

β

Π

ΠΠΠ

ΠΣΠ

where :

SRij = share of return of product category i in store j Attij = intrinsic attractiveness of category i in store j Crossi,j,m = cross-category effect of category m on category i in store j Pi,j,r = value of product category characteristic r for category i in store j Sj,s = value of store characteristic s for store j Lj,t = value of location characteristic t for store j

Ci = set of product categories with potential asymmetric cross-category effects on the return share of category i

∃0,i , β1,ι,r ,β2,i s , β3,i t ,γi,m,r = parameters

B. The (overall) store performance model

The impact of location characteristics on overall store sales is assessed by means of a

multiplicative model with overall store return as the dependent variable, and store characteristics

as well as location characteristics as explanatory variables. The coefficients of these latter

variables capture what we referred to as the direct location effects. In addition, we include ‘total

store attraction’ as an explanatory variable. Using a procedure similar to Mason (1990) and

Bultez et al. (1995), we use the denominator of the asymmetric MCI model as a measure of total

store attraction. This measure accounts for the effect of location factors on the attractiveness of

product categories within the store, and therefore captures any indirect location effects on store

sales that might exist. The store return model then takes the following form:

7)2()()()( Totatt* L * S * = R jtj,

tsj,

s0j

2,ts1, µδδδ ∏∏

where Rj = total returns in store j Totattj = total attraction of store j (denominator of the attraction model (1)) δ0, δ1,s, δ2,t, µ = parameters

From the foregoing discussion, it is clear that our perspective and modeling approach is different

from that in previous micro-marketing studies. Instead of considering a subset of store categories,

and modeling sales or choice processes for specific items in these categories separately, we

essentially start from the perspective of the store as a whole. Category performance is assumed to

result from (i) the store’s overall capability of attracting business, and (ii) the category’s ability of

securing a portion of this business, taking its interactions with other categories into account. Note

that taken together, models [1] and [2] are a generalization of many category return models used

elsewhere. For instance, when µ equals 1, the product of [1] and [2] reduces to a double

logarithmic category return model similar to the one used by Hoch et al. (1995) and Montgomery

(1997), with possible cross-category effects for all category pairs.

C. Variables affecting category and store performance

As indicated in figure 1, three types of variables determine store performance and category return

share : overall store characteristics, location-specific variables, and variables related to separate

categories within each store. We discuss each of these variable types in turn.

Store Characteristics

A variety of store characteristics might affect performance at the store and category level. Store

chain image can influence overall store return as well as performance of specific categories

(Desmet and Renaudin 1998). In a similar vein, the store’s retail format and strategy (assortment

depth, quality level, service, price and promotion policy) is sure to affect store and category

8

performance (Bell and Lattin 1998, Desmet and Renaudin 1998, Sirohi et al. 1998, Dhar and

Hoch 1997). Specific outlet characteristics like store size influence performance in several

respects. Store size may be positively related to assortment width and depth, service level,

convenience, and lower likelihood of stockouts, and hence positively contribute to overall store

performance (Sinigaglia 1997). In addition, store size may differentially affect the performance in

various departments. Following Desmet and Renaudin (1998), large stores attract a higher

percentage of customers with lower store loyalty, higher assortment sensitivity and more impulse

buying – these customers are likely to have different basket compositions and across-category

spendings.

Location Characteristics

Three types of location features can be distinguished: variables related to the competition, socio-

economic characteristics of the trading zone, and urbanization variables.

Supermarket competition in the trading zone has been identified as a potential determinant of

store performance in several studies, with conflicting expectations and results. The presence of

more or larger retailers in the trading area may increase competitive pressure and exert a negative

impact on store performance (Dhar and Hoch 1997, Hoch et al. 1995). At the same time,

supermarket presence may point to high economic potential and buying power in the trading zone

(Ingene 1984), and hence be positively related to store performance. The impact of supermarket

competition may also vary by category. For example, consumers are more likely to ‘shop around’

in different supermarkets for beauty care items than for basic groceries like rice or salt. Also, the

chain’s competitive strength (quality/price ratio) compared to other supermarkets may strongly

vary by category (Dhar and Hoch 1997).

9

The impact of socio-economic variables on retail performance is widely discussed and

documented. Previous studies point to significant effects of variables like age (Montgomery

1997, Dhar and Hoch 1997, Johnson 1997, Webster 1965), education (Bawa and Shoemaker

1987, Narasimhan 1984), employment status (Bawa and Shoemaker 1987, Kim and Park 1997),

ethnicity (Dhar and Hoch 1997, Johnson 1997, Hoch et al. 1995, Kim and Park 1997), income

(Bawa and Shoemaker 1987, Chiang 1995, Kim and Park 1997, Zeithaml 1985, Johnson 1997),

family composition and household size (Kim and Park 1997, Bawa and Shoemaker 1987, Gupta

1988, Chiang 1995). These socio-economic variables influence store and category performance in

three main ways. First, they constitute indicators of buying power, and hence affect overall

spending levels as well as the allocation of resources over more versus less income-elastic

product categories. Second, they influence the pattern of needs of the population in the trading

zone. Obvious examples are a higher demand for children clothes in large family areas, or a

lower meat consumption among certain ethnic groups. Third, they clearly affect consumers

shopping behavior and store selection. Through their influence on price sensitivity, mobility and

time cost; socio-economic variables exert a major influence on the type of supermarket most

likely visited (e.g. EDLP vs. Hi-Lo: see Bell and Lattin 1998, Kim and Park 1997). Whether

consumers engage in one stop shopping, or tend to buy certain items/categories in specialty stores

rather than supermarkets, also depends on their socio-economic profiles (Kim and Park 1997,

Dellaert et al. 1998).

10

Degree of urbanization of the trading area may influence performance at the store and category

level in two main ways. First, consumer lifestyles - and hence category consumption patterns –

are expected to be different in urban versus rural areas. For example, the demand for outdoor

leisure products or garden equipment will be higher in rural environments. Degree of

urbanization is also linked to consumer mobility and distribution density, variables found to

clearly affect shopping behavior (Ingene 1984, Hoch et al. 1995). So far, few studies have

systematically accounted for the potential impact of ‘degree of urbanization’ on store or category

performance (Walters and Bommer 1996, Bearden et al. 1978).

Local versus non-local sources of business : An important issue is that the retail store’s clientele,

though mainly recruited among local inhabitants, does not exclusively consist of people living in

the trading zone. Fragmented surveys suggest that, at least in some locations, people working but

not living in the trading area may constitute a non-negligible source of business. There are two

reasons to distinguish between ‘local’ versus ‘non-local’ sources of business. First, as the group

of workers-commuters in a trading area becomes more important, the socio-demographic profile

of local inhabitants should receive less weight in the model. In addition, apart from having a

possibly different socio-demographic profile, workers-commuters may have different buying

patterns in the supermarket than local shoppers. As they typically shop during lunch hours or

after work, they are more likely to engage in minor or fill-in shopping trips; and/or experience

more severe time constraints. To our knowledge, the distinction between local and non-local

business- though potentially important - has not been taken up in previous research.

Category variables

Category performance is expected to vary from location to location as a result of differences in

category-specific competition. The presence of more specialty stores in the category’s line of

business might act as an ‘attraction’ mechanism, and stimulate category shoppers to purchase in

the trading area (Ingene 1984). At the same time, more category stores will increase category

competition and put pressure on sales. From the supermarket’s perspective, the latter negative

effect is expected to prevail: category return in the outlet is likely to decrease with the number of

specialty stores in the trading zone.

11

A considerable number of studies document a positive impact of space allocated to a category on

category performance (Corstjens and Doyle 1981, Bultez and Naert 1988, Bultez et al. 1989,

Desmet and Renaudin 1998). This positive effect can result from increased visibility (Desmet and

Renaudin 1998), or follow from the fact that more stocking space on the shelves reduces

stockouts (Borin et al. 1994). Visibility depends on absolute space allocated to the category. To

the extent that the category competes with other categories for customer attention, visibility also

depends on the category’s share in the store’s total sales. Similarly, reduced likelihood of

stockouts in a given store is linked to absolute stocking space. Yet, to the extent that larger stores

have higher overall demand, and hence, higher rotation rates, stockouts for a given absolute space

vary with store size. It follows that, if space share is included as an explanatory factor of

performance – as is done in most studies on shelf impact- overall store size should also be

included to capture the absolute space effect.

III. Empirical Application

In this section, we provide an empirical analysis of location differences in relative category

attractiveness and their impact on overall store performance. We start with a description of the

data set and variables, and then present estimation results of the category and store performance

models developed in the previous section. Micro-marketing implications are studied in section

IV.

A. Data and Measures

Our data set covers information on 55 supermarkets of a European retail chain. For each of these

outlets, the retail chain has delineated the trading area based on extensive surveys. Data provided

by the retail chain on store characteristics and performance is supplemented with information

from two other sources. Information on competition in the store’s trading zone is constructed

12

from the yellow pages’ electronic database. Socio-demographic characteristics of inhabitants

living in each store’s trading area are derived from census data. As this information is collected

once every 10 years, we used data of one year only and performed a cross-sectional analysis of

the relationship between location characteristics and category and store performance.

Store Characteristics

Overall store performance is measured by the outlet’s yearly return (R). The outlet’s total sales

surface is used as a measure of store size (STSIZE). Store chain image and retail format are not

included in the analysis: as all the stores in our sample belong to one chain and format, these

variables show no variation.

Location Characteristics

Intensity of supermarket competition is captured by STCOMP, defined as the store's own sales

surface relative to that of competing supermarkets in the trading zone. Thirty indicators are

available on socio-economic characteristics of the trading area. These indicators relate to age,

sex, income, family size and structure, employment, nationality, type of habitat, mobility,

education levels, and spending patterns of the households living in the trading area. As these

socio-demographic variables are highly correlated and cannot possibly be incorporated

simultaneously into the models, we performed a principal component analysis on these data.

Results of the analysis are reported in appendix B. Four principal components are retained from

the 30 original variables, accounting for about 84 % of the variation. The first factor (FACTOR1)

represents the presence of young households with two or more children. Factor two (FACTOR2)

loads heavily on single households, and low income families. Factor three (FACTOR3) has a

high score in locations dominated by >middle class’ families, where middle class refers to

13

intermediate income levels and social status. Factor 4 (FACTOR4), finally, is high in regions

where single child households are predominantly present.

While our census data do not allow to assess the socio-demographic profile of people working

but not living in the trading area, we do have indications on the number of persons who work in

each trading area without being a local inhabitant (EMPLOY). We will use this number as an

indicator of the potential source of business coming from outside the trading zone.

Our measure for the degree of urbanization is derived from the typology of Van Hecke and

Cardyn (1995), which classifies regions into several groups with different degree of urbanization,

based on their service functions and density. For reasons of parsimony, the original classification

was reduced to a 2-level classification2. Degree of urbanization is thus incorporated in the models

as a dummy variable, which equals 1 when the store is located in a highly urbanized city, and 0

otherwise.

Taken together, the ‘location’ variables used to characterize the trading zones (i.e. the socio-

economic variables, and ‘degree of urbanization’) are comparable to those used in the

development of neighbourhood classifications like ACORN or MOSAIC (see, e.g., Johnson

(1997) for a basic description and additional references).

Category Variables

For each store, information is available on 17 product categories. The second column of table 1

describes the composition of these product categories, as defined by the chain’s managers.

14

The first 7 product categories are situated in the food, the other 10 in the non-food sector. For

purposes of interpretation, the categories defined by the chain are linked to the classification of

store assortments suggested by Van der Ster and Van Wissen (1993). The latter distinguish 4

types of product categories: the core assortment, image-enhancing assortment, varying

assortment, and profit-increasing assortment. Results of a correspondence analysis mapping

product categories and stores together largely confirms this classification (see appendix C). The

core assortment (cat. 1, 2 and 4) is clustered in the centre, and more to the right we find a group

of loyalty improving product categories (which are part of the image-enhancing assortment

according to Van der Ster and Van Wissen, 1993; cat. 3, 5, 6 and 7). For the varying assortment, a

distinction can be made between clothing categories grouped together in the upper left corner

(cat. 9, 10, 11 and 12), and luxury products situated somewhat below this group (cat. 14, 15, 16

and 17). The category ‘Health and Beauty Care’ (cat. 8) is positioned in between the core

assortment and the luxury assortment, which can be explained by the fact that it contains a

mixture of products. Health and Beauty Care includes core products such as toothpaste, as well as

luxury products such as higher-priced cosmetics and perfume.

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Category performance is measured by its share in the store's total return (SR; based on yearly

figures to avoid seasonality influences). Relative shelf space allocated to the category is measured

by its share in overall store sales surface (SS). Discussions with several chain managers reveal

that the allocation of store space to categories is decided upon when the store is set up, and is not

systematically adapted over time. In addition, space allocation is said to be rather ad hoc, and not

formally linked to characteristics of the location. This anecdotal information is confirmed by

preliminary regressions which reveal no significant links between category space share (as the

dependent variable) and location features (as explanatory variables). At the same time, variation

in space shares for given categories across stores is found to be large. These insights are

important in the estimation stage: they suggest that while space shares are potential explanations

for return share, we need not worry about reversed causal effects.

Table 1: Product category classification

Cat. N° Category

Type

1 Groceries

Core assortment

2 M eat

C ore assortment

3 F ruit & Vegetables L oyalty improving assortment 4 D airy & Fine Meat - self service C ore assortment 5 D airy & Fine Meat - counter L oyalty improving assortment 6 F ish L oyalty improving assortment 7 B ake-Off L oyalty improving assortment 8 H ealth & Beauty Care B etween Core – Luxury assortment 9 M en's Wear & Underwear C lothing 10 L ady's Wear C lothing 11 C hildren's Wear C lothing 12 S hoes C lothing 13 A udio, video, micro-electronics L uxury products 14 H ousehold L uxury products 15 F abrics (Interior) L uxury products 16 L eisure (outdoor) L uxury products 17 Leisure (indoor) Luxury products

Category-specific competition in the store’s trading zone (CCOMP) is derived from the Yellow

Pages data base. Per product category offered in the store, a category-specific competition index

is constructed measuring the number of stores in the trading area in direct competition with the

considered product category (e.g. the competition variable for the fish category is equal to the

number of fish stores located in the trading area, relative to the number of inhabitants).

B. Differential effects of location characteristics on category performance

16

Explanatory variables in the asymmetric attraction model are indicated in the left column of

tables 2 and 3: they cover store size, competition at the store level, the four principal components

reflecting socio-demographic profiles of inhabitants of the trading area, degree of urbanisation,

number of people employed but not living in the area, level of category-specific competition in

the area and the share of surface of the product category.

Estimation of the asymmetric attraction model is carried out in three steps (see Carpenter et al.

1988). First, a symmetric model version is estimated, containing - besides store and location

characteristics - the category's own surface share as an explanatory variable for its attraction.

Since store and location features are not category-specific, we can only assess their differential

impact on category attraction in comparison with a reference category (see e.g. Maddala 1990).

We select category 1 (Groceries) as the reference category, a choice inspired by the fact that in

the correspondence analysis, category 1 was positioned near the origin representing the average

return share profile. Our choice of groceries as the reference category leads to the following

interpretation for the store and location feature coefficients: a positive (negative) coefficient for a

given variable and category means that the variable affects this category's share of return more

(less) strongly than the share of return of groceries. Share of surface and category-level

competition are category-specific measures, the impact of which can be estimated in an absolute

way rather than compared with groceries.

Having estimated the symmetric model, a next step is to look for significant cross-category

effects. Using an approach similar to Carpenter et al. (1988), we investigate to what extent the

share of return residuals of the symmetric model can be explained by other categories' share of

surface variables. After identification of significant cross-effects, the third step involves

estimation of an asymmetric model, including those selected cross-category effects. The results of

this final estimation step are included in tables 2 and 3.

As can be seen from these tables, the model exhibits significant explanatory power (R5 values

17

between 0.24 and 0.80), predominantly positive own surface share coefficients, and positive as

well as negative asymmetric surface share interdependencies. Also, the tables point to differential

influences of store and location features on the return share of various categories. To get a better

grasp on the importance and nature of these location influences on categories’ share of return, we

determined the improvement in fit compared to a model without location variables, and computed

return share elasticities3 for various categories and location variables. Table 4 compares R²adj

values for various share of return models. While allowing for category-specific (differential)

space effects and asymmetric interactions greatly improves fit over a base model not accounting

for these effects, including store and location variables leads to a substantial additional

improvement. On average, the adjusted R² increases from .27 to .51.

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Table 2: Estimation results asymmetric attraction model:

Core and Loyalty Improving assortment

Variable Cat.1 Cat.2 Cat.3 Cat.4 Cat.5 Cat.6 Cat.7 Cat.8

Constant Factor1 Factor2 Factor3 Factor4 Ccomp Urban Stcomp Stsize Employ SS1 SS2 SS3 SS4 SS5 SS6 SS7 SS8 SS9 SS10 SS11 SS12 SS13 SS14 SS15 SS16 SS17

-0.027

0.127b

0.075a

-0.284a

-0.020b

-0.849

b -0.028

b 0.035 b -0.033

b 0.030 b -0.001 -0.005 -0.068

b -0.001 -0.041

b

0.116 b

0.201a

0.025

-0.255a

0.015

-0.919 b

0.0030.021

-0.032 b

-0.007-0.330a

0.007-0.072a

-0.250 a

-0.007-0.773 a

0.0180.041

0.099 a

-0.138 a

-0.333 a

-0.056 a

0.163-0.027 a

0.076 a

-0.043 a

0.028 a

0.007-0.020

-0.035b

-0.132 a

-0.0130.009

0.067 a

0.157 a

-0.251

-0.017

-0.3170.182 a

-0.136 a

0.032b

0.040b

0.0270.235 a

-0.011-0.190b

0.063b

-0.580 a

0.212b

0.360 a

0.110b

-0.321 a

-0.007

-2.779-0.0070.047 a

-0.097

a

-0.0240.383 a

-0.014-0.091

a

0.215 a

0.055b

0.187 a

0.478 a

-0.217

a

-0.344

a

0.042 a

0.055 a

-2.723 a -0.127 a 0.083 a 0.013

-0.025 -0.245 a -0.045 -0.020 0.009 0.000

0.193 a -0.126 a -0.265 a

-0.103 a

-1.590 a

0.049 a

0.0090.054 a

-0.015-0.215b

-0.0010.0330.047

0.073 a

0.162 a

0.071 a

-0.280 a

R² 0.359 0.346 0.471 0.746 0.792 0.747 0.691

a Significant at the 5% level b Significant at the 10% level

19

Table 3: Estimation results asymmetric attraction model: Clothing and Luxury assortment

Variable Cat.9 Cat.10 Cat.11 Cat.12 Cat.13 Cat.14 Cat.15 Cat.16 Cat.17

Constant Factor1 Factor2 Factor3 Factor4 Ccomp Urban Stcomp Stsize Employ SS1 SS2 SS3 SS4 SS5 SS6 SS7 SS8 SS9 SS10 SS11 SS12 SS13 SS14 SS15 SS16 SS17

-2.673a

0.001 0.151 a

-0.054b

0.035b

0.018 -

0.185a 0.027

-0.042 0.013

-0.049

-0.104a -0.010

-0.110a

0.069 a 0.015

-4.303a 0.053

0.115a -

0.059b

0.039 0.145b

-0.246a -0.034 0.270b

-0.040

-0.387a

0.327a -0.030

0.044b

0.073a 0.058a

-

0.160a

0.202a

-6.173a 0.049

0.139a -0.015 0.030

-0.103 -

0.143b

0.036 0.411a

-0.081b

0.407b

-

0.343a

0.320a -

0.072b

0.080a 0.073a

-0.080a

-

0.111a

0.151a

-1.478-0.0630.195a

0.0340.062b

-0.274a

-0.227a

0.046-0.0930.009

0.321a

0.0680.318a

-0.185a

-0.045

0.172a

1.0310.104a

-0.020-

0.081b

-0.016-

0.186a

-0.1260.038

-0.524a

0.101b

-0.203a

0.158a

-0.034

-3.747a

-0.052b

0.160a

-0.0240.011

0.172a

-0.123b

0.089b

0.103-

0.058b

0.087b

-0.115b

0.168a

-0.206a

-0.029b

0.021

-5.427a 0.055

-0.044 -0.033 -0.031 0.142b

0.039 0.043 0.158 0.047 0.192

-0.119a

0.003

0.048

-2.887a -0.004 0.070a -0.016 0.031b

-0.006 -

0.141a 0.083b

-0.079 0.051b

-0.195a

-0.073a

0.071a

-0.383 -0.027 0.030b

0.074a -

0.035a -

0.096a -

0.071b

0.018 -

0.123b

0.048b

0.439a

-0.227a

0.056b

R² 0.605 0.799 0.331 0.754 0.649 0.629 0.653 0.243 0.521

a Significant at the 5% level b Significant at the 10% level

20

Table 4: Improvement in fit through introduction of location factors and cross-category effects (overall R²adj of category performance models).

Model Store factors Location factors Differential effects

R²adj

Symmetric Symmetric Asymmetric Symmetric Asymmetric

- - - + +

- - - + +

- + + + +

0.028 0.097 0.274 0.405 0.513

Figures 2 to 5 provide bar charts of the return share elasticities. Overall, location characteristics

appear to have the strongest effect on clothing and luxury items, and the weakest influence on

core assortment. Clothing is relatively more attractive in low income locations and less attractive

in highly urbanized and in employment regions. Luxury products are fairly susceptible to local

competition, are relatively more attractive in employment areas and undergo a mixed impact of

socio-demographic variables. For loyalty improving categories, the results are highly category-

specific. Return shares of Dairy & Fine Meat, Fish, and Bake-Off are affected by socio-

demographic characteristics (factor 1, 2 and 3; urbanisation and employment), but in different

degrees and directions. Return share of Fruit & Vegetables, in contrast, does not vary with the

location’s socio-demographic profile4. Even though these results are derived from a relatively

small database, they have face validity, and are intuitively acceptable to the chain’s managers.

21

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

Factor1 Factor2 Factor3 Factor4 Ccomp Urban Stcomp Stsize Employ

groceriesmeatdairy & fine meat (self service)health & beauty care

Figure 2: Return share elasticities, core assortment

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

Factor1 Factor2 Factor3 Factor4 Ccomp Urban Stcomp Stsize Employ

dairy & fine meat (counter)fishbake-offfruit & vegetables

Figure 3: Return share elasticities, loyalty improving assortment

22

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

Factor1 Factor2 Factor3 Factor4 Ccomp Urban Stcomp Stsize Employ

men's wear & underwearlady's warechildren's wearshoes

-0,6

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

Factor1 Factor2 Factor3 Factor4 Ccomp Urban Stcomp Stsize Employ

householdfabrics (interior)leisure (outdoor)leisure (indoor)audio, video & micro-electronics

Figure 5: Return share elasticities, luxury products

Figure 4: Return share elasticities, clothing

23

C. Impact of location characteristics on overall store performance

Having assessed the impact of location factors on categories’ return shares within the store, the

next step is to investigate whether location-category interactions translate into differences in

overall store performance. Table 5 reports the results for the multiplicative store return model,

with the four socio-demographic factors, urbanization, store level competition, store size, and

number of employed living outside the trading zone, as explanatory variables (variables such as

the socio-demographic factors and urbanization dummy can take on a zero- or negative value and

are for this reason incorporated in exponential form). The parameters of these variables measure

direct effects of store and location factors on store return. Indirect effects through category

location-interactions are captured by the last variable reported in the table, TOTATT, which is

the denominator of the category return share model of the previous section.

Table 5: Estimation results of the multiplicative store return model Variable

Coefficient

t value

Factor 1 0.0015 0,050 Factor 2 0.0253 0,923 Factor 3 -0.0334 -1.231 Factor 4 0.0050 0.205 URBAN 0.0940 1.399 STCOMP 0.0456 0.912 STSIZE 0.7495 6.356 EMPLOY -0.0562 -1.460 TOTATT 0.1383 1.202 R² 0.602

The table suggests that returns are systematically higher in urban areas and in large stores. The

presence of many people working but not living in the trading area negatively affects store sales.

This is not too surprising since, having corrected for ‘economic activity’ in the variable URBAN,

24

the negative coefficient can be interpreted as commuters typically shopping for smaller baskets in

the store than local inhabitants. Except for factor 3 – which points to a dominant presence of

middle class families - none of the socio-economic factors directly affect store sales, nor is store

level competition a determinant of store return. The latter finding is in line with results obtained

by Hoch et al. (1995). Note, though, that while our overall store sales are not affected by

supermarket competition, competition affects our category sales through the return share models.

The coefficient of TOTATT is marginally significant, suggesting that location characteristics

affect store returns in an indirect rather than direct way, but that the overall effect is rather weak.

Yet, given that profit margins strongly differ between categories, location characteristics will

have a more pronounced effect on store margins through their impact on the category’s share of

business within the store. Both issues will be taken up in the next section.

IV. Implications for micromarketing: an illustration

Identifying the differential influence of location features on category attractiveness may provide a

formal basis for location-specific category management. The implications of the analysis are very

diverse. The results can help managers determine the appropriate spread of promotional budgets

over categories, depending on local conditions. They could also provide insights into appropriate

assortment composition. In the retail chain studied here - like in many multiproduct-multimarket

companies to date - the assortment offered in various locations is essentially the same. At present,

variations in store size only imply more or less facings assigned to items in the given assortment.

Insights into the characteristics of regions where certain categories are more successful may

reveal information on their preferential target groups, and form a basis for more appropriate

assortment composition, in line with the requirements of those segments. Eventually, the analysis

25

could form a first basis for locally differentiated assortment decisions. Our analysis suggests, for

instance, that clothing is predominantly bought in the supermarkets by single low income

families. The chain may want to tailor its clothing assortment to the requirements of these groups.

In brief, companies who want to exploit micro-marketing opportunities may benefit from the

insights offered by the type of research presented here. An alternative use of our models could

be to derive efficient allocations of scarce resources across product categories, as a function of

local conditions. The allocation of shelf or floor space is one example, where not only the

categories’ position in the store (store layout) could be locally determined, but also the share of

total store space each category is assigned.

In the remainder of this section, we empirically illustrate the implications of local differences in

category attractiveness on space allocation decisions within the store. More specifically, we

examine whether location-specific space allocation strategies allow to increase the chain’s overall

profit margin. As a benchmark, margins at the local or micro-marketing optimum are compared to

(1) results obtained with the current surface allocation scheme, and (2) store margins at the global

optimum, i.e. the optimal allocation of store surface for the chain as a whole disregarding location

characteristics. A mathematical specification of the global optimization problem can be found in

equation [5], the local optimization problem is displayed in equation [6].

26

outletsallovericategoryofsharesurfaceaverageSS

jstoreinicategoryoftscostsplenishmenReRCwhere

SStoSubject

SSSSRR

RC

RCSRRmargNetmMAX

i

ij

i i

ij

ijjiij

ijj i ijjiSSi

=

=

=

=

−=

∑

∑ ∑

.

.

.

1

)5()(.

α

jstoreinicategoryofsharesurfaceSSwhere

jSStoSubject

SSSSRR

RC

RCSRRmgarNetmMAX

ij

i ij

ijj

ijjiij

ijj i ijjiSSij

=

∀=

=

−=

∑

∑ ∑

,1

)6()(

α

Objective function of both optimization problems is the chain’s overall net margin, consisting of

the sum of category margins over all categories (i) and outlets (j). Surface share decisions for a

given product category (i) can affect chain margins in three different ways: through a direct effect

on the category’s share of return (SRij), an indirect effect on the store’s total returns (Rj), and

through cost implications (RCij). Given our model specification, surface share variables interact

with location characteristics, and hence may produce a different effect on category return share

and total store returns in different locations. For more details, we refer to appendix A, where

mathematical expressions for the elasticities of return share and store sales with respect to space

share are developed. Although surface share adjustments do not involve a direct cost, they may

affect the store’s operating costs indirectly through an effect on the required replenishment

frequency. In order to avoid out-of-stocks and sales losses, substantial reductions in a category’s

27

surface share will have to be compensated with more frequent replenishments, while increases in

surface share allow to reduce the number of replenishments. Replenishment costs (RCij) are

computed by means of a simplified cost function, similar to the one used by Bultez et al. (1989)

to solve shelf space allocation problems. Following this approach, costs are specified as a linear

function of the number of replenishments. This number is approximated by the ratio of a

category’s sales to its sales surface. Because our sales data are expressed in value rather than

volume, we rescaled the unit cost coefficients ∀i to arrive at similar cost levels as those applied

by Bultez et al. (1989).

Gross margin indices were obtained from the retail chain. These indices reflect the gross margins

of the different categories relative to groceries (with index 100). To convert these figures into

percentage margins per category, we combine them with an estimated base margin of .25.

Previous discussions with retailing experts indicate that for the type of chain studied here,

average gross margins amount to approximately this figure, which is supported in Messinger and

Narasimhan (1998).

As expected, the local optimum clearly outperforms the global optimum, and yields an additional

yearly return of some 750000 EURO. It is important to recognize that this monetary gain comes

at virtually no cost: it only necessitates a one-time reallocation of store space to categories.

Moreover, these ‘necessary’ space shifts are modest, and clearly exhibit face validity.

Compared to the global optimal allocation plan, largest differences in location-tailored optimal

surface shares are observed for the categories that are most sensitive to location characteristics,

such as clothing and luxury products. In the local optimal allocation plan, these categories receive

28

a substantially smaller share in some outlets and a much higher one in others. For example, where

category 16 (Outdoor Leisure products) receives an optimal surface share of 1.11% in the global

optimization solution, its optimal surface share ranges between 0.24% and 1.54% in the local

optimal allocation. On the contrary, local optimal surface shares of categories belonging to the

core assortment hardly differ from the global optimum. For instance, the optimal share of

category 2 (Meat) is equal to 7.71% in the global optimum, and ranges between 6.04% and 8.31%

in the local optimum. Although indicative and to be interpreted with care, these results clearly

illustrate the potential benefits of a geo-marketing approach to assortment and allocation

decisions.

V. Conclusions

This paper provides evidence of interaction effects between the characteristics of the area in which a

store is located, and the relative attractiveness of categories offered by the store. Using an

asymmetric attraction model suggested by Carpenter et al. (1988), we show that, besides store

characteristics (like store size) and category specific decision variables (like space share allocated

to the category), environmental factors may significantly affect the intrinsic appeal of different

categories. These results confirm the findings of previous authors that socio-demographic

characteristics of people living in the trading area may influence performance, and call for store-

specific marketing strategies.

Our study differs from previous papers on geo-marketing in several respects. First, the present study

differs in the location, competitive, and category-specific variables included. Our results show

that accounting for the relative presence of workers-commuters in a trading area provides a

‘correction’ on overall store returns and affects the type of categories bought. Future studies may 29

benefit from taking the presence, and possibly also the socio-demographic profile, of workers-

commuters into account. Second, we accounted for store ánd category specific competition, and

found that supermarket competition may not be the only competitive influence to be dealt with.

Even though we used only a crude measure to assess competition at the category-level, this

variable was found to have a significant and strong impact on categories’ share of return, and

hence sales.

At a more substantive level, the present study has a different scope and focus than previous studies.

It starts from the whole mix of categories offered by a store (instead of concentrating on a

specific subset in isolation), and basically adopts a ‘store view’. Consistent with this, it uses a

model that accounts for complex interdependencies between categories in producing store level

results. Also, unlike previous studies who assessed sensitivity to specific marketing instruments

(across categories) as a function of location characteristics, this analysis concentrates on

differences in intrinsic local appeal between categories. Such differences in intrinsic appeal will

have consequences for effective allocation of store resources to categories, and affect appropriate

category composition. To illustrate this, we examined the implications of location differences in

category attractiveness for store surface allocation decisions. Although indicative and to be

interpreted with care, the results suggest that location-tailored space allocation strategies may

provide a net improvement in overall chain margins, and may lead to different allocation patterns

for product categories that are most sensitive to geo-demographic and competitive charcateristics

of the store’s trading area.

30

Clearly, the present study has a number of limitations. The data set is fairly restrictive, in the sense

that it is purely cross-sectional, and considers relatively ‘aggregate’ product categories. Future

research may benefit from looking at more detailed category definitions. Also, considering time-

based observations may lead to better measures of causal space share effects. In addition, the

model adopted here is restrictive in the sense that location factors only influence the categories’

intrinsic appeal. Consumers’ sensitivity to space for a category, or interdependency patterns

between categories, are only ‘indirectly’ affected by characteristics of the location, through

differences in intrinsic category appeal. Future models could consider making space-related

parameters a direct function of location characteristics. Finally, our illustration was limited to the

impact of space shifts for otherwise stable assortments. This largely explains why only limited

gains in store return or margin were obtained with location-tailored strategies. Future research

should consider the effect of more fundamental changes in the category’s composition as a

function of location characteristics. Our study provides some basic directions on what location-

specific category adaptations may prove appropriate, but specific experimental research is needed

to quantify the implications of such adaptations.

31

Appendix A

Cross-category return elasticities

For any store j, the return elasticity of category k with respect to changes in the space share of

category i, is the sum of the elasticity of total store returns, and of the elasticity of category k=s

return share with respect to i=s space share:

jstoreinkcategoryproductofreturnRwhere

A + =

jk

SS,SR SSR SS,R ji,jk,ji,jji,jk,

=,

)1(ηηη

The store return elasticity is easily found to equal:

)2(A]*SR+ *SR[* = rm,i,jm,Cm

ri,1,ji, SS,Ri

ji,jγβµη Σ

∈

The return share elasticity for category k with respect to i’s space share equals:

otherwisequal to if i=k, el to iable equavardummy Dwhere

ASR+*SR[-]*D+*)D-[(1 =

=

]*

ki,

rm,i,jm,Cm

ri,1,ji,ri,1,ki,rk,i,ki,SS ,SRi

ji,jk,

01

)3(γββγη Σ∈

Both the store sales (A2) and share of return (A3) elasticities indirectly depend on location

characteristics, through the impact of the latter on the category’s return share. If the impact of

space share shifts on category returns is location-specific, so will be their effect on store margins.

Hence, space allocations maximizing net store margin may vary by location.

Appendix B

Principal Component analysis of socio-demographic variables

32

The 30 original variables describe the distribution of several socio-demographic

characteristics over the population of each store’s trading area. For example, income

distribution is described by 5 variables corresponding to 5 different income classes. Each

Table 7: Principal component analysis of socio-demographic variables

Var. Description Factor1 Factor2 Factor3 Factor4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

% of single households % of 2 person households % of 3 person households % of households with 4 or more persons % of population under 14 years of age % of population between 15 and 64 years of age % of population over 64 years of age % employed % unemployed % in military service % of population under 18 years of age % of students % of population who has never had a profession % retired % of population with no profession % unmarried % married % widowed % divorced % of households without children % of households with 1 child % of households with 2 children % of households with more than 2 children % of European Mediterranean nationality % of Islamitic origin % with income between 2500-6250 Euroa % with income between 6250-12500 Euroa % with income between 12500-17500 Euroa

% with income between 17500-25000 Euroa % with income over 25000 Euroa

-.698 -.151 .533 .853 .788 .702

-.936 .146 .114 .326 .816 .235 .359

-.893 .098 .351 .414

-.925 -.853 -.843 -.378 .922 .501 .016 .103

-.113 -.455 .102 .237 .128

.608 -.858 -.518 -.271 .167

-.261 .011

-.759 .525

-.657 .146 .368 .028

-.388 .467 .872

-.878 .090 .431

-.438 .186

-.157 .591 .584 .675 .881 .711 .026

-.346 -.438

-.077 -.005 .112 .038 .266

-.018 -.186 -.508 .761

-.319 .244

-.801 .553

-.004 .732

-.133 .095 .046

-.024 -.112 .303

-.197 .127 .449 .379

-.006 .415 .951 .754

-.845

.101 -.105 .194

-.056 -.456 .264 .200 .006 .201

-.088 -.412 .144 .095 .052 .073

-.170 -.036 .280 .155 .029 .718 .096

-.597 .329

-.496 -.109 -.049 -.121 .132 .077

Cumulative % of variance explained 34.12 63.83 78.26 83.61 a Household income indicated in tax declarations

variable indicates the percentage of the population belonging to the corresponding income

class. A description of the other variables can be found in the second column of table 7.

In order to reduce these variables to a manageable set, we performed a principal components

analysis. Based on a scree-test, a solution with 4 factors was selected, explaining 84% of total

variation. Factor loadings – obtained after a varimax rotation – are displayed in column 3 to 6

of table 7.

33

Appendix C

Correspondence analysis of product categories and outlets To facilitate interpretation of the results, we sought for a re-grouping of the 17 product

categories into larger classes with similar characteristics. In view of the research objective,

product categories belonging to the same class should exhibit a similar variation in relative

attractiveness over various outlets of the supermarket chain. To derive clear management

implications from the results, categories in the same product class should also serve similar

functions within the retail store. The classification of Van der Ster and Van Wissen (1993) is

based on the second criterium, and distinguishes between 4 broad product classes: the core

assortment consisting of the product categories that are essential to the store’s business, the

image-enhancing assortment which is made up of product categories that may improve the

store’s image (such as service goods, loyalty-improving products, high-quality products, and

innovative products), the varying assortment comprising temporary product offerings (such as

seasonal products), and the profit-increasing assortment consisting of products with a higher

than average profit margin to compensate for the low margins on core and other products.

To check whether a re-grouping of the 17 product categories along these principles yields

product classes with similar attractiveness profile, we performed a correspondence analysis

mapping product categories and stores together, based on the stores’ distribution of sales over

the 17 categories. The two-dimensional configuration is displayed in figure 6. As this figure

demonstrates and was discussed above, the results largely confirm Van der Ster and Van

Wissen’s classification. The major exceptions are that (1) there are two different types of

varying product classes (clothing and ‘luxury’ products), and (2) one product category is

situated in between two category clusters (Health and Beauty Care).

34

-1.5 -1 -0.5 0 0.5 1 1.5-1

-0.5

0

0.5

1

1

23

4

5

6

7

8

9

10

11

12

13

14

1516

17

X-Axis

Y-A

xis

Figure 6: Correspondence analysis of product categories by stores

35

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Footnotes

1 Price differences between product categories should have no impact on the results, as we concentrate on variation in return shares over stores (and not over product categories), and uniform pricing strategies are used across stores. 2 Preliminary analysis indicated that a higher-level classification does not substantially improve model results. 3 For the dummy variable >degree of urbanization=, the strength of effect was measured as the percentage difference in share of return observed for the product category in urban (dummy 1) vs rural (dummy 0) areas. In addition, elasticities of socio-demographic variables cannot be evaluated at the mean value, since each factor has zero mean. For this reason, we computed the % change in return share, resulting from a marginal change in the factor value.

4 As an additional check on the stability of the results, we re-estimated the model using a new set of data on store characteristics and category variables (that is, for a subsequent year), together with our base information on location characteristics (which is not available year by year, and assumed stable over time). The re-estimated model exhibited similar location effects, except for the clothing category where deviations were observed. A discussion session with the chain’s managers revealed that these changes could be attributed to a (temporary) turnaround in the management team for clothing. Overall, the check confirmed our findings for all other categories.