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Using Shopping Baskets to Cluster Supermarket Shoppers
Tom Brijs Gilbert Swinnen Koen Vanhoof Geert Wets Department of Applied Economic Sciences
Limburg University Center B-3590 Diepenbeek, Belgium
Email: [email protected]
Abstract. Today, competition forces consumer goods manufacturers and
retailers to differentiate from their competitors by specializing and by offering
goods/services that are tailored towards one or more subgroups or segments of
the market. Retailers understand that shoppers are heterogeneous in nature,
that they possess different wants and needs and that it is impossible to satisfy
them all. However, the retailer in the FMCG sector is highly limited in his ability
to segment the market and to focus on the most promising segments, since the
typical attraction area of the retail store is too small to afford neglecting a
subgroup within the store’s attraction area. In fact, the supermarket should
appeal to as many of the heterogeneous public in its attraction area as possible.
Nevertheless, given the huge amount of transactional and loyalty card data being
collected, this leads to the intriguing question whether in-store segmentation
may be a viable alternative to discover homogeneous customer segments based
on their shopping behaviour. Indeed, the discovery of homogeneous customer
groups who tend to use the store similarly could be used for instance to target
individuals with customized promotions. Therefore, in this paper we will
introduce a new methodology for behaviour-based customer segmentation.
More precisely, we will use the method of latent class mixture modelling to
discover hidden customer segments on the basis of the contents of their
shopping baskets. Furthermore, loyalty card data is used to find out if these
customer segments differ in terms of socio-demographic or lifestyle
characteristics and whether these characteristics can be used to target different
customer segments with more relevant product offers. The method is carried
out on market basket data from a major Belgian supermarket store. Results
indicate that a number of distinct segments can be identified who differ
significantly in terms of the average purchase rate within a pre-determined set of
product categories.
1 Introduction
Today’s retail environment is characterized by increased competition: retailers are fighting over
the consumer’s “share of wallet” and satisfying the diverse wants and needs of the consumer
forces the retailer to offer a wide variety of products in an environment where shelfspace is
limited and there is a pressure to stock new products every day. This is a continuous balancing
act and the extent to which a retailer understands and is able to satisfy the wants and needs of
the consumer better than his competitors will ultimately determine his success. It is in this
context that most supermarket retailers have quickly understood the need to fathom consumer
behaviour. Since the late 70’s, this has resulted in large investments in retail information
systems to collect shoppers’ data. Today, most retailers possess electronic scanner systems
and offer customers frequent shopper programs in order to collect customer data to better
understand consumer shopping behaviour.
However, according to Corstjens [5] the FMCG retailer is highly limited in his ability to
segment the market and to focus on the most promising segments. Indeed, the typical
attraction area of the retail store is too small to afford neglecting a subgroup within the store’s
attraction area. In fact, a supermarket should appeal to as many of the heterogeneous public
in its attraction area as possible.
Despite this limitation, the availability of huge amounts of transactional data about
customers’ purchases offers retailers some excellent opportunities for in-store segmentation.
Indeed, knowledge about how different customers tend to use the store enables a more
customized treatment of these customers, for instance in terms of targeted promotions or
services. In fact, the retailer involved in this study is currently evaluating a segmentation
strategy based on customer purchase behaviour in the store. The idea is that segmentation
places customers in groups on the basis of their similarity on a chosen set of variables.
Afterwards, members of different segments will be treated differently in marketing strategies to
achieve different marketing objectives with greater overall effect.
In this paper, we will propose a behaviour-based customer segmentation methodology.
More specifically, we will use the purchase history of supermarket shoppers to discover groups
of shoppers who show similar purchase rates in a number of product categories by means of
latent class cluster modelling. Segmentation based on shopping data has the advantage of
grouping customers having similar shopping patterns. Information of this kind can be useful for
retailers to target customers with more personalized marketing actions.
The remainder of the paper is organized as follows. Section 2 provides an overview of
popular bases for segmentation and justifies the use of behavioural based segmentation in this
paper. In section 3 the technique of latent class cluster models will be introduced and a
specific model for clustering customers based on their purchase rates in different product
categories will be developed. Subsequently, section 4 will illustrate the empirical results of the
developed latent class clustering model on sales transaction data that were obtained from a
major supermarket in Belgium. Finally, section 5 is reserved for conclusions and limitations of
this research.
2 Popular bases for segmentation
The measures most frequently used for segmentation are typically drawn from either one or a
combination of the following: (geo-)demographics, behaviour, benefits, and psychographics
[21]. However, the concrete choice for one or a combination of these segmentation bases
largely depends on the business question under study [28].
2.1 Demographic segmentation
Since, as a result of loyalty programs, demographic information (such as residence, profession,
age, gender, ethnicity, marital status, education, etc.) about customers is widely available,
segmentation based on demographic variables is probably the most popular method for
segmentation (e.g. [11]). For instance, in their study, Segal and Giacobbe [23] carry out a
Ward’s hierarchical cluster analysis on 10.000 customers in a large metropolitan area in the USA
on the basis of a set of demographic characteristics, such as occupation, household
composition, income, housing, etc. Their analysis revealed 4 demographic segments for which
they subsequently analysed and found profound differences in the market share of 4 major
supermarket chains. Gensch [11] clustered 700 individuals on 19 demographic variables, such
as income, age, marital status, number of cars, etc. in order to test the advantage of
disaggregate choice models. Gensch shows that meaningful segmentation can increase the
predictive fit of choice models and can lead to different managerial actions and strategies when
the assumption of homogeneity is too restrictive. McCurley Hortman et al. [14] carried out a
demographic segmentation based on the age of the head of household, number of working
adults in the household, and years in residence at the current address. Their cluster analysis
produced three distinct segments, i.e. a baby boomers segment, a middle-aged family group
and an elderly segment. These segments were subsequently used to study the differences in
the respondent’s emphasis on price, selection, and convenience when selecting supermarkets.
Despite the above mentioned studies, researchers often criticize that segmentation on the
basis of demographics lacks a direct link with purchase behaviour [6, 9]. In other words, it is
not theoretically clear whether differences in socio-demographic background produces a
significantly different purchase behaviour. While some studies found some slight differences in
responsiveness to marketing variables, many studies conclude that these differences are in fact
too small to be relevant for practical purposes [10, 17]. Probably, the usefulness of (geo-)
demographic variables can best be evaluated in the context of backward and forward
segmentation [27].
In the forward approach to segmentation, demographics are used to apriori form a number
of clusters which are subsequently related to product-specific measures of consumer behaviour.
For instance in our case, the supermarket retailer could segment the shoppers according to the
ownership of a microwave and/or freezer, to subsequently test whether this affects the
purchase frequency of pre-packed meals in the supermarket.
In the backward approach, product-specific measures of consumer behaviour are used to
form apriori segments, to subsequently profile these segments according to general customer
characteristics, such as (geo-)demographics. For instance, the supermarket retailer would
define a number of segments of ready-made meals in terms of the usage frequency, to
subsequently profile heavy versus light users in terms of the ownership of a microwave or a
freezer. In other words, the retailer would then test whether the ownership of a microwave
and/or freezer are able to discriminate between light versus heavy users of the ready-made
meals category. In our opinion, this backward approach is preferable.
2.2 Behavioural segmentation
Segmentation based on purchase behaviour aims at discovering groups of customers that
exhibit a similar purchase behaviour. However, the definition of behaviour in this context may
cover many loads. For instance, retailers often distinguish between light and heavy users of a
product (category), or regular stock-up shoppers versus emergency top-up shoppers, or lunch-
time shoppers versus evening shoppers, or home and daytime shoppers versus work and
weekend shoppers, or fast-checkout customers versus regular checkout customers, etc. In the
case of in-store segmentation, differences in behaviour of this kind can be relatively easily
extracted from receipt data. For example, Reutterer [22] uses Kohonen Self-Organizing Maps
(SOM) to cluster supermarket shoppers into 9 segments based on their preference for different
brands within the margarine product category. Preference in this context was measured by the
relative purchase frequency of a brand within its category. He found distinct segment
differences in the preference for private label and national brands. Kopp et al. [15] applied a k-
means clustering on a group of 1.650 supermarket shoppers based on a vector of share-of-
shopping-visits for eight competitive retailer groups. They identified six distinct segments and
subsequently tested their difference in profile on the basis of a number of descriptor variables,
such as fashion lifestyles, attribute importance, demographics, etc. McCurley Hortman et al.
[14] carried out an a-priori segmentation of shoppers based on whether the majority of their
shopping trips were made to a discount-image store or to a nondiscount-image store. In yet
another study, Ghosh [12] applies an a-priori segmentation to classify consumers into loyal or
deal-prone segments based on their past purchase patterns in order to facilitate evaluation and
adjustment of promotional plans.
In general, it is believed that clusters of behaviour based segmentation have relatively
strong predictive power to reveal differences in actual purchase behaviour. Therefore, in this
study we will use purchase behaviour to cluster customers into groups.
2.3 Benefit segmentation
Benefit segmentation aims at discovering clusters of consumers that seek similar benefits when
evaluating and choosing or purchasing (in) products or retail stores. Benefits may be
measurable, such as economical or durable, but may also be rather abstract concepts, such as
trendy or sportive. The difference in importance that consumers attribute to these benefits
offers an interesting basis for segmentation since they reflect the needs that consumers have.
For instance, Miller and Granzin [20] discover segments of consumers on the basis of a number
of benefits from fast-food retail chains, such as speed of delivery, price, friendliness of
employees, taste of the food, etc.
2.4 Lifestyle/psychographic segmentation
Lifestyle segmentation involves discovering groups of consumers that have a similar lifestyle
pattern, also called psychographic pattern. Lifestyle segmentation variables refer to how
consumers spend their leisure time (activities), what are their interests, and how they think
about themselves and the society in general (opinions) [3]. Gutman and Mills [13] cluster
fashion merchandise shoppers based on a number of fashion lifestyle related variables, such as
fashion leadership, fashion interest, etc. An important motivation to use these specific fashion
related lifestyle variables is that other studies have argued that ‘general’ lifestyle variables
provide a poor discrimination towards purchase behaviour. Gutman and Mills’ study revealed 7
fashion segments, such as leaders, followers, independents, uninvolveds, etc. which were
subsequently profiled on a set of demographic variables.
3 Latent class (LC) cluster models
In latent class (LC) cluster models [19, 24, 25, 26], the observed data is assumed to arise from
a number of apriori unknown segments or latent classes that are mixed in unknown
proportions. The objective of LC cluster models is then to ‘unmix’ the observations and to
estimate the parameters of the underlying density distributions within each segment. The idea
is that observations (in our case supermarket shoppers) belonging to the same class are similar
with respect to the observed variables in the sense that their observed values are assumed to
come from the same density distributions, whose parameters are unknown quantities to be
estimated. The density distribution is used to estimate the probability of the observed values of
the segmentation variable(s), conditional on knowing the latent class from which those values
were drawn. Therefore, it is critical to choose an appropriate density distribution depending on
the type of variables being analysed. Some popular distributions include the normal distribution
for continuous variables, the multinomial logit or multinomial probit for multichotomous
variables, and the Poisson distribution for frequency data.
3.1 Formulation of the LC cluster model
In this study, the objective is to cluster supermarket shoppers based on the contents of their
shopping baskets, being measured by the purchase rates in different product categories of the
assortment over a certain period of time. Since it is known that purchase rates can be
modelled with the Poisson distribution [8], we will use the Poisson distribution throughout the
rest of this paper.
3.1.1 Single variable LC cluster model
Given a number of subjects (i.e. supermarket shoppers) on which the variable yn (i.e. purchase
rate within one particular product category) is measured, and assume that these values arise
from a population that is a mixture of S segments of unknown sizes π1, …, πS, then the
unconditional LC cluster model can be defined as:
ƒ(yn|Φ) = �=
S
s 1
πs ƒs(yn|θs) with �=
S
s 1
πs = 1 and πs ≥ 0 for s=1, …, S (1)
From the above formulation, it can be observed that the unconditional distribution of yn is equal
to the product of the conditional probability given S times the probability of S and that
expression summed over all values of S. Φ denotes the vector of all unknown parameters
associated with the specific form of the density distribution being chosen, which in this case, is
the collection of λs, i.e. the segment-specific average purchase rates for the product category
under consideration, and the segment sizes π1, …, πS.
3.1.2 Multivariate LC cluster model
In this study, we are however not interested in clustering supermarket shoppers only on their
purchase rate in just one product category, but rather in a number of product categories. In
other words, the basis for segmentation is defined as the consumer’s purchase rate within
multiple product categories of the assortment. This leads to the following definition of the
unconditional multivariate LC cluster model for the case where the observations ynk are
assumed to be conditionally independent within each segment S:
ƒ(ynk|Φ) = �=
S
s 1
πs ∏=
K
k 1
ƒs(ynk|θks) with k=1, …, K (product categories) (2)
The assumption of conditional independence implies that we assume that the observed
purchase rates for the different product categories are mutually independent within each latent
class, such that the joint densities can be expressed in terms of the product of the independent
marginal densities. This assumption may be too restrictive. However, allowing for correlations
between the observations would lead to the specification of a multivariate Poisson distribution
and would make the estimation of the LC cluster model much more complex. Indeed, clusters
would not only differ with respect to their segment-specific average purchase rates for each
product category, but also with respect to the correlations between the observed variables.
This leads to a rapid increase in the number of parameters to be estimated, specifically the
number of free parameters in the variance-covariance matrices, and makes the estimation of
this kind of LC cluster models computationally very complex.
3.2 Estimation of the LC cluster model
The purpose of the LC cluster models defined above, is to estimate the parameter vector Φ.
The two main methods to estimate this parameter vector are maximum likelihood (ML) and
maximum posterior (MAP) [25] of which the former is the most frequently used. Basically, the
objective of maximum likelihood estimation is to maximize the likelihood for Φ given the data
ynk , i.e.
L(Φ ; ynk) = ∏=
N
n 1
ƒ(ynk|Φ) (3)
The likelihood (3) is thus expressed as the product over the densities of the N supermarket
shoppers, for which the observation vectors are assumed to be mutually independent. In other
words, the objective is to find the optimal values for the parameter vector, say Φopt , such that
the observations ynk are more likely to have come from ƒ(ynk|Φopt) than from ƒ(ynk|Φ) for any
other value of Φ.
In order to maximize (3) most software tools either use Newton-Raphson [18] or
expectation – maximization (EM) [7], or a combination of both. Although the Newton-Raphson
algorithm requires fewer iterations compared with the EM algorithm, convergence to a local
optimum is not guaranteed [19]. Furthermore, because of its computational simplicity, the EM
algorithm is the most widely used [24]. The EM-algorithm will not be discussed in detail in this
paper, but it is an interactive algorithm that sequentially improves upon sets of starting values
of the parameters, and enables simultaneous estimation of all model parameters [26], in our
case both the average product category specific purchase rates for each segment, and the
respective segment sizes. More details about the EM computation can be found in [7, 19, 26].
Once an estimate for the optimal value of Φ has been found, the estimates of the posterior
probability pns , i.e. the posterior probability for subject n to belong to latent class s, can be
obtained for each observation vector ynk according to Bayes’ rule. Indeed, after estimation we
know the density distribution ƒs(yn|θs) for each product category k within each latent class s and
we know the segment size πs of each latent class such that we can calculate the posterior
probability as
Pns = πs ∏=
K
k 1
ƒs(ynk|θks) / �=
S
s 1
πs ∏=
K
k 1
ƒs(ynk|θks) (4)
The problem however with EM-estimation is that the procedure may converge to a local but not
a global optimum. The best way to prevent a local solution is to use multiple sets of starting
values for the EM-algorithm.
Information criteria to evaluate the quality of a cluster solution include BIC (Bayes
information criterion), AIC (Akaike information criterion) and CAIC (Consistent AIC, which
penalizes for sample size as well as model complexity) [2]. These are goodness of fit measures
which take into account model parsimony (that is, it penalizes for number of parameters in
relation to maximum possible number of parameters). The lower the BIC, AIC or CAIC values,
the better the model in comparison with another. Other methods for evaluating LC cluster
models are based on the separation of the clusters and measure how well the segmentation
variables are able to predict class membership. The entropy statistic is such a statistic and will
be used in this paper.
4 Empirical work
The multivariate latent class cluster model, identified in (2), was fitted on purchase frequency
data on the 30 most frequently sold product categories for a random sample of 1962
households of a Belgian supermarket store. Hereto, individual product purchase frequency data
were collected over a period of 17 weeks and aggregated into product categories according to a
product taxonomy provided by the retailer. As such, we obtain a vector of purchase rates of
length 30 for each subject, representing the total number of times a supermarket shopper has
purchased within each product category.
4.1 Model estimation
The process to discover the optimal segmentation in terms of the number of latent classes is
carried out in two phases.
Firstly, in order to get an approximation about the optimal number of latent classes, a LC
cluster analysis was carried out for 1 to 15 segments without restarting the EM-algorithm with
different random starting values. The reasons for this are twofold. First of all, to save
computing time, since rerunning different cluster solutions with different random starting values
is computationally very intensive. Second of all, running a range of different cluster solutions
for only one starting value is usually sufficient to obtain a good impression of the range within
which the optimal cluster solution can be found. Figure 1 illustrates the results of this
experiment and shows the quality of the cluster solutions for different numbers of latent
classes. One can observe that the evaluation statistics (CAIC, BIC, AIC) reach a minimum value
in the range of 13 to 15 segments. This means that the optimal number of latent classes must
be situated approximately in that range.
AIC CAIC, BICMAIC
plot of statistics against the number of segments
GLIMMIX 2.0 ProGAMMAClasses
15141312111098765432
Stat
istic
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Figure 1: Determining the optimal number of latent classes
Similar conclusions can be drawn from examining the evolution of the likelihood for the
different cluster solutions against the EM-iterations, as illustrated by figure 2. This figure
illustrates that the maximum likelihood is obtained for 13 latent classes or higher, with only
slight differences in the likelihood after 20 iterations.
Clas s es : 2Clas s es : 3Clas s es : 4Clas s es : 5Clas s es : 6Clas s es : 7Clas s es : 8Clas s es : 9Clas s es : 10Clas s es : 11Clas s es : 12Clas s es : 13Clas s es : 14Clas s es : 15
p lo t of the like lihood aga inst E M -itera tions
G LIMMIX 2 .0 P ro GAMMAIteration
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Func
tion
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Figure 2: evolution of the likelihood against the EM-iterations
Given the range of latent classes within which the optimal segmentation can be found (i.e. 13
to 15 classes), the second phase consists of rerunning the experiments for this range of latent
classes, but with a larger number of random starting values. Indeed, the first stage consisted
of a rough approximation of the neighbourhood where the optimal segmentation can be found.
The second stage then consists of a more careful search of this neighbourhood (i.e. fine-tuning)
by investigating each cluster solution more in detail.
Therefore, experiments for 12 to 15 latent classes with different sets of random starting
values were carried out to identify the most optimal segmentation solution. Table 1 shows the
likelihood values, the entropy statistic, the R² and the CAIC statistic for each cluster solution for
3 different restarts with random starting values of the EM-algorithm.
Table 1: evaluation statistics for different cluster solutions
Nr of segments 12 13 14 15
Run nr 1 2 3 1 2 3 1 2 3 1 2 3
Likelihood -125348 -125049 -125091 -93844 –94811 -95987 -92826 –92449 –92989 -91746 -91494 -94153
entropy 0.984 0.981 0.982 0.988 0.984 0.987 0.983 0.983 0.983 0.981 0.985 0.984
R² 0.437 0.438 0.438 0.614 0.609 0.603 0.619 0.622 0.618 0.626 0.627 0.613
CAIC 255142 254544 254629 192506 194439 196791 190841 190087 191166 189052 188550 193867
Table 1 shows that the optimal clustering can be found for 15 latent classes, although the
average CAIC and likelihood values for 14 and 15 segments across multiple runs of the EM-
algorithm are not significantly different. In general, the separation between the clusters is
excellent, as shown by the entropy statistic.
4.2 Model description
Given the optimal cluster solution of 15 latent classes, we are interested in the differences
between the clusters, both in terms of their size and in terms of the average purchase rates for
the product categories being included in the analysis. Table 2 below shows the size of the
different clusters.
Table 2: Cluster sizes
Cluster nr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Size (in % of total) 4.69 1.22 11.67 17.69 2.29 4.03 3.72 6.27 3.50 0.76 28.49 7.70 4.13 0.05 3.11
From table 2 it can be observed that three clusters (i.e. cluster 3, 4 and 11) add up for more
than 57 per cent of the instances, whereas most of the clusters have sizes between three and
eight per cent of the total number of instances. Two clusters contain an extremely low
percentage of instances, namely cluster 10 and cluster 14, with respectively 0.76 per cent and
0.05 per cent of the total number of instances. Detailed analysis of cluster 14 revealed that it
contained just one instance with extremely high purchase rates in most of the product
categories compared with the overall sample average purchase rates. A closer look at the
loyalty card data about this instance in turn revealed that it represents a large household with 5
children and 2 parents, which may explain its extremely high purchase rates. Cluster 10
contains 15 instances in total and is separated as a result of its extremely high purchase rates
of Dutch cheese. A closer look revealed an extremely high purchase rate of Dutch cheese and
quark by an independent/tradesman and by one large family with 4 children. It is likely that
the tradesman runs a business where he needs large amounts of cheese (e.g. restaurant), but
unfortunately we cannot verify this by means of the loyalty card. The other households in the
cluster also have higher than average purchase rates of Dutch cheese, but not as high as the
tradesman and have more than average number of children per household, which again may
explain their higher consumption rates of Dutch cheese.
By examining the product category segment specific average purchase rates in table 2,
some interesting differences between the clusters can be discovered. The observations in table
2 illustrate that segments differ significantly in terms of the average purchase rates of the
product categories being studied. However, besides the segmentation variables, which can be
used to describe the differences between the discovered clusters, we also possess descriptor
variables to profile the segments. Indeed, the loyalty card data about the customers provides
some interesting socio-demographic and lifestyle information that can be used to analyse the
profile of each segment. More specifically, we will use the following descriptor variables per
customer:
• Profession: employee, labourer, self employed, housewife, retired, unemployed;
• Car ownership: Boolean variable;
• Pets owned: dog, cat;
• Club membership: Boolean;
• Ownership of a freezer: Boolean;
• Ownership of a microwave: Boolean;
• Ownership of a garden: Boolean.
Furthermore, the purchase history information of each customer enables the creation of
aggregate shopping characteristics, such as:
• The average number of items purchased per ticket;
• The average amount paid per ticket;
• The average number of store visits per week;
• The distribution of shopping visits over the week.
Table 2: product category segment specific average purchase rates (λks)
Cluster nr 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Fresh fruit&vegs. 58.7 59.8 44.4 33.2 88.6 55.0 40.4 99.5 31.9 124.1 13.4 27.7 33.9 171.6 40.7
Fresh meat 26.8 24.2 24.9 15.9 52.9 25.4 19.4 41.4 20.4 54.6 9.6 22.5 13.7 62.3 36.8
Milk 14.4 20.8 12.3 14.9 33.8 42.0 13.9 17.6 10.8 13.8 7.0 9.6 15.2 53.1 16.9
Bake-off 4.7 13.0 12.5 2.4 83.6 17.9 13.2 9.9 95.1 81.6 3.9 10.1 6.1 44.2 18.1
Dutch cheese 1.0 14.2 0.4 0.3 1.3 1.1 1.0 0.5 0.9 185.0 0.1 0.4 164.1 17.7 1.2
Dry Biscuits 8.3 10.9 7.7 11.8 21.9 33.5 6.8 14.6 9.8 11.9 4.0 9.4 9.4 49.7 12.9
Candy 7.7 12.4 6.3 7.9 24.5 27.9 12.6 19.9 10.8 22.9 4.1 15.1 7.9 37 11.8
Soft drinks 7.0 6.0 3.5 8.7 27.7 24.2 12.7 13.5 9.9 8.8 4.2 21.3 8.3 50.5 21.0
Chocolate 5.1 8.6 5.6 8.9 24.1 18.8 7.2 25.2 10.3 20.2 2.8 7.5 6.3 17.3 11.6
Small bread 5.5 23.4 8.0 1.2 43.1 5.6 5.7 5.3 24.1 28.6 2.5 4.7 7.2 81.1 70.6
Sauses 6.6 9.5 5.6 8.3 20.3 12.8 12.8 14.0 9.6 29.9 4.1 9.6 6.3 70.8 14.2
Quark 5.3 8.2 3.7 5.9 14.6 8.8 48.5 15.9 3.0 45.1 2.8 2.9 5.3 9.7 5.8
Fresh biscuits 5.8 15.5 6.1 7.2 17.0 19.5 7.6 12.5 6.6 11.7 3.1 7.2 6.2 78.5 7.7
Yoghurt 11.9 12.3 3.6 7.3 10.4 7.3 10.6 12.9 7.9 26.5 3.0 3.7 5.3 14.4 21.1
Bread 3.7 6.5 17.0 2.4 15 3.1 4.7 16.6 7.2 31.3 2.2 5.2 6.3 52.1 5.1
Crisps 5.9 4.0 3.1 7.6 11.8 36.5 2.8 5.8 6.2 15.6 2.8 7.9 4.2 35.7 11.2
Butter 6.0 11.2 4.9 6.5 12.2 9.4 6.3 12.1 6.2 23.3 2.7 4.9 5.7 9.3 7.1
Canned vegs. 6.9 7.9 6.6 4.1 16.8 11.6 6.9 11.3 3.0 11.6 3.4 5.5 4.9 8.8 6.7
Coffee 2.5 6.4 2.8 7.1 12.7 7.8 7.5 8.9 5.4 4.2 2.7 3.99 3.5 9.8 2.5
Cat food 55.6 10.1 1.9 1.6 27.9 3.2 1.6 2.8 2.2 22.6 1.5 1.8 8.4 101.9 2.2
Water 3.3 7.6 3.6 4.6 14.5 14.0 6.7 13.1 2.7 4.9 2.5 7.9 4.5 123.4 5.2
Fresh salads 5.3 5.3 5.5 3.6 13.0 7.9 5.6 8.6 5.5 10.1 2.6 5.3 4.00 20.5 4.7
Cigarettes 4.6 3.6 1.2 0.8 22.9 9.6 8.5 2.7 3.0 14.5 2.0 19.3 3.5 23.6 18.1
Packed fruit 0.4 380.3 0.4 0.2 0.9 0.4 0.3 0.8 0.2 1.0 0.1 0.3 0.1 9.0 0.3
Topping 2.6 6.7 3.2 6.3 8.4 12.4 5.5 10.4 3.3 8.3 2.2 2.8 4.8 11.7 5.9
Pastry 1.9 9.4 11.4 0.8 13.7 2.2 4.8 6.0 7.3 7.6 2.1 2.4 1.6 19.9 4.9
Fruit juice 2.6 7.1 2.0 3.2 8.6 19.1 3.9 5.1 1.8 10.7 1.2 4.7 3.2 65.0 5.1
Pasta 2.6 4.5 3.2 3.7 12.4 9.7 6.3 6.5 3.0 14.5 2.2 4.4 4.5 39.4 5.5
Newspaper&Mag. 5.8 6.8 5.0 4.0 9.0 6.4 3.4 8.5 2.6 22.5 1.7 5.2 3.0 19.8 4.1
Maintenance art. 3.5 7.6 3.7 3.5 6.0 7.9 3.9 5.7 2.9 7.6 2.3 4.0 3.1 16 5.4
The next paragraph will summarize the most prevalent characteristics per segment, both in
terms of the difference on the segmentation variables, as in terms of its profile based on the
descriptor variables. Because of space limitations and the high number of clusters, we will only
focus on the most striking differences. The interested reader is referred to appendix 1 where
more detailed figures can be found about the profile of each cluster.
4.3 Description of the segmentation results
Segment 1 is a relatively small segment (4.69%) and can be characterized by a significantly
higher than average purchase rate of cat food, but significantly lower than average purchase
rates of bake-off products, bread, pastry, packed fruit, Dutch cheese and coffee. Thus, it
seems that for ‘bread products’, the supermarket is not the preferred store. The profile of the
segment turns out that there are slightly more employees in this segment compared to the
entire sample, and that almost 60% of them owns a cat which is much higher than the overall
sample average (14%).
Segment 2 is even smaller in size (1.22%) than segment 1, and shows an extremely high
purchase rate in the packed fruit category, but a lower than average purchase rate of cigarette
products. The proportion of retired customers (25%) in this segment is significantly higher
than the overall sample average (11.9%), whereas the proportion of housewives (12.5%) and
labourers (8.3%) is significantly lower than average (resp. 21.5% and 17.2%). Their shopping
cart contains almost twice (31.4) the number of items per store visit than on average (17.2),
although their ticket price is only 16% higher.
Segment 3 is a relatively large segment (11.7%) and shows higher than average purchase
rates of bread and pastry, but lower than average purchase rates of Dutch cheese, soft drinks,
yoghurt, crisps, cat food, cigarettes and packed fruit. This segment contains a higher
proportion of retired customers (17%) than average (11.9%) and purchase a slightly less
number of items per store visit (15.0) than on average (17.2), which explains their lower
average ticket price (1081 Bef.) when compared to the overall sample average ticket price
(1259 Bef).
Segment 4 is even bigger (17.7%) than segment 3 and shows relatively low average
purchase rates in almost all product categories with some extremely low average purchase
rates in bake-off products, small bread items, pastry, cat food, cigarettes, packed fruit and
Dutch cheese. At least, it is clear that this segment does not appreciate the ‘bread products’ in
general in this supermarket. The cluster has an average socio-demographic and lifestyle
profile, but cluster 4 contains proportionally less cat owners than on average. They visit the
store less frequently per week (1.2) than the overall sample average (1.5), but they purchase
slightly more products per ticket (20.9) than average (17.2) which may explain their higher
average ticket price (1597 Bef).
Segment 5 is a relatively small segment (2.3%) and shows slightly higher average purchase
rates in almost all product categories (except for Dutch cheese and packed fruit) but possesses
very distinct characteristics in terms higher than average purchase rates in the product
categories fresh fruit & vegetables, fresh meat products, milk, bake-off products, pastry, dry
biscuits, candy, soft drinks, chocolate, small bread products, fresh biscuits, canned vegetables,
coffee, fresh salads and cigarettes. Segment 5 not surprisingly consists of relatively large
families (3.9 members) compared with the sample average (3.0) and contains higher proportion
of employees (35.5) than average (23.4) and a significantly lower proportion of retired
customers (4.4) than the overall sample average (11.9). Their shopping baskets on average
contain significantly more items (25.8) than the overall sample average (17.2) which is also
reflected in the average ticket price (1840 Bef). Furthermore, customers in segment 5 shop
twice as often per week (3.3) compared to the average customer (1.5). Thus, although
relatively small, this segment should deserve a special treatment since they are loyal and
represent a high monetary value per customer. It seems that these customers do all of their
shopping in one store.
Segment 6 is a small segment (4.0%) and could be characterized as ‘sweet tooth’ as
reflected by their higher than average purchase rates of dry biscuits, candy products, fresh
biscuits, crisps, fruit juice, topping and milk. Just as in most other segments, the average
purchase rate of Dutch cheese and packed fruit is much lower. This segment contains
proportionally much more labourers (27.8%) than the overall sample average (17.2%) but
significantly less retired customers (5.0%) than the overall average (11.9%). The average
household size in cluster 6 equals 3.7 which is higher than the overall average family size (3.0).
This is reflected in the average number of items purchased per ticket (28.2 compared to 17.2
for the overall sample) and in the average price per ticket (2045 Bef). The proportion of
customers who are a member of a club is, however, significantly less (33.7%) than the overall
sample average (43.7%).
Segment 7 is a small cluster (3.7%) and has a very distinct preference for quark. Indeed,
the average purchase rate of quark (48.5) is much higher than the overall sample average (11).
The average purchase rates in the other product categories is comparable with the overall
sample averages, except again for cat food, Dutch cheese and packed fruit. Also the socio-
demographic profile of this segment shows some interesting differences. We observe a much
higher proportion of labourers (30.1%) and a much lower proportion of retired customers
(4.1%) compared with the overall sample average (resp. 17.2% and 11.9%). The average
number of items per ticket for customers of cluster 7 is slightly higher (22.9) than the overall
sample average (17.2) and this is also reflected in the average ticket price (1692 Bef.).
Segment 8 is of a moderate size (6.3%) and has relatively high average purchase rates of
fresh fruit & vegetables, fresh meat and chocolate products. Purchase rates in other product
categories are comparable with the overall sample mean, except for Dutch cheese, cat food,
packed fruit and cigarettes with much lower average purchase rates. The lower purchase rate
of cat food is also reflected in the proportion of cat owners of this cluster which is lower (8.1%)
than the overall sample mean (14.0%). Furthermore, a higher proportion of customers in
cluster 8 possesses a garden (81.3%) compared to the overall sample mean (71.2%) and size
of the households is slightly higher (3.3) than average (3.0). This slight difference is also
reflected in the average number of items purchased per ticket (20.0) and the average ticket
price (1474). Moreover, customers from this cluster tend to shop more frequently (2.5 times
per week) than the average customer (1.5 times per week).
Segment 9 is a small segment (3.5%) and has a significantly higher than average purchase
rate of bake-off products (95) compared with the overall sample mean (21.8). For the other
product categories, we observe relatively average purchase rates, except for canned
vegetables, fruit juice, Dutch cheese, cat food and packed fruit. In terms of socio-
demographics, cluster 9 has a significantly higher proportion of housewives (30.1%) than
average. Furthermore, cat ownership is lower (8.7%) than average (14.0) and the average size
of the household is higher (3.5) when compared with the overall sample average (3.0).
Segment 10 is very small (0.76%) and has higher than average purchase rates in almost all
product categories, and especially in Dutch cheese, quark and yoghurt, as well as in fresh fruit
& vegetables, fresh meat, bake-off, bread, candy, and others. This segment is dominated by
employees and relatively large family sizes (3.9). The average number of items purchased per
ticket is significantly higher (36.2) than the overall sample average (17.2) and this is also
reflected in the average price per ticket (1969 Bef). Shoppers in this segment visit the store
almost twice as much as the average customer.
Segment 11 is the largest segment of all (28.5%) and is characterized by relatively low
average purchase rates on most of the product categories, but especially in
newspapers&magazines, fruit juice and packed fruit. This segment has an average socio-
demographic profile compared with the overall sample and has a slightly smaller family size
(2.9) than average (3.0). The number of items per ticket is slightly higher (17.8) than average
(17.2) which is also reflected in the ticket price (1386 Bef.). Striking however is that this group
visits the supermarket only half (0.75) as often per week as the average customer in the
sample (1.5)!
Segment 12 is of a moderate size (7.7%) and is characterized by slightly higher than
average purchase rates in most product categories, except for Dutch cheese, quark, yoghurt,
cat food, and packed fruit. It looks like this segment does not appreciate dairy products or
purchases them in another store. In terms of the socio-demographic profile of this segment,
we observe a slightly more ‘active’ proportion of customers (both employees and labourers) and
less ‘inactive’ customers (retired and unemployed). The average family size is slightly higher
(3.2) than the overall sample average (3.0). Both the number of different items per ticket, the
ticket price and the average number of weekly visits are slightly higher than average.
Segment 13 is a relatively small segment (4.1%) but has an extremely high average
purchase rate of Dutch cheese, and average purchase rates in other categories. In contrast,
purchase rates of cigarettes, packed fruit and pastry are much lower than average. In terms of
socio-demographics, the customer in this segment is comparable to the customer in the overall
sample, except from a slightly smaller family size (2.8) than average (3.0). In terms of
profitability per ticket however, we observe twice the number of items per ticket (34.5) than
average (17.2), but this is not proportionally reflected in the average ticket price which is only
34% higher (1685 Bef). The higher number of items per ticket may possibly be explained by
the lower average number of visits per week (1.0) compared to the overall sample average
(1.5), which may be an indication of one-stop-shopping or stocking-up behaviour.
Segment 14 consists of just one household and can be considered an outlier in the dataset.
The clustering procedure separated this household because of its extremely high purchase rates
in most of the product categories, which can be explained by the family size (5 children and 2
parents).
Segment 15 is a small cluster (3.1%) and it is characterized by significantly higher than
average purchase rates of small bread items and yoghurt. Just as in most of the other
segments, cat food, Dutch cheese and packed fruit have lower average purchase rates. The
socio-demographic profile of this cluster shows a significantly higher proportion of labourers
(26.2) than expected from the overall sample average (17.2), but a much lower proportion of
retired customers (3.2) than the overall sample average (11.9). The average household size is
also much larger (4.0) than in the overall sample (3.0). This is partially reflected in the average
number of items per ticket (23.1) and the ticket price (1586 Bef) which are significantly (but not
extremely) higher than the overall sample average (resp. 17.2 and 1259 Bef).
4.4 Recommendations for retail decision making
Based on the characteristics and the profile of each customer segment (see section 4.3),
actions or recommendations can be undertaken stimulate or alter the purchase behaviour of
each cluster. For instance, cluster 5 is a relatively small cluster, but is highly valuable in terms
of the size (25.8) and the turnover (1840 Bef) per ticket being realized. However, compared to
cluster 10, which contains households of equal size (3.9), cluster 10 on average purchases even
more items per ticket (36.2). Therefore, it would be valuable to think of ways to stimulate the
purchase rate of customers in cluster 5. Market basket analysis techniques, such as association
rules [1] or association coefficients [4], may help to discover frequently purchased
combinations of products within a segment and stimulate purchase behaviour by promoting
certain products or product combinations.
Segment 11 which visits the store only half as much (0.75 times per week) as the average
customer (1.5 times per week) should be stimulated to shop more frequently. Given the size of
this segment (i.e. 25% of the sample), it could be worthwhile printing a customized promotion
leaflet including tokens or reduction vouchers for the purchase of particular products during the
week, but not during the weekend, in order to stimulate shop visits during the week.
Segment 4 is a relatively large segment (17.7%) and has very low purchase rates in all
bread related categories, such as bread, pastry, small bread and bake-off products. Thus, it is
likely that these customers prefer purchasing their bread items at a local bakery instead of in
the supermarket. Given the size of this segment, this should be investigated and actions should
be taken upon. Possibly, this segment should be treated separately by means of a customized
promotion leaflet, or by stimulating future purchases by printing messages on their cash
receipts relating the purchase of bread items in the supermarket, e.g. special offers, quality
improvements, wider variety, …
Segment 6 was described as ‘sweet tooth’ and this could be exploited by presenting special
offers in the ‘sweet’ product categories, or by putting the most popular ‘sweet’ items closer
together in order to stimulate sales, etc. The selection of particular products to put on
promotion should however be carried out very carefully. It is not because this segment shows
a higher purchase rate in dry biscuits, that all of the consumers seek the same kinds of benefits
from this category. For instance, heavy users of dry biscuits maybe consists of two groups:
those who eat biscuits and for which price is not an issue (e.g. people who buy biscuits for a
dinner evening), and those for which price is in fact an issue (e.g. children who come to buy
bisuits after school and who are short of money).
5 Conclusion and limitations
5.1 Conclusions
The objective of this paper was to investigate the potential of behavioural segmentation based
on scanning and loyalty card data, and to provide a methodology how this can be
accomplished. We introduced a latent class clustering model that groups customers based on
their purchase rates within a set of predefined product categories. The developed model was
tested on sales transaction data from a Belgian supermarket store. More specifically, 15
segments were identified in the data, of which 1 segment was an outlier. The segments
significantly differ according to their average purchase rates in the set of product categories,
and were profiled based on loyalty card data from these households. This enabled us to
suggest some segment-specific actions for retail decision making. Furthermore, we believe that
the results of this kind of product category based in-store segmentation analysis may provide
useful input for further analysis on a more detailed scale, for instance to investigate the success
rate of individual SKU’s within each segment.
5.2 Limitations of this research
Firstly, the latent class cluster model was tested on real-life data from a Belgian supermarket
store. Real-life sales transaction data of this kind is typically subject to noise and outliers which
may hinder the discovery of good clusters. For instance, it could be argued that households in
cluster 14 should not be removed from the data before the analysis. Nevertheless, the
technique of latent class clustering was able to separate this observation from the other
observations by putting it into a separate cluster.
Secondly, the presence of outliers influences the interpretation of the results by comparing
segment-specific purchase rates for a category with the overall sample average purchase rate
that may be highly skewed as a result of the outlier observations. A better way of comparing
the segment-specific results would be to compare them with overall sample median values, for
instance.
The latent class cluster model in this paper was developed on the basis of a subset of
product categories from the assortment. In fact, only the thirty most frequently purchased
product categories were included in the analysis. This is of course a limitation of the research,
but given the restrictions of the software, hardware and computation times, we were not able
to include more categories. Nevertheless, with regard to the stability of the clustering solution,
we believe that the most frequently purchased product categories are the most important to
influence the cluster solution since purchase rates for infrequently purchased categories are
typically close to or equal to zero for all customers and will thus not influence the cluster
solution significantly.
Finally, in our model we have assumed mutual independence of the observations, i.e.
uncorrelated purchase rates. In other words, we assume that a higher purchase rate within
one category does not imply a higher (or lower) purchase rate in any other product category.
This is against the idea of one-stop-shopping or consumption complementarity where
customers tend to purchase products from different product categories to fulfil a complex set of
needs. This limitation should be viewed within the trade-off between model complexity and the
ease of computation of the model. Indeed, allowing for correlated observations would
necessitate the use of the multivariate Poisson distribution and would complicate the estimation
of the cluster model.
References
[1] Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A. (1996), Fast discovery
of association rules, in: Advances in Knowledge Discovery and Data Mining, AAAI Press, pp.
307-328.
[2] Akaike, H. (1974), A new look at statistical model identification, in: IEEE Transactions on
Automatic Control, AC-19, pp. 716-723.
[3] Asseal, H. (1984), Consumer Behaviour and Marketing Action, Boston, Kent Publishing
Company.
[4] Böcker, F. 1978. Die Bestimmung der Kaufverbundenheit von Produkten, in: Schriften zum
Marketing, band 7.
[5] Corstjens, J., and Corstjens, M. (1995), Store Wars: The Battle for Mindspace and
Shelfspace, John Wiley & Sons: England.
[6] Danneels, E. (1996), Market segmentation: normative model versus business reality: an
exploratory study of apparel retailing in Belgium, in: European Journal of Marketing, Vol. 30(6),
pp. 36-51.
[7] Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977), Maximum likelihood from incomplete
data via the EM-algorithm, in: Journal of the Royal Statistical Society, Series B39, pp. 1-38.
[8] Dillon, W.R., and Kumar, A. (1994), Latent structure and other mixture models in
marketing: an integrative survey and overview, in: Advanced Methods in Marketing Research,
Cambridge, MA: Blackwell, pp. 295-351.
[9] Forcht, K.A., and Cochran K. (1999), Using data mining and datawarehousing techniques,
in: Industrial Management & Data Systems, Vol. 5, pp. 189-196.
[10] Frank, R.E., Massy, W.F., and Wind, Y. (1972), Market Segmentation, Englewood Cliffs, NJ:
Prentice Hall.
[11] Gensch, D.H. (1985), Empirically testing a disaggregate choice model for segments, in:
Journal of Marketing Research, Vol. 22(4), pp. 462-467.
[12] Ghosh, A.K. (1997), Targeted promotions using scanner panel data, in: Journal of Product
& Brand Management, Vol. 6(6), pp. 405-416.
[13] Gutman, J., and Mills, M.K. (1991), Fashion life style, self-concept, shopping orientation,
and store patronage: an integrative analysis, in: Journal of Retailing & Distribution
Management, Vol. 19(3), pp. 64-86.
[14] McCurley Hortman, S., Allaway, A.W., Mason, J.B., and Rasp, J. (1990), Multisegment
analysis of supermarket patronage, in: Journal of Business Research, Vol. 21, pp. 209-223.
[15] Kopp, R.J., Eng, R.J., and Tigert, D.J. (1989), A competitive structure and segmentation
analysis of the Chicago fashion market, in: Journal of Retailing, Vol. 65(4), pp. 496-515.
[16] Luce, R. (1959), Individual choice behavior, New York, John Wiley and Sons.
[17] McCann, J.M. (1974), Market segment response to the marketing decision variables, in:
Journal of Marketing Research, Vol. 11, pp. 399-412.
[18] McHugh, R.B. (1956), Efficient estimation and local identification in latent class analysis, in:
Psychometrika, Vol. 21, pp. 331-347.
[19] McLachlan, G.J., and Basford, K.E. (1988), Mixture Models: Inference and Applications to
Clustering, New York.
[20] Miller, K.E., and Granzin, K.L. (1979), Simultaneous loyalty and benefit segmentation of
retail store customers, in: Journal of Retailing, Vol. 55(1), pp. 47-60.
[21] Myers, J.H. (1996), Segmentation and Positioning for Strategic Marketing Decisions,
American Marketing Association.
[22] Reutterer, T. (1998), Competitive market structure and segmentation analysis with self-
organizing feature maps, in: Proceedings of the 27th EMAC Conference, Stockholm, pp. 85-115.
[23] Segal, M.N., and Giacobbe, R.W. (1994), Market segmentation and competitive analysis for
supermarket retailing, in: International Journal of Retail & Distribution Management, Vol. 22(1),
pp. 38-48.
[24] Titterington, D.M. (1990), Some recent research in the analysis of mixture distributions, in:
Statistics, Vol. 4, pp. 619-641.
[25] Vermunt, J.K., and Magidson, J. (2000), Latent class cluster analysis, research paper.
[26] Wedel, M., and Kamakura, W. A. (2000), Market Segmentation: Conceptual and
Methodological Foundations, 2nd ed., International Series in Quantitative Marketing, Kluwer
Academic Publishers, MA.
[27] Wilkie, W.L., and Cohen, J.B. (1977), An overview of market segmentation: behavioral
concepts and research approaches, Marketing Science Institute Working Paper.
[28] Wind, Y. (1978), Issues and advances in segmentation research, in: Journal of Marketing
Research, Vol. 15 (August), pp. 317-337.
Appendix
Legend: Var 1 = employees (in %) Var 10 = club members (in %)
Var 2 = labourers (in %) Var 11 = freezer owners (in %)
Var 3 = housewives (in %) Var 12 = microwave owners (in %)
Var 4 = unemployed (in %) Var 13 = garden owners (in %)
Var 5 = retired (in %) Var 14 = family size (parents + children)
Var 6 = self employed (in %) Var 15 = different SKU’s per ticket
Var 7 = car owners (in %) Var 16 = revenue per ticket (in Belgian Francs)
Var 8 = dog owners (in %) Var 17 = tickets per week
Var 9 = cat owners (in %) Var 18 = distribution of shop visits (in %)
Table: segment specific profile information
CLUSTER NR All 1 2 3 4 5 6 7 8 9 10 11 12 13 15
Employees 23.4 33.7 29.2 27.5 23 35.5 29.1 30.1 27.6 30.4 40 24.3 27.1 22.2 24.6
Labourers 17.2 16 8.3 14.4 19.3 20 27.8 30.1 17 15.9 13.3 17.5 23.1 18.5 26.2
Housewives 21.5 25 12.5 19.6 24.2 22.2 25.3 21.9 26.8 30.1 20 18.8 21.2 19.7 21.3
Unemployed 1.3 0 4.2 0.4 2.0 0 1.2 0 1.6 0 0 1.8 1.3 0 3.3
Retired 11.9 6.5 25 17.0 12.4 4.4 5.0 4.1 14.6 7.2 6.6 9.8 8.6 14.8 3.2
Self employed 2.2 2.2 4.2 1.7 2.9 4.4 1.2 2.7 1.6 2.9 6.6 2.3 2.6 4.9 1.6
Car owners 88.3 84.1 90.1 90.8 90.5 95.3 90.9 94.4 94.9 92.4 100 88.8 86.5 87.6 87.5
Dog owners 25.4 17.4 29.2 23.6 25.1 31.1 32.9 30.1 23.6 18.8 40 24.1 26.5 28.4 27.8
Cat owners 14.0 58.7 8.3 12.6 8.6 35.5 12.6 8.2 8.1 8.7 26.6 11.0 13.9 16 13.1
Club members 43.6 44 59 49.7 45 53.5 33.7 36.6 48.7 51.5 53.3 44.4 41.2 44.4 37.5
Freezer owners 73.1 75 66.6 75.1 79.2 80 73.4 68.5 78 76.8 100 72.1 68.8 76.5 72.1
Microwave owners 62.8 67.4 58.3 62.4 65.4 68.8 63.3 63 61.8 69.6 80 62.6 61.6 62.9 62.3
Garden owners 71.2 76 58.3 75.1 80.4 82.2 74.7 76.7 81.3 76.8 100 68.8 68.8 76.5 72.1
Family size 3.0 3.16 2.9 3.0 3.2 3.9 3.7 2.9 3.3 3.5 3.9 2.9 3.2 2.8 4.0
SKU’s per ticket 17.2 22.6 31.4 15 20.9 25.8 28.2 22.9 20 22.3 36.2 17.8 18 34.5 23.1
Revenue per ticket 1259 1529 1459 1081 1597 1840 2045 1692 1474 1449 1969 1386 1455 1685 1586
Tickets per week 1.5 1.4 2.2 1.8 1.2 3.3 2.0 1.6 2.5 1.6 2.6 0.75 1.6 2.3 1.9
Distr. of shop visits:
Monday 13.8 15.4 10.9 14.8 12.8 14.6 15.3 13.5 14.4 11.7 15.2 13.8 15.6 12.4 12.4
Tuesday 12.2 10.2 10.6 13 11.6 12.8 12.4 12.4 13.3 12.1 11.2 11.3 13.5 11 11.4
Wednesday 13.1 14.2 13.2 13.6 12.1 15.3 13.9 11.8 16.3 12.4 8.7 11.7 13.9 11.5 13.6
Thursday 18.4 17.6 18.6 17.8 20 14.6 16.9 16.7 17.0 12.7 19.3 18.4 17.6 19.2 16.6
Friday 20.5 21.5 16.8 19.2 22.4 20.3 20.4 22.1 20.1 19.9 25 22 19.9 23.9 19.4
Saturday 21.9 21.1 29.7 21.4 21 22.3 20.9 23.3 18.8 31 20.5 22.7 19.4 21.9 26.5
Note 1: segment-specific profile information should be treated with care because:
• these figures are derived from loyalty card data for which the statistical reliability is not
known
• some segments have only few observations (e.g. segments 2, 10 and 14).
Note 2: profile data for cluster 14 are not shown because the cluster contains only one outlier
observation and thus the profile information is not relevant.
