Segmenting Members of a Retailer Loyalty Programme
Using Personnel-Related Service Quality Dimensions
Irena Ograjenšek
Vesna Žabkar
University of Ljubljana
Faculty of Economics
Kardeljeva pl. 17
1000 Ljubljana
Slovenia
irena.ograjensek@ef.uni-lj.si
vesna.zabkar@ef.uni-lj.si
Abstract
Ever since the advent of the smart loyalty cards, loyalty programmes (LP) have been
transcending their traditional role as creators of exit barriers by transforming
themselves into facilitators of customer data collection. Apart from demographic and
socio-economic data, behavioural (transaction) as well as psychographic (survey) data
are being collected for known LP members. Integral analysis of these data can be of
immense value for retailers striving to improve service quality for different customer
segments. Using the SERVQUAL model as a starting point, our study focuses on the
issue of LP members’ segmentation on the basis of three distinct personnel-related
perceived service quality dimensions (appearance, empathy and assurance), which
serve as inputs into clustering process. For cluster profiling, selected demographic,
socio-economic, and transaction variables are used. Apart from methodological issues,
managerial implications of findings are discussed.
Keywords
loyalty programmes, service quality, customer data analysis, customer segmentation
2
Segmentiranje članov programa zvestobe na osnovi vpliva
prodajnega osebja na zaznano kakovost storitev v trgovini na drobno
Povzetek
Programi zvestobe so tradicionalno sicer zasnovani kot sredstvo preprečevanja prebega
kupcev h konkurenčnim podjetjem. Z razvojem informacijske in telekomunikacijske
tehnologije pa postaja njihova glavna privlačnost vloga, ki jo igrajo v procesih zbiranja
in analize podatkov kupcev – članov programa zvestobe. Integralna analiza
demografskih, socioekonomskih, transakcijskih in anketnih podatkov kupcev omogoča
podjetjem učinkovito usmerjanje procesov celovitega obvladovanja kakovosti storitev
na ravni posameznih segmentov kupcev. Izhodišče pričujoče študije so na osnovi
anketnih podatkov kupcev identificirane dimenzije zaznane kakovosti storitev, ki se
nanašajo na prodajno osebje (videz, vzbujanje zaupanja in odzivnost) in služijo kot
vhodni elementi v proces segmentacije kupcev. Profili segmentov so pripravljeni s
pomočjo izbranih demografskih, socioekonomskih in transakcijskih podatkov. V
prispevek vključujemo iz rezultatov analize izhajajoča priporočila vodstvu podjetja.
Ključne besede
programi zvestobe, kakovost storitev, analiza podatkov kupcev, segmentacija kupcev
3
1. INTRODUCTION
Companies in the most developed economies have been aware of the importance of product and
process quality for many decades due to theoretical and practical contributions of quality
experts such as Deming, Juran, Crosby, Feigenbaum, Ishikawa and Taguchi [Peace, 1993;
Drummond, 1994; Hagan, 1994; Cole and Mogab, 1995; Swift, 1995; Bisgaard, 1998; Easton
and Jarrell, 2000]. Service industries embraced the basic quality improvement ideas
simultaneously with the manufacturing sector, yet neglected the use of statistical methods in
quality improvement processes even more than their manufacturing counterparts. One of the
reasons for such a state of affairs is given by differences in nature of services and manufactured
goods. They have always been emphasised in the literature, especially with regard to
measurability of service quality attributes and, consequently, characteristics of the measurement
process. Therefore, the use of traditional statistical quality control toolbox in quality
improvement of service processes has usually a priori been limited to the most basic of tools
(e.g. control charts). This does not mean that complex statistical methods have not been used in
the service sector. The toolbox developed by social sciences (containing e.g. exploratory and
confirmatory factor analysis) has been proposed as the one to be of use in quality improvement
of service processes by authors such as Parasuraman et al. [1985, 1988, 1994], Teas [1993,
1993a, 1994], Zeithaml et al. [1993], Cronin and Taylor [1992, 1994], Lytle et al. [1998], etc.
For the better part, the toolbox consists of a series of instruments for measurement of perceived
(subjective) service quality which, among other things, differ importantly with regard to
applicable measurement scores. SERVQUAL as the most frequently applied model (proposed
by Parasuraman et al.) is based on comparisons of customers’ quality expectations and
perceptions.
Although our study does not classify as another attempt to prove the SERVQUAL
generalisability, it does use this model as a starting point. However, our discussion focuses on
the issue of retailer loyalty programme members’ segmentation from the viewpoint of those
service quality dimensions, which pertain to retailer’s sales personnel. Data provided by a large
Slovenian retailer are used in the project. Our paramount research goals are to find out how
many loyalty programme member segments can be identified with regard to sales personnelrelated perceived service quality dimensions, what are their characteristics, and what kind of
managerial measures (if any) are necessary to improve the level of sales personnel-dependent
perceived service quality.
4
2. RETAILER LOYALTY PROGRAMMES AND THEIR CHARACTERISTICS
Before going into a detailed discussion of our empirical research project, it is necessary to
devote some attention to the issue of retailer loyalty programmes and their characteristics. A
brief historical overview shows that loyalty programmes as we know them today have existed
for a relatively short period of time: a little over twenty years. Large airlines were the first
companies to start playing with the idea of creating special customer programmes with two
distinctive features:

they would include a large number of customers;

activities conducted in the programme framework would stimulate members to increase
their purchase frequency.
In 1981 American Airlines launched the programme called AAdvantage [Kasper et al.,
1999]. It quickly turned into a source of competitive advantage and other airlines were forced to
respond in kind. Further development is well known: loyalty programmes soon outgrew the
confines of the airline industry. The expansion that started in closely linked industries (e.g. car
rental and hospitality industry) later advanced into retail trade, banking and insurance, etc.
Hopf and Ograjenšek [1999] define a loyalty programme as a company’s organised and
structured form of loyalty efforts. Rayner [1996] is more technical and speaks of a loyalty
programme as a mechanism for identifying and rewarding loyal customers - those staying with
the company for longest, purchasing most frequently and spending on average most per
purchase.
A loyalty programme could be viewed either as a functional (marketing) or strategic
(management) instrument, which includes various different measures to improve customer
loyalty in the framework of a special customer club. Measures such as for example organisation
of exclusive events, regular mailings (including monthly or quarterly information bulletins,
birthday cards, invitations to exclusive events, etc.), have a sole purpose of emotionally
involving and binding club members, and, consequently, increasing purchase frequency.
Practical value of loyalty programmes in continuous quality improvement of service
processes can be determined from two points of view, namely business and methodological
[Ograjenšek, 2002]:

Business aspect historically precedes the methodological and could therefore also be called
the traditional one. It is focused on creation and maintenance of loyalty as part of the
defensive business strategy. Defensive strategy aims at reducing the number of exiting and
5
switching customers. In other words, it tends to minimise customer turnover and maximise
customer retention. One way of accomplishing these goals is to introduce switching costs
[Bharadwaj et al., 1993]. The other is to “produce” highly satisfied customers. If positioned
correctly, loyalty programmes create a long-term customer loyalty (manifested in repeat
purchase), and do not turn into a relentless short-term chase after rewards. Members do not
perceive programmes as devices, which limit their freedom of choice [Marshall, 1999].
Additionally, according to Bolton et al. [2000] although members are increasingly exposed
to a complete spectrum of service experiences (including service failures) with the same
company, they seem to be more tolerant of failures and less ready to switch to competition.

Methodological aspect, which could also be called the modern one, has gained in
importance with development of modern information technology and use of the so-called
smart loyalty card for customer data collection. From this point of view, loyalty
programmes could be regarded as an important data source for analyses necessary in
continuous quality improvement of service processes.
The framework which brings both aspects together is Fornell’s [1992] classification of
business strategies, which can be supplemented as shown in Figure 1.
Figure 1: Bringing the Business and Methodological Aspects of Loyalty Programmes Together
in the Framework of Fornell’s Classification of Business Strategies
Business Strategy
Offensive
(aimed at new customers)
Increase market
Defensive
(aimed at present customers)
Capture market
share
BUILD SWITCHING
BARRIERS
Increase cust.
satisfaction
LOYALTY
PROGRAMMES
Predict profiles
and behaviours
DATA GENERATION
& ANALYSIS
6
Identify failures &
try to recover
While maintenance of loyalty (creation of switching barriers) is the focus of the business
aspect of a loyalty programme, methodological aspect is accounted for by data generation and
analysis. Figure 1 also shows that methodological aspect of loyalty programmes is of vital
importance not only for creation and implementation of a defensive, but also to form an
offensive business strategy. It could therefore be argued that due to recent advances in
information technology, loyalty programmes are transcending their traditional role as creators
of exit barriers for the customer. More and more, they are becoming facilitators of data
collection and analysis. In this role, they help companies segmenting the customer database and
reshaping its profile, focusing on those segments that can be served well for a profit. As
Reichheld and Sasser [1990: 109] put it: “Achieving service quality doesn’t mean slavishly
keeping all customers at any cost. There are some customers the company should not try to
serve.”
It goes without saying that in retailing, the role sales personnel play is crucial for shaping
customer perceptions of service quality and determining the level of customer satisfaction. The
effect is probably even larger in the framework of a loyalty programme aiming at creation of
long-term relationships, since liberally made and often repeated promises which are not kept
create more room for development of negative feelings towards the company in question
(especially if the customers feel trapped). On a more basic level, however, our research
proposition is the following:
Proposition: Tangible and intangible characteristics of sales personnel (such as
physical appearance, willingness to listen, assure and oblige, etc.) are the key to high
levels of perceived service quality.
Our empirical research strives to verify this research proposition using a subset of items
from the SERVQUAL scale.
3. SERVQUAL: AN OVERVIEW
SERVQUAL is a result of a systematic on-going study of service quality that begun in 1983.
The model defines quality as the difference between customers’ expectations and perceptions
with regard to the service delivered in the past. The respondents are asked to answer two sets of
questions dealing with the same subject. One set of questions is general (e.g. quality of service
in financial institutions), the other pertaining to a company in question (e.g. quality of service
in bank X).
7
Respondents choose from a seven-point modified Likert scale to indicate the degree of their
agreement with each of the given statements. For each of the items (service attributes), a quality
judgement can be computed according to the following formula:
Perception ( Pi ) - Expectation ( Ei ) = Quality ( Qi )
(1)
The SERVQUAL score (perceived service quality) is obtained by the following equation:
Q
1 22
 (Pi  Ei )
22 i 1
(2)
The Pi  Ei gap scores can be subjected to an iterative sequence of item-to-item correlation
analyses, followed by a series of factor analyses to examine the dimensionality of the scale.
Using the oblique rotation that identifies the extent to which the extracted factors are
correlated, Parasuraman et al. [1988] discovered five quality dimensions:

tangibles: physical facilities, equipment, and appearance of personnel;

reliability: ability to perform the promised service dependably and accurately;

responsiveness: willingness to help customers and provide prompt service;

assurance: knowledge and courtesy of employees and their ability to convey trust and
confidence;

empathy: caring and individualised attention that company provides its customers with.
As shown in Llosa et al. [1998], the SERVQUAL scale has been adopted as a standard by
other researchers in the field of service quality both in business-to-business and mass markets.
This, however, does not mean that the scale is not subject to constant re-examination and
criticism concerning issues such as:
 object of measurement: Kasper et al. [1999] state that it is not clear whether the scale
measures service quality or customer satisfaction;
 questionnaire wording: the same group of authors [Kasper et al., 1999] points out that it
might be better not to use negatively worded questions in order to avoid interpretation
problems;
 questionnaire length: Johns and Tyas [1996] deem the questionnaire in its original form
too long;
 timing of questionnaire administration: the issue of whether to distribute the
8
questionnaire before or after the service experience is discussed by Carman [1990], Smith
[1995] as well as Johns and Tyas [1996];
 problems of Likert scale application: a comprehensive overview given in Krosnick and
Fabrigar [1997] includes number and labeling of points, inclusion of the middle alternative,
equality of distances between points, etc.;
 use of (Pi-Ei) difference scores: Teas [1993, 1993a, 1994] shows that increasing P-E scores
may not always correspond to increasing levels of perceived quality;
 generalisation of service quality dimensions: Buttle [1995] lists a number of replication
studies to illustrate that the number of distinct perceived quality dimensions found in
replication studies conducted in different service industries varies from one to nine, thus the
generalisation is low, or, as stated by Babakus and Boller [1992: 253], “the dimensionality
of service quality may depend on the type of services under study”;
 static nature of the model: Haller [1998] points out that for a number of long-term service
processes (such as e.g. education), both perceptions and expectations - and consequently
quality evaluations - change in time, therefore a dynamic model of service quality should be
developed.
Given the fact that ours was not to be a SERVQUAL replication study, we applied a shorter
version of the questionnaire, concentrating on tangible and intangible characteristics of sales
personnel in relation to perceived service quality. We included only the items of performance
and decided to adopt a five-point Likert scale with the goal of increasing the response rate and
response quality. With regard to the timing of questionnaire administration, we resolved to
collect data away from the actual point of service delivery (unrelated to a single encounter).
4. EMPIRICAL RESEARCH
4.1 A Brief Introduction of the Retailer
The retailer whose loyalty programme members participated in our research project belongs to
the group of those large Slovenian companies whose almost monopolistic status (market share
above 70 per cent over the last several years) will probably be seriously eroded in the decade
following the EU accession. The company has a widespread network of retail outlets where, as
early as in 1996, more than 5,000 different products were available for sale. It is renowned for
its environmental concerns. Apart from them, the company has also been involved into a
number of cultural, educational, sport, and humanitarian projects.
9
4.2 A Brief Introduction of the Retailer Loyalty Programme
Rayner [1996] argues that there is considerable communality between the systems required to
run payment card programmes, and those required to run customer loyalty programmes. The
former can be easily amended to add customer loyalty benefits. She also points out that retailerissued payment cards are typical differentiating services aimed at developing long-term loyalty.
They might have a narrower appeal than loyalty programmes, but generate a stronger customer
commitment.
To benefit from advantages of both loyalty programmes and payment card, retailers may
pursue one of the two paths:

they introduce the payment card first and either add loyalty benefits later, or run the card in
conjunction with a separate customer loyalty programme;

they capitalise on the infrastructure developed for a loyalty programme by expanding it into
payment options.
In the year 1992, our retailer trial-issued a payment card to its own employees and
employees of affiliated companies. The card was only accepted at retailer’s points of sale and
was not promoted in any way. With this act, foundations for a long-term development of a
loyalty programme were laid. After very good results of the trial card issue, the public nationwide card launch followed a year later, and an independent unit (the Card Centre) was
established as an IT-support and transaction authorisation centre. During the following years, in
co-operation with several external partners, a number of loyalty initiatives were undertaken in
the card promotion framework, bringing it closer and closer to the final transformation into a
formal loyalty programme. Implementation of the final step (gradual replacement of bar-code
cards with smart cards and transformation of the Card Centre into the Customer Care Centre)
started in 2002.
10
4.3 Sample Selection Procedure and Sample Characteristics
Before taking a closer look at the sample characteristics, it is necessary to describe the sample
selection procedure, which consisted of three steps:

In the first step, a stratified random sample of 600 units was selected from the retailer
database of 45,958 members (the ZIP code areas serving as strata).

Cross-verification of each unit’s address and phone number was carried out in the second
step. 36 units (or 6 per cent) had to be excluded from the sample because their phone
numbers were either non-existent or could not be positively identified for various reasons
(e.g. phone number registered under a different name).

The remaining 564 units were contacted by phone in the three-day period from June 26th to
June 28th, 2001. 201 or almost 40 per cent (39.7 per cent of the applicable sample) agreed to
participate in the survey.
Although the final result is not a perfect stratified sample due to elimination of several
selected units without replacement (because of temporal and financial project constraints), it
comes close enough for the goals of the proposed empirical project.
Sample characteristics closely resemble characteristics of the population of loyalty
programme members. As shown in Table 1, there are 62.7 per cent males and 37.3 per cent
females in the sample, with the average age of 42.9 years. The youngest cardholder is 24 and
the eldest 79 years of age.
Table 1: Gender Structure and Age Characteristics of the Cardholders’ Sample
GENDER
Male
Female
Total
NUMBER
OF UNITS
% OF UNITS
126
75
201
62.7
37.3
100.0
AVERAGE
AGE
43.3
42.2
42.9
MINIMUM
AGE
24
24
24
MAXIMUM
AGE
69
79
79
2.5 per cent of the respondents finished only the primary and 62.2 per cent the secondary
school (in the population of cardholders, these percentages amount to 7.3 per cent and 65.9 per
cent). The remaining 35.3 per cent held at least a college if not a university degree (26.9 per
cent in the population of cardholders).
As in the cardholder population, a little more than 50 per cent of respondents came from the
two largest Slovenian metropolitan areas. The average length of membership, however, was
slightly longer (3.5 as opposed to 2.9 years).
11
More than 80 per cent of respondents were owners or co-owners of a house or an apartment;
roughly the same percentage of them were married or living together with a partner, and almost
70 per cent had to provide for one or two dependent persons.
4.4 A List of Variables
Apart from demographic and socio-economic variables used to describe population and sample
characteristics and to profile clusters later on, the following transaction and survey variables
were used in the analysis:

survey variables (perceived service quality items pertaining to retailer’s sales personnel
measured on the five-point Likert scale with options 1 – strongly disagree, 2 – disagree, 3 –
neither disagree nor agree, 4 – agree, 5 – strongly agree):
–
SQ_1: Retailer’s sales personnel are properly dressed.
–
SQ_2: Retailer’s sales personnel are neat.
–
SQ_3: Retailer’s sales personnel are always friendly.
–
SQ_4: Retailer’s sales personnel can be trusted.
–
SQ_5: Retailer’s sales personnel are very busy, therefore it is understandable that they
cannot help me immediately.
–
SQ_6: Retailer’s sales personnel are very busy, therefore it is understandable that they
cannot spend a lot of time dealing with my requests.

transaction (behavioural) variables (for the period January – June 2001):
–
total amount spent in the six-month period using the payment card issued by the
retailer;
–
number of retailer’s outlets visited in the six-month period;
–
maximum number of cardholder’s visits to one outlet in the six-month period;
–
total number of visits to retailer’s outlets in the six-month period.
Following is a brief description of methodology used in the empirical project.
12
4.5 Methodology
To define the underlying structure in the data matrix of service quality perceptions we used
factor analysis which enabled us to identify the separate dimensions of the structure and
determine the extent to which each variable was explained by each dimension. Oblimin with
Kaiser normalisation rotation method was used due to expected correlation among factors.
Significant loadings were interpreted. Factor analysis was further used for data reduction by
calculating scores for each underlying dimension and substituting them for the original
variables [Hair et al., 1998].
Factor scores were then applied to group respondents into clusters, which should exhibit
high within-cluster homogeneity and high between-cluster heterogeneity. Distance measures of
similarity (Euclidean distance) were applied. A combination of hierarchical and nonhierarchical clustering algorithms was employed (hierarchical method was used to specify
cluster seeds for a non-hierarchical method). Cluster analysis respecification showed that one of
the observations had to be deleted as an outlier and clustering algorithm repeated. Finally, the
clusters were interpreted and named, followed by validation and profiling of the clusters.
5. RESULTS
Perceptions of service quality pertaining to retailer’s sales personnel (6 survey items) were
factor-analysed to determine the underlying factors related to the SERVQUAL instrument.
Correlation matrix of these variables showed that over half of the correlations were significant
at the 0.01 level (see Table 2).
Table 2: Pearson Correlation Coefficient Matrix (with p-Values in Parentheses)
ITEM
SQ_1
SQ_1
1.000
SQ_2
0.481
(0.000)
SQ_3
0.237
(0.001)
SQ_4
0.286
(0.000)
SQ_5
0.094
(0.189)
SQ_6
0.205
(0.004)
SQ_2
0.481
(0.000)
1.000
0.261
(0.000)
0.401
(0.000)
0.108
(0.133)
0.227
(0.001)
SQ_3
0.237
(0.001)
0.261
(0.000)
1.000
0.432
(0.000)
0.266
(0.000)
0.209
(0.003)
SQ_4
0.286
(0.000)
0.401
(0.000)
0.432
(0.000)
1.000
0.259
(0.000)
0.287
(0.000)
SQ_5
0.094
(0.189)
0.108
(0.133)
0.266
(0.000)
0.259
(0.000)
1.000
0.516
(0.000)
SQ_6
0.205
(0.004)
0.227
(0.001)
0.209
(0.003)
0.287
(0.000)
0.516
(0.000)
1.000
13
The Kaiser-Meyer-Olkin Measure of Sampling Adequacy with value of 0.700 was in the
acceptable range. Bartlett's Test of Sphericity (216.4, df. 15, Sig.0.00) showed that non-zero
correlations existed at the significance level of 0.000. This provided an adequate basis for
proceeding with the factor analysis.
The first step in the factor analysis procedure was to select the number of components to be
retained for further analysis. The importance of each component as well as their relative
explanatory power as expressed by their eigenvalues were analysed. The scree test indicated
that three factors might be appropriate. Although the eigenvalue for the third factor was low
(0.755) relative to the latent root criterion value of 1.0, we considered inclusion of this factor as
well. The three factors represented 53 per cent of the total variance of the six variables (two
factors accounted for 48 per cent of the variance).
The size of communalities (see Table 3) shows variance in a particular variable accounted
for by the three-factor solution. Extraction method used was the Principal Axis Factoring,
followed by the Oblimin rotation method with Kaiser Normalisation.
Table 3: Communalities
VARIABLE
CODE
SQ_1
SQ_2
SQ_3
SQ_4
SQ_5
SQ_6
VARIABLE DESCRIPTION
COMMUNALITIES
Retailer’s sales personnel are properly dressed.
Retailer’s sales personnel are neat.
Retailer’s sales personnel are always friendly.
Retailer’s sales personnel can be trusted.
Retailer’s sales personnel are very busy, therefore it is understandable
that they cannot help me immediately.
Retailer’s sales personnel are very busy, therefore it is understandable
that they cannot spend a lot of time dealing with my requests.
0.436
0.666
0.478
0.520
0.540
0.554
As shown in Table 4, each factor is composed of variables with loadings of 0.50 or higher.
Variable SQ_1 and SQ_2 loaded significantly on Factor 1, variables SQ_3 and SQ_4 on Factor
3 and variables SQ_5 and SQ_6 on Factor 2. All three pairs of variables vary together (for all
three pairs, both variables are of the same sign, suggesting that these perceptions are quite
similar among respondents). Factor 1 seemed to capture personnel appearance, Factor 2
tapped into empathy and Factor 3 revealed assurance.
14
Table 4: Pattern Matrix
VARIABLE
CODE
SQ_2
SQ_1
SQ_6
SQ_5
SQ_3
SQ_4
VARIABLE DESCRIPTION
Retailer’s sales personnel are neat.
Retailer’s sales personnel are properly
dressed.
Retailer’s sales personnel are very busy,
therefore it is understandable that they
cannot spend a lot of time dealing with my
requests.
Retailer’s sales personnel are very busy,
therefore it is understandable that they
cannot help me immediately.
Retailer’s sales personnel are always
friendly.
Retailer’s sales personnel can be trusted.
PERSONNEL
APPEARANCE
0.79
0.62
EMPATHY
ASSURANCE
- 0.00
0.03
- 0.05
- 0.05
0.15
0.73
0.09
- 0.14
0.70
- 0.14
- 0.01
0.00
- 0.69
0.20
0.06
- 0.55
In our case it was reasonable to expect that perceptual dimensions would be correlated (see
Table 5). The application of an oblique rotation was thus justified. Validation of factor analysis
was performed by splitting the sample into two sub samples and re-estimating the factor model
to test for comparability [Hair et al., 1998]. The results proved to be stable within our sample.
Table 5: Factor Correlation Matrix
FACTOR
1
2
3
FACTOR LABEL
Personnel appearance
Empathy
Assurance
1
1.00
0.30
- 0.59
2
0.30
1.00
- 0.51
3
- 0.59
- 0.51
1.00
Factor scores for each of the three factors were saved. Each of the respondents was therefore
assigned three new variables (factor scores for Factors 1–3) that replaced the original six
variables in the cluster analysis. Our next objective was to segment customers into groups with
similar perceptions of identified service quality dimensions (personal appearance, empathy and
assurance).
The sample had been examined for the outliers and one strong candidate for deletion was
found. Given that the set of three factor scores was metric, squared Euclidean distances were
chosen as the similarity measure. The standardisation of the variables was not undertaken
because all variables were measured on the same (five-point) scale. The within-case
standardisation was not appropriate, as the magnitude of the perceptions was an important
element of the segmentation objectives.
In the clustering process, we decided to combine the hierarchical and non-hierarchical
cluster methods. First we used the hierarchical procedure to identify the appropriate number of
clusters. Then we used the non-hierarchical procedure to fine-tune the results.
15
In hierarchical cluster analysis, Ward’s algorithm was chosen to minimise the within-cluster
differences and to avoid problems associated with linkage methods. The within-cluster sum of
squares coefficient in agglomeration schedule revealed large increases in going from four to
three clusters (223.35 - 180.71 = 42.64), three to two clusters (310.08 - 223.35 = 86.73) and two
to one cluster (579.00 - 310.08 = 268.92). Because the largest increases were observed when
going from three to two clusters and from two to one cluster, the three-cluster solution was
selected (from a manageable number of clusters in the range of two to five). Furthermore, a
three-cluster solution was suggested based on the visual representation by a dendrogram. The
absolute and relative sizes of the three clusters are given in Table 6.
Table 6: Number and Percentage of Cases in Each Cluster
CLUSTER
1
2
3
Total
NUMBER OF CASES
115
37
41
193
% OF CASES
59.6
19.2
21.2
100.0
The non-hierarchical technique was then applied, using cluster centroids as seed points.
Identified groups were of the same size as the groups resulting from the hierarchical clustering
procedure. A three-cluster solution was thus confirmed, with Clusters 2 and 3 being of the
similar size (containing about 20 per cent of respondents each).
Interpretation and profiling of clusters was provided through the mean values (centroids) on
each of the three rating variables (see Table 7). It could be noted that members of Cluster 1,
which is the largest cluster, focus their attention, relative to members of Cluster 2 and Cluster 3,
on personnel appearance and empathy. Quite the contrary can be stated for members of Cluster
2 who are focusing on assurance and not on personnel appearance or empathy. For members of
Cluster 3, the focus is more on personnel appearance and less on assurance than Cluster 2,
however also less on empathy than Cluster 1.
Table 7: Final Cluster Centres
FACTOR
Personnel appearance
Empathy
Assurance
CLUSTER 1:
High Perceived
Service Quality
0.38
0.56
- 0.50
16
CLUSTER 2:
Low Perceived
Service Quality
- 1.29
- 0.61
1.09
CLUSTER 3:
Low Perceived
Empathy
0.22
- 0.97
0.31
For the profiling stage, we focused on demographic and behavioural (transaction) variables
not included in the cluster solution (see Tables 8 and 9).
Table 8: Comparisons for Nominal and Ordinal Variables,
Description of Clusters (n = 193)
VARIABLE
VARIABLE VALUE
Gender
Male
Female
Total
Ljubljana
Maribor
Other
Total
Primary school
Secondary school
College or university
Total
Married or living together
Other
Total
Owner or co-owner
Tenant
Total
Region
Education
Marital
status
Residence
ownership
NUMBER OF CASES
Cluster 1
Cluster 2
Cluster 3
73
28
22
42
9
19
115
37
41
37
14
14
18
10
7
60
13
20
115
37
41
3
1
1
81
15
24
31
21
16
115
37
41
99
31
31
14
5
9
113
36
40
96
31
35
19
6
6
115
37
41
TOTAL
123
70
193
65
35
93
193
5
120
68
193
161
28
189
162
31
193
The Pearson Chi-Square statistics for some of the demographic variables revealed the
following:

Gender: Pearson Chi-Square = 4.08, d.f. 2, Sig. 0.13;

Region: Pearson Chi-Square = 3.99, d.f. 4, Sig. 0.40;

Education: Pearson Chi-Square = 11.40, d.f. 4, Sig. 0.02;

Marital status: Pearson Chi-Square = 2.42, d.f. 2, Sig. 0,30;

Residence ownership: Pearson Chi-Square = 0.08, d.f. 2, Sig. 0.96.
The profiling showed that clusters vary with regard to the gender structure (the smallest
percentage of females was found in Cluster 2, the largest in Cluster 3 where both genders are
almost equally represented). There were no significant differences according to region.
Regarding education, Cluster 1 has less university-educated respondents than the other two
clusters and the highest percentage of secondary school graduates (70.4 per cent). As far as
marital status and residence ownership are concerned, no significant differences among clusters
were found.
17
Table 9: Comparisons for Ratio Variables, Description of Clusters (n = 193)
VARIABLE
Age
Length of
membership
Number of
dependent
persons
Total amount
spent with the
retailer using its
payment card
Number of
retailer’s outlets
visited
Maximum
number of visits
to one outlet
Total number of
visits to retailer’s
outlets
CLUSTER
N
1
2
3
Total
1
2
3
Total
1
2
3
Total
1
2
3
Total
1
2
3
Total
1
2
3
Total
1
2
3
Total
115
37
41
193
115
37
41
193
115
36
40
191
115
37
41
193
115
37
41
193
115
37
41
193
115
37
41
193
MEAN
STD.
DEVIATION
43.03
41.00
42.85
42.60
3.31
4.05
3.56
3.51
1.40
1.19
1.35
1.35
113,838.92
86,755.27
89,863.99
103,553.60
5.39
5.19
6.78
5.65
14.95
7.68
11.17
12.75
24.64
14.89
24.83
22.81
10.02
9.98
8.85
9.76
2.26
1.94
1.95
2.15
1.08
0.92
0.86
1.01
156,980.38
86,579.13
73,467.65
131,603.24
5.19
4.25
9.20
6.11
16.19
7.51
15.91
15.08
24.18
12.81
44.24
28.29
STD.
ERROR
0.93
1.64
1.38
0.70
0.21
0.32
0.30
0.15
0.10
0.15
0.14
0.07
14,638.50
14,233.52
11,473.72
9,473.01
0.48
0.70
1.44
0.44
1.51
1.24
2.49
1.09
2.25
2.11
6.91
2.04
The F-ratios showed differences in the group means for several behavioural (transaction)
variables:

Length of membership in the loyalty programme: F-ratio = 1.69, Sig. 0.19;

Maximum number of cardholder’s visits to one retailer’s outlet (the most frequently used
outlet by a given cardholder) in the six-month period: F-ratio =3.64, Sig. 0.03;

Total number of visits to retailer’s outlets in the six-month period: F-ratio = 1.81, Sig. 0.17.
Here, the profiling process indicated that members of Cluster 1, which rated higher on
perceived personnel appearance and empathy, tend to have a shorter length of membership in
the company’s loyalty program, visit one (“the preferred”) of the company’s outlets
significantly more often than members of Cluster 2, and are characterised by an overall higher
total number of visits to retailer’s outlets than members of Cluster 2.
As far as Cluster 2 (which contains loyalty programme members focusing primarily on
assurance) is concerned, it could be ascertained that of all three clusters, this one is
18
characterised by the longest average length of membership on one, and the lowest average
number of visited outlets as well as the lowest average number of visits to retailer’s outlets in
the six-month period on the other hand. Total amount spent is also the lowest in this group of
loyalty programme members, although differences among clusters are not statistically
significant. Interestingly, Cluster 2 is also marked by the lowest percentage of females (24 per
cent).
Cluster 3 with low perceived empathy (caring and individual attention provided to
customers) is - controversially - characterised both by larger than average number of visited
outlets and larger than average number of visits to retailer outlets in the six-month period.
Given the largest percentage of females in this cluster (46 per cent) this comes as no surprise
since research indicates that women are prepared to visit an outlet several times to check on
alternatives [Barletta, 2003; Quinlan, 2003]. Also, female customers might appreciate more
help from retailer’s sales personnel and therefore show less understanding for them being busy,
not being able to help immediately, or not being able to spend some time dealing with their
requests.
6. MANAGERIAL IMPLICATIONS
As we pointed out earlier, we believe that in retailing in general, and in the framework of a
loyalty programme in particular, the role sales personnel play is crucial for shaping customer
perceptions of service quality and determining the level of customer satisfaction. In our
empirical project we investigated tangible and intangible characteristics of sales personnel
(such as physical appearance, willingness to listen, assure and oblige, etc.) in relationship to
perceived service quality. In order to exclude possibility of sales personnel bias, we collected
the customer data on perceived service quality away from the point of sale. All results of the
empirical project discussed in this paper are in line with our research proposition.
Managerial implications of our findings can be summarised as follows:

Cluster 1 represents the solid membership base with high perceived quality and high
number of visits to retailer outlets on one, yet with relatively shorter length of membership
in the loyalty programme on the other hand. The main managerial challenge in this group
would be keeping the members happy and in spending mood. Giving more emphasis on
friendliness of sales personnel as well as their ability to convey trust could be the key.

Cluster 2 are disgruntled loyalty programme members (predominantly male with high
education), who “had been there and seen it all”. They cannot be bought with friendliness
19
and trust without delivery; they need to be regularly invited back to retailer outlets and
shown that their individual needs can be taken care of immediately.

Cluster 3 members (females form the majority in this group) do not need any further push
to visit several retail outlets of this particular company a number of times. They should,
however, be well attended to once in the shop. Sales personnel should be prepared to
dedicate enough time to answer their questions and help them find solutions to their
problems.
A loyalty programme is defined as a company’s organised and structured form of loyalty
efforts. Can these efforts produce negative effects? Not directly, perhaps, but sometimes
indirect influences turn out to be the ones that are most problematic. Our empirical findings
indicate that both the retailer in question and retailers in general should keep an eye on links
between the length of membership and behavioural (transaction) variables to avoid the negative
“loyalty programme effect”. The retailer  loyalty programme member relationships might go
stale due to predictability of the shopping experience. Also, interactions with the same sales
personnel might become a put-off for long-term members. Carefully timed promotions and
events, thorough employee training and rotation among outlets, as well as continuous analysis
of customer data could help prolong the active stage in the loyalty programme member
lifecycle.
7. THE FUTURE OUTLOOK
The results of our empirical analysis are a snapshot at a given point in time for a known retailer
and should be interpreted with regard to this limitation. The variables used in the analysis were
cut out of the broader model of SERVQUAL and give us only a limited view of the service
quality dimensions. Several presented findings, however, might be of general interest to
retailers involved in loyalty programmes.
The following issue would certainly need further investigation: in what ways could customer
data analysis of current loyalty programme members be applied to identify specific segment
behaviour and needs in order for management to act upon them with the goal of improving
perceived service quality level, and, ultimately, the bottom line. In the future, we hope to
broaden the scope of our analysis in this regard.
Further analytical challenges include introduction of the time perspective (to account for
effects of implemented managerial measures against the negative “loyalty programme effect”)
as well as predictions of profiles and behaviour of potential new members based on customer
20
data analysis of current loyalty programme members (to improve the efficiency of direct
marketing activities). Additionally, we would like to analyse the influence of demographic
shifts in the population (longer life expectancy, higher percentage of females in higher age
classes, etc.) both on effectiveness of loyalty programmes and on personnel-related service
quality dimensions. Finally, the general trend towards multi-company and multi-industry
loyalty programmes (European examples include Airmiles in the Netherlands, Payback in
Germany as well as Nectar in the UK) should present us with interesting personnel-related
quality problems to tackle, starting with the most pressing issue of employees’ dilemma
whether to be loyal to their respective companies or the loyalty programme in question.
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