delighting customers: an exploration into the discriminating factors
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Delighting customers: An explorationinto the discriminating factorsUtpal Kumar Chowdhury aa Goenka College of Commerce & Business Administration ,Kolkata, West Bengal, IndiaPublished online: 06 Feb 2009.
To cite this article: Utpal Kumar Chowdhury (2009) Delighting customers: An exploration intothe discriminating factors, Total Quality Management & Business Excellence, 20:2, 253-266, DOI:10.1080/14783360802351678
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Delighting customers: An exploration into the discriminating factors
Utpal Kumar Chowdhury�
Goenka College of Commerce & Business Administration, Kolkata, West Bengal, India
A robust customer-friendly approach in the form of Total Customer Satisfaction (TCS) hasevolved as a consequence of round-the-clock competition in this globalised era. Themarketers of today, find themselves involved in an unending journey towards optimisingTCS. To set this journey in the right direction, this article investigates the determinantsthat maximise TCS.
Keywords: factors that delight customers; discriminating between factors that delightcustomers
Preamble: a journey towards customer satisfaction
A new marketing philosophy – making customers the centre of company culture – is now
emerging as a consequence of the toughest business competition ever. Today’s customer-
centred companies consider themselves as customer satisfiers and are adept at customers not
just mere products/services. Such companies are aiming at Total Customer Satisfaction (TCS)1
as a solution for problems relating to outperforming competitors or going about winning customer
expectations of benefits and the like. It is in tune with TCS that a company finds ways of delight-
ing customers. This article will briefly describe some of the relevant aspects of delighting custo-
mers before exploring, on the basis of a case study, the attributes/variables that discriminate most
in delighting customers.
An overview
Delighting customers
Today’s outstanding companies aim to delight customers by assuring only what they can
provide, then providing more than their assurance.2 The logic behind their aim is: a company
can win a customer for life if he or she is continuously delighted.3 The key is to stay above cus-
tomers’ expectations through a company’s top-notch efforts.4
A customer has expectations of both an implicit and explicit nature. A delighted customer, in
addition to the normal fulfilment of expectations of both natures, gets some unthinkable satisfac-
tion. In other words, a delighted customer finds the largest perceived value-cost gap. Such a cus-
tomer feels an emotional bond5 with the product/service of a company, not simply a rational
preference.
Why delight a customer?
Delighted customers can do a lot – repeated buying, remain loyal6 for a longer period, less
price sensitive, good words of mouth,7 low service cost, less attention to competitor’s
product/service – in favour of a company. On the other hand, a dissatisfied customer can do
ISSN 1478-3363 print/ISSN 1478-3371 online
# 2009 Taylor & Francis
DOI: 10.1080/14783360802351678
http://www.informaworld.com
Email: [email protected]
Total Quality Management
Vol. 20, No. 2, February 2009, 253–266
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much harm to a company. In fact, a practical case study (Kotler & Armstrong, 2003) showed that
the market share of a global giant has plunged from more than 80% to less than 35% in just five
years because dissatisfied customers gleefully defected to the new competitors. Another study8
reveals that a dissatisfied customer gripes to 11 people, against a satisfied customer who talks
favourably to three people only. Clearly, the bad word of mouth runs around four times faster
than good word of mouth.
Today, more and more companies are realising the importance of delighting customers who,
in fact, constitute the company’s relationship capital. The observations of Reichheld (1996) are
highly relevant in the present context:
. Acquiring new customers can cost five times more than the costs involved in delighting
existing customers.. It requires the utmost effort to attract/capture delighted customers to switch away from
other companies.. The customer profit rate tends to increase over the life of the delighted customers.
How to keep customers delighted
In an open market economy, customer satisfaction is both an ultimate target and a marketing tool
for all customer-centred enterprises. Companies that achieve high ratings in terms of customer
satisfaction make sure that their target market knows it.9 Such companies apply the necessary
tools for tracking and measuring customer satisfaction almost on a regular basis. Briefly,
these tools are:
. Complaint and suggestion system;
. Customer satisfaction surveys;
. Ghost shopping; and
. Lost customer analysis.
These tools basically help a company to make the necessary adjustments that favour its
customers.
This study is directed towards finding out the attributes that discriminate most in delighting
customers. ‘What attributes discriminate most in delighting customers?’ is the basic question of
this study and it is the most fundamental question for any study relating to customer satisfaction
surveys. This is because, in our opinion, a company can keep its customers delighted only when
it has a transparent view about the attributes/variables that discriminate most in delighting cus-
tomers. The following part of our analysis is devoted to an in-depth search for these attributes/
variables.
An exploration
Our field survey was conducted over the customers of the Indian banking sector. The following
aspects are noteworthy in this regard.
Major objectives
Customers have different perceptions of the banking service in different situations commonly
faced by them. It is these perceptual differences that can evaluate the quality of banking services
and accordingly determine customer satisfaction levels. There are many predictor variables that
dominate the process of evaluation. Our objective is to make an assessment of perceptually
different levels of customer satisfaction with different groups of banks in such a way as to
show the discriminant attributes/variables.
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Research design
Our process of assessment follows the techniques of discriminant analysis. X11 is the criterion
variable and X1 to X9 are the predictor variables. X10 is a criterion variable and is of non-
metric type. This variable has a positive effect in attracting more customers. A description of
variables has been provided in Table 1.
The entire sample size of 57 observations has been classified into analysis samples and hold
out samples (that is, samples which do not behave in tune with the majority samples observed for
a study). We conducted our survey over three groups of banks – nationalised banks: group 1,
first-generation banks: group 2 and gramin (village) banks: group 3.
Our analysis proceeds with special attention paid to the interpretation of results of these
groups of banks in terms of customer satisfaction.
Assumptions
The principal assumptions underlying our analysis involve the formation of a discriminant
function and its estimation. We have employed pooled variance estimates to allow the cross-
validation procedure to be used.
Estimation of the discriminant function
A review of the significance levels of the individual variables is given in Table 2, which reveals
that, on a univariate basis, all of the variables except X7 and X8 display significant differences
Table 2. Wilk’s Lambda (u-statistics) and univariate F ratio with 2 and 54 degrees of freedom.
Wilk’s Lambda F Sig.
X1 0.447 33.400 0.000X2 0.364 47.168 0.000X3 0.677 12.902 0.000X4 0.425 36.483 0.000X5 0.475 29.843 0.000X6 0.613 17.037 0.000X7 0.557 21.472 0.000X8 0.557 21.480 0.000X9 0.259 77.262 0.000
Table 1. Description of variables.
Description Type
X1: Managerial control MetricX2: Service quality MetricX3: Work culture MetricX4: Alienation MetricX5: Loyalty MetricX6: Perceived value MetricX7: Machiavellianism MetricX8: Image MetricX9: Fulfilment of expectations Metric
X10: Awards/prizesRecipient (Coded 1) Non-metricNon-recipient (Coded 0)
X11: Customer satisfaction Non-metric
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between the group means. We do not know whether the differences are between groups 1 and 2,
2 and 3 or 3 and 1. But we do know that there exist significant differences.
The results shown in Table 3 show that the first variable to enter the model is X2. Review of
the F to enter data reveals that of the variables not in the model at Step 1, all but X7 have F values
greater than 3 and can thus be expected to be considered for inclusion in the model in the later
steps (we have set our cut off for F to enter at 3.0 or larger).
The information provided in Table 4 summarises the relevant steps of the three-group dis-
criminant analysis. Note that variables X2, X4, X9, X3 and X5, were all entered into the discrimi-
nant model. These variables are now entered into the canonical discriminant procedure and
linear composites are formulated. The discriminant functions are based only on the variables
included in the discriminant model, i.e. X2, X3, X4, X5 and X9.
However, after the linear composites are calculated, the procedure correlates all nine inde-
pendent variables with the canonical discriminant functions to develop a structure (loadings)
matrix. This procedure enables us to see where the discrimination would occur if all the nine
Table 4. Summary table of the relevant steps in three group discriminant function.
StepNo. of
variables Entered RemovedWilk’s
Lambda SignificanceMin. Dsquared Significance
Betweengroups
1 1 X2 – 0.364 0.000 1.482 0.004 1 and 32 2 X4 – 0.293 0.000 2.554 0.001 2 and 33 3 X1 – 0.219 0.000 3.537 0.000 1 and 34 4 X6 – 0.196 0.000 3.925 0.000 1 and 35 5 X9 – 0.119 0.000 4.033 0.001 1 and 36 6 X3 – 0.107 0.000 4.390 0.002 1 and 37 5 – X6 0.112 0.000 4.118 0.001 1 and 38 4 – X1 0.117 0.000 3.815 0.000 1 and 39 5 X5 – 0.107 0.000 3.906 0.002 1 and 3
Table 3. First step in three-group discriminant function.
At Step 1, X2 was included in the analysis.
Degrees of freedom Significance Between groups
Wilk’s Lambda 0.364 1 2 54.0Exact F 47.168 2 54.0 0.000
Min. D squared 1.482 1 3Exact F 9.069 1 54.0 0.004
Variables in the analysis after Step 1Variables Tolerance (T) F to remove D squared Between groupsX2 1.000 47.168
Variables not in the analysis after Step 1Variables Tolerance (T) ¼ Min (T) F to enter Min. D squared Between groupsX1 0.722 7.094 2.540 1 and 3X3 0.960 4.939 2.110 1 and 3X4 0.886 6.378 2.554 2 and 3X5 0.986 10.655 1.596 1 and 3X6 0.938 3.975 1.661 1 and 3X7 0.873 2.629 1.491 1 and 3X8 0.891 3.305 1.524 1 and 3X9 0.968 29.053 1.864 1 and 3
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variables were included in the model (i.e. if none were excluded by multicollinearity or lack of
statistical significance).
The first linear composite is developed to explain the largest amount of variation in the dis-
criminant groups. The second linear composite, which is orthogonally independent of the first,
explains the largest percentage of the residual variance after the variance for the first composite
function is removed.
After the linear composites are developed, they can be rotated to redistribute the variance. In
the present context, we have chosen varimax rotation.
Table 5 contains the results for the canonical discriminant functions. The functions are
statistically significant, as measured by the chi-square statistics and that the first function
accounts for 92.8% of the variance. After the first function is extracted, the chi-square is recal-
culated. The results show that significant differences are present in the remaining variance.
Thereafter, we have placed the discriminant function coefficients (weights) for the predictive
models in Table 6 and the unrotated structure matrix in Table 7.
Since we have chosen to rotate the linear composites there is no need to interpret the struc-
ture loading at this point.
Canonical discriminant functions: determining its validity
At this stage, we must determine that the functions are valid predictors. This is accomplished by
examination of the classification matrices. We shall refer to Tables 8(a) and (b) to show that the
discriminant functions are valid. The hit ratio for the analysis sample is 100%, whereas that for
the hold out sample is 62.5%. These results again point out the upward bias that is likely without
a hold out sample. Both of these hit ratios are high. But in order to evaluate the effectiveness of
our model, these hit ratios should again be compared to maximum chance criteria (mcc) and pro-
portional chance criteria (pcc).
Table 5. Canonical discriminant functions.
Functions 1� 2�
Eigen values 5.544 0.427% of variance 92.8 7.2Cumulative % 92.8 100.0Canonical correlation 0.920 0.547Test of function(s) 1 through 2 2Wilk’s Lambda 0.107 0.701Chi-square 116.189 18.507Degree of freedom 10 4Significance 0.000 0.001
Note: �Marks the two canonical discriminant functions remaining in the analysis.
Table 6. Standardised canonical discriminant function coefficients.
Function
1 2
X2 0.194 0.529X3 0.266 20.431X4 0.395 0.540X5 0.301 20.218X9 20.715 0.440
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In our sample of 57 observations, 26 were in group 1, 23 in group 2 and 8 in group 3. Table 9
exhibits that the highest probability/mcc would be 46%. In other words, it shows that we would
be correct 46% of the time. Based on the mcc our model is, therefore, firmly established.
A Review of the pcc makes it clear that cmax . cpro. It indicates that the mcc is the criterion
to outperform. The hit ratio (for the hold out sample: Table 8 (part b)) of 62% exceeds the cmax
criterion substantially. It again determines the validity of our discriminant model.
Table 8. Classification matrices for analysis sample and hold out sample forthree-groups discriminant function.
Predicted group membership
Actual groups No. of cases 1 2 3
(a) Analysis: Classification results for cases selected for use in the analysis1 17 17 0 0
100.0% 0.0% 0.0%2 18 0 18 0
0.0% 100.0% 0.0%3 6 0 0 6
0.0% 0.0% 100.0%Percent of ‘grouped’ cases correctly classified: 100.0%.
(b) Hold out sample: Classification results for cases not selected for use in theanalysis
1 9 4 1 444.4% 11.1% 4.44%
2 5 0 5 00.0% 100.0% 0.0%
3 2 1 0 150.0% 0.0% 50.0%
Percent of ‘grouped’ cases correctly classified: 62.5%.
Table 7. Unrotated structure matrix.
Function
1 2
X9 20.712� 0.341X2 0.542� 0.522X5 0.444� 20.186X1
a 0.426� 0.012X8
a 0.332� 20.042X6
a 0.264� 0.110X4 0.451 0.725�
X3 0.279 20.330�
X7a 0.151 0.228�
Pooled within-groups correlations between discriminating variables (variablesordered by absolute size of correlation within function).�Largest absolute correlation between each variable and any discriminant function.aThis variable is not used in the analysis.
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Case wise diagnostics
Table 10 exhibits additional classification data for the three-group discriminant analysis. The
‘Case number’ column shows the individual observation. In the ‘Actual group’ column, a ‘1’
indicates group 1, a ‘2’ group 2 and a ‘3’ group 3. The asterisk beside the numbers indicates
that a particular observation was misclassified by the discriminant function. The ‘Highest
group’ column shows the group assignment of an observation by the model that is most likely
using the discriminant function whereas the ‘2nd highest’ column shows the second most
likely assignment using the discriminant function. The first column of the ‘Squared Mohalnobis
distance to centroid’ shows the squared distance from the centroid of the highest probability
group while the squared distance from the centroid of the second highest probability group is
being shown by the second column. The discriminant scores for each observation on each func-
tion are shown on the extreme right-hand side of the table. We have considered a case’s values
on all functions simultaneously since there are several groups.
Interpretation of multiple discriminant analysis
The final stage of a discriminant analysis involves interpretation of solutions usually done
through the following approaches.
Plotting group centroids
We have plotted the group centroids to demonstrate the results of multiple discriminant analysis.
Applying the results of the three-group canonical discriminant function, as shown in Table 11(d),
we have plotted each group’s centroid in reduced discriminant space.
The values have been exhibited in Figure 1 showing the position of each group. This shows
that there appear to be differences in the groups on the nine predictor variables. But it fails to
explain what these differences are. We therefore need to depend on the other relevant approaches
to get a satisfactory explanation.
Plotting discriminant loadings
We have plotted actual loadings to depict the differences in the groups on the nine-predictor vari-
ables. The unrotated loadings from the structure matrix information have been shown in Table 7
while Table 11 (part b) exhibits the rotated correlations (loadings). We prefer to consider the
latter for the current purpose since loadings are considered more valid than weights.10 The plot-
ting of discriminant loadings is displayed in Figure 2.
Stretching vectors
An even more accurate approach for the present context of depicting the differences involves
what is called stretching the vectors. A vector, as we have considered here, is a straight line
Table 9. Calculation of chance criteria.
Maximum chance criteria (mcc) Proportional chance criteria (pcc)Group 1: 26/57 ¼ 46% Cpro ¼ (p1)2
þ (p2)2þ (p3)2
Group 2: 23/57 ¼ 40% Cpro ¼ (0.46)2þ (0.40)2
þ (0.14)2
Group 3: 8/57 ¼ 14% Cpro ¼ 0.3912 ¼ 39.12%Cmax ¼ 46%
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Table 10. Classification date for three-group discriminant function case wise statistics.
Casenumber(original)
Actualgroup
Highest group Second highest group Discriminant scores
Predictedgroup
P(D ¼ djG ¼ g)
P(G ¼ gjD ¼ d)
SquaredMohalnobisdistance to
centroid Group P(G ¼ gjD ¼ d)
SquaredMohalnobisdistance to
centroid Function 1 Function 2p d.f.
1 1 1 0.755 2 0.936 0.963 3 0.064 5.919 21.400 20.9142 1 1 0.961 2 0.921 0.090 3 0.079 4.965 22.144 20.6523 1 1 0.660 2 0.977 0.831 3 0.023 8.299 22.007 21.3394 1 �2 0.103 2 0.962 4.545 3 0.038 11.002 24.069 20.6715 1 1 0.378 2 0.980 1.944 3 0.020 9.753 21.195 21.5856 1 1 0.817 2 0.961 0.404 3 0.039 6.797 22.180 21.0287 1 1 0.379 2 0.985 1.941 3 0.014 10.405 22.969 21.4108 1 �2 0.062 2 0.664 5.931 3 0.336 7.293 24.153 0.6559 1 1 0.735 2 0.922 0.616 3 0.078 5.547 22.748 20.540
10 1 �3 0.799 2 0.830 0.450 1 0.170 3.625 20.995 1.20711 1 1 0.699 2 0.582 0.747 3 0.418 1.379 22.019 0.41712 1 1 0.438 2 0.989 1.696 3 0.011 10.749 22.247 21.70713 1 1 0.798 2 0.942 0.450 3 0.068 6.029 21.541 20.94314 1 �2 0.016 2 0.704 8.217 1 0.291 9.965 0.739 22.06215 1 �3 0.212 2 0.869 3.101 1 0.111 7.255 23.307 1.91116 1 1 0.729 2 0.965 0.632 3 0.035 7.248 21.708 21.47817 1 �2 0.862 2 0.938 0.321 3 0.062 5.756 22.452 20.72818 1 1 0.304 2 0.744 2.381 3 0.295 4.514 23.334 0.29619 1 1 0.626 2 0.630 0.938 3 0.380 1.917 21.217 0.47920 1 1 0.794 2 0.939 0.461 3 0.061 5.918 22.591 20.70721 1 �3 0.078 2 0.568 5.096 2 0.282 6.499 0.283 –0.54722 1 1 0.635 2 0.539 0.907 3 0.461 1.222 21.516 0.40923 1 1 0.534 2 0.978 1.256 3 0.022 8.813 21.536 21.45524 1 �3 0.754 2 0.658 0.565 1 0.342 1.871 22.075 0.93625 1 �3 0.549 2 0.506 1.201 1 0.494 1.251 21.289 0.45826 1 1 0.990 2 0.778 0.211 3 0.222 2.748 21.615 20.13727 2 2 0.478 2 0.964 3.454 1 0.015 11.871 1.374 21.25428 2 2 0.128 2 0.924 4.112 1 0.067 9.354 1.023 21.05029 2 2 0.379 2 0.991 1.940 1 0.007 9.354 1.023 21.05030 2 2 0.201 2 0.999 3.212 1 0.006 12.085 1.501 20.569
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31 2 2 0.680 2 0.996 0.770 3 0.001 17.319 2.005 21.65732 2 �3 0.209 2 0.999 3.134 1 0.001 14.484 1.973 0.29633 2 �1 0.369 2 0.996 1.994 1 0.001 17.537 2.035 21.64734 2 �3 0.835 2 1.000 0.361 3 0.001 15.163 1.862 21.11335 2 2 0.865 2 1.000 0.290 1 0.000 27.423 3.240 20.43136 2 2 0.523 2 1.000 1.296 3 0.000 25.220 3.050 20.50937 2 �1 0.447 2 1.000 1.610 3 0.000 33.404 3.875 20.36238 2 2 0.593 2 1.000 1.037 3 0.000 26.547 3.533 0.96139 2 2 0.978 2 1.000 0.045 3 0.000 30.625 3.778 0.47240 2 2 0.654 2 1.000 0.850 3 0.000 22.426 2.931 0.10941 2 2 0.565 2 0.999 1.178 3 0.000 28.125 3.613 0.35842 2 �3 0.227 2 1.000 2.961 3 0.000 16.418 2.125 0.96343 2 2 0.769 2 1.000 0.526 3 0.000 40.176 4.423 20.36144 2 2 0.884 2 1.000 0.246 3 0.000 26.224 3.394 0.34545 2 2 0.439 2 1.000 1.646 3 0.000 20.478 2.808 0.45346 2 2 0.712 2 1.000 0.680 3 0.000 24.618 3.353 1.40747 2 2 0.896 2 1.000 0.215 3 0.000 28.550 3.590 0.16848 2 2 0.449 2 1.000 1.801 3 0.000 17.973 2.324 20.11349 2 2 0.014 2 0.951 8.568 3 0.049 30.957 3.891 0.86850 3 �1 0.739 2 0.622 0.605 3 0.378 14.517 2.001 2.77951 3 3 0.794 2 0.655 0.462 1 0.345 1.603 22.077 0.34252 3 3 0.635 2 0.965 0.907 1 0.035 1.747 21.572 0.83253 3 3 0.963 2 0.861 0.076 1 0.139 7.514 22.112 2.31054 3 3 0.709 2 0.579 0.687 1 0.421 1.326 21.343 1.39555 3 �2 0.365 2 0.944 2.017 1 0.050 3.719 21.810 0.71256 3 3 0.139 2 0.997 3.945 1 0.003 7.894 20.191 1.74657 3 3 0.718 2 0.772 0.662 1 0.228 3.103 22.374 1.287
Note: � Misclassified case.
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Figure 1. Plot of group centroids in reduced discriminant space.
Table 11. Results for varimax rotated three group discriminant function.
Function 1 Function 2
(a) Varimax rotation transformation matrix% Variance 83.58 16.42Function 1 0.979 20.206Function 2 0.206 0.979
(b) Rotated correlations between discriminating variables and canonical discriminant functionsVariablesX3 0.974� 0.002X6 0.897� – 0.282X9 0.805� 0.005X2 0.412� – 0.308X1 0.296 0.524�
X7 0.161 – 0.393�
X8 – 0.160 0.699�
X5 0.144 0.884�
X4 0.110 20.748�
(c) Rotated standardised discriminant function coefficientsVariablesX2 20.153 20.167X3 0.356 0.008X4 0.034 20.298X5 0.325 0.354X9 0.294 20.155
(d) Canonical discriminant functions evaluated at group centroidsGroup1 21.971 20.4282 2.782 20.0413 21.592 1.512
Note: �Indicates variables that discriminate in both functions 1 and 2.
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drawn from the origin (centre) of the graph to the coordinates of a particular variable vector. The
length of each vector, as shown by Figure 3, is an indicator of the relative importance of each
variable in discriminating among the groups. Our plotting process of stretching vectors involves
all of the variables, since we find their Univariate F ratios (Table 2) are all statistically
significant.
Figure 2. Plotting of discriminant loadings.
Figure 3. Plot of stretched attribute vectors (variables) and stretched group centroids in reduced discrimi-nant space.
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It should be noted here that if the rotated discriminant loadings of the attribute vectors are
stretched, then the group centroids should also be stretched in order to plot them accurately
on the same graph (for details see Hair et al., 1990). Figure 3, therefore, displays the stretched
attribute vectors (variables) and the stretched group centroids in reduced discriminant space. The
remaining part of our analysis will justify this.
Stretching the vectors causes them to point to the groups having the highest mean level on a
respective predictor and away from the groups having the lowest mean score. Figure 3 clarifies
that the first discriminant function is the primary source of differences between the less satisfied
(group 3) and least satisfied (group 1) versus highly satisfied (group 2) customers. It also shows
that function 1 corresponds most closely to variables X3 (Work culture) and X9 (Fulfilment of
expectations). Thus, the distinguishing features of banks with highly satisfied customers are
that they have provided a good work culture and take great care about the fulfilment of custo-
mers’ expectations.
Visual inspection of the plots for function 2 shows that variables X2 and X5 are closely
associated with function 2 and that it is the primary source of difference between group 1
Figure 4. Territorial map for three-group discriminant function.Note: �Indicates group centroid.
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versus groups 2 and 3. The distinguishing characteristics of the banks with the most satisfied cus-
tomers are that they provide quality service and have a good many loyal customers.
We still have to mention two more important points for a complete interpretation of results.
First, territorial maps. These do not include the vectors, but do plot the centroids (see
Figure 4). Each group centroid is designated by the asterisk and group boundaries are established
by a group’s corresponding number.
Second, the rotated correlations (loadings) and the group centroids. These are shown in Table
11(part b). The asterisks beside the loadings show which are significant for each function.11 In
order to determine which groups each function discriminates between, the group centroids of
Table 11(part d) should be considered. It will help us to know where the differences lie. This
table shows that, for function 1, the centroid for group 1 is –1.971, for group 2 it is 2.782
and for group 3 it is –1.592. From this we conclude that the primary source of the differences
for this function is between groups 1 and 3 versus group 2. A similar approach can also be made
with function 2. However, it is prudent to consider function 1 since it represents substantially
more variance than function 2.
Conclusion: a lesson to management
Our analysis was aimed at understanding the perceptual differences of customers’ satisfaction
levels based on typical predictor variables, with the following findings.
First, there are two dimensions of discrimination between the levels of customer satisfaction.
The first dimension is typified by work culture and fulfilment of expectations, which may be
indicative of relational qualities. The second dimension is characterised by quality service and
customers’ loyalty, and more objective features of the dealings between banks and customers.
Second, the three groups can also be profiled on these two dimensions and variables associ-
ated with each dimension to understand the perceptual differences among them. Customers of
the first generation banks are the delighted customers (i.e. enjoying highest level of satisfaction)
compared with those of the other two banking groups on the first dimension. The first generation
banks have higher perceptions of customers’ satisfaction on work culture and fulfilment of
expectations. Groups 1 and 3, lower on the first dimension, are distinguished by the second
dimension, with customers of the gramin (village) banks enjoying a higher level of satisfaction
when compared with those of the nationalised banks. The gramin (village) banks, notably, have
higher perceptions of customers’ satisfaction on service quality and loyal customers.
These general patterns can be extended to profiling the groups on the separate independent
variables and focusing on the key differentiating variables, in our case, X2 (Service quality), X3
(Work culture), X5 (Loyalty) and X9 (Fulfilment of expectations).
The discriminant analysis, as presented by us, identifies the attributes/variables that have the
most impact, on the basis of which the respective authority:
. Can develop relevant concise programmes incorporating a smaller set of attributes/vari-
ables, and. May specifically consider the extent to which their products/services can deliver on these
attributes/variables from a strategic planning point of view.
Notes
1. In particular, TQM is the whole process which is always focused by customers’ pleas. In fact, TCS isconceptually developed from the fundamentals of the Total Quality Management (TQM) concept andhas gained currency since early 1950s. The Deming Prize (Japan), The Malcolm Baldrige Award
Total Quality Management 265
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(USA) and The European Quality Award (Europe) are some of the examples of awards given on thebasis of high grades on certain criteria of which TCS is a prime area.
2. Lexus, the global giant motor car manufacturer, is a case in point. It sets a high standard for delightingcustomers and often makes outlandish efforts to beat that standard. For details, see Kelly (1988).
3. Studies show that keeping customers continuously delighted, although costly, is good in terms of finan-cial performance of a company. See Ellis (2001) and Sellers (1993).
4. Dell left no efforts untried to delight its customers and aimed most of its resources at this purpose. Thecompany achieved great success. About 80% of Dell’s business, today, has been made up by thedelighted customers. Source: Ramstad (1997).
5. It is neither a mystery nor magic. Xerox found that its delighted customers are six times more likely toreturn to Xerox products than its other customers. For details, please see Hatch and Schell (2002).
6. It is interesting to note here the case of Saturn (General Motors’ newest car division). It enjoyscustomer loyalty rates in the 60% range compared with overall industry rates below 40%. Source:www.Saturn.com; Louisville Courier – Journal (1999).
7. The case of Boeing may be cited here. It delights its customers by increasing fuel efficiency – from 5%to an unexpected 8%. Being delighted, customers tell other potential customers that Boeing lives up toits promises (Cohen, 1997). In fact, many other cases can be cited here. The Blair Witch Project wassuccessful through internet ‘buzz’. Companies such as the Body Shop, USAA, Starbucks, Palm Pilot,BMW 23, Roadster and Amazon were essentially built by favourable word of mouth with very littleadvertising. Source: Kotler (2003).
8. See Walker (1995). This study also showed that about 96% of unhappy customers never tell thecompany about their problems and 13% of the troubled customers complained about the companyto more than 20 people.
9. For example, Maruti Udyog Ltd. acquired top position in the Indian Customer Satisfaction Index (ICSI)rankings from JD Power & Associates for several years. Maruti’s advertising of this fact helped it sellmore Maruti cars. Source: Ramaswamy and Namakumari (2003).
10. The other reasons for such preference are:
. Multicollinearity and other factors often preclude a variable from entering the equation but that doesnot necessarily mean that it does not have a substantial effect. This can be determined by evaluatingthe discriminant loadings.
. The researcher may be interested in the interpretations of the individual variables that have statisticaland practical significance. Such interpretations are accomplished by identifying the variables withsubstantive loadings and understanding what the differing group means on each variable indicate.
11. For example, X3, X6, X9 and X2 are significant for function 1. Of these, we have already identified X3
and X9 in Figure 3 as being closely associated with function 1.
References
Cohen, A. (1997). Boeing. Sales & Marketing Management, 11(10), 68.Ellis, L. (2001). Customer loyalty. Executive Excellence, 24(3), 13–14.Hatch, D., & Schell, E. (2002). Delight your customers. Target Marketing, 8, 32–39.Hair, J.F., Anderson, R.E., & Tatham, R.L. (1990). Multivariate data analysis (2nd ed.). New York:
Maxwell Macmillan.Kelly, B. (1988). Five companies that do it right – and make it pay. Sales & Marketing Management, 6(8),
57–64.Kotler, P. (2003). Marketing management (11th ed.). New Delhi: Prentice Hall.Kotler, P., & Armstrong, G. (2003). Principles of marketing (10th ed.). New Delhi: Prentice Hall.Louisville Courier – Journal (1999). Saturn illustrates value of customer loyalty, September, 3–7.Ramaswamy, V.S., & Namakumari, S. (2003). Marketing management (3rd ed.). Delhi: Macmillan India Ltd.Ramstad, E. (1997). Dell fights PC wars by emphasizing service – focus wins big clients and gives IBM and
Compaq a run for their money. Wall Street Journal, August 15, B4.Reichheld, F. (1996). The loyalty effect. Boston: Harvard Business School Press.Sellers, P. (1993). Companies that serve you best. Fortune, May 31, 74–88.Walker, C. (1995). Word of mouth. American Demographics, July, 40.
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