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Analysis of relations between CSI and COI
Jiyoun Lim, Sangchan Park
Korean advanced institution of science and technology, Industrial Engineering
ABSTRACT
Staying competitive in business, Company should understand the customer’s preferences, behaviours,
and ways to interact with the customers and so on. Customer Satisfaction Index contributes to
improvement of company’s service quality through research customer satisfaction in aspect of outside of
company. However not only CSI, but also the service quality improvement through employees’
evaluation for work is required. Because employees’ working ability can become higher when they are
satisfied their work. Therefore, we analyze relation between Customer satisfaction index and employees’
evaluation. The employees’ evaluation can be taken by customer orientation index (COI) that evaluates
individual employee’s ability which is related with customer satisfaction.
In this paper analysis of employees’ ability for service quality improvement can be studied through data
mining method. The self-organizing map (SOM) is an efficient tool for visualization of multidimensional
numerical data. The purpose is to give an idea of what kind of information can be acquired from different
presentations and how the SOM can be utilized in exploratory data visualization. Through visualization of
COI and CSI data, what is most important part of the work for customer satisfaction and what is the
source of influence for improvement of employees’ ability can be taken. In this paper, we propose
appropriated marketing strategies for each department in company.
Keywords: CSI, COI, SOM
HEADING
Introduction
Motivation
CSI is one of the popular researches for company’s customer satisfaction strategy. CSI analyze how
customers are satisfied with the service of company, and gives the basis for improving what process for
which customers. Service quality improvement can be made in aspect of 4M that means Man, Machine,
Method, and Material. CSI focuses the service improvement on only Method and Material so that the
strategy that can improve service quality in aspect of MAN and Machine.
Last study suggested CSI method with employee’s satisfaction index but it consists of numerical
analysis so that it needs much time to understand. More powerful and visually understandable idea is
needed in this methodology. In this paper, the self-organizing map (SOM) which is an efficient tool for
visualization of multidimensional numerical data is used for visualization of CSI data. In the
visualizations, a real data set of company in service part is used. The data contains information on the
satisfactory index about employees’ works in each part. The input space dimension is 40, and even if
there clearly many clusters in the data, they are highly overlapping.
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Literature Review
CRM based on CSI
Present CSI research used to survey the customer satisfaction and evaluate the result. There is study that
CSI connects on CRM strategy. This study offers not only the method of business evaluation, but also the
strategy of the improvement of service quality.
Figure 1 – Model of CSI analysis
The method of CSI analysis in Model of CSI analysis (Figure 1) largely consists of analysis and strategy
of service view, customer view and cooperate view. In service view, important quality variables that stand
for many questions of the survey are extracted from service quality variables. In customer view, target for
the target marketing and service operation are suggested and the forecast the effectiveness and result of
those methods. Lastly in cooperate view, the method of comparison with competition and the advice for
investment priority are suggested. The strength of connective analysis between CSI and CRM strategy is
the part of company’s strategy for segmented customers. However, this CSI model has limitations that
this focuses only process and it is difficult to monitor indexes continuously.
COI
COI research is the survey for employees’ performance evaluation in the company which is research
about response of call, visiting service and etc. When questions of survey are constructed, they must have
initial response attitude, fundamental rate of work, work performance, work transference, and finishing
response attitude.
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Figure 2 – CO effect on SO and JS
CO effects on SO and So effects on JS, so that CO indirectly effects on JS. Considering all CO, SO and
JS to be indexes based on customers, this result that is CO affects on JS, gives motivation on the study
about connection between CO and customer satisfaction.
CSI strategies connected with COI
CSI strategies connected with COI can be suggested for target customers who are maturity customers
and Attrition customers in aspect of CRM. Table 1 shows that analysis of CSI strategies connected with
COI.
Large COI variation
Small COI variation
Table 1 – Analysis of CSI connected with COI
Ability of human resource
 Working knowledge improvement is needed
High CSI
Low CSI
High High service satisfaction
Low service satisfaction
COI Good performance of
Good performance of Service
service and process
and Process
 Stay
 Improvement of
service and product is
needed
Low High service satisfaction
Low service satisfaction
COI Bad performance of
Bad performance of Service and
service and process
process
 Tasks which
 Overall improvement
don’t relate
is needed
with human
resources
If the variation of COI is large, the knowledge of work improvement is needed based on the judgment
that there is problem on human resources. If the variation of COI is small, Strategy can be deduced by the
analysis of connection with CSI survey result.
SOM-based data visualization
Data mining is an emerging area of new research efforts, responding to the presence of large databases
in commerce, industry and research. The self-organize map (SOM) [11] is a neural network algorithm
based on unsupervised learning. The SOM has proven to be a valuable tool in data mining and KDD with
applications in full-text and financial data analysis. It has also been successfully applied in various
engineering applications in pattern recognition, image analysis, process-monitoring and fault diagnosis.
The use of the SOM in exploratory data analysis in studied in.
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The SOM consists of neurons located on a regular low-dimensional grid, usually 1- or 2-dimnsional (1D
or 2D). Higher dimensional grids are possible, but they are not generally used since their visualization is
problematic. The lattice of the grid can be either hexagonal or rectangular.
Methodology
Framework
Figure 3 – Flow chart of Analysis of relations between CSI and COI
In the model of CSI research analysis suggested, analysis of connection with CSI-COI. Figure 3 shows
several steps of the model. Before survey, appropriate questionnaires should be made. They have to
contain 4M1E and service industry processes. 4M1E consists of man, machine, material, method, and
environment. ‘Man’ is about ‘the work attitude of employees’. ‘Machine’ is about ‘the systems and
procedures of work’. ‘Material’ is about ‘employees’ experience-level of working manual’. ‘Method’ is
about ‘detailed method or procedures of working manual’ and last, ‘environment’ is about ‘environment
which affects works’. Service industry index is scenario-based access. This method reflects customer
behavior that put to use service. It consists access, registration, diagnosis/explore, payment, after sales
service and return to community. ‘Access’ is about ‘convenience of access the service’. ‘Registration’ is
about ‘convenience of reservation and registration’. ‘Diagnosis/explore’ is about ‘easy understanding,
variety and convenience about service options’. ‘Payment’ is about ‘convenience of payment’. ‘After
sales service’ is about ‘service level of after sales service’, and, ‘Return to community’ is about
‘management about customer who have not used the service for specific duration’.
Figure 4 –4M1E and service industry index
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When analyze data, first thing to do is that recognize the key variables that affect customer satisfaction.
Factor analysis reduces the number of dimension. Correlation and partial correlation show what are
influencing variables more than other variables. Second, priority of factors is decided by SOM-based
visualization of data. SOM-based visualization gives possibility of analysis in various aspects. It shows
correlation between customer satisfaction and employee’s satisfaction visually. It is very convenient to
recognize the key factors.
Analysis
There are data of 3 parts in some company. Team A has 40 employees. CSI survey is examined for each
part and also, COI survey is examined.
Table 2 – Result of factor analysis (team A)
Factor
Factor 1
Factor 2
Activity for CI and brand attraction of
0.8225
employees (Q6_3)
Satisfaction of active service for
employees after change CI, character and
0.816
symbol (Q9)
Presentation meeting satisfaction (Q6_2)
0.8137
Employees’ full knowledge of work
0.3751
0.7859
(Q3_2)
Satisfaction of information about new CI,
0.7735
new forms and new logo file (Q8_1)
Satisfaction of event (Q6_1)
0.767
Satisfaction of guideline about new CI,
0.766
new forms and new logo file Q8_2)
Satisfaction of employees’ kindness
0.2475
0.7622
(Q3_1)
Satisfaction of sales hooks (Q4_4)
0.44
0.7244
Souvenir based on symbol reflects well
0.7136
company’s brand image (Q4_2)
Satisfaction about work speed (Q3_3)
0.5765
0.7118
Satisfaction of souvenir (Q4_3)
0.3015
0.6584
Interest about CI, logo, character and
0.6114
symbol (Q4_1)
Factor 3
0.1987
0.2896
0.2996
0.4393
0.1059
0.0374
0.2836
Based on the result of factor analysis, team A has only one factor.
Factor
Q6_3
Q6_2
Q6_1
Q4_2
Q3_3
Q8_2
Q3_2
Q3_1
Q4_4
Q8_1
Q4_3
Q4_1
Table 3 – Result of partial correlation (team A)
Partial correlation
0.7605
0.7199
0.7141
0.6631
0.6131
0.6073
0.5982
0.5969
0.5671
0.5557
0.5361
0.4575
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Partial correlation
0.1972
0.0522
0.043
0.212
0.4002
-0.0093
-0.0949
-0.0554
-0.0205
-0.1474
-0.1229
0.1142
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Based on the result of partial correlation, team A has 4 important variables among representative
variables. Those 4 variables are important, because they have high relationship between other variables.
variables
Q4_1
Q4_2
Q3_3
Q6_3
Table 4 –Contribution rate and Kano’s diagram type
Contribution rate
Kano’s diagram type
Contribution
Contribution
Fitting Line
Type
rate1
rate2
(Graph)
1.0153 +
0.5186
0.4476
Basic
1.5559 * ln(x)
1.1354 +
0.5073
0.4378
Basic
1.522 * ln(x)
1.1124 +
0.4918
0.4244
Basic
1.4753 * ln(x)
1.5379 +
0.3898
0.3364
Basic
1.1693 * ln(x)
r2
0.9813
0.9714
0.9786
0.9907
With this result and COI result, visualization based on SOM is constructed. The visualization of SOM
is constructed by ‘SOM toolbox for Matlab 5’[12].
Figure 5 – Visualization of the SOM of CSI and COI data
Figure 5 shows U-matrix on top left, then component planes, and map unit labels on bottom right. The
six figures are linked by position: in each figure, the hexagon in a certain position corresponds to the
same map unit.
The map unit in top left corner has low values for Q4_1, Q6_3, Q3_3, and relatively low value for
Q4_2. The labels associated with the map unit are ‘low’ and ‘high’ and from the U-matrix it can be seen
that the unit is not very close to its neighbours. It means that customers who are satisfied with key
processes that are Q4_1, Q4_2, Q3_3 and Q6_3 are not so much related to the employees’ performance.
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Services themselves are more important for these key processes. The map unit in bottom right corner has
high values for Q4_1, Q3_3 and relatively low values for Q4_2, Q6_3. The labels associated with the
map unit are ‘med’ and ‘high’ and from the U-matrix it can be seen that the unit is closed to its
neighbours. It means that the customers who are unsatisfied with key processes are much related to the
employees’ performance.
Conclusion
Usually it is important to know the information about satisfied customers among valuable customers
for company in customer segmentation. However, it’s possible to get customer’s information in various
aspects with CSI and COI data. The clustering information can get from visualization of the SOM. :
Customer who satisfied with service but with unskilled employees and customer who satisfied with
service and skilled employees. From this analysis, it is possible to make strategy for satisfied and
unsatisfied customers with employees’ performance.
REFERENCES
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Management
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financial services
[3] CAROLYN A. STRONG(2004) , The drivers of customer orientation: an exploration of relational, human
resource and procedural tactics
[4] Peter Tonks and Hugh Flanagan(1994) , Positioning the Human Resource Business Using Service Level
Agreements
[5] JOS J.M. TRIENEKENS(2004) , Specification of Service Level Agreements : Problems, Principles and Practices
[6] K.S Chen and H.H. Yang , A new decision-making tool: the service performance index
[7] Juha Vesanto , SOM-based data visualization methods
[8] Gregor Leban , VizRank : Data Visualization Guided by Machine Learning
[9] Alfred Inselberg , Visualization and data mining of high-dimensional data
[10] Lada Lavrac , Data mining and visualization for decision support and modeling of public health-care resources
[11] T. Kohonen , Self-Organizing Maps
[12] Juha Vesanto, Johan Himberg , SOM toolbox for Matlab 5
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