This document summarizes research on using knowledge mining techniques to study customer satisfaction levels in super bazaars. It first introduces the importance of customer satisfaction for super bazaars and defines knowledge mining. It then describes various knowledge mining techniques that can be applied, including classification, regression, time series analysis, clustering, and association rule mining. The document proposes a model for conducting customer satisfaction surveys, applying knowledge mining techniques to the data, and using the results to enhance customer satisfaction. The goal of the research is to better understand customer preferences and behaviors to improve business performance for super bazaars.

1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
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AN IMPACT OF KNOWLEDGE MINING ON SATISFACTION OF
CONSUMERS IN SUPER BAZAARS
Dr. S. D. Mundhe1
, Mr. D.R. Vidhate2
1
(Sinhgad Institute of Management and Computer Application (SIMCA), S.No. 49/1,
Off Westerly Bypass Pune-Mumbai Highway, Narhe-Top, Pune -411041, India)
2
(College of Computer Application for Women, Satara, C/O Kanya Shala, Bhavani Peth,
Rajpath, Satara-415002, India)
ABSTRACT
Customer satisfaction in super bazaars is important in the situation of intense
competition where super bazaars fight for every individual customer. It is a dynamic field,
critically important to the success of super bazaars. So, the measurement of customer
satisfaction provides important information for the super bazaars and serves as a warning
signal about future business developments. The best way for the super bazaars is to find out
what their customers think about them is to conduct a customer satisfaction survey. The
objective of the research is to identify possibilities for the increase in customer satisfaction.
Different knowledge mining techniques are suggested by studying satisfaction level of
consumers for successful retention by different super bazaars to improve business
performance. Therefore, we proposed a model for efficient implementation of knowledge
mining for studying customer satisfaction. The paper also summarizes researchlimitationsaswell
astheworkcontributionandfuture researchguidelines.The findings of the study states that consumer
satisfaction is linked with knowledge mining.
Keywords: Customer Behaviour, Customer Satisfaction, Knowledge Mining, Retail Outlets,
Super Bazaar.
1. INTRODUCTION
The concept of customer satisfaction has attracted much attention in recent years.
Customer satisfaction is widely recognized as an important factor in the formation of
consumers' future purchase intentions. A satisfied customer is more valid than a good
advertisement or ads. A satisfied customer stays loyal longer, buys more, talks positively
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2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
427
about the super bazaar and its products, pays less attention to competitors and is less price-
sensitive. Satisfaction with a product or service is a construct that requires experience and use
of a product or service. [4] Organizations that try to analyze this concept should begin with
an understanding of various customer satisfaction parameters. It is a state that reflects the
evaluation of relationship between customer and super bazaar. As customers are increasingly
more aware of the conveneince and their lifestyle, there are many cases preferring to go for
super bazaar for their everyday shopping rather than small departmental store around. So,
customer satisfaction is important for growth of this sector. Customer satisfaction is simple
term stated as level of shopping experience in super bazaar where customers expected service
level is met with actual service provided by retailer. The customer of super bazaar are mainly
higher income group people.So, they want satisfactory level of products and services rather
than traditional shopping places. The customers also want to minimize their time and hazards
for shopping. The management and owners of super bazaar have to consider the influencing
factors of customer satisfaction to hold the market growth. The achievement of customer
satisfaction leads to bazaar loyalty and product repurchase. Knowledge mining combines the
statistic and artificial intelligence to find out the rules that are contained in the data, letters,
and figures.[2] The central idea of knowledge mining for customer retention is that data from
the past that contains information that will be useful in the future. Appropriate knowledge
mining tools, which are good at extracting and identifying useful information and knowledge
from enormous customer databases, are one of the best supporting tools for making different
customer retention decisions. Within the context of customer retention, knowledge mining
can be seen as a business driven process aimed at the discovery and consistent use of
profitable knowledge from organizational data.
By applying knowledge mining techniques we can discover customer behavior,
customer satisfaction, and loyalty or background of the customer. Assessment and analysis in
this model may strengthen customer behavior and loyalty for particular super bazaar. Using
knowledge mining techniques customer would be satisfied under the bazaar policy and
limitations. The most commonly used techniques include artificial neural networks, Decision
trees, Nearest-neighbor method, Genetic algorithm, Rule induction and Data visualization.
1.1 Artificial neural network
It is non-linear, predictive models that learn through training which can then be used
to make predictions on data with unknown outcomes.
1.2 Decision tree
It is tree shaped decision models that utilize rules to classify a data set. Each model
represents sets of decisions. For example, whether the organization is using an appropriate
cost-effective marketing strategy that is based on the assigned value of the customer, such as
profit.
1.3 Nearest-neighbor method
It classifies dataset records based on similar data in a historical dataset.
1.4 Genetic algorithms
It is a technique that is based on the concepts of natural evolution using genetic
combination, mutation and natural selection as a form of optimization.

3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
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1.5 Rule induction
It is the use of if-then rules from data based on statistical significance.
1.6 Data visualization
It is the pictorial representation of relationships among data.
2. KNOWLEDGE MINING
Knowledge mining tools take data and construct a representation of reality in the form
of a model. The resulting model describes patterns and relationships present in the data.
Knowledge mining is used to construct six types of models aimed at solving business
problems: classification, regression, time series, clustering, association analysis, and
sequence discovery. The first two, classification and regression are used to make predictions,
while association and sequence discovery are used to describe behavior. Clustering can be
used for either forecasting or description. Each business is interested in predicting the
behavior of its customers through the knowledge gained in knowledge mining.
Super bazaar business collects large amount of data on sales and customer shopping
history. The quantity of data collected continues to increase rapidly. It provides a rich source
for knowledge mining. Knowledge mining helps super bazaar betterunderstand their business, be
able to better serve their customers and increase the effectiveness of the bazaar in the long
run. For super bazaars, knowledge mining can be used to provide information on product
sales trends, customer buying habits and preferences etc.
Knowledge Mining functions:
2.1 Classification
It aims at building a model to predict future customer behaviors through classifying
data records into a number of predefined classes based on certain criteria .For example,
assigning customers to market segments. In this case the target variable is the category and
the techniques discover the relationship between the other variables and the category. When a
new record is to be classified, the technique determines the category and the probability that
the record belongs to the category.
2.2 Regression
Regression is used to map a data item to a real valued prediction variable. It assumes
that the target data fit into some known type of function and then determine best function of
this type that models the given data. For example, super bazaar owner wishes to reach certain
level of sale before specific month. Periodically, he predicts what his sale would be based on
its current value and several past values. He uses simple linear regression formula to predict
this value by fitting past behavior to a linear function then using this function.
2.3 Time Series analysis
Time series analysis comprises those knowledge mining techniques that are applied to
the analysis of time ordered data records. These knowledge mining techniques attempt to
detect similar sequences or subsequences in the ordered data. It is used in sales forecasting. In
super bazaar business, it is used to find amount of sale by super bazaar for a period of week,
month or year.

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2.4 Prediction
Prediction is predicting future state rather than current state. It is about predicting the
value of a continuous variable from the other variables in a data record. For example,
predicting the likely expenditure of a customer from their age, gender and income group. The
most familiar value prediction techniques include linear and polynomial regression.
2.5 Clustering
It is the term for a range of techniques which attempts to group data records on the
basis of how similar they are. For example, super bazaars owner could store description of
each of their customers. In this case clustering would group similar customers together, at the
same time maximizing the differences between the different customers groups formed in this
way. Common tool for clustering include neural networks.
2.6 Summarization
Summarization is the abstraction or generalization of data. A set of task-relevant data
can be abstracted and summarized, resulting a smaller set which gives general overview of
the data and usually with aggregation information. For example, purchase by all customers in
bazaar can be summarized into weekly purchases, monthly purchases, product-wise
purchases etc. Such high level summary information instead of detailed purchase is presented
to bazaar owners for customer analysis.
2.7 Association rule
Association rule describes a family of techniques that determines associations
between data records. The most well known type of association rules is market basket
analysis. It discovers the combinations of items that are purchased by different customers in
super bazaar, and by association we can build up a picture of which types of product are
purchased together. Common tools for association modeling are statistics and apriori
algorithms.
2.8 Sequence Discovery
It is used to determine sequential patterns in data. These patterns are based on time
sequence of actions. These patterns are similar to associations in that data are found to be
related but relationship is based on time. It discovers the combination of items that are
purchased by different customers in super bazaar but item are purchased over time in some
order.
3. STATEMENT OF PROBLEM
In a super bazaar, behavior of buyer is important because the buyers are scattered
according to their convenience of purchasing. So, research is carried out on super bazaar to
study satisfaction level of consumers using different knowledge mining methods. This study
is useful for effective decision making by studying customer purchase patterns and trends
over time.

5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
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4. OBJECTIVES OF RESEARCH
1. To study the factors of satisfaction influencing buying decisions of the consumer
visiting super bazaars.
2. To extract the information using knowledge mining to measure the level of
satisfaction of consumers from super bazaars.
5. CUSTOMER SATISFACTION SURVEY PROCESS
The customer satisfaction survey process can be started by designing well structured
questionnaire. Customer satisfaction survey can be carried out by filling questionnaire from
consumers of super bazaar. Knowledge can be extracted by studying survey data. Different
knowledge mining techniques are applied to mine data. This data can be tested for accuracy
of knowledge. If results are satisfactory then it is used for customer satisfaction survey
process otherwise redesigning a questionnaire is needed for survey process. The proposed
model is represented in following steps:
No
Yes
Design
Questionnaire
Customer
Satisfaction
Survey
Study
Knowledge
Gained
Apply
Knowledge
Mining
Techniques
Accurate
Knowledge
Data
Analysis
Testing
Discovered
Knowledge
Apply Knowledge to
Study Customer
Satisfaction

6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
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6. CONCLUSION
The research model presented in this paper is for the study of customer satisfaction in
super bazaar. Beside the monitory benefits to the super bazaar, it will understand the customer's
problem and will enhance the customer satisfaction. More and more customer will interact easily
to the super bazaar. This approach will be applicable to those super bazaars which have problems
with the customers understanding. Knowledge mining tools are important to study the customer
satisfaction for any super bazaar. Adopting a knowledge mining based business will create
foundation for improved business efficiency. It will help increase the profit of super bazaars.
Application of knowledge mining techniques in customer satisfaction of super bazaar business is
an emerging trend in the global economy. It has attracted the attention of practitioners and
academics.
7. FUTURE RESEARCH
The research model presented in this paper will be updated through the customer survey
or questionnaire. This will enhance our model for customer satisfaction. We can improve our
model structure by surveying customers and generating new rules and patterns that will give
some fruit full results to the super bazaar. By using different knowledge mining techniques the
model can lead to more enhanced.
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