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Volume 9, No. 4, July August 2018
International Journal of Advanced Research in Computer Science
Available Online at
© 2015-19, IJARCS All Rights Reserved 24
ISSN No. 0976-5697 ISSN No. 0976-5697
M Mubasher Hassan
Sr. Assistant Professor, Dept. of ITE
Baba Ghulam Shah Badshah University
Rajouri (J&K)-India
Tabasum M
Lecturer, Dept. of Computer Science
School Education
Govt. of J&K-India
Abstract: The objective of achieving profitability is one of the main targets of any banking sector for longer sustainable existence. The customer
satisfaction index determines the longevity in relation of customer-bank and thereby provides the idea of devising new policies and strategies for
healthy connection of customers with the bank. Offering the services and products to the customer based on his choice and needs requires
understanding the customer. The customer data available in the bank can provide the deep insights to the bank for designing the customized
service and products. Deriving useful information from customer data using data mining techniques is of paramount importance in these days.
Leveraging existing granular customer data can help banks gain deep actionable customer insights useful to understand the customers and to
reveal and unlock opportunities for increasing profitability .customer segmentation and profiling are vital in achieving two main objectives of
CRM(Customer Relationship Management)i.e.; customer retention and customer development. The main aims of customer profiling and
segmentation include expanding customer base, design of tailor made products, micro targeting of sales, aligning right channels for right
products, increasing effectiveness of cross selling and up-selling, enhanced customer experience by focused customer relationship, prioritizing
relationship with high value customers, effectively managing cost with low value customers based on the profiling and segmentation of
customers. In this paper we are using data mining techniques i.e. Naïve Bayes classification algorithm for customer profiling and BIRCH
clustering algorithm for customer segmentation
Keywords: Customer profiling, customer segmentation, retail
banking, data mining, BIRCH algorithm Introduction Customer is the
most important asset in banking business and banks around the world
are trying to make their business customer centric i.e. based on deep
understanding of customer needs with the help of analytics,
customization of services and products to meet the requirements of
different segments of customers. Providing outstanding service,
flexibility in customer orientation, convenience orientation, pricing
orientation and relationship orientation is vital for customer retention,
churn prevention, deeper market penetration, preventing downward
migration in terms of value as well as increasing profitability with
existing customers by effective cross selling of products in current
competitive business scenario. In present multichannel banking
environment, social and demographic characteristics of customers are
changing rapidly creating demand for dynamic customer management
that can respond to customer needs in an adaptive manner. As the
banking customer data is multidimensional, data mining techniques
can be used for analysis of customer information useful for achieving
goals form (Customer Relationship Management).Customer profiling
and segmentation are vital tasks in CRM (Customer Relationship
Management) and provides basis for managing trustworthy
relationship with existing customers and customer development.
CRM focuses on customer retention by enhancing experience of
existing customers and increasing profitability by targeting new
customers, deeper market penetration, effective cross selling and
providing tailored offers to high value customers.
Customer profiling means classification of customers as per
their factual and transactional attributes. Customer profiling is
an important tooling CRM and data mining techniques can be
used to increase accuracy of customer profiling methods as the
customer data of banks is very sparse and complex. With the
help of data mining tools customer behavior can be analyzed
to derive patterns from huge customer records, this
information can be used as predictive tool for futuristic
behavior of customers[1].Retention of highly profitable
customers is the key challenge in current highly competitive
business scenario and can be achieved by continuous effort of
up gradation of customer centric products/services.
Understanding the customer is prerequisite for building strong
relationship with the customer. When we properly understand
customer needs, product preferences, buying pattern, purchase
history etc. we can improve or customize products/services
suitable for them that will contribute to customer satisfaction
and loyalty that will in turn result in increased profitability and
customer retention[2].
Customer segmentation also known as consumer segmentation
or client segmentation is key technique to understand
customers, gain customer insight for decision making and
strategy formulation. Customer Segmentation is an important
task in Commit divides customer base into discrete,
homogenous customer groups based on various attributes,
having similar characteristics or buying preferences. Customer
segmentation is defined as partitioning of markets into
homogenous sub markets in terms of customer demand and
characteristics resulting in identification of customer groups
that are similar in nature[3].
Customer segmentation also known as consumer segmentation
or client segmentation is key technique to understand
customers, gain customer insight for decision making and
strategy formulation. Segmentation divides customer base into
discrete, homogenous customer groups based on various
attributes, having similar characteristics or buying preferences.
Customer segmentation is defined as partitioning of markets
into homogenous sub markets in terms of customer demand
M Mubasher Hassan et al, International Journal of Advanced Research in Computer Science, 9 (4), July-August 2018,24-29
© 2015-19, IJARCS All Rights Reserved 25
and characteristics resulting in identification of customer
groups that are similar in nature.
Segmentation helps in identification of segments of particular
interests to business depending upon business goals[4].
Customer data has many dimensions and depending upon
business requirements and goals we can segment data on
particular attributes describing particular customer behavior
E.g. if main priority of business is customer retention, business
may interested in one dimension. So, the first step is to define
business goal and then go for segmentation.
Segmentation can be objective (supervised) or non-objective
(unsupervised).objective segmentation is used to identify
customers who respond to particular services or product offers
or for identification of high risk customers who will not repay
loans etc. Non-objective segmentation is used to understand
customers, for profiling of customers, to understand specific
customer groups that exist within customer base for different
marketing processes and channelizing of resources. In this
paper non-objective segmentation will be done using cluster
analysis techniques.
Segmentation process can be apriori i.e. when number and
type of segments is known in advance or adhoc when number
and type of segments are based on results of data analysis. We
are using apriori segmentation
Fig.1. Schematic diagram of customer profiling and segmentation
Every customer has some attributes associated with him that
comprises of demographic data like age, gender, education,
occupation, income, location, psychographic characteristics,
financial parameters etc. and transactional data associated
with his banking transaction history i.e. buying preferences,
purchase history, repayment pattern, churn history etc.
These attributes can be used to build customer profiles that
will act as descriptors of customers and will be suggestively
used for customer assessment, marketing of suitable
products, enhanced experience ,direct marketing,
customizing of products/services, cross-selling, deep-selling
,up-selling to increase profitability, churn prevention, risk
categorization, default prediction and considering
customer’s eligibility for different banking
products/services. Then, segmentation of customers can be
done using clustering algorithms as per their profiles into
M Mubasher Hassan et al, International Journal of Advanced Research in Computer Science, 9 (4), July-August 2018,24-29
© 2015-19, IJARCS All Rights Reserved 26
high value customers i.e. highly profitable, low risk
customers, low value customers i.e. customers who are less
profitable and pose high risk, medium value customers who
are moderately profitable and risk level associated with
them is also average and negative value customers who
incurs more cost to bank than the profit generated and poses
high risk also[5].
Fig.2. Block Diagram of Customer Profiling
III. Data Mining
Data mining is the process of knowledge discovery from
large complex databases helpful to companies like banks to
predict customer behavior and decision making from available
data by analyzing it and extraction of patterns[6]. Data mining
techniques helps banks to increase accuracy of customer
profiling and segmentation by using
classification and clustering algorithms[7]. This can be
done by personal data and transactional data of customers[8].
Based on this customer data, clustering is the way of
identification of segments and split customers into distinct
groups. Then, customer profiling is followed to label these
segments based on their characteristics. Customer segmentation
is done so that customer belongs to one of the following
Fig.3. Customer Segmentation
M Mubasher Hassan et al, International Journal of Advanced Research in Computer Science, 9 (4), July-August 2018,24-29
© 2015-19, IJARCS All Rights Reserved 27
A. High value
These are low risk customers having high net worth, large
deposits, loans with the bank and have major contribution
towards banks profitability. These customers should be
provided best quality customer service, on-priority grievance
response and timely offers and incentives to
ensure retention. There should be close monitoring of churn
risk and use of maximum resources to prevent churn.
Communication should be done through preferred channel.
B. Medium Value
These can be medium risk customers who have maximum of
their business with our bank and have scope of up gradation to
high value customers by gaining their trust and providing
better service and offers than competitors. They provide
significant profit to bank and this profit can be increased by
cross selling of products/services. Or these are the customers
falling into mediocre income group where focus should be on
providing feasible products/services they are eligible for and
moderate efforts should be made for retention.
Communication should be done through preferred but cost
effective channel
C. Low Value
These are customers who fall into low income group or high
risk customer group or low interest or need of banking
products/services. They contribute to little profit of the bank
and there is little scope for upward migration of such
customers. Or they can be customers who have maximum
portion of their business with other financial organizations and
can be migrated to medium value group by making suitable
efforts. Limited resources should be allocated for churn
prevention. Communication should be done through lowest
cost channel.
D. Negative Value
These are the high risk customers who incur more costs to
banks in terms of maintenance, operational costs, revenue etc
than the profit generated by them. These can be customers
with NPA loans or non operational accounts. Efforts should be
made for upward migration and reducing costs to serve and to
drive up revenue generated.
After value of customer is identified efforts can be made to
avoid downward migration of customers and increase upward
migration of customers from low or negative value to high
value to increase profitability of banking business[9].
Fig. 4. Segmentation based on customer value
IV. Customer Profiling
This personal data in combination with transactional data can
be used to build customer profiles and these customer profiles
can be segmented to fall into one of the above mentioned
segments using classification and clustering techniques
[10].we are using Naïve Bayes algorithm for customer
classification.NB classification algorithm is powerful
probabilistic algorithm used for predictive modeling and
classification problems .Based on Bayes theorem, it is
particularly useful with large and high dimensional data sets.
Bayesian classifiers are used to predict class membership
probabilities such as the probability that a given record
belongs to a particular class[11]. Naive Bayes is a supervised
classification algorithm for binary i.e. two class and multi-
class classification problems. It is easy to use and works well
with real time and multi class prediction
A. Personal data of every customer consists of
following attributes:-
No. of dependents
Marital status
Demographic location
Social class
Relationship time with bank
M Mubasher Hassan et al, International Journal of Advanced Research in Computer Science, 9 (4), July-August 2018,24-29
© 2015-19, IJARCS All Rights Reserved 28
Contingent liabilities
Deposits with bank
Risk level
Threshold level
Customers can be segmented according to value, behavior or
other characteristics. Segmentation is necessary to prioritize
customer handling for customer retention, identify most and
least profitable customers, and develop products which are
best suitable to different segments of customers, acquiring new
customers, design and development of customer tailored
products/services, selecting proper marketing and sales
channels for products/services[12]
Table.1. Attributes of Customer profile
marital status
no of
contingent liabilities
risk level
threshold level
with bank
with bank
buying preferences
transaction history
Clustering techniques use data mining algorithms to analyze
data and identify clusters, the clusters identified form basis for
segments[14]. Clustering algorithm will find correlation
among attributes to identify association rules. Clusters should
change dynamically to ensure accuracy in reflecting proper
state of customer data[15]
We are using BIRCH (Balanced iterative reducing and
clustering using hierarchies) clustering method. BIRCH is
unsupervised data mining clustering algorithm to perform
hierarchical clustering over large data sets. BIRCH is one of
the fastest algorithms and mostly requires single scan on data
is space and time efficient i.e.; has less memory and time
constraints, reduces I/O cost involved in clustering and is well
suited for multidimensional databases.[13] It can be used in
spatial databases with noise. BIRCH is scalable clustering
method and can be used for concurrent or parallel clustering
.BIRCH works by building a dendrogram called (Clustering
Feature) tree while scanning data set.CF tree is in memory
structure, a height balanced tree that stores the clustering
features for a hierarchical clustering. Each entry in CF tree
represents cluster of objects or cluster of data points is
represented by triple of numbers(N, LS, and SS)
Where N=no of items in sub cluster
LS=Linear sum of points
SS= Sum of the squared of points
The first step builds CF tree out of data points. Clustering
features are organized in a CF tree, with two parameters
branching factor B i.e. maximum no of children in non leaf
node and threshold T i.e. upper limit to radius of cluster in a
leaf node, L is no. of leaf node entries.
Second step is optional and condenses initial CF tree into
smaller CF trees. The algorithm scans all the leaf entries in the
initial CF tree to rebuild a smaller CF tree
In third step, apply existing algorithm on leaf nodes of CF tree
to combine these sub clusters into clusters. Optionally refine
these clusters.
A single scan yields clustering results and additional scans can
be used to refine the clustering results to yield better clusters.
Birch algorithm yields clusters that can be seen as segments of
customers e.g. segment a, segment Basement C and segment D
in our case.
M Mubasher Hassan et al, International Journal of Advanced Research in Computer Science, 9 (4), July-August 2018,24-29
© 2015-19, IJARCS All Rights Reserved 29
0 1 2 3 4 5
A L G O R I T H M.
Seg A
Seg B
Seg C Seg D
The main objective of this paper is to help banks in achieving
goals of CRM by customer segmentation and profiling with
the help of data mining algorithms. This has been done by
classification and identification of customers segments by
clustering of customer data and then profiling of customers to
label these segments by analyzing behavioral, transactional,
psychographic and demographic data of customers.
Segmentation and profiling helps in identification of different
customer typologies that helps banks in understanding
customers to serve them better, design of suitable market
strategies, customer retention and customer development.
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Malik Mubasher Hassan: Received his
B.Tech degree from University of Jammu
followed by M.Tech degree from NIT
Srinagar in 2007. He is presently working as
faculty in the Department of Information
Technology and Engineerong (ITE) at Baba Ghulam Shah
Badshah University Rajouri (J&K), India-185234. His
specialization is in wireless communication, optical
wireless, computer Networks, Data Mining and Cloud
Tabasum Mirza: She has receceived Masters
Degree in Computer Aplications from
University of Kashmir in 2008. She is presently
working as Lecturer in the Department of
Computer Science School Education, Government of
Jammu and Kashmir, India. She has a 6.5 years
experience of working in JK Bank Pvt. Ltd. Her
specialization are software Engineering, Java
Prograamming and Data Mining.
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The leading introductory book on data mining, fully updated and revised!When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised— is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problemsCovers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediatelyTouches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and moreProvides best practices for performing data mining using simple tools such as ExcelData Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
Data clustering is an important technique for exploratory data analysis, and has been studied for several years. It has been shown to be useful in many practical domains such as data classification and image processing. Recently, there has been a growing emphasis on exploratory analysis of very large datasets to discover useful patterns and/or correlations among attributes. This is called data mining, and data clustering is regarded as a particular branch. However existing data clustering methods do not adequately address the problem of processing large datasets with a limited amount of resources (e.g., memory and cpu cycles). So as the dataset size increases, they do not scale up well in terms of memory requirement, running time, and result quality. In this paper, an efficient and scalable data clustering method is proposed, based on a new in-memory data structure called CF-tree, which serves as an in-memory summary of the data distribution. We have implemented it in a system called BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and studied its performance extensively in terms of memory requirements, running time, clustering quality, stability and scalability; we also compare it with other available methods. Finally, BIRCH is applied to solve two real-life problems: one is building an iterative and interactive pixel classification tool, and the other is generating the initial codebook for image compression.
Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000–2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.
Using Data Mining Techniques in Customer Segmentation
  • H Ziafat
  • M Shakeri
H. Ziafat and M. Shakeri, "Using Data Mining Techniques in Customer Segmentation," vol. 4, no. 9, pp. 70-79, 2014.
Profiling bank customers behaviour using cluster analysis for profitability
  • R Baradaran
  • K Zadeh
  • A.-A Highway
  • A Faraahi
R. Baradaran, K. Zadeh, A.-A. Highway, and A. Faraahi, "Profiling bank customers behaviour using cluster analysis for profitability," pp. 458-467, 2011.
Building Data Mining Application for Customer Relationship Management
  • D Bhardwaj
D. Bhardwaj, "Building Data Mining Application for Customer Relationship Management," vol. 3, no. 1, pp. 33-37.