A study of Data Mining concepts used in Customer Relationship Management (CRM) with application in Themed Wedding Management

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613
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A Study of Data Mining Concepts used in Customer Relationship
Management (CRM) with Application in Themed Wedding Management
Karuna Subhash Kak
HSBC GLTi
Abstract— Customer relationship management (CRM) has
evolved as an approach based on generating positive
relationships with customers, increasing customer loyalty,
and expanding customer lifetime value [1]. To understand
the needs of customers and providing value-added services
are recognized as factors that regulate the success or failure
of the organizations. In the recent years, technology
enhancement made customer relationship easier in various
fields such as marketing, sales, service and Management
Information Technology [2]. To deliver customer value,
there are concepts such as data mining and data warehousing
with the use of technology. Even through data mining
concepts, organizations can easily find out their valuable
customers and helps in making better decisions. There are
data mining tools which answer business questions that were
time-consuming consuming in the past. These tools simplify
these questions and make customer relationship
management effective [3]. This researcher work is focused
on understanding the consumer’s behavior for themed
weddings. The themed weddings management strategies are
based on technology, business and customer perspectives.
The customer preferences are measured using Regency,
Frequency and Monetary (RFM) method. Business
strategies are defined to understand the customer preference
towards themed weddings management and the technologies
such as WEB 2.0 and data mining tool Weka are used. The
survey technique, and thematic content analysis using data
mining tools, to accomplish the goals of today’s customer
relationship management philosophy for themed weddings
management.
Key words: CRM; Wedding Management; Data Mining,
Case study, RMF
I. INTRODUCTION
The current business trends have changed. Within it, the
economics of customer relationships are changing in
fundamental ways, and companies are facing the need to
implement new solutions and strategies that address these
changes. Firms are concerned with increasing customer
value through analysis of the customer lifecycle. This
revolution motivated the concern of customer values. These
values are established using the analysis of the customer
lifecycle. The analysis is carried out using the tools and
technologies of data warehousing, data mining, and other
customer relationship management (CRM) techniques. The
core parts of CRM activities are to understand customer’s
profitability through their values and retain the same. There
are studies presented that; it is more costly to acquire than to
retain customers. So that it is important to determine
companies’ success or failure by evaluating customers’
value and retaining the most valuable customer. To measure
the customer values, generally, this method Recency,
Frequency and Monetary (RFM) methods have been used.
As per the Stone’s view, different weights should be
assigned to RFM variables depending on the characteristics
of the organization. Even decision support tools of the group
could be used to improve the objective rationality of
selected RFM weights [4].
This research is focused on the study of data
mining concepts used in CRM using RFM method to
establish the customer value for theme wedding
management. This has focus on identifying various variables
to evaluate customer value and correlate with RFM
methods. The knowledge of RFM weights will be evaluated
and analyzed using data mining tool called Weka.
This research examines the concepts of customer
relationship management and its components people,
process and technology stated by Chen and Popovich in
2003 [5]; data mining in order to find out various factors
influencing the demand of wedding packages. It begins with
an overview of the concepts of data mining and CRM,.
Further, it integrates the concepts of themed wedding
management to illustrates the relationship, benefits, and
approaches to implementation, and the limitations of the
technologies.
II. LITERATURE REVIEW
A. Customer Relationship Management:
1) Definition:
Customer Relationship Management is defined by four
elements of a simple framework: Know Target, Sell, and
Service [6]. CRM requires the organizations to know and
understand its markets and customers. This includes
customer intelligence in order to select the most profitable
customers. CRM helps to identify which products to sell to
which customers and through which channel. CRM seeks to
retain its customers through services such as call centres and
help desks [6].
2) Perspectives in CRM:
There are two forms of CRM; first form is related to service
marketing, which focuses on the organization's capabilities
to foster relationship with the customer. The second form
concerns using technology such as data marts and data
consolidation to deal with sales force automation. Peppers
and Rogers [7] considered CRM as a business strategy that
is to serve as the norm. To reflect the different aspects of
CRM emphasized in previous research, we adopt a holistic
view of CRM that encompasses all three perspectives. In the
following sections, we discuss the three perspectives in
detail.
a) Technology perspective:
On the technology prospective, organizations require an
integrated information system to provide relevant, real-time
and accurate information to all employees in the
organization [7]. This integrated information system
requires integration of the marketing, sales and service
functions. Hence, in this perspective, CRM is the
infrastructure with the important applications for
understanding and interacting with customers efficiently [8].

A Study of Data Mining Concepts used in Customer Relationship Management (CRM) with Application in Themed Wedding Management
(IJSRD/Vol. 3/Issue 10/2015/108)
b) Business perspective:
The business perspective finds CRM as an industrial
strategy related to customer demographics and predicting
consumer behavior, segmenting customers into customer
groups, one-to-one marketing, analyzing purchasing patterns
of customers, and basically knowing who the customers are,
where they are and what they need [9]. Such analysis is
crucial for decision making.
c) Customer perspective:
The customer perspective focuses on the interaction of the
customer with the organization. A customer usually does not
care about the internal business processes of an
organization. However, customers are influenced by
interaction opportunities with the organization. Interactions
include call centres, frontline sales personnel, the Internet,
wireless communication channels, email, fax and many
others [10]. Such interactions instil loyalty, and serve as
demonstrations of service efficiency and customer
friendliness of the organization.
3) Components of Customer Relationship Management:
Customer relationship management is a combination of
components such as people, process and technology and
techniques that seek to provide understanding customers and
support business accordingly [2].
B. Data Mining:
1) Definition:
“Data mining” is defined as a data search capability that
uses statistical algorithms to discover patterns and
correlations in data [3]. The data mining approach is
complementary to data analysis techniques such as statistics,
on-line analytical processing (OLAP), spreadsheets, and
basic data access. In simple terms, data mining is another
way to find meaning in data.
2) Data Mining Approaches:
Data Mining approaches are divided into three categories:
logical, cross tabulation, and Equational [11]. These
technologies extract patterns from a data set and then use the
patterns for various purposes. Table 1 summarizes the pros
and cons of these categories.
Approach Pros Cons
Logical
Works well with
multidimensional and
OLAP data.
Able to deal with
numeric and
nonnumeric data in a
uniform manner.
Unable to work
with smooth
surfaces that
typically occur in
nature.
Cross-
tabulation
Simple to use with
small number of non
numeric values.
Not scalable.
Equational
Works well on large
sets of data.
Works well with
complex multi
dimensional models.
Ability to approximate
smooth surface.
Able to handle
numeric value.
Ability to handle
conjunctions.
Require all data to
be numeric value.
System can quickly
become a black
box.
Table 1: Pros and cons to Data mining approaches [11]
C. Relationship between Data mining and Customer
Relationship Management:
Customer lifecycle refers to the stages in the relationship
between a customer and a business. It is important to
understand customer lifecycle because it relates straight to
customer revenue and customer profitability. Marketers say
there are three ways to increase a customer’s value:
- Increase their use (or purchases) of products they
already have;
- Sell them more or higher-margin products; and
- Keep the customers for a longer period of time.
However, the customer relationship changes over
time, evolving as the business and the customer learn more
about each other. So why is the customer lifecycle
important? Simply put, it is a framework for understanding
customer behavior. In general, there are four key stages in
the customer lifecycle:
- Prospects: people who are not yet customers but are in
the target market
- Responders: prospects who show an interest in a
product or service
- Active Customers: people who are currently using the
product or service
- Former Customers: may be “bad” customers who did
not pay their bills or who incurred high costs; those
who are not appropriate customers because they are no
longer part of the target market; or those who may
have shifted their purchases to competing products.
Data mining plays a critical role in the overall
CRM process, which includes interaction with the data mart
or warehouse in one direction, and interaction with
campaign management software in the other direction. In the
past, the link between data mining software and campaign
management software was mostly manual. It required that
physical copies of the scoring from data models be created
and transferred to the database. This separation of data
mining and campaign management software introduced
considerable inefficiency and was prone to human error.
Today the trend is to integrate the two components in order
to gain advantage.
III. STUDY BACKGROUND
A. Case Study: Wedding Management System:
Wedding management organisers provide different themed
packages to its customer. They are keen to know the
frequency of the people interested in their service and they
need to change their packages accordingly.
Therefore, the wedding planners did the survey
were found by searching the following online resources: The
Knot (www.theknot.com), Wedding Wire
(www.weddingwire.com) , and the Association for Wedding
Professionals International (www.afwpi.com) . These
websites were chosen because they are popular wedding
websites that provide lists of professional wedding planners
throughout the nation. Also a survey was conducted for the
people planning for wedding.
Our next step is to make a website and provide
themed wedding services to people and get the Recency
Frequency Monetary (RMF) variables such as budget, trends
for catering, hospitalization and improve the quality of the
services. Also when a person hit on the site and fills the

registration form, all the details will be recorded in the
database and using the data sets from the site we will
generate results from a Data Mining tool called Weka which
will help us in decision making, for clustering and
classifying the theme packages and to provide a better
service to the customers.
B. Study Design:
The is study design is focused on various elements such as
stakeholder identification is in line with the different
activities.
1) Identifying stakeholders and customers:
Figure 1 represents the various identified stakeholders such
as Data miner who mines the data and establishes various
patterns which helps the wedding planner to make decisions;
Domain expert, customer /client and the web developer in
the themed wedding management.
Fig. 1: Identified Stakeholders in Themed Wedding
Management
Figure 2 represents the Organization Chart for Themed
Wedding Management, Top level consists of the Wedding
Planner; Middle level consists of Department Heads such as
consultants, co-ordinators, accountants and assistants and
the Bottom level consists of Sub departments such as
Catering, Photographers, Videographers, Costumes,
Decorations, Music, Travel Agents, Accommodations,
Electricity, Budget, Hospitality, Event Mangers, Make-Up,
First-Aid.
Fig. 2: Organization Chat for Themed Wedding
Management
Customer
Type
Customer Description
Prospects The people who are not customer but are
hitting on the wedding sites.
Responders The people who fill the
registration/enquiry form on the site.
Active
Customers
Customers who have placed order for
theme wedding.
Former
Customers Customers who had a bad experience.
Table. 2: Represents Various Customers Identified in The
Themed Wedding Management.
2) Identifying Data Elements:
- Wedding Type: List the religion and caste of the
wedding couple.
- Budget: Track all expenses with a bodacious budgeter
which includes a complete list of typical wedding
costs. Additionally, the planner is able to budget the
wedding and create an estimate of segmented costs.
- Catering: Plan for the food and beverages in an
appropriate manner for all the events such as reception,
engagement etc.
- Hospitality: Keep a track of hotel contact information
and details of where the guest will be staying and the
reservation information.
- Transportation: Keep a track of all the transportation
contacts, budget and schedule details as well as the
agenda for pick-up and drop-off services.
Also, the personal data collected were collected such as
name, address, mobile number; along with the data elements
mentioned above.
3) Define Customer Relationship Management Approach:
- To implement client’s ideas:
The wedding planner must focus on maintaining a cordial
relationship with the client, providing the best output to the
client in the given budget and providing the customer value.
4) Define Data Mining Approach:
Figure 3 represents the data mining approach in terms of
Logical, Cross- tabulation and Equational incorporated
within the themed wedding management system.
Fig. 3: Data Mining Approach for Themed Wedding
Management
5) Data Analysis and Result:
Objective of data analysis is to understand the inclination of
the customer in selection of packages and future trends in
the wedding management field.
a) Following steps were performed:
- Conduction of survey which focused on various
elements such as wedding type, catering, hospitality,
decoration themes and most importantly the budget.
- This survey collected data considered as an input to a
Data Mining tool called Weka and
- Generated a decision tree as a Result.
- The outcome was a decision tree which would be
helpful to the wedding planner to classify and make a
good wedding package in near future.
- Limitations of Research were as follows:
- Data was collected from wedding couples, common
people and other service providers in wedding

business. However, this study incorporates data only
from wedding planners. This is an acknowledged
limitation of this research.
- Another limitation of this study was the time and
resources. However, due to lack of time for both
researcher and wedding planners, the sample
population ended up having thirteen participants only.
In addition, several documents derived from the course
of desk and internet researches had been considered
and used in this study in order to support some theories
and claims.
IV. CONCLUSION
The researcher has adopted a qualitative methodology and
analysis module of RFM consumer’s behavior, which is
often used to assess the relationship of consumer’s loyalty.
Primary data were gathered by conducting a survey. Data
were transcribed for analysis. Thematic content analysis is
used to understand the consumer’s behaviour for themed
weddings. The themed weddings management strategies are
based on technology, business and customer perspectives
are in implementation phase using WEB 2.0. The analysis
for customer preferences will be measured continuously
using Regency, Frequency and Monetary (RFM) method to
help wedding planner to deliver customer values. This
research is an effort to accomplish the goals of today’s
customer relationship management philosophy for themed
weddings management.This research is an example to
present that data mining tools are used to accomplish the
goals of today’s customer relationship management
philosophy for themed weddings management.
This study employs the customers’ data and
consumers’ records of purchase from the general
merchandise industry as a real case study. So, designing a
website for themed wedding management and calculating
the RMF weights is the future direction for further study.
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A study of Data Mining concepts used in Customer Relationship Management (CRM) with application in Themed Wedding Management

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