Data Mining: Key Component for aCRM Success
MANJEET KUMAR
(ASSISTANT PROFESSOR-IT),
NIILM CMS,GREATER NOIDA,UP
e-mail-manjeet2005@gmail.com
Abstract
The author tried to present role of the data mining (DM) in success of analytic
Customer relationship Management (aCRM). The paper attempts to present the
benefits the enterprise will get by implementing the CRM analytics using the DM.
Analytical CRM is the part of CRM that aims at storing, analyzing and applying
the knowledge about customers and about ways to approach customers, typically
using databases, statistical tools, data mining (DM), machine learning, Business
Intelligence (BI) and reporting methodologies. They provide the hidden patterns,
finding predictive information that experts may miss because it lies outside their
expectations which was not possible from traditional databases analysis using
analytical tools like DM. Analytical CRM helps in better understanding of the
customers by evaluating customer-related data. Greenberg (2004) defined
analytical CRM as the capture, storage, extraction, processing, interpretation,
and reporting of customer data to a user. We had gone through the systems of
many organizations and had studied the development of analytic CRM in detail of
many organizations that have implemented aCRM using DM techniques. The
efficiency increase which has been brought by implementation of the CRM
analytic using DM in the organization are analyzed and presented in form of
importance of DM for aCRM through this paper.
Keywords: - Customer relationship management (CRM); Analytical Customer
Relationship Management (aCRM); Data Mining (DM); Information Technology
(IT).
1. Introduction.
The competition is every service sector of business is increasing which has
forced companies to not only to attract customers, but also to retain them.
Customers are the key factor for the success of any service sector companies.
To manage the relationship with the customers the data related to the customers
are kept and managed through the customer relationship management (CRM).
CRM is a process or methodology used to learn more about customer’s needs
and behaviors in order to develop stronger relationships with them. The database
of customer service as repositories of invaluable information and knowledge that
can be utilized to improve customer service. The Data Mining helps firms to
analyze the customer data and extract the useful information, to gain competitive
advantage over others.
(Swift, 2001) defined Customer Relationship Management (CRM) as process
designed to gather data of customers, to grasp features of customers, and to
apply those qualities in specific marketing activities. CRM as suggested by
(Choy, et al., 2003) is an information industry term for methodologies, software,
and usually internet capabilities which focuses on leveraging and exploiting
interactions with the customer to maximize customer satisfaction, ensure return
business, and ultimately enhance customer profitability. (Xu and Walton, 2005)
argued that CRM should be equipped with some Information Technology (IT)
tools for better analysis of the customer data. Bose (2002) indicates that CRM
involves acquisition, analysis and use of knowledge about customers in order to
sell more goods or services and to do it more effectively. This type of CRM is
defined as Analytical CRM (aCRM). The analytical CRM is also referred by
Kotorov (2002) as a 360 degrees view of the customers. This type of CRM as
suggested by Rowley (2004) helps in online order, knowledge bases that can be
used to generate customer profiles etc. Analytical CRM involves understanding
of the customer by evaluating the knowledge extracted from the customer data
that helps in understanding the behavior of the customers. The analytical CRM is
the capture, storage, extraction, processing, interpretation, and reporting of
customer data to a user (Greenberg, 2004). The implementation of the analytical
CRM in the firm has boosted the production and profit of the organizations.
The urge for Data mining in analytical CRM was felt due to the massive amount
of the data, which was present in the data warehouse for the analysis purpose.
The Data Mining helps firms to analyze the customer data and extract the useful
information, to gain competitive advantage over others. Data mining (Frawley,
Piatetsky-Shapiro and Matheus, 1992) is the nontrivial extraction of implicit,
previously unknown, and potentially useful information from data. Data Mining is
the process of extracting information from large data sets through the use of
algorithms and techniques drawn from the field of statistics, machine learning
and database management systems (Feelders, Daniels and Holsheimer, 2000).
The effectiveness of the data mining in analytical CRM can be felt by the various
complexes modeling algorithm available with the data mining. We have tried to
bring the importance of the data mining in the analytical CRM through this paper.
2. Motivation and related work.
Analytical CRM has gained immense popularity in the business world that more
and more companies are attracting towards its implementation. But the CRM
implementation is effective only if it is implemented with the latest Data mining
techniques. The importance of the Data Mining in analytical CRM was felt and as
much research has not being done, we authors have being motivated for
showing the importance of DM in aCRM.
(Sap.com (2003) discussed the importance of the analytical CRM in the business
of an enterprise.
Fletcher (1999) showed the changing focus on customers by the companies.
(Tzokas and Saren, 2002) discussed how competitive advantage is achieved
through knowledge and relationship marketing. (Jones and Ranchhod, 2007)
argued building marketing strategies through customer attention. The work
contributed by various researchers in the field of operational CRM and Analytical
CRM is presented in the tabular form in table 1.0.
Table 1.0 Related Research on CRM and aCRM
SR.N
AUTHORS
NATURE OF WORK
O
1
Ahmed (2004)
Provided a model to use prediction and classification
to discover the characteristic of customers who are
likely to leave.
2
Ahn, et al., 2003 Developed the design of CRM model and examined
analytical CRM on distributed data warehouse.
3
Bose, 2002
Examined CRM and found it as key component for
information technology success.
4
Bose and
Stressed on the application of Knowledge
Sugumaran,
management in CRM
2003
5
Campell, 2003 Explained the customer knowledge creation in CRM.
6
Hansen, et al., Provided the strategy for managing the customer
1999
knowledge
7
Kotorov, 2002
Provided the 360 degree view of the customer data
through the analytical CRM.
8
Paiva, et al.,
Examined the manufacturing units from knowledge
2002
management perspective.
9
Park and Kim,
Developed the framework for dynamic CRM for
2003
marketing information strategy.
10
Qiaohong, et al., Developed a frame work for analytical CRM based on
2007
distributed data warehouse.
11
Rowley, 2002
Suggested the eight questions for Customer
knowledge management in e-business.
12
Tzokas and
Examined the competitive advantage with knowledge
Saren, 2002
management and relationship marketing
13
Xu and Walton, Showed the advantage and knowledge about
2005
customers the enterprise get through the analytical
CRM
14
Minna and Aino, Framed a theoretical framework for customer
2005
knowledge management competence.
15
Garcia-Mriillo
Defined the customer knowledge management
and Annabi
16
Bersons, Smith Showed Data Mining has a direct impact on the
and Thearling,
analytical results that will drive business decisions
1999
17
Cabena, et.al,
Explored Data mining that uses the extracted
1998
18
19
20
Han and
Kamber, 2006
information from large databases to make critical
business decisions
Defined Data Mining as extracting or mining
knowledge from large amount of data. Data Mining
includes various queries, reports and decision support
tools based on historical data of the organization and
can even predict for the future perspective.
Explores the emerging trend for data mining
Clifton and
Thuraisingham,
2001
Rygielski, Cheng Explores the emerging standards for data mining for
and David, 2001 CRM.
From these studies, one can understand that limited research exist on showing
the importance of the data mining in analytical CRM which has motivated us for
our work.
3. Analytical Customer Relationship Management (aCRM).
Analytical CRM helps to measure, predict and optimize customer relationship by
gaining the in-depth from the already existing data of the customers. Greenberg
(2004) showed analytical CRM as the capture, storage, extraction, processing,
interpretation, and reporting of customer data to a user. Analytical CRM follows a
customer centric approach that helps building high-value customers and build
more profitable customer relationships by targeting marketing, sales and service
more effectively. The success of the analytical CRM depends how well the firms
manage the answers to questions like:




Whether all relevant customers related information is collected from
various sources or not and whether the collected information is put in
uniform and symmetric data warehouse to create a knowledgeable
customer base which can answer all questions?
Does a sophisticated set of analytical tools available for the analysis of the
existing data in the warehouse?
Are the personnel’s in the company utilizing the analytical results obtained
from the aCRM?
How well is the result being deployed or implemented in the firms?
CRM analytics helps to create a through analysis of the operational data of the
CRM for getting the insight of the business. Smith (2006) suggested that the
analysis of the customer data is key for the success of CRM. That is CRM
analytics provides a single view of the customer data, which is integrated and
refined to answer the complex problem faces by the organizations. CRM
analytics help in the analysis of customer data that is collected from various
sources.
The results generated through the analysis of the data by CRM analytic helps the
business process from two different angles.
 It helps to predict the future behavior of the customers, trends in future
and scope of implementing the policies from future perspective.
 It also helps the firms to streamline the existing setup of the organizations
by guiding some fundamental changes that are required to be
implemented in the firms for better results.
The customer knowledge acquisition is the key factor behind the success of the
CRM analytics in any company’s set up. This process of knowledge acquiring is
a dynamic process which means it’s cyclic, the analysis of the data which is
collected from CRM analytics need to be again utilized for long term benefit for
the firms. The CRM analytics provides a significant difference in the performance
of the some of the key areas of the firm. They are:

Strategic level executives have access to all relevant information, which
were hidden earlier. They can provide better answers to the questions
which they earlier found difficult.
 The effort put by the organizations in term of the marketing will be
improved significantly if the company is more focused on some important
factor rather than all factors which will be revealed through the analysis of
the customer data by CRM analytics.
The overall business process of the company increases as all the business
processes are more and equally informed about the existing setup since the
knowledge extracted from the customer data is available through the CRM
analytics.
4. Data Mining
Data Mining, a synonym to “knowledge discovery in databases” is a process of
analyzing data from different perspective and summarizing it into useful
information. It reveals patterns and trends that are hidden among the data. It is
often viewed as a process of extracting valid, previously unknown, non-trivial and
useful information from large databases (Rao, 2003). (Noonan, 2000) addresses
that data mining is a process for sifting through lots of data to find information
useful for decision making. (West, 2005) opinions that by relying on the power of
data mining, retailers can maintain the consistency and accuracy of their
underwriting decisions; they can significantly reduce the impact of fraudulent
claims; and can have a better understanding of their customer’s wants and
needs. Data mining can be used to control costs as well as contribute to revenue
increases (Two Crows Corporation, 2005).
Data Mining includes various queries, reports and decision support tools based
on historical data of the organization and can even predict for the future
perspective. Data Mining can be performed on a number of different databases
and data repositories that include relational data, transactional data, data
warehouse, web data, data streams, flat files, time series data and many more.
(Clifton and Thuraisingham, 2001) explores the emerging trend for data mining.
Data Mining plays a leading role in every facet of customer relationship
management. Only through the application of data mining techniques can a large
enterprise hope to turn the myriad records in its customer databases into some
sort of coherent picture of its customers (Berry and Linoff, 2001). Most retailers
collect and have access to huge amount of data, collected from day to day
operations e.g. customer loyalty data, retail store sales and merchandise data,
demographic projection data etc. Currently retailers are data rich but information
poor. There is a great potential to develop systems that enable analysts and
decision makers to manage, explore, analyze, synthesize and present data in a
meaningful manner for decisions. Retail managers are in a constant need for
right kind of information for making effective decisions (Sharma and Vyas, 2007).
Data mining techniques helps in achieving the target.
The techniques of data mining like Classification, Prediction, Clustering,
Association, Genetic algorithms and Neural network help achieve the goal of the
data mining to extract the hidden, unknown patterns from the database. Ahmed
(2004) points Classification as the way to discover the characteristics of
customers who are likely to leave and provides a model that can be used to
predict who they are. The cluster detection algorithm searches for groups or
cluster of data elements that are similar to one another. K-means is one of the
major methods of clustering. It aims at partitioning the data that have similarity
and distinguishing it with other different one. Decision tree methods provide a set
of human readable, consistent rules, but discovering small trees for complex
problems.
5. Importance of Data Mining in Analytical CRM
The importance of the Data mining in analytical CRM can be realized by
understanding the application area, which is benefited through the data mining
techniques. Some of the key benefits achieved are mentioned below:


Improved Shopper-to-buyer conversion rate: The use of the data mining
techniques help the aCRM to better analysis the data through various
techniques like clustering etc through which the more efficiency in the system
is achieved. The shoppers are better aware about the taste of the customers
if the analysis is done through the sophisticated algorithms of the data mining.
Increased cross-selling and up-selling: The cross-selling and up-selling
can be increased if the data of customers are analyzed over a period of time.
The Data mining techniques like the classification, association and decision
tree helps in identifying the link or association between the items sold
together. The complex algorithm of the data mining helps in better dealing
with issues like the selling of the profitable item with less profitable items.







Improved Customer retention: The clustering technique of the data mining
helps in better segmentation of the customer data which helps to identify
those customers which are not satisfy with the performance or product of the
organization. Such customers can be identified and special offers and
incentives can be given to them to improve customer churn or retain the
customers.
Detect high profitable customers: The clustering also helps to identify
those customers which are doing the maximum dealing with the companies.
The cluster or group of such customers can be increased if proper analysis of
the customer data pertaining to those segments is taken care. This can be
performed better by DM techniques.
Enhance Marketing Strategy: The marketing strategy is dependent on the
future vision of the company. The DM techniques help to identify the pattern
hidden and also help to identify the future trend through the techniques like
neural network etc.
Improves the supply chain of the firm: The integration of the whole data
from various department helps to improve the whole supply chain which is
possible if analytical CRM is applied with Data mining as it helps in better
analysis of the complex data and help to increases the efficiency.
Predicting and forecasting possible through analytical CRM: The
prediction and forecasting are one of the key features which are achieved
through the DM application in the aCRM as hidden trend can be revealed.
Customer loyalty: The customer loyalty is dependent on the satisfaction
level the firms provide to the customers. The customer behaviour modeling is
possible through the aCRM if data mining is effectively applied. This helps to
now the buying pattern of the customers.
Customer life time value: The customer life time value analysis is possible
only if the customer data is analyzed from the entire angle. The complex
algorithm of the data mining helps to achieve very efficiently.
6. Conclusion
The authors tried to present importance of DM in CRM analytics, which helps
organization for successful decision making and customer intelligence. The
paper presented the benefits the organizations should get by implementing the
CRM analytics using DM.
Organizations should incorporate and adopt data mining in analytical CRM.
Successful implementation of CRM analytics in the organization using data
mining will increases the efficiency of the organization work. We believe that
generating useful patterns from customer data using CRM analytics principles by
implementing data mining, the firms can realize their return on investment (ROI).
This will enhance the strategic position of the organizations.
We conclude that as firms grow with evolving mountains of data generating,
more sophisticated DM tools can provide additional benefit for the executive of
the firms. Improved customer satisfaction, higher profitability and improvisation in
the whole supply chain for company can be achieved through the CRM analytics
implementation using Data mining techniques.
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