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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 5, Issue 10, October 2016
ISSN 2319 - 4847
A Survey on Application of Data mining in
CRM
P.Deepa
Assistant Professor(Junior),SITE ,VIT University, Vellore
ABSTRACT
In today’s business driven world retaining the customers is the major issue in the customer relationship management. Data
mining technique helps in addressing this issue. The main aim of this paper is to analyze how the different data mining
techniques have been used in various CRM stages and thus making the CRM better.
1.Introduction:
CRM started in the late 1990’s when the organization realized that According to chen et.al Customer relation
management can be defined as the combination of people, process and technology. Another definition says that CRM is
the process of interacting with the current and future customers and analyzing the history of customers with the
organization which helps in improving the relationship with the customers which increases the customer retention
which in turn improves sales growth of the business. CRM is broadly classified into 3 types i) operational CRM ii)
Analytical iii) Collaborative. Operational CRM is nothing but the automation of marketing activities. Analytical CRM
plays major role in analyzing the customer and predict the futuristic returns from the customer so proper plan on the
investment can be made. Collaborative CRM deals with how various departments work collaboratively by sharing the
information among them. In this paper we consider the analytical CRM and we will be studying how data mining
techniques play an important role in customer relation management.
2.Over view of CRM
CRM usually considered having the four dimensions.
i. Customer identification
ii. Customer Attraction
iii. Customer Retention
iv. Customer Development
Customer identification:
The very first step in CRM is to identify the target customers that is who will be most profitable customers to the
organization. This also involves the classification or segmentation of the customers.
Customer Attraction:
After identifying the customers the organization has to make efforts in attracting the customers. For Example providing
gift coupons will be one of the ways of attracting the customer.
Customer Retention:
This is the major concern of all the organizations. This is measured in terms of customer satisfaction. Retaining the
gained customer is most important than getting the new customers.
Customer Development:
This Deals with customer loyalty, i.e how far the transaction performed by a customer can be increased which benefits
for both the organization and the customer. This involves some analysis like market basket analysis, cross selling
analysis and customer lifetime analysis.
3.DATA MINING:
Data mining is the process of extracting useful information from raw data. Data mining can also be defined as
analyzing the data from different perspectives and summarizing it into useful information.
Volume 5, Issue 10, October 2016
Page 145
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 5, Issue 10, October 2016
ISSN 2319 - 4847
4.DATA MINING TECHNIQUES
The main objective of data mining is to develop a data model from the data. Following are the various data modeling
techniques,
 Association
 Classification
 Clustering
 Forecasting
 Regression
 Sequence Discovery
 Visualization.
Classification and clustering is used in customer identification, Association, classification and clustering is used in
customer attraction, Forecasting, Regression and Sequence discovery is used in Customer retention and Visualization
is used in customer development.
5.Application of Data Mining in CRM:
Data mining acts as the backbone for the effective CRM. Data mining has its evolution phases like Data Collection in
late 1960’s, Data Access in 1980’s, Data Navigation in1990’s Data Mining in 2000. Customer provides data to the
organization
[1]. Bang ngyen et al.,have discussed about the CRM ,What are the advancements in CRM, its pitfalls and the
opportunities for research in CRM
[2]. A Detailed Study on how IT has emerged into CRM and how different the CRM has become after incorporating IT
was studied by Linda D. Peters.
[3]. This paper deals with how data mining helps in improving the marketing channel of the organization. A
Systematic methodology of applying Data mining and knowledge management for marketing has been provided by
the authors.
[4]. The Data has to be processed before applying any Data mining technique. This paper studies the various
preprocessing Techniques and its impact on the classifiers performance of decision trees, neural networks and
SVM. The impact of preprocessing is also empirically verified.
[5]. Though there are many Data mining techniques which can be applied for the effective CRM, Cost effectiveness is
maintained only when the appropriate technique is used. A comparative study is done between CHAID and neural
networks in the situation where the cost of customer acquisition has to be lowered and the life time value of
customer has to be improved.
[6]. Liao et al., have studied how association rules and cluster analysis will help in adopting the new business method
to improve the sales of hypermarket in Taiwan. At first, the association rules are applied on the customer data
according to the customer consumption and then the clusters have been formed, Catalog is redesigned for each
cluster based on each cluster consumption preferences. Implementing these two techniques the Care four- Hyper
market in Taiwan was able to know whether providing the online purchasing option to the customer will be
profitable or not.
[7]. Apparel industry is one of the most profitable industries, but these industries suffer from production management
and marketing because of lack of standard size charts. In this paper the authors empirically show how k-means
algorithm and ANOVA are used in developing the standard size charts thereby improving the production and
marketing management of the apparel industry.
[8]. In this data driven era the customer behavior can be studied in many ways, one among them is the usage of mobile
networks, In this paper the authors have proposed a model where the customer is studied based on movement,
consumption, communication, and 3 different clusters have been formed which then integrated with ARPU model
which gives the behavior pattern in detail which in turn helps the service provider.
[9]. Today everything goes online; In this paper the author describes the role of online customer reviews in affecting
the online purchase of a product.
[10]. This paper deals with how event logs in the CRM platform can be used to study the behavior of the customer.
Event log is maintained in the CRM platform like SAP since the event has been initiated by the customer. The
author has proposed a technique where the process mining clusters and predicts the customer behavioral pattern.
[11]. In this paper the author describes how the sentiment analysis helps in achieving the customer relationship
management.
[12]. Customers provide their opinion on products in many social websites. A framework has been proposed where the
opinion of the customers are mined which is used to compare the product features.
Volume 5, Issue 10, October 2016
Page 146
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 5, Issue 10, October 2016
ISSN 2319 - 4847
[13]. In this paper the author proposed a new approach to detect the customer behavior by combining the process of
clustering and sequence mining. This framework helps in finding the customer decisions which in turn help in
improving the business which are influenced by customer actions.
[14]. The author has taken an effort to explain the role of information fusion in opinion mining.
Opinion mining is the area where most of the research is going on which has information fusion as the major part.
[15]. Social media is accessed by most of the customers today the authors have taken effort to identify the data mining
techniques which are often used in the social media. As a result of the survey it is identified that 19 data mining
techniques have been applied confined to 9 research objectives.
[16]. In this paper a novel approach has been proposed to assess the customer loyalty. A new framework has been
proposed in addition to k-means algorithm. The methodology has been applied and it has given the tremendous
results to the organization
[17]. In this Paper the author has proposed the Fast Lead User Identification framework which is used in identifying the
Lead users in social site (Twitter). Lead Users are those who tend to experience needs before the rest of the
marketplace and stand to benefit greatly by finding solutions to those needs. FLUID is the system which identifies
the lead user in the micro blogging site twitter.
[18]. Competitive intelligence is one of the most important risk driven factor in business. The proposed methodology
helps in visualize and compare the products from the online reviews given by the customer. This in turn helps the
product manufacturer to gain the competitive advantage and improve their process of development.
Conclusion:
The goal of CRM is to maintain the relationship with the customer. In this paper various Data mining approaches to
CRM have been reviewed from the published work. As per the analysis whatever done so far we can understand that
Currently the Data mining in CRM is moving towards the web mining.
References:
[1]. Bang ngyen "A review of customer relationship management: successes, advances, pitfalls and futures", Business
Process Management Journal, Vol. 18 Iss 3 pp. 400 – 419
[2]. Linda D. Peters, "IT enabled marketing: a framework for value creation in customer relationships", Journal of
Marketing Practice: Applied Marketing Science, Vol. 3 Iss 4 pp. 213 – 229
[3]. J. Shaw ,Chandrasekar Subramaniam a , Gek Woo Tan , Michael E. Welge” Knowledge management and data
mining for marketing “Michael , Decision Support Systems , 2001 127–137
[4]. Sven F. Crone a, Stefan Lessmann b,*, Robert Stahlbock,”The impact of preprocessing on data mining: An
evaluation of classifier sensitivity in direct marketing”
[5]. Chris Rygielski , Jyun-Cheng Wang , David C. Yen, “Data mining techniques for customer relationship
management” , Technology in Society 24 (2002) 483–502
[6]. Shu-hsien Liao , Yin-ju Chen, Yi-tsun Lin, “Mining customer knowledge to implement online shopping and home
delivery or hypermarkets”, Expert Systems with Applications 38 (2011) 3982–3991
[7]. Chih-Hung Hsu, “Data mining to improve industrial standards and enhance production and marketing: An
empirical study in apparel industry”, Expert Systems with Applications 36 (2009) 4185–4191
[8]. Zhenhua Wanga, Lai Tua, , Zhe Guob, Laurence T. Yangc, Benxiong Huanga,” Analysis of user behaviors by
mining large network data sets”, Future Generation Computer Systems ,37 (2014) 429–437
[9]. Mohammad Salehan a, Dan J. Kimb,” Predicting the performance of online consumer reviews: A sentiment
mining approach to big data analytics”, Decision Support Systems ,81 (2016) 30–40
[10]. Massimiliano deLeoni a,n, WilM.P.vanderAalst ,”A general process mining framework for correlating, predicting
and clustering dynamic behavior based on event logs”, Information Systems (2016)235–257
[11]. Kumar Ravi a,b, Vadlamani Ravi ,”A survey on opinion mining and sentiment analysis: Tasks, approaches
and applications”,Knowledge-Based Systems ,2015
[12]. Haiqing Zhang , AichaSekhari , YacineOuzrout , AbdelazizBouras,” Jointly identifying opinionmining elements
and fuzzy measurement of opinion intensityto analyze product features Engineering”, ApplicationsofArtificial
Intelligence
[13]. Alex Seret , Seppe K.L.M. vanden Broucke , Bart Baesens, Jan Vanthienen“A dynamic understanding of customer
behavior processes based on clustering and sequence mining”, Expert Systems with Applications 41 (2014) 4648–
4657
[14]. Jorge A. Balazs, Juan D. Velasquez, “Opinion Mining and Information Fusion: A survey”, Information Fusion 27
(2016) 95–110
[15]. AliBouNassif, MohammadNoor Injadat , FadiSalo,” Data miningtechniquesinsocialmedia:Asurvey”,
Neurocomputing.
Volume 5, Issue 10, October 2016
Page 147
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected]
Volume 5, Issue 10, October 2016
ISSN 2319 - 4847
[16]. Seyed Mohammad Seyed Hosseini , Anahita Maleki, Mohammad Reza Gholamian, “Cluster analysis using data
mining approach to develop CRM methodologyto assess the customer loyalty”, Expert Systems with Applications
37 (2010) 5259–5264
[17]. Sanjin Pajo, Paul-Armand Verhaegen, Dennis Vandevenne, Joost R. Duflou,”Fast Lead User Identification
Framework” Procedia Engineering 131 ( 2015 ) 1140 – 1145
[18]. Kaiquan Xu , Stephen Shaoyi Liao , Jiexun Li , Yuxia Song ,”Mining comparative opinions from customer
reviews for Competitive Intelligence”, Decision Support Systems 50 (2011) 743–754
AUTHOR:
Deepa Completed her Integrated Masters in software Engineering in Vellore Institute of Technology,
Vellore in 2005 and working as Assistant Professor Junior in VIT University from 2005 till Date. Her
Research interest are Datamining, CRM and Big Data.
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