The document discusses various data mining strategies and techniques utilized in Customer Relationship Management (CRM) systems to enhance customer identification, retention, and service delivery. It outlines the integration of data mining into CRM, particularly for retail, aiming to improve decision-making and business outcomes while noting the need for further practical research. Key techniques such as decision tree algorithms and predictive analysis mechanisms are highlighted as tools for better customer profitability assessment and effective management.

1. By:
Ankita Dubey

2.  Research Paper details
 Introduction
 Paper 1 discussion
 Paper 2 discussion
 Paper 3 discussion
 Conclusion
 References
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3.  Title: Data Mining Strategies andTechniques for CRM Systems
 Authors:
 Dr.Abdullah S.Al-Mudimigh,
 Zahid Ullah ,
 Farrukh Saleem
 Title: Improving The Retailers Profit For CRM Using Data Mining
Techniques
 Author:
 K.Deepa, S.Dhanabal
 Vishnukumarkaliappan
 Title: Application of Data Mining Technology in the Tourism Product's
MarketingCRM
 Author:
 Shenglei PEI
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4.  CRM: Customer Relationship Management
 Strategy and process of
▪ identifying,
▪ retaining and
▪ associating
selective customers in order to sustain their relationship
with the organization.
 With CRM, greater efficacy and effectiveness in delivering
strategies could be achieved.
 CRM involves all of your organizations “CustomerTouch
Points” and includes every part of your company that has
direct or indirect interaction with your customers and
prospects.
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5.  Technology Management
 Sales Management
 Marketing Management
 Customer Support Management
 Supply Chain Management
 Facilities Management
 Knowledge Management
 Retail Management
Data Mining
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6.  Paper 1 proposed that
 Data mining is also a successful factor of CRM.
 In this model the association mining techniques
for finding loyalty and background of the
customer, and making some prediction for the
contacting customer.
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8.  Knowledge discovery plays important role in CRM.
 Stuck or loop problems can be resolved by including
data mining into CRM.
 Enhance the capability of CRM in :
 Customer services
 Organization services
 Online services.
 The applications of data mining applied on the
existing database is generating new rules and
patterns from the experienced data.
 The conclusion is, data mining is the part of CRM.
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9. Retailing?
Data mining has :
 Identifying
 Attracting
 Developing
 Retaining
Missing…
Predict the demand of
products

10.  The main drawbacks in retail business are if
products are sold in huge amount and high
demand arises and subsequently if stock is
not available it leads to inconsistencies of
profit to the retailers.
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11.  CRM + SCM
 Grouping of similar customers
▪ Valuable customers
▪ Regular customers
▪ Occasional customers
 Business development
▪ Occasional customers are changed to regular or valuable
customers by providing some attracting programs, discounts
etc.
▪ Apply Data cube technology the yearly, monthly, weekly,
daily and seasonal based sold products are verified and
stored on the database
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12.  Customer attraction and retaining the customers
 Attraction
▪ Discounts , loyalty programs
are conducted which
motivates the customer to
place an order immediately.
▪ Direct marketing and coupon
distribution are some
examples of customer
attraction
 Retaining
 Predictive analysis of data
mining techniques
 Analysis of customer will
retain or not are identified by
hidden markov model.
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14.  Reinforcement of relationship marketing.
 Development of supporting strategies can
increase the sales.
 Effective integration of CRM and SCM is
designed by considering parameters of
product, customer and sales information.
 More parameters can be added.
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 Data mining process in CRM

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17.  Decision tree algorithm for customer
profitability analysis.
 Data sorting: In order to facilitate the
operation the data should be processed first.
 Carry out data mining by using decision tree
algorithm.
 The key point of using decision tree algorithm
is to calculate the information gain and look
for a branch node.
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Gain(A) represents information gain of attribute A ;
I( S1 ,S2,…. Sm) is the expectations of the element information,
m in which says the number of attribute values.

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20.  CRM system based on data mining
 Better use of customer information
 Quickly and efficiently get valuable knowledge
 Realize efficient management and operation.
 But a lot of research is still stay in theoretical
and lack of practice; many theories need to
be tested and perfected in practice.
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21.  [1] Dr. Abdullah S. Al-Mudimigh, Zahid Ullah and Farrukh Saleem,
“DATA MINING STRATEGIES AND TECHNIQUES FOR CRM
SYSTEMS”
 [2] K.Deepa, S.Dhanabal and Vishnukumarkaliappan “Improving
The Retailers Profit For CRM Using Data Mining Techniques”, 2014
World Congress on Computing and Communication
Technologies,978-1-4799-2876-7/13 2013 IEEE DOI
10.1109/WCCCT.2014.23
 [3] Shenglei PEI, “Application of Data Mining Technology in the
Tourism Product's Marketing CRM”,2013 2nd International
Symposium on Instrumentation and Measurement, Sensor
Network and Automation (IMSNA), 978-1-4799-2716-6/13 2013
IEEE
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22. Any questions?
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