Business Analytics and Data mining Techniques for Banking Industry

This presentation explores the applications of business analytics and data mining in the banking industry, specifically in customer relationship management. It discusses how these techniques can improve profitability, customer satisfaction, and retention.

TECH7406- Business Intelligence
and Data Warehousing
Research Report
Individual Report – 3 Customer Relationship
Management industry

Introduction
Main objective of this project is to prepare the report to analysis the business analytics
and data mining techniques to the selected domains, here we are chosen the Customer
Relationship Management industry and, also this report is used to review the applications of
business intelligence analytics and data mining in Customer Relationship Management
industry for decision making contents. This project is used to enable user to understand the
how business intelligence analytics and data mining technique revolutionize business today.

Business Analytics and Data mining
Techniques for Banking Industry
 Basically, the customer relationship management is a most important of every business
which is used to encompass the al the interactions in the point of view of a business may
it be a people on the network through a phone calls.
 It has only one goal which is improve the organization profitability. It is system tool
which is used to aids the management like productivity, work flow process, sales, contact
and many more. It is used to examine and analyze the interaction with customers and
data throughout the customer lifecycle.
 It is used to provide the information and data that the company can use to improve the
customer services to have the good customer retention resulting which is used to
increases the revenue and sales.

Business Analytics and Data mining
Techniques for Banking Industry
 Basically, the data mining has various applications for the customer relationship
management ("Business Intelligence for Customer Relationship Management |
Business Intelligence Tool", 2019).
 It creates the value for the customer which is key determinant of a successful
business.
 The customer satisfaction is used to ensure the profitability for the business.
 It is helps to increasing the business product and services and it increased the
loyalty among the existing customers while trying to attract the new ones.

Business Analytics and Data mining
Techniques for Banking Industry
 It facilitates the prospective analyst which is used to improve the business and it
creating the opportunities for business to determine the innovative ways to
implement the effective customer relationship strategies.
 The business intelligence and data mining techniques are greatly enhance the all
the phases of the customer relationship management which is used to provide
the actionable and timely information, maintain the high levels of the customer
satisfaction and retention of profitable customers (Rodpysh, 2012).

Applications of the Banking Industry
 The applications of CRM is based on inputs form the classification of the data
mining process and business intelligence process which are combined to creates
the integrated model for CRM.
 The integrated mode is consist of the three types of the process of the data
mining which could be used in the CRP process and possible applications of the
these two technique.
 The application of these two techniques in CRM is become the significant
triggers for creating the successfully strategic decision which is related to the
process of customer relationship management.

Applications of the Banking Industry

Business Opportunities
 Basically, the data mining and analyst is having the availability tools which is CRM to
creating the opportunities for business to find the innovative ways to implement the
effective customer relationship strategies.
 The CRM is used to create the activities critical to building the relationship and generating
the future opportunities such as managing sales opportunities in CRM which is easy and it
is used to monitor and track the revenue opportunities from the initial contact to closure.
 It also record and share the competitor activities, weakness and strengths. It manages the
leads and sales opportunities in database. It tracks the sales opportunities against the
marketing campaign which is used to understand the ROI ("Customer Relationship
Management & Opportunities - Sunrise Software", 2019).

Challenges
The challenges for the customer relationship management industry based
on data mining and business analyst is represented as below (K S, 2015).
 Define the clear objective to be linked to both business
objectives and processes.
 Integrating CRM with existing system
 Determine the right partner
 Achieving the user adoption
 Managing the applications

Conclusion
This project was successfully prepared the report which is effectively
analyses the business analytics and data mining techniques to the Customer
Relationship Management industry and, also this report is also effectively review
the applications of business intelligence analytics and data mining in Customer
Relationship Management industry for decision making contents. This project is
used to enable user to understand the how business intelligence analytics and data
mining technique revolutionize business today.

References
 Akhavan, P., & Heidari, S. (2012). Application of Knowledge Management in
Customer Relationship Management: A Data Mining Approach. SSRN
Electronic Journal. doi: 10.2139/ssrn.2188230
 Business Intelligence for Customer Relationship Management | Business
Intelligence Tool. (2019). Retrieved from
https://www.elegantjbi.com/elegantjbi/business-bi-crm.html
 K S, R. (2015). A Data mining model for Customer Relationship Management -
A Review. International Journal