3.5. Shenandoah Life insurance company United States.
Challenges
Policy approval process was paper based and cumbersome.
Routing of these paper copies to various departments, there was delays in approval.
Results
Empowered management with current information on pending policies.
Reduced the time required to issue certain policies by 20 percent.
Improved underwriting and employee performance review processes.
3.6. Soft map Company Ltd., Tokyo
Challenges
Customers had difficulty making hardware and
software purchasing decisions, which was hindering
online sales.
Results
Page views increased 67 percent per month after the recommendation engine went live.
Profits tripled in 2001, as sales increased 18 percent versus the same period in the previous year.
4. Conclusion
Data mining has importance regarding finding the patterns, forecasting, discovery
of knowledge
etc., in different business domains. Data mining techniques and algorithms such as classification,
clustering
etc., helps in finding the patterns to decide upon the future trends in businesses to grow. Data mining has
wide application domain almost in every industry where the data is generated that’s why data mining is
considered one of the most important frontiers in database and information systems and one of the most
promising interdisciplinary developments in Information Technology.
5. References
1. Jiawei Han and Micheline Kamber (2006), Data Mining Concepts and Techniques,
published by Morgan Kauffman,
2nd ed.
2. Dr. Gary Parker, vol 7, 2004, Data Mining:
Modules in emerging fields, CD-ROM.
3. Crisp-DM 1.0 Step by step Data Mining guide from http://www.crisp-dm.org/CRISPWP-0800.pdf.
4. Customer Successes in your industry from
http://www.spss.com/success/?source=homepage&hpzone=nav_bar
.
5.
https://www.allbusiness.com/Technology /computer-software-data-management/ 633425-1.html
,
last retrieved on
15th Aug 2010.
6.
http://www.kdnuggets.com/
.
ISSN : 0976-5166
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