Final Bi Ppt
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Transcript of Final Bi Ppt
7/29/2019 Final Bi Ppt
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BUSINESS INTELLEGENCE
By
Apoorva Gopinath A 02
Manish Khuswaha A 04
Sandeep Godhey A 21
Ranvir Shinde A 20
Jyoti Rana A 23
Swapnil Joshi A 58
Vikas Singh Bisht A 62
BENTLEY
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Predictive Analytics - Transition from “what happened” to “why is it
happening”—leading ultimately to “what will happen.”
Business applications
Techniques
Business Application Predictions
Customer Retention customer defection/churn/attrition
Direct Marketing customer Response
Product recommendations what each customer wants/likes
Behavior-based advertising which ad customer will click on
Email targeting which message customer will respond to
Credit scoring debtor risk
Fundraising for nonprofits donation amount
Insurance pricing and selection applicant response, insured risk
Predictive Model Descriptive Model Decision Model
Find relationships and patterns
between variables
Find clusters of data elements
with similar characteristics
Find optimal and most certain
outcome for a specific decision
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Insurance Industry
An insurers ability to forecast a policy's ultimate cost determineshow accurately it prices its products and in turn the extent to
which it can avoid adverse selection.
Applications in the Insurance Functions
1) Insurance Marketing : Analysis of purchasing patterns of insurance customers.
This Information can then be used to increase marketing functions hit ratio and
retention ratio
2) Underwriting : Filtering out of applicants who do not meet a predetermined
model score. Increases efficiency by reducing employee hours spent on
researching and analyzing an applicant who is not a desired insured
3) Claims : Prediction of fraudulent claims and efficient allocation of resources to
high priority claims.
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PREDICTIVE ANALYSIS FOR MARKETINGDECISION TREE FOR CROSS SELLRESPONSE MODELING FOR DIRECT MARKETING CAMPAIGN PROFIT CURVE
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Screen clipping taken: 17/02/2013, 13:09 Data was available till March 2004. Prediction till Dec 2004