VCare Case Study shows how data can be analysed based on providing two solutions, one based on aggregate data and other based on granular level of data.

1. Case Study:
Data Granularity and Business Decisions
Presented by:
Akshata Dandekar
Dilip Kumar
Rahul Lakkadwala
Rahul Yadav
Shashank Tiwari
Suchali Pal

2. Case Summary
 VCare Insurance Company operating in US had dominating market share till
2007, but due to new entrants it lost market share and was forced to go for a
makeover.
 Company was offered two solutions from CMO Steve based on aggregated
consumer data, and CEO Debbie based on Data analysis at granular level and
then create a scorecard.

3. Case Overview
 VCare Insurance Company (US-based)
 Problem:
 Had Dominant Market Share till 2007 in its home state in professional liability
market. Since 2007, Declining market share, Higher claims & Pressure on
rates.
 Two solutions were offered in 2010, the 7-member board had to decide upon
one, considering:
 Availability of Data and Analysis tools
 Ease of implementation

4. Background of VCare
 Mid-size mutual insurance company - earned premium - $109 million in 2010
 Operated in Niche segment of professional liability market in East Coast
 Dominant market share - Over 75% market share in its primary state of operation in
2006
 25% Market share in two other states
 Performed above Industry levels
 Decided to enter – Fidelity & Surety

5. Financial Summary

6. Investigation on two solutions of
Steve(CMO) & Debbie (CEO) for selection
by 7-member board led by Chris Collins ?

7. DEBBIE
 Joined as underwriter
 Became chief underwriter and then took
over the marketing department
 In 2007 she became the CEO

8. DEBBIE’S PERCEPTION
 Wanted to analyze the data at a granular level
 Looked at the existing performance
 Did many brainstorming sessions
 Hired AVIZARE solutions as a consultant
 The solutions provided by her were:
Data Analysis
Strategy intelligence
Decision support system

9. SOLUTIONS
 Data Analysis is a process of inspecting, cleaning, transforming, and
modeling data with the goal of discovering useful information,
suggesting conclusions, and supporting decision-making
 Initial data analysis:-The most important distinction between the
initial data analysis phase and the main analysis phase, is that during
initial data analysis one refrains from any analysis that is aimed at
answering the original research question. The initial data analysis
phase is guided by the following four questions

10. SOLUTIONS
 Strategic intelligence (STRATINT) pertains to the collection,
processing, analysis, and dissemination of intelligence that is required
for forming policy and military plans at the national and international
level. Much of the information needed for strategic reflections comes
from open source intelligence
 Quality of data:-The quality of the data should be checked as early as
possible. Data quality can be assessed in several ways, using different
types of analysis: frequency counts, descriptive statistics (mean,
standard deviation, median), normality (skewness, kurtosis, frequency
histograms, variables are compared with coding schemes of variables
external to the data set, and possibly corrected if coding schemes are
not comparable.

11. AVIZARE SOLUTIONS
 Identification of premium growth opportunities
 Estimating likelihood of claim
 Sweet spot identification
INTERNAL DATA
 UW Application
 Loss Control
 Claims and relevant data
EXTERNAL DATA
 Doctors data
 Demographics
 Other publicly available data

12. PARAMETERS
 Quantified parameters
Considering that the volume was too large for customization at
individual
level the aim was to segment the market by country or cluster of
countries
 Classified
Conservative and Demanding
Aggressive and Persuasive
 Prioritized and relationship
Based on the priorities and the relationship with the other organizations
and clients

13. Solutions Provided by Steve
1. Aggregate consumer data
2. OLAP
3. DSS
The source information for data aggregation may originate from public
records and criminal databases. The information is packaged into
aggregate reports and then sold to businesses, as well as to local,
state and government agencies. This information can also be useful
for marketing purposes.

14.  OLAP (online analytical processing) is computer
processing that enables a user to easily and
selectively extract and view data from different
points of view.
 OLAP can be used for data mining or the discovery of
previously relationships between data items.
 An OLAP database does not need to be as large as
a data warehouse, since not all transactional data is
needed for trend analysis.

15. Functions of OLAP
OLAP cube is an array of data understood in terms of its 0
or more dimensions. OLAP is an acronym for online
analytical processing. OLAP is a computer based technique
for analyzing business data in the search for business
intelligence
Operation which help them
 Slice
 Dice
 Drill up
 Drill down

16. DSS
What is DSS?
A Decision Support System (DSS) is a computer-
based information system that supports business
or organizational decision-making activities.
Properly designed DSS is an interactive system
intended to compile raw data, documents and
personal knowledge or business model.

17. Process of DSS

18. Functions by DSS
 DSS was used by the insurance company for verifying credentials of the
customers.
 Allow monitor negative trends and better allocation of resources.
 DSS helps in long term planning.

19. ComparisonCEO Steve
•Fixing Underwriting Practices & Optimize Pricing based on Statistics
•Performance related to adverse customer selection
•Analyse risk based on aggregate demographics
CEO Debbie
•Refused to prejudge and analyse the data at a aggregated level
•Suggested system centred on counties-Drill Down
•Performance based on lowest level customer data-Data Granularity

20. Decision of Chris Collins
 Approaches of Steve & Debbie individually doesn’t solve the problems
 Exact Solution in between the two approaches
 Balance between the Aggregation & Granularity
 Final Solution to be oriented to growth of market share to get back on the
track

21. Thank you