Insurance Pitch
Analytics Saves at Work - Company Introduction
An analytics consulting firm to support clients improve their business profitability, customer
experience and achieve regulatory compliance
Listed in 20 Best Big Data Startups in India
Our Insurance offerings:
1. Data Governance Framework and improving Data Quality
2. Operational Excellence – Cost Saves through optimising channel, process and people
performance
3. Increased Revenues through optimising contact, retention strategy, cross selling
leveraging web analytics and Big Data Analytics
4. Complaint Handling
Our Team*
•
Data Scientists with extensive knowledge of statistics and quantitative techniques in predictive and descriptive
modelling
•
Senior Analytics Consultants with experience in providing solutions to various business problems building models
using techniques ranging from linear regression to random forest on multiple platforms like R, SAS
•
Business Intelligence Consultants with wide experience in reporting and building dashboards and expertise in SQL
Server, MS BI, MS SharePoint, MS Excel, Toad, CRM, Toad and Teradata
•
Big Data Analysts with experience in text mining and modelling using different kinds of high-volume unstructured data
External Tie-ups
Industry Experts
& Consultants
• We work with accomplished industry experts who have extensive experience and industry knowledge. The
purpose of collaboration is to attract talent globally and to add more value to the clients
Academia
• Some of our research projects are being done at Indian Institute of Technology, Kharagpur in the area of
Big Data
*The team comprises members who are educated from Indian Institute of Technology and premiere Management and Economics institutes of India.
Offering 1
Data Governance - Framework
It has been observed that more than 20% of operational staff spends time doing rework due to poor data
quality
Assessing the existing
practices in Data
Governance
Employing a quality
monitoring and control
mechanism right from data
capture points
Defining the Critical Data
Elements (CDEs), the core
building blocks required
for business compliance
and their golden source
Validating/Establishing
data transformation and
data flow in the
organisation
Identifying different
roles in the Data
Ownership model
Get your data right the first time
Data Governance Framework…continued
Benefits
•
Reduced effort in rework
•
Better data quality helps in insightful analysis
•
Savings on policy returns due to wrong addresses
•
Improved turnaround time for account on-boarding
•
Enhanced customer experience
•
Regulatory compliance
End Result
Data will be relevant, accurate, timely, consistent, non-duplicate and accessible satisfying all the
attributes of Data Quality
Offering 2
Operational Excellence
Monitoring of results,
Training programs  Cost Saves
Continuous
Improvement
Performance
Framework
•
As-Is assessment of existing practices
and Benchmarking with industry best
practices
•
Optimising distribution channels
•
Sales Force Effectiveness
•
Rolling out people and process
performance efficiency framework
Efficiency Framework rollout
for process and people
Performance KPIs, Volume
Forecasting , Benchmarking
Data driven Solutions
As- Is assessment study, findings and
recommendations
Strategic Assessment
Guaranteed cost saves of 10% or more
Overall Operating Efficiency
How can we run operations as a production unit?
The Water Pump Case Study
A pump under the ideal / design situation is expected to deliver as follows:
1.) Availability for production = 24 hours everyday
2.) Production rate = 15 liters/hour
3.) Quality (measured in terms of a operating temperature) of
36 °C
1.) Available for production = 22 hours everyday
2.) Production Rate = At a production rate of 14 liters
per hour
3.) Quality at 34 °C
1
Actual availability 22
Availability for Production (A) = ------------------------- = ---- =
Design availability 24
2
91.6%
3
Actual rate 14
Work Rate (W) = ---------------- = ---- = 93.3%
Design rate 15
= A
x
W
x
Q
= 91.6% x 93.3% x 94.4%
= 80.6%
Actual
34
Quality (Q) = --------- = ---- = 94.4%
Design 36
Illustration of Performance Efficiency Framework
Methodology
1. As – Is Measurement
Right allocation of resources with efficient utilisation of each resource
results in increased efficiency of employees
2. Project Implementation
3. Sustained Improvement
4. Continuous Improvement
Employee Efficiency
3
85%
75%
70%
80%
77%
80%
1
70%
77%
70%
65%
60%
Jan
Feb
Mar
Apr
May
Pre Project Implementation
Jun
Jul
Aug
Sep
Oct
Nov
Post Project Implementation
 Our Improvement Model Covers Employee Productivity, Efficiency and Quality of deliverable
Performance Efficiency Framework - Benefits
•
Better awareness of management of employee skills and training needs
•
More transparency in employees appraisal and benefits
•
Better resource planning
•
More effective processes and people
Visible results in 6 months time frame after rollout of Performance Efficiency Framework
Offering 3
Increased Revenues through analysis
Customer Acquisition
• Customer Segmentation based on
demographic and Psychographic data
to generate leads
• Propensity models to score
customers based on Purchase data,
Social Media Data, Web log data from
website browsing etc. to identify
target Customers
Customer Retention
Growth from existing base
• Identifying customers with high
lifetime value based on product
details, demographics and
transactional data
• Identifying customers who have
a higher risk of lapse based on
transactional, channel and
demographic data
• Cross Selling to potential high
lifetime value customers and
customers who are more likely to
purchase
• Overlaying Customer Lifetime
Value and Lapse rates to
identify customers to target
with offers for retention
• Identifying next likely insurance
product the customer might buy
and cross sell accordingly
Improved topline through efficient targeting of
customers
Offering 4
Complaint Handling
Consolidated customer complaint handling leads to enhanced customer service
Social Media Sites
•
Internet Blogs/ Reviews
Consolidate all customer queries and complaints from
social media and web into one source automatically
•
Unified source to view and respond to queries and
complaints made on the web
•
Complaints not directed at the official support forums to
be gathered as well
•
Queries/ Complaints can be mapped to CRM at a later
stage
•
Reduce Manual search for the queries made on the web
•
Improve turnaround time for social media queries and
reduce detractors on the web
•
Unified source for all Social Media & Internet Queries
Decrease inbound calls into contact centre and reduce
customers’ effort
Better Customer experience by efficient customer
service
Social Media – Complaint Analysis
•
No consistency in responding to complaints posted on
social media
•
Several unanswered complaints on social media
•
Social media teams seem to have a disconnect when
handling customers with existing case history
Appendix
Case Studies/ Demonstrations
Social Media Scoring
Objective
To score online customers applying for insurance product based on their social media activity
Social Media Score
 Shortlist the social media websites which can be linked with email-id or other unique identifiers
 Extracting data from those websites and summarizing attributes from each of them as shown in the
example below
 Designing an algorithm to score individuals based on these attributes
Name
Number of
Profiles
(Facebook/
LinkedIn)
No of Friends
Frequency of
Posts
Tenure of Profile (in
days)
Other Variables
From Facebook
Other Variables from
LinkedIn/Twitter
Archna
Wadhwa
1
150
20
1825
*Based on the
availability of data
*Based on the
availability of data
Zubair Shaikh
1
0
0
0
*Based on the
availability of data
*Based on the
availability of data
Dyuti Sen
2
1000
48
1460
*Based on the
availability of data
*Based on the
availability of data
Lapsation Propensity Model
Objective
To measure the propensity of lapsation of customers to channelise the retention efforts to customers highly
likely to lapse
Methodology
 Univariate, Bivariate and Multivariate profiling of customers to observe the relation between
lapsation rates and multiple variables like Age, Gender, Geographical Region, Annual Income,
Premium payment frequency etc.
 Based on the insights from profiling a set of hypotheses is formed which guides the predictive model
development
 Building the models corresponding to each hypothesis and testing the hypothesis based on the
model output and refining the models if necessary
 Validating the models on the test data
Benefits
•
•
Improved retention rates resulting in increased revenues
Cost saves on retention efforts with increased efficiency - a result of right targeting
www.analyticssavesatwork.com
India - Office
U.K. - Office
101, Evoma Business Centre,
Prestige Featherlite Tech Park,
EPIP Zone – 2nd Phase
Near KTPO, Whitefield
Bangalore - 560066
5 Park Court, Pyrford Road,
West by fleet,
Surrey,
KT14 6SD