Insurance Pitch

Analytics Saves at Work - Company Introduction

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

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 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

• Some of our research projects are being done at Indian Institute of Technology, Kharagpur in the area of Big Data Academia

• 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

Industry Experts & Consultants

External Tie-ups

*The team comprises members who are educated from Indian Institute of Technology and premiere Management and Economics institutes of India.

Offering 1

Assessing the existing practices in Data Governance

Defining the Critical Data Elements (CDEs), the core building blocks required for business compliance and their golden source

Identifying different roles in the Data Ownership model

Employing a quality monitoring and control mechanism right from data capture points

Validating/Establishing data transformation and data flow in the organisation

Data Governance - Framework

It has been observed that more than 20% of operational staff spends time doing rework due to poor data quality

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

Strategic Assessment

As- Is assessment study, findings and recommendations

Performance KPIs, Volume Forecasting , Benchmarking

Efficiency Framework rollout for process and people

Monitoring of results, Training programs Cost Saves

Data driven Solutions

Performance Framework

Continuous Improvement

Operational Excellence

• 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

Guaranteed cost saves of 10% or more

How can we run operations as a production unit?

Overall Operating Efficiency

1.) Availability for production = 24 hours everyday2.) Production rate = 15 liters/hour3.) Quality (measured in terms of a operating temperature) of 36 °C

The Water Pump Case Study

A pump under the ideal / design situation is expected to deliver as follows:

1.) Available for production = 22 hours everyday2.) Production Rate = At a production rate of 14 liters per hour3.) Quality at 34 °C

Actual availability 22 Availability for Production (A) = ------------------------- = ---- = 91.6%

Design availability 24

Actual rate 14 Work Rate (W) = ---------------- = ---- = 93.3%

Design rate 15

Actual 34Quality (Q) = --------- = ---- = 94.4%

Design 36

= A x W x Q = 91.6% x 93.3% x 94.4% = 80.6%

1

2 3

OOE

Illustration of Performance Efficiency Framework

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov60%

65%

70%

75%

80%

85%

Employee Efficiency

70% 70%

77% 77%80%

12

34

Pre Project Implementation Post Project Implementation

1. As – Is Measurement

2. Project Implementation

3. Sustained Improvement

4. Continuous Improvement

Our Improvement Model Covers Employee Productivity, Efficiency and Quality of deliverable

Methodology

Right allocation of resources with efficient utilisation of each resource results in increased efficiency of employees

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 Growth from existing base Customer Retention

• 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

• Identifying customers with high lifetime value based on product details, demographics and transactional data

• Cross Selling to potential high lifetime value customers and customers who are more likely to purchase

• Identifying next likely insurance product the customer might buy and cross sell accordingly

• Identifying customers who have a higher risk of lapse based on transactional, channel and demographic data

• Overlaying Customer Lifetime Value and Lapse rates to identify customers to target with offers for retention

Improved topline through efficient targeting of customers

Offering 4

Complaint Handling

Internet Blogs/ ReviewsSocial Media Sites

Unified source for all Social Media & Internet Queries

• 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

• Decrease inbound calls into contact centre and reduce

customers’ effort

Consolidated customer complaint handling leads to enhanced customer service

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

AppendixCase 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 VariablesFrom 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

101, Evoma Business Centre,Prestige Featherlite Tech Park,EPIP Zone – 2nd PhaseNear KTPO, WhitefieldBangalore - 560066

India - Office U.K. - Office5 Park Court, Pyrford Road,West by fleet,Surrey,KT14 6SD