This document discusses data storytelling techniques for insurers. It provides three case studies as examples: 1) using visualization to determine why enrollment is down in new areas, 2) visualizing predictive models to identify why some prospects are being underscored, and 3) integrating separate online and offline data sources to accurately measure performance across channels. The case studies demonstrate how data can be combined and visualized to identify issues, evaluate models, and optimize marketing efforts.

1. Data Storytelling for Insurers

2. TEVJE MAILLARD
VP of Analytics &
Software Engineering
tmaillard@tpgdirect.com
www.linkedin.com/in/tevjemaillard
@tevjeM
ALEX COHEN
Sr. Marketing Analyst
acohen@tpgdirect.com
www.linkedin.com/in/acohen23

3. OVERVIEW
• Business Problems Solved With Geospatial Analysis
• Evaluating Predictive Models Visually
• Visualizing Complex Data Sets By Using Business
Intelligence Tools
• Combining Online & Offline Data While Maintaining Data
Integrity
• Delivering Automated Versatile Retention Analysis

4. OBSERVATIONS
• Insurers seldom have an analytics view that allows them to
accurately compare all of their marketing efforts’ effectiveness.
• This is often due to the myriad of sources both online and offline that
drive business.
• Opportunities are often missed due to information not being
effectively visualized.
• Frequency of reporting factors highly into the utility of an analytics
program. It has a direct impact on pattern/trend recognition & media
mix optimization.

5. Initial Issues Suspected Causes
Case Study 1
• Enrollment Down in New Service Areas
• Conversion Rates Had Larger Than
Expected Variance Between Counties
• Pressure From Senior Management to
Efficiently Grow Business in New Areas
• Brand Awareness
• Strength of Hospital
Network In New
Areas
Task: Build a Visualization That Demonstrates Which of the
Two Suspected Causes is Suppressing Performance the Most

6. Case Study 1

7. Case Study 2
Gleaned From Data Mining Predictive Evaluation
• Historically Direct Mail results show that
the customer segments are the top
prospects. With them we can realize
performance 3 to 5 times higher than
average.
• Current LTV Modelling process did not
Match Historical Results.
• Appeared to Be Underscoring Prospects
• Response
• Conversion
• Attrition
• Premium
• Loss
Task: Determine which model(s) is/are causing prospects to be
underscored.

8. Case Study 2
The circles represent
expected model
scores.
The bars represent
actual premium
collected per piece of
mail

9. Call Center Data Web Data
Undervalued
Website is treated as
secondary concern.
Disconnected
Web & Call Center are
treated as separate
entities.
Inaccurate
Digital CPPs appear
unreasonably high
Case Study #3
• Appears to account for 82% of NAP
• Backend Quoting Data Stored on a
Separate SQL Server than Web Data
• Fields & XML Feeds do not match layout
of Web Data
• Analytics Stored In GA
• Backend Quoting Data
Stored On Independent
SQL Databases

10. Case Study 3
Assigned Each Campaign a
Unique Toll Free Number
Toll Free Number on
Website Changes
Depending on Channel
Backend Databases Unified
All Quoting Data
Standardized
Call Center Servers
Modified to Pass Call
Information to Google
Analytics

11. Case Study 3
Joined GA Data with Backend Data
Provided LTV View Across All Channels
Enable Automated Retention reporting to Expand Focus Beyond CPP

12. Questions & Answers