Predictive Analytics and Business Insights

Predictive Analytics and Business Insights Real-Time Data Visualization and Analytics (A Story) Brent Cavan Manager, Predictive Analytics Tokio Marine North America Services, LLC 1

R-T Data Visualization and Analytics Provided Agenda: Utilize real-time data measurement, data integration, analytics and data visualization to drive decision making» Gain insight into customer behaviors in real time through Big Data analytics» Optimize marketing spend by allocating resources real-time for more effective engagement» Use Just-In-Time decision making to enhance customer interaction in real-time» Generate more moments of interaction via media properties, brands and each other Advanced Analytics and the Zanzibar Argument "It is not worth the while to go round the world to count the cats in Zanzibar" Henry David Thoreau Industries and data are different, challenges can be different, tools leave you with mixed capabilities, but the core of any Analytics Program should be the same The Really, inward a focused exploration voyage of for Meaning soulsearching But We don t do it like that 2

Background to the Story Tokio Marine Group, Japan's oldest and leading property/casualty insurer Tokio Marine North America, Inc. (TMNA Group) was established as a holding company in the US market The TMNA Group includes» Philadelphia Insurance Companies (Property & Casualty)» Tokio Marine Management & Insurance Companies (Property & Casualty - Global)» First Insurance Company of Hawaii (Property & Casualty)» Reliance Standard Life Insurance Company (Employee Benefits Solutions)» Safety National (Worker s Compensation) Tokio Marine North America Services, LLC TMNAS was established in 2012 as the shared services company to Tokio Marine North America, Inc. (TMNA Group) Legal Internal Audit Actuarial Human Resources Facilities Finance & Accounting Information Technology Me Look Outside the Box 3

The Challenge in the Story Advanced Analytics are designed to answer critical questions facing business users when dealing with large and ever-changing data sets. Keep this rough dialectic, however, in mind: Thesis Antithesis Synthesis Look Outside the Box But We don t do it like that But We don t do it like that The Really, inward a focused exploration voyage of for Meaning soulsearching 4

Prequel to the Story In a general sense, business users need to know» What Happened? Identifying which customers are performing well or poorly» Why Did It Happen? Identifying what might be causing some to perform poorly» What Will Happen? Identifying which other customers might perform poorly in the future» What Should I do? Identifying what can be done to change their behavior/performance Business Intelligence Programs Most BI Programs (And Tools) focus only on the What and somewhat the Why questions the historical / operational 5

Prequel to the Story Advanced Analytics allow business users to compress initial analysis» The Big Thought: Positioning information for business users that is predictive and prescriptive in the right areas Data Visualization 6

Prequel to the Story Traditional BI capabilities score somewhere at Level 2 moving to Level 3 You? GO GREEN (ADOPTION) Key Soft Concepts Avoid Over-Structuring / Barriers Make It Viral! 7

Prequel to the Story How to get to a Level 4 or 5 IT Score?» Migrating from State1 ( Row-Column Reporting) to State2 ( Data Discovery )» The Big Thought: Transition to State3 ( Data Virtualization / Visualization and Business Consumerization ) 2) Prototyping State DISCOVERY Dashboards / Predictions / Forecasts IBM Cognos Active Reports / Work Spaces / SPSS 3) Future State VISUALIZATION Presentation Layer / Dynamic Queries SAP Hana / Lumira and IBM Cognos Work Spaces / SPSS 1) Current State FILTERING Reports WIRED (SSRS) / Cognos Report Studio 8

The Story Board Program First Principles» Program Vision - Conceptual Design and Goals» Project Design and Sequence» Data Visualization - Story Telling Skills» Action Plans» Measured Results» Tool Selection» Resources The Big Thought Amplified: Projects Must Follow This Model Analysis Understanding (Visualization) Action Plans Decision Rules System Displays (User Actions) 9

The Story: Our Program Conceptual Design 10

The Story: Our Program Goals 11

The Story: Any Project Project Design Modeling Analysis Rules New Assignments Migration Results Improve Speed of Service Feedback Improve New Quote Process Improve Problem Resolution Improve Renewal Process Increase UW Flexibility Action Plans System Display (Actions) 12

The Story: A Project Project Sequence» Analyze Annual Customer Surveys against agency production, submissions, and sales activity to identify patterns» Analyze patterns to produce market segments (clusters of agencies) that merit attention» Select key market segments for a test (000 Agencies worth $000 Million)» Identify all agency needs from surveys» Classify agency sentiment, performance, and needs» Create Action Plans to meet needs (Service Model changes)» Set campaign to contact agencies and initiate service changes» Test Action Plans with selected agents (90-day test period)» Analyze test results (improved surveys / submissions / production)» Use analysis to feed Agency Behavior predictions» Apply / Automate Agency evaluation updates» Model Decision Support Rules» Embed Action Plan Elements in CRM display 13

The Story: Data Visualization Part A: Story Telling Skills Data Analysis CHAID Decision Trees K-Means Clustering Cluster of Policyholders with High Av. Claims Costs Guys Buying Diapers (Thurs./Sat.) Also Buy Beer Kohonen Self-Organizing Maps Classic Salesman Route Problem 14

The Story: Data Visualization Part B: Story Telling Skills Meaning What We Didn t Know Money on the Table Segment Large Agencies growing New Business > 20% / Year Promoters Satisfied / Very Satisfied Hidden Issues New Biz Quote Process UW Flexibility Technology 15

The Story: Data Visualization Part B: Story Telling Skills Meaning What We Didn t Know Unhappy with Something to Say Segment Premium Increases are Slowing Passives or Detractors Negative Coded Comments Poor Service Poor Responses Poor Salesperson Knowledge Agency Visits Down 16

The Story: Data Visualization Part B: Story Telling Skills Meaning What We Didn t Know On the Fence Segment Premium Still Trending Up Previously Satisfied / Very Satisfied Now Passive / Neutral Hidden Issues Slow Response UW Flexibility Declining Sales Activity 17

The Story: Data Visualization Part C: Story Telling Skills Classification CLASSIFICATION Clean Quadrants Multi-layered Drill-thrus Migration (Before / After) 18

The Story: Data Visualization Part D: Story Telling Skills Display Dashboard - Upper Dashboard - Lower 19

The Story: Data Visualization Part D: Story Telling Skills Display DASHBOARD / ANALYTIC THREADS AVOID the So What? Moment 20

The Story: Data Visualization Part D: Story Telling Skills Display DASHBOARD / ANALYTIC THREADS AVOID the So What? Moment 21

The Story: Action Plans Uncommon Services (Harvard Business School) - Inspired 22

The Story: Measured Results The Magic Quadrant (Before / After) 23

The Story: Tool Selection State of the Market - 24

The Story: Tool Selection State of the Market - 25

The Story: Tool Selection State of the Market - 26

The Story: Tool Selection State of the Market - 27

The Story: Tool Selection Our Selections IBM SPSS, IBM Cognos, SAP Hana/Lumira/CEI 28

The Story: Tool Selection Future ipad Delivery iphone Delivery (Geo-Locational) Agency Action Plan Action Plan (New Quote Process): Intake Concierge Service (800 #, Dedicated Staff) Intake Centralized email delivery of submissions Data Entry Training Agency Data Entry Staff Process Submission Analysis - Down Agency Visits Schedule Quarterly Agency Feedback Agency Manager Surveys Due 29

The Story: Resources Leverageable Advanced Analytics Team Resources» Design, Modeling, Data Analysis, Business Analysis, Programming Manager, Predictive Analytics Brent Cavan (Analytics Leadership / Design) Architect XXX (Visualization Design / Data Integration) Data Scientist XXX (Statistical / Model Design) Intelligence Analyst XXX (Analytics Development) Data Analyst XXX (Analytics Development) Business Analyst XXX (Analytic Requirements / Evaluation) Intelligence Analyst Intelligence Future Analyst Intelligence Future Analyst Future Data Analyst Data Future Analyst Data Future Analyst Future Business Analyst Business Future Analyst Business Future Analyst Future 30

Conclusion: Stay Out of Zanzibar Program First Principles Program Vision - Conceptual Design and Goals Project Design and Sequence Action Plans Measured Results Data Visualization Story Telling Skills Resources Tool Selections Expected Important Very Important 31

Questions Program First Principles» Program Vision - Conceptual Design and Goals» Project Design and Sequence» Data Visualization - Story Telling Skills» Action Plans» Measured Results» Tool Selection» Resources 32