IMCA 2013 Big Data in Insurance


Published on

Big Data presentation at 2013 Insurance Marketing Communication Assn. annual conference made by Pat Saporito, SAP

Published in: Data & Analytics, Technology
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

IMCA 2013 Big Data in Insurance

  1. 1. Insurance Marketing Communication Assn. Annual Conference, 2013 Big Data in Insurance & Implications for Marketing Pat Saporito, CPCU, Sr. Director, Global COE for Business Intelligence
  2. 2. © 2012 SAP AG. All rights reserved. 2Confidential Agenda Digital Evolution & Innovations Big Data Growing Insurance Needs & Implications Challenges for Marketing, Communications & IT How to Play Nicer Together
  3. 3. © 2012 SAP AG. All rights reserved. 3Confidential Digital Marketing Innovations 1:1 Marketing , Gamification/ Game Design  Angry Birds, CastleVille Group/Collective Buying • Groupon, Living Social Social Networking • Facebook, Linked In Inbound Marketing Retargeting, remarketing  Travelocity Location Based Marketing • Google Places Video • YouTube, Hulu User Generated Content • Facebook, Linked In, Twitter Mobile Technologies • Smartphones, iPads/tablets
  4. 4. © 2012 SAP AG. All rights reserved. 4Confidential Drive Better Profit Margins New Strategies and Business Models Operational Efficiencies Value Velocity Volume Variety Mobile CRM Data Planning OpportunitiesTransactions Customer Sales Order Things Instant Messages Demand Inventory Big Data Matters Potential to Provide Transformational Business Value
  5. 5. © 2012 SAP AG. All rights reserved. 5Confidential “Today in 64% of enterprises, fewer than 10% of decision makers use BI” Gap between End Users and Big Data Leading to a future ‘Flash Point’ between both Source: Forrester Research, 2012 BI Maturity Survey and Pioneer self assessment Projected Growth in End User Enablement  50% by 2014  75% by 2020 Sustained Explosive Growth in Data Volumes  80% Data Growth Year on Year
  6. 6. Analytics 3.0 │The Era of Impact 1.0 Traditional Analytics Data Economy: Rapid Insights Providing Business Impact Big Data2.0 3.0 • Primarily descriptive analytics and reporting • Internally sourced, relatively small, structured data • “Back room” teams of analysts • Internal decision support • Analytics integral to running the business; strategic asset • Rapid and agile insight delivery • Analytical tools available at point of decision • Cultural evolution embeds analytics into decision and operational processes • All businesses can create data- based products and services • Complex, large, unstructured data sources • New analytical and computational capabilities • “Data Scientists” emerge • Online firms create data- based products and services Today 2013 © IIA All Rights Reserved
  7. 7. © 2012 SAP AG. All rights reserved. 7Confidential What Big Data Means at SAP Marketing SAP Insight Driven Marketing Team 1.0 B Records on SAP Community Network 500M Transactions in our Mktg Intel Platform 800 Users of our Mktg Effectiveness Platform/200 reports/views 2.0B Behavior combinations based on event attendance/234 behaviors 27M companies/25M contacts
  8. 8. © 2012 SAP AG. All rights reserved. 8Confidential Insurance Analytic Evolution Where are you today? Where do you need to be? Pricing & Underwriting Traditional Class Rated Portfolio Analysis Household Analysis, Tier Rating Plans Risk Based Pricing, Ad-hoc or On Demand Rate Reviews Data Poor Quality, Silo’d, Inaccessible Data Data Assembled Across Product Lines/Historical Consistent Enterprise View Knowledge/ Data Mining Atomic Detail Data Wisdom/ Predictive Product Development One Product Fits All Unbundled Coverages Cafeteria/ Menu Approach Customer & Profitability Driven Marketing Product Value Customer Segment Value Customer Lifetime Value Dynamic Value Management Accounting & Finance Unit focused claims mgmt. Integrated, but reactive claims mgmt. Driver based historical claims mgmt. Driver based predictive claims mgmt. Metrics Silo’d, Functional, Lagging Metrics SBU-Strategic Objective linked, historical drivers Strategic & Cross-SBU objective linked, predictive drivers Integrated predictive models & metrics Claims Traditional Planning & Budgeting Driver Based Planning & Budgeting Integrated Planning Predictive Planning Less Advanced More Advanced Reactive Predictive
  9. 9. New information signals :-) Brand Sentiment Higher NPS 360O Customer View Loyal Customers Product Recommendation More Sales Propensity to Churn Greater Retention Real-time Demand/ Supply Forecast More Efficient Predictive Maintenance Less Downtime Fraud Detection Lower Risk Network Optimization Lower Cost Insider Threats Greater Security Risk Mitigation, Real- time Retain Market Value Asset Tracking Increase Productivity Personalized Care Loyal Customers What signals are you missing? “ ” In 2011 the amount of data surpassed 1.8 Zettabytes 90% of the data in the world today has been created in the last two years alone IDC Digital Universe Study Extracting Value from Chaos
  10. 10. A changing relationship with information From mass production to mass specialization Personalized Insights Advanced Planning and Forecasting Sensing and Responding Predictive Modeling Real-time Reporting and Analysis “ ” Every product and service will be offered to us in exactly the way we need it, not how manufacturers want to deliver it. A Demographic of One, Michael S. Malone
  11. 11. Information Culture Use information as a strategic asset in decisions Build and tell fact-based stories Maximize performance with effective use of information Connecting people to data “ ” The stone age was marked by man's clever use of crude tools; the information age, to date, has been marked by man's crude use of clever tools. Anon
  12. 12. © 2012 SAP AG. All rights reserved. 12Confidential Big Data: Analysis Tools Variety of tools for analyzing big data; new end user tools 78% are using reports and dashboards 68% are exploring predictive analysis (programmatically or analytic tools) 67% are exploring visualization tools 50% are using custom applications The Challenge of Big Data Benchmarking Large-Scale Data Management, Ventana Research, January 2012
  13. 13. © 2012 SAP AG. All rights reserved. 13Confidential Standard Reports Ad-hoc Reports OLAP & Visualization Dashboards & Scorecards Exploration & Visualization Value Predictive Modeling Traditional Business Intelligence Big Data Analytics Organizational&CompetitiveImpact Moving from Traditional BI to Big Data Analytics The Analytical Tools Continuum Self Service Sweet Spot
  14. 14. © 2012 SAP AG. All rights reserved. 14Confidential Where‘s the Value? Spending More Time Analyzing vs. Acquiring Data Source: SAP – ASUG Value Engineering Benchmark Study
  15. 15. © 2012 SAP AG. All rights reserved. 15Confidential SAP Vision for Intelligent Data Art of the Possible – Customer Success Stories Intelligent Data Data Explosion User Proliferation and Expectations Align
  16. 16. © 2012 SAP AG. All rights reserved. 16Confidential SAP BusinessObjects Business Intelligence Vision Five Innovation Pillars Mobile  First experience for BI  Content to point of impact  Expand to untapped users Extreme  Big data  Real-time  Predictive BI Core  Core for innovation  Complete BI Suite  Continued Leadership  Extendable foundation Creative  For IT and Department  Fast time-to- value  Connected to the Enterprise  Visualization Social  Capture the decision  Opinion and Facts  Leverage the network  Text analytics, sentiment analysis Innovation without Disruption
  17. 17. © 2012 SAP AG. All rights reserved. 17Confidential Analytics for the CMO Aberdeen Best in Class PACE Framework Source: Aberdeen Group “Analytics for the CMO: How Best in Class Marketers Use Customer Insights to Drive More Revenue”
  18. 18. Analytics Marketing Resource Management Customer & Segment Analysis Multi-Channel Engagement Campaign Management Mobile Inbound Response 21st Century Marketing SAP Marketing Analytics Approach Customer Social Monitoring & Filtering Loyalty & Rewards Real Time Offers
  19. 19. © 2012 SAP AG. All rights reserved. 19Confidential Challenges in Getting to Value Marketing Issues with IT • IT too slow is responding to requests • Have to go through IT to get data • Data quality/confidence • Data manipulation tools are lacking • Not responsive or innovative enough IT Issues with Marketing • Too many requests • Don’t know what they want • Create additional data islands/marts; more data to manage • Create more data ambiguity • Never satisfied
  20. 20. © 2012 SAP AG. All rights reserved. 20Confidential Common Marketing Analytic Pains Copyright SAP AG. Road to BI Success/BI Strategy Self Assessment Survey.
  21. 21. © 2012 SAP AG. All rights reserved. 21Confidential Copyright SAP AG. Road to BI Success/BI Strategy Self Assessment Survey. Common Marketing Analytic Pains (Cont’d)
  22. 22. © 2012 SAP AG. All rights reserved. 22Confidential Copyright SAP AG. Road to BI Success/BI Strategy Self Assessment Survey. Common IT Analytic Pains
  23. 23. © 2012 SAP AG. All rights reserved. 23Confidential Self-Service BI Strategy Assessment A self-service online assessment tool that can help you identify business challenges across your organization.
  24. 24. © 2012 SAP AG. All rights reserved. 24Confidential How to Play Nicer Together • Marketing & IT alignment; IT needs to focus on business-driven BI • Marketing defines business needs (describe strategic initiatives, current pains and business value to address; build your business case) • IT provides an “Innovation Sandbox” • IT provisions data and database infrastructure • IT helps identify/vet more self-service tools for Marketing • Consider Cloud environment for your Sandbox or use “trial” programs • Look outside the insurance industry for ideas/innovations
  25. 25. There’s a Data Maestro in all of us Join the SAP data challenge “ ” What story is your Data telling? Free download of SAP Lumira (data exploration & visualization tool)
  26. 26. Thank You! Pat Saporito, CPCU Sr. Director, BI Global COE for Analytics (201) 681-9671 Twitter: @Pat.Saporito LinkedIn: www.linkedin/in/patriciasaporito SAP Collaboration Network SAP Decision Factor Blog