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Institutionalizing Analytics_ Predictive Analytics World_fractal analytics


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Fractal Analytics CEO Srikanth Velamakanni shares insights on the path to institutionalizing analytics with a fresh perspective on applying art to problem solving.

Fractal Analytics CEO Srikanth Velamakanni shares insights on the path to institutionalizing analytics with a fresh perspective on applying art to problem solving.

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  • 1. ® What got you here Won’t get you there Institutionalizing Analytics 10 Slides, 5 Case Studies Predictive Analytics World 2011 San FranciscoConfidential | Copyright © Fractal 2010
  • 2. ® Companies see analytics as a key differentiator in their business Financial Management & Budgeting Operation Production Strategy & Business Development Customer Service MIT-IBM Research Workforce Planning Top-performing organizations use analytics five times more than others Overall Average Top Performers 0 2 4 6 8 Lower Performers Tendency to use Analytics*Survey covered 3,000 executives, managers and analysts working across more than 30 industries and 100 countriesConfidential | Copyright © Fractal 2010 2
  • 3. ® Implementing analytics driven decisions is the critical barrier to greater adoption Demonstrating Clear ROI 21% Implementing Analytics Driven Decisions 39% Hiring People 7% Fractal research found that ~40% of executives polled, thought Getting Leadership Buy-In for Analytics that implementation was the 19% biggest roadblock for Cleaning Dirty or Very Large Data institutionalizing analytics 21% 0% 10% 20% 30% 40% 50%*Poll covered 116 executives from analytics user groups across the globeConfidential | Copyright © Fractal 2010 3
  • 4. To compete on analytics, companies need to make analytics a ®central part of their way of functioning, a part of their DNA The New Questions Companies Are The IA Journey Asking of Analytics Institutionalizing Questions Fractal Examples Analytics How do we transform our Internal Cross Sell Operationalizing business through Analytics? Analytics Solving Problems, Optimization How do we set up a centralized A Korean bank analytics unit? Predictive Analytics & How do we operationalize Operationalizing Models analytics in every instance? CLTV How do we achieve greater Pricing Optimization Actionable Insights innovation through use of analytics? How do we make information A CPG major “come alive”?Confidential | Copyright © Fractal 2010 4
  • 5. ® Case study 1: Transforming a Bank through Internal Sourcing of Asset CustomersLiability Portfolio RMs  Risk (Debit) Scorecard Risk Pre (****) TeleMktg Qualified (******)  Approve d High  (****) Branch Respons Score Channel  Risk Grey Zone e (**) (***) OptimizationScores (******) Mailers Model Respons (***) Risky e Scores No Segment No Low Offers Offers Score (*******) (***) The bank outperformed its peers during the credit crisis of 2008. 70% of bank’s credit card revenue come from existing customers Confidential | Copyright © Fractal 2010 5
  • 6. ® Case study 2: Creating a Centralized Analytics Unit for a Korean Bank • Establishment of anon AnalyticsCompeting centralized Analytical MIS unit • Centralized Customer level analytics • Fully functional sourcing and training engine • Multiple analytics toolsAnalytics • More than 90% of theAd hoc models unused • Inadequate staff and training Time Day 0 Day 1000 Confidential | Copyright © Fractal 2010 6
  • 7. ® Case Study 3: Operationalizing CLTV for 15,000 Field Agents Report • Benchmark producer against their peers • Describe the quality of business written • Inform producers about best & worst customers • Deep dive to identify drivers of LTVConfidential | Copyright © Fractal 2010 7
  • 8. ® Case study 4: Bringing Information to Life Concept Larger real estate for Data Visualization Customized iPad remote control application Visuals answer business question Technology Infrastructure Analytics Infrastructure • iPAD Application Platform • Business Abstraction • Content Management Enablers • Analytics Manufacturing Server facilitiesConfidential | Copyright © Fractal 2010 8
  • 9. ® Case study 5: Innovation in a Tightly Regulated P&C Insurance Industry LOSS LIFE TIME MODELING VALUE Cost of doing Loyalty business NEXT GEN Index for Customer PRICING Measures customer Loyalty Risk PRICE Additional SENSITIVITY Customer Measures customers’ reaction Characteristics to price changes Helps realize different profit margin depending on price sensitivity This pricing strategy increases premiums by up to 5% while staying loss neutralConfidential | Copyright © Fractal 2010 9
  • 10. ® Questions to Get Started 3 How do we get there? Demand Creation Supply structure 2 What is the mandate for Analytics? Planning & Prioritization Vision & Purpose Execution1 Where are we now? 5 year plan Funding Analytics 3 year plan Readiness Audit Confidential | Copyright © Fractal 2010 10
  • 11. ®What are the steps in Institutionalizing Analytics? Visioning & Purpose Scope of Influence Organizational Buy-in Success Metrics Solution mapping Institutionalizing Business Case Processes, SLAs Generation Partnership Strategy Analytics Calendar Organization Structure Data Access Talent Management Infrastructure Working with the Technology & Tools Organization Techniques & SolutionsConfidential | Copyright © Fractal 2010 11
  • 12. ® Fractal Analytics helps companies compete on analytics by helping them institutionalize analytics in the areas of understanding, predicting and influencing consumer behavior.United States United Kingdom India1840 Gateway Drive Russell Bedford House Corporate EnclaveSan Mateo, CA 94404 Unit 10, City Forum Andheri (E)Tel: (650) 378 1284 250 City Road Mumbai - 400099 London - EC1V 2QQ2500 Plaza Five Tel: + 91 22 4067 5800Jersey City, NJ 07311Tel: (201) 633 8728