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Wealth management
The document outlines a wealth management framework emphasizing customer-centricity and personalized service through analytics. It details critical components for success, including alignment of interests, effective relationship management metrics, and objectives to enhance customer experience and retention. Additionally, it discusses the importance of leveraging data analytics for improving efficiency, identifying client segmentation, and offering tailored financial solutions.
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Wealth management
- 1. CONFIDENTIAL & LEGALLYPRIVILEGED Wealth Management Solutions
- 2. Page 2© adiyanth– Distribution Restricted Content • Wealth Management • Analytics as Enabler • Typical Analytics Roadmap
- 3. Page 3© adiyanth– Distribution Restricted Key Players in Wealth Management Delivery 1. Trusted Advisors 2. Bouquet of Services 3. Delivery Channels Customer Value Proposition • Single Point of Contact • Personalized Attention • Bundled Products across Personal Banking, Wholesale Banking & Treasury Services • Portfolio-Specific customized views and reports • Other Facilities • Special FD Rates • Concierge Services Critical to Success: “Alignment of Interests” Role of Advisors: Aid in Diversification and Risk Mitigation
- 4. Page 4 Wealth ManagementBusiness Model is based on Customer-Centricity, Service Offering & IT Platform Wealth Management Value Chain Technology – Direct Banking / CRM / Personalized View Bouquet of Offers – Partnership Tie-ups / Investment Services / Portfolio Management Trusted Advisors – Qualified Financial Planners, Relationship Managers, Branch Managers
- 5. Page 5 Wealth Managementby Objectives – Primary Performance Metrics Relationship Manager’s KPI Length of Relationship Beginner Seasoned FinancialObjectives Share of Mind Share of Wallet 1. Number of Sales Contacts 2. Enrollment in Concierge Service 3. Product Holding Ratio 4. Size of Loan Portfolio 5. Assets Under Management
- 6. Page 6 Critical toSuccess – Managing Changing Client Expectations (Alignment of Interests) 3. Activation Efforts - High Value Proposition - Unique and customized 1. Solicitation Efforts - Open & Honest - Setting realistic expectations - Upfront on Fee Structure 2. Account Set UP - On-time - First Time Right - Welcome Kit 6. Retention Efforts - Value Matching - Highly Subsidized “White Goods” offers 4. Value Enhancement Efforts - Cross-Sell /Up-Sell - Add-on: Family/Business Associates - International Offerings 5. Loyalty Building Efforts - Speaking Opportunities - Access to “Page 3” events - Personalized Attention Impacts on Life Time Value Solution should enable a Bank’s Wealth Management team to “Keep Eye on the Ball” across stages
- 7. Page 7 Achieving Targets- Focusing on Pipeline and Efficient “Closing” Opinions – Favorability in Usage Positive Neutral Negative 1. Loyalty Programs 2. Value Enhancement Programs 3. Retention Program 4. Winback Programs 5. Courtesy Calls Behavior-SpeedtoDecision Aggressive Fast Slow 2 2 2 51 1 44 3 Objective: Increase efficiency of Relationship Manager Potential Attrition
- 8. Page 8© adiyanth– Distribution Restricted Content • Our Approach • Analytics as Enabler • Typical Analytics Roadmap
- 9. Page 9© adiyanth– Distribution Restricted Data is the Lubricant of the Engagement Engine • Our Approach • Analytics as Enabler • Typical Analytics Roadmap
- 10. Page 10 Analytics asEnabler – Overview Marketing Mix Models Promotion Response Models Forecasting Models Personal Visits Campaigns Programs Execution Planning Analytics will increase Relationship Manager’s Effectiveness and Efficiency
- 11. Page 11 Analytics asEnabler (Effectiveness) - Identifying my Wealth Management Candidate Segmentation framework follows three-step segmentation approach evaluation process, centering around the solution’s business objectives. 1. Both attitudinal and behavioral segmentation approaches are equally well-suited for usage as customer classification and/or description mechanisms – Identify desired functionalities • Understanding the structure of the market • Derive preference/affinity segments • Use consumer attitude/preference-driven descriptive segmentation • Require segmentation schema capable of driving cross-sell / up-sell programs • Require assigning segments to the newly acquired customers 2. Leverage your current informational assets, but do not lose sight of the marketing need at hand – Investigate data availability • Rich in transactional data - use behavioral segmentation • Rich in abounding reservoir of customer preferences – use attitudinal data 3. Differences between population and customer samples – Evaluate spatial population drift
- 12. Page 12 Analytics asEnabler (Efficiency) - Dashboards INR 0 INR 1,000 INR 2,000 INR 3,000 INR 4,000 INR 5,000 INR 6,000 INR 7,000 INR 8,000 INR 9,000 INR 10,000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec (INR 5,000,000) INR 0 INR 5,000,000 INR 10,000,000 INR 15,000,000 INR 20,000,000 INR 25,000,000 1 2 3 4 5 INR 0 INR 10,000,000 INR 20,000,000 INR 30,000,000 INR 40,000,000 INR 50,000,000 INR 60,000,000 INR 70,000,000 INR 80,000,000 East West North South Central Assets by RegionDaily Average in Flow Net Flows by Product Net Flows by Region Example – Advisor’s home page providing overview of performance
- 13. Page 13 Analytics asEnabler - Dashboards Daily Average in Flow 2% 9% 30% 20% 8% 10% 21% Cash International Large Cap Fixed Income Other Small Cap Mid Cap INR 0 INR 100,000 INR 200,000 INR 300,000 INR 400,000 INR 500,000 INR 600,000 Top Advisor Opportunities Asset Allocation Advisorswiththegreatestriskfactors Advisor AUM RollingProduction ShyamSundar INR3.5Crores INR12Lakhs AmeyaJoshi INR1.45Crores INR20Lakhs RahulBhagat INR4.34Crores INR0.48Lakhs SureshPatwardhan INR0.76Crores INR0.12Lakhs ShyamalSurana INR0.54Crores INR0.23Lakhs 0 50 100 150 200 250 INR 0 INR 2,000,000 INR 4,000,000 INR 6,000,000 INR 8,000,000 INR 10,000,000 INR 12,000,000 INR 14,000,000 INR 16,000,000 INR 18,000,000 UGDC AHDF DSER FGHU HJIU Top Recruitment Profiles Example – Manager’s home page providing overview of business and opportunities.
- 14. Page 14 Analytics asEnabler - Dashboards Example – Advisor’s home page providing further drilldown of customers. Sharukh Salman Preity Nagesh Chiranjeevi Nagarjuna Mahesh Firoz Philip AUM Days Since Last Contact Days Since Last Service Service Pipeline Eligible For Attrition Risk Potential Score Cross-Sell Score Satisfaction Index Next Revision Date Age Income Prescence of Children Occupation Education Lifestyles Opinions Brand Loyalty Shopping Pattern Distribution Channel Preferences Product Preferences Perception of Product Needs to be fulfilled Age Occupation Education Gender Geography Nature of Employment Address Industry Ownership Sales Turnover / Profit Year of establishment Number of Locations Geography Export / Import IT Budget II. Pscychographic Information III. Demographic Information IV. Firmographic Data I. Financial Drivers
- 15. Page 15© activecubes– Distribution Restricted Content • Wealth Management • Analytics as Enabler • Typical Analytics Roadmap
- 16. Page 16 Structuring AnalyticsSolution Engagement – 3 Year Timeline Year 1 Define Analytics Objectives: MIS/Decision Analytics / Predictive Modeling/ Strategy Design Contact Strategy: Developing Contact Intensity Cross-Sell Framework: Identifying Eligible Customers Year 2 Refine Analytics Objectives: MIS/Decision Analytics / Predictive Modeling/ Strategy Design Automating data preparation & Improving campaign processes Year 3 Imbibe Analytics Objectives: MIS/Decision Analytics / Predictive Modeling/ Strategy Design Automating data preparation & Improving campaign processes Cross-Sell Framework: Evaluating Product/Service Sequencing based on LTV Contact Strategy: Developing contact schema based on contact channels and messages Automating data preparation & Improving campaign processes Cross-Sell Framework: Integrating LTV metric across service lines and products Contact Strategy: Instituting organization-wide contact rules including DNC Management • Agree on timeline for start of engagement. • This will help plan the appropriate team to work with Wealth Management team • Agree on sequence of projects • This will help identify the type of data needed; and the teams can work to get this ready in the background, while other paperwork is being worked on. This will ensure a quick start.
- 17. Page 17© adiyanth– Distribution Restricted Social Media Website Logs Search Logs Journal entries Online Enquiries Product feedbacks Analytics Architecture Data is the Lubricant of the Engagement Engine
- 18. Page 18© adiyanth– Distribution Restricted Solution Considerations – Big Data Capabilities on a Hype Cycle
- 19. Page 19 Solution Considerations– Data Stream C-Sat Data Agent Logs CRM Data Call Transcripts Payment Data Data Linking & Cleaning Text Mining Framework Derived Attributes Framework Common Text Representation Indexed XML/ CSV files Data warehouse Data Sources Data Processing & Conversion Stage Data Storage Stage Analysis & Reporting Stage Assisted Insight generation Decision Matrix Reporting & Automation Social Signals Digital Pathways Enabling highest data quality and governance
- 20. Page 20© adiyanth– Distribution Restricted Solution Considerations – Structuring the Unstructured !! Cruising Altitude (Fitness Value): 1. Sum of mutual information between cue & environment 2. Linear function of environment probabilities Transition Altitude (Half-Life Value): 1. Qualitative Data Value =( Data Usefulness ) * ( Loss to Competitive Advantage ) * ( Timeliness ) 2. No. of days it takes for Qualitative Data Value to Half itself Landing Altitude (Quality Value): 1. Completeness 2. Consistency & Integrity Ground Level (Decision Value): 1. Quantitative Decision Matrix 2. Behavioral Decision Matrix
- 21. Page 21© adiyanth– Distribution Restricted Solution Considerations –Decisions enablement
- 22. Page 22© adiyanth– Distribution Restricted Questions?
- 23. CONFIDENTIAL & LEGALLYPRIVILEGED Thank you