This document discusses wealth management solutions and the role of analytics. It outlines a typical analytics roadmap over 3 years to define objectives, automate processes, and integrate lifetime value metrics. Analytics can increase relationship manager effectiveness and efficiency by identifying wealth management candidates, providing dashboards on key metrics, and enabling insights from both structured and unstructured data sources. The goal is to use data and analytics to improve customer targeting, cross-selling, contact management, and decision-making across the client lifecycle.

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Wealth Management Solutions

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Content
• Wealth Management
• Analytics as Enabler
• Typical Analytics Roadmap

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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

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Wealth Management Business 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

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Wealth Management by 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

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Critical to Success – 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

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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

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Content
• Our Approach
• Analytics as Enabler
• Typical Analytics Roadmap

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Data is the Lubricant of the Engagement Engine
• Our Approach
• Analytics as Enabler
• Typical Analytics Roadmap

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Analytics as Enabler – Overview
Marketing Mix
Models
Promotion Response
Models
Forecasting Models
Personal
Visits
Campaigns
Programs
Execution Planning
Analytics will increase Relationship Manager’s Effectiveness and Efficiency

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Analytics as Enabler (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

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Analytics as Enabler (Efficiency) - Dashboards
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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

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Analytics as Enabler - Dashboards
Daily Average in Flow
2%
9%
30%
20%
8%
10%
21% Cash
International
Large Cap
Fixed Income
Other
Small Cap
Mid Cap
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INR 100,000
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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
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UGDC AHDF DSER FGHU HJIU
Top Recruitment Profiles
Example – Manager’s home page providing overview of business and opportunities.

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Analytics as Enabler - 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

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Content
• Wealth Management
• Analytics as Enabler
• Typical Analytics Roadmap

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Structuring Analytics Solution 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.

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Social Media
Website Logs
Search Logs
Journal entries
Online Enquiries
Product feedbacks
Analytics Architecture
Data is the Lubricant of the Engagement Engine

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Solution Considerations – Big Data Capabilities on a Hype Cycle

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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

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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

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Solution Considerations –Decisions enablement

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Questions?

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