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How Customer Intelligence Capabilities Enable
Customer Centric Organizations
National Conference on Database
Marketing December 2008
Tony Branda Executive Head of Business Analysis,
RBS Citizens NA
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Agenda
I. Why Customer Intelligence?
1. Retail Bank Data-Mining Evolution
2. Level Setting Business Intelligence and analytics
3. The Evolution in World Class Customer Mgt.
4. Best Practices in Customer Centric Architecture
5. Killer Customer Applications
6. Vision
7. Who Is Your Customer?
8. What Does Top Analytical Talent Need?
9. What Is The Impact To Your Bottom Line?
II. Steps To Deploy Customer Intelligence
1. Assess Your Organization
2. Create A Vision
3. Create A Phased Plan
4. Procure Executive Sponsorship
5. Enact The Right Governance
6. Implementation Considerations
III. Major Pitfalls
1. Top Ten Reasons Customer Intelligence Projects Fail
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Retail Bank Data-Mining Evolution

• As Retail Banks move from focusing on pushing
  products to managing customer relationships,
  their approach to data analytics has gone from
  being one dimensional to multi-dimensional
• The multi-dimensional nature of operating at a
  customer level has forced a more collaborative
  organization and common infrastructure to
  maximize customer value and experience in
  addition to shareholder value
• Certain lines of business by their very nature
  have been early adopters of analytics to drive
  revenue growth. The Commodity nature of
  businesses like Cards and HELOC have facilitated
  heavy data-mining to differentiate themselves in
  commodity markets
• Customer Centricity will best leverage economies
  of skill and scale derived from analytical
  platforms
• Methodologies, techniques and best practices have
  proven transferable to other retail finance
  products

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Level Setting Business Intelligence and
analytics
Optimization Whats the best that can happen?
Predictive modeling What will happen next?
Forecasting/extrapolation What if these trends continue?
Statistical analysis Why is this happening?
Alerts What actions are needed?
Query/drill down Where exactly is the problem?
Ad hoc reports How many, how often, where?
Standard reports What happened?
Analytics
Competitive advantage
Access and reporting
Degree of intelligence
Source Adapted from a graphic produced by SAS,
reprinted by permission in Competing on
Analytics, The New Science of Winning, Authors
Thomas H. Davenport Jeanne G. Harris
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The Stages of Evolution in World Class Customer
Mgt.
Integrated Information Drives the Entire Customer
Lifecycle
To facilitate the evolution from a single
product/ channel focus to an end-to-end
customer-centric vision
4th Generation
5th Generation
3rd Generation
1st Generation
2nd Generation
Multiple segmentation schemes enhanced
predictive modeling by LOB and rolled up to
CFG-Enterprise
1 customer view drives marketing strategy,
planning and execution decisions
Processes focused on balancing improved
efficiency with improved effectiveness by LOB
Focus on identifying the best channel for
reaching the customer by LOB
Focus on single channel execution by LOB
Integrated CFG customer data and single
repository across organization
Actualization of Customer Centric Vision
Customer-oriented org alignment by LOB
Learning Agenda and supporting Framework
established for CFG supported by the LOB
Pre-emptive CFG customer cross-sell retention
strategies employed
No org alignment
Optimizing
Awareness
Development
Leading
Practicing
Stages of Actualization
Vision adapted from Forrester Study on customer
centricity
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Best Practices in Customer Centric Architecture
Business Intelligence, Marketing Support
All data at the Customer Level stored here
Service Manager Layer
Enterprise Data Warehouse (RDR)
Customer Marketing System (SmartFocus) (Unica)
Best Product Option at POS
Product Needs Assessment tool, taking into
consideration Bank requirements and Customer needs
Enterprise Business Services (ODS)
Business Intelligence
SFDC
Prospect System (Equifax Credit Bureau)
Universal Loan Fulfillment
Loan fulfillment and Processing Shows total
contingent liability at the Customer level
Pipeline to distribute leads, offers, referrals
to Different platforms
SAS
CIS MCIF
New sales platform over the Existing sales
tool Creates a standard process for selling in
any channel
Customer profile And householding creator
Sales and Servicing Support
Knowledge Expands Customer Choice
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Killer Customer Applications

• Manage at the customer profitable level RNI
• Next logic product
• Channel Optimization based on customer level
  channel usage and preferences
• Offer Sequencing
• Contact Management Offer Coordination/Bundled
  Offers.
• Relationship Pricing based value exchange
• Channel Optimization Best Offer for each
  customer in the right channel.
• Full Spectrum Lending. Willingness to Lend.
• Quality Customer / Best Customer mindset
• Continuous Pre-approval at the customer level.
• Product Development

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Vision
Program Strategy and Management
Information Delivery Development And Management

• Who are the most lucrative customers?
• How do we retain and deepen those relationships?
• What is the next logical product to offer?
• What product/features do customers want?
• Data Analysis Predictive Modeling
• Segmentation Optimization

• Get the right information to the right user at
  the right time
• Solution Planning and Development
• Database Management and Maintenance
• Develop and Provide Access to Metrics
• Standardized Tool Suite
• Standard Reporting (Static Interactive)
• Data Acquisition
• User Training and Support

• Campaign Management and Execution
• Program Planning
• Vendor and Channel Management
• List Development
• Creative Development
• Reporting Standard and Ad-hoc

• What is the competition doing, and how do we
  compare?
• Market and Sizing Potential
• Market and Share Analysis
• Competitive Intelligence
• Market Research

• Test and Learn Discipline
• Short-Term Measurement
• Performance Alerts
• Forecasting and Extrapolation
• Long Term Performance Assessment and Business
  Decisioning
• Program Optimization for Gen 2, 3, etc.

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Who Is Your Customer?

• Partial views are completely wrong!
• Take off your businesss blindfolds and see your
  customer holistically.

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What Does Top Analytical Talent Need?

• More insight, less data matching and cleansing.
• No duplicative functions.

• Enterprise-wide Scope provides greater impact
  opportunities.

Feel Valued
To Have Impact

• Enterprise-wide Analytical Teams provide the best
  environment for growth.

• Multi-product and channel applications provide
  intellectual challenges.

Growth Potential
Challenge
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What Is The Impact To Your Bottom-Line?
Time spent analyzing not linking data. Typical
gains 20-30.
Analyst Efficiency
Eliminate redundancies. Gain from economies of
scale. 20-30 cost reduction.
Lower IT Cost
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Agenda
I. Why Customer Intelligence?
1. Retail Bank Data-Mining Evolution
2. Level Setting Business Intelligence and analytics
3. The Evolution in World Class Customer Mgt.
4. Best Practices in Customer Centric Architecture
5. Killer Customer Applications
6. Vision
7. Who Is Your Customer?
8. What Does Top Analytical Talent Need?
9. What Is The Impact To Your Bottom Line?
II. Steps To Deploy Customer Intelligence
1. Assess Your Organization
2. Create A Vision
3. Create A Phased Plan
4. Procure Executive Sponsorship
5. Enact The Right Governance
6. Implementation Considerations
III. Major Pitfalls
1. Top Ten Reasons Customer Intelligence Projects Fail
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Assess Your Organization
Be brutally honest. What skills do you need? What
silos do you need to break?
People Org
A GREAT offer to our valued customer
We regret to Inform you
How many systems? What is the scope of each? How
do they promote a unified view?
Systems
Customer
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Create A Vision
Strategic Customer Insight Through CRM
Consistent and Seamless Customer Experience
Analytical Environment
Channels
Source Data
Strategic Analytics
Legacy Systems MCIF
Branch
Real Time Integration
Enterprise Data Warehouse
Market Research
Competitive Intelligence
Call Center
Segmentation
Modeling/ Optimization
Reporting
Customer Data
Retain
Web
CDW
External Data
Multi-Channel Communication
E-mail
Attract
Value Proposition
List Generation
Campaign Design
DM
Contact Data
Value Driven Decision
Mobil
Analytical Tools
Market Research
Real-Time Event
Enterprise Rules
Deepen
Loyal Customer
Response
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Create A Phased Plan

• The vision is vital. It will avoid dump and
  runs.
• The phased plan is what you will sell to finance
  partners.
• Each phase should have positive ROI independent
  of all subsequent phases.
• All phases should add up to the vision.

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Procure Executive Sponsorship

• Executive support is key because it
• Accelerates analytical growth by eliminating
  diversionary paths to growth.
• Mitigates risk of reaching dead-end terminal
  states.

Source Competing on Analytics Davenport
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Enact The Right Governance
Role Description
Expected MeetingFrequency
Executive Champions

• Sets the overall vision
• Holds ultimate accountability for the success of
  the CIM Program
• Role models and communicates leaderships
  commitment

Quarterly

• Approves CIMP Program Vision, Standards and
  Guiding Principles
• Ensures integration across LOBs
• Approves Project and Initiative Prioritization
• Reviews Program Progress with Steering Committee
• Ensures program is appropriately staffed and
  funded

Use this as a starting point. But,
be flexible. Every organization is unique.
Key Executive Stakeholders
Six Times Annually

• Reviews proposals to add new projects to the
  program
• Tracks and reports on the progress
• Provides strategic direction and ensures
  alignment to the vision
• Define Enterprise Policies and LOB Requirements
• Committee Chairs from Lines of Business

Steering Council
Monthly

• Provides Overall Program Management
• Program Communications
• Initial Program Vision and End State
• Program Schedules, Milestones and Control
  Processes
• Ensures Integration with In-flight Processes and
  Projects

Program Planning Committee
Weekly

• Develops and manages the program work-plan (and
  content) working with the CIMP Steering Council
  and Planning Committee
• Program Director endorses program team solutions
  for Executive Approval
• Executes on CIMP Initiatives

Program Director, Core Team and SMEs.
Weekly
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Enact The Right Governance (Cont)
The Org shouldbe carefullycrafted toensure
thatCustomer Intelligence is trulyenterprise-wid
ein scope.
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Implementation Considerations
Senior Executive Sponsorship and Enterprise
funding
Year 1
Year 2
Year 3
Year 5
Year 4
Determine and load critical data to deliver against Business for highest priority deliverable Consumer Commercial (RBS) Greenwich Load critical data and develop prioritized application by business Begin design of CRM solution to interface with analytical environment Expand data sourcing to next immediate data by business area Develop additional application Phase 1 implementation of CRM solution Enhance existing applications based on lessons learned previous Phase Complete data sourcing Continue CRM implementation based on business priorities Continue Enhancements to existing applications Continue CRM implementation based on business priorities Continue CRM implementation based on business priorities
Benefit Accrued
Senior Executive Sponsorship and Business Area
funding
Year 1
Year 2
Year 3
Year 5
Year 4
Selected Line of Business projects identified and funded by individual LOBs Only data related to the LOB and project loaded Work based on LOBs willingness and ability to pay Next phase of LOB prioritized and funded projects. Next phase of LOB prioritized and funded projects. Next phase of LOB prioritized and funded projects. Next phase of LOB prioritized and funded projects.
Benefit Accrued
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Implementation Considerations

• The data environment has the following component
• Robust Database
• Point and Click ad-hoc query and reporting tool
• Slice and dice drill down tool (cubes)
• Demographic and mapping capability
• Campaign Management
• Analytical and predictive modeling
• Data cleansing and quality assurance
• Ability to extract, transform and load data (ETL)
• Skills to develop and support the analytical
  environment are different from the transaction
  environment
• Ability to process large amount of data quickly
• Design of the database is significantly different
  from transactional systems
• Tools are specialized for this environment
• Need the ability to quickly implement changes
• Daily (very small changes)
• Weekly (small changes)
• Monthly (medium changes)
• Quarterly (large changes)
• Satisfy all levels of knowledge worker

• Unica Affinium Campaign (v6.4)
• Targeted selection and list generation engine
  for all standard campaigns
• eMessage module supports dynamic email
  marketing (RedAlerts)

• Claritas/MapInfo
• Demographics and Mapping

• Hyperion Essbase
• Advanced Ad Hoc Query Engine (Cubes)

• Business Objects Reporting Platform
• Client and Web-based Report creation
  distribution
• Ad Hoc Query Point and Click capability

• SAS Data Mining/Modeling
• Predictive model development
• Acquisition, Retention, Attrition models for both
  Products and Relationships
• Each model may have 40 to250 input variables

• Oracle 9i Database
• Robust database with
• Real time data update
• Daily, weekly and monthly data update
• ----------------
• Able to store multi-terabytes of data

• Data Mentors DataFuse v4
• Address cleansing
• Household Definition

• Data Extraction/Transformation/Load
• Informatica PowerCenter 7.1
• Able to process multiple data loads at once
• Runs daily and Ad Hoc
• Supports file import/export and direct database
  connections to other systems

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Agenda
I. Why Customer Intelligence?
1. Retail Bank Data-Mining Evolution
2. Level Setting Business Intelligence and analytics
3. The Evolution in World Class Customer Mgt.
4. Best Practices in Customer Centric Architecture
5. Killer Customer Applications
6. Vision
7. Who Is Your Customer?
8. What Does Top Analytical Talent Need?
9. What Is The Impact To Your Bottom Line?
II. Steps To Deploy Customer Intelligence
1. Assess Your Organization
2. Create A Vision
3. Create A Phased Plan
4. Procure Executive Sponsorship
5. Enact The Right Governance
6. Implementation Considerations
III. Major Pitfalls
1. Top Ten Reasons Customer Intelligence Projects Fail
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Major Pitfalls

• Lack of Support at the Most Senior Levels of the
  Organization. No Mandate or Top Down driven
  approach to developing a Customer Intelligence
  capability and lack of understanding of how it
  drives growth or enables the customer experience.
  
• Mistaking CIM/CRM or Data Warehouse initiative
  for a Technology project and not a business
  initiative.
• Not recognizing CRM/CIM as a separate discipline
  that includes marketing, risk, ops and IT skills
  but is also broader than any one of these.
• Not selecting the Customer Intelligence head
  carefully. This is a demanding job that
  includes
• Budget oversight of such a large initiative. IT
  spend can get out of control.
• Broad expertise with technology, techniques
  (modeling, etc) and vision.

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Major Pitfalls

• When companies assume that building the
  capability internally with IT is the only option
  when several ASP or hosted solutions may provide
  a better value equation and speed to market.
• Taking a Build it and they will come or Big
  Bang approach.
• Customer Intelligence Projects need to include an
  End State/Vision.
• A Phased Implementation is always better. This
  can be done in several ways. By Subject Area, By
  Data Type, By Business Line etc.
• Decoupling the analytical areas who are the users
  from the database itself. Adoption is always
  quicker when both teams are together and
  learnings are self contained.

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Major Pitfalls

• When companies assume that building the
  capability internally with IT is the only option
  when several ASP or hosted solutions may provide
  a better value equation and speed to market.
• Taking a Build it and they will come or Big
  Bang approach.
• Customer Intelligence Projects need to include an
  End State/Vision.
• A Phased Implementation is always better. This
  can be done in several ways. By Subject Area, By
  Data Type, By Business Line etc.
• Decoupling the analytical areas who are the users
  from the database itself. Adoption is always
  quicker when both teams are together and
  learnings are self contained.
• Not defining any quick wins from the project.
• Holding Customer Intelligence to a one year ROI.
  This is a long-run investment with major
  milestones and achievements along the way, but
  each phase will only pay off in 2-3 years.

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Conclusions

• The customer centric nature of retail banking
  today is driving more complexity in management of
  data and more sophisticated business analytics
• Knowledge sharing and collaboration across
  geographies, lines of business and platforms is
  an important part of achieving this vision
• Optimization techniques can be a helpful tool in
  achieving the maximum return on customer
• Technology has increased response/activation and
  decreased the customer annoyance factor.

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Appendices
A. Biographies Tony Branda
B. Customer Centricity Case Study Relationship Indicator
C. Optimization Handles the Increasing Complexity Of Our Marketplace
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Biography and Case Studies
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Biographies

• Tony Branda
• Tony Branda leads the Business Analysis team
  within RBS National Bank. The Business Analysis
  team provides world class business insights for
  internal clients and partners through the use of
  leading edge data-mining techniques and tools.
  Tony joined RBSNB in June of 2006
• Prior to RBSNB, Tony was Senior Vice President
  and Program Director for a Division wide customer
  information strategy at Wells Fargo. Tonys
  strategic planning unit created the enterprise
  wide approach to Customer Data, Business
  Intelligence and Marketing Infrastructure. Tony
  built out a 30 million customer cross sell
  marketing platform and associated analytics as
  well as a customer experience enhancing contract
  strategy.
• Prior to Wells Fargo, Tony Branda was Senior Vice
  President and Team Leader for Consumer Real
  Estate Database Marketing as well as Enterprise
  Statistical Modeling at Bank of America.
• Tony Branda has held several key positions in
  financial services at American Express and MBNA
• Tony Branda received his B.B.A and M.B.A in
  Marketing from Pace University. He received a
  Certificate in Direct Marketing from New York
  University

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Customer Centricity Case Study Relationship
Indicator
A Better Relationship Indicates Better Asset
Quality - e.g. Segment 1 has lowest Bad Rate
across all Customer Scores

• Citizens assigns its customers a relationship
  indicator from 1 to 5 (1 being the best). For
  example
• Citizens Relationship value of 1Customer for at
  least two years, at least two accounts, and
  have at least 50,000 in total balances
• Citizens Relationship value of 3 Customer with
  at least one account, and at least 5,000 total
  balances
• Citizens Relationship value of 5 Customers with
  less than 1,000 total balances

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Optimization Handles the Increasing Complexity
Of Our Marketplace
Dynamic Predictions On-Going Recalibration
and Scalability over Brands
1 to 1
More products
Optimization, predictive models and segmentation
Many to Many
Competitiveness
Scores rank orders prospects on a single
dimension
Few to Many
Segmentation Based on customer profile data
Few to Few
Less products
One offer fits all
1 to All
Customer Complexity
More Customers Segments
Less Customers Segments