tony branda executive head of business analysis, rbs citizens na how customer intelligence...

Tony Branda

Executive Head of Business Analysis, RBS Citizens NA

How Customer Intelligence Capabilities Enable Customer

Centric Organizations

National Conference on Database Marketing

December 2008

2

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

3

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

4

Level Setting: Business Intelligence and analytics

Optimization What’s 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

Access and reporting

Co

mp

etit

ive

adva

nta

ge

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

5

The Stages of Evolution in World Class Customer Mgt. Integrated

Information Drives the Entire Customer

Lifecycle

1st Generation 2nd Generation 3rd Generation 4th Generation 5th Generation

Focus on single channel execution by LOB

Focus on identifying the best channel for reaching the customer by LOB

No org alignment

Awareness LeadingDevelopment Practicing Optimizing

Processes focused on balancing improved efficiency with improved effectiveness by LOB

Multiple segmentation schemes & enhanced predictive modeling by LOB and rolled up to CFG-Enterprise

Integrated CFG customer data and single repository across organization

Pre-emptive CFG customer cross-sell retention strategies employed

1 customer view drives marketing strategy, planning and execution decisions

Ac

tua

liza

tio

n o

f C

us

tom

er

Ce

ntr

ic V

isio

n

Stages of Actualization

Customer-oriented org alignment by LOB

Learning Agenda and supporting Framework established for CFG supported by the LOB

To facilitate the evolution from a single product/ channel focus to an end-to-end customer-centric vision

Vision adapted from Forrester Study on customer centricity

6

Best Practices in Customer Centric Architecture

EnterpriseData Warehouse

(RDR)

CISMCIF

Customer Marketing System

(SmartFocus)(Unica)

ProspectSystem(Equifax

Credit Bureau)

Bu

sin

ess

Inte

llig

ence

EnterpriseBusiness Services

(ODS)

Best ProductOption at POS

Universal Loan

Fulfillment

SFDC

All data at the CustomerLevel stored here

Customer profileAnd householdingcreator

Pipelineto distributeleads, offers, referrals toDifferentplatforms

Business Intelligence, Marketing Support

New sales platform over the Existing sales tool;Creates a standard process for selling in any channel

ServiceManager

Layer

Sales and Servicing Support

Product NeedsAssessment tool, taking into considerationBank requirements andCustomer needs

Loan fulfillment and Processing;Shows total contingentliability at the Customer level

SAS

Knowledge Expands Customer Choice

7

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

8

Vision

InformationDelivery

DevelopmentAnd

Management

Market and Competitive Intelligence

Exploration and Discovery

Execution

Performance Assessment

• 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

• What is the competition doing, and how do we compare?

• Market and Sizing Potential• Market and Share Analysis• Competitive Intelligence• Market Research

• 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

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

• 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.

Program Strategy and ManagementInformation Delivery Development

And Management

9

Who Is Your Customer?

Partial views are completely wrong!

Take off your business’s blindfolds and see your customer holistically.

10

What Does Top Analytical Talent Need?

More insight, less data matching and cleansing.

No duplicative functions.

Enterprise-wide Scope provides greater impact opportunities.

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

Multi-product and channel applications provide intellectual challenges.

Feel

Valued

Growth

Potential

To Have

Impact

Challenge

11

What Is The Impact To Your Bottom-Line?

Time spent analyzing not linking data.

Typical gains 20-30%.

Eliminate redundancies.

Gain from economies of scale.

20-30% cost reduction.

Analyst

Efficiency

Lower

IT Cost

Silo 1

Silo 2

Silo 3

Silo 4

ODS

DWDis

para

te S

ilos

Ent

erpr

ise

Sol

utio

n

12

Agenda







6. Vision






2. Create A Vision





III. Major Pitfalls


13

Assess Your Organization

HELOC ProspectingCredit CardUnderwriting

A GREAT offer

to our valued

customer

We regret to

Inform you…

Mortgage AnalystBranch Rep

Customer

Be brutally honest.

What skills do you need?

What silos do you need to break?

How many systems?

What is the scope of each?

How do they promote a unified view?

People

& Org

Systems

14

Create A Vision

Strategic Customer Insight Through CRMConsistent and Seamless

Customer Experience

Legacy Systems

MCIF

Customer Data

External Data

Contact Data

Market Research

Source Data

Enterprise

Data

Warehouse

Analytical

Environment

Branch

Call Center

Web

E-mail

DM

Mobil

Real T

ime In

tegratio

n

Channels…

Retain

Attract

Deepen

Loyal Customer

$$$

SegmentationModeling/

OptimizationReporting

Strategic Analytics

Value

Proposition

List

Generation

Campaign

Design

Multi-Channel Communication

Real-Time

EventEnterprise Rules

Value Driven Decision

Response

Market

Research

Competitive

Intelligence

Analytical

Tools

CDW

15

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.

16

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.

Stage1

Organization has some data and management interest in Analytics

Analytically Impaired

Stage3

Executive support:Full steam-ahead path

Managerial support:Prove-it path

Functional management builds analytics momentum and executive interest through basic applications

LocalizedAnalytics

Executives commit to analytics by aligning resources and setting a

timetable to build a broad analytical capability

Analytical Aspirations

Terminal stage: some companies’ analytics efforts never receive

executive support and stall here as a result

Enterprise-wide analytics capability under development; top executives

view analytic capability as a corporate priority

Analytical Companies

Organizations routinely reaping benefits of its enterprise-wide

analytics capability and focusing on continuous analytical renewal

Analytical Competitors

Human Capital:Low skill,

knowledge-allergic culture

takes pride in “gut instinct.”

Technology:No or poor data

quality. Unintegrated

systems lead to multiple truths.

Stage 2

Human Capital:Pockets of

isolated analytics. Early successes

stir attention.

Technology:Isolated data;

Some important data is missing.

Human Capital:Executive support

for fact-based culture may meet

considerable resistance.

Technology:Proliferation of

data warehouses and BI tools.

Stage 4

Human Capital:Skills exist, but

often not aligned to the right level

or role.

Technology:High quality data – have plan for enterprise-wide

BI strategy.

Stage 5

Human Capital:Highly skilled,

leveraged workforce. Fact-

based culture.

Technology:Enterprise-wide BI architecture

largely implemented.

Source: Competing on Analytics: Davenport

17

Enact The Right GovernanceRole Description

Executive ChampionsExecutive Champions

Key ExecutiveStakeholders

Key ExecutiveStakeholders

SteeringCouncilSteeringCouncil

Program Planning

Committee

Program Planning

Committee

Program Director, Core Team and

SME’s.

Program Director, Core Team and

SME’s.

Sets the overall vision Holds ultimate accountability for the success of the CIM

Program Role models and communicates leadership’s commitment

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

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

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

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

Quarterly

Expected MeetingFrequency

Six Times Annually

Weekly

Weekly

Monthly

Use this as a

starting point.

But, be

flexible.

Every organization

is unique.

18

Enact The Right Governance (Cont’)

Cards

Lending

Business Banking

CFO

Head of Strategic Planning

CKO - EVP

Analytical Strategy Liaisons (Client Managers)

Cards SVP

Lending SVP

Business Banking

SVP

Analytical COE’s Info Management COE’s

Modeling and Optimization

Campaign and Program

Measurement

Strategic Analysis

Database Development

Information Delivery / Self Service COE

Campaign Management

CIO CTO

LOB’sEDM Steering

Tec

hno

logy

an

d M

anuf

actu

ring

The Org shouldbe carefullycrafted toensure thatCustomer Intelligence is trulyenterprise-widein scope.

19

Implementation Considerations:

• 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



Senior Executive Sponsorship and Enterprise funding

Year 1 Year 2 Year 3

Year 1 Year 2 Year 3

Year 5Year 4

Year 5Year 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.

Senior Executive Sponsorship and Business Area funding

Benefit Accrued

Benefit Accrued

20

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

OracleDatabase

Data Mentors DataFuse v4•Address cleansing•Household Definition

Oracle 9i DatabaseRobust database with Real time data updateDaily, weekly and monthly data update

----------------Able to store multi-terabytes of data

Data Extraction/Transformation/LoadInformatica 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

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

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

Hyperion Essbase•Advanced Ad Hoc Query Engine (Cubes)

Claritas/MapInfo•Demographics and Mapping

SAS Data Mining/Modeling•Predictive model development•Acquisition, Retention, Attrition models for both Products and Relationships

•Each model may have 40 to250+ input variables

21

Agenda







6. Vision






2. Create A Vision





III. Major Pitfalls


22

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.

23

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 learning’s are self contained.

24

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 learning’s 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.

25

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.

26

Appendices

A. Biographies: Tony Branda

B. Customer Centricity Case Study: Relationship Indicator

C. Optimization Handles the Increasing Complexity Of Our Marketplace

27

Biography andCase Studies

28

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. Tony’s 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

29

Customer Centricity Case Study: Relationship Indicator

Citizens assigns its customers a relationship indicator from 1 to 5 (1 being the best). For example:

Citizens Relationship value of 1:Customer 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

Decision Power ChannelCumulative Bad Rate

By Custom Score 06 and Relationship Segment

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

Segment 1

Segment 2

Segment 3

Segment 4

Segment 5

Combined

Current cut off

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

30

Optimization Handles the Increasing Complexity Of Our Marketplace

SegmentationBased on customer

profile data

SegmentationBased on customer

profile data

Optimization, predictive models and segmentation

Optimization, predictive models and segmentation

1 to All

Few to Few

Few to Many

Many to Many

1 to 1

More Customers Segments

Less Customers Segments

Com

peti

tiven

ess

Customer Complexity

One offer fits allOne offer fits all

“Scores” rank orders prospects on a single

dimension

“Scores” rank orders prospects on a single

dimension

Dynamic Predictions On-Going Recalibration andScalability over Brands

Dynamic Predictions On-Going Recalibration andScalability over Brands

Less p

rod

ucts

More

pro

du

cts

tony branda executive head of business analysis, rbs citizens na how customer intelligence...

Documents

customer level

customer value

customer relationships

customer centric architecture

world class customer

killer customer applications

business intelligence

data analytics