using information to build customer value

Using Information to Build Customer Value

Randall B. Grossman([email protected])

October 28, 1998

Boston College

Fleet’s Investment in Information-Based Marketing

- 2 -Customer Data Management & Analysis

Information-Based Marketing at Fleet Boston CollegeOctober 28, 1998

Agenda

• Context: Fleet and the Evolution of Marketing in Banking

• Deciding on a Strategic Investment in Technology: The Business Case

• Fleet’s Investment in Marketing Technology and Skills

• How We are Using the New Capability: Fleet’s Retail Strategy and its Use of Information-Base Marketing

Context



Fleet Financial Group, headquartered in Boston, is:• A $102 billion diversified financial services company listed on

the New York Stock Exchange (NYSE:FLT).

• Fleet's lines of business include: – Retail banking– Consumer Lending– Small business banking– Credit Card lending– Student loans– Mortgage banking– Private Banking and Trust services– Investment management / Mutual Funds– Discount brokerage– Insurance brokerage

A Little Bit About Fleet Today

– Commercial banking– Corporate finance– Government banking– Commercial real estate

finance– Asset based lending– Specialized lending

(sports lending, high technology, etc.)



The Retail Business at Fleet: Scope and Scale

• 6.2 million customers in traditional geographic footprint

– New England, New York, New Jersey, and Florida.

• 7.0 million additional customers in recently acquired national consumer businesses

• 400,000 small business customers

• 20% market share of New England deposits

• 26% market share of New England small business customers

• 42% of Fleet Financial Group’s net income.



Branches :1200 outlets in 8 states

ATMs : 2400 machines,including 900 at remote sites

Telephone :80 million callsper year

Online Services :85,000 activecustomers

The Retail Business at Fleet:Scope and Scale



The Challenge

By late 1995, Fleet had successfully grown to become the Northeast’s largest retail bank (outside NYC):• The question on analysts’ lips:

“How will Fleet leverage this presence to build revenue?”

At the same time, research was teaching us three lessons:• Nearly half of our customers were unprofitable; almost 20%

are very unprofitable.

• Balances are only loosely correlated with profitability.

• Demographics are even more poorly correlated with profitability.

Yet, our marketing efforts remained product-oriented and focused on response rates and volume generated, not customer profitability

Yet, our marketing efforts remained product-oriented and focused on response rates and volume generated, not customer profitability



Fleet’s Response to this Challenge Was Threefold

• Invest in developing alternative channels, to meet the evolving needs of the customer base:

– PC and Web banking.– Telephone banking.

• Restructure Retail Banking to integrate the channels in one organization:

– Manage to a multi-channel distribution model optimized around customer needs and behavior.

• Invest in information-based marketing to provide the information to manage the customer base profitably.

– Create the ability to look at an integrated view of the customer . . .– . . . With sufficient “granularity” to permit the data to be cut any way

analytical needs require.– Build the skills and staff to use the new capability.

The third element is the one we will discuss today.The third element is the one we will discuss today.



Banks Have Been Undergoing Two Decades of Change

Regulated Era• All banks sell the same

thing: bank products.

• Glass-Steagal strictly interpreted.

Product Era• Monoline specialists

emerge:– Credit cards.– Mutual funds.

• Banks introduce investment products:– Mutual funds and

discount brokerage.

• Increasing, marketing is organized around products:– The product

management model.

– Product features proliferate

Needs-Based Era• Products and product

bundles become tuned to customer needs:– Features are fine-tuned to

match usage and customer needs.

– Single-product and bundled product offerings targeted to customer preferences.

– Channel and product become intertwined.

• Banks become true financial services providers:– Full range of products

available.

– Offers tuned to customer needs.

1980 1995



Pricing Is Emerging as a Critical Tool for Profitability Management

Regulated Era• Rates are regulated;

with minor exceptions there is little difference among institutions.

• “3-6-3”

Rate Competition Era• Rate competition

emerges:– Rates become the

featured item.– Product profitability

erodes as interest margin is reduced.

• Fees replace interest margin as the focus for revenue:– Customer and consumer

activists’ suspicions piqued.

– Monolines exploit this in credit card.

• Volumes remain the focus of marketing.

Price Differentiation Era• Product and customer

profitability puts spotlight on pricing:– Price elasticity becomes a

key variable– Segment-based pricing

emerges.– Product features, channel

availability, and pricing jointly determined.

• Relationship pricing becomes quantitatively based:– Lifetime profitability

measurement.– Profit potential versus

actual.

Regulated Products Product Management Needs-Based Products1980 1995



The Channels Through Which We Sell and Service Customers Have Expanded

Branch Banking Era• Branches are the sole

sales and service vehicle . . .

• . . . Except for commercial customers, who are all served through a relationship manager channel.

Cost Control Era• Alternative delivery

introduced to reduce the cost of service:– ATMs.– Telephone customer

service.

• Small business segregated as a separate business, driven through the branches:– Relationship

management approach becomes too costly.

• Meanwhile, monoline credit card companies and mutual funds introduce direct mail and telephone sales . . .

Multi-Channel Era• Customer can choose from

multiple channels for sales or service:– Telephone sales.– PC Banking– Internet banking– Branches (which become

increasingly sales oriented).

– ATMs.– Direct mail / direct

response.

• Channel profitability drives positioning:– Channels oriented toward

segments they serve profitably.

Regulated ProductsRegulated Pricing

Product ManagementPrice Competition

Needs-Based ProductsPrice Differentiation1980 1995



Finally, How Customers Expect Us to Speak to Them Is Undergoing a Steady Change

Mass Advertising Era• Mass media advertising

the exclusive vehicle for communication:– Newspapers– Television– Radio

• Same message to everyone:– “Traditional bank

image.”– Goal is to have us in

mind when you choose to bank.

Pro-Active Era• Direct marketing (mail)

introduced as a means to reach out directly to customers:– Monoline credit card

companies the most aggressive.

• Segmentation used as a means to vary the message:– Demographics (age,

income, wealth, home ownership, etc.) used.

– Segments targeted with product offers typically used by those demographics.

Segment-of-One Era• Communications refined to

be individual-specific:– Usage and behavioral-

based segmentation overlaid on demographics

– Customer-specific information, derived from interactions with the customer, drives how we speak to him/her.

• Branding increases in importance as means of defining the company to the customer.– Provides the context for

one-to-one messages.

Regulated ProductsRegulated PricingBranch Banking

Product ManagementPrice Competition

Cost Control

Needs-Based ProductsPrice Differentiation

Multi-Channel1980 1995



These Changes Have Made Financial Services Marketing A Substantially More Difficult Task

Segment-of-One Marketing

Multi-Channel Sales and Service

Needs-Based Product Design

Profitability Management



The Key to Information-Based Marketing is to Create a Cycle of Learning

Design Tests,Hypotheses

Execute throughappropriate channels

Analyze ResponseAnd Subsequent Usage

SelectAnd Code

Lists

Customize OffersBy Cell / Segment

IdentifyLikely

Targets

ExistingCustomers

ExternalLists

Model Behavior a

nd

Profitabilit

y



This Learning Cycle Provides the Basis for Improved Profitability

Over repeated promotions, we can develop the knowledge base to understand:• The right product . . .

– What service bundles represent the most effective way to meet the needs of our customers.

• . . . Offered through the right channel:– How is it that a customer likes to purchase products from us (branch, on-line,

phone, relationship manager, etc.)– And what channels does the customer prefer for servicing?

• . . . At the right pricing:– What is the tradeoff between rate / fees and response rate (or attrition rate)

that maximizes profitability over the lifetime of the customer?

• . . . And with the right promotional support:– Do teaser rates work, and how well?– What messages perform best, with what frequency?

• All of this optimized by customer segment and -- where possible -- by individual customer:

– Using segmentation models and predictive models that allow us to differentiate offers by customer group.



Total NPVNPV per Mktg $

Promotions Over the Past Two Years Illustrate the Power of Information-Based Marketing

Example Campaign: NPV Impact

($2MM)

($1MM)

$0

$1MM

$2MM

$3MM

$4MM

$5MM

$6MM

$7MM

$8MM

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Ma

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To

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($2.00)

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

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Types of analysis typically undertaken:• Customer / prospect segmentation:

– Identity behavioral, psychographic, demographic, and other attributes that predict channel usage, product / service needs, profitability.

– Incorporate total customer relationship and knowledge of customer goals / objectives.– Overlay customer profitability measures to produce segment profitability.

• Customer behavior modeling, e.g.:– Next purchase– Attrition– Channel usage.

• Marketing lifecycle models:– Acquisition / Cross-sell / Retention

• Response analysis:– Channel attribute utility– Product / service attribute utility

• Lifetime value models:– Net present value of a customer today.– Likely potential value of a customer.

• Pricing management:– Dynamic pricing trade-offs between spread

and sales / retention.

Fleet Employs Analytical Modeling to Tease Out Insights from Customer Behavior

Analytics employs tools such as:

• Statistical analysis• Linear programming and

stochastic modeling• Neural networks• Genetic algorithms• Data visualization• Geographic mapping• Campaign tracking

Analytics employs tools such as:

• Statistical analysis• Linear programming and

stochastic modeling• Neural networks• Genetic algorithms• Data visualization• Geographic mapping• Campaign tracking



Customer Behavioral Analysis Plays a Role in Every Area of Marketing Decision-Making

• Identification of strategic opportunities:– Understanding which customers are profitable, and which are not -- and why.– Modeling potential customer profitability.– Understanding how customers use our distribution channels, and where

opportunities exist for channel usage migration.

• Tactical decision-making:– Assessing what the right resource expenditure is for a given customer

segment or product.– Developing pricing tactics to maximize portfolio profitability.– Tracking run-off and implementing intervention programs where appropriate.– Modeling attrition and developing early warning systems

• Evaluation of program success (and not only direct marketing programs!):

– Tracking response by promotion.– Evaluating the incremental effect of different promotional methods in a

campaign:» Advertising expenditures, by type.» Branch merchandising.» Branch training and sales efforts.» Direct mail.» Telephone solicitation.



Example: Analysis of Customer Data Highlights Product Flaws

0

10

20

30

4050

60

70

80

90100

92 93 94 95 96 97 98 99 100Cumulative Percent of Households

Cu

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lati

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of

Ca

lls

Analysis such as this is helping us pinpoint where we need to set limits in the redesign of our deposit products.

Analysis such as this is helping us pinpoint where we need to set limits in the redesign of our deposit products.

Calls taken by live agents in Fleet’s call center:



As Important as Modeling Is the Availability of Effective Management Reporting

Example: Fleet’s CD Portfolio Manager tracks changes in the portfolio composition and their sources through trend reporting:

Apr-95 May-95 Jun-95 Jul-95 Aug-95 Sep-95 Oct-95 Nov-95 Dec-95Regular Time CDsBeginning Balance 2,994,234 2,957,845 2,885,517 3,024,460 3,032,911 3,012,898 2,976,444 3,023,057 3,009,528Dollars Maturing

$$$ Maturing 342,275 274,871 206,604 195,077 168,832 249,400 284,354 170,072 158,827Net Rollovers 140,611 141,997 126,804 124,553 109,838 143,600 142,583 112,758 104,466Net Transfers 98,295 36,656 18,645 19,041 14,477 20,023 66,244 19,449 17,434Retention Percentage

ClosedAt maturity (97,264) (90,784) (57,431) (46,488) (42,765) (80,958) (71,198) (35,717) (34,748)Other** (18,669) (31,181) (13,621) (11,474) (12,965) 16,571 (18,907) (14,066) (15,924)

Sub-total, closed (115,933) (121,965) (71,052) (57,962) (55,730) (64,387) (90,105) (49,783) (50,672)Sales

Baseline 76,733 45,746 70,488 58,923 28,512 22,985 133,775 29,443 30,148Campaign 0 0 0 0 0 0 0 0 0

Sub-total, Sales 76,733 45,746 70,488 58,923 28,512 22,985 133,775 29,443 30,148Other Transfers 3,012 2,494 947 1,229 706 1,320 2,173 925 1,059Other Activity 5,904 6,831 142,284 11,256 8,251 8,447 5,099 8,034 5,888Ending Balance 2,957,845 2,885,517 3,024,460 3,032,911 3,012,898 2,976,444 3,023,057 3,009,528 2,993,772

* Includes accounts counted as rollovers in previous month that closed during the grace period in the following month (overlaps).** Difference between transfers in and transfers out; due to partial redemptions or additions in the process of transfer, or bus. line transfers.

All figures in $ thousands

Deciding on a Strategic Investment in Technology: The Business Case



The Fleet Data Warehouse Project

12/95 12/9812/96 12/97

• Fleet completes merger with Shawmut Bank, announces acquisition of NatWest US.

• CDMA organization created to spearhead information-based marketing and customer analysis

• Fleet completes merger with Shawmut Bank, announces acquisition of NatWest US.

• CDMA organization created to spearhead information-based marketing and customer analysis

• Scoping phase completed.• Scoping phase completed.

• Integration team selected; project begins.

• Integration team selected; project begins.

• Prototype warehouse in operation.

• Prototype warehouse in operation.

• Initial load completed.• Initial load completed.

• Warehouse and datamarts in production.

• Warehouse and datamarts in production.



The Project Was Championed by Two Senior Executives

The project was championed by two senior executives:• Gunnar Overstrom -- Vice Chairman

• Bob Hedges -- Managing Director, Retail Banking

At the same time, the industry was abuzz with the power of information-based marketing:• The credit card “monolines” had blazed the trail -- and been

rewarded with high multiples.

• Influential analysts -- lead among them, Tom Brown -- were writing favorably of the institutions that were embracing information based strategies.



Building a Strong Constituency Was Critical

Principles we followed:• Get the right number of people involved:

– Not too many, but not too few either.– Representing a reasonably broad set of interests:

» Business lines» Technology» Marketing

• Make it the right level:– People who can make a contribution to the discussion.– Nominated by senior managers.

• Listen to what they have to say:– They have to see their views reflected in the result.

• Take the time to do it right.

The #1 reason that data warehouse efforts fail:A visionary built it, and no one used it.

The #1 reason that data warehouse efforts fail:A visionary built it, and no one used it.



Fleet Conducted a Six-Month “Scoping Phase”

UnderstandBusiness

Needs

UnderstandBusiness

Needs

3/11/96

DefineNext

GenerationImplications

DefineNext

GenerationImplications

4/3/96(3/31/96)

SetPriori-

ties

SetPriori-

ties

4/24/96(4/5/96)

DefineFunctional

Requirements

DefineFunctional

Requirements

4/24/96(4/22/96) Finalize

andDistribut

eRFP

Finalizeand

Distribute

RFP

5/17/96(5/10/96)

UnderstandCurrent

Environment

UnderstandCurrent

Environment

RecommendTarget

ManagementApproach

RecommendTarget

ManagementApproach

RecommendTarget

WarehouseArchitecture

and Tools

RecommendTarget

WarehouseArchitecture

and Tools

2/7/96 4/30/96(4/15/96)

5/10/96(4/30/96)



The Scoping Phase Process

Functional Requirement




Business Objective

Business Objective

“Next Generation”Implication




Scope/Timing Analysis & RecommendationsScope/Timing Analysis & Recommendations

• What are the ambitions of the business given marketplace trends, business goals, competitor positioning, etc.?:

– Our starting point, which was reviewed in the first Steering Group meeting

• In broad terms, what data and capabilities will be required (e.g., analytical and reporting tools) that will support and enable the business lines in achieving objectives? e.g.:

- Support for managing campaign- Targeting of prospective customers based

on profit potential and likelihood of purchase

• Which “Next Generation” implications should we address, and when?:

– Tough decisions will be required that consider business priorities, technical feasibility, etc.

• What are the specific capabilities that must be built?



Once There Was Agreement on Scope, We Built the Business Case

Justification is only part of the reason for a business case. The real value comes from:• Showing what people are going to get.

– Making it concrete: “Here is what we will do with the information once we have it.”

• Putting a stake in the ground, to return to later.– Putting businesses on the hook to get the benefits that were claimed

when funding was requested.

• Making you think about what you need to be successful.– It is more than just and investment in technology!– Using the technology means:

» Hiring people with new skills.» Creating new management processes.» Changing aspects of the culture.



So, How Do You Build A Business Case?

Start by asking:• “What would I do differently if I had better information?”

• “What decisions would I make, and what would be the result of making them?”

Then, figure out what that is worth:• Will it make you more efficient?

– In what areas, and how much?

• Will it help retain profitable customers?– How much of a lift will it provide? How will that be accomplished?

• Will it improve our ability to sell (profitably!)?– To what extent? How much?

• Can it improve how we manage our customers?– More precise pricing, better product design, better engineered service, etc.

This has to be driven by a business manager, because the business, ultimately, must step up to delivering the benefit.

This has to be driven by a business manager, because the business, ultimately, must step up to delivering the benefit.

Take the time to do it right!Take the time to do it right!



For Fleet, the Payoff Comes From Success in Four Areas

• Target marketing efficiency improvement, as a result of:– Disciplined response analysis and iterative application of learning– Segmentation-based direct mail and sales efforts– Targeted list management activities– Data-based sales management and analysis

• Pro-active identification of profitable cross-selling opportunities:– Event-triggered sales efforts– Next product to sell modeling– “Segment-of-one” sales and service

• Customer loyalty and other retention programs:– Behavior analysis and attrition modeling– Cumulative product usage-based pricing and rewards programs

• Management of customer profitability):– Driving product design off of models of customer usage and preferences– Transaction-intensity-driven pricing– Margin optimization through segmented price elasticity– Channel configuration analysis and optimization



Fleet has Targeted Benefits in Each Area of Payback

• Year five benefits expected, by category:– Target marketing efficiency $ 3.7 million– Cross-selling $ 20.6 million– Retention $ 19.1 million– Customer profitability management / pricing $ 72.0 million

• Most opportunities relate to increasing revenues . . .– Achieving better margins through response analysis.– Selling to -- and retaining -- the most profitable customers.

• . . . Though some involve expense savings:– More efficient use of resources through increasingly effective

targeting.– Elimination of pricing that encourages excessive transaction use by

low value customers, resulting in a migration to lower cost channels and/or a reduction in use.



The Analysis Drew on Several Sources -- Most Already in Existence

• Several data sources were employed in developing the analysis of opportunities:

– A random sample of 58,000 households from our existing customer database, analyzed by First Manhattan Consulting Group to produce a breakdown of customer and account profitability.

– Analysis of the price elasticity of demand for interest checking, savings, money market, and CD deposits. Conducted using Shawmut Bank data for the period 1993-1995.

– Current balance sheet and P&L statements for consumer banking, to provide a baseline.

– 1997 Plan sales targets and direct marketing expenses.– Results shared by consultants from consulting efforts elsewhere in the

banking industry.– Results shared by database marketing colleagues at industry

conferences, as well as information obtained from Fleet staff hired from other institutions.



A Cross-Sell Example: What We Would Do Differently If We Had the Data Warehouse

Where the benefit will come from: • Identify which customers are most likely to buy . . . and which are the

most profitable products to suggest:– Develop predictive models to identify likely cross-sell prospects, and likely

post-sales usage:» The inputs: past promotional response data, existing customer usage data (12 to 36

months history).» Statistical and neural network techniques can be used to:

• Predict the likelihood of interest in a particular product, and identify “trigger events” that indicate a new need to be filled (logistic regression models, CHAID analysis, and neural network data mining are all techniques we would use to do this).

• Predict likely usage patterns for a product, including channel preference, transaction volume, balances likely to be held, likely life before attrition, etc.. (Multivariate regression and cluster analysis models that predict usage profiles).

– Match this with product profitability:» Patterns of usage can be matched with product profitability algorithms to indicate

likely profitability of alternative cross-sell options.– Provide the SSR with information to suggest more profitable rather than less

profitable products -- and products the customer is likely to buy:» Warehouse feeds can be created to both telephone and platform representatives to

provide indicators for cross-selling.» Direct mail campaigns -- with options for mail, branch, or telephone response -- can

stimulate interest and traffic from customers most likely to be profitable sales and avoid those least likely to be profitable.



Many of our low-profit customers are high-profit somewhere else. For 2% of our customers lying in the top half of profitability deciles, increase the number of products purchased per household by one:

Sources

Data source: CDMA database, with customer profitability measures computed by FMCG. The measure is profit before tax. Expenses are fully loaded and transaction based. Customer base is the retail banking footprint, excluding NatWest (4.43 million households).

Assumptions: Market Planning studies indicate that we have captured less than 20% of our customers’ full financial services potential, a figure that is consistent with industry studies. Above benefit assumes that we target customers in the top half of our current profitability distribution, based on predictive models of which customers are likely to have unmet needs and/or existing financial services business at other institutions, achieving a 2% success rate in increasing profitable sales by 1 product per household. Benefit is assumed to be incremental of selling expenses, which are estimated at $150 per account sold.

Example: Benefits Calculation

Impact ($ millions)Increase Share of Wallet with Top 50% of Household Base 1997 1998 1999 2000 2001

Balance Sheet Impact (Year-End)Loans $0.0 $8.2 $32.4 $64.2 $94.0Deposits 0.0 45.3 172.2 341.6 499.9

P&L Impact Net Interest Income $0.0 $1.2 $4.5 $8.9 $12.9Fees 0.0 0.2 0.9 1.9 2.7Expenses 0.0 0.6 2.3 4.4 6.3

Net Contribution before tax $0.0 $0.8 $3.1 $6.4 $9.3



Of Course, We Can’t Forget the Expense Side of the Equation

• The cost of building it:– Hardware.– Software.– Integration expense (a.k.a.,

“consultants”)– Internal technical staff.– Business staff.

• The cost of operating it:– Hardware, software

maintenance.– Growth in capacity (additional

investment).– Technical staff (you’ll need

more than a normal system requires; there is constant tuning).

• The cost of using it:– Business lines will have to add

staff with new skills.

One thing that worked well for Fleet: having selected the

integration team, we worked for 2 months on a time-and-

materials letter of intent, while the final workplan and budget

were determined.

One thing that worked well for Fleet: having selected the

integration team, we worked for 2 months on a time-and-

materials letter of intent, while the final workplan and budget

were determined.

• Budget two phases. Put 90% of the value in Phase I (and less than 90% of the cost). Remember, Phase II will never happen.

• Make the consulting contract fixed price.

• Budget two phases. Put 90% of the value in Phase I (and less than 90% of the cost). Remember, Phase II will never happen.

• Make the consulting contract fixed price.



Fleet’s Budget Was $37.7 Million in Capital, With an Incremental Run Rate of $12.4 Million

(All figures are in thousands)

Phase IICapital Expense Phase I Ramp-up New Dev. Total

Hardware 7,813$ 2,409$ 1,150$ 11,372$ Software 6,510 1,407 822 8,739Integration 12,528 250 3,909 16,687Other 749 193 0 942TOTAL 27,600$ 4,259$ 5,881$ 37,740$

Annual Operating ExpenseDepreciation 5,520$ 852$ 1,176$ 7,548$ Hardware maintenance 871 282 134 1,287Software maintenance 959 224 131 1,314Systems staff [1] 1,575 840 2,415DPOT staff [2] 2,000 200 2,200Other 763 208 971Expense elimination (1,930) (1,400) (3,330)TOTAL 9,758$ 1,358$ 1,289$ 12,405$

Notes:

1 15 FTE in Phase I; another 8 FTE in Phase II. 17 of these would be maintenance.

2 20 FTE in Phase I; another 2 FTE in Phase II. All are ongoing expense.

Budget approved by the Board of

Directors, October 16, 1996.



The Payback for Fleet is Significant: An IRR of 138%; NPV of $90 Million

Impact ($ millions)Total Phase I Contribution 1997 1998 1999 2000 2001

Balance Sheet ImpactLoans $0.0 $14.7 $55.9 $111.4 $164.3Deposits 0.0 97.3 383.8 791.3 1,199.7Fixed Assets (Phase I investment) 4.5 13.6 10.6 7.7 4.7

P&L ImpactNet Interest Income ($0.5) $5.9 $28.0 $49.8 $65.8Fees 0.0 1.7 2.1 3.6 4.6Expenses Benefits-case related 0.0 (3.8

)(24.8) (37.6) (43.0)

Next Generation operating expense 11.7 9.2 9.5 10.0 10.5 CDMA staff (incremental over 1996 levels) 2.1 5.0 5.5 6.1 6.4

IRR: 138%NPV (@18.5%): $90 million

Net Contribution before tax ($14.3) ($2.8) $39.9 $74.9 $96.5

Additional staff skilled in using the technology (database marketing, statistical analysts, DSS analysts, data content analysts): 60 FTE

Hardware & software maintenance, technical staff (37 FTE), depreciation, less the cost of systems eliminated by the data warehouse.

Fleet’s Investment in Marketing Technology and Skills



There Are Four Principal Components to Fleet’s Data Warehouse

• The data

• The database environment

• Marketing promotion software

• Analytical tools

Architecturally, the Next Generation will be a marketing and sales applicationimplemented in a data warehouse environment -- which ensures that the database

can be extended in the future, as needed, to encompass other functions.

Architecturally, the Next Generation will be a marketing and sales applicationimplemented in a data warehouse environment -- which ensures that the database

can be extended in the future, as needed, to encompass other functions.

• Over 1 terabyte of data

• Sun hardware with Informix DBMS

• Exchange Applications’ ValEx software

• Open architecture supporting multiple software tools



What Data Are in the Warehouse?Customer information (consumer and business):• Demographics

• Profitability (using EAS factors as a feed)

• Household and business relationship linkages.

Account (Product & Service) information:• Deposits

• Loans (consumer & commercial, including origination data)

• Mortgages

• Credit cards

• Mutual funds

• Annuities

• Trust & Private Banking

• Cash management

• AM Fax

• Interpay (payroll services)

36 months of account history and 12 months of transaction history will be maintained in the warehouse.

36 months of account history and 12 months of transaction history will be maintained in the warehouse.

Channel usage information:• Branch transactions (by location)

• ATM transactions (our customers and other banks’ customers)

• Telephone transactions (VRU, live agent) -- by type

• PC banking transaction

• ACH transactions



The Technical Design Is Intended to Support a Wide Range of Users

Analytics Compute Server• Statistical analysis• Neural networks• Data Discovery• Geodemographic analysis• Ad hoc query and analysis

Marketing Data Mart• Summarized, Pre-formatted data• Promotion Design, Tracking and

Analysis• “Point & Click, Drill-Down”

analysis

Data Marts

Servers: Sun 6000

DBMS: Informix Online

Analytics: SAS, Cognos Powerplay, Impromptu, other analytic tools

Campaign Mgmt: ValEx

ManagementReporting Server

• On-Demand Parameterized Reports

Weekly feedsAd hoc extracts, as needed.

Business Analysts & ManagersMarketing Analysts

Client / server

Power Users

Workstations

Workstations: PCs

O/S: Windows NT, 95 & 3.1

Access: LAN / WAN, secure dial-up

Browser-based(client/server for list selection)

Browser-based

SOURCE DATA(Internal & External)

Staging ServerCleansing, Transformation, Merge/Purge, Householding

Data Warehouse

Server: Clustered Sun 6000s

DBMS: Informix XPS

Daily, weekly, and monthly loads

Warehouse Server Cluster• Fully normalized data, maintained with full detail

• 36 months account level history• 13 months transaction level history



Exchange Applications’ ValEx Software Provides the Tools for Marketing Automation

Design Tests,Hypotheses

Execute throughappropriate channels

Analyze ResponseAnd Subsequent Usage

SelectAnd Code

Lists

Customize OffersBy Cell / Segment

IdentifyLikely

Targets

ExistingCustomers

ExternalLists

Model Behavior a

nd

Profitabilit

y

• Tools for analysis

• Closed Loop Response Capture

• Cleansing, Matching, and Suppressions

• Point & Click design tools

• Seamless linkage of modeling and targeting



Two Types of Analytical and Reporting Environments Are Available

For advanced (“power”) users:• Statistical analysis tools

• Access to marketing datamart and to the data warehouse for:

– Exploratory queries and analysis.

– Extracts of data subsets to Analytics computing server for further analysis.

• Data mining tools:– Neural network software.– Data discovery software.– Data visualization

• Geographic analysis software– Geographic mapping with

linkage to the database.– Geodemographics.

For most business line and marketing analysts:• A marketing datamart optimized

for management analysis:– Both summarized and detailed data

sources.

• Query tools configured to permit:– Ad hoc query.– Extracts of data into desktop tools

such as Lotus 1-2-3 and Excel.

• User-configured reporting:– Menu-driven, permitting users to

determine what they want when they want it.

• Linkage to the campaign management environment:

– Ability to look at and analyze campaign results.



At the same time, the business line marketing groups have been steadily increasing their analytical skills.

At the same time, the business line marketing groups have been steadily increasing their analytical skills.

Equally Important, Fleet Has Invested in Building the Skills to Use the Technology

CDMA is a central database marketing and customer behavioral analysis division. It serves as an internal direct marketing and customer analysis consultancy for the business lines:

Information Acquisition, Management, and Access

Information Acquisition, Management, and Access

• 16 Business analysts

• 18 Systems development staff

• 16 Technical staff (DBAs, etc.)

ManagementReporting &

Analysis

ManagementReporting &

Analysis

• 19 DSS programmer / analysts

AnalyticsAnalytics

• 12 Quantitative Analysts (Ph.D.s)

Database Marketing

Database Marketing

• 17 Database marketing professionals and analysts

Using the New Capability:Fleet’s Retail Strategy



SALES ANDSALES ANDREVENUEREVENUE

Maximizing the sales potential of our channels, and usingcustomer data to better manage customer and business profitability, will lead to revenue growth.

DISTRIBUTIONDISTRIBUTIONPERFORMANCEPERFORMANCE

Reconfiguring channels, re-engineering our basic processes, building new capabilities to maximize efficiency and actively managing customer behaviors, based on a sound knowledge of consumer behavior and costs, will lead to stronger performance.

Strengthening the customer experience will result in greater satisfaction and retention levels. We will achieve this by making it easier to do business with Fleet.

CUSTOMER CUSTOMER EXPERIENCEEXPERIENCE

BUILD THEBUILD THEORGANIZATIONORGANIZATION

Establish a culture of continuous market-driven performance improvement. Make investments in people to build the capabilities required to compete in the future.

These New Capabilities Are Central to Fleet’s Retail Strategy



SALES ANDSALES ANDREVENUEREVENUE

Maximizing the sales potential of our channels, and usingcustomer data to better manage customer and business profitability, will lead to revenue growth.

DISTRIBUTIONDISTRIBUTIONPERFORMANCEPERFORMANCE

Reconfiguring channels, re-engineering our basic processes, building new capabilities to maximize efficiency and actively managing customer behaviors, based on a sound knowledge of consumer behavior and costs, will lead to stronger performance.

Strengthening the customer experience will result in greater satisfaction and retention levels. We will achieve this by making it easier to do business with Fleet.

CUSTOMER CUSTOMER EXPERIENCEEXPERIENCE

BUILD THEBUILD THEORGANIZATIONORGANIZATION

Establish a culture of continuous market-driven performance improvement. Make investments in people to build the capabilities required to compete in the future.

Where Information-Based Marketing Fits In



Questions, Anyone?

using information to build customer value

Documents