On October 23rd, 2014, we updated our
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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:
Small business banking
Credit Card lending
Private Banking and Trust services
Investment management / Mutual Funds
A Little Bit About Fleet Today
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.
The Retail Business at Fleet: Scope and Scale Branches : 1200 outlets in 8 states ATMs : 2400 machines, including 900 at remote sites Telephone : 80 million calls per year Online Services : 85,000 active customers
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
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.
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.
Banks Have Been Undergoing Two Decades of Change
All banks sell the same thing: bank products.
Glass-Steagal strictly interpreted.
Monoline specialists emerge:
Banks introduce investment products:
Mutual funds and discount brokerage.
Increasing, marketing is organized around products:
The product management model.
Product features proliferate
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.
Pricing Is Emerging as a Critical Tool for Profitability Management
Rates are regulated; with minor exceptions there is little difference among institutions.
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.
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 through appropriate channels Analyze Response And Subsequent Usage Select And Code Lists Customize Offers By Cell / Segment Identify Likely Targets Existing Customers External Lists Model Behavior and Profitability
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.
Promotions Over the Past Two Years Illustrate the Power of Information-Based Marketing Total NPV NPV per Mktg $ Example Campaign: NPV Impact ($2MM) ($1MM) $0 $1MM $2MM $3MM $4MM $5MM $6MM $7MM $8MM Mailing 1 Mailing 2 Mailing 3 Mailing 4 Mailing 5 Mailing 6 Total NPV ($2.00) $0.00 $2.00 $4.00 $6.00 $8.00 $10.00 NPV per Marketing $
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.:
Marketing lifecycle models:
Acquisition / Cross-sell / Retention
Channel attribute utility
Product / service attribute utility
Lifetime value models:
Net present value of a customer today.
Likely potential value of a customer.
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:
Linear programming and stochastic modeling
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.
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 training and sales efforts.
Example: Analysis of Customer Data Highlights Product Flaws
Calls taken by live agents in Fleet’s call center:
Analysis such as this is helping us pinpoint where we need to set limits in the redesign of our deposit products. 0 10 20 30 40 50 60 70 80 90 100 92 93 94 95 96 97 98 99 100 Cumulative Percent of Households Cumulative Percent of Calls
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-95 Regular Time CDs Beginning 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 3,009,528 Dollars Maturing $$$ Maturing 342,275 274,871 206,604 195,077 168,832 249,400 284,354 170,072 158,827 Net Rollovers 140,611 141,997 126,804 124,553 109,838 143,600 142,583 112,758 104,466 Net Transfers 98,295 36,656 18,645 19,041 14,477 20,023 66,244 19,449 17,434 Retention Percentage Closed At 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 30,148 Campaign 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 30,148 Other Transfers 3,012 2,494 947 1,229 706 1,320 2,173 925 1,059 Other Activity 5,904 6,831 142,284 11,256 8,251 8,447 5,099 8,034 5,888 Ending 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 12/98 12/96 12/97
Fleet completes merger with Shawmut Bank, announces acquisition of NatWest US.
CDMA organization created to spearhead information-based marketing and customer analysis
Scoping phase completed.
Integration team selected; project begins.
Prototype warehouse in operation.
Initial load completed.
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:
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.
Fleet Conducted a Six-Month “Scoping Phase” Understand Business Needs 3/11/96 Define Next Generation Implications 4/3/96 (3/31/96) Set Priori-ties 4/24/96 (4/5/96) Define Functional Requirements 4/24/96 (4/22/96) Finalize and Distribute RFP 5/17/96 (5/10/96) Understand Current Environment Recommend Target Management Approach Recommend Target Warehouse Architecture and Tools 2/7/96 4/30/96 (4/15/96) 5/10/96 (4/30/96)
The Scoping Phase Process
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?
Functional Requirement Functional Requirement Business Objective “ Next Generation” Implication “ Next Generation” Implication Scope/Timing Analysis & Recommendations
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. 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
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:
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 $94.0 Deposits 0.0 45.3 172.2 341.6 499.9 P&L Impact Net Interest Income $0.0 $1.2 $4.5 $8.9 $12.9 Fees 0.0 0.2 0.9 1.9 2.7 Expenses 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:
Integration expense (a.k.a., “consultants”)
Internal technical 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.
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 II Capital Expense Phase I Ramp-up New Dev. Total Hardware 7,813 $ 2,409 $ 1,150 $ 11,372 $ Software 6,510 1,407 822 8,739 Integration 12,528 250 3,909 16,687 Other 749 193 0 942 TOTAL 27,600 $ 4,259 $ 5,881 $ 37,740 $ Annual Operating Expense Depreciation 5,520 $ 852 $ 1,176 $ 7,548 $ Hardware maintenance 871 282 134 1,287 Software maintenance 959 224 131 1,314 Systems staff  1,575 840 2,415 DPOT staff  2,000 200 2,200 Other 763 208 971 Expense 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 Impact Loans $0.0 $14.7 $55.9 $111.4 $164.3 Deposits 0.0 97.3 383.8 791.3 1,199.7 Fixed Assets (Phase I investment) 4.5 13.6 10.6 7.7 4.7 P&L Impact Net Interest Income ($0.5) $5.9 $28.0 $49.8 $65.8 Fees 0.0 1.7 2.1 3.6 4.6 Expenses 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 database environment
Marketing promotion software
Over 1 terabyte of data
Sun hardware with Informix DBMS
Exchange Applications’ ValEx software
Open architecture supporting multiple software tools
Architecturally, the Next Generation will be a marketing and sales application implemented in a data warehouse environment -- which ensures that the database can be extended in the future, as needed, to encompass other functions.
What Data Are in the Warehouse?
Customer information (consumer and business):
Profitability (using EAS factors as a feed)
Household and business relationship linkages.
Account (Product & Service) information:
Loans (consumer & commercial, including origination data)
Trust & Private Banking
Interpay (payroll services)
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
The Technical Design Is Intended to Support a Wide Range of Users
Analytics Compute Server
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
Management Reporting Server
On-Demand Parameterized Reports
Weekly feeds Ad hoc extracts, as needed. Business Analysts & Managers Marketing 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 Server Cleansing, 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
Tools for analysis
Closed Loop Response Capture
Cleansing, Matching, and Suppressions
Point & Click design tools
Seamless linkage of modeling and targeting
Design Tests, Hypotheses Execute through appropriate channels Analyze Response And Subsequent Usage Select And Code Lists Customize Offers By Cell / Segment Identify Likely Targets Existing Customers External Lists Model Behavior and Profitability
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.
Geographic analysis software
Geographic mapping with linkage to the database.
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.
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.
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:
At the same time, the business line marketing groups have been steadily increasing their analytical skills. Information Acquisition, Management, and Access
16 Business analysts
18 Systems development staff
16 Technical staff (DBAs, etc.)
Management Reporting & Analysis
19 DSS programmer / analysts
12 Quantitative Analysts (Ph.D.s)
17 Database marketing professionals and analysts
Using the New Capability:
Fleet’s Retail Strategy
These New Capabilities Are Central to Fleet’s Retail Strategy SALES AND REVENUE Maximizing the sales potential of our channels, and using customer data to better manage customer and business profitability, will lead to revenue growth. DISTRIBUTION PERFORMANCE 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 EXPERIENCE BUILD THE ORGANIZATION 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 Maximizing the sales potential of our channels, and using customer data to better manage customer and business profitability , will lead to revenue growth. 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. Establish a culture of continuous market-driven performance improvement. Make investments in people to build the capabilities required to compete in the future . SALES AND REVENUE DISTRIBUTION PERFORMANCE CUSTOMER EXPERIENCE BUILD THE ORGANIZATION