CASE STUDY II-3 - Pearson Educationwps.prenhall.com/wps/media/objects/5547/5681051/5e_Cases/Case_Study_II-3.pdfCase Study II-3 CRM Using Data Warehousing at First American Corporation

Introduction

In 1990, First American Corporation (FAC) lost $60 million andwas operating under letters of agreement with regulators. Today,FAC is a profitable, innovative leader in the financial servicesindustry. This change in fortune is the result of an ambitiousstrategic vision, and a major investment in data warehousingthat made the vision possible.

FAC’s strategic vision is called Tailored Client Solutions (TCS),a customer relationship–oriented strategy that positions FACclients at the center of all aspects of the company’s operations.Though many organizations espouse customer relationshipmanagement, FAC has redesigned every aspect of its operationsto meet its clients’ needs as well as its own profitability goals.Underlying these efforts is the recognition that, to succeed withthis strategy, it must know its customers exceptionally well, andleverage that knowledge in product design, in distribution chan-nel decisions, and in every interaction with its clients.

The execution of this strategy would be impossible withouta data warehouse called VISION that maintains client behaviors(e.g., products used, transactions), client buying preferences(e.g., attitudes, expressed needs), and client value positions(profitability). Using information from VISION, FAC has

• identified the top 20 percent of its customers who pro-vide virtually all of the consumer profits, and the 40 to50 percent who are not profitable

• developed strategies to retain the top high-value customers

• developed strategies to move unprofitable customers tolower-cost distribution channels, different products, orpricing structures that boost profitability, while still focus-ing on customer needs and preferences

• developed strategies to expand relationships with all customers

• redesigned products and distribution channels toincrease profitability and better meet customers’ needsand preferences

• redesigned information flows, work processes, and jobsin FAC’s branches in order to meet customers’ needsand increase their use of profitable products

To implement TCS, FAC had to change the way its employ-ees think about banking and about their jobs, shifting from“banking by intuition” to “banking by information and analysis.”All of these actions combined have moved FAC from losses of$60 million in 1990 to profits of over $211 million in 1998.

This case describes FAC’s transformation and emphasizesthe information technology that was essential to its success.The first two sections discuss First American Corporation andthe Tailored Client Solutions strategy. Then it presents theVISION data warehouse and the way in which it was imple-mented. The final sections describe the uses and applicationsof VISION, and the resulting benefits.

About First American Corporation

Founded in 1883, FAC is a comprehensive financial servicesholding company headquartered in Nashville, Tennessee. Itsholdings include

• First American National Bank

• First American Federal Savings Bank

• Deposit Guaranty (acquired in 1998 and now operatingas First American National Bank)

• First American Enterprises, Inc.

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CASE STUDY II-3

CRM Using Data Warehousingat First American Corporation

Revised case copyright © 2004 by Barbara Wixom, Hugh Watson,and Dale Goodhue, based on an original case report in collaborationwith Brian L. Cooper. The authors would also like to thank CarrollKimball, Jay Phillips, Theresa Leahy, and Connie White from FirstAmerican Corporation. An earlier version of this case report was the1999 first-place winner of the Best Paper Award from the Society forInformation Management (SIM).

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296 Part II Applying Information Technology

• IFC Holdings (formerly INVEST Financial Corporation,the nation’s largest third-party marketer of investmentproducts, headquartered in Tampa, Florida) (98.75 percent ownership)

• The SSI Group (the largest processor of hospital health-care claims in the U.S. headquartered in Mobile,Alabama) (49 percent ownership)

With operations in Tennessee, Kentucky, Virginia, Mississippi,Arkansas, and Louisiana, FAC had $20.7 billion in assets, 7,195employees, 391 banking offices, and 650 ATMs in 1998. It hadthe largest deposit market share in Tennessee, the second-largest deposit market share in Mississippi, and the largest smallbusiness and the middle deposit market share in Tennessee.

Conditions were not as good, however, in 1991 whenDennis Bottorff became chairman and chief executive officer.Because of FAC’s problems, there was concern that the bankwould be closed, and that the larger banks would “come inand pick the pieces they wanted.” Bottorff and his manage-ment team started by “fixing the broken things in the com-pany.” However, they realized that FAC needed a long-termstrategy if it were to survive in the increasingly competitivebanking industry. It needed a new way of thinking and a newbusiness model. FAC could not be the low-cost providerbecause it lacked the economies of scale of larger financialinstitutions. Product differentiation was not a feasible strat-egy because other banks could quickly duplicate productsthat showed promise in the marketplace. It could not com-pete for large accounts (e.g., Fortune 500 companies),because they are the province of large national and interna-tional banks.

Tailored Client Solutions

Bottorff decided FAC should focus its attention on three marketsegments—consumers and small and midsize businesses—inwhich the bank could compete effectively. It also needed toshift from the traditional banking emphasis on products tobeing much more customercentric. This new customer focusrequired a data warehouse to support it. Top management feltthat the effort should be driven from the marketing departmentand, after a year-long search, Brian Cooper was hired as execu-tive director of marketing.

The marketing department, senior management, and keymembers of the finance department worked together todevelop the Tailored Client Solutions strategy. Exhibit 1 showsthe four interlocking components of the strategy: excellentclient information, a flexible product line focused on mass cus-tomization, a consistent sales and service approach centeredon meeting client needs, and a distribution managementapproach focused on channels of choice. All this was coordi-nated under a broad new brand—“About life. About you.”—thatput the customer at the center of everything the bank did.

Client information is the first component of TCS. All bankspossess a great deal of information about their customers, butoften the information is not easily accessed, and most of it isnot leveraged in the many interactions that the bank has withits clients. FAC wants to know more about its customers, and touse that knowledge in every aspect of its business. In additionto demographic information and information about transac-tions with the bank (e.g., ATM card use), FAC includes informa-tion about how its customers prefer to do business and howprofitable each customer relationship is to FAC.

Client Informationknow the client

better than anyone

Flexible Product Lineprovide what the

client needs

Consistent Servicehelp the clientachieve goals

DistributionManagementoffer the client

preferred channels

The Client

“About life. About you.”

EXHIBIT 1Tailored Client Solutions


Case Study II-3 CRM Using Data Warehousing at First American Corporation 297

The second part of TCS is a flexible product line. Althoughall banks can offer similar products, it is more important to offerproducts that meet client needs and are profitable. By under-standing its clients and having accurate profitability information,FAC phases out or modifies unprofitable products, and designslucrative alternatives that meet clients’ needs.

The third component of TCS—consistent service—focuseson determining and meeting customer needs. Banks tradition-ally have sold either what the customer asks for or the hottestproduct on the market at the time, but neither approach explic-itly considers what is in the client’s best interest. TCS calls for amajor shift in sales culture because a FAC representative beginsany customer relationship by discussing that person’s currentfinancial situation and future goals. Then, together, the cus-tomer and representative explore products that would help theclient accomplish personal objectives. Because FAC representa-tives have comprehensive information about each customer’sbanking history, they are able to provide customers with tailor-made solutions.

Distribution management is the final component of thestrategy. Banking services are delivered through various chan-nels (e.g., branch offices, ATMs, PC banking), and banks mustmake long-term decisions about the best way to make servicesavailable to customers. These decisions require knowledgeabout how customers currently use the bank, how they wouldprefer to use the bank under different circumstances (e.g., ifthere were a fee for using a teller), and the costs of the alter-native distribution channels. With this information, FAC is ableto optimize the design of its distribution channels to meet cus-tomer needs at the lowest cost.

All four of these strategic components fit under the umbrellaof the new brand identity for the bank: “About Life. About You.”Each component is conspicuously focused on meeting clientneeds, and together they create a powerful synergy.

Interestingly, although TCS was sold to the board of directorsas an integrated strategy, it was introduced in stages to the restof the organization. According to the marketing director, the rea-son was that “it would have been overwhelming, given wherethe company was at the time.” The champions of TCS commu-nicated only parts of the vision to those groups involved indeveloping the separate components. Five separate projects,each one representing a major piece of the TCS strategy (e.g.,client information, brand identity), were pursued by five sepa-rate groups concurrently. Each project was designed to have sig-nificant positive financial “lift” (i.e., impact) and to embodysome of the basic changes in the way the bank would operate.

On September 20, 1997, after each component was suc-cessfully installed, the complete Tailored Client Solutions strat-egy was formally announced at the 1997 FAC LeadershipConference. This conference was also the kickoff for theDestination 2000 challenge to all FAC employees, with the goal

that by the year 2000, FAC would join the “Sweet 16,” a groupof 16 banks that led the industry in terms of financial perfor-mance and valuation measures. A videotape that presentedDestination 2000 was distributed to all bank employees, whowere encouraged to join the effort and to adapt creatively to theupcoming changes. The theme was reiterated at companywidemeetings, in internal correspondence, and through visiblechanges in work practices.

The VISION Data Warehouse Project

Because every piece of the Tailored Client Solutions strategydemanded better and more accessible information about theclient than FAC, or most banks, ever had, senior managementrealized that it needed information technology to support thestrategy. The critical piece of IT—a data warehouse calledVISION—would contain integrated customer information, prod-uct profitability information, and distribution revenues and costs.(See Exhibit 2.) Similar to many other IT organizations in 1996,FAC’s IT group had no substantive experience in data warehous-ing. Because of the strategic importance of the VISION project,FAC’s management decided to discontinue an internal project(using Sybase) begun the first quarter of 1996, and to insteaddepend heavily on outside help from vendors and consultantswho would then be expected to transfer knowledge to FAC per-sonnel as the project progressed.

Phased Development Approach

Preparing VISION to be the foundation for TCS’s core compo-nents took a phased approach, clear goals, the right people,and a careful coordination between IS, marketing, finance, andexternal consultants. Connie White was named the projectmanager. Her job was to establish expectations, define respon-sibilities, and coordinate the many interdependencies amongthe project teams. The development of VISION was to be amultiphased initiative, with each phase designed to producetangible business benefits while also moving the overall projectforward with technical deliverables. The timeline and goals forthe VISION phases are presented in Exhibit 3. According toWhite, the phased approach enabled FAC to “get the end resultand bring in the revenues as we worked on the project, asopposed to many companies who go out and want to doeverything at one time—and aren’t successful.”

Phase 1 was designed to help managers understand theoverall revenue picture. The First Manhattan Consulting Group, arecognized leader in understanding profitability in the financialservices industry, provided benchmark data to FAC. Togetherwith First Manhattan, Jay Phillips, senior vice president for deci-sion support, and his team developed revenue, cost, and profitformulas to use with VISION data. These formulas and actualtransaction data were added to an existing customer information



system within 3 months, and the project team began to under-stand which data would need to be stored in the data ware-house. In the next two phases, additional products and transac-tion data were added to VISION, and the profit formulas wereenhanced. The data warehouse team also developed extractionand transformation processes, while developing the data mod-els for physically storing the data in VISION.

Implementing a complete data warehouse was the majortarget of the final phases. FAC relied on external vendors suchas NCR to provide hardware, software, and methodologicalsupport through much of the initial data warehouse develop-ment. According to Emery Hill, executive vice president ofoperations and technology, “we just jumped into the poolbecause they [NCR] said that they would jump in with us and

Phase 1 Phase 2 Phase 3 Phase 4 Phase 5

1–2Q 1996 2–3Q 19963Q 1996–1Q 1997

2Q 1997–1Q 1998

2–4Q 1998

BusinessGoals

Identify the top revenue producers

Identify the least profitable customers

Include actual transaction and product data in profitability formulas

Understand all aspects of client and product profitability

Incorporate profitability understandings in business processes

TechnicalGoals

Enhance the existing customer information system with retail revenue

Enhance the existing customer information system with direct contribution view for consumers

Enhance existing customer information system with net income after capital charges(NIACC) for consumers

Deploy the warehouse– proof of concept (consumer business)

Commercial profitability integration

Complete production testing of the warehouse

PHASE

DATE

EXHIBIT 3VISION Goals by Project Phase

Client Informationintegrated client

information

Consistent Serviceclient contact

history

Flexible Product Lineproduct profitability

DistributionManagement distribution

revenues and costs

VISIONData Warehouse

“About life. About you.”

EXHIBIT 2VISION Data Supports TCS Initiatives



keep us afloat.” Yet the company was also careful to appointan FAC employee to manage the process. By 1997 the datawarehouse proof of concept was delivered, and the datawarehouse team spent 1998 validating the data, rolling outthe warehouse, and helping develop applications for financeand marketing.

Warehouse Architecture

FAC’s production data warehouse was an NCR platform (NCR5150M) configured with five SMP nodes running the TeradataRelational Database System.1 This configuration would pro-vide FAC with 1.5 terabytes of storage. At the end of 1998 itsupported 200 gigabytes (GB) of raw data, which was grow-ing at a rate of 10 GB per month. The database held 2 million

1SMP (symmetric multiprocessing) is the processing of programsby multiple processors that share a common operating system andmemory. A relational database is a collection of data organized as a setof tables from which data can be accessed or extended with little effort.

accounts and information about 1.2 million households, andFAC planned to store up to 37 rolling months of history foranalysis. Exhibit 4 shows the warehouse architecture.

Data Sources

The warehouse uses more than 100 source files extractedfrom 26 legacy applications. From the mainframe environ-ment, VSAM and IMS files are FTP’d to a file server, whereInformatica’s Powermart software applies business rules writtento transform the data from the legacy systems (e.g., makingaccount numbers consistent across banks) into data ele-ments consistent with the relational database structure of thedata warehouse. The resulting files are loaded into a ware-house staging area where business users manually validate asubset of the data (e.g., financial data is validated within fivepercent of the general ledger). After the users approve thedata conversions, the data moves into Teradata base tablesthat are organized by account and by activity. Concurrently,files from external data sources, such as student loans from

Source Systems

Consumer LoansDemand Deposit Commercial LoansTrustInvestmentsMortgage LoansTrans. Files (ATM, Voice Response Unit, Call Center)Customer Info SystemCredit DeskTeller AutomationPC BankingTime DepositsEmployee FileCorporate LeasesSelect RewardsAnnuitiesBrokerage

26 Applications/100 files

GeneralLedger

InformationDelivery

ProfitabilityAnalysis &

Reporting (PAR)

Commercial Call System

Relationship Work Station

Performance Measurements

Contact Management

Campaign Management

External Sources:MCIF–Harte–Hanks

Student Loans–Sallie MaeDun & BradstreetCredit Cards–FDR

VISION

• Client Profitability• Product Profitability• D&B Information • Transaction Detail • Retention Tracking• Cross-Sale Information• Credit Reports• Householding

EXHIBIT 4The VISION Data Warehouse Architecture



Sallie Mae, geographic and financial data from Dun &Bradstreet, and psychographic and demographic appends todata from Harte-Hanks, are also incorporated into the ware-house. Exhibit 5 describes the various kinds of data that existin VISION after the warehouse is populated. The entireprocess for populating the data warehouse currently takesapproximately 10 business days, although the team plans toreduce that time by half in 1999.

Customercentric Data

All of the data are organized around the client to provide acomprehensive understanding of the client’s demographiccharacteristics, the banking products used, transaction activities,interactions with the bank, measures of the client’s relationshipto the bank, and psychographic insights about client prefer-ences and propensities. (See Exhibit 6.) The data can be ana-lyzed in multiple ways, including “slicing and dicing” by time,products, geographical regions, and market segments asdimensions; planning marketing campaigns for specific prod-ucts and markets; and detecting which clients are at risk ofleaving the bank.

Data Access

About 50 marketing and finance analysts have direct access tothe warehouse data. For the marketing area, the data ware-house team uses Cognos Corporation’s tools to create multidi-mensional cubes of data that are stored on a local file server.Users can access these data cubes on the server or downloadthe information to their desktops. They then use the Cognos

2Since FAC took an enterprise-level data management approachfrom the outset, and began with a large number of legacy systemsthat were not integrated, the VISION project focused on the enter-prise data model from the outset. At FAC, the data warehouse wascreated first and then used to populate its data mart. A differentapproach often found in practice is for data marts created for depart-ments or divisions to be used to populate an enterprisewide datawarehouse.

tool suite, including PowerPlay, Impromptu, and Scenario, tomanipulate and analyze the data.

The finance analysts access warehouse data using thesame Cognos Corporation tools, as well as via a data martextracted from the data warehouse that uses an Oracle rela-tional database management system.2 The data mart supportsthe custom-developed profitability analysis and reporting toolcalled PAR (Profitability Analysis Reporting), which supports adhoc SQL queries against the mart and also provides users withpredefined reports.3

Data Warehouse Team

Eighteen full-time IT employees comprise the team that sup-ports all of the company’s data warehousing initiatives. As shownin Exhibit 7, warehouse services manages the extraction, trans-formation, and load processes for the Teradata warehouse. Thewarehouse development team—the “gatekeepers” of the ware-house—is responsible for developing new and enhanced appli-cations for data feeds. The business access tool support group

Data Description Source

Client Behaviors

Client Buying Patterns

Client Value Propositions

ProductsDelivery channels TransactionsSegmentsAttitudesExpressed needs

Profitability

• IBM mainframe

• Sallie Mae

• IBM mainframe• Dun & Bradstreet geographic

and demographic data• Harte-Hanks household data

• IBM mainframe• Profitability algorithms

Data can be analyzed at any level of aggregation, from bankwide or line of businessdown to individual account or client relationship.

EXHIBIT 5VISION Data

3SQL is a standard query language used in querying, updating, andmanaging relational databases and is the de facto standard for data-base products.



Client

Transactions

Products

Psychographics

Demographics

ClientInteractions

Measures

• Teller Transactions

• PC Banking• ATM Transactions

• Loans• Deposits • Investments

• Name • Occupation • Head of Household

• Buying Preferences • Financial Segments • Lifestyle Groups

• Balances• Fees• Profitability

• Marketing Campaigns• Customer Calls• Mailer Responses

EXHIBIT 6The Customercentric Data Model

implements and supports the data access tools, such as Cognos.The analytics group performs analytical studies and ad hocanalyses of warehouse data. The data mart team extracts, trans-forms, and loads data into the VISION data mart (PAR system).

Early in the project (1996–1997), most of the data ware-house positions were filled by internal IT employees who werenewly trained, because there were few experienced data ware-housing professionals available on the market. Data warehous-ing expertise was provided by consultants and especially thevendor NCR. After the arrival of Lance Mattingly, who was hiredas a new applications development manager in 1998, mostnew data warehousing positions were filled using a “contract-to-hire” approach: data warehousing professionals werebrought into FAC first as contractors, and the bank offered thempermanent positions after they had an opportunity to “take atest drive” with them.

VISION Users and Applications

The Users

The VISION data warehouse has both direct and indirectusers. The direct users are the 20 marketing analysts and 30finance analysts who directly access the warehouse data andprovide the analyses that are used for decision making by

FAC’s management. There are also hundreds of indirect userswho use reports generated from the VISION warehouse data.Exhibit 8 profiles some of these indirect users and the typesof information that they receive.

Software Applications

Many applications that support the attraction, enhancement,and retention of customers rely on the VISION data warehouse.A description of the major applications, organized by the TCSstrategy, follows.

Client Information

Customer Preferences and Profiles

Customer preferences are important to many banking deci-sions, and FAC has created preference information in VISIONusing a technique called “conjoint analysis.” The companyselected a sample of over 3,000 customers and asked eachone what he or she would do under different circumstances.For example, would you use an ATM to make a deposit if thetransaction were free and the same transaction performed bya teller cost fifty cents? a dollar? Based on the answers, anumber of different types of customer were identified, and by



Data Warehouse

Manager

Warehouse Services

Business Access Tool Support

Analytics Team

Data Mart Team

Warehouse Development

• Leader

• 3 Programmer/Analysts

• Leader• Data Modeler• Business Analyst• Programmer/Analyst• Project Coordinator

• Leader

• 2 Programmer/Analysts

• 2 Analysts

• Leader • 2 Programmer/Analysts

EXHIBIT 7IT Data Warehouse Organization Chart

matching transaction and demographic patterns for each typeof customer with those of the remaining customers, FAC wasable to generate preference information for its entire cus-tomer population.

FAC also generates and uses market segmentation informa-tion. Data are purchased from Claritas, which are thenappended to FAC household records by Harte-Hanks. Usingfinancial segmentation software from Claritas, demographicand financial transaction data are used to place clients in oneof 10 financial categories (e.g., wealth market, wealth pre-servers). A similar process is used to place customers in one of62 lifestyle groups (e.g., young influentials, pools & patios). Thecategory placements are based on the assumption that “youare like your neighbors.”

Preference and profile information is used in many ways,such as targeting marketing efforts and designing the best mixof distribution channels for the bank.

Customer Retention

VISION calculates the profitability of every customer so thatappropriate actions can be taken. High-value customers areidentified and targeted for retention programs, primarilybecause this group is responsible for nearly all of the bank’sconsumer profits. The mid-value customers receive targetedmessages about bank products that should be attractive tothem (and will move them into more profitable relationships).Low-value customers are migrated to more profitable productsand lower-cost distribution channels (e.g., PC banking ratherthan using tellers). However, FAC’s goal is to retain all of itsclients, regardless of their current profitability.

The “Top 20 List” is one example of a retention programbased on VISION data. Using the results from customer prof-itability analysis, each branch receives a weekly list of its top 20clients. Service representatives then discuss ways to retainthese customers and to expand their use of the bank. Arequired approach is to telephone the top customers to thankthem for their business and then to discuss their financial goalsand how the bank can help the client realize them.

Retaining clients and expanding their use of the bank’sproducts are important components of how FAC employeesare evaluated and compensated. For example, the bank mayhave a campaign to increase the number of savings accountsand each branch makes a commitment for their contributionto the campaign. Information from VISION is used to profilecustomers who are most likely to open a savings account, andemployee compensation is tied to how well the commit-ments are met. On Mondays, branch managers commit toperformance objectives for the week, and on Fridays theirperformance is evaluated.

Select Rewards

Like airlines’ frequent flyer programs, Select Rewards isdesigned to increase customer loyalty and to increase cus-tomers’ use of the company’s products. VISION data is usedto analyze the profitability of the bank’s products, determinethe points that are awarded, and establish the point levels fordifferent awards. With Select Rewards, a customer earnspoints for longevity with the bank and the breadth (numberof products) and depth (average account balances) of thebanking relationship. Each month a customer receives astatement that reports the points earned and a redemptioncertificate. (See Exhibit 9.) The points can be redeemed in avariety of ways—discounted banking services, items from a



Indirect User Information Provided by VISION

Senior Management Profitability by line of business

Corporate Banking Managers Profitability by industry segment

Small Business Banking Managers Sales performance by area

Retail Sales Managers and Sales Force Retention of high-value households

Product Managers Profitability of products within product groups

Distribution Managers Channel migration opportunities

Marketing Managers Segment profitability

Asset and Liability Managers Product profiles

Credit Managers Analysis of deciles and risk-taking

EXHIBIT 8Indirect Users and the Information Provided by VISION

4A “join” is a relational database command that results in data frommultiple tables (relations) being combined based on the value of a dataelement (such as an account type code) common to the tables.

local retail store, gift certificates for meals at a restaurant, orfree airline tickets.

Flexible Product Line

Product Profitability

It is critical for FAC to know the profitability of its various prod-ucts. This is a complex calculation that requires cost-accountingequations and data from more than 20 relational database“joins”4 using VISION data warehouse tables. Provided withproduct profitability information, financial analysts look at prod-ucts in a variety of ways. To illustrate, an analyst might begin byexamining the profitability of deposits, loans, and fee-basedservices (e.g., managing trust accounts). Drilling down intoloans, the analyst sees that commercial loans are the least prof-itable when returns are adjusted for risk and capital charges, afinding contrary to conventional banking wisdom, whereincommercial loans are thought to be very profitable. The analystsees that floor plan loans are the most profitable of the com-mercial loans. The analyst then rank orders all of the commer-cial loans to see which ones are the most and least profitable.For the least profitable loans, the analyst examines all of theloans, by the relationship managers who are responsible forthem. The analyst learns that a particular relationship manager

is consistently making loans at rates that do not cover their fullcosts. This information is marked for management’s attention.

Seniors Account

Product profitability information is also used to redesign prod-ucts. Like many banks, FAC offers a free checking account tocustomers who are age 55 and older. Working with VISION,financial analysts discovered that the bank was making moneyfrom a few senior accounts, but was experiencing a loss withmany others. Further analysis revealed that the average size ofthe checking account balance was the key difference betweenprofitable and unprofitable accounts.

Based on profitability information and input from clients, aredesigned product was introduced that created value for bothFAC and its customers. Next, FAC implemented a marketing planto retain profitable senior account customers while improvingthe profitability of the non-profitable accounts. The 20 percentof the customers whose account balances were normally highenough to make them profitable received a letter and a per-sonal call saying that the new minimum balance requirementwas unlikely to affect them. The other customers received a let-ter notifying them of the new minimum balance change andencouraging them either to increase their average balance or toswitch to another type of account. The results of this changewere impressive: there was a significant increase in averageaccount balances, net income after capital charges, and servicefees. Less than 2 percent of the clients closed their accountsand many of them ultimately returned as profitable customers.



EXHIBIT 9Select Rewards Statement and Points Redemption Certificate



Next Most Likely Purchase

FAC is applying data mining techniques to the VISION ware-house data in order to better understand and predict customerbehaviors. Applications that are either in place or under devel-opment include accurate identification of buying trends, pre-cise definition of market segments, optimization of promotionalprograms, customer retention and acquisition, customer crossselling and upgrading, and fraud detection. To illustrate, onedata mining application calculates the likelihood that a cus-tomer will adopt a specific product. Exhibit 10 shows the stepsthat are used to transition VISION data into this useful prefer-ence information for marketing campaigns.

Consistent Service

Contact Management System

The contact management system helps FAC service represen-tatives develop a one-to-one relationship with customers bygiving them a clear picture of the customer’s entire banking

relationship. Drawing from VISION and other data sources, itprovides personal information about the customer, how prof-itable the customer is to the bank, how long the customer hasbeen with the bank, buying preferences (e.g., serious saver,price shopper), the products used, transaction history, and thecustomer’s financial goals. All of this information is accessibleto the service representative’s desktop computer in order tosupport better-informed, more personal interactions with thecustomer. Because it is a bankwide system, it also creates moreconsistent, seamless service. A customer can go into any FACbranch and the service representative has access to the sameinformation about the client.

The service representatives and the contact managementsystem work together to

• build a more personal relationship between customersand FAC

• provide opportunities to learn more about customers’needs and goals

• market additional products to customers

Step

1 Analysts determine that the target is to determine the likelihood that a household would use Product A.

2 Examples of households with and without Product A are extracted from VISION. 5,000 of each are selected.

3 Data are pulled on the 10,000 households and a file is created using an SQL query tool.

4 The file is transformed into a format readable by the data mining application and loaded into the product.

5 The data is analyzed using several statistical techniques. Each data variable is examined for importance, relevance, coverage, etc.

6 A first pass is made to select variables for the initial modeling effort. Between 40 and 60 variables are usually selected.

7 The data is divided into training, test, and validation sets.

8 A neural net makes its first attempt at predicting the target variable (use of Product A). Several hundred iterations may be made before a reasonable model is created.

9 The initial model is reviewed and tested. Adjustments are made. A new model is created. The process continues until no other improvements are seen.

10 The neural net is released in the form of a scoring algorithm, which is usually a multivariate nonlinear regression calculation.

11 Using a reporting tool, every household meeting the correct profile is downloaded from VISION.

12 The data file is formatted and then run through the data mining application. Each household is scored for its propensity to use Product A.

13 A household score file is created.

14 The household scores are appended to VISION where they are used to create target marketing lists.

Activity

EXHIBIT 10Analytical Steps to Determine the Next Most Likely Purchase



When service representatives meet with clients, they follow astructured interview process that includes “meet and greet”questions that may take into account a recent conversation witha bank employee (e.g., How are you? Did your daughter enjoyher trip to Europe?), questions about financial needs (e.g., Areyou still planning to buy a cottage at the lake and might you wantto finance it with a home equity loan?), and questions aboutinvestment goals (e.g., Have you thought about opening an IRAto help with retirement plans?). The answers to these questionsare entered into the contact management system by the servicerepresentative either during the conversation or later on. Whenthe client is interested, the service representative prepares adetailed financial plan that ties the client’s goals to appropriatebank products. The contact information is also used in identifyingwhich customers to include in direct marketing campaigns.

Redesigning Bank Operations

FAC has redesigned its network of branch banks, how processesare performed in each branch, and the jobs that people per-form in each branch, with the goal of increasing the time spentwith clients in order to better understand their needs and toenhance the opportunities for marketing products. For example,the jobs of the customer service representatives (i.e., tellers)were expanded to handle routine tasks such as changes ofaddress in order to allow personal bankers to have more timefor sales-related activities.

The personal banker job itself was divided and is handled byfour different types of people. The personal banker still exists tohandle walk-in traffic and sell additional products as time per-mits. Then there are the consumer relationship managers whoperform an expanded personal banker role. Because of theirabilities, they are entrusted with the most valuable relationshipsidentified by VISION and they have more time for marketingefforts. Small-business-relationship managers do the same thingfor the most valuable “mom and pop” businesses. The final roleis handled by investment specialists who sell an expanded set ofinvestment products. With these changes, it is estimated that 60percent of the time of the business relationship managers andinvestment specialists is spent better understanding customers’needs and marketing new and additional products.

Distribution Management

Channel Distribution Costs

In order to make good decisions about investments in thebank’s multiple distribution channels, it is important to knowthe costs associated with each channel, and VISION datamakes this possible. The cost calculations require informationabout how frequently the channels are used, the nature of thetransactions, and the costs of operating the channels.

An interesting example of how this information can be usedcomes from ATM channel data. As a general rule, ATM transac-tions cost less than a teller. However, a recent FAC study foundthat the cost of accepting a deposit through a teller was 77 centsas opposed to $1.79 with an ATM. This surprising result wasbecause there are fixed costs associated with processing ATMdeposits (the deposits must be collected from an ATM, posted atthe branch, mailed to a central location, and inspected at thecentral location), and these costs were being distributed across arelatively small number of ATM deposit transactions. In fact, only4 percent of FAC’s ATM transactions were for deposits, whichwas below the industry average. FAC’s response was a programto educate customers on using ATMs for deposits and to extendthe hours whereby clients would receive “same day” credit formaking their deposits through an ATM. For a specified period oftime, customers who were Select Rewards members alsoreceived bonus points for making ATM deposits.

Distribution Management System

The Distribution Management System (DMS) is a highly analyti-cal application that helps FAC plan distribution channels for vari-ous market segments in a way that is profitable to the bank, yetstill meets the needs and preferences of customers. Using inputsfrom VISION, such as household profitability, how customerscurrently use the bank’s products, customer preferences, seg-ments and channel costs, and external data that match potentialnew customers to the customer segments, DMS calculates thebest way to distribute products to customers.

For example, in a particular market area, FAC was operatinga main bank (a hub), three branch banks (spokes), and variousATMs. Using DMS, it was decided that closing one branch andincreasing the number of ATMs would increase the bank’s prof-its while better meeting the area clients’ needs.

TCS and Data Warehousing Impacts

FAC’s TCS strategy, supported by the data warehouse, haschanged the mind-sets of employees, which in turn hascreated tangible benefits at the application and organizationallevels. Further, FAC’s senior management believes that thecompany is realizing strategic benefits thanks to the organiza-tional transformation and the way in which the bank is nowperceived within the industry.

A Change in Mind-set

The organizational transformation at First American Corporationincluded major shifts in the mind-sets of its staff, primarilybecause of the information available through the data ware-house. Jay Phillips, SVP for decision support, says, “We reengi-neered all of finance, changed all of the systems, and made ahuge shift in marketing. You see a real change in how the lines



of business do business; you see the branches really goingthrough a redesign process.” Finance has moved from being“bean counters” to aggressively working to find better ways ofcreating revenue. “Good customers” are now determined bythe profitability of their overall relationship with FAC. Marketinghas moved from a “lollipops and balloons” mentality to predict-ing customer actions through careful analysis, and using thisinformation to promote profitability.

Across the bank, units that previously took a passiveapproach to business innovation now see themselves asresponsible for creatively improving the bottom line. For exam-ple, accounts payable took an existing, internally used purchas-ing-card application and turned it into a product that could besold to smaller corporate customers. (A purchasing card isbasically a credit card for projects or groups to convenientlypurchase items and automatically record the expenses against

Application FAC Users Benefits

Client Information

Customer Attrition and Retention

Segment managersFinancial analystsMarketing analysts

Revenues from high value clients grew by more than 15%.

Customer Complaints Service representatives

Improvement in NSF fee collection efforts increased fee income by more than $1.3 million in 1997 and 1998.

Select Rewards Marketing analystsSegment managers

More than 43,000 customers are Select Rewards members, accounting for over $1.4 billion in loans, deposits, and investments.

Flexible Product Line

Seniors Account Marketing analysts Improved risk-adjusted returns on equity (RAROC) from less than 25% to over 50% while maintaining deposit balances.

Repackaging of budget checking, student checking, 55 and better, and small business checking

Financial analysts Enhanced revenues in excess of $1.8 million in 1997 and $3.2 million in 1998.

Repricing of certificates of deposit, express mortgages, NOW accounts, FAIR accounts, and savings accounts

Financial analysts $1.7 million additional revenue in 1997 and 1998.

Next Most Likely Purchase

Service representatives

Improved sales effectiveness.

Consistent Service

Contact Management Sales representatives Assisted in improving retention of high-value clients by 1%; worth $4 million in revenue.

Redesigning Bank Operations

Sales representativesService representatives

Assisted in improving retention of high-value clients by 1%.

Distribution Management

Distribution Management System

Financial analystsDistribution managers

In 1998, 22 higher-cost “hub” locations were replaced by 30 lower-cost “spoke” sites. Over 20% return on investment.

EXHIBIT 11TCS Application Benefits Realized



their budgets). This was quite a shift in mind-set for accountspayable, which had never before been involved with revenuecreation. Brian Cooper adds: “It is gratifying to see that VISIONhas transformed the organization. People think differently nowbecause of it, and new employees don’t even know the [old]ways we used to think.”

However, this level of change has not been easy or comfort-able for everyone. Throughout the organization, those who couldadapt to frequent changes and could take the initiative toenhance performance prospered, while those who could not, left.Some areas experienced 100 percent turnover in one year, andmany others experienced 25–30 percent turnover over 3 years.Those who stayed, however, are part of exciting changes.

Achieving the Benefits

In order to assess the success and impact of TCS, senior man-agement initiated some project tracking metrics. Exhibit 11describes selected applications, the users of the applications,and the resulting benefits. For example, the seniors accounthas seen an improvement in the risk-adjusted return on equity

from less than 25 percent to more than 50 percent whilemaintaining deposit balances.

FAC’s Tailored Client Solutions strategy, powered by theVISION data warehouse, had also fundamentally changed howthe bank is managed, and had generated quantifiable financialreturns. It has allowed FAC to emerge as a profitable, innovativeleader in the financial services industry, as the highlighted datain Exhibit 12 show.

The TCS strategy also has favorably affected how otherbanks perceive FAC. The CEO of Deposit Guaranty (which FACacquired in 1998) considered FAC to be an attractive buyerbecause he wanted to be part of “a financial institution of thefuture and not a bank of the past.” He had learned of FAC’s datawarehousing initiatives and had not encountered banks of sim-ilar size, or even larger size, with the same capabilities.

FAC was clearly at the forefront of a new wave of enterprisesystems—customer relationship management systems using adata warehouse solution—and after many years of effort wasbeginning to leverage its IT investments. The CEO's goal forFAC to join the "Sweet 16" was also within reach as the com-pany's financial performance significantly improved.

Financial Indicators 1996 1997 1998

Return on assets 1.33% 1.40% 1.55%

Return on earnings 15.20% 15.91% 18.07%

Earnings per share $1.98 $2.18 $2.62

Productivity ratio 58.98% 57.93% 53.44%

Average assets (in billions) $16.6 $17.9 $19.3

Average core deposits (in billions) $11.6 $12.4 $12.6

Stock price (as of)$29.56

(1/17/97)$45.875

(1/15/98)$41.3125(1/21/99)

EXHIBIT 12FAC’s Financial Performance 1996–1998


Documents

CASE STUDY II-3 - Pearson Educationwps.prenhall.com/wps/media/objects/5547/5681051/5e_Cases/Case_Study_II-3.pdfCase Study II-3 CRM Using Data Warehousing at First American Corporation