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This Research Note is reprinted by Sybase with permission of The Tower Group.

The Tower Group Research Notes are available to subscribers on the Internet at www.towergroup.com.

1998 The Tower Group, Newton, MA USAMay not be reproduced by any means without express permission. All rights reserved.

Case Study: Building an Analytical Data Mart for CreditCards at the Bank of Montreal

016:40RCSeptember 1998Kathleen Khirallah+1.617.965.9090, ext. [email protected]

Highlights

Bank of Montreal embarked upon building a data mart for its credit card portfolio at a time whenmany banks and solution providers were focusing upon enterprise-wide solutions. However, thechallenges facing the banks card portfolio were not inconsiderable, and the bank was forced tochoose between quickly saving the card portfolio and slowly building an enterprise-wide solution.

As 1995 approached, Bank of Montreal was anticipating a considerable increase in competitionfrom US-based card issuers. Card issuers in the United States enjoyed the twin benefits of scaleand skilllarger scale of marketing budgets and greater skill in information marketing. The bankunderstood that it might never be able to match the budget capabilities of the US issuers, but itcould work to augment its marketing skill.

One of the insights Bank of Montreal gained early in its efforts to construct the data mart was thatthe skills required for model building and sophisticated analytical work are not typically foundamong bank employees. The bank was forced to recruit and hire people with highly specializedskills. The bank also realized that motivating and retaining these analysts would require differentincentive plans. Bank of Montreal needed to address cultural as well as technology issues inbuilding their credit card data mart.

While the credit card division was building its data mart, Bank of Montreal was also building aBank Information Warehouse (BIW). While the data mart represented a bottom-up initiative, theBIW is a top-down approach to customer relationship management (CRM), and the integration ofthe two projects will be a challenge for the bank.

This Research Note is reprinted by Sybase with permission of The Tower Group.Case Study: Building an Analytical Data Mart for Credit Cards at the Bank of Montreal

1998 The Tower GroupMay not be reproduced by any means without express permission. All rights reserved.

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IntroductionIn 1995, the credit card division at Bank of Montreal surveyed the competitive landscape and quicklyrealized that the card business was changing dramatically. As the bank reviewed its portfolio of creditcard products, it estimated that 20% of its customers were generating approximately 150% of theportfolios profit. Moreover, the portfolio had achieved a stasis point. Instead of growing in responseto marketing efforts, it was slowly eroding. Perhaps most important, Bank of Montreal realized thatsoon it would be facing significant competitive pressure from US issuers of cards, especially the US-based monoline issuers. The bank was convinced of the need to take actionif only in self-defense.Although the eventual solution would contain capabilities to expand the portfolio and battle for marketshare, the primary driver was the need to defend and protect the portfolio.

This Tower Group Research Note explores the strategies and actions that Bank of Montreal tookwithin its credit card and electronic banking divisions to focus on customer relationship management(CRM) and to stimulate growth within the banks portfolio of credit card products. Bank of Montrealfaced a dilemma not unlike that faced by many financial service institutions (FSIs). It had developed aseparate, silo structure for its business units, and yet the banks management understood theburgeoning need to work at an enterprise level. Bank of Montreal realized that it could not simply waitfor the implementation of an enterprise data warehousethe business unit required action sooner.Ultimately, Bank of Montreal decided to allow the credit card division to function like a monoline andmaintain its independence while the bank would simultaneously pursue enterprise-wide solutions forCRM.

BackgroundBank of Montreal had approximately US$150 billion in assets as of April 30, 1998, which places itsquarely among the top five FSIs within Canada. In January 1998, the bank announced its intention tomerge with Royal Bank of Canada. If approved, the new institution is expected to have approximatelyUS$333 billion in assets. Bank of Montreal has 1,200 branches and offices dispersed throughoutNorth America, Europe, Latin America, and East Asia. The organization was Canadas first charteredbank in 1817 and served as Canadas Central Bank until 1935. Significant subsidiaries of Bank ofMontreal include Harris Bank, Chicago, Illinois, and Nesbitt Burns, a full-service securities firmcreated from the merger of Nesbitt Thomson and Burns Fry in 1994.

Bank of Montreals StructureWithin the Canadian market, Bank of Montreal provides a full range of consumer banking andcommercial/wholesale banking services. Exhibit 1 provides an overview of the consumer and businessbanking services offered by the bank.



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Not unlike most other FSIs, Bank of Montreal experienced the growth and development of specialtyproducts (securities, credit cards, mortgages) in environments that were often separate from the retailbank. These specialty products typically fall under different banking regulations than depositoryproducts, run on specialized systems, and have operational requirements that are highly product-specific. The knowledge and expertise required to manage these products often resulted in separate,independent departments and/or operating divisions. Since sales and account servicing were highlyspecialized functions interfacing with separate systems, the segregation of personnel made sense.

Essentially an independent, silo division, the credit card division at Bank of Montreal recruited andhired specifically for its own purposes and maintained separate marketing and information technologyresources within the division.

The Credit Card DivisionBank of Montreal has one of the largest credit card portfolios in Canada. The Tower Group estimatesthat of Canadas approximate total of 11 million households, Bank of Montreal has a credit cardrelationship with more than 5 million. Additionally, the bank is one of the largest merchant acquirersin Canada, currently serving approximately 60% of all Canadian merchants. Further, the bank alsoprocesses the second highest volume of point-of-sale (POS) transactions. The practical import of thisis that the bank not only has access to a tremendous amount of consumer behavioral data but also hasinvaluable insight into the behavior of merchants.

Exhibit 1Corporate and Consumer Banking Services at Bank of Montreal

Consumer Banking Services Commercial/Wholesale BankingServices

Deposit services and retirement accountsDeposit services

Consumer loans Business loans

Mortgages Cash management

Student services Treasury services

Personal MasterCard Corporate trust

Personal investing Foreign exchange

On-line trading Corporate MasterCard issuer

Mondex Electronic Cash On-line trading

Merchant acquiring services and POS

Source: The Tower Group



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Threats to the Credit Card PortfolioIn 1995 the managers of the credit card division undertook an extensive analysis of the card portfolio.The results were not pleasing. The banks card portfolio had achieved a sense of stasisnot only wasit unresponsive to marketing efforts, but it appeared to be shrinking due to steady attrition ofcustomers. Similarly, when the bank examined the makeup of the portfolio, it was surprised to findthat 20% of the customers were generating 150% of the portfolios profit contribution. Not only wasgrowth of the portfolio stagnant, but there was also some question as to the appropriateness of thecustomer base.

Besides examining the portfolio closely, Bank of Montreal also surveyed the competitive landscape andfound clouds on the horizon. Unlike the US market, banks in Canada do not have the option of issuingboth MasterCard and Visa credit cards. Bank of Montreal is an issuer of MasterCard and animportant member of MasterCards governing board. Over the course of the 1990s, as FSIs in Canadaunderwent mergers, the resulting bank would choose whether to issue Visa or MasterCard.MasterCards share of market was eroding very quickly as newly merged banks chose to issue cardsfrom Visa. The steep decline in the number of banks issuing MasterCard in Canada led Bank ofMontreal to sponsor regulations that would ease the rules of entry for FSIs wishing to issueMasterCard in Canada. The result of this easing of entry rules was an opening of the Canadian marketfor US-based FSIs that were not already eligible to issue cards in Canada as Schedule II Banks.

Exhibit 2 provides an overview of the internal and external threats pressuring the credit card division in1995.



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US-based Card IssuersOf all the threats to Bank of Montreals credit card portfolio, the threat of increased competition,particularly from US-based organizations, was the most worrisome. Organizations such as First USAor Capital One have the twin attributes of marketing scale and marketing skill that are less wellrepresented within the Canadian market. While Canadian card issuers can achieve the marketing skillsto compete with specialty US issuers, they would be hard pressed to match the scale of the marketingbudgets. Exhibit 3 provides an overview of marketing budgets for specialty monoline issuers in theUS.

Exhibit 2Convergent Pressures on Canadian Credit Card Issuing Banks

The Changing World: A Matter of Survival

ProfitabilityPressures

IncreasedCompetition

IncreasedRegulation

New TechnologyMandates

Mergers andAcquisitions

Minimal ProductDifferentiation

Non-TraditionalBusinesses

Source: Bank of Montreal and Sybase Professional Services



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The foregoing exhibit is focused solely on the monoline issuers of the US. When US-based banks suchas Citicorp are added into the picture, the competitive threat is indeed formidable. For Bank ofMontreal, the inability to compete in terms of scale of marketing budget forced the bank to emphasizeimprovements in the banks marketing skills, and hence the creation of the analytical data mart.

The Analytical Data MartThe proposal to build the analytical data mart was submitted to bank management in late 1995.Approval and funding for the project were granted in early 1996 and work on the underlyingarchitecture began almost immediately. In the initial phase of the project, the bank worked with SybaseProfessional Services to develop the business and technical requirements for the data mart.

The initial scope of the project was limited to providing risk analysis, segmentation capabilities, andenhanced marketing functionality to the credit card division. Because the data mart was developed toserve solely the credit card division, the number of data feeds was limited to two. The bank also addedexternal data from third-party sources to enrich its understanding of customer behavior. Although thisdata set is somewhat limited, the bank was able to launch its first campaign in August 1996.

It should be noted that the banks first campaign was executed from the divisions heritage mainframesystem with a DB2 database from IBM. In an effort to achieve early successes from the analyticaldata mart, Bank of Montreal ran its first few campaigns in parallel, using the banks existingprocessing environment as well as the new architecture. The first campaign ran solely on the newlycreated data mart occurred in January 1997. The data mart is currently positioned for 1.2 terabytes ofdata and the bank hopes to carry two full years of transaction history within the data mart.

ArchitectureSoon after receiving funding and management approval in early 1996, Bank of Montreal begansearching for solution providers for its analytical data mart. At that time, the credit card division hadvirtually no personnel who were experienced with data warehouses or Unix-based systems. The bankprocessed its credit card portfolio in a mainframe environment, and its existing technical personnelwere unsuited to align the technical requirements with the business requirements for the data mart.Once the hiring of personnel experienced in data warehouses and Unix had been completed, the bank

Exhibit 3North American Marketing Budgets for Credit Card Monolines (US$ millions)

First USA $375

MBNA $343

Capital One $224

Providian $143

Metris $30

Source: Bank of Montreal and Furst Annapolis Consulting Estimates



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worked with Sybase Professional Services to develop the technical architectural plan for the analyticaldata mart shown in Exhibit 4.

The solution providers that Bank of Montreal chose to work with are listed in Exhibit 5.

Exhibit 4Bank of Montreals Credit Card Data Mart

Staging Area

Datamart

NCCS,CCAPS,EFT/POS

ExtractionProcesses

Transformation & Administration Processes

Analysts

MVSUNIX

Client

Extraction

DeltaCreation

Metadata

Transport

Cleansing

Transform DisplayManager

PerfManager

BackupManager

ArchiveManagerSecurity

BIW

ExtTapes

EIS SAP

BIW

ExportingProcesses

Timer

Extraction

UNIX/MVS

DataMining

Security

Architectural Components of Analytical Data Mart




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The analytical data mart is updated weekly via a delta process. Transactions are captured daily andheld in the staging area. Given the limited number of data feeds and the specialty product nature of thedata mart, the bank considers weekly refreshing of the data mart to be sufficient. Users of the datamart are almost exclusively analystsapproximately 30 in number. With the adoption of easy-to-usegraphical user interface (GUI) front end tools, the bank expects to extend access to the data mart tomanagement.

BudgetBank of Montreal spent US$710 million on the initial implementation of the technologies for the datamart. The bank defines this category as the solution tools listed in Exhibit 5. The cost of internalresources for technologists and marketers has not been included in this figure, nor has spending forexternal services. Although the bank has not disclosed the amount spent on these items, The TowerGroup estimates the total spending for the analytical data mart to be approximately US$1215 million.

Management at the bank expects the spending on the data mart to increase significantly over the nextfew years as additional data sources are added for analytical purposes. The data mart is consideredvery much a work in progressnew software tools, new modeling capabilities, and new data sourcesare expected to represent an additional expense, albeit a relatively small one.

Goals for the Analytical Data MartThe analytical data mart was given the green light for development by the bank as a result of theportfolio and competitive analysis completed in 1995. The bank acknowledged that immediate actionwas needed if the portfolio was to grow in the face of an intensifying competitive threat. The purposeof the data mart was to mimic the customer relationship management (CRM) capability of a monolineover the short term. Bank of Montreal wanted to institute a cradle to grave management of customer

Exhibit 5Solution Providers for Bank of Montreals Data Mart

Software/Services Provider

Extraction and transformationsoftware

Prism, Inc.

Data mining and statistical analysisSAS InstituteHarte Hanks

Relational database managementsystem (RDBMS)

Sybase, Inc.

Database server Sybase, Inc.

Data storage Amdahl

Hardware platform Sun Microsystems, Inc.

Campaign management Proprietary

Source: Bank of Montreal



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credit card relationships. More specifically, the bank hoped to cut customer defections by 5%,resulting in an anticipated 25% increase in card portfolio profitability. Exhibit 6 provides an overviewof the customer strategies and tactics driven out of the data warehouse.

Business ResultsSince its inception in 1996, the analytical data mart has provided an internal rate of return (IRR)greater than 100% and the bank has seen its credit card market share grow by 70 basis points. Theaverage card balance outstanding on the newly acquired accounts is 129% higher than the existingportfolio, and transaction volume on these new accounts is 59% higher than the average account in theportfolio. The payback on the project was expected in 2.1 yearsa point that Bank of Montreal willreach shortly.

The bank was focused intensely on achieving quick results, and an early change to the marketingprocess has been an increase in the numbers of campaigns. Campaign cycle time has been increasedtwo campaigns are initiated every six weeks. The campaigns are developed on the output of severalkey models, such as the following two:

Exhibit 6Business Strategies and Tactics of the Data Mart

Information Technologys Role in Providing BetterCustomer Management

Call to Action

l Develop New Products andServices

l Consolidate Systems fromMergers and Acquisitions

l Create Automated System toTransfer Information

l Provide Differentiation withBetter Customer Service

l Create Closed-Loop CustomerTracking System

Create SustainableAdvantage

Create SustainableAdvantage

BusinessStrategies

Increase Revenue/Increase EfficiencyIncrease Revenue/Increase Efficiency

Meet RisingCustomer Expectations

Meet RisingCustomer Expectations

Targeted/Niche Marketing

Targeted/Niche Marketing




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Propensity to Borrowthe likelihood that the customer needs or is willing to use a credit cardproduct

Propensity to Respondthe likelihood of the customer to respond to the direct mail solicitationusing the call to action outlined in the offer

The banks response rate has improved dramatically as a result of the modeling process. Previouslythe bank had averaged solicitation response rates that were slightly above the industry average at 1.52.0%, whereas currently the bank averages 3% for its response rate. When using internal lists, thebank has also seen response rates as high as 16%. The increases in response may not seemearthshaking, but it should be noted that the increases in response rate occurred during a period ofheightened competition from US issuers.

Aside from experiencing an increase in its response rate, the bank has also experienced a slight declinein its write-offs, with a current rate of 1.75%. The bank has traditionally been a conservative lenderand has not changed its underwriting criteria since starting to use the analytical data mart. At present,the rate of delinquencies and write-offs is much lower in the accounts generated from the newcampaigns than for the portfolio as a whole. However, as the new accounts age within the portfolio,this performance may change.

Key Lessons LearnedSince the analytical data mart came on line, the bank has arrived at several important realizationsregarding the marketing of credit cards and the managing of customer relationships. First and foremosthas been the need to continually test marketing offers, solicitation treatments, lists, and responsemechanisms. Bank of Montreal conducts champion/challenger tests for every solicitation. This isparticularly important for the propensity to purchase model and the propensity to borrow model. Asnoted in Exhibit 6, the bank is striving to create a closed loop tracking system. It is only throughrigorous testing, measuring, tracking, and reporting that the bank is able to draw reliable conclusionsabout what is working in their solicitations and why.

One additional insight gained that has had a tremendous impact upon the bank is the realization that theskill set necessary for sophisticated modeling and statistical analysis typically does not reside withinbanks. The quantitative skills required for this type of work are found in individuals who are notusually bankers. Bank of Montreal has acknowledged this fact and has gone to some lengths to assurethat the analytical employees hired for modeling are retained as long-term employees. Bank ofMontreal has instituted the following guidelines to assure the satisfaction and ultimately, the retentionof these key employees:

Employees sign a noncompete agreement as a prerequisite for employment.

Incentives for analysts are team-based.

Analysts are accountable for their results for 18 months following a campaign.



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Outside professional training for analysts averages 11 days per year.

Analysts are encouraged to spend 10% of their time on pure research. The analysts present theirresearch findings to their peers in regularly scheduled Knowledge Seminars.

The realization that statistical analysts and modelers require different challenges and rewards formotivation is quite interesting. Building an analytical data mart, committing resources to continuallytesting of new strategies, and searching for new human resources policies for nontraditional employeesreflects the depth of the banks commitment to the credit card business.

Developing an Enterprise-wide Solution

Bank Information Warehouse (BIW)For the Bank of Montreal, its enterprise-wide data warehousethe BIWrepresents a considerablymore extensive expenditure of capital. The BIW is expected to be the engine that will enable the bankto effectively execute a customer relationship management strategy. In the current environment, theanalytical data mart is updated more frequently than the BIW. The data mart provides information tothe BIW, but at present the BIW does not yet feed the data mart.

A significant challenge facing the Bank of Montreal will be the convergence of the BIW with theanalytical data mart. The data mart represents a carefully bounded set of functionalitiesa bottom-upapproach to developing a CRM capability. The BIW represents a top-down projectone that isenterprise-wide in nature and is designed to meet the CRM needs of the entire bank. Unfortunately,enterprise-wide projects are far more complicated in scope, always requiring considerable planning andbusiness unit participation. With these two projects Bank of Montreal has elected to allow the creditcard business unit to function like its most nimble competitora monoline issuer of credit cards. Yetthe bank has not forsworn an enterprise-wide CRM-oriented solution.

Merger with Royal BankWhile Bank of Montreal must look to reconcile the BIW with the analytical data mart, it must also lookahead to its impending merger with Royal Bank. Although the merger with Royal Bank has not yetreceived regulatory approval, Bank of Montreal must start considering how to merge the best practicesof both organizations for CRM. Royal Bank has been working toward developing a CRM capability atan enterprise level. The challenge of merging the CRM initiatives of both banks should not beunderestimated. Similarly, the banks will need to merge the specialized solutions developed within theseparate business units of the banks, like the credit card portfolio. Since Royal Bank currently has inexcess of 10 separate lines of business, it should be expected to have these specialized solutions aswell. Over time it will be interesting to note which solutions and tools are chosen to serve the larger,merged bank.



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ConclusionThe credit card division at Bank of Montreal has built a focused, analytical data mart to enhance itsability to market in the face of increased competition. While some resources within the bank continuedto focus on an enterprise-wide solution to CRM via the BIW, the credit card division was allowed tobuild a solution bounded by the needs of one business unit.

In assessing the efficacy of bank data warehousing projects of recent years, one common complaint isthe lack of business unit value and focus. Projects that are developed for enterprise-wide purposes, ifnot designed carefully, are often unable to deliver true value to the business units. Often the businessunits are required to expend considerable time and energy to the design and definition of such a projectbut must wait years before seeing any practical benefit. In this instance, Bank of Montreal has decidedupon a parallel development trackthe credit card division has invested in its immediate future whilethe bank continues to work on its Bank Information Warehouse. It will be interesting to evaluate theprogress and success of the bank on both initiatives over time.

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