CRM: From ‘art to science’ - Springer · 2017. 8. 27. · associated technology have been adopted from back ofﬁce departments within IT (data marts for DSS and data warehouses

financial and performance data. Thisapproach would diverge from thetheoretical approach to developing acustomer-centric approach tomanagement.

Customer relationship management(CRM) has been in existence for a longtime, however, it did not come into itsname and officially gain momentum —especially in terms of its name — untilthe 1990s.2 CRM is a business strategyevolved to manage the development of acompany, the acquistion and retention ofits customers and to create long-termvalue between them.3 CRM has spurnedthe development of the newbusiness-to-consumer (B2C),business-to-business markets (B2B) and

INTRODUCTIONDecision support systems (DSS) havebeen around for over 35 years.1 Theygained popularity and greater adoptionwith technical advances and improvedeconomics in computing technologyduring the late 1970s to 1990s. Manyvarieties of DSS were related to systemssuch as expert systems multidimensionalanalysis, query and reporting tools, onlineanalytical processing (OLAP), businessintelligence and executive informationsystems (EIS). Much of the evolution ofDSS was not integrated acrossdepartments and focused more onoperational and company performancemetrics. Hence many DSS evolvedtoward a silo, aggregate level type of

76 Database Marketing & Customer Strategy Management Vol. 13, 1, 76–92 � Palgrave Macmillan Ltd 1741-2447/05 $30.00

CRM: From ‘art to science’Received (in revised form): 15th September, 2005

Tyrone W. Jacksonis an associate professor in marketing at California State University in Los Angeles, California. He has more than 17 years’industry experience working with Global 500 companies. He has held senior management positions for the globalmanagement consulting firms Accenture and KPMG Consulting (BearingPoint). His areas of expertise are in direct marketing,data mining, analytics, CRM and business intelligence. Dr Jackson earned his MA in economics from Yale University and hisMS and PhD in marketing science and econometrics at the University of California at Berkeley.

Abstract Early research and methods concerning customer relationship work oftenfocus on more intuitive approaches to customer management. Many of the initialtheories, such as one to one marketing and value-based management, were lessanalytical in their approach. Likewise, too often companies that have implementedcustomer relationship management (CRM) systems have done so with an unstructuredapproach (art) as opposed to a structured and by-the-numbers approach (science). Thispaper focuses on developing a decision support system (DSS) that allows for bettermeasurement and management of a CRM system.

This paper identifies the core components of a CRM/DSS that generateimprovements to decision making in all stages (acquisition, development and retention)of customer relationships. Unlike previous research and discussions on this topic byacademics and industry practitioners alike, a cross-functional approach thatincorporates different disciplines and departments — such as finance, marketing, risk,operations, market research and technology — is adopted here. An application of aCRM/DSS is then applied to financial services as a case example.

Tyrone W. JacksonSchool of Business andEconomics,California State University,Los Angeles, CAUSA.

Tel: �1 (0)323 343 2970,e-mail:[email protected]

Both DSS and CRM utilise a widearray of data and technology. CRM isprimarily a customer-focused strategywhereas DSS can be thought of as theinfrastructure that can measure, captureand deliver performance. Together CRMand DSS can generate a knowledgeenvironment that maximises customer valueand measures, monitors and provideintelligence to company and customerperformance profitability.

BENEFITS TO CUSTOMERSAND COMPANIESCRM/DSS are systems that integratevaried data and apply quantitativetechniques that define the most attractivemessage, the optimum marketing mix ofproduct features, the right price points,the best delivery channels andappropriate level of service assigned toeach customer.

The system accounts for eachcustomer’s propensity for specificproducts and services, based on theirunique perception of what constitutesvalue along a continuum of productfeatures, service capabilities and pricepoints. It also factors in the strength ofthe company’s image and its influence onthe buying decision. It allows companiesto identify, monitor and change theirvalue propositions to reap maximalbenefits to their stakeholders (customersand shareholders).

Greater yields are realised from eachcustomer by organising the business andmanaging their resources aroundcustomers’ needs, preferences and pricesensitivities. CRM/DSS employ atechnology infrastructure that wouldpredict changes in market share and theexpected profitability. Market share willfluctuate in response to changes inproduct and delivery characteristics underdifferent pricing strategies for customerswith distinctly different preferences and

business approaches to database marketingand personalisation through amulti-channel environment. Although itdoes not have the longer history of DSS,it does have a higher acceptance rate interms of notoriety among and adoptionby academics and practitioners alike.CRM, like DSS, however, has seenfragmented adoption within companieswhich has had led to stovepipe growthwithin organisations. For instance, manyvendors have developed systems calledCRM systems, but which aretechnologies that focus primarily onselected company operations such as salesforce automation (SFA) and call centresupport.

INTEGRATING CRM AND DSSIdentifying the lessons learned from bothCRM and DSS implementation can helpidentify the weaknesses and strengths ofboth systems. As discussed, the ‘silo’adoption and implementation of bothCRM and DSS has been a majorshortcoming of both systems. This hasnot allowed either system to reap itsmaximum benefits, and various studieshave emphasised the shortcomings ofreturn on investment.4 Both systems,however, have broad adoption and widecoverage by industry and functional areaswithin a company. For example, bothsystems are used by a vast array ofindustries. Also, although CRM and DSShave often been focused within particularareas of a company, they can both coverthe entire organisation, from operationsto marketing, finance, service, humanresources and technology.Organisationally, these systems and theassociated technology have been adoptedfrom back office departments within IT(data marts for DSS and data warehousesfor CRM) to the executive offices (EISfor DSS and executive dashboards forCRM).5

� Palgrave Macmillan Ltd 1741-2447/05 $30.00 Vol. 13, 1, 76–92 Database Marketing & Customer Strategy Management 77

CRM: From ‘art to science’

market share models define how value isperceived by each customer segment,relative to competing products in thatmarket. By developing a modellingframework for measuring customers’perceptions about value, customerprofiles that define the customer’s choiceattributes and purchase criteria for eachproduct in each customer segment havebeen formulated. These also weigh theimportance each customer places on theindividual quality and price factors thatcount in their purchase decision.

By simulating the product choicebehaviour within individual customersegments and by changing the specificproduct attributes and pricing structures,reliable forecasts of market share andprofitability are generated. Strategicplanning is greatly enhanced andattractive products can be targeted atthose customer segments with thegreatest potential for long termprofitability. An unlimited number ofscenarios can be developed toaccommodate anticipated changes in thebusiness environment, including changesin economic factors, customerpreferences and competitor’s strategies.Individual customer strategies can bedeveloped to maximise the expected netpresent value (NPV) of cash flows andbe integrated with corporate marketingstrategies to enhance target marketing,customer acquisition and relationshipmanagement functions.

These analysis tools can be used toincrease the profitability of each channeland achieve competitive advantages byoptimally configuring each branch orstore for specific opportunities within itslocal market through the development ofsuperior product, service and pricingstrategies.

Some of the capabilities of aCRM/DSS system include:

— Evaluating profitability and market

price sensitivities. For each proposedmarketing programme, the influencefrom each competitor’s counter reactionis used to develop effective product andmarketing strategies that will exceedcustomers’ expectations for value. Thesestrategies can be directly linked tofinancial performance and shareholdervaluations. Investment in new products,marketing, new delivery channels andbranch re-engineering can be scrutinisedand evaluated for their potential forsuccess, given the anticipated economicand competitive environment scenarios.

CRM/DSS accurately measure themarket demand for the company’sproducts and provide control over themarket by simulating the changes indemand that occur by changing product,pricing and service characteristics. Thesemodels are based on sophisticated discretechoice technologies and other non-linearmathematical techniques that accuratelyrepresent how customers will respondwhen given a choice among differentcompeting products and services. Thesemodels categorise customers by theirsimilar choice behaviours, attitudes andtransaction activities. Customers’ likelyresponses and subsequent predictivebehaviour patterns can then be correlatedwith profitability levels and expectedduration. These models are frequentlycalibrated with new customer,competitive and economic data so thatthe changes occurring in the marketplacecan be accurately interpreted and theireffect on the customer determined.Through ‘test and learn’ scientificmethods, these models can be modifiedfor continuous improvement (paretoimprovement). This helps managers tomake better decisions over productdevelopment, marketing and pricingstrategies so that they can maximiseprofitability and penetration into eachpotential market segment. For eachspecific product, accurate profitability and


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determining the best prospects foreach different product based oncustomer events and preferences.

— Pricing models that define specificpricing strategies for profitability andmarket share based on evaluations ofcompetitive pricing and customersown-cross price elasticity (substitutesand compliments).

These models are developed using avariety of techniques and data sources.Some examples include publicly availablegovernment and industry trade datasources, market research data and avariety of proprietary forecastingtechniques with over ten years of proveneffectiveness in production environments.

DSS help firms develop an edge overthe competition by taking advantage ofthe dynamics and changes within themarket through a greater understandingof:

— competitors’ strategies and how theyimpact market demand;

— the competition’s ability to respondgiven its revenue and cost structures;

— the components of market demandand what influences its size;

— the nature of the buying decisions foreach product within each behaviouralsegment; and

— the profitable actions that can betaken with each customer.

A FINANCIAL SERVICES CASERetail banking has been faced with theissue of how to market to customers andnon-customers while at the same timeminimising risk exposure. Marketingmanagement and risk management are atodds in applying the basic tenets of theirtheories in the pursuit and managementof the customer markets. Marketingdevelops value propositions and targetmarkets the population based upon who

share for products within each line ofbusiness.

— Forecasting the impact on the marketmarketing campaigns and productchanges.

— Estimating market demand, individualcustomer revenue, cost, margincontribution and profitability at theproduct and service level.

— Using sophisticated forecastingalgorithms and powerful ‘what if’analysis capabilities to identify revenueopportunities in competitive markets.

— Developing optimal resourceallocation methods for minimum costand maximum revenue.

— Evaluating how different customersegments perceive value.

— Continuously monitoring andmeasuring the value of productsrelative to that of the competition.

— Evaluating customer servicepreferences and determine analyticallytheir value (eg life time value [LTV],NPV in determining optimalcustomer servicing levels acrossmultiple touchpoints.

Sophisticated mathematical models areintegrated into a comprehensiveCRM/DSS for developing forecasts andcreating relationship managementstrategies. For example:

— Market share models that are sensitiveto the factors that influence aconsumer’s choice behaviour.

— Resource optimisation models thatdetermine profitable service anddistribution strategies.

— Profitability models that analyse profitimpacts from specific marketingstrategies and product changes.

— Market size models that forecast totaldemand for each product within eachcustomer segment as well as the targetmarket potential.

— Target marketing models for



parameters, than too many consumersassociated with higher risks could betargeted. To address this target marketdilemma faced by many financialservices, a balanced or ‘risk-weighted’marketing approach is employed,whereby both marketing and riskparameters are incorporated throughoutall stages of CRM in order to maximiseprofitability and customer value. Potentialcustomers who might otherwise beeliminated solely on risk criteria wouldbe eligible to receive marketing offersand the expected default or charge-offrate would be applied to the marketingprogramme portfolio along with thecustomer portfolio. Thus risk is managedat both the individual- andportfolio-based levels. Expectation of risktolerances and expected market response,acceptance, approval, cross-sell ratio,retention and reacquisition are all factorsthat are applicable to a risk-weightedapproach to marketing.

CRM/DSS environment forfinancial servicesAdaptive and unbiased decisions andtrend analyses can be conducted in asequential time series approach todynamic monitoring of the risk/rewardratio in a portfolio. Extensive use of datawarehouse-based DSS techniques is thekey to achieving this functionality. Themodelling process is both interactive anditerative and is conducted at each of thekey phases of portfolio management.Hence:

— Decisions are made on an objectiveand uniform bases throughout theportfolio;

— The decisioning criteria can be‘adaptive’ as new time series ofrelevant factors are folded in duringiterative updating of the model;

— With a data warehousing system in

is more likely to respond, whereas riskmanagement develops risk criteria andsets risk offers based upon who is morelikely to default. In isolation, bothmarketing and risk management woulddevelop a myopic DSS environment in‘managing customers by the numbers’.Companies without a strategy,coordination and clear objectives forcustomer profitability and performancewill develop suboptimal decision criteriathat can lead simultaneously to lowercustomer value and lower companyprofits.

This section of the paper will developa CRM/DSS environment that integratesmultiple disciplines of risk, marketingand technology to maximise managementand measurement for financial companiesusing both CRM and DSS. Companiessuch as Wells Fargo, E*Trade andDeutsche Bank are considered industryleaders for taking a very quantitativesystems-oriented approach to theiradoption of CRM. Their performanceand customer numbers help bear out thevalue of their approaches, using, in part,a customer insight technology such asthe ‘customer dashboard’. With first inindustry rankings for retail bankingcross-selling and cross-channel customerexperience, Wells Fargo has achievedremarkable growth with an aggressiveapproach to new channel development,innovative product strategy and sales. Its2003 annual report said: ‘Eighty percentof our growth comes from selling moreproducts to existing customers.’6

Joining risk and marketingAll too often, marketing programmesthat are developed using arisk-dominated approach to marketingcan prelude certain population groupsamong the target market based solely onrisk performance. Likewise, if marketingprogrammes are devoid of risk


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customers with high profitability andacceptable risk profiles.

2 Develop customer qualification strategy— develop approval decision anddetermine optimal risk base pricing formultiple products.

3 Develop customer conversion strategy— identify the optimal productattributes to overcome consumer-initiated rejection of offer.

4 Develop customer activation strategy— determine the criteria ofacceptance; utilisation needs or firstmover effect.

5 Optimise balance building strategy —enhance product attributes such as linesize, interest rate and other corefeatures best aligned with customerrisk profiles for maximum portfolioprofitability to generate value benefitsfor the customer and profitability forthe financial institution.

6 Redefine product developmentstrategy — develop new products andredefine existing products to provideoptimal match with customer valueperceptions.

7 Optimise retention strategy —minimise defection of profitablecustomers.

8 Optimise reacquisition strategy —create inducement and valuepropositions to recapture profitablecustomers.

Key data and information categoriesIn any viable system, data are the keybuilding blocks: modelling and analysisare critically dependent upon data.Internal data — sometimes called‘household data’ — are coredifferentiators in the analytical CRMprocess. Ceteris paribus, the internal data isthe one differentiation and competitiveadvantage available to a companyconcerning its customers.

Internal data will need to be

place, these models can uncoverhitherto ‘hidden’ customer segmentsthat can be anomalously loss-inducingor profitable;

— Relative weightings forresource-related costs can be assigned,for multiple products, on an optimalrisk/reward ratio basis for effective‘relationship’ maintenance;

— Customer growth management andretention criteria can be effectivelyimplemented using adaptive riskscores;

— Marketing opportunities can beenhanced through cross-selling usingrisk-optimal profitability;

— Customer servicing can be enhancedby analytics that incorporate attitudes,preferences and willingness to pay (ie‘listening to the customer’) relative tocustomer value.

To build a CRM/DSS environmentcompanies must adapt a CRMframework which addresses all stages(acquisition, development and retention)and which consists of the corecomponents — processes, data,modelling/analyses and technologies.These key core components are outlinedin subsequent sections of the paper.

Key CRM processesAs with any plan, a mis-step can occurin implementation if the processes arenot linked well or, more importantly, arenot aligned with the strategic objectivesand goals of the initial strategy. Poorlinks between the process and strategyare often cited as CRM pitfalls at thisstep.7 Core processes for financialinstitutions can be further categorisedwithin the CRM stages as:

1 Develop customer acquisition strategy— identify criteria to determine thegreatest likelihood of responding for



will result in designing buyer valuepropositions — that will be part of thecampaigns — to be tested and ultimatelyvalidated. The various internal, external,market research and other sources of datathat would be recommended for aCRM/DSS for financial services areoutlined below.

— Demographics/firmographics/geodem-ographics: examples would includeStandard Industrial Classification (SIC)or North American IndustryClassification System (NAICS) code,zip code, years in current occupation,income, assets, contact information,channel usage and preference.

— Credit history: examples wouldinclude — Fair Isaac Corporation(FICO) and bureau scores (iecalculated risk parameters),debt/income ratios, judgments, liensand inquiries.

— Deposit history: examples wouldinclude balance in various financialproducts, fraud indicators, number andtype of overdrafts.

— Financial: examples would includecredit and deposit history (borrowingcapacity, leverage potential, repaymentsources) and debt/income ratios (typesof income, net worth, loan types,deposits, interest rate, fees by type,NPV).

— Vintage history: examples wouldinclude delinquency status,charge-offs, utilisation rates over time,renewal and line increases.

— Performance score modelling:examples would include utilisationhistory, current and past productmixes, repayment history, bureauderogatories, profitability metrics.

— Triggered events: examples wouldinclude expiration of promotionoffers, birth dates, anniversary dates,college, change of marital status,change of address, income change.

supplemented with data from externalsources for business, commercial andconsumer targets. External sources allowone to determine some of the marketpotential assessment as well asgeo-demographic, lifestyle and economicinformation on a customer basis. Thisdata will then be merged to form adataset on a customer and householdlevel that will be used in the next phaseof the project, the benchmarking process,where assessment of current behaviourand targeting for future marketing willoccur. This allows penetration ofsegments where opportunities exist fordeveloping new profitable relationshipsor increasing the value of existingrelationships by extending a company’sshare of customers’ wallets (creatingswitching behaviour).

In order to identify the company’smarket in terms of existing customers’preferences and prospects, marketresearch is typically conducted. Thisallows the information obtained from theprofiling models to be supplementedwith customer and non-customerpreference data to obtain a full picture ofcustomer behaviour, preference andpotential profitability. This informationwill then be used to help design buyervalue propositions that can be structuredand validated with marketing campaigns.

By combining market research andbenchmarking models of marketing andrisk behaviour, quantitative assessment ofpreference behaviour and market sharecan be developed. Using the outputsresults and information data from themodels and research one can constructmeasures and simulations to decide howvarious product features, services andmarketing mix factors affect customerand non-customer behaviour.Furthermore, the tradeoffs between thesefeatures and their effect on customerbehaviour and company profitability canbe measured. This optimisation process


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— measure and monitor ex ante and expost preferences, behaviour andprocesses;

— support recurring customervalue-based analysis;

— provide consistent customerinformation back to the operationalenvironment; and

— design an environment for scalabilityand flexibility to meet other businessneeds.

Some key types of analytics that arenecessary components of a CRM/DSSapproach are examined in the followingsections.

Profiling models

Profile, or look-alike, models identifyand differentiate customers who purchaseproduct(s)/service(s). Both modelsmeasure extremes in the decile analysisand can identify those customers whowill not purchase and, among those whodo purchase, those that are unprofitable.Some of the types of models that areused by financial institutions are:

— Likelihood of purchase models —determine the likelihood of whether acustomer purchasing aproduct/service.

— Product propensity models —determine how much of a product orservice a customer will use.

Balance building models

These models determine the usage mixof the various products and services heldby customers. This class of modelsmeasures the percentage mix of producttypes by customer and includes:

— Profitability product-mix clusteringmodels — determine the current andpotential customer relationship

— Campaign marketing: examples wouldinclude package codes, number oftimes contacted, time of response,channel of entry, offer amount, rate,fees.

— External (eg FICO, Claritas,Donnelley, Polk): examples wouldinclude credit attributes, lifestylefactors, psychographic,geodemographics, purchase intent,economic, business products/services.

— Customer servicing patterns andlevels: examples would includechannel preferences, product andservice trade-off preferences, serviceusage, channel touchpoints preferencesand satisfaction.

— Market research: examples wouldinclude customer preferences, analyticsderived from conjoint primaryresearch customer and non-customerdata, prior purchased data, pricingpreferences, customer satisfactionratings.

AnalyticsIncluded in the process will be thequantification of the branch network,centralised service functions and customermanagement processes to determine theireffectiveness in (1) delivering superiorvalue within each local market; and (2)attracting and sustaining profitablesegments of the market and determiningthat the cost of service is properlyaligned with the profitability capturedfrom each customer. As part of anyscientific method that captures customerand market behaviour, a ‘test and learn’approach should be developed to plan,measure, monitor and modify CRMprogrammes. The CRM/DSS analyticsframework is designed to:

— integrate customer information fromvarious sources (eg internal, external);

— design ‘test and learn’ approaches;



Note that there may be times when aspecific modelling task cannot besimulated mathematically due to asparsity of data and the indeterminateeffects of the dataset utilised.

— Build risk-based pricing models usingthe originations score — with anacceptable origination score, theperformance/ behaviour scores andprofitability metrics typically can bemonitored over 12–18 months foreach account booked. Once thecharge-off population is identified, acomposite ‘risk score’ can beconstructed using a linearly weightedcombination of applicants’ financials,FICO scores, risk parameters andother relevant demographic variablesfor the surviving population. Acorrelative analysis of this risk scorewith respect to the correspondingbehaviour scores can yield a ladderedpricing scheme. The pricing strategyevolved in this approach will berisk-optimised for the expected losson a portfolio basis. These risk-basedpricing models can add value by:• assigning each new account a price

for the risk profile of its appropriatesegment;

• automatically accounting foreconomic and business cycle effectsin pricing, as time-correlatedperformance scores are adetermining factor;

• optimising the portfolio profitabilityby correcting pricing imbalances inmarginal or non-performingaccounts. This re-adjustment mayplay a critical role inretention/attrition schemes.

— Develop performance/behaviourscoring models — these modelsestablish a dynamic metric based oncustomer behaviour related to defaultpotential. Repayment anddelinquency patterns observed over

presence among a financial servicescompany’s customers by measuringthe depth and length of thisrelationship. It determines usage ofclusters of products — eg assets,deposits, credit (loan/line) — percustomer segment in order todetermine product usage patterns.

— Customer temporal cross-sell models— provide a time sequence of howcustomers purchase products/servicesover time. These models determinewhat clusters of products and servicescustomers appear to migrate to intheir relationship with the financialinstitution; hence these models can bestructured around a customerrelationship view.

— Develop utilisation/balance buildingmodels — measure the behaviourderived from the percentage utilisationof product types by customers. Theydetermine usage of clusters ofproducts — eg asset, deposits, credit(loan/line) — per customer segmentin order to determine currentutilisation rates dimensionalised acrossproducts and services.

General observations on quantitativerisk modellingOften the input data utilised are derivedfrom the financial institution’s ownmarketing, risk and transactional databasesin a time series form. This allows forcustomised modelling reflecting thecharacteristics of the financialinstitution-specific customer base.Customer information based ondemographics and prospect informationfor new acquisitions/campaigns is oftenderived using industry/local standards or‘like-behaving’ populations in specificuser segments. Where bank-specific dataare lacking, some of these data sourcescan be substituted for some of themodelling requirements outlined below.


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• balance building and retentionmanagement strategies optimised forthe changing patterns ofperformance scores on a periodicbasis;

• new customer acquisitionprogrammes incorporating behaviourcharacteristics and profitabilitypotential given economicconditions.

Loyalty models

This series of models measure, thelikelihood of the numbers of purchasersof products and services being ended.These loyalty-based models directlydetermine the duration or length ofrelationships with various customers.

— Attrition models — provide the

the previous 12–18 months, inconjunction with the in-periodcharge-off/non-accrual rates, canpredict the probability of a customerdefaulting. These scores can beevaluated at the account level or theobligor level in the portfolio.Estimating the likelihood that acurrent obligor will go ‘bad’ haspotential uses in:• early warning through automatic

computation of risk;• improving process efficiencies

through adaptive asset quality ratingbased on this score;

• pro-active triage and forecasting oflosses through charge-offs;

• Risk-based pricing mechanismsbased on expected loss rates andcurrent payment patterns;

• servicing of low-risk customers;



Table 1: CRM/DSS requirements in the customer acquisition stage

CRM process DSS analytics DSS data requirements

Customer acquisition.Develop selection criteria thatwill elicit responses fromcustomers with high profitabilityand acceptable risk profiles

• Profitability behaviour• Customer profiling

— product share— bundling— market share/conjoint

• Customer segmentation• Market testing

• Internal: profitability data;transactional behaviours; purchasehistory patterns; product groupings;account information; productattributes; other bank account data;historical performance data; FICOscores

• External: demographic attributes;economic census information;geodemographic and lifestyle factors;credit bureau attributes

Customer qualification.Make approval decision anddetermine optimal risk basepricing for multiple products

• Origination modelling— Demographic patterns— Purchase patterns— Financials— Market research

• Risk-based pricing

• Internal: see above• External: see above• Other: FICO scores; financial data;

credit and deposit history; borrowingcapacity; leverage potential;secondary repayment sources;leverages; debt-servicing;debt/income ratios; discretionaryincome; net worth; liquidity factors;outstanding loans; loan types;product attributes

Customer conversion.Develop right product attributesto overcome consumer-initiatedrejection of offer

• Booking rate analysis— timing— bundling— product shares— market share/conjoint

• Internal: profitability data;transactional behaviours; purchasehistory patterns; product groupingsaccount information on productattributes; financial data

• External: demographic attributes;economic census information;geodemographic and lifestyle factors;channel preference; points of entry

CRM, customer relationship management; DSS, decision support systems; FICO, Fair Isaac Corporation.

environment, a key challenge that faces acompany is where to begin. Differentcompanies vary in the outcomes theywant to achieve as well as in theircurrent state of readines, as isdemonstrated in the Wells Fargo andE*Trade case studies. Starting with anend state in mind (eg a strategic plan)and then choosing the area with thehighest potential marketing NPV interms of implementation and execution,readiness is to be advised, however. Asdiscussed later, neither Wells Fargo norE*Trade tried to build a CRM/DSSenvironment all at once. Instead, bothcompanies adapted CRM/DSS in a way

metrics for determining retention ratesof customers.

The integration of the CRM processes,DSS analytics and DSS data requirementsfor individual CRM stages — customeracquisition, customer development andcustomer retention — are listed in Tables1–3 respectively.

Organisational readinessThe process changes needed to create aCRM/DSS environment can bedaunting. Upon making the decision tomove forward into a CRM/DSS


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Table 2: CRM/DSS requirements at the development stage


Customer activation.Develop right productattributes to overcomeacceptance criteria

• Discrete choice & conjointmodeling— first mover effects— purchase patterns— market research

• Internal: data as used in customeracquisition stage

• External: data as used in customeracquisition stage

• Other: FICO scores; financialdata–credit and deposit history;borrowing capacity; leveragepotential; secondary repaymentsources; leverages; debt-servicing;usage rates

• Transactional: purchase patterns anddestinations; channel usage productattributes

Balance BuildingEnhance product attributessuch as line size, interestrate and other core featuresbest aligned with customer riskprofiles for maximum portfolioprofitability

• Adaptive optimisationmodelling for— credit line management— loss forecasting— profitability-based— segmentation

• Delinquent/overlimitcollection strategies

• Market testing for— first-mover effects— triggered marketing

• Internal: profitability data;transactional behaviours; purchasehistory patterns; product groupings,account information on productattributes; financial data

• External: augment with other lendingrelationships, lifestyle factors, NPVprofile/optimisation model; customerneed to migrate to other customerproducts

• Other: account level risk attributescontinually tested, challenged andupdated

Product development.Develop new products andredefine existing productsto provide optimal matchwith customer valueperceptions

• Adaptive optimisationmodelling for:— credit line management— loss forecasting— profitability-based— segmentation

• Delinquent/overlimitcollection strategies

• Market testing for:— first-mover effects— triggered marketing

• Internal: profitability data;transactional behaviours; purchasehistory patterns; product groupings;account information on productattributes; financial data

• External: other lending relationships;lifestyle factors; NPVprofile/optimisation model; data onmigration to other customer products

• Other: account level risk; marketingattributes continually tested viacampaign; service level results

CRM, customer relationship management; DSS, decision support systems; FICO;

management. The importance of having adata warehouse and applying decisionsupport cannot be over-emphasised: it isimpossible to fine tune operations todeliver superior value profitably to a largepopulation of customers with varyingneeds and preferences without them. Thisis why so many companies today losemoney on the vast majority of theircustomers. The CRM/DSS environmentcan help a financial institution build betterinformation management and analyticalsystems that significantly enhance itscompetitive advantage through thedelivery of offerings that are designed tosatisfy the priority needs of thosecustomers that represent the greatestpotential for profitability.

Using data warehouses allows dataresearch and analysis, informationmarketing and consistent, subject areacoordination of data with high integritywhen information is needed to solve aspecific business question. Thearchitecture of the warehouse can showthe end-user what to look for and whereto find the required data. Adiagrammatic representation of theCRM/DSS infrastructure is given inFigure 1.

that allowed early successes and choseproduct(s) and service(s) areas that weremore CRM and DSS ‘change processready’ both in terms of physical andhuman resources as well as organisationalchange readiness. Some of the biggestobstacles facing companies in moving toa CRM/DSS environment are thenecessary organisational process changes.Lack of experience, uncertainty, humanand financial performance can slowadoption and even lead to theabandonment of a CRM/DSS approach.Early successes can be the catalyst toovercoming some of the organisationalissues facing CRM/DSS. Additionally,the outcomes from initial CRM/DSSinplementation can become benchmarksfor future enhancements andmodifications needed to integrate thisapproach with other areas of a company’sorganisation.

TECHNOLOGY INFRASTRUCTUREThe CRM/DSS essentially entails acentralised storage/update facility forsupporting the decision models thataddress marketing management, riskmanagement and operational



Table 3: CRM/DSS requirements at the customer retention stage


Customer retention.Minimise defection ofprofitable customers

• Weighted preference modelfor competitive offers

• Survival analysis• Root cause analysis• Switching models

— bundling behaviour— purchase patterns— market research— market testing

• Internal: profitability data;transactional behaviours; purchasehistory patterns; product groupings;account information on productattributes; financial data

• External: data (other lendingrelationships, lifestyle factors),customer need to migrate to othercustomer products

Customer reacquisition.Create inducement torecapture profitable customers

• Time–purchase patterns• Market research• Loyalty models

• Internal: profitability data;transactional behaviours; purchasehistory patterns; product groupings;account information on productattributes; financial data; root causeinformation

• External: (lifestyle factors), customerneed to migrate to other customerproducts

CRM, customer relationship management; DSS, decision support systems.

DSS capabilities. The growing smallbusiness banking area was tasked withfinding ‘how to grow a small businesswithout a bricks-and-mortar nationalpresence’.

In the mid-1990s, Wells Fargoembarked on a CRM/DSS approach togrowing its small business banking byleveraging its data warehouse and datamarts on existing customers andnon-customers. It targeted bothcustomers and non-customers in its keyterritories based upon its brandrecognition. The strategy was to startwith one or two products (ie businessloans/lines) and measure the performanceof the programme before scaling up theCRM/DSS approach. The ‘test andlearn’ approach derived from earlier

COST–BENEFIT RESULTSCompanies employing a CRM/DSSapproach have realised significantbenefits. The following two case studiesare useful in illustrating lessons learnedand results obtained by companies thathave integrated CRM/DSS at differentstages of development.

Case one: Wells Fargo BankIn the early 1990s, prior to its mergerwith Norwest Financial, Wells FargoBank was known in the financialservices industry as a super-regionalbank. It was considered a leader inemploying DSS in areas of riskmanagement and fraud detection;however, it lacked advanced marketing


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Figure 1: Customer relationship management (CRM) decision support system (DSS) technical architectureOLAP, on-line analytical processing.

SuggestionsSuggestions

OrdersOrders

ProductInquiriesProductInquiries

StatusInquiriesStatus

Inquiries

Customers / Business Partners

Risk WebWeb

Back-Office Source Systems

CustomerInsight

CampaignMgmtCRM/DSS

Channels

Consumer Business Supply Chain

INTERACTIVE

ANALYTICAL

Web

HelpDesk

Store

Mail-in

Email

SalesRep

Messaging Architecture

OLTP CustomerDatabases

Data Warehouse

Data Marts

Common Customer Reference

SalesForce

Customercare

Personal -ization

Clickstream

Data Standardization

ERPERP

OtherLegacyOther

Legacy

Marketing

Internal External

ASP

Population Architecture

Quality Hygiene Security Privacy

RiskModels

BehaviorModels

Future ValueModels

RetentionModels

QUANTITATIVEMODELING

SegmentationModels

ProfitabilityModels

Market research data

Scoring

Updates

SuggestionsSuggestions

OrdersOrders

ProductInquiriesProductinquiries

StatusInquiriesStatus

inquiries

Customers/Business Partners

Risk WebWeb

Back-office source systems

CustomerInsight

Campaignmgmt

CRM/DSS

Channels

Consumer Business Supply chain

INTERACTIVE

ANALYTICAL

Web

Helpdesk

Store

Mail-in

Email

Salesrep

Messaging architecture

OLAP customerdatabases

Data warehouse

Data marts

Common customer reference

Salesforce

Customercare

Personal -isation

Clickstream

Data standardisation

ERPERP

OtherLegacyOther

Legacy

Marketing

Internal External

ASP

Population Architecture


OtherLegacyOther

legacy

Marketing

Internal External

ASP

Population architecture


RiskModels

BehaviorModels

Future ValueModels

RetentionModels


SegmentationModels

ProfitabilityModels

RiskModels

BehaviorModels

Future ValueModels

RetentionModels


SegmentationModels

Riskmodels

BehaviorModels

Behaviourmodels

Future valuemodels

Retentionmodels

QUANTITATIVEMODELLING

Segmentationmodels

Profitabilitymodels

Scoring

Updates

which was an alternative to traditionalinvestment institutions such as MerrillLynch and other very large investmentfirms. Many other dot.com companiessoon mimicked E*Trade’s valueproposition. Faced with the highlycompetitive self-directed investordot.com companies, E*Trade waschallenged with ‘how to grow itscustomer base and create a solution thatallowed it to grow withoutcompromising servicing its existingcustomers’.

E*Trade had leading brand recognitiondue to its innovative ads; however, itlacked a structured and integratedmarketing strategy and framework toengineer continuous growth of profitablecustomers. E*Trade lacked a DSScapability to apply a CRM structure toits customers. In 1999, E*Trade beganbuilding a CRM/DSS approach from theground up. First, it looked at its internalsystems of data, process, capital (physicaland human) and organisation to developits CRM strategy. It looked atenhancing, analysing and changing itsresources and organisational structure.Initial work involved analysing, surveyingand segmenting its customers and theuniverse of investors in order to developinitial value propositions to attract, growand retain customers that wouldmaximise its profits. The advancedsegmentation factored the preferences,attitudes, behaviour and tastes of existingcustomers and non-customers whosepreferences mirrored E*Trade’s variouscore competitors. E*Trade started smallby developing initial risk-weightedmodelling for marketing programmestargeted at customers and non-customers.Based on the ‘test and learn’ experience,it was able to continuously modify andre-segment the universe of investors tocreate more effective offers. Eventually,multiple products/services weredeveloped and the prior learning was

campaigns allowed its marketing andcombined risk methods to calibraterisk-weighted models to targetnon-customers in non-brand-recognizedlocations as the bank moved further fromits bricks-and-mortar base in the USA.Multi-channel touchpoints were usedalong with extending CRM/DSS toother product groups in the smallbusiness environment. DSS were used tomeasure and increase customerdevelopment (eg cross-selling andup-selling) and, eventually, to retaincustomers to yield maximal profits andbenefits over the bank’s previousnon-DSS approach.

The results were astonishing and madeWells Fargo Bank the No. 1 SmallBusiness Banking Lender in the USA.8

The NPV per account ranged from $417to as high as $1,800, depending uponmulti-channel acquisition. CRM/DSSwas instrumental in growing sales to over$1bn in outstandings for in- andout-of-state business in less than sevenmonths. Wells Fargo developed severalnew product offerings based on theoutcomes of its CRM/DSS approach.Eventually, Wells Fargo Bank — which isconsidered a pioneer and a leader —leveraged the success of CRM/DSS toother areas of the bank and extended itsreach outside the USA. The success ofits CRM/DSS approach continues today.Wells Fargo enjoys the No. 1 industryranking for retail banking cross-sellingand cross-channel customer experienceand the highest cross-sell ratio amongbanks internationally.9

Case two: E*TradeOne of the most recognised earlydot.com companies during the 1990s wasE*Trade. As part of the dot.com boom,E*Trade enjoyed a large growth becauseof its value proposition of attractingdo-it-yourself or self-directed investors,



per share of 250 per cent for E*Trade.10

E*Trade’s success continues today withthe continuous development andenhancement of the CRM/DSS systemit built in 2000. E*Trade’s CEOattributed the company’s strongest year in2004 to the success of its CRM/DSS-based customer segmentation andincreasing integration.11

Key lessonsBoth Wells Fargo and E*Trade realisedsignificant improvements uponimplementing a CRM/DSSenvironment. Some key lessons learnedby both Wells Fargo Bank and E*Tradewere:

— Think big and start small — have aclear CRM strategic plan of desiredoutcome and targets. Start small inboth the adoption of CRM and DSS;for example, pick a small number ofproducts or services and small sectionsof the organisation so that early winscan be realised.

— Success leads to easier adoption —organisational and process changes aretypically the biggest challenge togetting all areas of a company’sorganisation on board with aCRM/DSS environment. Early winsallow other parts of the organisationto listen, learn and move forwardwith implementing CRM/DSS intheir own areas.

— Be ready for change — develop astructured process plan but be flexiblein its implementation so as toaccommodate any unexpectedobstacles with resources, requirementsand external factors.

— Use scientific methods — employscience to ensure that measurementand targets are optimised (eg use ‘testand learn’ approaches) and factor keyactors (eg customers, competitors,

extended to multiple touchpoints. Anend-to-end approach allowed initialprospects to be scored and theirbehaviour to be monitored at nearreal-time as they interacted with the weband other touchpoints. Thus appropriateservicing levels were applied throughoutthe customer lifecycle (ie from prospectto customer) by tracking customerinteractions with the various touchpoints.At various stages, appropriate offers weregenerated, tracked and measured at thecustomer transaction level asprospects/customers researched, inquired,purchased and obtained servicing. Inaddition, E*Trade could measure theimpact of its decisions to see how theyeffected outcomes in the market(customers and competitors).

E*Trade developed new products andservices based upon feedback oncustomer preferences and willingness topay feedback. E*Trade built a fullCRM/DSS environment consisting ofCRM, information technology, datawarehousing, multi-channel strategies,marketing programmes, marketingcommunications, data warehousing, webmarketing, database marketing, datamining, campaign management,personalisation, servicing, organisationalchange, performance metrics andreporting. The results were nothing shortof spectacular, yielding a NPV percustomer ranging from $390 to $740.Predictive modelling improved directmail response rates by 250 per cent with30 per cent savings on expenditure.

The resulting CRM/DSS environmentsegmented customers’ needs, wants andpreferences, delivered personalisation toexisting customers and strong webmarketing value propositions to newcustomers that together resulted ingenerating over $15 million in revenuesper quarter. It achieved a growth incustomers’ assets of 121 per cent overthe previous year and increased earnings


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approaching marketing and operationsfrom an overall profitability perspective,decisions are no longer made in isolation.An integrated DSS enables firms to makedecisions based solely on the impact oncustomers’ perceptions and profitability.This level of effectiveness is achieved byusing sophisticated mathematical modelsto evaluate the impact of variousdecisions and by integrating the systemsand data used to make those decisions.Therefore the various departments willbecome more unified and jointlyinvolved in the decision-makingprocesses that affect the customer, whichwill undoubtedly improve competitiveposition in the marketplace.

Having CRM/DSS significantlyenhances the firm’s competitiveadvantage. This will allow a company’sresources to maximise market demand bydeveloping products and delivery optionsthat are strategically priced for growingmarket share within those customersegments that represent the greatestlong-term value. Individual companyresults with CRM/DSS will vary giventhe various stages of adoption andabsorption rates of the CRM/DSSenvironment. Nonetheless, as marketsemploy CRM/DSS, they will becomemore efficient and companies’performances will be based more ontheir ability to absorb CRM/DSS basedon efficiency and innovation.

References1 Power, J. D. (2001) ‘A brief history of decision

support systems’, DSSResources.com, retrievedApril 13, 2005 from http://dssresources.com/history/dsshistory.html.

2 Xu, Y., Yen, D. C., Lin, B. et al. (2002)‘Adopting customer relationship managementtechnology’, Industrial Management & Data Systems,Vol. 12, No. 8, pp. 442–452.

3 Kotler, P. (1994) ‘Marketing management’,Prentice Hall, Englewood Cliffs, NJ.

4 Business Tecnology editors (2002) ‘What works’,BusinessWire, March 6, p. 1.

5 Roberts, L. P. (2005) ‘The history of CRM —Moving beyond the customer database’, retrieved

shareholders) into decision making.— Build institutional memory —

planing, capturing, measurement,implementation and change should bedocumented and tracked throughoutthe CRM/DSS environmentconversion process. This helpscompanies to have benchmarks (egbalanced scorecards) to better capturepast and current resources and metricsof CRM/DSS environments and toimprove them in the future.

— Continuously improve — just ashardware suffers obsolescence, so toocan systems. Companies shouldcontinuously improve theirinfrastructure and systems, includingCRM and DSS. For example, thesegmentation strategy that workstoday needs continuous upgrading andenhancements to be effectivetomorrow.

CONCLUSIONCRM and DSS evolved from years ofresearch and development in consumerbehaviour, resource optimisation andmarketing science. Separately they havenot achieved the expected results toutedby many vendors of these technologies.Together, however, CRM and DSS haverealised increased benefits. CRM/DSSallows an organisation to evaluate theimpact that changes in product attributes,prices and customer preferences will haveon market share, profitability andshareholder value. Therefore, marketreactions and financial consequences fromproduct development and pricingstrategies can be anticipated prior to theirdeployment.

Typically, marketing, risk and servicefunctions operate independently. Eachbusiness decision made by a departmenthas a ‘ripple effect’ on other parts of thecompany, which influences customers’perceptions and company profitability. By



achieves break-even results from ongoingoperations’, PR Newswire, retrieved April 21, 2005from http://media.prnewswire.com/en/jsp/search.jsp?searchtype=full&option=headlines&criteriadisplay= show&resourceid=1442120.

11 Anon. (2004) ‘E*TRADE financial corporationannounces strongest year in company history andreports Q4 2004 GAAP earnings of $0.26’, PRNewswire, retrieved April 21, 2005 fromhttp://media.prnewsiwre.com/en/jsp/search.jsp?searchtype=full&option=headlines&criteriadisplay=show&resourceid=2861339.

March 13, 2005 from http://ezinearticles.com/?The-History-of-CRM----Moving-Beyond-the-Customer-Database&id=6975.

6 Forbes, S. (2004) ‘Creating profitable customerexperiences’, American Banker, November 17, p.22.

7 Business Technology editors (2002) op cit., p. 1.8 Zuckerman, S. (1996) ‘Mom and Pop, you are

prequalified!’ BusinessWeek, No. 3471, pp. 98–101.9 Forbes, S. (2004) op cit.10 Anon. (2000) ‘E*TRADE Surpasses $1 billion in

revenues for first nine months of fiscal 2000,


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Documents

CRM: From ‘art to science’ - Springer · 2017. 8. 27. · associated technology have been adopted from back ofﬁce departments within IT (data marts for DSS and data warehouses