The quality of customer information management in customer ... · relationship management (CRM)...

their customer management strategieson their customer knowledge

— IBM’s global banking team’s work onconfiguring systems to use customerdata

— the IBM Business Consulting Servicesanalytics practice, which helps clientsget value from their customer data —including improving analysis andbusiness processes. In this paper, thisinformation is presented with specificreference to the mobile telephony

INTRODUCTIONIn the first paper in this series of three,the authors examined the theory andconcepts of data quality management andbriefly highlighted some empiricalfindings. This paper explains in moredetail how these findings relate to usingdata to develop customer insight. Theconceptual and experiential material usedin this paper comes from:

— QCi’s work in helping clients base

240 Journal of Database Marketing Vol. 10, 3, 240–254 � Henry Stewart Publications 1479-182X (2003)

The quality of customerinformation management incustomer life cycle managementReceived (in revised form): 24th February, 2003

Merlin Stoneis IBM Professor of Business Transformation at the University of Surrey, Business Research Leader for IBM BusinessConsulting Services and Director of QCi Ltd, and The Database Group Ltd.

Bryan Fossis CRM Solutions Executive with IBM Global Financial Services.

Iain Hendersonis a consultant with QCi Ltd.

Dave Irwinis Solutions Designer for Acxiom Corporation.

Jon O’Donnellis Head of Telecommunication Customer Analytics with IBM Business Consulting Services.

Neil Woodcockis Chairman of QCi Ltd.

Abstract This paper reviews the detailed findings from empirical research into howwell companies plan and manager the acquisition and use of customer data. Theresearch shows that few companies reach good standards in this area, and so run therisk of their data not being able to support their CRM strategies and policies or indeedprivacy or data protection requirements. The paper then turns to how data can be usedto support the management of the customer life cycle, particularly when combined withadvance analytics.

Professor Merlin StoneBusiness Research Leader,Business ConsultingServices, IBM UK Ltd, 76Upper Ground, South Bank,Mailpoint SOUTHBANK2PB2, London SE1 9PZ,UK.

Tel: �44 (0)20 7202 3000;Fax: �44 (0)20 7202 5887;e-mail:[email protected]

which market it is in and where it wantsto be — but that is all. The scoringcriteria for each of the 260 factors arelisted in Table 1.

GENERAL RESULTSThe average results of the assessments in2000–2002 are shown in Table 2. Thisshows that despite strong consultancy andresearch support, companies are not usingbusiness practices that allow them tomanage customers profitably. The lack ofapplied best practice occurs throughoutthe model of customer management.Although systems, data and measurementscore relatively well, analysis andplanning are weak, and this is the areawhere data are turned into profit.

market, a market where data volumesand the return to improved datamanagement are both very high.

The empirical data come from twosources. The first is the full databasearising from the use of the QCiCustomer Management Assessment Tool(CMAT�). The second is from a studycarried out in the USA using theresearch version of the tool. QCi’sCMAT is one way to measure thequality of customer management. Thiswork is summarised in a recent book,‘The Customer Management Scorecard’.1

The model is summarised in Figure 1.The model covers main elements ofcustomer management — 260 factors inall. It assumes that a company knows

� Henry Stewart Publications 1479-182X (2003) Vol. 10, 3, 240–254 Journal of Database Marketing 241

The quality of customer information management in customer life cycle management

Table 1: CMAT scoring process

No real progress: nothing/very little happening, possibly isolated small initiativesIsolated activity: something happening, not systematic, not broadly deployedSome commitment and some progress: concept understood, plan to implement, resource

allocatedFull commitment and real progress: plans exist, resources allocated, implementation begunClear evidence and being implemented: doing it, can be seen, no evidence of effect yetFully implemented and having an effect: company is doing it, it can be seen, proper evidence

it is working

01530

6080

100

Figure 1: The CMAT customer management model

Win back

Targeting

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Managingproblems Welcoming

Getting to knowcustomer

Customerdevelopment

Competitors

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Processes

People and organisations

Technology

Analysisand

planning

Theproposition

Measuringthe

effect

Customer management activity

study, a CMAT-Research (CMAT–R)assessment was used. This consists of atwo-hour, face-to-face interview withone or more customer management ormarketing executives from eachorganisation. CMAT–R uses the mostimportant 49 of the full CMATquestions.2 To this base questions wereadded which focused on datamanagement and usage. While a fullassessment requires evidence of progress tobe presented about each practice, aCMAT–R assessment relies on discussionwith the interviewee, so that scores froma CMAT–R assessment are often higherthan those from a full assessment.

The project focused on the role ofcustomer data integration (CDI), as wellas data management and usage incustomer management. The researchcovered 15 large corporations withactivities in the USA and, except in onecase, the study used face-to-faceinterviews.

The authors now consider in moredetail the areas relevant to this paper. Table3 shows the scores for a selection ofquestions related to the use of data inunderstanding and planning customermanagement over time. It is clear fromthese scores that use of data is a weak area.Particularly weak are the areas associatedwith the customer buying cycle andcustomer lifetime value. In some sectors(eg automotive, telecommunications,financial services, business travel), it is thisknowledge that is the key to competitiveadvantage in customer acquisition,retention and development; this isconfirmed by the US study.

THE US STUDYIn October, 2001, QCi and AcxiomCorporation investigated whyorganisations in the USA are finding somany difficulties in the area of customerdata management and usage. For this


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Table 2: Average assessment scores

Overall averageAnalysis and planning — knowing which customers you have, which you want, planning to

win and keep customersThe proposition — why customers should join, stay and buy morePeople and organisation — structure, motivation, communication etcInformation and technology — systems and dataProcess management — methodical approaches to all aspects of CRMCustomer management activity — the actual process of managing customers, as described in

the customer management stages aboveMeasuring the effect — whether what was planned was implemented and the results it

achievedUnderstanding the customer experience — ie, knowing what the company and its competitors

do to customers, seen from the customers’ point of view

3531

2540423232

48

27

Table 3: Scores for a selection of CMAT questions

Life cycle modelLife stage modelRetention ratesCustomer’s positioning in life cyclesLifetime value understandingWhich customers should you actively developSegment development plansCross-sell triggersProduct portfolio sequenceCustomer information planReason for loss storage

1012252222352220151123

many CRM programmes get stuck inthe strategy phase

— CRM accountability is often splitbetween different departments: sales,service, customer administration,marketing

— education in customer management isneeded but not made available acrossthe organisation

— poor programme implementation leadsto poor performance

Information management and usagefindingsTo evaluate the area of informationmanagement and usage more thoroughly,the analysis sections listed below arefurther defined and individual bestpractices are clustered by section asfollows:

— vision and strategy— investment

General findingsBased on detailed analysis, the researchuncovered the findings outlined in Figure2. The CMAT scoring approachdistinguishes between intent, reality andeffect. Fifty per cent is a roughbenchmark for acceptable businesspractice, while 100 per cent is the upperlimit for a fully deployed and maturedbusiness practice that shows clearbenefits. Figure 2 shows that theCMAT–R sample scored slightly lowerthan the full data set of US-basedorganisations in all but one of the broadsection categories.

The reasons why performance is notvery good despite significant customerrelationship management (CRM)investment include:

— senior executive ownership andleadership is required, but is oftenabsent

— too much thinking, too little doing,



Figure 2: Comparison of CMAT–R scores for USA and Europe

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Conclusions from the US studyThe two companies that scored highestin this study are companies that havetaken significant action to ensure thattheir customer information is properlymanaged and integrated. Thus, thestrongest performer brought togethertransactional data from many businessand product divisions withnon-transactional data, including contacthistory, demographics and preferencedata. While such integration does notguarantee customer management success,it enables companies to understand andserve customers better. The highscorers either had significant outsourcedIT relationships that helped themprioritise and manage key data relatedissues, or a well-designed informationgateway that managed data quality andintegration across different systemsplatforms. The authors see informationmanagement becoming more significantas new channels are used and as ahigher proportion of transactions require

— resourcing— information content— information usage— information management— technology support— integration.

Figure 3 shows that there are many areasfor improvement in customerinformation management, but also thatthere is a lot of work being done onimproving information management andusage. Companies are now more awareof the importance of leveraging customerdata to support customer management.However, an enterprise customerinformation plan is often absent. Anotherlow-scoring area, appropriate access tocustomer data across the organisation, is amajor issue for most of the organisationssurveyed. Furthermore, access to certainanalytical measures of customer value —customer worth, lifetime value or evenreason for loss — were the weakestscoring areas of the analysis.


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Figure 3: CMAT-R subset scores (based on 22 areas related to information management and usage)

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Presence of an enterprise customer information plan

Prioritised plan for data capture

Identification and mapping of information management

Access to sufficient specialist resources

Understanding and measuring the impact of data quality

Incentives and sanction supporting data quality

Ability to share customer data at multiple touchpoints

Appropriate access to data across the organisation

In depth understanding of 360° customer view issues

Access to robust transaction data

Ability to determine and use 'customer worth'

Ability to use 'lifetime value' in allocating marketing spend

Gathering and use of customer preference data

Understanding and use of 'reason for loss' in retention activity

Understanding and implementation of data update cycles

Ability to recognise and welcome new customers

Demonstration of good use of customer data

Use of external data to minimise requirement for data input

Issues or opportunities arising from intermediation

Understanding of privacy/data protection issues

Robust framework for outsourcing decisions

Understanding of technology support for customer management

Obtain resources to support the plan

Ensure that the resource requirements ofcustomer information management andusage are sufficient to support theenterprise plan. Build into therequirements that this is a specialist areawhere employee development andsourcing may be constrained. Considerstrategic outsourcing as an option.Customer information management andusage requires various skills that are inshort supply. Consequently, organisationsmust ensure that they not only havesufficient resources in place to meetcurrent business needs, but that successionplanning is also in place. Only one of theorganisations assessed felt it had this issuefully under control. Most recognised theissue but had not yet confirmed plans toimplement a robust process in this area.

Build measures of data quality

Build robust measures of data quality andtake remedial action when necessary,including incentives and sanctions forcustomer-facing employees. Organisationsshould understand and measure theimpact of data quality on the variouscustomer management processes andbuild appropriate business cases forinvestment in data quality improvementprogrammes. These measures shouldinclude, for example, financial data onwasted marketing activity generated bypoor data inputs. In addition, other lessobvious process measures, such as theamount of rework undertaken atinformation-intensive stages in customermanagement processes should beincluded. Customer-facing employeesshould have incentives and sanctions inplace relating to information quality onthe customer database since theseemployees are best placed to capture andvalidate data. ‘Understanding of dataquality’ scored well across the board inthe US sample but, interestingly,

direct links to a customer record. TheUS Security and ExchangeCommission’s requests for reporting ofcustomer information will create newpressures for already overburdenedinformation management infrastructures.

Customer information is the fuel inthe customer management/CRMengine. It is a key factor for morefacets of customer management thanmight appear obvious at first glance.Customer management needs a strongcustomer information infrastructure tosupport it. Organisations must haveenterprise plans for the managementand use of customer information,covering marketing, sales, customerservice, finance, operations and IT. Theindividual best practices in informationmanagement and usage, and the samplefindings, are detailed below. Theseareas were summarised in the authors’first paper, published in the previousissue of the Journal of DatabaseMarketing.3

Create an enterprise customerinformation management plan

Ensure that an enterprise plan for themanagement and use of customerinformation is in place. Organisationsspend vast sums of money onmanaging and using customerinformation, and the customer databaseis often quoted as a major asset of thebusiness. As such, it should be coveredby a comprehensive plan that does thisasset justice. Only one organisation inthe US study felt it had an enterprisecustomer information plan in place andactively being used. Only 4 per centof companies in the global assessmentbase felt they had such a plan in place.In many cases, departmental plansexisted (ie, in marketing or in CRMteams), but organisations found it hardto take an enterprise view.



three components of data quality takemonths or weeks to complete, acompany still will not be driving itsCRM strategy based on the mostrecent information available.

Theoretically, a company can representwhere it is in a ‘data quality continuum’to constantly improve its position. Eachimprovement directly influences theeffectiveness of a company’s CRMstrategy.

A company manages its data qualityby using CDI. This means the creationof a ‘comprehensive customer portrait’made available anytime, anywhere, acrossthe enterprise as needed, based onbusiness requirements. These CDIprocesses handle extremely complex datamanagement tasks and, in many ways,provide the foundation for the mostimportant component in the overallsuccess of a CRM strategy — anunderstanding of the customer.

For the past few years, one of theauthors’ companies has used assessmenttools containing data qualitymeasurement components. Datadiagnostics is the first step in buildingdata quality measures. In one test on aclient’s data, almost 64 per cent of theidentifying information for customerrecords was good — but over 36 percent of the records had data anomalies.This company also provides a dataquality index score that benchmarksperformance against an industry average.

Define a single customer view

Build a detailed understanding of theissues around the 360� view of thecustomer prior to making commitmentsto build such a view. This does not assertthat an organisation should already havebuilt such a customer view. Indeed thefindings confirmed that very feworganisations have such a view. This

‘incentives and sanctions’ had a lowoverall score, with only two organisationsclaiming to have them in place.

There are four components to dataquality:

— data completeness: this is thepercentage of all possible datasources and the coverage across alldefined data fields a company hasintegrated into its decision supportand operational CRM processes. Ifthere are data about customers inmany different operational datastores, then all of those sourcesshould be integrated into the CRMsystem. However, this also refers tosources of outside data that may beavailable

— data accuracy: this is the overallaccuracy of the data contentidentifying contact information andknown data (internal or external)associated with each customer record.Once a company has all of thesources and fields identified andpopulated, how accurate is that datacontent? In most companies, thisanswer will vary considerably witheach source

— grouping accuracy: this is theaccuracy with which a company canconsolidate data from disparatesources. Once a company has all ofthe data sources identified and hasperformed the necessary hygiene toensure that they are accurate, it ishow well it can identify and groupmultiple occurrences of the samecustomer in order to provide acomprehensive and accurate customerportrait

— data access: this is the speed withwhich a company can integrate itsdata and provide those data in ausable form across all decision supportand customer-facing applications. If allthe activities described in the first


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to transaction data was mid-range inthe US sample, but there were bigdiscrepancies across sectors. Financialservices and telecommunicationstypically scored well, with theautomotive sector less so. From thewider CMAT benchmark only 28 percent of companies had three years ormore of sales purchase information.

Use customer data to understandcustomer current and lifetime value,preferences and retention drivers

This includes the following:

— using customer data to improve thecustomer interface(s)

— understanding and determiningindividual customer value across thecustomer base

— using lifetime value data as key feedsinto marketing activity

— gathering and using customerpreference data to build customersatisfaction and reduce operating costs

— ensuring that retention activity isdriven by all valid data available.

Presenting data already known to theorganisation at the customer interface canshow customers the value of giving suchinformation in the first place. It can alsosupport personalised messaging. Twoorganisations flagged the capability toreturn comprehensive customer data ateach customer touchpoint as addingsignificantly to the customer experience.However, most organisations could notdo so at all or only in a limited numberof contact channels. Limitations in thearea of CDI and the single customerview were the most common barriersidentified. Customer data should be usedto recognise customers with multiplerelationships and, in some cases, multipleaddresses. Proper recognition ofhouseholds leads to accurate

paper suggests that organisations shouldbuild an understanding of what such aview entails, enables and costs. There arebig differences in the costs andimplications of the options. In the USsample, most organisations recognised thesignificance of the issue but tended to beinconclusive in their analyses and hadnot chosen a specific route to buildingthis understanding. For example, anorganisation in an e-commerce bankingstart-up sector may find that it needs acomplete view of its customers availablein real time, and it may even find thatthis view is relatively easy to attain as ithas no legacy systems or unclear issues toresolve. Another organisation that relieson an intermediary sales channel mayfind that, while it wants a 360� view ofthe customer for market and customeranalysis and planning, it does not need itto be in real time as it is used for offlinedata mining. The 360� view is a movingtarget, the key is to build anunderstanding of what is required by theorganisation, what is achievable and atwhat price.

Collect transaction history data

Hold and provide appropriate access toa minimum of three years oftransaction history in a form thatenables detailed analysis. Three yearstypically represents two or more salescycles, although some industries, suchas the automotive industry, may requireup to six years of data to achievesimilar analysis outputs. Data should beheld at the transaction level and shouldinclude a unique customer ID alongwith the date, product, volume, valueand channel/outlet for each transaction.Ideally, the data should also include thetransaction margin. For analysispurposes, postal codes and customersegmentation-related fields also shouldbe accessible. Overall scoring on access



One exception was found in theCMAT–R sample in the publishingsector. Inability to calculate and uselifetime value is probably a result ofweaknesses in both ability to isolatecomplete views of individual customersand possibly a weakness in resourcing ofthe analysis and planning function.Outsourcing this analysis to specialists, ifneeded, could be a good option in theshort term.

Customer preferences

The gathering and use of customerpreference data results from invitingcustomers to state their preferredcommunications frequency/channel/timings, etc. When gathered andimplemented in a robust manner,customer preference data help retentionand can help to cut costs. However,gathering preference data and thenignoring them or operating outside ofthe preferences shared is worse than notgathering them at all. Communicationbased on customer preferences canimprove the perception of theorganisation with regard to the privacyissues. Preferences are either overtly, orby implication, ‘opt-in’ and so in a safezone as far as customers are concerned.Two organisations in the sample showeda sophisticated understanding and use ofcustomer preference data. However, formost, gathering and use of these data wasat best sporadic and the data gatheredwere under used.

Retention activity

To support retention activity, reason forloss should be sought and stored on thecustomer database for every knowncustomer loss. This may be as simple as adrop-down list of possible options, eventhough it will not be able to becompleted in all situations. Given the

segmentation and modelling. It alsoenables organisations to perform the mostappropriate groupings for strategyexecution.

Customer current value

An organisation should be able todetermine the value of individualcustomers, combining sales margin, salesand marketing costs, management costs,logistics and service, etc. Armed with thisinformation, one can make betterdecisions on marketing activity, includingacquisition profiles and planned customerloss programmes. This capability wasrated poorly in both the CMAT–Rsample and across the CMAT database asa whole. This meant that organisationscould not conduct robust analyses,creating further weakness in customermanagement activity and ultimately inthe measurement of success. Developingthis capability is not as difficult andexpensive as one might think. Anindividual, customer-value figure basedon informed ‘guesstimates’ built on acore of robust data is better than nothaving one at all. CDI and good dataquality capability help to improve theaccuracy of customer value data. Onekey to progress in this area lies in a goodanalysis team and the senior managementcommitment to measuring customervalue.

Lifetime or long-term value

Organisations should recognise the likelylifetime of new and existing customers aswell as their short-term value whenallocating marketing budget andpriorities. This should be translated intoan allowable cost per sale — awell-established metric in the campaignevaluation and review process. Scores inthe CMAT–R sample and in the widerCMAT data set were very poor here.


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welcome a new customer who is actuallyan existing customer buying throughanother channel. The former is whereorganisations should be aiming, althougha full multi-channel solution takes timeto develop. The infrastructure consists ofa single customer view with a strongCDI capability to manage therecognition process for new and existingcustomers.

Account for third-party data(intermediation)

Take into account the impact thatintermediation has on a company’sinformation management capability andbe proactive in anticipating and resolvingproblems associated with exchanging datawith this third party. Where a third partysits between an organisation and its finalcustomers is an issue that has causedmany organisations problems withgaining access to managing and usingcustomer information. This issue is ofless concern to most organisations than itwould have been five years ago, even inthe sectors where intermediation is thenorm, such as brokerage, automotive andinsurance. The majority of organisationsinterviewed considered these issues nowto be well understood and addressed,typically through joint marketingprogrammes between the parties, withthe intermediary being encouraged toshare data with the supplyingorganisation.

Understand privacy

Understand privacy and its implicationsin all relevant geographies as both athreat and an opportunity. Best practicein this area goes well beyond themonitoring and implementation oflegislative imperatives. Organisationsshould understand the reasons forconcern. For example, are customers

business benefits of retention overongoing acquisition, any supportinginformation for retention is valid.Similarly, event data, such as priceenquiries, changing order patterns andlapsed accounts can be used as possiblepredictors of defection. This area wasrated very poorly in the CMAT–Rsample, with only two organisationsclaiming to have made significantprogress in this area. Other organisationsscored poorly even on recognition of theissue. Retention remains poorly addressedoverall. In the wider CMAT data set,some 63 per cent of organisations stilldid not even measure retention rates.

Build a customer infrastructure thatsupports recognition and welcoming ofcustomers

Build an infrastructure and set ofprocesses that enables recognition ofwhen a new customer has conducted afirst transaction with the organisation andthen trigger appropriate welcomingactivity. This affects customer perceptionsof an organisation and helps to build aplatform for communications.Organisations should use ‘welcomingactivity’ to capture data while thecustomer is receptive and build up thecapability to personalise communicationswith customers based on preferences.Organisations as a whole, in both theCMAT–R sample and the globalassessment base, were handling this issuewell, with the majority having robustwelcoming programmes in place. WhileQCi research in previous years showedthat welcoming programmes were scarce,there was diversity in practice in thecurrent sample. Welcoming activitiesrange from real time over multiplechannels — using the opportunity tocapture and validate preference data —to welcoming programmes which runoffline, such as mailing campaigns that



A METHODOLOGY FOR DATAUSAGE IN CUSTOMER LIFE CYCLEMANAGEMENT — CASE STUDY OFUK TELECOMMUNICATIONSUnpublished IBM studies have shownthat there is a big difference betweenoperators, both within the UK andbetween the UK and other countries.This applies to several performanceindicators, such as average revenue peruser, churn rates, cost to serve and, mostimportant of all, profitability. Thebusiness case for improved customeranalytics starts with this disparity. Despiteit, most mobile telecommunicationsoperators treat all customers the same.Improving customer analysis and using itsresults creates value through revenuegeneration and reducing costs. Revenueis increased by:

— increasing the volume and mix ofnew customers

— reducing the churn of profitablecustomers

— extending the use of existing products— cross-selling the use of new products.

Costs can be reduced by:

— reducing the cost to serve bymatching service to value

— increasing the use of self-care— achieving higher returns on lower

marketing expenditure— reducing fraud.

In all the above, however, returns areonly secure when business processes arechanged so that the analysis is used asthe regular basis for managing customers.The most successful organisations haveimplemented customer analytics by usinga three-stage approach:

— the development of a marketing andcustomer analytics vision,implementation blueprint and

actually happy to share data withsuppliers, but just do not like the currentterms and conditions? Or is itinvasiveness or distrust that is the issue?Most of the CMAT–R organisationsbelieved that they understood and hadacted upon the privacy issues they faced.This contrasts significantly with thewider, more European, internationalCMAT assessment base, in which fewerthan 40 per cent claim to have robustprogrammes in place to tackle thetougher legislation-affected issues. Farfewer organisations saw privacy as anopportunity to build deeper, trustedrelationships with customers. This begsthe question, is the privacy legislationthat has forced US-based organisations totake note of this issue only a forerunnerof what is to come? The authors’ view isthat privacy — as an issue thatorganisations must allocate resources to— is only just emerging and many twistsand turns have yet to emerge.

Consider outsourcing businessprocesses

Consider outsourcing of informationmanagement business processes whenappropriate or beneficial. Having a robustframework for determining whether tooutsource information managementactivities is a best practice that themajority of organisations assessed to dateindicated they had made significantprogress with.

Conclusion

The US study showed that, while manyorganisations were conscious of mostissues relating to managing data qualityto get value from the data, many werenot managing their data very well. It willnow be considered how data can beturned into value through applying acustomer life cycle approach.


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The main areas where change is requiredin order to embed an analysis-drivenprocess into customer management are:

— communication and changemanagement

— data availability— content tagging— contact strategy— process complexity— measurement— optimising offers— technical implementation.

Figure 6 shows the types of strategy that

prioritised business case— a step-by-step approach whereby a

small number of processes areimproved via pilots and successes usedto justify more significant investments

— phased development of the fullarchitecture, enabling analytics andrevised business processes.

This process must work both for thecustomer journey and for the company,as illustrated in Figure 4. Many differentvalue-generating actions can be takenwith customers at different stages of thecustomer journey, as shown in Figure 5.



Figure 4: The customer journey and the company process

Figure 5: Value-generating actions for customers who depend upon analysis

Source: Business Consulting Services


After salesInitial

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particularly successful strategy is to movecustomers to a higher base level ofservices, as shown in Figure 7. To do this,the following approach should befollowed:

— model the whole care and servicesystem to understand current costs

could be deployed for customers ofdifferent values. One of the mostprofitable is to provide higher servicelevels to customers of current or predictedhigh-value, conventional voice services,encouraging them to extend usage moreand to use additional products and serviceseg picture messaging and data services. A


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Figure 6: Aligning customer management strategy with value

Figure 7: Moving the customer to a higher level of base service

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— assess the data available from internaland external sources, their structure,content and quality

— identify the gap between the required,available and source data

— determine how to close this gap instages

— build data to an overall plan thatenables reuse, accelerating later stagedevelopments and supportingconsistency in customer managementpractices.

The CMAT assessment helps companiesto determine business priorities, as wellas the roadmap for change and the likelyreturn on investment. Specific datarequirements can be identified for eachstage of the roadmap. Assessment ofexisting and potential data sourcesprovides a basis from which to determinethe gap between data requirements andavailable data, enabling the developmentof a staged data integration roadmap.Often only small data items are requiredto be in the right place at the right time(eg an indicator of customer value, orretention risk, at customer touchpoint).This requirement allows companies toensure that the appropriate propositioncan be offered to the customer.

There really is no substitute for aproper data audit, covering which dataare available, from which internal andexternal sources, how they are definedand structured, what their quality andcontent are, what transformations areneeded and what derivations and analysisare required. Many companies start theirCRM projects by denying that data arean issue. This can lead to anunderestimation of the work required toget data into order, leading to delays,budget overruns, rework, reduced projectscope and even cancelled projects. Atone company they concluded that of allthe customer data they had (manydatabases and millions of records) the

throughout the value chain andimplications of different configurations

— determine how the company willmanage and deliver different servicepackages/levels for different segments

— define new product, tariff andcare/service packages

— determine how to integrate analyticsinto operational processes

— communicate internally to ensureconsistent delivery.

In conclusion, returns are only achievedwhen business processes are adaptedtogether with the effective use ofanalytics. The most successfulorganisations have implemented customeranalytics by using a three-stage approach,as follows:

— the development of a marketing andcustomer analytics vision,implementation blueprint andprioritised business case

— a step-by-step approach whereby asmall number of processes areimproved via pilots and successes usedto justify more significant investments

— phased development of the fullarchitecture, enabling analytics andrevised business processes.

Of course, none of this is possible unlessthe data required for customer life stageanalysis are available at the right qualityand for analysis and action.

CONCLUSIONSOne of the most significant challengesfaced by many companies is to ensurethat the data required for improvingcustomer management are available andof sufficient quality. Companies need to:

— identify the specific data, target useand timeliness for a customermanagement activity



loop through to customer managementactivity and measurement, to get somereturn on the investment. Businessownership is the key to data quality anduse. A closed loop approach is the keyto collecting, analysing, verifying andusing data.

References1 Woodcock, N., Stone, M. and Foss, B. (2002)

The Customer Management Scorecard, Part 1, KoganPage, London, UK.

2 For more on this, see Woodcock et al. ibid.3 Stone, M., Foss, B., Henderson, I., Johnson, P.

and Murray, D. (2003) ‘Managing the quality andconpleteness of customer data’, Journal of DatabaseMarketing, Vol. 10, No. 2, pp. 139–158.

only complete and accurate customerdata were the records self-maintained bycustomers through the web, althougheven here the data were suspect ascustomers have reasons not to sharecomplete and accurate data or tomaintain them. However, at anothercompany, the proper audit was done atthe beginning, ensuring that theyunderstood what issues had to be tackledand how new data needed to becollected, using better validation screensand contact centre staff training.Companies must avoid trying to tackletoo much too soon. It is important toprioritise and focus on getting a closed


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