A Measure for Companies' Customer Portfolio Management.pdf

PLEASE SCROLL DOWN FOR ARTICLE

This article was downloaded by: [Romanian Ministry Consortium]On: 2 March 2010Access details: Access Details: [subscription number 918910197]Publisher RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Business To Business MarketingPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t792303971

A Measure for Companies' Customer Portfolio ManagementHarri Terho a

a Turku School of Economics, Department of Marketing, Turku, Finland

Online publication date: 09 December 2009

To cite this Article Terho, Harri(2009) 'A Measure for Companies' Customer Portfolio Management', Journal of BusinessTo Business Marketing, 16: 4, 374 — 411To link to this Article: DOI: 10.1080/10517120902762542URL: http://dx.doi.org/10.1080/10517120902762542

Full terms and conditions of use: http://www.informaworld.com/terms-and-conditions-of-access.pdf

This article may be used for research, teaching and private study purposes. Any substantial orsystematic reproduction, re-distribution, re-selling, loan or sub-licensing, systematic supply ordistribution in any form to anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae and drug dosesshould be independently verified with primary sources. The publisher shall not be liable for any loss,actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directlyor indirectly in connection with or arising out of the use of this material.

http://www.informaworld.com/smpp/title~content=t792303971

http://dx.doi.org/10.1080/10517120902762542

http://www.informaworld.com/terms-and-conditions-of-access.pdf

374

Journal of Business-to-Business Marketing, 16:374–411, 2009Copyright © Taylor & Francis Group, LLC ISSN: 1051-712X print/1547-0628 onlineDOI: 10.1080/10517120902762542

WBBM1051-712X1547-0628Journal of Business-to-Business Marketing, Vol. 16, No. 4, Oct 2009: pp. 0–0Journal of Business-to-Business Marketing

A Measure for Companies’ Customer Portfolio Management

Customer Portfolio ManagementH. Terho

HARRI TERHOTurku School of Economics, Department of Marketing, Turku, Finland

Purpose/Contribution: Customer portfolio management (CPM) is oneof the key areas of customer-relationship and network managementin business markets. However, there is scant research about theimplementation of this concept in business. This article contributes tothis conceptually rich but empirically nascent field of CPM researchby (1) conceptualizing customer portfolio management, (2) forminga measure for it, (3) validating the suggested measure, and (4) sug-gesting implications for future research and management.

Methodology: A CPM construct is proposed based on the synthe-sizing of the theory and the findings from a qualitative field study ofcompanies’ management practices. The suggested construct isformative and consists of the following four dimensions: analysisefforts, analysis design, responsiveness efforts, and responsivenessdesign. Hence, this conceptualization takes into account both thestrength and style of companies’ CPM practices. The measure isvalidated following Diamantopoulos and Winklhofer’s (2001)guidelines for developing formative measures. Together with thecontent validity established in the conceptual phase of the research,the results from a cross-industry survey of 212 companies give sup-port to the construct validity of the suggested CPM measure. Partialleast squares modeling is applied in validating the measure.

Implications: This study gives an extensive, up-to-date review ofcustomer portfolio management and provides measures for futureresearch on companies’ CPM practices and performance. Further,the theory and the field study highlighted several central topics

This study is part of the LIKE Research Program “Finnish Companies and the Challengeof Globalisation,” tunded by the Academy of Finland.

Address correspondence to Harri Terho, Assistant Professor, Turku School of Economics,Department of Marketing, Rehtorinpellonkatu 3, FI-20500 Turku, Finland. E-mail: [email protected]

Downloaded By: [Romanian Ministry Consortium] At: 18:57 2 March 2010

Customer Portfolio Management 375

that should be addressed in future CPM studies. The resultingmanagerial implications derive from the discussion on the keyaspects of developing CPM practices in business.

KEYWORDS Customer portfolio management (CPM), customerrelationship management (CRM), formative measurement, partialleast squares (PLS)

Relationship marketing has been one of the main themes in marketing andindustrial markets for over two decades. The main idea behind it is that rela-tionship building and management are crucial success factors in currentbusinesses (Grönroos 1994; Morgan and Hunt 1994). However, an increasingamount of research has been published in recent years indicating that strongerrelationships are not always desirable. The profit distribution of companies’customer relationships is remarkably heterogeneous (Mulhern 1999; Niraj,Gupta, and Narasimhan 2001), and various relationships represent various rolesor serve different functions in the long term (Cannon and Perreault 1999;Wilson and Jantrania 1994; Walter, Ritter, and Gemünden 2001). Therefore, themyopic development of closer relationships, or a strict customer-retentionfocus, could be questioned. Many authors have suggested that the firm shouldadjust its relationship-management activities to the customer’s value andconcentrate on managing its whole spectrum of customer relationships—from transactions to strategic partnerships (Johnson and Selnes 2005).

Since the 1980s, a large number of various customer portfolio modelshave addressed this essential managerial topic in the B2B context (seeAppendix 1). The recent boom in customer relationship management (CRM)has made customer-relationship portfolio thinking highly topical for bothcompanies and academic research (Eng 2004; Johnson and Selnes 2004;Rajagopal and Sanchez 2005). The starting point in customer portfolio anal-ysis is the management of the whole portfolio of customers. Because firmshave only a limited amount of resources to use on their customers, it is notrational to treat and develop all relationships in the same way—it is preferableto differentiate the allocation in relation to the value of the relationship.Instead of only managing individual relationships, a firm should manage itswhole portfolio of relationships and consider whether it has the right kind ofportfolio of customers to secure its long-term performance (cf. Turnbull 1990).

Customer portfolio management (CPM), therefore, has a slightly differ-ent focus than the related CRM in terms of theory and research. CurrentCRM studies focus largely on business-to-consumer contexts, therefore,missing some central aspects of relationship management in business markets.Most research concentrates mainly on customer satisfaction or on customervalue in strictly financial terms, such as profitability in its managementinstead of taking a broader perspective. Further, CRM studies concentrate


376 H. Terho

largely on the treatment of individual relationships in management. Somestudies address the issue of managing customer relationships in differentlifetime cycles, but they do not focus on the future-oriented development ofrelationships and the whole portfolio of customers (cf. Bowman andNarayandas 2004; Reinartz, Krafft, and Hoyer 2004; Ryals 2005; Wilson,Daniel, and McDonald 2002). These are major issues in business markets inthat companies’ networks of relationships form a context that both enablesand constrains corporate performance (Ritter, Wilkinson, and Johnston 2004).

Portfolio management has been identified as one of the four key levels inmanaging relationships and networks in business markets (Möller and Halinen1999). Although there is considerable conceptual knowledge about customerportfolio analysis, there is almost no empirical research on the implementationof this concept (i.e., CPM) (see Leek, Turnbull, and Naudé 2002; and Terho andHalinen 2007, for exceptions). Further, current studies offer highly contradictoryviews about the value of portfolio management for companies. Tests and simu-lations of theoretical models support the proposition that CPM is of greatimportance (e.g., Zolkiewski and Turnbull 2002), whereas other researchersargue that formal, simplified portfolio analysis may even be counterproductivein the long run (Armstrong and Brodie 1994; Dubois and Pedersen 2002).

Clearly, new empirical research on CPM is needed. This article contrib-utes to this crucial area of relationship management by forming and validat-ing a measure for studying firms’ CPM practices in business markets. Morespecifically, it (1) conceptualizes customer portfolio management, (2) devel-ops a measure for studying companies’ CPM practices in business, and (3)validates this new measure with empirical data. Finally, avenues for futureresearch and management are suggested.

Current knowledge about CPM is almost entirely based on various customerportfolio models representing the “received view”—in other words, idealsset out in the literature—and may differ from the reality of the businessworld. The CPM measure developed here is thus based on the logic behindthe classic works of Kohli and Jaworski (1990) and Jaworski and Kohli (1993).In other words, this article starts from a theory-based definition of CPM fol-lowed by a field study of companies’ CPM practices. This synthesizing ofthe theory and the findings leads to the establishment of an operational def-inition. This definition and some additional interview material are then usedto build up the CPM measure. Finally, cross-industry survey data from 212companies acting in business markets are used to validate the measure.

THE CUSTOMER PORTFOLIO MANAGEMENT CONSTRUCT

A Theory-Based Definition of Customer Portfolio Management

A large number of customer portfolio models have been developed sincethe early 1980s. All these models focus on analyzing customer relationships



in order to help managers allocate scarce organizational resources andensure the long-term profitability of the relationships (e.g., Eng 2004). Overall,the suggested models form a heterogeneous and complex entity, because“portfolio analysis and management can be applied from various perspec-tives at various levels of aggregation and using different combinations offactors or portfolio components depending upon the purposes intendedand the specific situations confronting the company” (Turnbull 1990, 20).

Consequently, the models vary markedly from each other in their cen-tral elements of customer analysis and resource allocation strategies (Eng2004; Johnson and Selnes 2005; Turnbull 1990). For a detailed, analytic sum-mary of all academic models, including the analysis dimensions and mana-gerial implications, see Appendix 1. For the most part, the suggested modelsare matrix-type tools consisting of two- or three-dimensional axes with sin-gle, two, or three phases for analyzing the value of customers from the focalfirm’s point of view (Zolkiewski and Turnbull 2002). The analysis criteriavary from a strictly financial focus (Storbacka 1997) to very broad views aboutcustomer value (Fiocca 1982). Moreover, the suggested resource-allocationstrategies vary considerably from the allocation of sales-force time (LaForgeand Cravens 1982) and the more general adjustment of marketing strategiesto customer value (Shapiro et al. 1987), to strategies concerning which rela-tionship to develop and in what direction (Zolkiewski and Turnbull 2002).

Terho and Halinen (2007) have proposed the following theory-baseddefinition of CPM based on a literature review of academic customer portfo-lio models: “an activity by which a company analyzes the current and futurevalue of its customers for developing a balanced customer structure througheffective resource allocation to different customers or customer groups”(721). This definition is adopted here as a starting point. However, it is prob-lematic because it is based on theoretical models representing a relationship-management philosophy. It is probable that corporate CPM practices are notperfectly similar to academic models, and indeed that they differ from them.An empirical field study may give a significantly clearer idea of the domainof a construct and hence enable a more precise definition. It could also helpin forming an operational definition that is not only theoretically rigorous butalso matches business reality and is relevant to practitioners. It is importantfor such a definition to explicate the CPM activities that translate the underly-ing philosophy into practice (cf. Kohli and Jaworski 1990, 3).

A Field Study of CPM Practices in Business

A qualitative field study of CPM practices was conducted in seven companieswith the goal of developing an operational definition of CPM. The study wasbased on a theoretical sample of seven purposefully chosen firms assumed torepresent as much variation as possible in terms of the business and the cus-tomer base, the aim being to obtain an adequate picture of various management


378 H. Terho

practices (cf. Eisenhardt 1989, 537). A number of factors were taken into accountin the selection of the companies: differences in their strategies (cost vs.value-added emphasis), main products and services (simple and standard-ized vs. complex and tailored), customer-base size (large vs. narrow), mar-ket concentration (many vs. a few potential customers), and in the industrydynamics (mature and stable vs. developing and dynamic). The companiesinvolved are given a name based on the industry in which they are actingand a letter: energy A, insurance B, logistics C, paper D, E (two companies),and the information and communication technologies (ICT) businesses F, G(two companies). They were all among the 100 largest companies inFinland and were known to apply systematic portfolio management.

The study was based on semistructured interviews with carefullyselected senior managers responsible for customer management in each ofthe seven companies. This rather limited number of interviews was deemedsufficient because saturation was reached. The discussions revealed someclear patterns that contrasted with the theory-based definition of CPM. Theinterview themes included the companies’ CPM aims, the contents of theanalysis, the managerial implications, the various responsibilities, any prob-lems, and overall CPM-related experiences. The interviews lasted approxi-mately 1.5 hours and the discussions were kept as broad as possible with aview to obtaining rich data about CPM practices.

Yin (2003, 106–108) proposes two general strategies for analyzing casestudy data: case descriptions and theoretical propositions. Even though thisfield study was not a genuine case study aiming at a thorough understand-ing, both of these analytical strategies were applied. First, it was possible toform a description, or a broad overall picture, of the companies’ CPMpractices based on the interviews. Second, the interviews were analyzed bytesting the theoretical proposition that the theory-based definition and thepractice should be similar. In other words, the focus of the analysis was onfinding differences between the theory-based definition and the companies’management practices. Because the empirical data were not extensive, anal-ysis programs such as NUDIST were not used. The findings of the fieldstudy are illustrated in the form of quotations from the interviews.

A Field-Based View of Customer Portfolio Management

A comparison of the interviews and the theory-based definition revealed threemain patterns that are not present in customer portfolio models and, therefore,in the theory-based definition of CPM: the process nature of portfolio man-agement, the design of CPM activities, and the role of the various organiza-tional levels and functions involved. These three themes are discussed here.

First, the theoretical customer portfolio models concentrate on themechanical design of the portfolio analysis at one point in time, that is, theycarry out the analysis and develop strategies for managing different kinds of



customers. In practice this is a very limited view, however. According to theinterview data, CPM is likely to be an ongoing, continuous process ratherthan a separate act of analysis or strategy development taking place at onepoint in time. Portfolio management is clearly a strategic issue, but it takesplace largely in daily business dealings with customers. Several interviewersstressed the fact that the proper implementation of CPM into practice couldtake years, as illustrated here.

• Insurance B: “Basically, analysis tools or models can be taken into usequite quickly. The standardization of practices is a bit more time-consuming.However, the implementation takes time. . . . To get the people in thefield to understand what we are doing and to get them to act correctlycan take years. . . . To make the results visible takes time: we don’t act ina ‘vacuum’—if we could change our customer-base structure right awaythen the results would soon be seen.”

It seems that the main aspects of CPM processes concern customeranalysis, in other words, processing customer information and responding tothe new knowledge. These two activities emphasize a process nature andthey are somewhat present in the theory-based definition, although they areboth more generally apparent here. In particular, the idea of responsivenessis at the core of portfolio management and emphasizes the need to movefrom customer-management strategies to their implementation in concretedaily actions.

• Logistics C: “We have recently taken a new value-segmentation modelinto use. . . . The large [customer] mass is analyzed based on the numeri-cal data. For the larger customers we have this more qualitative evaluationas well. . . . I think the implementation represents a major challenge inmanagement. How to take the created operational models concretely intoour daily practices.”

• Paper D: “There are two levels in our customer management: customer-base management, which is the responsibility of upper management, andoperative customer management, when the plans are taken into practice. .. . These two levels interact the whole time through discussions, meetings,reports, and measurement.”

• Paper E: “It is crucial to move beyond the customer classifications and touse them as the basis of our operations. What relationships should wedevelop, where should we put our efforts? . . . The implementation ofcustomer-base management involves constant cooperation between cor-porate management, divisional management, and employees in the field.”

This notion of CPM as customer-information processing and responsive-ness to the knowledge gained is consistent with theories of organizational


380 H. Terho

learning (Huber 1991), information processing (Sinkula 1994), and marketorientation (Kohli and Jaworski 1990). Consequently, CPM in business couldbe regarded as an ongoing process involving two main activities: customeranalysis and responsiveness to customer knowledge.

Second, the interviews from the field study indicate that CPM practicesdevelop over time as companies adjust their portfolio-management practices.This adaptation may be based on the explicit planning and development ofportfolio-management activities. Even though many companies use consult-ants in building up their practices, they are not bound to ready solutions.On the contrary, most of the interviewees stressed the need to build and tailorthe management practices in-house. Interestingly, the adaptation of prac-tices is not necessarily in accordance with strict managerial planning or for-mal development, and could also be based on learning from feedback, newknowledge, insights achieved, and trial and error.

• Insurance B: “We also quite often do ad hoc analyses when we noticesome interesting issue or want an answer to a specific problem. Whenwe’ve done it we wonder if it was just a single project or if we also wantto monitor that issue in the future in our customer-base management.”

• Logistics C: “Our management practices are developing the whole time. . .There may be changes [to the CPM process] along the way to what wehave planned if we notice that it’s not exactly what we wanted.”

• Paper E: “The customer-management IT technology and databases play acentral role in management. . . . All our [customer management] softwareand the tools that we use have been tailored specifically for us.”

The interviews indicate that the adaptations in portfolio-managementpractices differed notably from company to company. Some firms had putlots of effort into formally planning and adapting their analysis and respon-siveness activities. Others did not place as much emphasis on explicit, for-mal design and took a more informal approach to portfolio managementthrough the management of individual customers and daily interaction. Inother words, CPM activities may but do not need to be formally designed, asLeek, Turnbull, and Naudé (2002) also noted.

• Energy A: “We have standardized customer-treatment ‘models’ for differ-ent types of customer relationships in our customer base. There are veryclear-cut instructions for our staff regarding the smaller customers . . . .For the large ones the customer classification and the implementation ofcustomer management are basically the responsibility of our very knowl-edgeable salespeople.”

• Insurance B: “Because of the nature of our business we need to have aformal management model for analyzing and treating our customers. . . .In [the] future we need to develop more thorough instructions for the



sales people. . . . A key issue would be getting them to move from a volumeto a profitability orientation in their actions.”

• Paper E: “You don’t want to ‘tie up’ your preferably innovative salespersonnel with too strict instructions. But of course you must have somecommon frame or values for them.”

• ICT F: “We do a customer-base-level analysis once a year. Still, the focusin our customer-base management lies in the continuous monitoring andmanagement of our customer relationships in our sales organization.”

• ICT G: “The customer base or ‘portfolio’ level is not relevant for us. . . . Inthe end the customer classifications are not important. . . . Our customer-base analysis and management are realized fully through the managementof individual customer relationships.”

The concept of customer-portfolio-management design is a useful startingpoint for examining these differences in portfolio-management style. Designhere refers to an explicit focus on the planning and adaptation of CPM activi-ties. Theoretically, the notion of CPM design is connected to the concept ofmechanistic and organic management styles or arrangements, which is dis-cussed widely in organization and marketing theory (e.g., Burns and Stalker1961, 96–126; Chakravarthy 1982, 38). The so-called mechanistic organizationalstyle combines the use of formal rules and procedures with limited participationin decision making, whereas organic decision making features few procedures,yet high involvement (Dahlström, Dwyer, and Chandrashekaran 1995, 43).

Third, the theoretical models give the impression of CPM as a thoroughlymanagerial marketing practice. According to the interviewees, however, it isstrongly cross-functional and multilevel. Instead of being an isolated market-ing tool it is connected to other managerial decisions in marketing such as keyaccount management (KAM), need-based segmentation and the developmentof offerings, and also to other functional areas of the company such as sales,accounting, production, supplier management, and R&D. Similarly, CPM deci-sions in one business unit are often linked to decisions in other related units orareas.

• Energy A: “One great challenge for customer analysis is the separateddatabases in our different business units.”

• Logistics C: “Value-based and need-based segmentation must be usedtogether. We cannot just look the value of the customer—we need to con-sider customer needs as well in managing customers of different value. . . .We work together with our customers in developing offerings for differ-ent customer groups.”

• Paper D: “We have started increasingly to take into account customerswho are buying several product categories from different business units.A good customer in one product area must be serviced well in the otherbusiness areas even though it may be a bad customer for those units.”


382 H. Terho

• Paper E: “There are three main parties who are important [in CPM]. Thesales personnel who are in contact with the customers, the production orits planning, and thirdly the accounting function, so that you can be cer-tain of the profitability.”

Even though CPM is a managerial activity, it cannot be separated fromother levels of the company. Its proper implementation often requires orga-nizational changes that need the support of top management. Similarly, theimplementation of portfolio management takes place at lower levels of theorganization, such as the sales level, as illustrated in many of the previouscomments.

• Logistics C: “It [CPM] is like any other change in the organization—itrequires the support of the top management. Some smaller changes thatonly affect a particular area, those you could implement right away, butas soon as their execution affects the whole organization you need thesupport of the management.”

Hence, CPM is heavily interconnected with other managerial questionsthat should be taken into account in the decision making. For a more thor-ough discussion on this topic, see Ritter (2000) on the interconnectionsbetween customers in a portfolio, Zolkiewski and Turnbull (2002) on theinterconnections between customer and supplier portfolios, and Tikkanen,Kujala, and Artto (2007) on the interconnections between customer, project,network relationship, and offering portfolios.

Synthesis: An Operational Definition of Customer Portfolio Management

A synthesis of the theory and the field-study findings suggests the followingoperational definition of CPM: “the company’s activities in analyzing itsportfolio of customers pertaining to their role in providing current andfuture value for the focal company, and its responsiveness to the analysisconducted.” The core of portfolio management lies in learning—companiesgain new knowledge about the value and the role of various customersthrough the analysis processes, and this enables them to better allocate theirlimited resources among their customers.

More specifically, CPM is a continuous process involving four maindimensions related to analysis and responsiveness activities. Given the goalof this study to form a measure for studying companies’ practices, the focusis naturally on their main CPM activities rather than on the overall longitudi-nal portfolio-management process. CPM is made up of four dimensions (seeFigure 1) approximating both the efforts (i.e., strength) and the design (i.e.,style) of the analysis and responsiveness activities.



This kind of conceptualization, which takes the design of activities intoaccount, is rare in marketing, although some similar managerial constructsexist (e.g., network competence, Ritter 1999; see also Kohli and Jaworski1990, 16). The different facets of CPM do not necessarily hang together. It ismore likely that the different activities together form the CPM level, indi-cated by the direction of the arrows in Figure 1. Moreover, it should benoted that this conceptualization makes sense only when there is a reason-able amount of efforts present. The presence of design alone would indicatethat a company plans carefully its CPM activities but does not implementthem into practice. Clearly future studies need to take this issue intoaccount. See the following section, “Limitations and Implications for FutureResearch,” for a discussion on how this issue should be solved. The keydimensions are discussed in more detail next, based on the theory and thefield study.

ANALYSIS EFFORTS

CPM activities are aimed at obtaining a thorough understanding of the roleof various customers in the customer base in order to ensure value for thefocal company over the long term. The development of analysis activitiesmay help firms minimize errors in understanding the value of differentkinds of customers. This is essential, because such errors are likely to leadin certain cases to the under- or overspending of resources (cf. Reinartz,Krafft, and Hoyer 2004, 296). When the company has an understandingabout the structure of its customer portfolio and the role of various custom-ers in providing value, it is arguably also better able to develop resource-allocation strategies to achieve its long-term and effectiveness goals (cf.Turnbull 1990, 21; Johnson and Selnes 2005).

Analysis efforts here refer to the focal company’s efforts to analyze itswhole portfolio of customers pertaining to their different roles in providing

FIGURE 1 Customer portfolio management construct.

Customer portfolio

management

Analysis efforts

Analysis design

Responsiveness efforts

Responsiveness design


384 H. Terho

current and future value for the focal company. According to the literatureand the qualitative study, it should be stressed here that customer valuemust be considered broadly in portfolio analysis, the activities being by nomeans restricted to assessing the direct, economic value (see also Appendix 1).Rather, the main focus is on the roles of different customers in the customerbase in terms of providing current and future value to the focal company(cf. Johnson and Selnes 2005). The various customer relationships serve a vari-ety of functions (Möller and Törrönen 2003; Walter, Ritter, and Gemünden2001; Wilson and Jantrania 1994). Similarly, the value of a relationship maybe realized through the customer’s broader role in the customer-base struc-ture, for example, via economies of scale rather than directly (Johnson andSelnes 2004, 3). In addition, customer-relationship characteristics such asstrength, commitment, trust, and mutuality influence its long-term develop-ment and success, therefore, affecting its future value. Because portfoliomanagement is a heavily future-oriented practice, the customer’s future valuepotential (e.g., growth, customer shares) also represents a major aspect ofCPM. It is not enough to understand the value of individual relationships,and the focus should also be on understanding how each relationship typefits into the larger portfolio (cf. Cannon and Perreault 1999, 457). Hence,activities such as comparing, grouping, and prioritizing customers based ontheir value are essential in analysis efforts.

ANALYSIS DESIGN

If the analysis activities miss some central aspects of the business of thefocal firm, are of poor quality, or concentrate on the wrong issues they willproduce unsatisfactory and possibly misleading outcomes (cf. Zolkiewskiand Turnbull 2002, 578–582). Hence, tailoring portfolio-management activitiesis crucial to companies’ CPM practices (cf. Terho and Halinen 2007; Salle,Cova, and Pardo 2000). This issue also arose in the field study, in which theanalysis practices were found to vary strongly based on perceived CPM con-tingencies. The development of analysis activities may take place informallythrough learning in everyday business, and also through explicit, systematicdevelopment. Significantly, the activities may but need not be formallydesigned.

The concept of analysis design refers to the focal company’s continu-ous efforts to plan and adapt its customer portfolio analysis activities tocompany needs. In other words, design refers to how much effort a com-pany has to put into planning its analysis activities; for example, establish-ing the criteria, methods, and procedures, as well as further developing itscurrent activities. Clearly, the adoption of highly designed and sophisticatedanalysis practices is close to the use of the formal portfolio models sug-gested in the literature. This also implies more managerial planning in port-folio management. Therefore, high levels of design indicate mechanistic



portfolio management with formal rules and procedures and more limitedparticipation in the decision making (cf. Burns and Stalker 1961).

RESPONSIVENESS EFFORTS

The third main CPM element is responsiveness efforts, referring to the focalcompany’s efforts to adjust its resource allocation according to the value ofdifferent customers in its current and future customer portfolio. Very little isaccomplished in a thorough analysis of customers unless the firm is able torespond to the knowledge it gains through such activities. CPM is a future-oriented practice because it basically helps the company understand its cur-rent portfolio so that it is equipped to produce better future resource alloca-tion among customers of different value. The resource-allocation strategiesof customer portfolio models fall into two theoretically meaningful classes:matching and development (see Figure 2; Appendix 1).

Matching relates to the cost-efficient treatment of customers of differingvalue in the current customer relationship portfolio. It involves issues suchas tailored offerings, different operational models (e.g., service, channels),and the allocation of sales resources to customers of differing value. On theother hand, the development of a relationship-portfolio structure focuses onthe future-oriented question of which relationships to develop and in whichdirection. Zolkiewski and Turnbull (2002, 578) separated the possible rela-tionship-development implications in the form of four questions: Do newrelationships need to be created? Which relationships should be developed?Which relationships should be maintained? Are there any relationships thatshould be broken or discarded? This division is not exactly clear-cutbecause both of these aspects of resource allocation overlap: customer-treatment decisions always include a relationship-development aspect, andvice versa.

RESPONSIVENESS DESIGN

Another side to responsiveness concerns its design. The responsivenessdesign refers to the focal company’s continuous efforts to plan and adapt itsresponsiveness activities to company needs with a view to implementingthem in practice. Responsiveness design is a continuous process, and

FIGURE 2 Two Main Aspects of Resource Allocation for Hypothetical Customer Portfolio (A,B, C, D, E).

E B A

D C

1. Strategies for adjusting resource allocation to match the value of different customers: How to manage efficiently customer groups A, B, C, D, and E?

2. Strategies for developing customer portfolio structure: How to develop customer groups A, B, C, D, and E?


386 H. Terho

includes the evaluation of current practices and their adaptation based onfeedback. Unlike analysis design, responsiveness design directly incorpo-rates implementation issues. Implementation is not self-evident: many of theinterviewees in the pilot study indicated that they had difficulties putting thedifferentiated resource allocation into practice. This was the result, in manycases, of a sales-volume-oriented culture in the sales department, for exam-ple, emphasizing volume rather than profitability. To be able to successfullyimplement differentiated resource allocation a firm must be able to realizeits strategies in the actions of its personnel at the customer interface in vari-ous functions (cf. Campbell 2003, 380–381). For example, instructions aboutcustomer-management principles may be crucial. In sum, responsivenessdesign concerns the careful planning of resource allocation and the adapta-tion of current practices. High levels of design in responsiveness indicatemore formal, mechanistic customer management.

MEASURES OF CPM

Formative Measurement

Ever since Churchill (1979) presented his article about measure develop-ment, researchers in marketing have devoted considerable attention to thedevelopment of multiple-item measures with sound psychometric proper-ties. Measurement in marketing has been largely based on the ideas behindclassical test theory and its assumptions about the relationship between aconstruct and its indicators. The basic assumption here is that the latentvariable results in the indicators (Bollen and Lennox 1991). Because thedirection of the causality is from the construct to the indicators, and changein the construct causes changes in the indicators, the classical measures arereferred to as reflective. In more formal terms, it is assumed in classical testtheory that the variation in the scores on measures of a construct is a func-tion of the true score, plus the error (Jarvis, Mackezie, and Podsakoff 2003,199). The reflective indicators should, therefore, be internally consistent, asthey all reflect the same underlying construct. For the same reason, the indi-cators in reflective measurement should be interchangeable and the con-struct validity should be unchanged when a single indicator is removed(Bollen and Lennox 1991).

However, the suggested CPM conceptualization does not fit easily intothe dominant reflective-measurement perspective (cf. Jarvis, Mackezie, andPodsakoff 2003). First, all four CPM dimensions are internally very broad,and each of them includes a wide variety of indicators that are not necessar-ily intercorrelated. For example, a company may analyze its current cus-tomer value but it does not have to analyze the future value potential.Similarly, it may manage customers of different value very efficiently but itdoes not have to try to develop its customer structure by driving customer



relationships in a certain direction. Second, the same applies to the fourCPM dimensions, which are not necessarily intercorrelated. This is obvious,because the existence of analysis activities does not mean that the companywill respond to this knowledge. Neither do the analysis nor responsivenessefforts need to be carefully designed.

CPM is better operationalized through the use of formative (cause)indicators (see Figure 1). Formative measurement is based on the idea thatthe indicators cause the concept measured. This has several effects on theproperties of such measures. First, the internal-consistency criterion is notvalid for the cause indicators (Bollen 1984, 381). On the contrary, a censusof indicators, not a sample, is required for indicator specification in forma-tive measures (cf. Bollen and Lennox 1991, 308). Second, as the formativeconstructs are caused by their indicators, dropping an indicator may alterthe meaning of the construct. Third, for the same reason, the measurementerror cannot be measured on the item level but must rather be estimated onthe construct level (Bollen and Lenox 1991). A natural consequence of thesecharacteristics is that the formation and validation of formative measures dif-fer from traditional reflective measurement.

Diamantopoulos and Winklhofer (2001) have developed guidelines fordeveloping and validating formative measures. They separate four criticalissues for successful index construction: content specification, indicatorspecification, indicator collinearity, and external validity. Next, the CPMmeasure is developed in accordance with these guidelines. The contentspecification and the indicator specification are discussed in the context ofdeveloping the survey instrument, while the indicator collinearity and exter-nal validity are based on the empirical survey data.

Survey Instrument

The above presentation of the CPM construct was based on theory and afield study. The first step in the construction of formative measures, contentspecification, is firmly rooted in the extensive definitions of CPM and itsdimensions. The careful literature review and the empirical field study sup-port the content validity of the construct.

The second step, indicator specification, is based on definitions of theCPM dimensions. In order to ensure that the indicators will cover the entirescope of the construct, conceptual matrices based on these definitions areused as a guide. They provide a structured means of ensuring that the ques-tions evenly cover all the main aspects. The list of items was tested in inter-views with experts. The aim of this qualitative process is to ensure that thequestions are understandable, relevant, and do not overlap too much. Itconsisted of an additional seven personal interviews with the senior manag-ers responsible for customer management (with an emphasis on the clarityand scope of the questions), 10 personal interviews with academic experts


388 H. Terho

(with an emphasis on the theoretical rigidity and scope of the questions),and a critical review with nine academics examining how the indicators fitthe definitions. This process resulted in modifications to several questionsuntil the indicators were found suitable. The result was a list of 10+6+9+6indicators covering all the main aspects of CPM without excessive overlap,presented in the questionnaire in Appendix 2. The theory-based matricesused in the formation of the indicators are presented below. The numbersin the matrices refer to the indicators in the questionnaire. The originalquestions were in Finnish.

Analysis efforts refer to the focal company’s efforts to analyze its wholeportfolio of customers pertaining to their different roles in providing it withcurrent and future value. According to the definition, the two main aspectsof analysis efforts comprise the temporal aspect of customer value analysis(current vs. future) and the degree to which individual customers and thewhole portfolio are analyzed. The codes in the matrix correspond to thequestions in the questionnaire (see Figure 3; Appendix 2).

Analysis design refers to the focal company’s continuous efforts to planand to adapt its CPM activities to company needs. According to the defini-tion, two main aspects are distinguished: time (what has been done/future-oriented development orientation) and the type of design (planning/adapta-tion of practices). The codes in the matrix correspond to the questions inthe questionnaire (see Figure 4; Appendix 2).

Responsiveness efforts refer to the focal company’s efforts to adjust itsresource allocation according to the value of different customers in its cur-rent and future customer portfolio. The main aspects are, once again, thetemporal aspect (current/future moment) and the focus of the resource allo-cation (matching/development). The codes in the matrix correspond to thequestions in the questionnaire (see Figure 5; Appendix 2).

Responsiveness design refers to the focal company’s continuous effortsto plan and to adapt its responsiveness activities to company needs with a

FIGURE 3 A conceptual matrix for forming items for analysis effort.

Current (backward-looking) value

Future value

Relationship level Portfolio level

AE1-AE2

AE3-AE4 AE8-AE10

AE5-AE7

FIGURE 4 A conceptual matrix for forming items for analysis design.

Current focus

Future focus

Planning of practices

Adaptation of practices

AD1-AD2

AD3 AD6

AD4-AD5



view to implementing them in practice. Again, two central aspects can bedrawn from the definition: time (what has been done/future-oriented devel-opment) and the type of design (planning/adaptation of practices). Thecodes in the matrix correspond to the questions in the questionnaire (seeFigure 6; Appendix 2)

The next stage was to test the suggested CPM measure empirically. Thedata collection is discussed first. The focus then shifts to multicollinearity andthe testing of external validity based on partial least squares (PLS) modeling.

Data Collection

Large companies are more likely to systematically apply portfolio manage-ment given their more extensive resources. Therefore, a purposive samplewas drawn from the Finnish Fonecta ProFinder B2B database for the largestB2B companies acting in all industries in Finland. There were no statisticalconsiderations in the company selection. In practice, selecting all firms witha turnover of more than €55 million gave a list of 630 companies. Those act-ing mainly in business-to-consumer businesses, nonprofit companies, andthose mainly supplying to their owners were dropped from the survey. CPMis frequently practiced in independent business areas in large companies,rather than as a centralized process. The researcher, therefore, contacted thesenior management in every company in the sample in order to (1) identifywhether there were one or more independent organizational units responsi-ble for CPM, (2) find the key persons responsible for CPM activities, and (3)motivate the respondents to participate in the study (cf. Huber and Power1985, 174–175). The final sample consisted of 493 independent units respon-sible for customer-base management. Given the difficult respondent group ofsenior marketing managers, a single-respondent approach was adopted toget a better response rate. Of the personally contacted independent unitmanagers, 446 promised to participate. They were all sent an electronic

FIGURE 5 A conceptual matrix for forming items for responsiveness efforts.

Current focus

Future focus

Matching focus Development focus

RE1-RE3

RE4 RE8-RE9

RE5-RE7

FIGURE 6 A conceptual matrix for forming items for responsiveness design.

Current focus

Future focus

Planning of practices

Adaptation of practices

RD1-RD2

RD3 RD6

RD4-RD5


390 H. Terho

questionnaire and two reminders in order to obtain as good a response rateas possible. After three months of data collection a total of 225 question-naires had been returned. In 18 cases two or more responses were receivedfrom a single company.

Removing responses with low respondent competency, measured bymeans of a separate question, and those with a substantial quantity of miss-ing values led to a total of 212 usable responses, which represented a highresponse rate of 43 percent. Even though the single-respondent strategy is alimitation, it helped in increasing the response rate, which in turn mayimprove the overall validity of this study. Armstrong and Overton’s (1977,396) guidelines for estimating nonresponse bias were used. First, the per-sonnel class, turnover, and industry of the respondent companies werecompared to all B2B companies in the focus area of the study for possiblebias. There were no statistically significant differences in turnover or indus-try, although the personnel size of the respondent companies was slightlylarger than that of the nonrespondent companies. Second, the early and laterespondents were compared, and no systematic differences were found. Insum, the responses could be considered to represent large Finnish B2Bcompanies well, and the results suggest that nonresponse-bias is not a prob-lem in this study.

Estimation and Results

The third step in the CPM measure formation process is the examination ofmulticollinearity. Excessive collinearity is a problem in formative measure-ment because it affects the stability of the indicator coefficients. Moreover,multicollinear indicators would include redundant information, making theindex problematic. In other words, index-construction procedures tend toeliminate highly intercorrelated items for minimizing multicolleniarity,whereas traditional scale-development procedures tend to retain highlyintercorrelated items for maximizing internal consistency (Diamantopoulosand Siguaw 2006, 271). Diamantopoulos and Winklhofer’s (2001, 272)guidelines include the use of the variance inflation factor (VIF), for whichthey suggest a cut-off threshold value of 10. According to the VIF values,none of the indicators were problematic. Diamantopoulos and Siguaw(2006, 271) suggest studying multicollinearity in terms of a related tolerancevalue with a more conservative a cut-off value of .30, and, as a result of thisprocedure, two items were deleted (questions AD1 and AD6). Furthermore,multicollinearity was studied by examining pairwise correlations where cor-relations greater than .8 indicate multicollinearity (Gujarati 2003, 359). Inthis case, most of the correlations remained less than .6 and all of themwere less than .7 (five variables greater than .6). Finally, Hair, Anderson,and Tatham’s (1995) two-part process for assessing multicolinearity did notreveal any such problems.



The fourth and final phase in formative measurement development isensuring the external validity of the construct. The formative indicators mayhave a positive, a negative, or no correlation between one another (Bollenand Lennox 1991, 307), which implies that the traditional assessment ofindividual item reliability and convergent validity is not meaningful for for-mative constructs (Hulland 1999). The error of an index may be approachedon the construct level rather than on the item level. According to Diamanto-poulos and Winklhofer (2001, 272), the external validity of an index shouldbe examined by means of a multiple indicators and multiple causes(MIMIC) model in which the formative indicators act as direct causes of thelatent variable, which, in turn, is indicated by one or more reflective indica-tors. If the overall model fit proves acceptable, it can be taken as supportingevidence for the set of indicators forming the index.

The CPM construct consists of the four dimensions of analysis efforts, anal-ysis design, responsiveness efforts, and responsiveness design, which togetherform the CPM measure. In the following, the MIMIC model is tested on theaggregate level, that is, all CPM dimensions are used as causes in an overallCPM measure using four reflective indicators. This is an established procedurefor validating formative constructs with multiple dimensions (e.g., Reinartz,Krafft, and Hoyer 2004; Ulaga and Eggert 2006). The indicators of the reflectiveCPM measure can be found in the questionnaire in Appendix 2.

Partial least squares, a component-based structural equation modelingtechnique, is used to test the MIMIC model. There are several reasons forchoosing this approach over maximum-likelihood-based methods such asLISREL. First, PLS can model latent constructs under conditions of non-nor-mality, which is the case in this research (e.g., Chin, Marcolin, and Newsted2003, 197). Second, it avoids two serious problems associated with maxi-mum-likelihood-based methods, namely, improper solutions and factorindeterminacy (Fornell and Bookstein 1982, 440). Attempts to explicitlymodel formative indicators in traditional structural equation modeling havebeen shown to lead to identification problems. One way of avoiding thisproblem is to apply components-based PLS, which can better model forma-tive indicators (Chin 1998a, 9–10). Partial least squares estimates the latentvariables as the exact linear combination of the observed measures, therebyavoiding the indeterminacy problem and providing an exact definition ofthe component scores. Finally, PLS is appropriate when the theory isuntested in an application domain (Gopal, Bostrom, and Chin 1992, 57).The results of the MIMIC model are discussed. The measurement modelresults are discussed first, followed by the results of the structural model.The SmartPLS 2.0 program was used to carry out the analysis (cf. Ringle,Wende, and Will 2005).

The four-item reflective CPM measure had a Cronbach’s alpha of .66 (.65acceptable), composite reliability of .80 (over .7), and an average varianceextracted (AVE) of .50 (should be larger than .5), see Table 1. The item


392 H. Terho

loadings were .67, .69, .72, and .73 (ideally over .7, but over .5 acceptable).Even though these figures are not ideal, they could be considered accept-able given the purpose of this research to develop measures, and the factthat the CPM measure is a very complex, widely varying multidimensionalconstruct. Its broadness makes it difficult to capture in its entirety with a sin-gle reflective measure (cf. Churchill 1979, 68).

Reliability and AVE are not applicable in formative measurement.Instead, item weights could be seen as validity coefficients. For this reasonDiamantopoulos and Winklhofer (2001) recommend removing nonsignifi-cant items in the MIMIC model. However, several authors stress that indicatorelimination, by whatever means, should not be separated from conceptualconsiderations when a formative measurement model is involved (Bollenand Lennox 1991, 308; Diamantopoulos and Winklhofer 2001, 273). Further-more, earlier studies have validated formative measures with several dimen-sions, based purely on structural relationships (e.g., Reinartz, Krafft, andHoyer 2004).

When the MIMIC model was tested several indicators turned out to havenegative or near-zero indicator weights, suggesting the need to remove indi-cators. The decision was made to drop those with weights of under 0.100, withtwo exceptions (RE1, RE4). This was possible because the suggested list ofindicators was very fine-grained, and there was slight overlap in the items.Therefore, removing the indicators did not alter the overall measure, as thefinal items still effectively covered the essential aspects of the CPM phenom-enon. More specifically, items AE3 and AE4 could be considered to be cov-ered by AE1 and AE2; AE6 by AE5 and AE7; AE8 by AE10; RE3 and RE7 byRE1, RE2, RE4; and RD4 by RD5 and RD6: AD1 and AD6 were removed ear-lier because of multicollinearity (see the questionnaire in Appendix 2) Themeasurement model results for the final purified CPM measure are shown inTable 2. Table 2 gives the item weights for the formative measures, the itemloadings for the reflective measures, and the t values for all measures.

Significantly, 19 of the 22 indicators were significant at least at the10 percent level, and all of the indicators had positive indicator weights(retained nonsignificant indicators: AE2, RE1, RE4). Because PLS is based onstandard ordinary least squares regression, misspecification due to the inclu-sion of “irrelevant” items will not bias the estimates of significant items

TABLE 1 Summary Statistics for Measures

Construct nameNumber of items Mean

Std. Deviation Range (1–7) Reliability AVE

Analysis efforts 6 4.75 1.00 1.40−7.00 -/- -Analysis design 4 4.25 1.34 1.00−7.00 -/- -Responsivness design 5 4.44 1.18 1.33−7.00 -/- -Responsivness efforts 7 5.07 0.92 1.44−6.78 -/- -Reflective CPM measure 4 4.89 1.04 1.00−7.00 .66/.80 .50



(Mathieson, Peacock, and Chin 2001, 107). The 10 percent significance levelfor the indicators was acceptable because of their strong conceptual sup-port. The difficulty of forming a good reflective CPM measure also supportsthis choice because the nomological context is important in assessing therelative importance of formative measures (Mathieson, Peacock, and Chin2001, 107).

Furthermore, the structural model depicted in Figure 7 supports the con-struct validity of the CPM measure. The figure shows the path coefficients,with the t values in parenthesis, and the R2 value for the structural model.The interpretation of the R2 values is identical to that of traditional regres-sion (Chin 1998b, 316). The corresponding path estimates could also beinterpreted in the same manner. Following the bootstrap procedureincluded in the SmartPLS (500 resamples, as recommended by Chin 1998b,323), all of the model path coefficients were found to be significant. The R2

of the CPM construct was substantial (.777), indicating that the reflectiveand formative measurement approaches shared 78 percent of their variance,supporting the construct validity of the formative CPM measure.

TABLE 2 Measurement Model Results for the MIMIC Model

Formative Indicators Indicator Weights t statistics

AD2 -> Analysis_Design .241 2.473AD3 -> Analysis_Design .274 3.485AD4 -> Analysis_Design .381 4.116AD5 -> Analysis_Design .317 3.644AE1 -> Analysis_Effort .165 1.718AE2 -> Analysis_Effort .117 1.248AE5 -> Analysis_Effort .230 2.496AE7 -> Analysis_Effort .413 4.152AE9 -> Analysis_Effort .192 1.817AE10 -> Analysis_Effort .301 3.122RD1 -> Responsiveness_Design .161 1.814RD2 -> Responsiveness_Design .318 3.214RD3 -> Responsiveness_Design .304 3.088RD5 -> Responsiveness_Design .187 1.964RD6 -> Responsiveness_Design .309 3.820RE1 -> Responsiveness_Effort .087 0.831RE2 -> Responsiveness_Effort .423 4.620RE4 -> Responsiveness_Effort .033 0.367RE5 -> Responsiveness_Effort .340 3.491RE6 -> Responsiveness_Effort .168 1.677RE8 -> Responsiveness_Effort .185 2.745RE9 -> Responsiveness_Effort .227 3.011

Reflective Indicators Indicator Loadings t statisticsRF1 <- Reflective_CPM .689 10.307RF2 <- Reflective_CPM .674 10.722RF3 <- Reflective_CPM .723 12.274RF4 <- Reflective_CPM .733 15.135

Note: t value 1.64 = 10%; 1.96 = 5%; 2.54 = 1% significance.


394 H. Terho

Interestingly, analysis and responsiveness efforts evinced weaker pathcoefficients than design in the structural model. This was clearly caused bythe narrow scope of the reflective CPM measure, and can also be seen if thecorrelations and squared AVE figures for the CPM constructs are examined(see Table 3). The correlation table shows that all the CPM dimensions arehighly correlated. However, design correlates more highly with the reflec-tive CPM measure than with efforts. Furthermore, the correlations betweenthe CPM dimensions are smaller than the squared AVE figures, the reflectiveCPM measure being an exception. This was expected, as the reflective CPMmeasure should overlap all formative measures.

The GoF figure (geometric mean of the average communality and theaverage R2) can be used to estimate the overall goodness-of-fit of the PLSmodel. Even though the AVE figure is not well suited to formative mea-surement, the GoF figure was examined, indicating a good fit of .644(cf. Tenenhaus et al. 2005, 173). Therefore, according to the path coeffi-cients, the R2 value and the GoF value, the MIMIC model has a good fitwith the empirical data.

Finally, an alternative way of conceptualizing customer portfolio man-agement is as a second-order construct. In the building of new measures,

FIGURE 7 Structural model results for the MIMIC model*t values 1.96 = 5% significancelevel. **t values 2.54 = 1% significance level.

Reflective CPM

Measure

Analysis design

Analysis efforts

Responsiveness design

Responsiveness efforts

R2 = .777.135* (2.403)

.226** (3.989)

.325** (5.916)

.366** (4.931)

TABLE 3 Latent Variable Correlations and Squared AVE for CPM Dimensions and ReflectiveCPM Measure

Ana_Des Ana_Eff Resp_des Resp_Eff Refl_CPM

Ana_Des (.823)Ana_Eff .569 (.683)Resp_des .669 .564 (.775)Resp_Eff .481 .647 .571 (.656)Refl_CPM .769 .673 .775 .675 .705

Note: The bolded figures are squared AVE. AVE is not well suited for formative measurement and is therefore in sparenthesis for formative measures.



choosing between first-order and second-order conceptualizations shouldalways be based on both theoretical and empirical reasoning. Hierarchicalcomponent analysis, as recommended by Wold (cf. Lohmöller 1989, 130–133),was used to test this alternative second-order conceptualization. In this case,it comprised the two second-order activities of analysis and responsiveness,both of which comprised CPM effort and design. Technically speaking, asecond-order factor is directly measured by observed variables for all thefirst-order factors. While this approach repeats the number of manifest vari-ables used, the standard PLS algorithm can be used to estimate the model(Chin, Marcolin, and Newsted 2003, 197). In other words, the second-orderconstructs repeat the indicators of the lower-order constructs (for analysis4+10 indicators and for responsiveness 6+9 indicators).

Significantly, the outer model results (indicator weights) are similar inthis model to the previously tested first-order conceptualization of CPM.Therefore, both of these MIMIC models suggest keeping and dropping thesame indicators, thereby providing further support for the validity of theconstruct. The R2 value of the tested second-order model was .775 and theGoF figure was .642, indicating that choosing a second-order conceptualiza-tion of CPM does not lead to a better model fit. Therefore, the simpler first-order model is a reasonable choice.

LIMITATIONS AND IMPLICATIONS FOR FUTURE RESEARCH

This study developed a conceptualization of customer portfolio manage-ment based on theory and a qualitative field study. The suggested constructis formative and consists of four dimensions: analysis efforts, analysisdesign, responsiveness efforts, and responsiveness design. Hence, it takesinto account both the strength and style of companies’ management prac-tices. Together with the content validity established in the conceptual phaseof this research, the empirical results based on survey data (N = 212) lendsupport to the construct validity of the suggested CPM measure.

However, there are also limitations related to the novelty of the mea-sure. The new measure should be cross-validated with a fresh set of data.Moreover, its nomological validity should be examined by linking it to otherconstructs (antecedents or consequences) to which it could feasibly belinked (cf. Diamantopoulos and Winklhofer 2001, 273). Avenues for futureresearch are suggested based on both the theory and the field-study results.

A crucial question for future research is whether companies’ CPM prac-tices are related to company performance (cf. Turnbull and Zolkiewski1997), or whether they could even be counterproductive (cf. Dubois andPedersen 2002). CPM involves the processing of customer information andresponding to the new knowledge and insights gained in this process.Therefore, it could be seen as organizational learning, which, at its most


396 H. Terho

basic level, is the development of new knowledge or insights that have thepotential to influence the behavior of the organization (cf. Huber 1991).Organizational learning is widely recognized as an important aspect of thestrategic performance of companies (e.g., Fiol and Lyles 1985, 803; Slaterand Narver 1995, 66–67). Thus it could be hypothesized that CPM activitiesare linked to performance.

Future research should consider three areas of performance. First, CPMactivities may produce new and more precise knowledge about the portfolio ofcustomers and their value to the focal firm. The learning that takes place insuch activities enables firms to allocate their resources more efficientlyamong customers, thereby avoiding underspending or overspending. There-fore, it could be hypothesized that the activities of analysis and responsive-ness are connected to financial customer performance, in other words tocustomer profitability. Second, CPM focuses on managing customers basedon their value to the selling company. This strong supplier focus raises theinteresting question of how its activities affect the customer perceived valuein the exchange. Based on earlier studies, both negative and positive effectscan be argued for (cf. Armstrong and Brodie 1994; Dubois and Pedersen2002; Johnson and Selnes 2004, 15). Too strict a focus on customer profit-ability and customer costs in management could potentially decrease per-ceived value creation, customer satisfaction, and customer retention, all ofwhich are reflected in overall customer performance. Then again, insightinto the selling company’s value creation could also result in openness andthe understanding of customer value. Consequently, a positive relationshipcould be hypothesized. Third, the two areas of customer performance dis-cussed are both key operational performance measures that form a meaning-ful link from CPM efforts to firm performance (cf. Venkatraman and Ramanujan1986). If these links are strong enough the former may also have a directrelationship with the latter.

Any testing of the link between companies’ CPM practices and perfor-mance should take into account the different roles of effort and design.First, behavior change is the necessary link between organizational learn-ing and performance improvement (Slater and Narver 1995, 66). It is,therefore, rational to consider that only analysis and responsivenessefforts, that is, behavior, can provide a direct link to performance. Second,analysis and responsiveness design approximates CPM style and cannottherefore affect performance in isolation. Given that CPM efforts may butdo not need to be highly designed, it would be very interesting to studywhether the careful design of CPM practices would amplify the relation-ship between efforts and performance. In other words, does analysis andresponsiveness design mediate the relationship between respective CPMefforts and performance? It would be rational to study mediation insteadof moderation because the four CPM dimensions are all highly correlated(cf. Baron and Kenny 1986, 1176).



Finally, future research should pay careful attention to the possiblefirm-external and firm-internal contingencies (moderators) in testing therelationship between CPM activities and performance with a view to creat-ing more managerially and theoretically relevant knowledge. For example, ahighly relevant proposition put forward by Möller and Halinen (1999, 2000)and Terho and Halinen (2007) is that the relational complexity and the con-text of exchange strongly affect what kind of customer management is rea-sonable in practice. On this basis, it could be hypothesized that market-likeexchange contexts are likely to favor highly designed, more mechanisticCPM practices whereas network-like exchange contexts are likely to favorless designed, more organic CPM practices. Several company-internal vari-ables could also be hypothesized to moderate the relationship betweenCPM activities and performance. The main issues identified concern theacquisition, quality, and adequacy of customer information; the use of infor-mation technology, organizational alignment, interdepartmental relation-ships; and the role of the accounting function.

REFERENCES

Armstrong, S. and R. Brodie (1994). Effects of portfolio planning methods ondecision-making: Experimental results. International Journal of Research inMarketing 11(1): 73–84.

Armstrong, S. and T. Overton (1977). Estimating nonresponse bias in mail surveys.Journal of Marketing Research 16(3): 396–402.

Baron, R. and D. Kenny (1986). The moderator–mediator variable distinction insocial psychological research: Conceptual, strategic, and statistical consider-ations. Journal of Personality and Social Psychology 51(6): 1173–1182.

Bollen, K. (1984). Multiple indicators: Internal consistency or no necessary relation-ship. Quality and Quantity 18(4): 377–385.

Bollen, K. and R. Lennox (1991). Conventional wisdom on measurement: A struc-tural equation modeling perspective. Psychological Bulletin 110(2): 305–314.

Bowman, D. and D. Narayandas (2004). Linking customer management effort tocustomer profitability in business markets. Journal of Marketing Research41(4): 433–447.

Burns, T. and G. Stalker (1961). The management of innovation. London: TavistockPublication.

Campbell, A. (2003). Creating customer knowledge competence: Managing relation-ship management programs strategically. Industrial Marketing Management32(5): 375–383.

Campbell, N. and M. Cunningham (1983). Customer analysis for strategy develop-ment in industrial markets. Strategic Management Journal 4(4): 369–380.

Canning, G. Jr. (1982). Do a value analysis of your customer base. IndustrialMarketing Management 11(2): 89–93.

Cannon, J. and W. Perreault (1999). Buyer–seller relationships in business markets.Journal of Marketing Research 36(4): 439–460.


398 H. Terho

Chakravarthy (1982). Adaptation: A promising metaphor for strategic management.Academy of Management Review 7(1): 35–44.

Chin, W. (1998a). Issues and opinion on structural equation modeling. MIS Quar-terly 22(1): 7–16.

Chin, W. (1998b). The partial least squares approach to structural equation model-ing, in Modern methods for business research, ed. G.A. Marcoulides, 295–336.Mahwah, NJ: Lawrence Erlbaum Associates.

Chin, W., B. Marcolin, and P. Newsted (2003). A partial least squares latent variablemodeling approach for measuring interaction effects: Results from a MonteCarlo simulation study and an electronic-mail emotion/adoption study. Infor-mation Systems Research 14(2): 189–217.

Churchill, G. (1979). A paradigm for developing better measures of marketingconstructs. Journal of Marketing Research 16(1): 64–73.

Cunningham, M. and E. Homse (1982). An interaction approach to marketing strat-egy, in International marketing and purchasing of industrial good, ed. H.Håkansson, 328–345. Chichester, UK: John Wiley & Sons.

Dahlstrom, R., R. Dwyer, and M. Chandrashekaran (1995). Environment, structureand performance in interfirm exchange. Journal of Business to Business Market-ing 55(2): 123–132.

Dhar, R. and R. Glazer (2003). Hedging customers. Harvard Business Review 81(5):86–92.

Diamantopoulos, A. and J. Siguaw (2006). Formative versus reflective indicators inorganizational measure development: A comparison and empirical illustration.British Journal of Management 17(4): 263–282.

Diamantopoulos, A. and H. Winklhofer (2001). Index construction with formativeindicators: An alternative to scale development. Journal of Marketing Research38(2): 269–277.

Dickson, P. (1983). Distributor portfolio analysis and the channel dependencematrix: New techniques for understanding and managing the channel. Journalof Marketing 47(3): 35–44.

Dubinsky, A. (1986). Customer portfolio analysis, in Advances in business mar-keting: A research annual, ed. A. Woodside, 113–139. Greenwich, CT: JAIPress.

Dubinsky, A. and T. Ingram (1984). A portfolio approach to account profitability.Industrial Marketing Management 13(1): 33–41.

Dubois, A. and A.-C. Pedersen (2002). Why relationships do not fit into purchasing port-folio models: A comparison between the portfolio and industrial networkapproaches. European Journal of Purchasing and Supply Management 8(1): 35–42.

Eisenhardt, K. (1989). Building theories from case study research. Academy of Man-agement Review 13(4): 532–550

Eng, T.-Y. (2004). Does customer portfolio analysis relate to customer performance?An empirical analysis of alternative strategic perspective. Journal of Businessand Industrial Marketing 19(1): 49–67.

Fiocca, R. (1982). Account portfolio analysis for strategy development. IndustrialMarketing Management 11(1): 53–62.

Fiol, M. and M. Lyles (1985). Organizational learning. Academy of ManagementReview 10(4): 803–813.



Fornell, C. and F. Bookstein (1982). Two structural equation modeling models:LISREL and PLS applied to consumer exit-voice theory. Journal of MarketingResearch 19(4): 440–452.

Freytag, P. and N. Mols (2001). Customer portfolios and segmentation, in Portfolioplanning in a relationship perspective, ed. In P. Freytag, 93–123. Copenhagen:Forlaget Thomson.

Gopal, A., R. Bostrom, and W. Chin (1992). Applying adaptive structuration theory toinvestigate the process of group support systems use. Journal of ManagementInformation Systems 9(3): 45–69.

Grönroos, C. (1994). Quo vadis, marketing? Towards a relationship marketing para-digm. Journal of Marketing Management 10(4): 347–360.

Gujarati, D. (2003). Basic econometrics, 4th ed. Boston: McGraw Hill.Hair, J., R. Anderson, and R. Tatham (1995). Multivariate data analysis. New York:

Macmillan.Hartley, R. (1976). Use of customer analysis for better market penetration. Indus-

trial Marketing Management 11(3): 183–194.Huber, G. (1991). Organizational learning: The contributing processes and the liter-

ature. Organization Science 2(1): 88–115.Huber, G. and D. Power (1985). Retrospective reports of strategic-level managers:

Guidelines for increasing their accuracy. Strategic Management Journal 6(2):171–180.

Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research:A review of four recent studies. Strategic Management Journal 20(2): 195–204.

Jarvis, C., S. Mackenzie, and P. Podsakoff (2003). A critical review of construct indi-cators and measurement model misspecification in marketing and consumerresearch. Journal of Consumer Research 30(2): 199–218.

Jaworski, B. and A. Kohli (1993). Market orientation: Antecedents and conse-quences. Journal of Marketing 57(3): 53–70.

Johnson, M. and F. Selnes (2004). Customer portfolio management: Toward adynamic theory of exchange relationships. Journal of Marketing 68(2): 1–17.

Johnson, M. and F. Selnes (2005). Diversifying your customer portfolio. MIT SloanManagement Review 46(3): 11–14.

Kohli, A. and B. Jaworski (1990). Market orientation: The construct, research prop-ositions, and managerial implications. Journal of Marketing 54(2): 1–18.

Krapfel, R. Jr., D. Salmond, and R. Spekman (1991). A strategic approach to manag-ing buyer–seller relationships. European Journal of Marketing 25(9): 22–37.

LaForge, R. and D. Cravens (1982). Steps in selling effort deployment. IndustrialMarketing Management 11(3): 183–194.

Leek, S., P. Turnbull, and P. Naudé (2002). Managing business to business relation-ships: An emerging model. Journal of Customer Behaviour 1(3): 357–375.

Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares.Heidelberg: Physica-Verlag.

Mathieson, K., E. Peacock, and W. Chin (2001). Extending the technology accep-tance model: The influence of perceived user resources. The DATA BASE forAdvances in Information Systems 32(3): 86–112.

Möller, K. and A. Halinen (1999). Business relationships and networks: Managerialchallenge of network era. Industrial Marketing Management 28(5): 413–427.


400 H. Terho

Möller, K. and A. Halinen (2000). Relationship marketing theory: Its roots and direc-tion. Journal of Marketing Management 16(1–3): 29–54.

Möller, K. and P. Törrönen (2003). Business suppliers’ value creation potential: Acapability-based analysis. Industrial Marketing Management 32(2): 109–118.

Morgan, R. and S. Hunt (1994). The commitment-trust theory of relationshipmarketing. Journal of Marketing 58(2): 30–38.

Mulhern, F. (1999). Customer profitability analysis: Measurement, concentration,and research directions. Journal of Interactive Marketing 13(1): 25–40.

Niraj, R., M. Gupta, and C. Narasimhan (2001). Customer profitability in a supplychain. Journal of Marketing 65(2): 1–16.

Pels, J. (1992). Identification and management of key clients. European Journal ofMarketing 26(5): 5–21.

Rajagopal and R. Sanchez (2005). Analysis of customer portfolio and relationshipmanagement models: Bridging managerial dimensions. Journal of Business andIndustrial Marketing 20(6): 307–316.

Rangan, K., R. Moriarty, and G. Swartz (1992). Segmenting customers in matureindustrial markets. Journal of Marketing 56(4): 72–82.

Reinartz, W., M. Krafft, and W. Hoyer (2004). The customer relationship manage-ment process: Its measurement and impact on performance. Journal of Market-ing Research 41(3): 293–305.

Ringle, C., S. Wende, and A. Will (2005). SmartPLS 2.0 (beta). Available fromwww.smartpls.de

Ritter, T. (1999). The networking company: Antecedents for coping with relationshipsand networks effectively. Industrial Marketing Management 28(5): 467–479.

Ritter, T. (2000). A framework for analyzing interconnectedness of relationships.Industrial Marketing Management 29(4): 317–326.

Ritter, T., I. Wilkinson, and W. Johnston (2004). Managing in complex networks.Industrial marketing Management 33(3): 175–183.

Ryals, L. (2003). Making customers pay: Measuring and managing customer risk andreturns. Journal of Strategic Marketing 11(3): 165–175.

Ryals, L. (2005). Making customer relationship management work: The measurementand profitable management of customer relationships. Journal of Marketing69(4): 252–261.

Salle, R., B. Cova, and C. Pardo (2000). Portfolio of supplier–customer relationships,in Getting better at sensemaking, ed. A. Woodside, 419–442. Stamford, CT: JAIPress.

Shapiro, B., K. Rangan, R. Moriarty, and E. Ross (1987). Manage customers forprofits (not just sales). Harvard Business Review 65(5): 101–108.

Sinkula, J. (1994). Market information processing and organizational learning.Journal of Marketing 58(1): 35–45.

Slater, S. and J. Narver (1995). Market orientation and the learning organization.Journal of Marketing 59(3): 63–74.

Smackey, B. (1977). A profit emphasis for improving sales force productivity.Industrial Marketing Management 6(3): 135–140.

Storbacka, K. (1997). Segmentation based on customer profitability: Retrospectiveanalysis of retail bank customer bases. Journal of Marketing Management13(5): 479–492.



Tenenhaus, M., E. Vinzi, Y-M. Chatelin, and C. Lauro (2005). PLS path modeling.Computational Statistics and Data Analysis 48(1): 159–205.

Terho, H. and A. Halinen (2007). Customer portfolio analysis practices in differentexchange contexts. Journal of Business Research 60(7): 720–730.

Tikkanen, H., J. Kujala, and K. Artto (2007). The marketing strategy of a project-based firm: The four portfolios framework. Industrial Marketing Management36(1): 194–205.

Turnbull, P. (1990). A review of portfolio planning models for industrial marketingand purchasing management. European Journal of Marketing 24(3): 7–22.

Turnbull, P. and J. Zolkiewski (1997). Profitability in customer portfolio planning, inUnderstanding business markets: Interaction, relationships and networks, 2nded., ed. D. Ford, 305–325. London: The Dryen Press.

Ulaga, W. and A. Eggert (2006). Value-based differentiation in business relation-ships: Gaining and sustaining key supplier status. Journal of Marketing 70(1):119–136.

Venkatraman, N. and V. Ramanujan (1986). Measurement of business performancein strategy research: A comparison of approaches. Academy of ManagementReview 1(4): 801–814.

Walter, A., T. Ritter, and H.G. Gemünden (2001). Value creation in buyer–sellerrelationships: Theoretical considerations and empirical results from a supplier’sperspective. Industrial Marketing Management 30(4): 365–377.

Wilson, D. and S. Jantrania (1994). Understanding the value of a relationship. Asia-Australia Marketing Journal 2(1): 55–66.

Wilson, H., E. Daniel, and M. McDonald (2002). Factors for success in customerrelationship management (CRM) systems. Journal of Marketing Management18(1–2): 193–219.

Yin, R. (2003). Case study research: Design and methods, 3rd ed. Thousand Oaks,CA: Sage Publications.

Yorke, D. and G. Droussiotis (1994). The use of customer portfolio theory: Anempirical survey. Journal of Business and Industrial Marketing 9(3): 6–18.

Zolkiewski, J. and P. Turnbull (2002). Do relationship portfolios and networks pro-vide the key to successful relationship management? Journal of Business andIndustrial Marketing 17(7): 575–597.


402

APPEN

DIX

1. CU

STO

MER P

ORTFO

LIO

MO

DELS

AN

ALY

ZED

Au

tho

rSt

eps

Dim

ensi

on

s an

d o

per

atio

nal

izat

ion

Man

ager

ial

imp

lica

tio

ns

Har

tley

(197

6)1)

Sal

es a

nal

ysis

2) M

arke

t pen

etra

tion s

trat

egie

sSa

les

volu

me,

cu

sto

mer

’s i

nd

ust

ry, sa

les

tren

dSt

rate

gies

for

mar

ket pen

etra

tion:

(pre

sent ge

ogr

aphic

al, oth

er S

IC

mar

ket)

Smac

key

(197

7)1)

Acc

ount an

alys

is2)

Sal

es fore

cast

and p

lannin

g of

sale

s re

sourc

e al

loca

tion

3) M

onito

ring

resu

lts

Sale

s vo

lum

e: h

isto

ry, cu

rren

t, t

arge

ted

Co

mp

are

sale

s vo

lum

es, p

rod

uct

lin

es, an

d

sale

s ef

fort

s

Allo

cate

sal

es forc

e re

sourc

es to

estim

ated

cust

om

er p

ote

ntia

l

Cunnin

gham

an

d H

om

se (

1982

)1)

Cre

ate

a portfo

lio for

anal

yzin

g cu

stom

ers

Sale

s vo

lum

e T

ech

nic

al i

nte

ract

ion

an

d a

ssis

tan

ce

(Cu

sto

mer

’s i

mp

ort

ance

as

a “r

efer

ence

p

oin

t)

(Cu

sto

mer

’s a

bil

ity

to

pro

vid

e im

po

rtan

t co

mm

erci

al i

nfo

rmat

ion

)

Hel

p u

nder

stan

din

g diffe

rent

cust

om

ers’ r

ole

s fo

r dev

elopin

g cu

stom

er s

truct

ure

Can

nin

g (1

982)

1) C

reat

ing

a pro

fit pro

file

Pro

fit

pro

file

:D

o d

edic

ated

sal

es p

rogr

ams

for

cust

om

ers

with

sim

ilar

valu

e an

d

requirem

ents

2) D

eter

min

ing

the

sourc

e of

pro

fits

Direc

t oper

atin

g co

sts,

sal

es o

rder

pro

cess

ing,

fiel

d s

ervi

ce c

ost

sSo

urc

e o

f p

rofi

ts:

Pro

duct

mix

, in

dust

ry s

erve

d, vo

lum

e so

ld,

ord

er fre

quen

cy/

size

, sh

are

of cu

stom

ers

busi

nes

s, len

gth o

f tim

e se

rved

, co

mpet

itive

nes

s of purc

has

e

3) A

sses

sing

the

valu

e el

emen

ts

bey

ond p

rofits

:4)

Est

ablis

h a

val

ue

ranki

ng

5) S

et u

p a

mar

ketin

g pro

gram

Val

ue

elem

ents

bey

on

d p

rofi

ts:

Gro

wth

, te

chnolo

gica

l is

sues

, cu

stom

er a

s a

refe

rentia

l so

urc

e, s

har

e of cu

stom

er v

olu

me

LaFo

rge

and C

rave

ns

(198

2)1)

Cla

ssify

PCU

for

selli

ng

effo

rt

dep

loym

ent

PC

U (

pla

nn

ing

and

co

ntr

ol

un

it)

op

po

rtu

nit

y:

Size

of ac

count,

sale

s gr

ow

th r

ate

of ac

count,

the

inte

nsi

ty o

f co

mpet

ition/a

ccount

Adju

st s

ales

res

ourc

es to c

ust

om

ers

with

diffe

rent pote

ntia

l

Sale

s o

rgan

izat

ion

str

engt

h:

Pro

duct

dis

trib

utio

n/t

he

item

s st

ock

ed, sh

elf

spac

e an

d tra

de

rela

tions


403

Fiocc

a (1

982)

1) A

nal

yze

all cu

stom

ers

in

gener

al lev

elT

he

imp

ort

ance

of

the

cust

om

er:

Volu

me

of purc

has

es, p

ote

ntia

l sal

es, p

rest

ige

of

the

acco

unt,

mar

ket le

ader

ship

, ove

rall

acco

unt des

irab

ility

Dif

ficu

lty

of

man

agin

g ea

ch a

cco

un

t:Pro

duct

char

acte

rist

ics (n

ove

lty a

nd c

om

ple

xity

),

acco

unt’s

char

acte

rist

ics,

(cu

stom

ers’ n

eeds,

buyi

ng

beh

avio

r, c

om

pet

enci

es, pow

er, an

d

com

pet

itive

nes

s) c

om

pet

ition for

the

acco

unt

(num

ber

, st

rengt

hs

and w

eakn

esse

s of

com

pet

itors

and c

om

pet

itors

posi

tion v

is-à

-vis

th

e cu

stom

er)

Gen

eral

lev

el: under

stan

d c

ust

om

er

stru

cture

Det

aile

d im

plic

atio

ns

for

the

key

cust

om

ers:

Impro

ve/

hold

/ w

ithdra

w p

osi

tion

with

a c

ust

om

er

2) A

nal

yze

the

key

acco

unts

se

par

atel

yC

ust

om

er’s

bu

sin

ess

attr

acti

ven

ess:

Com

pet

ition, m

arke

t, te

chnolo

gica

l, finan

cial

an

d e

conom

ic, so

cio-p

olit

ical

fac

tors

Th

e re

lati

ve s

tage

of

the

pre

sen

t b

uy

er/

sell

er r

elat

ion

ship

:Le

ngt

h o

f th

e re

latio

nsh

ip, v

olu

me

of purc

has

es,

importan

ce o

f th

e cu

stom

er, pow

er,

frie

ndsh

ip, co

-oper

atio

n in d

evel

opm

ent,

man

ager

ial &

geo

grap

hic

al d

ista

nce

Th

e m

on

etar

y v

alu

e o

f p

urc

has

es f

or

ever

y

pro

du

ctT

he

acco

un

t’s

mar

ket

sh

are

for

sell

ing

firm

’s e

ach

pro

du

ct

Dic

kson (

1983

)1)

Dis

trib

uto

r portfo

lio a

nal

ysis

Th

e gr

ow

th r

ate

of

the

dis

trib

uto

rs’ s

ales

Tra

din

g ta

ctic

s fo

r diffe

rent cu

stom

ers

the

man

ufa

ctu

rers

sh

are

of

dis

trib

uto

rs’

sale

s o

f th

e p

rod

uct

or

pro

du

ct g

rou

pM

anu

fact

ure

r’s

sale

s o

f th

e p

rod

uct

per

ea

ch d

istr

ibu

tor

Exp

licit

pla

ns

for

dev

elopin

g lo

ng-

term

Chan

nel

dis

trib

utio

n m

ix (

offen

sive

in

vest

men

t, def

ensi

ve e

ntren

chm

ent,

stra

tegi

c re

trea

t, ab

andonm

ent)

2) C

han

nel

dep

enden

ce m

atrix

Gro

ss p

rofi

t an

d d

irec

t m

anu

fact

uri

ng

cost

sM

anu

fact

ure

r’s

mar

ket

sh

are

Dis

trib

uto

r’s

mar

ket

sh

are

(Con

tin

ued

)


404

AP

PE

ND

IX 1

(Con

tin

ued

)

Au

tho

rSt

eps

Dim

ensi

on

s an

d o

per

atio

nal

izat

ion

Man

ager

ial

imp

lica

tio

ns

Cam

pbel

l an

d

Cunnin

gham

(1

983)

1) L

ife

cycl

e cl

assi

fica

tion o

f cu

stom

ers

“A g

ener

al a

nal

ysis

of

cust

om

ers”

Sale

s vo

lum

eU

se o

f st

rate

gic

reso

urc

esA

ge o

f th

e re

lati

on

ship

Shar

e o

f cu

sto

mer

’s p

urc

has

esP

rofi

tab

ilit

y

Short ter

m: ge

ner

al h

elp for

allo

catin

g re

sourc

es a

mong

cust

om

ers

2) C

ust

om

er c

om

pet

itor

anal

ysis

by

mar

ket se

gmen

ts

“Introduce

s co

mpet

ition”

Cu

sto

mer

’s s

har

e in

his

mar

ket

Gro

wth

rat

e o

f cu

sto

mer

’s d

eman

d f

or

the

pro

du

ctV

olu

me

of

sup

pli

er’s

pro

du

ct p

urc

has

edC

om

pet

ito

rs’ s

har

es o

f th

e p

rod

uct

Long

term

: ge

ner

al h

elp for

under

stan

din

g an

d D

evel

opin

g cu

stom

er p

ortfo

lio s

truct

ure

3a)

Portfo

lio a

nal

ysis

of ke

y cu

stom

ers

Co

mp

etit

ive

po

siti

on

of

the

sell

erG

row

th r

ate

of

cust

om

er’s

mar

ket

Sale

s vo

lum

e o

f ea

ch c

ust

om

er3b

) Portfo

lio a

nal

ysis

of a

singl

e cu

stom

erG

row

th r

ate

of

cust

om

er’s

pu

rch

ases

Life

cy

cle

clas

sifi

cati

on

of

cust

om

er’s

d

iffe

ren

t b

usi

nes

ses:

Tom

orr

ow

’s, to

day

’s s

pec

ial,

today

’s n

orm

al,

yest

erday

’s b

usi

nes

sC

ust

om

er’s

vo

lum

eD

ubin

sky

and

Ingr

am (

1984

)1)

Cust

om

er p

rofita

bili

ty

portfo

lioT

he

cust

om

er’s

po

ten

tial

pro

fit

con

trib

uti

on

:

Net

sal

es -

(co

sts

of go

ods

sold

+ d

irec

t se

lling

expen

ces

of sa

lesp

erso

n)

now

Th

e p

rese

nt

pro

fit

con

trib

uti

on

of

a cu

sto

mer

:N

et s

ales

- (

cost

s of go

ods

sold

+ d

irec

t se

lling

expen

ses

of sa

lesp

erso

n)

in futu

re

Adju

st s

ales

res

ourc

es to c

ust

om

er

valu

e an

d futu

re p

ote

ntia

l

Dubin

sky

(198

6)2)

Mak

e a

cust

om

er c

om

posi

tion

Dev

elop n

eeded

cust

om

er rel

atio

nsh

ip

cate

gories

Shap

iro, Ran

gan,

Moriar

ty a

nd R

oss

(1

987)

1) C

ust

om

er p

rofita

bili

ty

portfo

lioC

ost

to

ser

ve:

Pre

sale

cost

s, p

roduct

ion c

ost

s, d

istrib

utio

n

cost

s, p

resa

le s

ervi

ce c

ost

sN

et p

rice

Adju

st m

arke

ting

stra

tegi

es for va

lue

of

diffe

rent cu

stom

er g

roups

2) M

anag

emen

t: five

-ste

p a

ctio

n

pla

n

Gen

eral

hel

p for

under

stan

din

g an

d

dev

elopin

g cu

stom

er p

rofita

bili

ty

stru

cture


405

Kra

pfe

l, Sa

lmond

and S

pek

man

(1

991)

1a)

Rel

atio

nsh

ip typ

e m

atrix

(typ

ing)

1b)

Rel

atio

nsh

ip m

anag

emen

t m

ode

mat

rix

(typ

ing)

2) C

om

bin

e th

e re

latio

nsh

ip typ

e an

d the

man

agem

ent m

ode

(map

pin

g)3)

Man

agem

ent m

ode

mat

chin

g ac

ross

the

dya

d4)

Sig

nal

ing

about re

latio

nsh

ip

Rel

atio

nsh

ip v

alu

e:Fu

nct

ion o

f cr

itica

lity,

quan

tity,

rep

lace

abili

ty,

slac

k (R

V=f(

C, Q

, R, S)

)In

tere

st c

om

mo

nal

ity:

Act

or’s

econom

ic g

oal

s an

d thei

r per

ceptio

n o

f par

tner

’s e

conom

ic g

oal

s

Adju

st, m

atch

, an

d s

ignal

inte

ract

ion/

man

agem

ent st

yle

to d

iffe

rent

rela

tionsh

ip typ

e

Per

ceiv

ed p

ow

er p

osi

tio

nIn

tere

st c

om

mo

nal

ity

Hel

p u

nder

stan

din

g an

d d

evel

opin

g cu

stom

er s

truct

ure

Ran

gan, M

oriar

ty

and S

war

tz (

1992

)1)

Seg

men

t in

dust

rial

cust

om

ers

Pri

ce

Co

st t

o s

erve

(P

ow

er)

Adju

st p

rici

ng

and lev

el o

f se

rvic

e to

cu

stom

er v

alue

Hel

p u

nder

stan

din

g an

d d

evel

opin

g cu

stom

er p

rofita

bili

ty s

truct

ure

Pel

s (1

992)

1) A

nal

yze

cust

om

ers

to d

ivid

e th

e m

ain a

nd k

ey c

ust

om

ers

Th

e p

oss

ibil

ity

of

incr

easi

ng

sale

s vo

lum

eT

he

cust

om

er’s

cap

acit

y t

o d

evel

op

th

e se

ller

’s i

mag

eT

he

kn

ow

-ho

w w

hic

h t

he

clie

nt

can

tra

nsf

er

or

hel

p t

o c

reat

e is

th

e n

etw

ork

eff

ect

Tai

lor

mar

ketin

g te

ams

for

key

clie

nts

York

e an

d

Dro

uss

iotis

(19

94)

1) D

evel

opin

g a

cust

om

er

portfo

lioT

he

stra

tegi

c im

po

rtan

ce o

f th

e ac

cou

nt:

Acc

ount pote

ntia

l, fu

ture

cap

acity

exp

ansi

ons,

lin

ks to e

xport m

arke

ts, ac

count pre

stig

e

Adju

st r

esourc

e al

loca

tion to c

ust

om

er

valu

e

2a)

Anal

yze

importan

t cu

stom

ers

in d

epth

- p

rofita

bili

tyT

he

dif

ficu

lty

in

man

agin

g th

e cu

sto

mer

:Com

pet

itor

entren

chm

ent,

pay

men

t pro

ble

ms,

cl

aim

s put fo

rwar

d, buyi

ng

beh

avio

r

Pla

ns

whic

h r

elat

ionsh

ips

should

be

dev

eloped

stronge

r fo

r lo

ng-

term

portfo

lio b

alan

ce2b

) Anal

yze

importan

t cust

om

ers

in d

epth

- p

erce

ived

re

latio

nsh

ip s

tren

gth

Cu

sto

mer

pro

fita

bil

ity:

Direc

t co

sts,

pse

udo-d

irec

t co

sts,

indirec

t co

sts

3) C

om

par

e an

im

portan

t cu

stom

er’s r

elat

ionsh

ip

stre

ngt

h a

nd m

arke

t sh

are

Per

ceiv

ed s

tren

gth

of

rela

tio

nsh

ip:

Tec

hnic

al a

bili

ty, ex

per

ience

, prici

ng,

spee

d o

f re

sponse

, fr

equen

cy o

f co

nta

ct, co

oper

atio

n,

trust

+ len

gth o

f re

latio

nsh

ip, fr

iendsh

ip,

man

agem

ent dis

tance

Cu

sto

mer

s m

ark

et s

har

eSt

ren

gth

of

the

rela

tio

nsh

ip

(Con

tin

ued

)


406

AP

PE

ND

IX 1

(Con

tin

ued

)

Au

tho

rSt

eps

Dim

ensi

on

s an

d o

per

atio

nal

izat

ion

Man

ager

ial

imp

lica

tio

ns

Storb

acka

(19

97)

1) T

hre

e vi

ews

to c

reat

e an

under

stan

din

g of cu

stom

er

bas

e an

d its

pro

fita

bili

ty

Rel

atio

nsh

ip r

even

ue

Rel

atio

nsh

ip c

ost

Adju

st c

ust

om

er s

trat

egie

s to

cust

om

er

cost

s/pro

fita

bili

tyV

olu

me

Rel

atio

nsh

ip v

olu

me

Rel

atio

nsh

ip p

rofi

tab

ilit

y

Hel

p u

nder

stan

din

g an

d d

evel

opin

g cu

stom

er p

rofita

bili

ty s

truct

ure

Turn

bull

and

Zolk

iew

ski (1

997)

1) A

nal

yze

cust

om

ers

with

a

thre

e dim

ensi

onal

portfo

lioN

et p

rice

Co

st t

o s

erve

:Pre

sale

cost

s, p

roduct

ion c

ost

s, d

istrib

utio

n

cost

s, p

resa

le s

ervi

ce c

ost

s (S

hap

iro)

Adju

st c

ust

om

er s

trat

egie

s to

cust

om

er

cost

s (m

onito

r m

igra

tion p

atte

rns)

2) M

onito

r th

e m

igra

tion o

f cu

stom

er p

osi

tions

Rel

atio

nsh

ip v

alu

e:Fu

nct

ion o

f cr

itica

lity,

quan

tity,

rep

lace

abili

ty,

slac

k (R

V=f(

C, Q

, R, S)

) (K

rapfe

l)

Hel

p u

nder

stan

din

g cu

stom

er p

ortfo

lio

stru

cture

Frey

tag

and M

ols

(2

001)

1) A

sk fiv

e ques

tions

from

mai

n

cust

om

ers

and d

ivid

e cu

stom

ers

to g

roups

with

re

gard

s of th

eir

importan

ce

now

and in the

futu

re.

Wh

y t

he

cust

om

er i

s im

po

rtan

t to

th

e fi

rm?

Econ

omy

(tur

nove

r, e

arni

ngs, c

osts

, dem

and)

, kn

ow-h

ow a

nd le

arni

ng (

pro

cess

es,

tech

nolo

gies

, em

plo

yees

’ kno

w-h

ow),

com

pet

ition

(lim

its a

nd a

dvan

tage

s), o

ther

poi

nts

(rep

utat

ion,

eth

ics)

Maj

or

stre

ngt

hs

and

wea

kn

esse

s o

f th

e cu

sto

mer

?H

ow

do

es c

ust

om

er s

ee s

elli

ng

firm

an

d

ho

w i

t b

ehav

iors

?In

wh

at d

irec

tio

n a

re t

he

cust

om

ers

dev

elo

pin

g? c

o-o

per

atio

n/c

om

pet

itio

n?

Is t

he

dir

ecti

on

in

wh

ich

th

e cu

sto

mer

is

hea

din

g co

mp

atib

le w

ith

sel

ler?

Prioritiz

atio

n in r

esourc

e al

loca

tion;

how

to a

ct tow

ard c

ust

om

ers

Whic

h r

elat

ionsh

ips

to d

evel

op,

mai

nta

in, te

rmin

ate

2) C

om

bin

e ques

tions

with

the

Turn

bull

and Z

olk

iew

ski’s

(1

997)

model

(note

: re

latio

nsh

ip v

alue

under

stood

diffe

rently

)

Net

pri

ceC

ost

to

ser

veR

elat

ion

ship

val

ue:

e.g.

, ea

rnin

gs, ac

cess

to k

now

ledge

, ac

cess

to

certai

n m

arke

ts, hig

h turn

ove

r


407

Zolk

iew

ski an

d

Turn

bull

(200

2)1)

Iden

tify

the

indiv

idual

portfo

lio c

onst

ituen

tsCust

om

er p

rofita

bili

tyRel

atio

nsh

ip v

alue

Stra

tegi

c im

portan

ce o

f th

e ac

count

Adju

st r

elat

ionsh

ip m

anag

emen

t st

rate

gies

to c

ust

om

er v

alue

(such

as

KA

M, at

tentio

n, sa

les,

contrac

ts,

time,

etc

.)2)

Iden

tify

the

inte

ract

ions

bet

wee

n p

ortfo

lio c

onst

ituen

tsExp

licit

pla

ns

for

dev

elopin

g cu

stom

er

portfo

lio s

truct

ure

: w

hic

h

rela

tionsh

ips

to d

evel

op, m

ainta

in,

bro

ke, cr

eate

new

.3)

Iden

tify

the

inte

ract

ions

bet

wee

n d

iffe

rent portfo

lios

Cust

om

er, su

pplie

r an

d indirec

t portfo

lios

Rya

ls (

2003

)1)

Anal

ysis

for

risk

adju

sted

va

lue

of th

e firm

’s c

ust

om

er

portfo

lio

Ret

urn

s fr

om

cu

sto

mer

po

rtfo

lio

:Su

m o

f in

div

idual

cust

om

er lifet

ime

valu

es

(rev

enue-

cost

s)R

isk

fro

m c

ust

om

er p

ort

foli

o.

(How

wel

l m

anag

ed, kn

ow

ledge

about

cust

om

er, risk

of bei

ng

take

n o

ver,

rela

tionsh

ip s

tren

gth)

Under

stan

d a

nd d

evel

op c

ust

om

er

portfo

lio p

rofita

bili

ty s

truct

ure

: try

to r

educe

cust

om

er r

isk,

acq

uire

less

ris

ky c

ust

om

ers


408 H. Terho

APPENDIX 2. QUESTIONNAIRE

The following statements deal with the strategic management of the customerbase and customer relationships. Please indicate to what extent you agree ordisagree with the statements in terms of the practices of your business unit(company): 1 = strongly disagree, 7 = strongly agree. (* = removed item)

Analysis Efforts (AE)

AE1 We analyze the value of all customer relationships in our customer baseAE2 We analyze the costs of all customer relationships in our customer baseAE3 We evaluate the expected value of our customer relationships*AE4 We look for customers in our customer base that have high future

value potential*AE5 We look for diverse customer groups in our customer base that repre-

sent different sorts of value for our companyAE6 We make comparisons of our customers based on their value*AE7 We segment our customers based on their valueAE8 We analyze the roles various customers have for our company over the

long term*AE9 We analyze the development of various customer groups in our cus-

tomer baseAE10 We analyze the health of our customer base over the long term

Analysis Design (AD)

AD1 We have carefully thought out the essential criteria for analyzing ourcustomer relationships*

AD2 We evaluate the quality of our customer-base analysis practicesAD3 We tend to discuss how to develop our customer-base analysis practicesAD4 We have tailored the criteria of our customer-base analysis to match

the special characteristics of our businessAD5 We have invested in developing our customer-base analysis methodsAD6 We adapt our customer-base analysis practices according to the experi-

ences gained from current practices*

Responsiveness Efforts (RE)

RE1 We tailor various product and service entities to customers based ontheir value

RE2 We have created different operational models for treating customerswith different kinds of value (e.g., service channels, level of service)



RE3 We allocate our sales resources to customers in relation to their valueto our company*

RE4 We systematically direct resources to customers that have high futurevalue potential

RE5 In our actions we aim at converting low-value relationships to morevaluable ones

RE6 We systematically develop our most valuable customer relationshipsRE7 We try to retain customer relationships that do not have development

potential, but are careful about overly investing in them*RE8 We ignore or aim at terminating certain unprofitable customer relationshipsRE9 We put effort into finding new customers that have potential value to

our company

Responsiveness Design (RD)

RD1 We have carefully considered the main aspects of our customer-basemanagement practices

RD2 We evaluate the quality of our customer-base management practicesRD3 We try to find ways of improving our customer-base management practicesRD4 We put a lot of effort into applying the principles of our customer-base

management in our everyday business*RD5 We have created concrete instructions related to our customer-management

principles for our personnel who work at the customer interfaceRD6 We adapt our customer-base management practices based on the

experiences received from our practices

Reflective CPM Measure

RF1 We seek to develop our customer-base analysis practicesRF2 We analyze the current and future value of our customer relationships

extensivelyRF3 We seek to develop our customer-base management practicesRF4 Customer value is an essential factor in our customer-base management

practices

IMPLICATIONS FOR BUSINESS MARKETING PRACTICE

During the last two decades an increasing number of studies have under-lined the need for companies to manage their customer base as founded oncustomer value. Customer portfolio management (CPM) focuses on the entireportfolio of customer relationships, from transactions to strategic partnerships,


410 H. Terho

and their management based on the value of the customers to the sellingcompany. This article has conceptualized CPM and has created a measurefor studying this pivotal area of customer-relationship management basedon theoretical review, interviews, and survey data (N = 212). Several impli-cations can be drawn from a theoretical review and the results of the empir-ical study for developing customer portfolio management practices.

First, this article identifies key aspects of CPM activities, namely analy-sis and responsiveness. These two issues should be in focus when develop-ing analysis activities. On the one hand, a company should understand thevalue of individual customer relationships and on the other hand the rolesof different customers in the customer base in providing value for the focalcompany in the long term. In practice, the value of a customer to the sellingcompany can be approached by analyzing the positive value and costsrelated to the customer relationship, the realized historical value and thefuture value potential, or the various functions the relationship serves. Thestate and characteristics of the relationship should also be taken intoaccount in analysis because they may affect the future outcomes of cus-tomer relationships. It is important to note that the mere maximization ofthe lifetime value or revenues of single customers is a restricted view ofCPM. Instead, the analysis activities should concentrate on the comparing,grouping, and prioritizing of customers in the entire customer base as basedon their value.

Further, the companies should respond to knowledge about differentcustomers’ value in the customer base. Two general responsiveness strategiescan be distinguished. First, a company should match its resource allocationaccording to the value of different customers. This implies the cost-efficienttreatment of customers such as through tailored offerings, various operationalmodels (e.g., level of service, service channels), and the allocation of salesresources to customers of different value. Second, companies should payattention to developing their future customer-portfolio structure in the long term.Do new relationships need to be created? Which ones should be developed,which should be maintained, and are there any that should be broken ordiscarded? Both of these strategies greatly overlap because customer-treatmentdecisions always include a relationship-development aspect, and vice versa.

Second, this study has identified several issues that are likely to fosteror discourage the successful implementation of CPM. The results underlinethat customer portfolio management is a strategic multilevel and cross-func-tional practice, and the cooperation between various company levels andfunctions plays a critical role in its implementation. The support of top man-agement is crucial in terms of securing the necessary organizationalresources and changes. Further, the timeframe varies for the differentaspects of its implementation, which should be taken into account. Forexample, analysis procedures can be developed and taken into use quitequickly. However, the main challenge in customer portfolio management



lies in its proper implementation to everyday practices, which can be verytime consuming. The personnel at the customer interface play a key role inputting the portfolio-management strategies into practice. Hence, the resultshighlight the need to pay special attention to motivating, giving instructions,and also listening to the staff.

The results further indicate that CPM is highly interconnected to othermanagement decisions and activities. Hence, CPM decisions should not beconsidered in isolation, in terms of the marketing department, but as inter-connected to other functions such as supply, production, R&D, andaccounting. Furthermore, attention should also be given to the connectionwith other marketing decisions. For example, managing customers basedonly on their value to the selling company would probably be very danger-ous in the long term. CPM activities should, therefore, be well integratedinto need-based segmentation. Similarly, the connections within customerrelationships, between customer and supplier relationships, and amongother network relationships should also be considered in CPM decisions.For example, managers should pay attention to the issue of how to treatcustomers who are buying several product categories from different busi-ness units in a consistent way. Overall, interdepartmental communicationand cooperation should receive special attention.

Third, the results of this study further highlight the need for companiesto tailor CPM activities to their needs. For example, the appropriate analysiscriteria for estimating customer value are likely to differ from company tocompany depending, for example, on the strategy, nature of the business,and characteristics of exchange with the customers. Hence, the companiesshould carefully consider the central criteria in their business for estimatingcustomer value.

A further pivotal question managers should ask themselves relates tothe design of customer portfolio management. Do the company’s customerrelationships and customer-base characteristics imply the need for carefullydesigned, formal customer-base analysis practices, or do more informal,flexible customer-relationship-specific analysis procedures better suit itsneeds? Does it need carefully planned, top-down, formal procedures forresponding to the value of customers, or would more flexible responsive-ness procedures pursued in close interaction with customers be better?


Documents

A Measure for Companies' Customer Portfolio Management.pdf