A Review Of Customer Relationships Planning: Does Customer Profit

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A Review of Customer Relationships Planning: Does Customer Profitability and Portfolio Analysis Provide the Key to Successful Relationship Management?
This paper is written from a marketing and purchasing perspective. It provides an insight into how marketers interpret and describe companies' actions. It would be unusual to come across the term 'social capital' in a standard marketing or purchasing text. Yet as Walker et al (1997) observe:
"The notion of social capital implies a strategy of maintaining the structure of existing relationships" page 109.
This is the central theme of the paper - the tools that can be used to facilitate relationship management. Indeed, it could be said that the current interest in relationship marketing is based on the recognition of the need to preserve or develop social capital.
The development and management of customer relationships has, in recent years, become a central focus of marketing research and conceptualization as it has been realized that they are valuable assets of a firm. Although the roots of much current thinking about relationships can be found in the early work in business marketing of the International Marketing and Purchasing (IMP) Group (Håkansson, 1982; Turnbull and Valla, 1986; Ford, 1990), important contributions have also emerged in the services marketing literature (Grönroos, 1983, 1985; Berry, 1985; Gummesson, 1985, 1987) and more recently in consumer product marketing (Christopher, Payne and Ballantyne, 1991). The management and development of relationships has also attracted a number of other significant contributions, such as those from Jackson (1985), Dwyer, Schurr and Oh (1987) and Frazier, Spekman and O'Neal (1988) and more recent contributions from Ford, Lamming and Thomas (1992) and Morgan and Chadha (1993).
An implicit assumption, however, of much of this work is that having 'strong' customer or supplier relationships is necessarily 'good'. When this assumption is stated explicitly it is immediately and obviously not so - as any sales or customer account manager knows. Some customers are just not worth having, they are difficult to satisfy, are too demanding and/or will not pay a 'fair' economic price. It is therefore surprising that few research studies have addressed the key issue of customer/supplier costs and profitability and how effective management of customer/supplier relationships may contribute to the strategic development of the supplying firm. Additionally, there is also little research into the concept of how established customer relationships may provide a firm with a sustainable competitive advantage.
This chapter discusses the importance of the development and on-going management of customer relationships as a key activity of the firm. It begins with an introduction to relationship management. This is followed by a critical analysis of existing customer/supplier portfolio models and a discussion of the relevance of taking a network perspective when analysing the relationships surrounding a focal organization. Consideration is also given to the difficulty of defining a "˜profitable' customer and the associated notion of relationship value.
Relationship Management
It can be argued that relationship management is as important to marketing management as manipulating the marketing mix. Indeed, some would argue that relationship management is the most critical issue, particularly in a business-to-business situation where firms are often reliant on a small number of customers, their markets are relatively static and maintaining existing client relationships is often essential to their ongoing business success.
It is, therefore, important to understand why such 'relationship' based perspectives have developed. It is also necessary to consider how understanding the significance of relationships with individual customers can be translated into management strategy/actions. This has resulted in the development of relationship management theories over the last twenty years. The recognition of the importance of these theories and the different theoretical approaches to them can be said to have evolved in a step-wise manner. This also illustrates the different levels of interaction:
1. Study of either buying or selling units.
1. Study of the two-sided (dyadic) nature of relationships.
2. Study of a team approach to marketing.
3. Study of portfolios of relationships and the dimensions that can be used for resource allocation in portfolios.
4. Study of the whole network of relationships that surround a firm.
Most recently, there has been a realization of the importance of relationship management has also spread into the area of consumer marketing where discussions about the centrality of relationship marketing are now commonplace.
Much of the early work in the field of industrial buying and selling concentrated solely on organizational buying behaviour; for example:
 the Robinson, Faris and Wind buy-grid framework (1967)
 the Webster and Wind model (1972)
 the Sheth integrative model of industrial buying behaviour (1973)
 the Nielsen model (1973) that was developed with the intention of integrating the various models of industrial buying behaviour.
These models tend to concentrate on single, discrete purchases rather than the continuing process of purchasing that typifies organizational buying. Additionally, they neglect both the dynamic nature of buyer-seller relationships and the influence of the seller in the purchase.
The Interaction Approach postulated by the IMP Group (HÃ¥kansson, 1982; Turnbull and Cunningham, 1981) attempted to develop these early industrial buyer behaviour conceptualizations by integrating the dynamics of buyer/seller interactions and relationships. Understanding the importance of relationships is central to the IMP Approach. The approach recognized that both suppliers and buying firms are often involved in close, long lasting relationships within which episodes of exchange determine the nature of the relationships through adaptation. The relationship is itself seen as a dynamic process that is partially determined by the environment in which the parties operate and also by the atmosphere of the relationship itself. It is important to note that relationship management is important to all parties involved - buyers, sellers and any intermediaries. Cunningham (1982) and Cunningham and Homse (1982) were among the first to develop this concept. Axelsson and HÃ¥kansson (1986) investigated the role of the purchasing function within industrial firms. They noted, amongst other findings, the importance that the purchasing firm attaches to the handling of the various relationships it has with its suppliers and how the purchasing function can have an important role to play in balancing the roles of internal and external resources when adaptations/joint developments are being made.
More recently there has been an enormous increase in the study and operationalization of supply chain concepts and partnership sourcing. Turnbull and Valla (1986) demonstrate that the supplier does not always have total control over their customer relationships and strategy.
Ford (1990) comments on the importance of relationship management but notes that it is most often seen only in the context of industrial export management rather than in the role of maintaining the whole portfolio of relationships that a company has. Developing Turnbull and Cunningham's (1981) argument he stresses the importance of a team approach to industrial marketing (rather than letting sales personnel operate in isolation from R&D, manufacturing and other important supplier staff). When a team approach to customer relationship management is adopted, all the appropriate departments in both the selling and buying firms (e.g. sales, production, technical, administration, etc.) need to be involved. This invariably results in better understanding of both the seller's and buyer's needs and successful adaptations are more readily made and managed by both parties.
Recognition of the strategic importance of managing both supplier and customer relationships has led to two important themes of research:
1. Firstly, a number of authors address relationship management in the context of portfolio analysis, arguing that portfolio analysis provides useful models to aid understanding and analysis for both theoretical development and managerial control.
1. Secondly, there has been a rapidly evolving research interest in the concept of relationship networks (Axelsson and Easton, 1992; HÃ¥kansson and Snehota, 1995; Iacobucci, 1996). This work recognizes the importance that a variety of 'actors' in the network of relationships surrounding a focal firm can have on a firm's strategy and tactics. However, it can be argued that the contribution of network theory to the actual management of relationships has not yet been fully exploited: "Unfortunately, the pace of conceptual development in the network area has not always been matched by empirical study" (Turnbull, Ford and Cunningham (1996), page 55).
Relationship portfolio analysis could be equated to social capital analysis. It provides a framework for relationship management, the central tenet of which is to enable managers to invest their resources in the most efficient and effective way. It gives answers to the questions: which relationships should be developed?; which maintained?; and are there any that should be broken /discarded?
These contributions to the understanding of relationship management are critically reviewed in the following sections and recommendations are made about their relevance to strategic decision making and theoretical development in the area of customer portfolio management.
A Review of Portfolio Models for Relationship Assessment
Portfolio theories began within the sphere of financial investment (Markowitz, 1952), but their use as a strategic planning aid has developed into a more generalized management context. Indeed, no strategic management or marketing textbook is complete without their inclusion. When used effectively, they provide guidance for resource allocation and the Boston Consulting Group (BCG) model despite its inherent weaknesses is probably one of the most widely used management decision aids. Indeed, as Wind and Douglas (1981) note, with specific reference to using portfolio analysis in the context of customers/markets:
"The major advantages of a portfolio perspective are its focus on the interdependencies among the various decisions and the forcing of a discipline for resource allocation among the portfolio components - an allocation which takes explicitly into account the projected outcomes of each course of action on the relevant management objectives." (Wind and Douglas, 1981, page 70.)
An important question that managers need to address is which of the models that have emerged are most appropriate to their business and, indeed, at which level of their business should they be applied: SBU, market segment or individual customers and/or suppliers.
Although the application of portfolio theory to customer and/or supplier relationship analysis has essentially been 'borrowed' from traditional corporate and marketing strategy theory - Strengths Weaknesses Opportunities and Threats (SWOT) analysis, BCG etc., its application to customer/supplier analysis is still problematic particularly in relation to the appropriate dimensions of analysis. Despite this, the authors believe the approach to be valid and potentially very valuable. However, the development of customer and supplier portfolio planning has, to date, largely been related to business-to-business markets. This is probably due to the relative power of a small number of players in such markets; it is common for a firm serving business markets to be highly dependent on a small number of customers and, therefore, the addition or loss of a major customer can have dramatic effects on the company's turnover, profitability and indeed its viability.
During the last fifteen years a number of portfolio models have been specifically developed to address this situation, they have taken the relationship as the unit of analysis and can be assumed to be based upon an understanding that long-term, interactive relationships are often the norm in this type of market structure. These models include those proposed by: Cunningham and Homse (1982), Fiocca (1982), Campbell and Cunningham (1983), Yorke (1984a), Shapiro et al., (1987), Krapfel, Salmond and Spekman (1991), Rangan et al., (1992), Yorke and Droussiotis (1994) and Turnbull and Zolkiewski (1997). The most significant of these models are reviewed below.
Fiocca (1982) Two-Step Customer Portfolio Analysis
Fiocca (1982) proposed a two-step customer portfolio analysis, see Figure 1, and argued that the selling organization should firstly analyze customers at a general level, according to the strategic importance of, and the difficulties in, managing the relationship with each customer (account). The second step of analysis requires another two-dimensional matrix to be constructed for the key accounts identified in step 1, with the customers ' business attractiveness' on one axis and the 'strength of the supplier customer relationship' on the other.
Fiocca (1982) suggests a number of mechanisms for assessing the proposed axes: 'Difficulty in managing the customer' is a function of the level of competition for the customer, customer buying behaviour and the characteristics of the product bought by the customer. 'Strategic importance' is determined by the value/volume of purchases, the potential and prestige of the customer, customer market leadership, and the overall desirability to the supplier in making strategic improvements and adaptation to customer specifications. This mixture of subjective and actual values makes such calculations difficult especially when the main point of using such analysis is surely to produce data which can be used for comparison. 'Business attractiveness' is determined by considering a number of factors that are related to the customer's market (growth rate, competition, maturity, changes in the environment, etc.) and the status/position of the customer's business within the market. Such calculations are particularly difficult to assess and Fiocca does not take into account factors which can be critical in doing business internationally such as distance and cultural factors. The strength of supplier/customer relationships is again measured by applying a mix of objective, judgmental or subjective factors: length of relationship, importance of the customer, friendship, co-operation in product development, social distance, etc.
A criticism of the Fiocca model put forward by Yorke and Droussiotis (1994) is that it does not recognize the importance of considering customer profitability and, in fact, simply assumes that different cells can be associated with different levels of profitability. This assumption that customers are profitable simply because management perceive them to be was identified by Turnbull and Zolkiewski (1997) as a general problem in much analysis. In reality, customers were often found to be not as profitable as managers believed them to be (once full account was taken of real selling costs).
Figure 1, Two-Step Customer Based Portfolio Analysis (Fiocca, 1982, page 56)
These reservations have been confirmed by Turnbull and Topcu (1994) who tested the Fiocca matrix using detailed case study data from a Turkish industrial minerals manufacturer. They identified a number of problems with data calculation. Firstly, the axes scales (high and low; high, medium and low; and, strong, medium and weak) tend to be subjective. Secondly, they used different interpretations of 'Difficulty in Managing the Account' (one based upon the amount of problem solving needed and the other based upon relative service requirement) and 'Relative Buyer Seller Relationship' (where they used Ford's (1982) life-cycle stages as a proxy measure). Finally, they used a proportion of sales revenue to indicate strategic importance of the customer. This is questionable because there are many other factors which can contribute to strategic importance, such as technical leadership, which need consideration.
Not surprisingly, Turnbull and Topcu found that the use of different measures for each of the two dimensions, suggested by Fiocca, resulted in significant disparities in 'categorization' of customers. This was particularly marked in relation to the definition of non-key customers. However, they demonstrated that a number of non-key customers required disproportionately high levels of service from the supplier. It could also be postulated that some of the key customers were not receiving enough service. They also demonstrated that the matrix provides a great deal of valuable information which managers should consider when forming strategy.
Yorke and Droussiotis (1994) also undertook an empirical study to test and develop the Fiocca matrix using a Cypriot textile agency. They used the following variables to construct the first step of the portfolio: account potential, future capacity expansion, links with export markets and account prestige were used to calculate Strategic importance of the account; and degree of competitor entrenchment, payment problems, claims put forward and buying behaviour were used to calculate Difficulty in managing the account. They also developed Fiocca's model by introducing 'weighting' of the variables. While these variables are all important, they again exhibit the inherent problem of having a subjective element, which could cause problems when actually undertaking the analysis. Yorke and Droussiotis found a reasonable scatter of accounts on the Fiocca matrix and then proceeded to a second stage of analysis of two key customers as suggested by the Fiocca model. However, they undertook their analysis using two dimensions which were different from those suggested by Fiocca: customer profitability and perceived strength of the relationship.
Customer profitability was calculated by taking the revenue from that customer (gross value of sales minus the commission paid) and subtracting from it direct costs, pseudo-direct costs (those costs which could be attributed to groups of similar customers and therefore apportioned accordingly) and indirect costs. When the profitability of each customer was calculated they found that about 20 per cent of customers accounted for 80 per cent of profits.
Perceived strength of the relationship was calculated using the variables: technical ability, experience, pricing requirements, speed of response, frequency of contact, degree of cooperation, trust, length of relationship, friendship and management distance (frequency of contact). Their analysis of two key customers showed that while both were profitable, the company were currently not supplying even half of the customers' requirements and could potentially significantly increase their own net revenues. Yorke and Droussiotis suggest that such an analysis can be especially useful if strength of relationship is assessed vis-à-vis that of competitors.
This empirical test of customer analysis is interesting, but it is also problematic in a number of respects; it was conducted over a very short timescale (two months) and the authors recognize that it may not be representative of the usual situation in the industry and the company. This issue of timescale of analysis is very important; what is the most appropriate analysis period? In reality it will vary from industry to industry and market to market, with high technology companies perhaps needing to assess customer profitability quarterly while other industries probably need to consider it as part of their yearly planning cycle. Secondly, the way indirect and direct costs are allocated raises important questions; very often it is not easy to simply apportion management time and costs or even sales time and costs to a particular customer or contract.
Campbell and Cunningham (1983) Three-Step Portfolio Analysis
Based upon Cunningham and Homse 's pioneering work (1982), Campbell and Cunningham (1983) proposed a three step portfolio analysis strategy for marketing management. They see their emphasis on developing effective relationship management of customers as a challenge to the ideas of Porter (1980) who they suggest emphasizes the need to counteract customers' buying power. Using a case study of a major packaging supplier, they suggest a three step analysis using two variables at each stage.
The first step focuses on the nature and attractiveness of the customer relationship using customer life cycle stage on one axis and various customer data on the other. The customer life cycle stage is divided into tomorrow's customers, today's special customers, today's regular customers and yesterday's customers. The other dimension of analysis is multivariate, involving sales volume, use of strategic resources, age of relationship, supplier's share of customer's purchasers and profitability of customer to supplier. They believe that this type of categorization will facilitate the understanding of how "strategic resources, which will ensure the future health of the business, are allocated among customers" (Campbell and Cunningham, 1983, page 374). Two major problems arise in respect of this approach. Firstly, the conceptual validity and practicality of using a life cycle approach to customer analysis can be challenged. Secondly, the choice of appropriate variables for analysis can be difficult; obtaining the required data on the variables can also present major problems.
The second step of analysis focuses on the customer's own performance as an important aspect of customer portfolio planning. Thus, the customer's share of its own market is combined with the customer's demand for the supplier's product and is used to produce a second two-by-two matrix classification as shown in Figure 2. The various customers are represented by a circle which is indicative of volume of purchase, this circle can then be sliced to show the volume purchased from the supplier and the supplier's competitors.
Figure 2 Power balance in Buyer/Seller Relationships (Campbell and Cunningham, 1983, page 376)
Campbell and Cunningham believe that this type of analysis will allow management to better assess opportunities and threats. However, they note some of the inherent weaknesses in this type of analysis: should a whole product range or individual products be used and where do you draw the boundaries of the segment - domestically or internationally? Additionally, we would suggest that this type of analysis is fraught by another problem: how often in business-to-business marketing situations are their accurate figures for market share available; companies often do not have accurate figures for their own market share let alone the ability to collect this data from all but their closest customers (and this assumes that these customers have the data). Another potential difficulty arises from how the product is used by the customer; if it is utilized in his final product, then this type of estimation is inherently useful though difficult. However, if capital goods or service are being supplied then the estimations are unlikely to be as meaningful.
The third, and final, step involves the selection of the key customers for analysis. Another two-dimensional grid is proposed for this stage with growth rate of customer's market (high, medium, low and decline) on the vertical axis and competitive position (relative share of customer's purchases) on the horizontal axis. Companies are placed on the matrix and represented by a circle that represents their sales volume. Campbell and Cunningham (1983) acknowledge that such a matrix provides useful information on key customers but that it can be easily misunderstood if one customer consumes a major part of the suppliers output (that customer could easily appear to be the only key customer). Again, we would suggest that this type of analysis will be problematic due to lack of accurate data about customer's true market positions.
However, such a framework provides a useful conceptual starting point for undertaking strategic analysis of an organization's customer portfolio. It was also one of the first approaches to recognize the need for organizations to attempt detailed analysis on a customer-by-customer basis.
Shapiro et al. (1987) Customer Classification Matrix
Shapiro et al. (1987) in developing a customer classification matrix (Figure 3) focus on customers as profit centres. Three variables - costs to serve suppliers, customer behaviour and management of customers - were used to investigate the profit dispersion of the customer portfolio . Four types of costs - presale, production, distribution and postsale service costs - were used to define the cost to serve axis. Combining this calculation with the net price charged they found that such analysis identified a wide range of profit margins both by customer and type of product sold. Figure 3 shows the 'labels' which Shapiro et al ascribe to customers in each quadrant of the matrix, each type representing different profit contribution profiles.
Figure 3 Customer Classification Matrix (Shapiro et al., 1987, page104)
Shapiro et al suggest that while many suppliers believe that if they analyze the breakdown of their accounts most accounts will fall into the 'carriage trade' and 'bargain basement' quadrants. Yet, when analysis is actually performed, it will usually show that over half a suppliers' accounts fall into the 'passive' and 'aggressive' quadrants. They contend that "Four aspects of the customer's nature and position affect profitability: customer economics, power, the nature of the decision-making unit, and the institutional relationship between the buyer and seller" (Shapiro et al., 1987, page 104).
They also observe that the position of any one account is likely to change over time, often starting in the 'carriage trade' segment and migrating towards another segment. They argue that this dispersion of customer profitability can be managed by following an action plan, which involves: repeated analysis, pinpointing costs, preparing profitability dispersions, focusing strategy and providing support systems. However, they leave the interpretation of low and high values to the discretion of the analyst, which could cause difficulty when comparable data sets are required, especially if management make subjective judgements as to these values.
Despite these reservations, this classification matrix can be useful, as shown by Rangan, Moriarty and Swartz (1992). They further developed the approach and demonstrated that the grid can be successfully used to segment customers in mature industrial markets. Turnbull and Zolkiewski (1997) also tested this matrix using a case study of a UK-based Computer Systems house and identified a scatter of customer projects across the matrix. They also found that management in the company studied did not make any efforts to calculate the presale and postsale costs for individual projects or customers. This finding supports Shapiro et als' postulation that managers do not know the real cost to serve individual customers and that presale and postsale costs can form a significant percentage of costs.
However, the manner in which pre and postsale costs are recorded can prove to be extremely difficult to implement in a technically complex product context. The amount of time spent by R&D staff, sales engineers, managers etc., can be difficult to determine exactly or even approximately, especially if the relationships are long term. Also, the manner in which costs such as R&D and the preparation of detailed bids are apportioned is complex, as these costs are often directed towards the needs of both existing and potential customers. Regardless of the difficulties associated with calculating cost to serve, Turnbull and Zolkiewski (1997) believe it is essential that the cost to serve values are given due consideration by management, as they can give very important indications as to the true profitability of either individual projects or the overall profitability of different customers.
Turnbull and Zolkiewski (1997) also found, as had Shapiro et al, that the contribution of some of the customer projects were not where the management in the company concerned predicted and discuss the wider implications of using such a matrix. For instance: what is the optimum spread of customers on the matrix? Additionally, if an interactive perspective is taken, it is also interesting to contemplate if different buying departments in a customer organization (e.g. new projects and service and maintenance) can be placed in different grid positions at any one point in time. Such a view may be particularly pertinent when considering industrial relationships, where the links between buyer and seller are many and complex and set-up costs are high. In such circumstances it can only be expected that, when different operational groups are responsible for buying services, if the buyers change, or if the relationship has time to develop (perhaps even to a new stage), different behaviour is quite likely to be exhibited.
Krapfel, Salmond and Spekman (1991) Supplier Classification Matrix
Krapfel, Salmond and Spekman (1991) also use a portfolio approach to analyse customer-supplier relationships and propose a relationship classification matrix based upon the concepts of "˜relationship value' and "˜interest commonality'. This matrix is illustrated in Figure 4. They suggest that relationship management style should be varied according to the perception of power and interest commonality.
Figure 4 Supplier Classification Matrix (Krapfel, Salmond and Spekman, 1991, page 27)
Krapfel, Salmond and Spekman, page 26) define relationship value as "a function of four factors: criticality, quantity, replaceability and slack.....
RVi is the value of the relationship to the seller
Cj is the criticality of the goods purchased by the buyer
Qj is the quantity of the seller's output consumed by this buyer
Rj is the replaceability of this buyer (i.e. the switching cost of accessing other buyers)
Sj is the cost savings resulting from the buyer's practices and procedures".
Turnbull and Zolkiewski (1997) tested the Krapfel, Salmond and Spekman (1991) matrix. They used a customer-supplier perspective and utilized data from the same UK-based Computer Systems house as used in the test of the Shapiro et al., matrix. They note that relationship value is "˜softer' or more judgmental than the more specific cost data proposed by Shapiro et al (1987) and requires certain assumptions to be made as described by Zolkiewski (1994). Also, because the customer relationships being studied were long-term they were all classified as having a high interest commonality (they were all either repeat or follow-on purchases).
Consequently, Turnbull and Zolkiewski (1997) found that the customers were only positioned in two of the quadrants of the matrix (partner and friend). They suggest that because, in this situation, long term relationships are the norm they may have interpreted interest commonality in an inappropriate manner. However, they re-examine Krapfel, Salmond and Spekman's definition: "Interest commonality reflects an actor's economic goals and their perception of the trading partner's economic goals. When buyer and seller economic goals are compatible, interest commonality is high, and vice versa" (page 26) and conclude that when a holistic analysis of a total business environment is needed, interest commonality may well make a valuable contribution to the analysis. They also suggest that it is perhaps more pertinent when used in supplier analysis than in customer portfolio analysis.
Turnbull and Zolkiewski (1997) Three-Dimensional Customer Classification Matrix
Following their analysis based upon the Shapiro et al and Krapfel, Salmond and Spekman matrices, Turnbull and Zolkiewski (1997) proposed a three-dimensional basis for customer portfolio analysis, as illustrated in Figure 5. This proposal resulted from a consideration of the differences in the nature of the matrix axes (i.e. the variables being used), with the Shapiro et al., axes being relatively easy to measure while the Krapfel et al., axes are much more subjective. They argue that three-dimensional analysis based upon cost to serve, net price and relationship value is appropriate when segmenting the customers of any firm, especially because such an analysis provides a more comprehensive overview than can be gained from simply using two variables. They use these variables to analyze the case study data (from the Computer Systems House which was used to test the Shapiro et al and Krapfel at al matrices) and suggest that this three-dimensional approach is much more beneficial as a strategic planning aid because it provides a more refined set of analysis criteria.
Using three-dimensional analysis allows a more refined customer classification in terms of dimensions of analysis and number of classifications. For example, in this case eight classifications are possible - compared to the four groups in the two-by-two matrices - see Table 1 below.
Table 1 Segments of the Turnbull and Zolkiewski Matrix
Relationship Value        Net Price        Cost to Serve
Low        Low        Low
Low        Low        High
Low        High        Low
Low        High        High
High        Low        Low
High        Low        High
High        High        Low
High        High        High
These eight segments provide a mechanism for combining hard data (profitability of customers) with more judgemental data (relationship value). Managers can then assess customers in light of these findings and determine which relationships need developing and/or maintaining and which, if any, need to be broken. Initial analysis would suggest that customers/relationships which have a high value and net price combined with a low cost to serve are the most attractive and, those with low relationship values and net prices combined with a high cost to serve are least attractive. It is imperative, however, that such analyses of relationships are not a one-off process. The most informative data will be that which monitors the positions of customers/relationships over time, for instance can high net price be maintained over time (Shapiro et al (1987) and Turnbull and Zolkiewski (1997) both observe such migrations). The manager will then need to consider the investments in relationships which are needed to maintain the status quo.
It is unfortunate that the case study used by Turnbull and Zolkiewski was not extended to cover all the proposed customer matrices. Such an analysis would probably have provided much stronger evidence for their choice of axes. However, it is clear from Turnbull and Topcu's (1994) and Yorke and Droussiotis's (1994) analysis of the Fiocca model, that his axes are much more difficult to use in terms of provision of replicable and comprehensive data (they are almost too subjective). This lends support to the choice of axes made by Turnbull and Zolkiewski, where net price is obviously an extremely important factor; cost to serve calculations - presale, production, distribution and postsale costs - ensure that due consideration is given to all aspects of the product development and selling process; and, relationship value allows judgemental data (management intuition) to be included in the analysis.
Figure 5, Three-Dimensional Customer Classification Matrix (Turnbull and Zolkiewski, 1997, page 320)
Campbell and Cunningham's (1983) reasons for using customer analysis, especially in business-to-business markets still hold true today. They can be summarized as:
1. Customers are often an organization's greatest asset.
1. In mature markets it is often difficult to gain new customers (consequently losses cannot be easily replaced).
2. Industrial concentration is high in many markets.
3. Understanding customers' needs aids new product development and innovation.
Customer analysis allows clearer customer and segment targeting and should produce better returns from the allocation of scarce and expensive 'marketing' resources, such as sales, technical and product development etc. Additionally, it can ensure better relationship management to produce higher buyer loyalty and thereby competitive advantage.
Criticisms of and Problems with Portfolio Models
As the discussion surrounding the various models has developed, a number of important criticisms and problems have emerged. These are summarized below:
1. Is it viable to transpose product life cycle concepts into a "˜customer life cycle' and then use this as a basis for planning?
While a number of authors (Ford, 1982; Grönroos, 1983) have discussed this concept at length, its application to this sort of analysis can be problematic.
2. There are a wide range of variables and potential ways to calculate the dimensions of analysis, which mitigates against easy comparison of analyses.
3. The actual analysis may be easily distorted by a number of factors, including:
a. lack of accurate data
b. suppliers being reliant on one or two major customers
c. data being collected over too short a period
d. the subjective basis of many of the variables.
4. Many of the models do not explicitly include customer profitability; experience shows that customer profitability data is difficult to collect: although direct costs should be apportioned directly on a customer-by-customer basis, many companies do not have adequate mechanisms for allocating indirect costs.
5. When matrix positioning involves a mixture of actual and subjective data, the results may prove unsuitable for use in future comparisons. Although weighting of variables may go some way to alleviating this.
6. Generally, the scales proposed for axes are imprecise; for instance, what are low and high values? Again, such values implicitly involve subjective judgements and therefore become more difficult to assess. However, they can be very useful if it is accepted that they simply provide a rough conceptual guide to sorting out the major customers from the mass of customers, especially when it is not very clear what to do because the majority of customers occur in a large cluster.
7. Care must be taken if supplier and customer analysis dimensions are interchanged - substitution is not necessarily simple. Often different perspectives are implicit in the dimensions attributed to either customers or suppliers.
We would suggest that if these criticisms/limitations are considered, there is great potential for using portfolio analysis to assess both customer and supplier relationships. Indeed, such a focus should ensure that an organization is clearly in tune with both its customer and supplier objectives and enhance cooperation rather than confrontation.
Choice of Model/Variable
All the models discussed in this chapter have taken dyadic relationships as their unit of analysis. The resulting models are aimed at facilitating strategic management decision making by illustrating where human, technical and financial resources should be concentrated. There are also models which can be used at higher levels within the market hierarchy such as that proposed by Yorke (1984a) which provides insight to analysis at the strategic business unit and/or market segment level.
Clearly there is a wide range of relationship management portfolio models which could be used as a basis for analysis. An overview of all the proposed dimensions (or variables) is given in Table 2 which starkly illustrates two of main problems of portfolio analysis: the difficulty in choosing appropriate dimensions of analysis (the consequence of which is often ineffective implementation of strategy) and how macro environmental pressures can be effectively considered within the models. Additionally, they show that there seems to be a greater emphasis on developing new models than demonstrating the efficacy of existing models.
Indeed, it may be necessary for management to consider the whole range of suggested axes or variables, enumerated in Table 2, when considering customer portfolio management and select the most appropriate dimensions for their situation. Yorke (1984a) notes that:
"If objectives are to be met in both the short and the longer term, dimensions for a strategic portfolio should be market or customer oriented and not based solely on the perceptions of the supplier's own management thinking even though it claims to be outward looking to the marketplace." (Yorke, 1984a, page 336.)
Table 2 Analysis Dimensions Suggested For Customer Portfolio Analysis
        Cunningham and Homse
(1982)        Fiocca (1982)        Campbell and Cunningham (1983)        Yorke (1984)        Shapiro et al (1987)        Krapfel, Salmond and Spekman (1991)        Rangan et al (1992)        Yorke and Droussiotis (1994)        Turnbull and Zolkiewski (1997)
No. of dimensions        2        2        2        3        2        2        2        2        3
No. of steps        1        2        2        1        1        1        1        2        1
Technical interaction                                                                        
Sales volume                                                                        
Can the customer be used as a reference site?                                                                        
Can the customer provide useful commercial information                                                                        
Difficulty in managing the customer                                                                        
Strategic importance of the customer                                                                        
Customer's business attractiveness                                                                        
Relative strength supplier/customer relationship                                                                        
Price                                                                        
Cost to Serve                                                                        
Interest commonality                                                                        
Relationship value                                                                        
Customer's share in his market                                                                        
Growth rate of customer's demand for the product being supplied                                                                        
Growth rate of customer's market                                                                        
Competitive position (relative share of customer's purchases)                                                                        
Supplier's objectives                                                                        
Market/customer oriented objectives                                                                        
Customer profitability                                                                        
Perceived strength of the relationship                                                                        
Another main discussion area surrounds which is the best way to undertake portfolio analysis and at the same time incorporate as many salient variables as possible. Fiocca (1982) used a two step, two dimensional process while Campbell and Cunningham (1983) used a three-step, two-dimensional process. Both are attempts to simplify the process of analysis. On the other hand, Turnbull and Zolkiewski (1997) believe that two variables result in a too simplistic analysis and propose a three-variable matrix. Even three variables provide a simplified analysis and multi-variable (or dimension) analysis may be more appropriate, despite the difficulty of visualization.
Whichever model and dimensions are chosen, relationship portfolio analysis provides an effective strategic tool which can help managers to prioritize their allocation of resources to best manage their relationships. This allows the firm to identify the types of relationship in which it is involved. It can then concentrate on developing synergistic relationships for the business (i.e. maximising the firm's corporate social capital) and identify difficult or unprofitable relationships which need to be discarded or held (i.e. while reducing their corporate social liability).
Customer Profitability
The debate about which are the most appropriate dimensions to use in relationship and analysis management centres upon the notion of customer profitability. In the first instance it may seem obvious that firms should only deal with customers who yield profit. However, there are many reasons why a company may acquire and retain 'non-profitable' customer relationships. For example:
1. The relationship is an integral part of competitive strategy, e.g. selling a loss leader.
1. The relationship may be needed for R&D purposes.
2. The customer may be a 'status' customer.
3. The 'unprofitability' may be temporary as a result of environmental influences, e.g. strikes
4. Non-profitable relationships may be due to poor management within a firm, e.g. incorrect pricing of goods or inability to control costs.
5. Non-profitable relationships may be because managers do not know the real costs of selling to their customers (Shapiro et al., 1987; Turnbull and Zolkiewski, 1997).
Hence, it is important that the concept of customer (or account) profitability is understood by management.
Indeed, a number of authors have stressed the centrality of customer profitability to the concept of portfolio analysis (Shapiro et al., 1987; Yorke and Droussiotis, 1994; Turnbull and Zolkiewski, 1997). Yet, how do you define a profitable customer or the value of a relationship? Which variables should be used in such calculations?
Campbell and Cunningham (1983) recognized the importance of including customer profitability in any analysis and the difficulty associated with collecting such information. Shapiro et al. (1987) put forward a mechanism for calculating customer profitability (subtracting cost to serve from net price) while Yorke and Droussiotis (1994) use a very different set of variables. However, the Yorke and Droussiotis variables are too closely associated with one specific organization and are not easily transferable to comparable situations. On the other hand, cost to serve is, in the authors' opinions, operational - it simply requires the availability/collection of collect appropriate data.
However, the concept of customer profitability still needs careful consideration. It should not be taken as an absolute, the context of the relationship also needs consideration. Variables such as stage of relationship, strategic importance of the customer and relationship value all need to be part of the analysis. It is these more judgmental variables which become the most difficult to use, partly because of the subjective nature of the calculation and partly because of the difficulty in replicating these calculations in the future.
Moving from Relationship Portfolios to Networks
All the models discussed thus far are dyadic and ego-centric. The study of relationship management also includes the perspective of network management. It is important, therefore, to recognize that portfolios of relationships at various levels, both micro and macro, are positioned within a network of relationships. However, there can be difficulty in the analysis of such a network. A definition of a business network (in marketing terms) commonly used by network researchers is:
"a set of two or more connected business relationships, in which each exchange relation is between business firms that are conceptualized as collective actors" (Emerson, 1981, quoted in Ford, 1997, page 230).
Such a definition may be conceptually valid but contributes little to the pragmatic problems of managing a network of different customers and/or market segments. In order to achieve a more operationalizable view, we propose that the network be defined as:
The portfolio of customer relationships, supplier relationships and indirect relationships which directly relate to the focal organization's business environment.
Arising from this review of portfolios and work on networks of relationships (Axelsson and Easton, 1992; HÃ¥kansson and Snehota, 1995) we can identify three distinct sets of relationships within a network, as illustrated in Figure 6:
 customer relationships
 supplier relationships
 indirect relationships
We then suggest that strategic management is the identification and management of these different sets of relationships, whilst at an operational level, the challenge is to manage each portfolio optimally. Management is, basically, the optimum allocation of a firm's resources - physical, human, technical and intellectual - and its knowledge across these portfolios.
The constituents of the first two portfolios are, of course, relatively easy to identify, customers and suppliers are usually evident within the marketplace. The use of portfolio analysis can facilitate their management in terms of cash flow, technology development and market position, for example, to ensure the maximization of the social capital of the firm . However, the identification of important indirect relationships is more difficult as they can be potential suppliers or customers, competitors or regulatory bodies or even other divisions of the firm itself. These can only be determined by extensive analysis of the micro-environment of the organization in question. By including the notion of managing indirect relationships we aim to stress the importance of allocating resources to indirect relationships and the management of political positions within the network (this can be illustrated by effective use of public relations and lobbying as well as involvement in trade associations, university links etc.). The implication of this is that social capital is more than 'direct relationships' it surely must include 'indirect' and 'potential/new' relationships too. In this review we have not attempted to develop the issue of indirect relationships although it is an area which requires further development (see HÃ¥kansson, 1982; Ford 1997).
Figure 6 A Network Perspective of Portfolio Analysis
This review clearly shows that customer portfolio analysis can provide strategic input into a firm's planning processes and may also be the key to a successful relationship management strategy (managing the corporate social capital). However, the use of portfolio analysis should only be undertaken after due consideration has been given to the limitations inherent in the analysis and particularly the identification and definition of the important criteria for analysis.
The matrices which have been reviewed all appear to provide 'information' which can help with strategic planning. However, there are two main questions which result from this work. Firstly, how can subjective (management) values be incorporated into the calculations? Many of the examples showed to a greater or lesser extent the difficulties of this. Secondly, which variables are the most pertinent? In conjunction with this, it seems that apart from calculations of the profitability of the various projects and customers, quantitative measures of customer/portfolio management have not been easy to identify. Indeed, this review illustrates the difficulty of fitting data to concepts and how there is often, in reality, a mismatch between this data and the concepts it aims to support.
Choice of models or dimensions is not simple, it will partly depend on the nature of the firm itself and partly how it perceives its micro-environment - which factors are most important: relationship management; competitors' share; emergence of new markets etc. However, we believe that two-dimensional matrices do not provide enough depth of analysis. The answer may be in step-wise analysis (Fiocca, 1982; Campbell and Cunningham, 1983) or in multidimensional analysis (Turnbull and Zolkiewski, 1997).
It is apparent from the various practical attempts to use the portfolio models that although these models are inherently appealing as a means for analysis, in practical terms they are extremely difficult to define. The real problem lies in the fact that the definitions simply do not involve easily collected "˜hard' data; for example, many organizations do not have mechanisms which allow them to calculate the real "˜cost to serve' individual customers or even market segments.
The question of customer profitability and relationship value again has an inherent appeal. All firms want profitable customers and valuable relationships. The difficulty comes with the associated calculations. However, it is imperative that Shapiro et als' suggestion that the real costs of supporting various customers should not be considered in isolation by managers and that they should be aware that high variations in these costs do often exist. It is also crucial that the data used to calculate customer profitability takes into account adaptation/development costs for new products/services as well as the more 'tangible' indirect costs such as sales expenses. Yorke (1984b) notes how infrequently management attention is paid to the effects in terms of net profit of applying resources to a particular segment or even a particular customer.
Clearly, this work needs further testing especially in the area of optimum choice of dimensions and determining methods for incorporating subjectivity into the calculations. Also cross-model analysis would help clarify the situation. Indeed, Ford, McDowell and Turnbull (1996) suggest that the complexity inherent in the network of relationships which surround an organization result in a reliance on effective decision making by managers and analysis of experience of their existing relationships rather than on inherent planning. As Haspeslagh (1982) points out, the real issue for managers is not which portfolio model/grid to use per se, or even which axes, but the judgement associated with choosing the most appropriate model for their own administrative purposes. We would develop the argument further, to say that the critical element is the variables (which and how many) used in the analysis.
By taking the perspective of a firm as being embedded in three types of relationship portfolio and believing that portfolio analysis provides the key to successful relationship management we may have unwittingly described the inherent constituents of corporate social capital: customer relationships, supplier relationships and indirect relationships. Many of the variables that are proposed in the models reviewed in this paper are clearly related to the revenues and capital assets of the firm. As such they can impact very significantly on the social capital of the firm. Arising from this view is the recognition of the importance of further conceptualization and empirical research that more explicitly integrates the contributions of sociology and business-to-business marketing. Of course, it is widely recognized that different disciplines overlap and have blurred edges. What should also be recognized is that significant developments can be made when cross-fertilization of ideas is allowed to take place. Clearly this chapter shows a number of areas such as relationship management, social capital and networks, where multi-disciplinary approaches would be particularly fruitful.
Kind thanks are given to Malcolm Cunningham for his thought-provoking comments and encouragement.
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