Once developed primarily for investment decisions, portfolios are now used in many specialist areas, including direct marketing strategy development.
Originally, portfolios were developed to make financial investment decisions. Since then, however, portfolios have also been applied in other areas. One example is the product portfolio of the Boston Consulting Group, which arranges products or product groups according to their relative market share and market growth. However, portfolios were also used in strategic management, e.g. to classify different business areas of a company and to support the achievement of strategic goals. For the first time, Fiocca applied a portfolio to a company’s customer base. Common to all the portfolios mentioned is that they are intended to serve as a basis for making decisions on the allocation of scarce resources and to point out generic options for action.
One area of marketing that is particularly concerned with the company’s relationship with its customers is key account or key customer management. Key customers are generally regarded as “strategic” or “important” customers. The usual procedure for determining a company’s key accounts is to set up a matrix with a company-related and a customer-related variable. Müllner, for example, uses the variables “market influence potential of the customer” and “procurement complexity” (in the sense of complexity of cooperation with the customer). Portfolios are again used for the classification of key accounts. However, it should be noted that the profitability of individual customers is not sufficiently reflected in the portfolio classification, as sales are often used to measure the success of key accounts. Marketing portfolios visualize and support decisions, but their subjectivity is a major disadvantage: there is no connection to accounting or management accounting data.
In order to determine the profitability of a customer portfolio, the costs must be assigned to the cost objects, in this case individual customers, as far as possible according to the source. While this is easy for direct costs, it is challenging for indirect costs. The different allocation options are described below. An example is used for illustration purposes.
More differentiated cost accounting systems reduce the proportion of costs allocated as overheads. They work with a larger number of cost pools in which a number of costs that follow the same cost driver are collected. One of the most common instruments is Activity Based Costing [ABC-Costing]. The cost drivers are determined as events, tasks, or work segments, as well as the costs incurred. The overhead costs are allocated to the cost objects according to their use of the individual activities. In this way, a more cause-oriented cost allocation is achieved. To illustrate the problem, you can see a case study below. This shows that the different allocation procedures lead to very different statements regarding the contribution of individual customers.
Case study Allocation of indirect costs
Alfons GmbH has 3 customers (A,B,C). Currently, overhead costs of CHF 600 are allocated as a percentage of customer sales. The following customer fees will be charged:
In the following, various customer profitability models will be examined and their procedures demonstrated.
Shortly after the publication of Fiocca’s first qualitative approach, Campbell and Cuningham presented a three-stage customer portfolio model, including profitability, using a packaging company as an example. The portfolio was intended to facilitate decisions on the allocation of scarce marketing and technical resources, ensure effective relationship management and finally evaluate the strategic position of the company with different customer portfolios. The following approach is suggested:
This first step provides an overview of customer relationships, as the proportion of customers in each segment varies from company to company. This step should serve to question the current situation, to recognize dependencies, to illuminate in which customers how much is invested and to observe the development of individual customers in the long term.
In the second step, customers and competitors per market segment are examined on the basis of the growth rate of customer demand and the market share of the customer.
In the second step, customers and competitors per market segment are examined on the basis of the growth rate of customer demand and the market share of the customer.
The size of the circles indicates the total purchase volume for the customer’s primary packaging. The pieces indicate the share of the various competitors in the purchase volume.
The approach of Campbell and Cunningham, although one of the first approaches to client portfolios, proves to be very rewarding to see that there are many criteria by which a client portfolio can be built. The analysis included considerations of the customer’s life cycle, competition for customers and also the relative competitive position in relation to the customer’s growth potential. Although later authors praise Campbell and Cunningham for including profitability in their analysis, the portfolios remain rather qualitative. Profitability is only taken into account in the first step, the life cycle analysis, where it is only used to divide the customer into a section of the life cycle. The influence of customer behaviour on profitability is not yet taken into account. It is also neglected in the strategically important identification of key customers.
Ryals’ approach is based on applying the modern portfolio theory of financial markets to client relationships. A company’s customer portfolio is not only viewed according to earnings criteria, but the risk taken is also measured. This approach is interesting in that it views the client base as an investment of a company like a securities portfolio. The company also devotes time and resources to maintaining the customer portfolio.
For customer profitability, it is not enough to rely solely on revenue, the difference between revenues and costs. The company’s cost of capital must also be included and covered in the determination of the customer value.
The approach is as follows:
The first step is to calculate the customer value. The analysis is carried out for each individual customer or for homogeneous customer segments, depending on requirements. The customer value is determined from the difference between cash receipts and payments per customer or customer segment and discounted using the weighted average cost of capital.
Step 2: In
the second step, the risk per customer or segment is to be determined.
The application of portfolio theory to customer portfolio profitability shows that it is not only the return of the customer or the customer segments that should be considered, but that it also makes sense to include the risk component when considering the customer. Management can therefore not only choose profit-related measures, such as cross-selling, but also make efforts to reduce customer risk. In this way, customer-binding measures can be specifically tailored to lower-risk segments and stable purchasing behavior or good payment practices can be rewarded.
The Johnson and Selnes model, published in 2004, calculates the Customer Portfolio Profitability Lifetime Value (CPLV) for a company and shows that external environmental factors also play an important role in customer portfolio management decisions. The environmental factors are taken into account by considering various economies of scale. It is discussed what a good customer portfolio should look like under increasing or decreasing economies of scale.
The aim is to calculate the Customer Portfolio Profitability Lifetime Value for the company. The question arises as to how the limited resources should now be allocated to customer groups. The model is based on the following assumptions for operationalisation:
Step 1: The
customers go through the customer typology progressively, i.e. first as strangers and finally to the partner.
Step 2: It is
assumed that the closer the customer relationship is, the less likely it is to switch to the competition. The stronger the dynamic marketing capabilities, the less likely a customer is to migrate to the competition.
If economies of scale increase, the total CPLV increases and the proportion of contributions from customers with loose connections to the company increases.
The basic idea behind the Stobachoff curve model, which is known in the literature as the “whale curve model” or “Kanthal curve model”, is to focus on the strongly varying profitability of individual customers. Recent studies have shown that, as a rule, up to thirty percent of a company’s customers are unprofitable. In certain sectors, such as banking, there is an upward deviation from this average: Twenty percent of customers generate far more than one hundred percent of revenues
The stobachoff curve is calculated on the basis of individual customer profitability and can only be interpreted correctly and thus assist management in its decisions on the customer portfolio if certain restrictions are observed in the calculation of the individual profitability.
As shown, Stobachoff curves are an excellent tool for visually illustrating the profitability of client portfolios. In order to better understand the inherent dynamics of profitability in customer portfolios, a simulation model was constructed. With its help, several Stobachoff curves can be simulated under consideration of certain assumptions, so that a considerably better understanding of the handling of heterogeneous customer profitability is created. In order to achieve this understanding, however, the assumptions of the model must be understood as precisely as possible.
Since the Stobachoff curve model is closely oriented to practice (empirical studies prove the existence of heterogeneous customer portfolios), a simulation model also creates added value for the practitioner, who may understand the complexity of customer portfolios decisively through simulation.
The model calculates “Stobachoff curves” from the three components sales, discount and customer costs. Customer profitability is calculated according to the following customer contribution calculation:
Gross turnover at list prices
= Net sales
– customer costs
= Customer contribution
The model follows the argumentation of Shapiro, Rangan, Moriarty and Ross, who divide customers into a customer portfolio regarding the axes “realized net price” and “service costs”. Analyses of discount behaviour in certain sectors are already available empirically: The discounts granted fluctuate sharply. The net price paid by a customer is based on the list price minus various discount components: Advertising, orders, discounts, product lines etc.
The Z-AG is intended to draw attention to the importance of the customer portfolio for the company’s success. At the same time, current procedures in practice are pointed out and it is shown which methods are disadvantageous.
It is assumed that the customer portfolio of the Z-AG consists of 10 customers, which are characterized by different behavior and different size.
Case study Z-AG: Cumulative sales trend
As you can see in the figure, sales initially rise rapidly and then flatten out. This means that customers 10, 9 and 8 have very high turnover , while the remaining customers contribute only marginally to turnover . In practice, the majority of companies base the selection of their key accounts, i.e. strategically important customers, on turnover, as shown above. One example is Hilti AG, which uses turnover as a valuation yardstick for measuring the success of key accounts, among other things. The misleading nature of this approach is illustrated in the following, among other things, on the basis of the Z-AG.
Case Study Z-AG: Cumulated Sales History and Cumulated Customer Contribution (Sorted According to Sales Amount)
As can be seen, the customers with the highest revenues differ greatly in terms of their profitability. Customer 10 is the most profitable customer (160), customer 9 (-45) and 8 (-12), however, have a strong negative impact on the operating result. The remaining customers, on the other hand, contributed only marginally to the result. Figure 7 gives an even better overview of the relationship between sales and customer profitability, as the values per customer are only ordered by sales but not added.
Case study Z-AG: Turnover and customer contribution (ordered according to the amount of turnover)
It is clear that customers 9 and 8 generate a negative customer contribution, while the remaining customers are slightly positive.
Case study Z-AG: Stobachoff curve
This figure shows the corresponding Stobachoff curve. In contrast to the previous charts, the customers are arranged in order with regard to their profitability and then totaled.
The Stobachoff curve again illustrates the unsatisfactory profitability of the high-volume customers 8 and 9. At the same time, it illustrates the high inherent risk of the customer portfolio: If customer 10 migrates, Z-AG loses the profitability bearer in the company.
The Z-AG decides to shift the focus of its service efforts from the top-selling customers (8, 9, 10) to the most profitable customers (10, 2). Two possible scenarios are assumed:
Scenario 1 assumes that the former key account customers will remain loyal to the company even without special service.
Scenario 2 assumes that some of the former key account customers are migrating because the service level they were offered has fallen
Case study Z-AG: Scenario 1 – Development of turnover and profitability if former key account customers remain with the company.
In both cases, it is assumed that the new key account customers will slightly increase their sales. This figure shows the sales and profitability development (cumulative) for scenario 1.
Case study Z-AG: Scenario 2 – Sales and profitability development when former key account customers migrate
This figure shows the cumulative profitabilitydevelopment for scenario 2 when customers 8 and 9 have left the company and no new customers have been acquired. Since it is assumed that all costs must be regarded as fixed at least in the short term, the costs previously allocated to customers 8 and 9 are allocated to the remaining customers. As a result, we have a strongly negative result.
Different high-risk customer portfolios
The following options for action arise with regard to the customer portfolio of Z-AG:
The aim should be to achieve a customer portfolio that is as balanced as possible in terms of customer contributions. Otherwise, a company runs the risk of becoming dependent on certain customers. In the medium term, Z-AG must build up further profitable customers, otherwise it will become too dependent on the customer.
In the following, the challenges that arise with regard to data collection and the implementation of alternative courses of action will be discussed.
In order to represent a meaningful customer portfolio that includes profitability, the company needs an information system that can pass on direct and indirect costs to the individual customers.
In practice, however, these systems are not always available, as a study conducted in spring 2000 showed: According to this study, data maintenance, information capture and reporting are the greatest challenges when introducing customer value analyses in companies. This problem is compounded by the fact that the responsibilities in the company are often distributed among several departments or persons, e.g. sales, marketing, key account managers, so that information about individual customers has to be gathered from different systems.
If the client portfolio models used indicate an unfavourable portfolio, action should be taken. These options for action can be summarised as : Measures relating to cooperation, client behaviour, portfolio streamlining, internal measures to reduce cost rates and portfolio focus.
However, it is not easy to implement these measures, as the example of the Z-AG showed. It will often not be easy to persuade customers to change their behaviour, and there is always a risk that they will migrate. Decisions regarding cooperation with customers and portfolio streamlining should be carefully considered, as they can also harm the company in the long term.
It should also not be forgotten that the application of a model alone does not reduce overhead costs, but only provides information on their distribution to individual customers. Applying a model therefore does not free the company from looking for measures to increase efficiency, which ultimately have a positive effect on all customers. Activity Based Costing provides starting points, which can be used to make comparisons, for example, with industry rates.
Limendo business consulting is happy to support you in the preparation of important customer portfolio analyses. You can also get automated analysis of your customers with our highly innovative product, Limendo Smart. This allows you to continuously make better decisions.