A more realistic evaluation of customer value can be obtained by asking a representative sample of customers to rank the product’s attributes and then, using an analytical tool such as conjoint analy- sis, or trade-off analysis, to apply a weighting system to discover the weight given to different levels of each attribute. Here advanced computer analysis is used to calibrate the importance ‘weights’, which can then be aggregated to provide an objective measure of the
‘utility’ that customers prescribe to each elements of customer value.
This technique is based on the simple concept of trading off one attribute against another. For example, the purchaser of a new car is likely to trade off a number of specific product attributes in agreeing the purchase price and specifications. Vehicle performance, petrol economy, number of seats, safety features, boot capacity, low price, and so on will have factored in his decision. Trade-off analysis can also be used to identify customers who share common preferences in terms of product attributes and may reveal substantial market segments with service needs that are not fully catered for by existing offers.
Trade-off analysis possesses several advantages over more traditional forms of value assessment, as it:
1. Employs measures of attribute importance that do not rely on direct rating by respondents
2. Forces a trade-off among very important attributes to determine which are the most important; and
3. Achieves this for each customer separately.
There are two forms of trade-off analysis. The ‘full profile’ approach presents respondents with a full-profile description of an offer and asks them to rate the offer’s constituent elements. The ‘pairwise’
trade-off approach asks respondents to rank combinations of vari- ants of two attributes, from the least preferred to the most preferred and then repeats this for a series of other pairs of attributes.27
The ‘full profile’ form of trade-off analysis is a more commonly used approach and is often deemed more realistic by researchers as all the product’s aspects are considered at the same time. However, if the number of attributes is large then the judging process used for each individual profile in the ‘full profile’ approach can become very complex and demanding. For that reason other researchers prefer the ‘pairwise’ trade-off approach. The Robotic Components example (see box) demonstrates the use of the pairwise trade-off analysis.
Specialist research texts provide more detailed discussion of these trade-off approaches including the full profile form.28
Robotic Components Inc. is a manufacturer of components for the growing industrial robot market. As part of a new CRM initiative they are exam- ining various value propositions to improve their logistics to customers.
For example, they believe that buyers might be prepared to sacrifice some decrease in stock availability for an improvement in delivery relia- bility of a day or two. They decided to undertake a value assessment, using the pairwise trade-off approach, based on the following options of stock availability, order cycle time and delivery reliability:
Stock availability: 75 per cent 85 per cent 95 per cent
Order cycle time: 2 days
3 days 4 days
Delivery reliability: 1 day
3 days
With this pairwise form of conjoint analysis, the various trade-offs are placed before the respondent as a series of matrices. The respondent then completes each matrix to illustrate his/her preference for service alternatives. Thus, with the first trade-off matrix between order cycle time and stock availability, shown below, the most preferred combina- tion would be an order cycle time of 2 days with a stock availability of
95 per cent (where the number 1 in the matrix represents the first pre- ferred option). The last preferred combination is an order cycle time of 5 days with a stock availability of 75 per cent (where the number 9 in the matrix represents the ninth and least preferred option). For the other combinations the respondents complete the matrix to show their own preferences. An example of a typical response is given below for each of the three trade-off matrices:
Distribution service trade-off matrices Order cycle time
2 days 3 days 4 days
75% 6 8 9
Stock 85% 3 5 7
availability 95% 1 2 4
Order cycle time
2 days 3 days 4 days
Delivery 1 day 1 3 5
reliability 3 days 2 4 6
Stock availability
75% 85% 95%
Delivery 1 day 4 2 1
reliability 3 days 6 5 3
Once these trade-off matrices are completed, computer analysis is used to determine the implicit ‘importance weights’ that underlie the initial preference rankings. For the data in the above example the following weights are identified for a given respondent:
Service element Importance weight 1. Stock availability: 75% 0.480
85% 0
95% 0.480
2. Delivery time: 2 days 0.456
3 days 0
4 days 0.456
3. Delivery reliability: 1 day 0.239 3 days 0.239
Trade-off analysis can be used to identify customers who share common preferences in terms of attributes. Experience of researchers and consultants working in this area suggests that this form of analy- sis may often reveal substantial market segments with service needs that are not fully catered for by existing product or service offers.
Numerous studies using this approach have now been carried out by both consultants and market researchers. As a result its commer- cial acceptance as a means of value assessment has grown greatly.
Having completed our discussion of ‘the value the customer receives’, including its two main components – the value proposition and the value assessment – we now turn our attention to ‘the value the organization receives’.
The value the organization receives
The value the supplier organization receives from the customer has the greatest association with the term ‘customer value’. Customer value from this perspective is the outcome of providing and deliver- ing superior value for the customer, deploying improved acquisition and retention strategies and utilizing effective channel management.
Fundamental to the concept of customer value are two key elements.
First, determining how existing and potential customer profitability varies across different customers and customer segments. Second, Thus, for this respondent, stock availability appears to be marginally more important than delivery time and both were in the region of twice as important as delivery reliability. Information such as this can be most use- ful. For example, in this case, a stock availability of 85 per cent with 2 days’
delivery and a reliability of 1 day is seen as being almost equally accept- able as 95 per cent with 2 days’ delivery and a reliability of 3 day (a com- bined weight of 0.695 [00.4560.239] compared with 0.697 [0.480 0.4560.239]). This suggests that a tightening up on delivery reliability might reduce stockholding and still provide an acceptable level of cus- tomer service.
Robotic Components then repeated this for different customers, identified key customer segments and used this information to create appropriate offers to different customer segments.
Source: Adapted from an example by Professor Martin Christopher and used with his permission
understanding the economics of customer acquisition and customer retention and opportunities for cross-selling, upselling and building customer advocacy. How these elements contribute to increasing customer lifetime value is integral to this view of value creation.
Customer profitability
We emphasized the importance of market segmentation earlier in this chapter. Carefully segmenting the market and developing an approach that maximizes the value of your most desirable customer segments and the corresponding lifetime value that these customer groups produce for your company, lie at the heart of the value creation process. Companies need to understand the existing prof- itability of their key customer segments (and, in certain businesses, the profitability of individual customers) and initiate action to real- ize the potential profitability of those segments and consequently improve customer lifetime value.
It is somewhat surprising that most companies focus on identify- ing the profitability of products rather than customers,29 when it is customers who generate profits, not products. Products create costs but customers create profits. This distinction is not just semantic. We find that the difference between profit and loss is typically deter- mined after a product is manufactured. The costs of storing, moving and supporting products are significant. Customers differ widely in their requirements for delivery service, in their ordering patterns and, indeed, in the products they purchase. Each product has its own unique profile of margin, value/density, volume and handling requirements. Similarly customers will order different product mixes, will have their own unique requirements as to the number of delivery points and, of course, the number of times they order and the complexity of their orders will differ. Putting all these factors together can produce widely differing cost implications for the supplier.
The 80/20 rule, or ‘Pareto Law’, suggests that 80 per cent of the total sales volume of a business is typically generated by just 20 per cent of its customers and that 80 per cent of the total costs of servicing all the customers will probably be incurred by only 20 per cent of the customers (but probably not the same 20 per cent).
The profitability of customers varies considerably whether we are examining this at the customer segment or individual, or one-to-one, customer level. Figure 3.10 shows the shape of the profit distribution
resulting from the uneven spread of profits across the customer base.
From this example, it can be seen that there is a ‘tail’ of customers who are actually unprofitable and who therefore reduce total profit contribution. It is essential to understand which segment these customers fit into. The analysis in this figure, which is based on large corporate customers, applies equally at the customer segment level.
A key aim of CRM is to develop close relationships with customers in segments that are, or have the potential to become, highly profitable. So the ability to create customer segment profit and loss accounts at the appropriate level – segment, micro-segment or indi- vidual customer – is fundamental to a successful CRM strategy.
The problem is that traditional accounting systems make it diffi- cult, if not impossible, to identify the true costs of serving individual customers. Companies often assume that there is an ‘average’ cost of serving a customer, thus forgoing the opportunity to target those customers or segments who have real potential for transforming their own bottom line.