The goal of customer value management is straightforward: every decision should choose the option that contributes most to long-term customer value. The obvious challenge is measuring the value of each option. Less obvious but equally important is the challenge of identifying the decisions themselves.
Identifying decisions is difficult because companies interact with customers in many ways. Most interactions are standard operational processes that do not require customer-specific decisions. Decision points could be added if there was a reason to do so, hence the need to identify the points at which decisions are or could be made.
The reason to add a decision is that treating different customers differently would increase aggregate customer value. This brings us back to the original challenge of measuring the value of a choice.
The fundamental issue in measuring value is time. The immediate result of a decision is often obvious: the customer did or did not accept the offer, did or did not renew the contract. Long-term results are more subtle. Did the customer buy less after you refused to accept a return? Did explaining Web service features lead to fewer telephone inquiries? Did a prompt repair lead to more referrals? In many cases, the long-term impacts will have a much larger value than the immediate result.
Extending the time horizon raises its own issues. The most daunting is the difficulty of establishing causal relationships between a single interaction and final results. After all, customers have many interactions and they all have some impact on later behavior. How long must you wait before you measure lifetime value, anyway?
Methods of Measurement
A more practical approach lies between the extremes - measure something more than the immediate result but less than total lifetime value. One method is simply to look at a specified interval, perhaps behavior in the 90 days following a given interaction. This involves comparing behavior of customers who had a particular experience with behavior of similar customers who lacked that experience. Statistical techniques can extend this approach by looking at multiple experiences and finding the impact of common patterns.
The problem with a purely time-based approach is that it treats experiences as disconnected. This makes it difficult to understand the relationships among different interactions or the reasons for any observed results. An increasingly common alternative is to analyze discrete customer processes such as making a purchase or resolving a service request. This identifies a set of related interactions that can be analyzed as a unit. Many metrics, such as total cost to resolve a problem or revenue per lead, make the most sense when measured for the process as a whole. In fact, measuring results for individual interactions can be misleading; a focus on reducing time per repair call could result in more return visits and thus higher total cost per repair. Looking at the process as a whole also makes it easier to understand how the pieces fit together and to assess the net impact of any individual change. Measuring the impact of decisions on long-term value also becomes simpler because the number of input variables is reduced when the inputs are processes rather than individual interactions.
Customer processes are a natural unit of analysis because many businesspeople already think in customer process terms. In this context, it is important to distinguish between customer process and business process because a single customer process may intersect with multiple business processes. The customer process of buying a new home entertainment system may intersect with business processes for advertising, inventory, store operations, sales, financing, delivery and installation. The business may see these processes as separate, but they are all unified from the customer perspective. Even from the business perspective, changes in one area may have a major impact on another area - impacts that would likely go unnoticed unless the customer process is analyzed as a whole.
Customer process analysis is not a panacea. Data must still be collected, results measured, new options tested and policies deployed. But working with customer processes reduces the overwhelming complexity of the entire customer relationship into manageable pieces. This makes it a critical step in converting customer value management from theory into practice.
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