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Evaluating the Value of a Customer

Originally published February 28, 2013

In my previous article, Customer Value Analytics Driving Customer Centricity, I suggested that there are different kinds of customers who provide different kinds of value at different times during the lifetime of the customer relationship. But from an objective perspective, it is valuable to develop criteria for customer valuation that are relevant within the corporate business context. These criteria can provide quantifiable measures for characterizing customer types, enable the development of customer analytics models, and help in crafting a set of customer engagement strategies that maximize the different aspects of profitability.

In a perfect model, this could be characterized as an optimization problem by enumerating a collection of variables whose positive values we would seek to maximize or whose negative values we’d seek to minimize. We can attempt to work out this enumeration from the perspective of the most desirable optimized outcome by looking at the key dimensions of customer value and profitability. This would specify different variables to be applied to each customer, or potentially to a collection of customers, including:

  • Tangible aspects such as increased revenue and decreased costs.

  • Material, yet somewhat fuzzy aspects such as minimizing corporate risk.

  • Practical aspects such as minimizing effort of engagement or elongating the duration of the customer relationship.

  • Somewhat intangible beneficial aspects such as increased goodwill and word-of-mouth positive publicity.
Another way of saying this is that the value of a customer is a function of variables such as those provided in Table 1.

Table 1:
Sample Variables for Customer Valuation

Of course, attempting to focus on only one or a few of these aspects may impact maximizing the benefit. For example, increasing the duration of the customer relationship may be beneficial if the customer type is one that has a predictable revenue stream across that time horizon. But if the cost of elongating the relationship increases the level of effort and costs associated with retention, the anticipated benefit of the revenue stream may be offset by the increased costs. As another example, decreasing the investment in maintaining a customer may result in lowered overall costs, but may increase the probability of attrition, thereby reducing (or really eliminating) the predictability of the future revenue stream.

From a practical perspective, the customer valuation model should incorporate any variable that contributes to some measurable aspect of corporate value. Some of those shared in Table 1 seem obvious, such as the net present value of the customer’s future revenue stream. On the other hand, some have correlations that are more complex to envision, such as the revenue value of a contribution to a customer profile model. It is worth allocating a brainstorming session to identify those variables that can possibly contribute to corporate value and populate a table like the one provided here by taking these steps:
  • Begin with an enumeration of the high-level dimensions of value.

  • Suggest variables associated with customer engagement and touch points that potentially contribute to any of those value dimensions.

  • Specify a measure of value for each of the variables
Once that table is populated, the next step is to consider factors for weighting the measures as they contribute to the calculation of customer value. For example, if there are costs relating to customer risks, the refinement would seek to ascertain which customer characteristics are relevant in assessing those costs, such as the customer’s credit score or the number of times the customer has a missed or late payment. These identified customer characteristics coupled with the measure calculation become a part of the valuation model.

This highlights the fact that it would be a challenge to presume that a customer valuation model can be created in the absence of any history of the results of the various customer relationships and experiences. The implication is that the developed model is going to rely on analysis of customer engagement histories involving transactions and interactions at any touch point, as well as relationships between customers and products/services and relationships between customers and other individuals. On the other hand, it also suggests that there are some key variables that contribute more significantly to the valuation than others. This implies that you can suggest a baseline model whose variables and weightings can be refined over time.

My next article will look at the utility of the customer valuation model across the customer lifecycle and breadth of touch points.

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