Maximizando del valor de los clientes: Maximizing Asset Value One Customer at a Time

Derive a deeper understanding of customer value

by Don Peppers & Martha Rogers, Ph.D.
Regardless of the business you are in, your customers are your most important financial assets. As with other financial assets, customers need to be efficiently valued in order for you to increase the profitability of your customer portfolio over the long term. Yet, firms often fall short of realizing this goal because they don't fully understand the dynamics of customer value and the drivers of customer growth. What's a decision maker to do?

Assigning value to customers
Let's look at how one might arrive at a deeper understanding of customer value. Similar to other financial assets, a customer's value is determined by the profitability the customer will deliver in the future. It follows that you want to know when the value of your customer asset increases or decreases, and what kinds of events or situations drive these changes. This may sound simple conceptually, but it can be challenging to put into practice. You have to identify the dynamics that drive the future profitability of your customers in order to arrive at a meaningful measure of customer value.

This leads some decision makers to employ the concept of lifetime value (LTV) to measure a customer's value to the enterprise. At its most basic, LTV refers to the future economic worth of a customer and can be further defined as the net present value of the likely future income stream generated by an individual customer. For instance, LTV might be the expected number of purchase transactions from a customer each year times the number of years the customer is expected to remain loyal times the discounted net present value of the margins on those purchases each year. Ideally, lifetime value would capture all the various behaviors of a customer that have any bearing on the enterprise's profit from that customer. Technology is obviously a key part of the process. Customer transaction databases along with sophisticated analytics used by decision science professionals help to quantify LTV, in effect modeling your customer's behavior in the past to predict your customer's behavior (and value) in the future.

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