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The central thesis of Return on Customer by Don Peppers and Martha Rogers is that companies must optimize the mix between current-period cash flows and long-term customer equity, which is a function of the lifetime customer value (LTV) of a company's entire portfolio of customers.

The following equation for Return on Customer (ROC) was discussed in Part 1:

ROC = Πi + ΔCEi
           CEi-1

Πi = Cash flow from customers during period i
ΔCEi = Change in customer equity during period i
CEi-1 = Customer equity at the beginning of period i

ROC equals a firm's current period cash flow from its customers plus any changes in the underlying customer equity, divided by the total customer equity at the beginning of the period.

The second half of the book reinforces and amplifies the first: that is, customers are a company's scarcest resource, and different customers should be treated differently to best maximize ROC. According to the authors, sometimes it is more beneficial to defer some profitability in order to boost future earnings and growth.

Treating Different Customers Differently

Most companies already treat different customers differently, but only at a relatively basic level. Understanding each customer's unique needs and values is the key to maximizing ROC. The more challenging the economy, the more important it is to know which customers to focus on and leverage.

Another key task, according to the authors, is to know what each customer could be worth. The authors define a customer's "potential value" as the maximum LTV that a company could realize from a customer if it were to execute the best possible strategy. The most straightforward way to estimate a customer's potential value is to look at the range of LTVs for similar customers. In the business-to-business (B2B) world, this process might entail comparing the LTVs of corporate customers in the same industry, with similar sales, profit, or growth patterns.

Peppers and Rogers go on to write about the two key factors for revenue growth: customer loyalty and revenue stimulation.

Most research supports the idea that loyal customers buy more, cost less to serve, and generate higher margins. The key with customer loyalty is to focus on the most profitable customers and to quantify the value created by the increase in loyalty.

For example, one study found that a 1%improvement in the customer-retention rate could increase customer equity 3-7%. According to the authors, the goal is increased lifetime value, not customer loyalty per se, since that could be achieved, for example, by selling products below cost.

The revenue stimulation piece, according to the authors, is all about selling additional offerings to customers, above and beyond what they would otherwise be expected to buy. The authors point out that in many industries there is a strong correlation between the number of offerings bought by a customer and their loyalty. For example, a bank customer who buys three or more offerings is more likely to stay loyal than a customer who buys only one or two offerings.

The following are key questions to help determine potential value:

  • How much of your customer's business currently goes to your competition?

  • Why do some of your customers buy more offerings than others?

  • How can you identify latent needs of your customers?

  • How can you reduce the cost of serving your customers while still maintaining satisfaction?

  • Why are some of your customers more likely to provide referrals or be "referenceable"?

Predicting the Future

The art and science of business is about prediction, whether about market growth, diffusion of innovations, or pro forma profitability. LTV is no exception. It's important to look for leading indicators of LTV changes. The authors outline two steps for predictive modeling:

  1. Devise an equation for LTV with several years of transactional data that looks at customers' actual spending patterns and other characteristics that might be predictive and meaningful. This process will involve some assumptions and judgment, as all models do.

  2. Identify the best currently available predictive variables and determine correlations and relationships with the calculated LTVs. The included data could be demographic, firmographic, and/or psychographic.

Peppers and Rogers write that the leading indicators of LTV change fall into four categories:

  1. Lifetime value drivers: The elements of the LTV equation that help determine how much value the customer creates for the company over time.

  2. Lifestyle changes: When a customer takes a new job, gets married or divorced, or is involved in a merger or acquisition—that is, when there is a significant change in the customer's situation, there is likely to be an LTV impact.

  3. Behavioral cues: What does the customer do—more complaints, more or fewer touches or offerings purchased? At the end of the day, behavior trumps intentions.

  4. Customer attitudes: These include such areas as satisfaction, willingness to recommend your company or offerings, and likelihood of repurchase. These indicators, along with lifestyle changes, are likely to be predictive of behavior.

Behavioral cues are important in predicting future customer behavior. According to the authors, a customer that begins to subscribe to an email newsletter or begins to refer other customers is typically less likely to defect.

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ABOUT THE AUTHOR

image of Michael Perla

Michael Perla is Sr. director & life sciences people leader, Business Value Services, at Salesforce.

LinkedIn: Michael Perla