ROC: The Logic of the Return on Customer

Has the Time Come for "Return on Customer" At Last?

from Strategy Speaks: a Peppers and Rogers Blog by Don Peppers

 

There is an interesting and well-informed article discussing Martha Rogers' and my Return on Customer metric in the most recent issue of the UK's Marketing Week magazine. David Reed, who covers the "data strategy" beat for the magazine, writes that while the data side of marketing has benefited greatly from a renewed attention to the financial metrics of success, particularly ROI, this might be a short-term blessing for the discipline. What he means is that ROI metrics typically look at campaign or product profitability figures, but have little to say about the long-term value created (or often destroyed) by marketing efforts. On the other hand, he says, the ROC metric does capture long-term value, because it incorporates changes in customer lifetime value (LTV). [Note, please that Martha and I have trademarked the terms "Return on Customer" and "ROC." We grant permission to people to apply these terms to their own analytics efforts when we deem the terms are used correctly.]
Although Martha and I first wrote about Return on Customer in late 2003, our book by that name didn't appear until 2005. We argued that whenever a customer has an experience with a brand he creates current-period, short-term value, by buying the product or costing money to serve, but he also creates long-term value, because the experience itself makes him more or less likely to do business with the brand in the future. We also suggested the simplest way to measure such future value would be to compare the customer's lifetime value both before and after the experience. If he has a good experience, then his LTV will increase, and vice versa, and the amount by which LTV changes represents the long-term component of the value created by the experience. Although there was no shortage of analysis devoted to forecasting customer LTV, until our book came out the marketing community paid little or no attention to trying to understand or forecast changes in customer LTV. But these changes in LTV, by capturing the long-term impact of short-term events, will do the best job of eliminating the short-term bias of more traditional ROI and pay-back analyses of marketing efforts.



In addition to its inability to capture long-term value creation (or destruction), return-on-investment analysis doesn't speak to the other big problem confronted by marketing, which has to do with the fact that customers are a finite productive resource. Customers aren't unlimited in number, so a business must create whatever value it can from the customers and prospects available to it. If you have a good marketing program to invest in, and the ROI is appropriate, you can virtually always secure the capital to invest, because there is a robust secondary market for capital. However, there's no such secondary market for customers. No bank can lend you a few customers for you to employ to create some value, repaying them later with interest.
Reed writes that he remembers a presentation I gave in London a few months ago, in which I posed a question to the seminar attendees. Suppose, I said, you had to choose from two different marketing initiatives. Initiative A costs you £5 per customer, and yields a benefit of £10 per customer, for a net profit of £5, or a 100% ROI. Initiative B costs you £10 per customer, and yields a benefit of £16, or a net profit of £6 per customer, giving you an ROI of just 60%. Which initiative do you choose? Of course, Initiative A is by far the superior financial investment, because for every pound invested you get a pound of profit, instead of just 60 pence. So, whether your budget is £1000 or £10,000,000, you get more benefit from A, right? Well, yes - unless you just have one customer. If you only have a single customer, of course, then Initiative B is better, because the £6 you would earn from B is more than the £5 you would earn from A. But now the question is, would your answer be different if you "only" had a thousand customers? Or a million? Of course not.
This is the basic logic of Return on Customer. The ROC metric assumes that (1) customers create all value for a business, (2) this value is created not just in the short-term but also in the long-term, and (3) the number of customers is finite. ROC measures the efficiency with which a set of customers creates value. ROI, on the other hand, measures the efficiency with which a quantity of capital creates value. The simplest mathematical explanation of Return on Customer is by direct analogy with return on investment: Suppose you buy a stock for $100, you receive a dividend of $5 on this stock, and by the end of the year the stock has gone up in value to $110. In that case, your ROI would be 15%. Similarly, if you acquire a customer who has a $100 LTV, you make $5 in profit from this customer, and by the end of the year the customer's LTV has increased to $110, then your ROC would be 15%.
We predict that ROC will become more and more important to businesses as they continue to wrestle with the kinds of customer-specific marketing problems thrown up by today's assortment of interactive media and technologies.
ROC measures customer-specific efficiency, as opposed to dollar-specific efficiency. It is a way to "optimize" your business around an individual customer, a task that is becoming increasingly central to any company's success. Let's say you're designing a Web site. You want the communications and offers on your Web site to make the best possible impression on the customer who is visiting the site right now, based on what you know about the customer. And of course you will change what the Web site shows to the next customer, and the next, and the next, based on what you know about each different customer, in order to tailor the most appropriate communication and offer for each. In effect, you are optimizing your business around each customer, individually. And you will do the same thing at the contact center; you will want to do the same thing at the point of purchase, and for the sales force, and for your email marketing, and for all other one-to-one channels. In each such situation, your task is not to decide the best communication to go the same way to everyone, but the best communication for each individual customer, at this time. You are optimizing by customer.
Optimizing by customer is, increasingly, the central task of marketing, as the whole discipline continues to be transformed by interactive technologies. And Return on Customer is a metric designed for measuring the value created whenever a business optimizes by customer.