Consider All the Variables
Accepted wisdom cautions against measuring customers by how much revenue they generate. The key metric is their profitability. Measuring profitability allows marketers to recognize differences between customers based on the costs associated with the products they purchase, the channels they use and the special services they consume.
When these variables are considered, many marketers learn that some high-revenue customers are actually unprofitable. If customers are unprofitable, they can be let go, or marketers can study how they interact with the company with an eye to making them profitable.
A few years ago, a major telco released 1,200 customers after it determined this group accounted for 40,000 service calls a month. It was a bold step not to be undertaken lightly. In order to eliminate money-losing customers, organizations must be able to gather and analyze millions of data points.
What works?
What keeps the highest value customers coming back? Retaining a high-value customer, or making an unprofitable customer profitable, can be as simple as making an adjustment in the product or service mix. But marketers won’t know which approach is most effective if they can’t dig into their data. An expert approach to analysis is critical.
Here’s a good first step: When marketers consider customer value, they should break it down into three distinct elements: current value (or current customer profitability), lifetime value (LTV) and potential value.
Current value is a historical view of each customer’s profitability based on the products and services he or she has consumed to date from the company. Lifetime value is an extrapolation of each customer’s current value into the future based on the products he or she owns today. And potential value uses statistical techniques to estimate a customer’s propensity to become more profitable by buying additional goods and services.
This evaluation process may sound daunting, but there are cases of companies successfully separating unprofitable customers from profitable ones.
For instance, a check printing company experienced a gradual decline in the number and profitability of checks ordered by customers of a specific bank. Through measuring the ROI of customers in specific groups, the printer discovered that not all customers were profitable: The ones who were the biggest drains were those who still ordered everything the old-fashioned way, via paper orders. These same customers liked to order a lot of customized products, which generated lower margins.
It was costing the printing company too much to service this group. The printing company could have either fired the unprofitable customers or raised prices across the board so those continuing to place low-margin orders would pass the costs on to the other bank customers. Or the printer could have refused to provide customization services.
Rather than take these steps, which might have alienated customers and generated ill will, the printer showed its bank customers the data. It shared the fact that demand was lower and servicing them in traditional ways was costing more. The printer suggested that if the margin-tightening trend continued, it would have no choice but to drop the bank or raise prices.
The pitch worked. The printer’s bank customers adapted to the automated ordering technology.
A better slice
In another instance, a paper products company couldn’t get its largest customer, a major pizza chain, to accept a price increase. When it studied the value of all its customers, it discovered this huge client was costing the firm money. It didn’t drop the firm. Instead, the paper company shared with the pizza firm calculations showing that a few cents per case could keep the supplier in the black. The chain accepted a price increase after reviewing the calculations, and the paper supplier kept a customer with strong potential.
In today’s world, organizations have to understand customer profitability and potential in order to make smarter decisions that produce higher profits and happier customers. And to do that, marketers must be able to analyze their data in detail. With millions of customers creating millions of transactions each day over multiple customer touch-points, only technology can solve the problem and distribute the guidance to tens of thousands of employees every day — whether sales execs, store clerks or call center employees.
Jeff Gilleland is the global customer intelligence strategist at SAS. Karen Heath is a managing practice principal in HP’s BI Solutions group.
