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Selling Power Magazine Article
Know When to Hold 'Em
While customer retention may not be simple, the first step is spotting potential defectors, because it's more cost effective to focus on them. But recognize that some customers are going to drop out no matter what you do. Next, understand how much value you can expect from customers if they stay on board. This sets your retention budget for keeping them.
These challenges are tougher if you sell a high volume of low-dollar business. Personal conversations are too expensive in these markets. You have to use data and smart analytics to inform your retention decisions.
MIT professor Michael Braun has developed what he calls "a hierarchical competing risk model" to do just that. "Competing risk models are often used when observations include both the time and cause of an event, as in medical research, for example," he explains.
Braun's model addresses which customers may choose to terminate your services and why. It distinguishes the customer churn that can be influenced by something your firm can do (e.g., improve service, cut prices, or upgrade value) from uncontrollable churn due to customer relocation, change in economic circumstances, death, or bankruptcy.
Braun used data from a land-based telecommunications provider to estimate the incremental customer value a firm can earn by delaying its controllable churn while recognizing that some churn is unpreventable. The approach can be used to focus retention on customers you can keep.
"We used past customer behavior to determine churn due to controllable factors," Braun explains. "The method also yields a measure of the return on investment of retention activities."
The Braun model is directly applicable to contractual sales, such as those in communications, cable television, health clubs, or other services that are membership-based, even direct-debit charitable donations. Its basic principles could be adapted to any recurrent sales to regular customers.
The telecommunications provider had plenty of data on its customers, including initial service date, churn date, location, income, age, employment, homeownership, and education. More than three-quarters of the customers who left gave a reason for discontinuing service: competition, price, dissatisfaction with service, dissatisfaction with product, moving from service area, and so forth. Some customers had been cut off for nonpayment. In the case of this telecom provider, much of the churn turned out to be uncontrollable, a signal that retention efforts must be tightly budgeted to be cost effective.
Allocation, Allocation, Allocation
Braun, who worked in telecom before teaching at MIT, says allocation of retention resources is a very common challenge: "It's important to know what you can and cannot control. There are costs associated with any activity. And you must know the return on investment you want to achieve."
These are straightforward business principles. The hard part is applying them. To do so, you must recognize and allow for the uncontrollable churn or risk reducing the return on investment on whatever you do to hold on to the rest. Uncontrollable churn will likely differ by market segment and length of time a customer's purchased from your firm.
Although the model was applied to a retail telecom market, Braun says it is "absolutely relevant to B2B." Subscription-based B2B sales are most appropriate to the model, but any regular transactions that suddenly stop or sharply decline are good candidates.
"It can work for a repeat-purchase model, as in the purchase of office supplies, for example, that stops or is interrupted for a while or goes below its natural rate," Braun explains. A customer's going out of business is uncontrollable, for example, but you might control whether that customer moves his or her business to a competitor.
The model's math would differ for regular, rather than subscription, sales. But the vital decisions are still in whom you should invest and how much. Braun's approach can be used to tailor retention strategy and timing to individual customers.
Another implication of Braun's approach is the need for constant investment in customer acquisition. "You must be constantly acquiring new customers, because some are going to drop off no matter what you do," he stresses.
Estimates of how much you will spend holding on to even controllable churn can inform your new customer strategy. Some customers may be prevented from leaving or just delayed a while but at a higher cost than obtaining new customers. Furthermore, recognition of retention costs and inevitable churn is necessary to properly predict the value of each new customer.
Application of Braun's model does require data about or estimates of the reasons for churn. It is best to get this data from customers when they cancel a service, but sales reps can also make informed judgments. Additionally, companies can use customer surveys to uncover the reasons behind churn. "The better the data you have on the reasons for churn," explains Braun, "the more accurate the model will be."
Many companies have struggled with the retention puzzles Braun's model addresses. Business-intelligence tools address the problem by tapping into massive customer (continued on page 2)
– Henry Canaday
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