Churn is a bit easier to spot when there is a monthly billing relationship, as with credit cards. Even there, however, attrition might be silent. A customer stops using the credit card, but doesn’t actually cancel it. Churn is easiest to define in subscription-based businesses, and partly for that reason, churn modeling is most popular in these businesses. Long-distance companies, mobile phone service providers, insurance companies, cable companies, financial services companies, Internet service providers, newspapers, magazines,
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and some retailers all share a subscription model where customers have a formal, contractual relationship which must be explicitly ended.
Why Churn Matters
Churn is important because lost customers must be replaced by new customers, and new customers are expensive to acquire and generally generate less revenue in the near term than established customers. This is especially true in mature industries where the market is fairly saturated—anyone likely to want the product or service probably already has it from somewhere, so the main source of new customers is people leaving a competitor.
Figure 4.6 illustrates that as the market becomes saturated and the response rate to acquisition campaigns goes down, the cost of acquiring new customers goes up. The chart shows how much each new customer costs for a direct mail acquisition campaign given that the mailing costs $1 and it includes an offer of $20 in some form, such as a coupon or a reduced interest rate on a credit card.
When the response rate to the acquisition campaign is high, such as 5 percent, the cost of a new customer is $40. (It costs $100 dollars to reach 100 people, five of whom respond at a cost of $20 dollars each. So, five new customers cost $200
dollars.) As the response rate drops, the cost increases rapidly. By the time the response rate drops to 1 percent, each new customer costs $200. At some point, it makes sense to spend that money holding on to existing customers rather than attracting new ones.
$250
$200
$150
$100
Cost per Response
$50
$0
1.0%
2.0%
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Response Rate
Figure 4.6 As the response rate to an acquisition campaign goes down, the cost per customer acquired goes up.
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Retention campaigns can be very effective, but also very expensive. A mobile phone company might offer an expensive new phone to customers who renew a contract. A credit card company might lower the interest rate. The problem with these offers is that any customer who is made the offer will accept it. Who wouldn’t want a free phone or a lower interest rate? That means that many of the people accepting the offer would have remained customers even without it.
The motivation for building churn models is to figure out who is most at risk for attrition so as to make the retention offers to high-value customers who might leave without the extra incentive.
Different Kinds of Churn
Actually, the discussion of why churn matters assumes that churn is voluntary.
Customers, of their own free will, decide to take their business elsewhere. This type of attrition, known as voluntary churn, is actually only one of three possibilities. The other two are involuntary churn and expected churn.
Involuntary churn, also known as forced attrition, occurs when the company, rather than the customer, terminates the relationship—most commonly due to unpaid bills. Expected churn occurs when the customer is no longer in the target market for a product. Babies get teeth and no longer need baby food. Workers retire and no longer need retirement savings accounts. Families move away and no longer need their old local newspaper delivered to their door.
It is important not to confuse the different types of churn, but easy to do so.
Consider two mobile phone customers in identical financial circumstances.
Due to some misfortune, neither can afford the mobile phone service any more. Both call up to cancel. One reaches a customer service agent and is recorded as voluntary churn. The other hangs up after ten minutes on hold and continues to use the phone without paying the bill. The second customer is recorded as forced churn. The underlying problem—lack of money—is the same for both customers, so it is likely that they will both get similar scores.
The model cannot predict the difference in hold times experienced by the two subscribers.
Companies that mistake forced churn for voluntary churn lose twice—once when they spend money trying to retain customers who later go bad and again in increased write-offs.
Predicting forced churn can also be dangerous. Because the treatment given to customers who are not likely to pay their bills tends to be nasty—phone service is suspended, late fees are increased, dunning letters are sent more quickly. These remedies may alienate otherwise good customers and increase the chance that they will churn voluntarily.
In many companies, voluntary churn and involuntary churn are the responsibilities of different groups. Marketing is concerned with holding on to good customers and finance is concerned with reducing exposure to bad customers.
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From a data mining point of view, it is better to address both voluntary and involuntary churn together since all customers are at risk for both kinds of churn to varying degrees.
Different Kinds of Churn Model
There are two basic approaches to modeling churn. The first treats churn as a binary outcome and predicts which customers will leave and which will stay.
The second tries to estimate the customers’ remaining lifetime.
Predicting Who Will Leave
To model churn as a binary outcome, it is necessary to pick some time horizon.
If the question is “Who will leave tomorrow?” the answer is hardly anyone. If the question is “Who will have left in 100 years?” the answer, in most businesses, is nearly everyone. Binary outcome churn models usually have a fairly short time horizon such as 60 or 90 days. Of course, the horizon cannot be too short or there will be no time to act on the model’s predictions.
Binary outcome churn models can be built with any of the usual tools for classification including logistic regression, decision trees, and neural networks.
Historical data describing a customer population at one time is combined with a flag showing whether the customers were still active at some later time. The modeling task is to discriminate between those who left and those who stayed.
The outcome of a binary churn model is typically a score that can be used to rank customers in order of their likelihood of churning. The most natural score is simply the probability that the customer will leave within the time horizon used for the model. Those with voluntary churn scores above a certain threshold can be included in a retention program. Those with involuntary churn scores above a certain threshold can be placed on a watch list.
Typically, the predictors of churn turn out to be a mixture of things that were known about the customer at acquisition time, such as the acquisition channel and initial credit class, and things that occurred during the customer relationship such as problems with service, late payments, and unexpectedly high or low bills. The first class of churn drivers provides information on how to lower future churn by acquiring fewer churn-prone customers. The second class of churn drivers provides insight into how to reduce the churn risk for customers who are already present.
Predicting How Long Customers Will Stay
The second approach to churn modeling is the less common method, although it has some attractive features. In this approach, the goal is to figure out how much longer a customer is likely to stay. This approach provides more
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information than simply whether the customer is expected to leave within 90
days. Having an estimate of remaining customer tenure is a necessary ingredient for a customer lifetime value model. It can also be the basis for a customer loyalty score that defines a loyal customer as one who will remain for a long time in the future rather than one who has remained a long time up until now.
One approach to modeling customer longevity would be to take a snapshot of the current customer population, along with data on what these customers looked like when they were first acquired, and try to estimate customer tenure directly by trying to determine what long-lived customers have in common besides an early acquisition date. The problem with this approach, is that the longer customers have been around, the more different market conditions were back when they were acquired. Certainly it is not safe to assume that the characteristics of someone who got a cellular subscription in 1990 are good predictors of which of today’s new customers will keep their service for many years.
A better approach is to use survival analysis techniques that have been borrowed and adapted from statistics. These techniques are associated with the medical world where they are used to study patient survival rates after medical interventions and the manufacturing world where they are used to study the expected time to failure of manufactured components.