Berry

470 Chapter 14
Winback
Once customers have left, there is still the possibility that they can be lured
back. Winback tries to bring back valuable customers, by providing them with
incentives, products, and pricing promotions.
Winback tends to depend more on operational strategies than on data analysis.
Sometimes it is possible to determine why customers left. However, the
winback strategies need to begin as part of the retention efforts themselves.
Some companies, for instance, have specialized “save teams.” Customers cannot
leave without talking to a person who is trained in trying to retain them. In
addition to saving customers, save teams also do a good job of tracking the
reasons why customers are leaving—information that can be very valuable to
future efforts to keep customers.
Data analysis can sometimes help determine why customers are leaving,
particularly when customer service complaints can be incorporated into operational
data. However, trying to lure back disgruntled customers is quite hard.
The more important effort is trying to keep them in the first place with competitive
products, attractive offers, and useful services.
Lessons Learned
Customers, in all their forms, are central to business success. Some are big and
very important; these merit specialized relationships. Others are small and
very numerous. This is the sweet spot for data mining, because data mining
can help provide mass intimacy where it is too expensive to have personal
relationships with everyone all the time. Some are in between, requiring a balance
between these approaches.
Subscription-based relationships are a good model for customer relationships
in general because there is a well-defined beginning and end to the
relationship. Each customer has his or her own life cycle defined by events—
marriage, graduation, children, moving, changing jobs, and so on. These can
be useful for marketing, but suffer from the problem that companies do not
know when they occur.
The customer life cycle, in contrast, looks at customers from the perspective
of their business relationship. First, there are prospects, who are activated to
become new customers. New customers offer opportunities for up-selling,
cross-selling, and usage stimulation. Eventually all customers leave, making
retention an important data mining application both for marketing and forecasting.
And once customers have left, they may be convinced to return
through winback strategies. Data mining can enhance all these business
opportunities.