AN ENGINE FOR CHURN FORECASTING

Forecasting customer stops and customer levels plays an important role in businesses, particularly for planning future budgets and marketing endeavors. A forecast provides an expect value (or set of expected values), that can be used for comparing what actually happened to what was expected. This is a natural application of data mining, particularly survival analysis. The following figure shows what a forecasting engine looks like.

Existing Customer Base

X-Base Forecast,

Do Existing Base Forecast (EBF)

Do New Start Forecast (NSF)

r>

Do Existing Base Churn Forecast (EBCF)

Do New Start Churn Forecast (NSCF)

New Start Forecast

Churn Forecast

Churn Actuals

Compare

A forecasting engine uses data mining to predict customer levels (and hence churn) as well a providing explanations in the form of deviations from the expected.

New Start

There are five important inputs:

Effective Date. All numbers before this date are actuals; all numbers after this date are forecasts.

Forecast Dimensions. These are attributes of customers, such as product, geography, and the channel used for developing the forecast.

New Starts. This is a list of new starts broken down by the forecast dimensions after the effective date.

Active Customers. This is a list of all customers active on the effective date, including the forecast dimensions for each customer.

Actual Churn. These are actual stops broken into forecast dimensions; these are used for comparisons for explanatory purposes. This is not available when the forecast is being developed, but is used later.

The forecast is then broken into the following pieces. The existing base forecast (EBF) determines the probability of each active customer being active on given dates in the future; this forecast is a direct application of survival analysis. The new start forecast (NSF) determines the contribution to the future base from new starts. That is, these are the new starts who are active on future dates. This is a direct application of survival analysis with a twist, because every day, new customers are starting: NSF(t) = One Day Survival of NSF(t- 1) + New Starts(t).

The churn forecast is easily derived from the EBF and NSF. The existing base churn forecast (EBCF) is the number of churners on a given day in the future from the existing base. This is the difference in survival on successive days: EBCF(t) = EBF(t) - EBF(t+ 1). The new start churn forecast (NSCF) is the number of churners on a given day in the future from the new starts. This is a little trickier to calculate, because we have to take into account new starts: NSCF(t) = NSF(t- 1) - One Day Survival of NSF(t- 1). The churn forecast is the sum of these, CF(t) = EBCF(t) + NSCF(t).

All of the pieces of the forecast typically use forecast dimensions. The result is that the forecast can be compared to actuals, making it possible to explain the results in terms understandable and useful to the business.

The power of survival analysis is that it focuses on what is often the most important determinant of retention, customer tenure. Customers who have been around for a long time are usually more likely to stay around longer. However, survival analysis can also take into account other factors, through several enhancements to the basic technique. When there is a lot of data, different factors can be investigated independently, using a process called stratification. When there are many other factors, then parametric modeling and proportional hazards modeling provides a similar capability (these are not discussed in detail in this book). In either case, it is possible to get an idea of customers remaining tenures. This is useful not only for retention interventions, but also for customer lifetime value calculations and for forecasting numbers of customers, as discussed in the sidebar An Engine for Churn Forecasting.

An alternative approach is to predict who is going to leave for some small amount of time in the future. This is more of a traditional predictive modeling problem, where we are looking for patterns in similar data from the past. This approach is particularly useful for focused marketing interventions. Knowing who is going leave in the near future makes the marketing campaign more focused, so more money can be invested in saving each customer.

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.