New Start
Base Forecast
Forecast
Do Existing Base
Do New Start Churn
Churn Forecast
Forecast (NSCF)
(EBCF)
Churn
Churn
Forecast
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.
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.
A
.
ctual 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.
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Data Mining throughout the Customer Life Cycle 469
The forecast is then broken into the following pieces. The existing base
forecast (EBF) det
active customer
ermines the probability of each
being active
on given dates in the future; this forecast is a direct application of survival analysis. T
new start forecast
he
(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) = EB t
F( ) – 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 – ) – One Day Survival of NSF(
1
t – 1). The churn forecast is the sum of
these, CF( t) = EBCF( t) + NS
t
CF( ).
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.
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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.
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As more of the world is technology-driven, more and more data is available, particularly about customer behavior. Data mining seeks to use all this data to advantage, by summarizing data and applying algorithms that produce meaningful results even on large data sets.
In the midst of all this technology, though, the customer relationship still maintains its central position. After all, customers—because they provide revenue—are the one thing that businesses need to remain successful, year after year. Eventually, other funding sources dry up. No computer ever made a purchase from Amazon; no software ever paid for a Pez dispenser on eBay; no cell phone ever made an airline or restaurant reservation. There are always people, individually or collectively, on the other end.
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TEAMFLY
Team-Fly®
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C H A P T E R
15
Data Warehousing, OLAP,
and Data Mining
Since the introduction of computers into data processing centers in the 1960s, just about every operational system in business has been computerized. These automated systems run companies, spewing out large amounts of data along the way. This automation has changed how we do business and how we live: ATM machines, adjustable rate mortgages, just-in-time inventory control, online retailing, credit cards, Google, overnight deliveries, and frequent flier/buyer clubs are a few examples of how computer-based automation has opened new markets and revolutionized existing ones. This is not a new story; it has been going on for decades.
In a typical company, such systems create vast amounts of data spread through scads of disparate systems, from general ledgers to sales force automation systems, from inventory control to electronic data interchange (EDI), and so on. Data about specific parts of a business is there—lots and lots of data, somewhere, in some form. Data is available but not information— and not the right information at the right time. The goal of data warehouses is to make the right information available at the right time. Data warehousing is the process of bringing together disparate data from throughout an organization for decision-support purposes.
A data warehouse serves as a decision-support system of record, making it possible to reconcile reports because they have the same underlying source.
Such a system not only reduces the need to explain disparate results, but also provides consistent views of the business across business units and time. We 473
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believe that, over time, informed decisions lead to better bottom-line results over time, and data warehouses help managers make informed decisions.
Decision support, as used here, is an intentionally ambiguous concept. It can be as rudimentary as getting production reports to front-line managers every week. It can be as complex as sophisticated modeling of prospective customers using neural networks to determine which message to offer. It can be and is just about everything in between.