How long will a lightbulb last? What factors influence a cancer patient's prognosis? What is the mean time to failure (MTTF) of a disk drive? These questions may seem to have little relationship to each other, but they do have one thing in common. They are all questions that can be answered using survival analysis, because they involve time‐to‐event estimations. And, if we think about customers instead of lightbulbs, patients, and disk drives, they readily translate into important questions about customers, their tenures, and their value.
The scientific and industrial origins of survival analysis explain the terminology. Its emphasis on “failure” and “risk,” “mortality” and “recidivism” may explain why, once upon a time, survival analysis did not readily catch on in the business and marketing world. That time has passed, and survival analysis is recognized as a powerful set of analytic techniques for understanding customers. And, the combination of SQL and Excel is sufficiently powerful to apply many of these techniques to large customer databases.
Survival analysis estimates how long it takes for a particular event to happen. A customer starts; when will that customer stop? By assuming that the future will be similar to the past (the homogeneity assumption), the wealth of data about historical customer behavior can help us understand what will happen and when.
For customers that have ...