A Few Questions Before You Churn!

Everyone seems to be modeling customer churn these days. But before you roll up your sleeves and take a dive, here are a few things I learned from David Ogden’s webcasts.

How will you use your churn model?
- Do you want to identify/rank likely churners?
- Do you want to identify/quantify the churn drivers?

Data Collection Window
- How much historical data do you want to use – 3 years data, 5 years data?

Prediction Window
- Who will churn next month? Who will churn in the next 6 months?

You build a model and predict who will churn next month. But what if the client’s business is such that it usually takes 2-3 months to implement the results from your churn model - set up campaigns, target customers with customized retention offers, send out mailers, etc.? Understand the client’s business and decide on an appropriate prediction window before simply doing what they ask.

Involuntary Churn vs. Voluntary Churn
- Voluntary churn occurs when a customer decides to switch to a competitor or another service provider because of dissatisfaction with the service or the associated fees
- Involuntary churn occurs due to factors like relocation, death, non-payment, etc.

Sometimes models are built leaving out one or the other group of customers. There is a clear difference between the two; decide which one is more important for the client’s business.

Drivers vs. Indicators
- Both influence churn, but drivers are those factors/measures that the company can control or manipulate. Indicators are mostly demographic measures, macro-economic factors, or seasonality, and they are outside the company's control.

Expected time to churn, vs. probability to churn tomorrow
- Survival Time Modeling answers the question, “What is the expected time to churn?” The response variable here is the Time (months, weeks, etc. until a customer will churn).
- Binary Response Modeling answers the question – “Who is likely to churn next week/month/quarter?” The response variable here is the Churn Indicator (customer stays or leaves).