7.2
Buy-Till-You-Die Models
A family of probability models that predict, from purchase history alone, how many purchases a customer will make next and whether they have quietly stopped buying.
Schmittlein, Morrison & Colombo (1987) as Pareto/NBD; simplified as BG/NBD by Fader, Hardie & Lee (2005)
What it does
Removes the arbitrary churn definition. In non-contractual categories nobody cancels, so “churned” is whatever number of days someone picked: ninety, a hundred and eighty. These models replace that guess with a probability that a given customer is still alive, estimated from how often and how recently they bought, and they forecast future transactions well enough to price a customer base.
When it breaks
The models assume a customer's underlying purchase rate is stationary, meaning it does not change. So they break precisely when you intervene: sustained promotion, a price change, a category shift, a competitor entering. They are a forecast of what happens if nothing is done, which makes them a strong baseline and a poor evaluator of your own campaign. They also explain nothing: a customer with a 4% survival probability comes with no reason and no lever.
Case
Fader, Hardie and Lee introduced the BG/NBD as an easier alternative to the Pareto/NBD, showing it reproduced the original model's fit and forecasting accuracy on the same data with a far simpler estimation procedure. That is the reason buy-till-you-die modelling became practical outside academia.
Fader, Hardie & Lee · “Counting Your Customers” the Easy Way ↗In the wild
Unchecked · not part of the tier
What people have written about this tool in the last twelve months. A machine found these and nobody has read them. Everything above this line was checked by hand.
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