ParetoNBDModel.expected_probability_alive#

ParetoNBDModel.expected_probability_alive(data=None, *, future_t=None)[source]#

Compute the probability that a customer with history frequency, recency, and T is currently active. Can also estimate alive probability for future_t periods into the future.

Adapted from equation (18) in Bruce Hardie’s notes [3].

Parameters:

data (pd.DataFrame, optional) –
Dataframe containing the following columns:
- customer_id: unique customer identifier
- frequency: number of repeat purchases
- recency: time between the first and the last purchase
- T: time between the first purchase and the end of the observation period.
  Model assumptions require T >= recency
- future_t: Number of time periods in the future to estimate alive probability; defaults to 0.
- covariates: Purchase and dropout covariate columns if original model had any.
If not provided, the method will use the fit dataset.
future_t (array_like, optional) – Number of time periods in the future to estimate alive probability; defaults to 0. Not needed if data parameter is provided with a future_t column.

Return type:

DataArray

References