Calculates the probability of a customer being alive at the end of the calibration period, based on a customer's past transaction behavior and the BG/NBD model parameters.
bgnbd_nocov_PAlive
P(alive) for the BG/NBD model without covariates
bgnbd_staticcov_PAlive
P(alive) for the BG/NBD model with static covariates
bgnbd_nocov_PAlive(r, alpha, a, b, vX, vT_x, vT_cal) bgnbd_staticcov_PAlive( r, alpha, a, b, vX, vT_x, vT_cal, vCovParams_trans, vCovParams_life, mCov_trans, mCov_life )
r | shape parameter of the Gamma distribution of the purchase process |
---|---|
alpha | scale parameter of the Gamma distribution of the purchase process |
a | shape parameter of the Beta distribution of the lifetime process |
b | shape parameter of the Beta distribution of the lifetime process |
vX | Frequency vector of length n counting the numbers of purchases. |
vT_x | Recency vector of length n. |
vT_cal | Vector of length n indicating the total number of periods of observation. |
vCovParams_trans | Vector of estimated parameters for the transaction covariates. |
vCovParams_life | Vector of estimated parameters for the lifetime covariates. |
mCov_trans | Matrix containing the covariates data affecting the transaction process. One column for each covariate. |
mCov_life | Matrix containing the covariates data affecting the lifetime process. One column for each covariate. |
Returns a vector with the PAlive for each customer.
mCov_trans
is a matrix containing the covariates data of
the time-invariant covariates that affect the transaction process.
Each column represents a different covariate. For every column a gamma parameter
needs to added to vCovParams_trans
at the respective position.
mCov_life
is a matrix containing the covariates data of
the time-invariant covariates that affect the lifetime process.
Each column represents a different covariate. For every column a gamma parameter
needs to added to vCovParams_life
at the respective position.
Fader PS, Hardie BGS, Lee, KL (2005). ““Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model” Marketing Science, 24(2), 275–284.
Fader PS, Hardie BGS (2013). “Overcoming the BG/NBD Model’s #NUM! Error Problem” URL http://brucehardie.com/notes/027/bgnbd_num_error.pdf.
Fader PS, Hardie BGS (2007). “Incorporating time-invariant covariates into the Pareto/NBD and BG/NBD models.” URL http://www.brucehardie.com/notes/019/time_invariant_covariates.pdf.