lifetimes

value_10_pred.py

# the predicted number of transactions in the next 10 weeks
rfm_cal_holdout["n_transactions_10_pred"] = bgf.predict(t=10, 
                                                        frequency=rfm_cal_holdout['frequency_cal'], 
                                                        recency=rfm_cal_holdout['recency_cal'], 
                                                        T=rfm_cal_holdout['T_cal'])

# the probability of being alive
rfm_cal_holdout["alive_prob"] = bgf.conditional_probability_alive(frequency=rfm_cal_holdout['frequency_cal'], 
                                                                  recency=rfm_cal_holdout['recency_cal'], 
                                                                  T=rfm_cal_holdout['T_cal'])


# multiplication of alive probability x number of purchases x average past purchase
rfm_cal_holdout["value_10_pred"] = rfm_cal_holdout["alive_prob"]* \
                                   rfm_cal_holdout["n_transactions_10_pred"]*\
                                   rfm_cal_holdout["monetary_value_cal"]
                                   
                                     
rfm_cal_holdout[["value_10_pred", "alive_prob", "n_transactions_10_pred", "monetary_value_cal"]].head()