sklift.metrics.max_prof_uplift
- sklift.metrics.metrics.max_prof_uplift(df_sorted, treatment_name, churn_name, pos_outcome, benefit, c_incentive, c_contact, a_cost=0)[source]
Compute the maximum profit generated from an uplift model decided campaign
This can be visualised by plotting plt.plot(perc, cumulative_profit)
- Parameters
df_sorted (pandas dataframe) – dataframe with descending uplift predictions for each customer (i.e. highest 1st)
treatment_name (string) – column name of treatment columm (assuming 1 = treated)
churn_name (string) – column name of churn column
pos_outcome (int or float) – 1 or 0 value in churn column indicating a positive outcome (i.e. purchase = 1, whereas churn = 0)
benefit (int or float) – the benefit of retaining a customer (e.g., the average customer lifetime value)
c_incentive (int or float) – the cost of the incentive if a customer accepts the offer
c_contact (int or float) – the cost of contacting a customer regardless of conversion
a_cost (int or float) – the fixed administration cost for the campaign
- Returns
the incremental increase in x, for plotting 1d array-like: the cumulative profit per customer
- Return type
1d array-like
References
Floris Devriendt, Jeroen Berrevoets, Wouter Verbeke. Why you should stop predicting customer churn and start using uplift models.