Séminaire hybride : Qini-Based Uplift Regression

Séminaire hybride : Qini-Based Uplift Regression
Séminaire “Un chercheur du GERAD vous parle!”

Qini-Based Uplift Regression

5 avril 2023   11h00 — 12h00

Alejandro Murua Professeur titulaire, Département de mathématiques et de statistique, Université de Montréal, Canada

Séminaire en format hybride au local 4488 du GERAD ou Zoom.

Models for uplift are commonly used to isolate the marketing effect of a campaign. For customer churn reduction, uplift models identify customers who are likely to answer positively to a retention activity only if explicitly targeted. They are also used to avoid wasting resources on customers that are very likely to switch companies. In practice, the models' performance is measured with the Qini coefficient. We introduce a Qini-based uplift regression model to analyze a large insurance company's retention marketing campaign. Our approach is based on logistic regression. We show that a Qini-optimized uplift model acts as a regularization in uplift models, yielding interpretable models with few relevant explanatory variables. Our results also show that the parameter estimation based on our Qini-optimized regression significantly improves the Qini prediction performance of uplift models.

This is joint work with Mouloud Belbahri (TD Assurance), Olivier Gandouet (TD Assurance) and Vahid Partovi Nia (École Polytechnique de Montréal).


Mercredi 5 avril 2023
Débute à 11h00





Séminaire hybride au GERAD
Zoom et salle 4488
Pavillon André-Aisenstadt
Campus de l'Université de Montréal
2920, chemin de la Tour
Montréal Québec H3T 1J4