ULB Machine Learning Group | Defended PhD Theses

Verhelst, Theo

Causal and predictive modeling of customer churn - Lessons learned from empirical and theoretical research PhD Thesis

2024.

@phdthesis{nokey,

title = {Causal and predictive modeling of customer churn - Lessons learned from empirical and theoretical research},

author = {Theo Verhelst},

url = {https://difusion.ulb.ac.be/vufind/Record/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/368384/Holdings},

year  = {2024},

date = {2024-01-29},

urldate = {2024-01-29},

abstract = {Customer churn is an important concern for large companies, especially in the

telecommunications sector. Customer retention campaigns are often used to mitigate

churn, but targeting the right customers based on their historical profiles

presents an important challenge. Companies usually have recourse to two datadriven

approaches: churn prediction and uplift modeling. In churn prediction,

customers are selected on the basis of their propensity to churn in the near future.

In uplift modeling, only customers who react positively to the campaign

are considered. Uplift modeling is used in various other domains, such as marketing,

healthcare, and finance. Despite the theoretical appeal of uplift modeling, its

added value with respect to conventional machine learning approaches has rarely

been quantified in the literature.

This doctoral thesis is the result of a collaborative research project between

the Machine Learning Group (ULB) and Orange Belgium, funded by Innoviris.

This collaboration offers a unique research opportunity to assess the added value

of causal-oriented strategies to address customer churn in the telecommunication

sector. Following the introduction, we give the necessary background in probability

theory, causality theory, and machine learning, and we describe the state of

the art in uplift modeling and counterfactual identification. Then, we present the

contributions of this thesis:

• An empirical comparison of various predictive and causal models for selecting

customers in churn prevention campaigns. We perform several benchmarks

of different state-of-the-art approaches on real-world datasets and in

live campaigns with our industrial partner, we propose a new approach that

exploits domain knowledge to improve predictions, and we make available

the first public churn dataset for uplift modeling, whose unique characteristics

make it more challenging than the few other public uplift datasets.

• Counterfactual identification allows one to classify the different behaviors

of customers in response to a marketing incentive. This can be used to establish

profiles of customers sensitive to the campaign, and subsequently

improve marketing operations. We derive novel bounds and point estimators

on the probability of counterfactual statements based on uplift models.

• A comprehensive comparison of predictive and uplift modeling, starting

from firm theoretical foundations and highlighting the parameters that influence

the performance of both approaches. In particular, we provide a new

formulation of the measure of profit, a formal proof of the convergence of

the uplift curve to the measure of profit, and an illustration, through simulations,

of the conditions under which predictive approaches still outperform

uplift modeling.

Our theoretical and empirical assessments of uplift modeling suggest that it often

fails to deliver the anticipated advantages over predictive modeling, especially in

scenarios such as customer churn within the telecom sector, characterized by class

imbalance, limited separability, and cost-benefit considerations. These results are

broadly aligned with the practical experience of our industrial partner and with

the existing scientific literature. Our counterfactual probability estimators allow

us to characterize customers at a level inaccessible to conventional predictive modeling,

revealing new insights on the behavior and preferences of customers.},

keywords = {},

pubstate = {published},

tppubtype = {phdthesis}

}

Close

Customer churn is an important concern for large companies, especially in the
telecommunications sector. Customer retention campaigns are often used to mitigate
churn, but targeting the right customers based on their historical profiles
presents an important challenge. Companies usually have recourse to two datadriven
approaches: churn prediction and uplift modeling. In churn prediction,
customers are selected on the basis of their propensity to churn in the near future.
In uplift modeling, only customers who react positively to the campaign
are considered. Uplift modeling is used in various other domains, such as marketing,
healthcare, and finance. Despite the theoretical appeal of uplift modeling, its
added value with respect to conventional machine learning approaches has rarely
been quantified in the literature.
This doctoral thesis is the result of a collaborative research project between
the Machine Learning Group (ULB) and Orange Belgium, funded by Innoviris.
This collaboration offers a unique research opportunity to assess the added value
of causal-oriented strategies to address customer churn in the telecommunication
sector. Following the introduction, we give the necessary background in probability
theory, causality theory, and machine learning, and we describe the state of
the art in uplift modeling and counterfactual identification. Then, we present the
contributions of this thesis:
• An empirical comparison of various predictive and causal models for selecting
customers in churn prevention campaigns. We perform several benchmarks
of different state-of-the-art approaches on real-world datasets and in
live campaigns with our industrial partner, we propose a new approach that
exploits domain knowledge to improve predictions, and we make available
the first public churn dataset for uplift modeling, whose unique characteristics
make it more challenging than the few other public uplift datasets.
• Counterfactual identification allows one to classify the different behaviors
of customers in response to a marketing incentive. This can be used to establish
profiles of customers sensitive to the campaign, and subsequently
improve marketing operations. We derive novel bounds and point estimators
on the probability of counterfactual statements based on uplift models.
• A comprehensive comparison of predictive and uplift modeling, starting
from firm theoretical foundations and highlighting the parameters that influence
the performance of both approaches. In particular, we provide a new
formulation of the measure of profit, a formal proof of the convergence of
the uplift curve to the measure of profit, and an illustration, through simulations,
of the conditions under which predictive approaches still outperform
uplift modeling.
Our theoretical and empirical assessments of uplift modeling suggest that it often
fails to deliver the anticipated advantages over predictive modeling, especially in
scenarios such as customer churn within the telecom sector, characterized by class
imbalance, limited separability, and cost-benefit considerations. These results are
broadly aligned with the practical experience of our industrial partner and with
the existing scientific literature. Our counterfactual probability estimators allow
us to characterize customers at a level inaccessible to conventional predictive modeling,
revealing new insights on the behavior and preferences of customers.

Close

Ciortan, Madalina

Unsupervised analysis of scRNA-seq data with machine learning models PhD Thesis

2022, (Funder: Universite Libre de Bruxelles).

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