Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business

The purpose of this thesis is to analyze the behavior of customers within the Online Food Delivery industry, through which it is proposed to develop a prediction model that allows detecting, based on valuable active customers, those who will leave the services of Alpha Corporation in the near fut...

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Detalles Bibliográficos
Autor principal: Muñoz, Luis Ezequiel
Otros Autores: Fumagalli, Elena
Formato: Artículo acceptedVersion
Lenguaje:Inglés
Publicado: Universidad Torcuato Di Tella 2023
Materias:
Marketing
Comportamiento del Consumidor
Predicción tecnológica
Machine Learning
Boost
xgboost
Random Forest
Retention
Churn Prediction
Acceso en línea:https://repositorio.utdt.edu/handle/20.500.13098/11860
Aporte de:
Repositorio Digital Universidad Torcuato Di Tella de Universidad Torcuato Di Tella
id I57-R163-20.500.13098-11860
record_format dspace
spelling I57-R163-20.500.13098-118602023-06-06T07:36:37Z Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business Muñoz, Luis Ezequiel Fumagalli, Elena Marketing Comportamiento del Consumidor Predicción tecnológica Machine Learning Boost xgboost Random Forest Retention Churn Prediction The purpose of this thesis is to analyze the behavior of customers within the Online Food Delivery industry, through which it is proposed to develop a prediction model that allows detecting, based on valuable active customers, those who will leave the services of Alpha Corporation in the near future. Firstly, valuable customers are defined as those consumers who have made at least 8 orders in the last 12 months. In this way, considering the historical behavior of said users, as well as applying Feature Engineering techniques, a first approach is proposed based on the implementation of a Random Forest algorithm and, later, a boosting algorithm: XGBoost. Once the performance of each of the models developed is analyzed, and potential churners are identified, different marketing suggestions are proposed in order to retain said customers. Retention strategies will be based on how Alpha Corporation works, as well as on the output of the predictive model. Other development alternatives will also be discussed: a clustering model based on potential churners or an unstructured data model to analyze the emotions of those users according to the NPS surveys. The aim of these proposals is to complement the prediction to design more specific retention marketing strategies. 2023-06-05T21:41:06Z 2023-06-05T21:41:06Z 2022 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion https://repositorio.utdt.edu/handle/20.500.13098/11860 eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/2.5/ar/ 134 p. application/pdf application/pdf Universidad Torcuato Di Tella
institution Universidad Torcuato Di Tella
institution_str I-57
repository_str R-163
collection Repositorio Digital Universidad Torcuato Di Tella
language Inglés
orig_language_str_mv eng
topic Marketing
Comportamiento del Consumidor
Predicción tecnológica
Machine Learning
Boost
xgboost
Random Forest
Retention
Churn Prediction
spellingShingle Marketing
Comportamiento del Consumidor
Predicción tecnológica
Machine Learning
Boost
xgboost
Random Forest
Retention
Churn Prediction
Muñoz, Luis Ezequiel
Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business
topic_facet Marketing
Comportamiento del Consumidor
Predicción tecnológica
Machine Learning
Boost
xgboost
Random Forest
Retention
Churn Prediction
description The purpose of this thesis is to analyze the behavior of customers within the Online Food Delivery industry, through which it is proposed to develop a prediction model that allows detecting, based on valuable active customers, those who will leave the services of Alpha Corporation in the near future. Firstly, valuable customers are defined as those consumers who have made at least 8 orders in the last 12 months. In this way, considering the historical behavior of said users, as well as applying Feature Engineering techniques, a first approach is proposed based on the implementation of a Random Forest algorithm and, later, a boosting algorithm: XGBoost. Once the performance of each of the models developed is analyzed, and potential churners are identified, different marketing suggestions are proposed in order to retain said customers. Retention strategies will be based on how Alpha Corporation works, as well as on the output of the predictive model. Other development alternatives will also be discussed: a clustering model based on potential churners or an unstructured data model to analyze the emotions of those users according to the NPS surveys. The aim of these proposals is to complement the prediction to design more specific retention marketing strategies.
author2 Fumagalli, Elena
author_facet Fumagalli, Elena
Muñoz, Luis Ezequiel
format Artículo
acceptedVersion
author Muñoz, Luis Ezequiel
author_sort Muñoz, Luis Ezequiel
title Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business
title_short Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business
title_full Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business
title_fullStr Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business
title_full_unstemmed Customer Churn Detection and Marketing Retention Strategies in the Online Food Delivery Business
title_sort customer churn detection and marketing retention strategies in the online food delivery business
publisher Universidad Torcuato Di Tella
publishDate 2023
url https://repositorio.utdt.edu/handle/20.500.13098/11860
work_keys_str_mv AT munozluisezequiel customerchurndetectionandmarketingretentionstrategiesintheonlinefooddeliverybusiness
_version_ 1768086693484691456

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