Modelli di Churn Prediction

La tesi si pone l’obiettivo di descrivere il tema della churn prediction, che consiste nella previsione dello spostamento dei clienti da un’azienda all’altra. La churn prediction si colloca nell’ambito del Customer Relationship Management (CRM) ed è indispensabile per identificare i clienti più esposti al passaggio ad un’altra compagnia e per cercare di fidelizzarli. È un argomento molto importante che riguarda tutte le aziende di tutti i settori. Infatti date le risorse limitate di cui tutte le aziende dispongono, è indispensabile identificare i clienti che stanno per abbandonare. Inoltre occorre calcolare il valore dei clienti per identificare quelli più preziosi per l’azienda. Si presentano le varie fasi di modellazione da seguire per ottenere una buona previsione del churn del cliente. Sono descritte le varie tecniche maggiormente utilizzate che sono la regressione, gli alberi di classificazione, le reti neurali, le clustering analysis e il processo markoviano. Sono analizzati vari modelli presenti in letteratura, tra cui il modello di Polya per simulare la loyalty (fedeltà), il modello markoviano per il CRM e alcuni modelli di churn prediction nel settore del retail banking e in quello delle telecomunicazioni

Bruk av metoder innen forklarbar maskinlæring for kundefrafallsprediksjon

Kunstig intelligens baserte systemer blir stadig anvendt i beslutningstakingsprosesser. Det er ikke lett for mennesker å forstå de grunnleggende prinsippene bak hvordan disse systemene fungerer. Ved å anvende ulike metoder innen forklarbar maskinlæring er det mulig å få en bedre av forståelse av disse modellene. I denne oppgaven undersøkes det hvorvidt metoder innen forklarbar maskinlæring som SHAP, kan anvendes for å forstå hvilke faktorer som påvirker maskinlæringsmodeller for kundefrafallsprediksjon i telekommunikasjonsbransjen. Det blir undersøkt hvilke variabler som påvirker modellene på både lokalt og globalt nivå. Analysen viser at variablene som påvirker selve modellen, nødvendigvis ikke har like stor påvirkning på de individuelleprediksjonene.Artificial intelligence-based systems are constantly being used in decision-making processes.The concepts behind the systems are not always interpretable for humans. By using methods within explainable machine learning, it is possible to get a better understanding of these models. This thesis tries to explore whether methods within explainable machine learning such as SHAP can be used to understand which factors that influence machine learning models within customer churn prediction. The focus is to understand the feature importance on these models at both local and global level. The results shows that the features which influence the model, do not necessarily have the same impact on the individual predictions

A Combination of Multiperiod Training Data and Ensemble Methods in Churn Classification: the Case of Housing Loan Churn

Customer retention has been the focus of customer relationship management research in the financial sector during the past decade. The first step in customer retention is to classify the customers into binary groups of possible churners, meaning customers that are likely to switch to another service provider, and non-churners, referring to those that are probably staying with the current provider. The second step in customer retention is to take action to retain the most probable churners to either minimize costs or maximize benefits. As a result, churn classification is an important first step in customer retention. However, the main challenge in churn classification is the extreme rarity of churn events. For example, the churn rate in the banking industry is usually less than 1%. In order to overcome this rarity issue, a great deal of research has been found to improve the two main aspects of a churn classification model: the training data and the algorithm. Regarding the training data, the recently proposed multi-period training data approach is found to outperform the single period training data thanks to the more effective use of longitudinal data of churn behavior. Regarding the churn classification algorithms, the most advanced and widely employed is ensemble method, which combines multiple models to produce a more powerful one. Two popularly used ensemble techniques are random forest and gradient boosting, both of which are found to outperform logistic regression and decision tree in classifying churners from non-churners. To the best of the author’s knowledge, the proposed multi-period training data has not been applied to the ensemble methods in a churn classification model. As a result, the thesis would like to study whether this multi-period training data approach, when employed together with ensemble methods in a churn classification model, produces better churn prediction than with logistic regression and decision tree. The ensemble methods used in this thesis are random forest and gradient boosting. The study uses empirical data of housing loan customers from a Nordic bank. The churn models are evaluated based on three criteria: misclassification rate, Receiver Operating Characteristics (ROC) index and top decile lift. The key finding of this thesis is models that combine multi-period training data approach with ensemble methods perform the best in the housing loan context based on the aforementioned evaluation criteria

Churn prediction models tested and evaluated in the Dutch indemnity industry

Due to global developments customer churn is getting a growing concern to the insurance industry. Technological improvements like the internet makes it much easier for customer to compare their policies, obtain new offers or even churn from one provider to another. The insurance industry therefore has become a heavily competitive market in which insurance companies have to compete to protect and expand their customer base in order to maintain or expand their market position. Thus, retaining customers is becoming more and more important and therefore finding customers who are most likely to leave is a central aspect. Many different techniques are available to identify customers who are most likely to leave, however which technique can be used best is often not clear. Research clarifies that the characteristics of the industry and/or dataset which is used are mostly assessing related to performance. In advance it is impossible to determine the best suited technique to use if previous research in which performance was tested has not been published. This study presents a data mining methodology in which the four most used prediction techniques in literature are tested and evaluated using a real life voluminous insurance company dataset to determine which technique performs best. Using the same dataset makes results comparable and clears out which technique performs best based on the insurance data domain characteristics

Diseño de un programa de retención de usuarios de tarjetas de crédito

Durante los últimos años diferentes sectores del gobierno y la academia han discutido sobre la necesidad de ampliar el acceso a los servicios financieros para la mayoría de hogares posibles y así lograr unas mejores condiciones en términos de oportunidades y bienestar de la población. Con este fin han surgido diferentes iniciativas del gobierno para ampliar la población bancarizada, entendida como la proporción de los individuos con acceso al uso de los servicios financieros en Colombia. (Pabón, 2007) Como consecuencia de esto, el mercado de las tarjetas de crédito en Colombia ha tenido grandes cambios en los últimos años. Ha pasado de tener 8.240.506 tarjetas de crédito vigentes en diciembre del 2010 a 13.752.401 en diciembre del 2015 según los informes de la Superintendencia Financiera de Colombia, lo que representa un crecimiento del 67%. Pero conforme aumenta el número de tarjetas de crédito, también aumenta el número de plásticos cancelados; durante el 2010 se cancelaron 1.351.101 plásticos, mientras que en el 2015 el número de tarjetas de crédito canceladas fue 2.070.176, lo que representa un incremento de cancelaciones del 53% (Superintendencia Financiera de Colombia, 2016). Esto lo convierte en un sector altamente competitivo donde los clientes pueden elegir entre las diferentes entidades emisoras de tarjetas de crédito en Colombia basándose en el nivel de satisfacción, accesibilidad y calidad del servicio. Muchas empresas han comenzado a incluir la retención de clientes como uno de sus principales objetivos de negocio. Todos los proveedores de servicios pierden clientes que se van a otras empresas de la competencia, debido a diversas razones, pero debe quedar claro que la supervivencia de cualquier negocio depende de su capacidad para mantener y retener a los clientes. (Oghojafor, Mesike, Bakarea, Omoera, and Adeleke, 2012). El objetivo de este trabajo final de maestría con perfil de profundización es desarrollar e implementar un modelo estadístico que prediga la probabilidad de cancelación de una tarjeta de crédito, y así poder crear estrategias preventivas con los clientes que tienen una mayor probabilidad de deserción. Este trabajo se realizó con información real de un banco del sector financiero colombiano, que por políticas de privacidad de denominará en el resto del Diseño de un programa de retención de usuarios de tarjetas de crédito documento como Banco ABC. Todas las cifras y resultados se manejarán en porcentajes o valores ficticios respetando las proporciones reales. En el capítulo 1 se planteará el tema de la deserción como una problemática que afecta diferentes industrias haciendo un énfasis en el sector financiero. Luego en el capítulo 2 se mostrará cómo se ha abordado la deserción, qué metodologías se han usado para predecir el abandono y algunos resultados de estas. Posteriormente en el capítulo 3 se expondrá la metodología usada en el Banco ABC para dar solución al problema de deserción en el negocio de tarjetas de crédito; seguido de esto en el capítulo 4 se analizarán los resultados obtenidos y algunas conclusiones sobre el trabajo realizado.Maestrí

ADTreesLogit model for customer churn prediction

In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament). In fact, ADTreesLogit has better predictive accuracy than TreeNet® on two important observation points. © 2008 Springer Science+Business Media, LLC.link_to_subscribed_fulltex