5 research outputs found

    Causal Analysis of Customer Churn Using Deep Learning

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    Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar-value: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feedforward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian network to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.Comment: 6 page

    Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data

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    In todays competitive landscape, businesses grapple with customer retention. Churn prediction models, although beneficial, often lack accuracy due to the reliance on a single data source. The intricate nature of human behavior and high dimensional customer data further complicate these efforts. To address these concerns, this paper proposes a multimodal fusion learning model for identifying customer churn risk levels in financial service providers. Our multimodal approach integrates customer sentiments financial literacy (FL) level, and financial behavioral data, enabling more accurate and bias-free churn prediction models. The proposed FL model utilizes a SMOGN COREG supervised model to gauge customer FL levels from their financial data. The baseline churn model applies an ensemble artificial neural network and oversampling techniques to predict churn propensity in high-dimensional financial data. We also incorporate a speech emotion recognition model employing a pre-trained CNN-VGG16 to recognize customer emotions based on pitch, energy, and tone. To integrate these diverse features while retaining unique insights, we introduced late and hybrid fusion techniques that complementary boost coordinated multimodal co learning. Robust metrics were utilized to evaluate the proposed multimodal fusion model and hence the approach validity, including mean average precision and macro-averaged F1 score. Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models. Furthermore, the analysis demonstrates a positive correlation between negative emotions, low FL scores, and high-risk customers

    Spectrogrammes Mel exploités à l'aide de composants harmoniques et percussifs dans la reconnaissance des émotions de la parole

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    International audienceCustomer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollarvalue: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feed-forward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian networks to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollarvalue: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feed-forward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian networks to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.L'attrition des clients décrit la fin d'une relation avec une entreprise ou la réduction de l'engagement des clients sur une période spécifique. Deux principales stratégies de marketing d'entreprise jouent un rôle essentiel pour augmenter la valeur en dollars de la part de marché : gagner de nouveaux clients et conserver les clients existants. Le coût d'acquisition de clients peut être cinq à six fois supérieur à celui de la fidélisation de la clientèle. Il est donc judicieux d'investir dans des clients présentant un risque de désabonnement. L'analyse causale du modèle de désabonnement peut prédire si un client se désabonnera dans un avenir prévisible et aider les entreprises à identifier les effets et les causes possibles du désabonnement, puis à utiliser ces connaissances pour appliquer des incitations personnalisées. Cet article propose un cadre utilisant un réseau de neurones à rétroaction profonde pour la classification accompagné d'une méthode d'extraction de motifs séquentiels sur des données éparses de grande dimension. Nous proposons également des réseaux bayésiens causaux pour prédire les probabilités de cause qui conduisent à l'attrition des clients. Les métriques d'évaluation sur les données de test confirment que XGBoost et notre modèle d'apprentissage en profondeur ont surpassé les techniques précédentes. L'analyse expérimentale confirme que certaines variables causales indépendantes représentant le niveau de contribution de la super garantie, la croissance du compte et l'ancienneté du client ont été identifiées comme des facteurs de confusion pour le taux de désabonnement des clients avec un degré élevé de croyance. Cet article fournit une analyse du taux de désabonnement des clients dans le monde réel, de l'inférence de l'état actuel aux orientations futures des caisses de retraite locales

    Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions

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    Abstract Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent causal variables representing the level of super guarantee contribution, account growth, and account balance amount were identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds

    Analyse causale du taux de désabonnement des clients à l'aide du Deep Learning

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    International audienceCustomer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollarvalue: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feed-forward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian networks to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollarvalue: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feed-forward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian networks to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.L'attrition des clients décrit la fin d'une relation avec une entreprise ou la réduction de l'engagement des clients sur une période spécifique. Deux principales stratégies de marketing d'entreprise jouent un rôle essentiel pour augmenter la valeur en dollars de la part de marché : gagner de nouveaux clients et conserver les clients existants. Le coût d'acquisition de clients peut être cinq à six fois supérieur à celui de la fidélisation de la clientèle. Il est donc judicieux d'investir dans des clients présentant un risque de désabonnement. L'analyse causale du modèle de désabonnement peut prédire si un client se désabonnera dans un avenir prévisible et aider les entreprises à identifier les effets et les causes possibles du désabonnement, puis à utiliser ces connaissances pour appliquer des incitations personnalisées. Cet article propose un cadre utilisant un réseau de neurones à rétroaction profonde pour la classification accompagné d'une méthode d'extraction de motifs séquentiels sur des données éparses de grande dimension. Nous proposons également des réseaux bayésiens causaux pour prédire les probabilités de cause qui conduisent à l'attrition des clients. Les métriques d'évaluation sur les données de test confirment que XGBoost et notre modèle d'apprentissage en profondeur ont surpassé les techniques précédentes. L'analyse expérimentale confirme que certaines variables causales indépendantes représentant le niveau de contribution de la super garantie, la croissance du compte et l'ancienneté du client ont été identifiées comme des facteurs de confusion pour le taux de désabonnement des clients avec un degré élevé de croyance. Cet article fournit une analyse du taux de désabonnement des clients dans le monde réel, de l'inférence de l'état actuel aux orientations futures des caisses de retraite locales
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