International Journal For Multidisciplinary Research

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Ch-Oracle A Unified Statistical Framework for Churn Prediction

Author(s) S.SELVAKANI, K.VASUMATHI, D.VIGNESH
Country INDIA
Abstract The User churn stands as a consequential challenge within the realm of online services, posing a substantial threat to the vitality and financial viability of such services. Traditionally, endeavors in churn prediction have transformed the issue into a binary classification task, wherein users are categorized as either churned or non-churned. More recently, a shift towards a more pragmatic approach has been witnessed in the domain of online services, wherein the focus has transitioned from predicting a binary churn label to anticipating the users' return times. This method, aligning more closely with the dynamics of real-world online services, involves the model predicting the specific time of user return at each temporal step, eschewing the simplistic churn label. Nevertheless, antecedent works within this paradigm have grappled with issues of limited generality and imposing computational complexities. This paper introduces ChOracle, an innovative oracle that prognosticates user churn by modeling user return times through the amalgamation of Temporal Point Processes and Recurrent Neural Networks. Furthermore, our approach incorporates latent variables into the proposed recurrent neural network, effectively capturing the latent user loyalty to the system. An efficient approximate variational algorithm, leveraging backpropagation through time, is developed for the purpose of learning parameters within the proposed RNN.
Keywords framework, customer churn, prediction, Variational Algorithm, modeling, RNN
Field Computer
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-15
Cite This Ch-Oracle A Unified Statistical Framework for Churn Prediction - S.SELVAKANI, K.VASUMATHI, D.VIGNESH - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14986
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.14986
Short DOI https://doi.org/gtmzrt
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