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Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors
[article]
2017
arXiv
pre-print
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The paper describes in depth the application of Deep Learning in the problem of churn prediction. ...
secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance. ...
The authors would like to acknowledge Elliot Block, Andrew Berls, Dan Evans, and the rest of the Framed Data team for their engineering and data infrastructure assistance and technical guidance. ...
arXiv:1703.03869v1
fatcat:hrpzo3pt2balpo3zm5it3dmmi4
Experimental Parameter Tuning of Artificial Neural Network in Customer Churn Prediction
2017
Trendy Ekonomiky a Managementu
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Scientific aim: To present and execute experimental design for performance evaluation and hyperparameter optimization of classification models, which are used for customer churn prediction. ...
Conclusions: Results imply that placing hyperparameter optimization to ANN classification model leads to improved customer churn prediction ability. ...
Popular techniques used for customer churn prediction are logistic regression, decision tree, fuzzy logic, Bayesian classifier, SVM, and neural networks (Ngai et al., 2009; Kumar, Garg, 2013) , the paper ...
doi:10.13164/trends.2017.28.9
fatcat:3lzpxwvi4zf2vo2kh3g55xy6we
Customer Lifetime Value Prediction Using Embeddings
2017
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17
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The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. ...
We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. ...
We experiment with (1) deep feed-forward neural networks and (2) hybrid models combining logistic regression and a deep feedforward neural network similar to that used in [5] . e deep feedforward neural ...
doi:10.1145/3097983.3098123
dblp:conf/kdd/ChamberlainCLPD17
fatcat:vyosgrgxlvbhxkzg7ib26vikaa
I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application
[article]
2019
arXiv
pre-print
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Then we design a novel deep learning pipeline based on LSTM and attention to accurately predict user churn with very limited initial behavior data, by leveraging the correlations among users' multi-dimensional ...
The whole framework is deployed as a data analysis pipeline, delivering real-time data analysis and prediction results to multiple relevant teams for business intelligence uses. ...
We deployed ClusChurn in Snap Inc. to deliver real-time data analysis and prediction results to benefit multiple productions including user modeling, growth, retention, and so on. ...
arXiv:1910.01447v1
fatcat:mfea4xsmgffhxnc6diyjizxe2u
Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data
2019
IEEE Transactions on Games
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2018 pre-print arXiv:1802.02301v2 fatcat:vfbhw2w4z5dy5iavn4illigeliOther Versions
The main aim of the competition was to predict whether a player would churn and when the player would churn during two periods between which the business model was changed to a free-to-play model from ...
The results of the competition revealed that highly ranked competitors used deep learning, tree boosting, and linear regression. ...
Parameters were adjusted through cross-validation.
1) Binary Churn Prediction (Track 1)
1-1) LSTM A model combining an LSTM network with a deep neural network (DNN) was used for test set 1 ( Figure ...
doi:10.1109/tg.2018.2888863
fatcat:ifaukda6arhsjiz65jaasfjlly
Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers
2022
Intelligent Automation and Soft Computing
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Churn prediction is the number of customers who wants to terminate their services in the banking sector. ...
The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. ...
This model uses Multi-Layer Perceptron in Artificial Neural Network (ANN) [9] for customer churn prediction and resulted in the best accuracy compared to the traditional machine learning methods with ...
doi:10.32604/iasc.2022.022423
fatcat:rtpffzi4qzfw7cjwwtep3ljmhm
Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data
2018
Zenodo
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The main aim of the competition was to predict whether a player would churn and when the player would churn during two periods between which the business model was changed to a free-to-play model from ...
The results of the competition revealed that highly ranked competitors used deep learning, tree boosting, and linear regression. ...
Parameters were adjusted through cross-validation.
1) Binary Churn Prediction (Track 1)
1-1) LSTM A model combining an LSTM network with a deep neural network (DNN) was used for test set 1 ( Figure ...
doi:10.5281/zenodo.1696795
fatcat:rejyi4n7f5ht3l7cmjoiax4fgu
Analysis of WTTE-RNN Variants that Improve Performance
2019
Machine Learning and Applications An International Journal
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A prediction model called the WTTE-RNN was shown to be effective for predicting the time to event for topics such as machine failure. ...
These assets share a common trait in that at some stage they will fail or, in the case of customers, they will churn. ...
The focus of the research explores using an RNN for predicting churn based on customer lifetime value (CLV) time series.
C. PREDICTING CUSTOMER CHURN USING RECURRENT NEURAL NET-WORKS
D. ...
doi:10.5121/mlaij.2019.6103
fatcat:5t6qapvx2jehpopcvrbaa2ea44
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems
[article]
2021
arXiv
pre-print
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In recent years, deep neural networks have had great success in machine learning and pattern recognition. ...
Architecture size for a neural network contributes significantly to the success of any neural network. ...
ACKNOWLEDGMENT We would like to acknowledge Drexel Society of Artificial Intelligence for its contributions and support for this research. ...
arXiv:2101.06511v1
fatcat:y5xjh6aktbfspjn7rli4rfx2iq
Randomness In Neural Network Training: Characterizing The Impact of Tooling
[article]
2021
arXiv
pre-print
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In this work, we address a less well understood and studied question: how does our choice of tooling introduce randomness to deep neural network training. ...
Our results suggest that deterministic tooling is critical for AI safety. ...
Churn (churn)-Predictive churn is a measure of predictive divergence between two models. ...
arXiv:2106.11872v1
fatcat:7shulcpu3bb3tikwl3d3naweku
Large scale distributed neural network training through online distillation
[article]
2020
arXiv
pre-print
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However, due to increased test-time cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. ...
Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made. ...
ACKNOWLEDGMENTS We would like to thank Avital Oliver for feedback on a draft and Oriol Vinyals for many helpful discussions. ...
arXiv:1804.03235v2
fatcat:ftkf2wofpjhqxaeilzksiwpfsq
RICON: A ML framework for real-time and proactive intervention to prevent customer churn
[article]
2022
arXiv
pre-print
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We consider the problem of churn prediction in real-time. ...
In addition to churn propensity prediction, RICON provides insights based on product usage intelligence. ...
for churn detection in telecom industries [1, 9] ; difficulty-aware churn prediction framework [15] and survival ensemble-based multi-dimensional churn prediction in online gaming [2] ; Deep-CNN and ...
arXiv:2203.16155v1
fatcat:6en2ry4e3jacdbt3adwiylwbfq
Visual Reasoning of Feature Attribution with Deep Recurrent Neural Networks
[article]
2019
arXiv
pre-print
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Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks. ...
Beyond predicting a correct class for each data instance, data scientists also want to understand what differentiating factors in the data have contributed to the classification during the learning process ...
RELATED WORK Visualization for Deep Neural Networks (DNN) Many visualization techniques have been developed to facilitate the DNN model building process, covering domains such as image understanding [ ...
arXiv:1901.05574v1
fatcat:5ikq3zzflzdqffv2wmsx3qikru
A Blended Deep Learning Approach for Predicting User Intended Actions
[article]
2018
arXiv
pre-print
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To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. ...
Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. ...
ACKNOWLEDGMENT We thank Sagar Patil for proofreading the manuscript. ...
arXiv:1810.04824v1
fatcat:6okce4rlrrd6hn4bpot7zfvmbi
Assessment of an Identification Strategy for the Prediction of the Dynamics of Two-phase Flows
2015
Energy Procedia
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Such a model has been implemented by means of Multilayer Perceptron artificial neural networks, trained using input-output data detected during experiments expressing different flow patterns. ...
The proposed strategy consists in the assessment and optimisation of a generalised NARMAX model for the input-output identification of the dynamics of the experimental time series of the void fraction, ...
to be required for the attractors of more complex flow patterns, such as those of churn and annular flows. ...
doi:10.1016/j.egypro.2015.12.039
fatcat:xi2yk7e3zrhjxhrlfwog6aonju
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