Paper shows distillation can maintain low model churn.
problem Maintaining low model churn in real-world systems.
method Equivalence between distillation and explicit churn constraints.
result Distillation significantly reduces model churn with minimal accuracy loss.
Paper analyzes churn behavior in mobile games at micro and macro levels.
problem Understanding churn behavior in mobile games, especially at micro and macro levels.
method Developed a semi-supervised and inductive embedding model for micro-level churn prediction and constructed a relationship graph for macro-level churn ranking.
result Accurate micro-level churn prediction and macro-level churn ranking were achieved using novel techniques.
Survival ensembles improve churn prediction in mobile social games.
problem Predicting user churn in mobile social games to retain players.
method Survival analysis and ensemble learning techniques.
result Survival ensembles provide more accurate and robust churn predictions.
Study analyzes player churn and purchasing behavior in games.
problem Retaining players and predicting churn in video games.
method Deep behavioral analysis and ensemble learning models.
result Discarding certain churners improves prediction models.
Deep learning models predict customer churn with high accuracy.
problem Predicting customer churn using temporal behavioral data.
method Used deep convolutional neural networks and autoencoders on labeled customer data.
result Deep learning models achieved an AUC of 0.743 on churn prediction.
XGBoost predicts customer churn from time-series data.
problem Predicting customer churn from time-series data.
method Extreme gradient boosting with temporal feature engineering.
result XGBoost model achieved first place in WSDM Cup 2018 Churn Challenge.
ChOracle predicts user return times to improve churn prediction.
problem Churn prediction in online services.
method Combining Temporal Point Processes and Recurrent Neural Networks with latent variables.
result Superior performance on various real-world datasets.
Deep learning predicts customer churn from abstract features.
problem Predicting customer churn in subscription-based companies.
method Unsupervised feature learning using deep neural networks on abstract feature vectors.
result Deep learning achieves excellent churn prediction performance across different companies.
New model analyzes customer churn with tensor completion and binary data.
problem Analyzing the impact of interventions on customer churn.
method Tensorized latent factor block hazard model with 1-bit tensor completion.
result Effective categorization of interventions by similar impacts.
Study on rapid policy changes in reinforcement learning.
problem Rapid change of greedy policy in reinforcement learning.
method Empirical study and ablation analysis.
result Policy churn is a beneficial form of implicit exploration.
Model predicts telecom customer churn with high accuracy.
problem Predicting customers at risk of leaving telecom companies.
method Machine learning and social network analysis on big data platform.
result Model achieved 93.3% AUC, significantly improving churn prediction.
ProfitTree uses evolutionary algorithms to build churn prediction models that maximize profit.
problem Predicting customer churn with profit maximization in mind.
method Integrates EMPC metric into profit-driven decision trees using evolutionary algorithms.
result Significant profit improvements over classic accuracy-driven models.
Deep learning predicts customer churn in retail.
problem Accurately predicting which customers are likely to stop purchasing.
method Survival model parameters learned by recurrent neural networks.
result Individual level survival models for purchasing behavior.
New method predicts customer churn using mixed-penalty logistic regression.
problem Predicting customer churn in CRM systems.
method Mixed-penalty logistic regression for big data analysis.
result Proposed method enhances logistic regression for better predictive analytics.
Paper presents a churn prediction model for mobile games.
problem Churn prediction for mobile games.
method Semi-supervised and inductive embedding model using deep neural networks.
result Model outperforms existing methods in churn prediction.
Study compares classification techniques to predict customer churn in banking.
problem Predicting customer churn in banking industry.
method Comparison of six supervised classification techniques (ANN and random forest) on 10000 European bank customers.
result ANN structure with five nodes in a single hidden layer is the best performing classifier.
Bayesian model reduces TV watching data to 11 parameters for churn prediction.
problem Predicting customer churn in telecommunications with high-dimensional data.
method Bayesian hierarchical joint model for time-to-event and count data.
result Model reduces data from thousands to 11 customer-level parameter estimates.
Proposes a deep learning churn prediction system for telecom using TL and meta-classification.
problem Churn prediction challenges in telecom due to large data, high dimensions, and imbalanced data.
method Transfer Learning (TL) and Ensemble-based Meta-Classification. Two stages: TL on Deep CNNs, then GP-AdaBoost meta-classifier.
result TL-DeepE system achieved 75.4% and 68.2% prediction accuracy on Orange and Cell2cell datasets, respectively.
This paper compares neural networks with traditional methods for churn prediction using financial data.
problem Churn prediction with sequential data and deep neural networks.
method Assesses LSTM neural networks combined with RFM variables against logistic regression models.
result LSTM neural networks outperform traditional methods in churn prediction.
Method detects critical events in complex systems by learning latent causal structure.
problem Detecting onset of epileptic seizures, customer churn, or pandemics from hidden causal interactions.
method A machine learning method that learns an optimal feature representation from powers of the empirical covariance or precision matrix.
result Proves structural consistency and demonstrates competitive results in seizure and churn prediction.
Mobile game developers use a scalable churn prediction model to predict player abandonment.
problem Predicting player abandonment in mobile games.
method Survival ensembles approach for accurate churn prediction.
result Accurate predictions on player abandonment and playtime.
Model predicts customer churn in financial institutions using neural networks.
problem Manual feature engineering in customer churn prediction.
method Developed a Multi-layer Perceptron model using Artificial Neural Network architecture.
result Artificial Neural Network model achieved comparable performance to Neuro Solution Infinity software.
Predicts employee turnover and designs retention policies.
problem Employee turnover prediction and retention policy design.
method Classical machine learning techniques for prediction, policy design based on model outputs.
result Developed model outputs for designing effective retention policies.
Unified metric SCV models subscription business revenue.
problem Analyzing revenue contribution of subscription businesses.
method Bayesian probabilistic model with exponential decay for churn.
result Exact and approximate closed-form solutions for revenue.
Proposes a greedy algorithm for telecom offers to retain subscribers.
problem Maximizing revenue while preventing churn in telecom subscribers.
method Combinatorial algorithm for offer optimization under heterogeneous incentives.
result Efficient and accurate solution for large subscriber bases.
Study uses time series analysis to predict player churn and conversion in games.
problem Predicting player churn and conversion in free-to-play games.
method State Space time series approach with Autoregressive Integrated Moving Average and Unobserved Components models.
result Unobserved Components approach fails to detect marketing campaigns and predicts abandonment poorly.
Quantum machine learning boosts financial forecasting accuracy.
problem Churn prediction and credit risk assessment in finance.
method Used quantum and classical Determinantal Point Processes for churn prediction, and quantum neural networks for credit risk assessment.
result Significant improvement in precision for churn prediction (6% increase). Quantum models match classical performance with fewer parameters.
New method solves non-convex constrained optimization problems with non-differentiable constraints.
problem Training non-convex models with non-differentiable constraints.
method Proxy-Lagrangian formulation and semi-coarse correlated equilibrium.
result Solves non-convex constrained optimization problems with theoretical guarantees.
ALICE combines feature selection and inter-rater agreeability for ML model insights.
problem Improving interpretability of black box machine learning models.
method Integrates feature selection and inter-rater agreeability into a user-friendly Python library.
result Initial experiments on customer churn modeling show promising insights.
Model predicts user engagement and survival time in games.
problem Building data-efficient engagement models for diverse games.
method Data-driven approach using minimal metrics.
result Joint estimates of survival time and churn probability.
Winning solution for predicting player churn in a video game.
problem Predicting when players will stop playing a game.
method Long Short-Term Memory (LSTM) approach and conditional inference survival ensemble model.
result Models accurately predicted player churn and were robust to changing business models.
Study analyzes MMOG Glitch's auction house data over 14 months.
problem Understanding player behavior in MMOGs.
method Analysis of auction house data from MMOG Glitch, visualization of player migration.
result Template for analyzing player progression and churn in MMOGs.
PolicySynth improves synthetic data alignment with real data for better campaign decisions.
problem Synthetic data used in decision support systems often leads to incorrect decisions.
method PolicySynth framework that conditions synthetic data on churn scorer to align with real data decisions.
result PolicySynth achieves high strategy simulation fidelity (0.923-0.960) on churn and acquisition datasets.
Survival analysis improves playtime measurement in games.
problem Measuring and improving player retention in games.
method Survival analysis for playtime data without covariates.
result Survival and hazard estimates provide a visual and analytic interpretation of playtime.
This study evaluates methods for clustering mobile game player behavior data.
problem Clustering time series data of player behavior in free-to-play games.
method Evaluation of various similarity measures and dimensionality reduction techniques.
result Identification and validation of temporal patterns of player behavior.
SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.
problem AI exclusion of SMEs due to data scale mismatch.
method Bayesian transfer learning with hierarchical pooling and conformal prediction.
result 96.7% AUC on 100 obs SMEs, 24.2 point improvement over logistic regression.
Simplifies complex regression model interpretation.
problem Interpreting complex machine learning models.
method A method for grounding interpretations in actual learning examples.
result Validated on academic and industrial regression tasks.
The paper targets optimal interventions for long-term outcomes using imputed data and policy learning.
problem Maximizing long-term outcomes observed only in the future.
method Imputing missing long-term outcomes and using a doubly-robust approach for policy evaluation and optimization.
result The approach outperforms simple short-term proxies and achieves significant revenue impact over three years.
Unified approach adjusts classifiers to meet system-level constraints.
problem Multi-class classification under system-level constraints.
method Post-processing approach using linearly constrained stochastic program and entropic regularization.
result Finite-sample guarantees for risk and constraint satisfaction.
Deep learning improves survival analysis for customer behavior prediction.
problem Predicting customer behavior such as buying, churning, or defaulting.
method Multi-Task Logistic Regression (MTLR) combined with a deep learning architecture.
result The deep learning method outperforms MTLR and CoxPH models in predicting nonlinear dependencies.
Firms miscount their customers who stop buying without saying goodbye.
problem Counting non-contractual customers accurately.
method Estimating repeat purchase probabilities and extrapolating to infinite time.
result The count of alive customers is only partially identified, with a wide range of estimates.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
problem Manual sales account prioritization is inefficient and under-invested.
method Developed an AI-based Account Prioritizer using machine learning and explanation algorithms.
result Generated a +8.08% increase in renewal bookings.
SurvMixClust clusters survival data and predicts individual survival curves.
problem Integrating clustering into survival analysis for precision medicine.
method SurvMixClust learns latent representations for clustering and predicts survival functions using a mixture of non-parametric experts.
result SurvMixClust creates balanced clusters with distinct survival curves, outperforming clustering baselines and competing with non-clustering models in predictive accuracy.
A Qini-based uplift model improves retention marketing campaign performance.
problem Isolating the marketing effect of a campaign and identifying responsive customers.
method Qini-based uplift regression model using logistic regression.
result Qini-optimized uplift models improve performance and provide interpretable models.
Paper introduces a dynamic reference frame strategy to predict events with a buffer time.
problem Lack of time buffer for predictions to enable timely action.
method Introduces a new concept of dynamic reference frame creation.
result Enables organizations to act on predictions with a buffer time.
Improves classifier fairness and other constraints by optimizing on two datasets.
problem Training classifiers to satisfy fairness and other data-dependent constraints.
method Two-player game framework, optimizing on two independent datasets.
result Significant improvement in constraint satisfaction at evaluation time.
A new game-theoretic approach optimizes complex rate metrics.
problem Optimizing non-decomposable performance metrics and rate constraints.
method Extending two-player game approaches to a three-player game, seeking equilibrium.
result Generalizes and improves upon existing algorithms for constrained optimization.
We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse …