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.
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.
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these pre…
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.
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.
Customer temporal behavioral data was represented as images in order to perform churn prediction by leveraging deep learning architectures prominent in image classification. Supervised learning was performed on labeled data of over 6 million customers using deep convolutional neural networks, which achieved an AUC of 0…
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.
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.
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.
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.
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.
We have created a framework for analyzing subscription based businesses in terms of a unified metric which we call SCV (single customer value). The major advance in this paper is to model customer churn as an exponential decay variable, which directly follows from experimental data relating to subscription based busine…
Accurately predicting customer churn using large scale time-series data is a common problem facing many business domains. The creation of model features across various time windows for training and testing can be particularly challenging due to temporal issues common to time-series data. In this paper, we will explore …
As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Usin…
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are bu…
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.
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.
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.
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.
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.
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.
Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will benefit many st…
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.
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/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI. In this paper, we introduce a new method to calculate survival functions using the Multi-Task Logistic Regression (MTL…
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.
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.
The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 m…
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.
The emergence of mobile games has caused a paradigm shift in the video-game industry. Game developers now have at their disposal a plethora of information on their players, and thus can take advantage of reliable models that can accurately predict player behavior and scale to huge datasets. Churn prediction, a challeng…
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention dis…
Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suite…
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study the problem of training non-convex models subject to these rate constraints (or any non-convex and non-differentiable constraints). In the non…
Interpretation of a machine learning induced models is critical for feature engineering, debugging, and, arguably, compliance. Yet, best of breed machine learning models tend to be very complex. This paper presents a method for model interpretation which has the main benefit that the simple interpretations it provides …
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.
The study models life insurance policy cancellations using statistical and machine learning methods.
problem Forecasting individual contract cancellations in life insurance policies.
method Statistical and machine learning methods applied to data from private pension and endowment policies.
result Identified key features affecting contract cancellations.
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.
Study user engagement in mobile health apps for health workers in resource-poor settings.
problem Detect churn and tailor content for health workers in mobile health apps.
method Probabilistic and survival analysis of behavioral logs.
result Personalized measures of meaningful engagement can enhance health outcomes.
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.
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.
Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the generalization performance for such constrained optimization problems, in terms of how well the constraints are satisfied at evaluation time, gi…
Customer Satisfaction is the most important factors in the industry irrespective of domain. Key Driver Analysis is a common practice in data science to help the business to evaluate the same. Understanding key features, which influence the outcome or dependent feature, is highly important in statistical model building.…
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.
A new geometric metric identifies true data changes from parametrization artifacts in high-dimensional representations.
problem Quantifying representation drift in high-dimensional data using Euclidean or cosine distances can misattribute changes due to arbitrary parametrizations.
method Introducing the Fubini Study metric to identify representations that differ only by gauge transformations.
result The Fubini Study metric isolates intrinsic evolution by remaining invariant under gauge-induced fluctuations, providing a diagnostic for meaningful structural changes.