The study builds a customer selection model grouping and ranking customers based on multiple dimensions.
problem Traditional grouping methods based on assets are insufficient and ineffective.
method K-means unsupervised learning for grouping, weighted customer value calculation for ranking.
result Differentiates and ranks customers based on their values, not just assets.
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.
New method for selecting clusters in residential electricity data.
problem Selecting useful clusters in electricity consumption data.
method Formalizing expert knowledge as external validation measures.
result Successfully reconstructed customer archetypes.
COTA improves customer support speed and accuracy with machine learning.
problem Improving speed and reliability of customer support.
method Combining feature engineering and deep learning for automated ticket classification and answer selection.
result COTA v2 outperforms COTA v1 in reducing issue resolution time by 10%.
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.
We study the dynamics of co-evolution of producers and customers described by bit-strings representing individual traits. Individual ''size-like'' properties are controlled by binary encounters which outcome depends upon a recognition process. Depending upon the parameter set-up, mutual selection of producers and custo…
Ebay uses forecasting and simulation to decide when to disable a vendor.
problem Determining the optimal time to disable a vendor to avoid customer loss.
method Data-driven approach involving multiplicative seasonality model, Monte Carlo simulation, and linear model.
result Identifies the best time to disable a vendor to minimize customer loss.
Supervised randomization makes randomized experiments more cost-effective for uplift modeling.
problem Costly randomized experiments for uplift modeling.
method Integrates existing scoring models into randomized trials to target relevant customers while correcting for selection bias.
result Cost-efficient data collection under supervised randomization with competitive uplift model performance.
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.
System optimizes product images for e-commerce, enhancing customer engagement.
problem Optimizing product images for e-commerce to improve customer engagement.
method Machine learning, deep learning, and computer vision techniques applied to large e-commerce catalogs.
result System produces superior image sets tailored to customer preferences.
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.
One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borr…
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.
Adaptive framework improves airline pricing models' performance.
problem No single model dominates other models for all customer requests.
method Adaptive meta-decision framework using Thompson sampling.
result Improves expected revenue per offer by 43% and conversion score by 58%.
Outfittery uses machine learning to help stylists choose appropriate fashion items.
problem Selecting appropriate fashion items and ensuring relevance to customers.
method Combining machine learning with human expertise to recommend items by style fit and relevance.
result The method successfully recommends fashion items by style fit and relevance.
The paper provides an algorithm for the risk estimation when a company selects an outsourcing service provider for innovation product. Calculations are based on expert surveys conducted among customers and among providers of outsourcing. The surveys assessed the degree of materiality of species at risk.
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.
Paper solves annuity pricing puzzle by minimizing risk.
problem Inconsistency between theory and empirical observations in annuities pricing.
method Risk minimization approach to price annuities for both producer and customer.
result Different pricing solutions for annuity price and rate of payments.
Tests for Esophageal cancer can be expensive, uncomfortable and can have side effects. For many patients, we can predict non-existence of disease with 100% certainty, just using demographics, lifestyle, and medical history information. Our objective is to devise a general methodology for customizing tests using user pr…
Machine learning optimizes box selection for efficient warehouse operations.
problem Optimizing the selection of shipping boxes for thousands of products.
method Formulated as a clustering problem, parameters estimated via analytics.
result Improves box utilization rate by over 10%.
Improved stock price prediction using LSTM with customized loss function and analyst calls.
problem Forecasting stock prices using LSTM neural networks.
method Customized LSTM model with improved loss function, analyst calls integration, and attention units.
result Improved performance of LSTM model over ARIMA for stock price prediction.
This study integrates cost-sensitive and causal classification methods.
problem Improving classification model performance in business decision-making.
method A unifying evaluation framework for cost-sensitive and causal classification.
result Conventional classification is a specific case of causal classification.
System optimizes retrieval for personal assistants using reinforcement learning.
problem Needing a neural retrieval-based Q&A system for user memory.
method Direct optimization of F1-score using reinforcement learning.
result Improved retrieval performance on test sets.
DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
Reduce survey questions to scale market research without annoying customers.
problem Performing market research by surveying customers with many questions is inefficient and annoying.
method Used Bayesian networks to model and reduce the number of questions asked to customers.
result Demonstrated the effectiveness of the approach using an example of broadband customer segmentation.
Algorithm reduces audit costs by identifying best service configurations from biased textual evidence.
problem Designing service systems from textual evidence requires accurate selection despite biased automated scoring.
method Developed PP-LUCB algorithm combining LLM scores and selective audits to minimize costs.
result Correctly identified the best model in 40/40 trials with 90% cost reduction.
Financial institutions use LSTM models to predict customer goals.
problem Predicting customer goals and actions in financial services.
method Used LSTM models with state-space graph embeddings on historical customer traces.
result Demonstrated the effectiveness of LSTM models in predicting customer goals and actions.
Customer momentum is a positive relationship between a firm's returns and past returns of its customers.
problem Understanding the relationship between a firm's returns and its customers' past returns.
method Examined customer momentum using a long-short equally-weighted decile portfolio and Fama-French factor models.
result Customer momentum generates significant monthly returns and is statistically significant.
Proposes robust assortment optimization from observational data.
problem Real-world scenarios often violate assumptions of stable customer preferences and correct choice models.
method Develops a robust framework that accounts for potential distributional shifts in customer choice behavior.
result Uncovered the notion of ``robust item-wise coverage'' as the minimal data requirement for sample-efficient robust assortment learning.
Method generates realistic customer transaction sequences for retail analysis.
problem Modeling customer behavior and purchasing patterns in retail databases.
method Customer embedding using RNN and GAN for generating plausible baskets.
result Generated baskets resemble real transactions and replicate sequential patterns.
A method selects key genes from tumor transcriptomics data using kernel methods and improves classification performance.
problem Feature selection for tumor classification using gene expression data.
method Multiple Kernel Learning with latent regularization and non-linear dimensionality reduction.
result Improved tumor classification performance on unseen test samples.
Paper proposes a new topology for AML analysis using Poincaré embeddings.
problem Complex money laundering schemes and regulatory constraints hinder AML analysis and information sharing.
method Proposes a new topology for AML analysis using Poincaré embeddings.
result Demonstrates improved AML analysis and information sharing through Poincaré embeddings.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
problem Cold start problem and under-representation of new or under-impressed products in e-commerce search results.
method Aggregates customer engagements within a day for the same query as input training data for machine learning models.
result Training models on aggregated data leads to better ranking of new and under-impressed products.
We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a 'selection function' to reveal the relevant latent variables, and use this …
Study uses Open Banking data to estimate customer value, showing potential 21% increase.
problem Limited CLV estimation using single-entity data.
method Introduces PCLV framework using Open Banking data for comprehensive customer value estimation.
result Open Banking data can estimate PCLV per competitor, showing a 21.06% increase over Actual CLV.
Predicts customer call intent for auto dealerships using CNN.
problem Understanding customer intent from phone calls for better service.
method Developed a CNN-based supervised learning model for multi-class classification.
result CNN model performs well on customer call intent classification.
Model predicts customer behavior from incomplete data.
problem Predict customer behavior from missing demographic data.
method Structured regression on deficient data in evolving networks with neural features embedding.
result 4% to 130% improvement in accuracy over alternatives.
FedACS uses attention to select clients with similar data for federated learning.
problem Non-IID data and data scarcity in federated learning.
method FedACS integrates an attention mechanism to prioritize clients with similar data distributions.
result FedACS improves federated learning performance by addressing non-IID data and data scarcity.
VAE learns latent representations for bank customers' creditworthiness.
problem Improving marketing, CRM, and credit risk assessment in retail banks.
method Adopted Variational Autoencoder (VAE) to learn latent representations.
result VAE latent representations capture customers' creditworthiness and generalize to new data.
The paper uses RFM and clustering to segment bank customers.
problem Challenges in customer retention and profitable segmentation in banking.
method RFM technique and clustering algorithms applied to real data.
result Successful customer segmentation improves conversion rates.
The study improves CLV predictions in retail banking with machine learning.
problem Estimating customer lifetime value in retail banking is challenging.
method Developed a novel framework for CLV predictions over arbitrary time horizons.
result 43% improvement in out-of-time CLV prediction error.
This study enhances sales forecasts by integrating market indicators into forecasting models.
problem Traditional forecasting models rely solely on historical demand data.
method Automated integration of macroeconomic time series data (GDP growth) into forecasting models using feature selection methods.
result Feature selection methods, especially Forward Feature Selection, significantly improve forecasting accuracy.
Study clusters bank customers using LSTM and DTW.
problem Efficiently segmenting bank customers for targeted offers.
method Encoder-decoder LSTM network and Dynamic Time Warping (DTW).
result Hybrid method yields more accurate clusters.
Silent abandonment reduces contact center efficiency by 5%-15%.
problem Measuring customer abandonment and patience in text-based contact centers is challenging due to uncertainty.
method Developed methodologies to identify silent-abandonment customers and estimate customer patience using text analysis and queueing models.
result Silent abandonment accounts for 30%-67% of customer abandonments and reduces system efficiency by 5%-15%.
DALC customizes LSTM models for detectors in large-scale traffic networks.
problem Fine-grained traffic prediction for large-scale transportation networks.
method Formulated as a finite Markov decision process, introduced ALC algorithm for automatic customization, and developed DALC for distributed customization.
result DALC provides higher prediction accuracy than Apache Spark MLlib approaches.
DoGS improves Gibbs sampling quality with variable selection orders and bounds.
problem Improving Gibbs sampler scan quality.
method Using Dobrushin influence to optimize Gibbs sampling.
result DoGS delivers higher-quality inferences with smaller sampling budgets.
New algorithm balances limited resources for unknown customer types over time.
problem Allocating limited resources to diverse customers with uncertain demands.
method Synthesizes inventory balancing and online learning.
result Performance guarantee is tight, showing both competitive ratio and regret losses are relevant.
Adaptive sampling method optimizes DNN compression for resource-constrained platforms.
problem Efficiently compressing DNNs for resource-constrained platforms with high accuracy.
method Adaptive sampling using genetic algorithm-inspired operations to optimize hyperparameters.
result Adaptive sampling outperforms rule-based and reinforcement learning methods in compression rate and accuracy.