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.
We present a Bayesian framework for estimating the customer lifetime value (CLV) and the customer equity (CE) based on the purchasing behavior deducible from the market surveys on customer purchasing behavior. The proposed framework systematically addresses the challenges faced when the future value of customers is est…
Proposes a new model to predict irrational customer behavior.
problem Irrational customer behavior in decision-making.
method Nonparametric choice model using decision trees and probability distributions.
result Decision forest model accurately predicts non-rational customer behavior.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
Binary choice forests model customer choices in retailing.
problem Estimating DCMs using transaction data is challenging and prone to misspecification.
method Random forest of binary decision trees to represent DCMs, interpretable and consistent predictions.
result Random forest can predict choice probabilities and assortments unseen in training data.
Deep learning model predicts online fraud using customer behavior data.
problem Predicting online financial fraud from customer behavior data.
method Recurrent Neural Network (RNN) integrated with Markov Transition Field (MTF).
result The proposed model significantly improves fraud prediction compared to traditional methods.
Data-driven method clusters and analyzes heat load patterns in district heating networks.
problem Lack of knowledge about customers' heat load behaviors in district heating networks.
method Data-driven approach that clusters customer profiles and detects unusual patterns.
result High potential for deploying the method to analyze customers' heat-use habits in practice.
Study optimizes crowdfunding platform offerings based on customer behavior.
problem Maximizing crowdfunding platform revenue through optimal product assortment.
method Multinomial logit model and machine learning methods (multivariate regression, classification) for revenue prediction.
result Optimal assortments can significantly increase platform revenue.
This study benchmarks AI agents for personalized retail promotions using simulations.
problem Optimizing coupon targeting for sparse customer purchase events.
method Comprehensive simulations of customer shopping behaviors; training RL agents on batch data.
result Contextual bandit and deep RL methods outperform static policies in sparse reward environments.
We consider a planning problem where the dynamics and rewards of the environment depend on a hidden static parameter referred to as the context. The objective is to learn a strategy that maximizes the accumulated reward across all contexts. The new model, called Contextual Markov Decision Process (CMDP), can model a cu…
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…
Study predicts customer data sharing in Open Banking and explains key factors.
problem Predicting and explaining customer data sharing in Open Banking environments.
method Hybrid data balancing strategy with ADASYN and NEARMISS, XGBoost models, SHAP, CART.
result 91.39% accuracy for inflow and 91.53% for outflow predictions, revealing influential features.
Predicts gender and age from mobile phone data for marketing.
problem Enhance marketing offers by predicting customer demographics.
method Machine learning algorithms applied to CDRs, CRM, and billing info.
result 85.6% accuracy in gender prediction, 65.5% in age prediction.
Study examines retailer responses to stockouts in wholesale environments.
problem Effect of stockouts on future demand in wholesale settings.
method Statistical analysis of historical customer order and delivery data over 4 years.
result Stockouts negatively impact future demand frequency but not value, with effects being short-term.
Personalized Influence Estimation helps understand key factors influencing individual observations.
problem Understanding key factors influencing individual observations in various business problems.
method Joint behavior of feature dimensions and relative feature importance.
result Encouraging results justify key reasons for churn in majority of the sample.
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.
It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period. Knowing as much as possible about each customer is crucial for such predictions. However, their demographic data, preferences, and other information that might be useful for building loyalty programs …
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 model for learning customer preferences in a dynamic product launch setting.
problem Learning customer preferences in a setting with new product launches.
method Proposes a sequential multinomial logit (SMNL) model and a learning algorithm with a regret bound.
result Demonstrates the tier structure can mitigate risks associated with learning new products.
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.
Study examines pricing strategies in competitive supply chains with discrete prices.
problem Inaccurate assumptions in traditional SC models for pricing decisions.
method Examines a SC model with one supplier and two manufacturers, considering customer demand segmentation and discrete price setting.
result Nash equilibria among manufacturers are not unique, and low denomination factors can lead to instability.
This work aims to separate buying preferences from merchandise commercials in fashion e-retail.
problem Difficult to infer customer preference from sales data due to implicit signals.
method Extends earlier work on explicit signals to implicit signals from user behavior.
result Derives a metric to separate buying preferences from merchandise commercials.
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%.
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.
ML system reduces overdraft fees for Mint users.
problem Overdraft fees burden Americans, leading to financial hardship.
method ML-driven overdraft early warning system (ODEWS).
result Saved $3 million in overdraft fees for Mint customers.
Predicting delayed outcomes in recommender systems using proxies.
problem Predicting delayed outcomes in recommender systems.
method Formalized as adversarial, delayed online learning problem; proposed Factored Forecaster (FF) and Residual Factored Forecaster (RFF) neural network architectures.
result Residual Factored Forecaster (RFF) outperforms direct forecaster and Factored Forecaster (FF) in predicting human behavior.
Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a …
Paper presents a novel time series clustering algorithm for financial inclusion.
problem Difficulty in understanding consumer financial behavior without restrictive credit scoring.
method Developed a novel time series clustering algorithm.
result Allows institutions to offer unique financial products based on customer needs.
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…
Study predicts purchasing decisions of online food delivery customers.
problem Understanding and predicting consumer purchasing decisions in online food delivery.
method Used machine learning techniques including CART, C4.5, random forest, and rule-based classifiers to predict purchasing decisions.
result C4.5 decision tree model outperformed others with 91.67% accuracy.
Study tackles ranking fraud in online platforms by learning robust rankings.
problem Fraudulent fake users manipulate product rankings.
method Developed algorithms for robust ranking in two informational environments.
result Our algorithms converge to optimal rankings, robust to fake users.
Model assesses credit risk using behavioral data from Experian and Bank of Italy.
problem Improving credit risk assessment in financial institutions.
method Statistical and machine learning techniques applied to behavioral data from Experian and Bank of Italy.
result Demonstrates transferability of the model from private to central data.
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors) is sparse. A company that can only observe the interactions between its own customers will generally not be able to accurately estimate its …
This study designs a financial risk control platform using big data and machine learning.
problem Traditional risk management models are inadequate for modern financial complexities.
method Big data mining, real-time streaming data processing, statistical analysis, and precise customer behavior mining.
result The platform effectively identifies and responds to potential risks in real-time.
Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.
problem Understanding how discounts influence train ticket buying and rescheduling behavior.
method Machine learning techniques, including causal machine learning, to analyze survey data.
result Increasing a discount rate by 1% increases the rescheduled trip share by 0.16% among always buyers.
Model predicts Chinese stock market liquidity and customer order behavior.
problem Understanding market liquidity and customer order behavior in the Chinese stock market.
method Dual state-space model using Fourier transform to connect volume-at-price buckets to correlations.
result Customer orders are correlated with market sentiment and stock returns, not with bond returns.
Study uses Bayesian regression to analyze consumer behavior changes in restaurants post-COVID-19.
problem Impact of COVID-19 on consumer behavior in the restaurant industry.
method Bayesian regression with Hamiltonian Monte Carlo.
result Estimates change in consumer behavior before and after the pandemic.
This paper addresses dynamic price discrimination with fairness constraints.
problem Dynamic price discrimination with fairness constraints in online retailing.
method Nonparametric demand models, dynamic pricing policy, regret minimization.
result Optimal dynamic pricing policy with ildeO(T4/5) regret for price fairness. The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retai…
A new algorithm adapts to changing user behaviors in finance.
problem Adapting to changing user behaviors in financial recommendations.
method History-Augmented Collaborative Filtering using a custom neural network.
result The algorithm provides dynamic financial recommendations.
This research extends the Pareto/NBD model using neural networks for better out-of-sample predictions.
problem The limitations of the Pareto/NBD model in predicting out-of-sample data.
method A neural network-based extension of the Pareto/NBD model.
result The proposed method shows extraordinary predictability on repeat purchases at individual and aggregate levels.
Valid certifies LLMs' domain adherence, bounding out-of-domain behavior.
problem Adversarial susceptibility of LLMs to generate out-of-domain outputs.
method VALID approach providing adversarial bounds as a certificate.
result Validates LLMs' domain adherence with meaningful certificates.
Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
problem Survival strategies of unorganized retailers challenged by organized and e-retailers.
method Field investigation, conjoint analysis, design of experiments, agent-based simulation.
result Unorganized retailers' performance influenced by their own attribute levels and price discounts.
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.
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.
This study compares logistic regression and XGBoost for predicting credit risk.
problem Predicting credit risk in financial services.
method Advanced machine learning techniques (logistic regression and XGBoost) with data preprocessing.
result XGBoost outperforms logistic regression in predicting credit risk.