arXiv research

DCE learns customer embeddings from digital activity and financial context.

Paper proposes a new topology for AML analysis using Poincaré embeddings.

Reduce survey questions to scale market research without annoying customers.

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…

Telecommunications operators (telcos) traditional sources of income, voice and SMS, are shrinking due to customers using over-the-top (OTT) applications such as WhatsApp or Viber. In this challenging environment it is critical for telcos to maintain or grow their market share, by providing users with as good an experie…

The study explores machine learning for predicting customer propensity-to-pay uncertainty.

Dynamic pricing model for airline ancillaries improves revenue and conversion.

Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…

A new pricing strategy learns customer valuations without noise distribution knowledge.

FinBloom enhances LLMs for real-time financial queries.

Outfittery uses machine learning to help stylists choose appropriate fashion items.

Personalized pricing analytics is becoming an essential tool in retailing. Upon observing the personalized information of each arriving customer, the firm needs to set a price accordingly based on the covariates such as income, education background, past purchasing history to extract more revenue. For new entrants of t…

MTDS improves sequence generation adaptability via latent code control.

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 …

The paper proposes a new method for product recommendation that considers revenue contributions and user similarity.

Study online pricing with contextual elasticity and heteroscedastic valuation.

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…

This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action orga…

Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.

A new method for uplift modeling using learning-to-rank techniques.

LLMs learn probability density functions in-context, showing distinct learning trajectories.

Financial institutions use LSTM models to predict customer goals.

In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inference. The feedback from a customer in any of these situations is only 'bandit feedback' - that is, a partial feedback based on whether we chose…

Customer momentum is a positive relationship between a firm's returns and past returns of its customers.

Method generates realistic customer transaction sequences for retail analysis.

PROPS personalizes complex models for sequential data.

Automates molecule design with simpler SMILES generation and reinforcement learning.

Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.

Study uses Open Banking data to estimate customer value, showing potential 21% increase.

Predicts customer call intent for auto dealerships using CNN.

This research tackles unsupervised topic extraction in noisy social media data.

VAE learns latent representations for bank customers' creditworthiness.

The paper uses RFM and clustering to segment bank customers.

The study improves CLV predictions in retail banking with machine learning.

Paper tackles AI risks by customizing metrics and models.

Adapts large transformer model for search query intent understanding.

Study clusters bank customers using LSTM and DTW.

ADMM solves constrained CASH problems by breaking them into smaller, manageable pieces.

Statistical test verifies long-term rating system calibration with overlapping time windows.

Study compares classification techniques to predict customer churn in banking.

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 …

KodeXv0.1 improves financial question answering over GPT-4.

Silent abandonment reduces contact center efficiency by 5%-15%.

DALC customizes LSTM models for detectors in large-scale traffic networks.

Insurance firms use RL to optimize customer offers for desired target portfolios.

Deep learning predicts customer churn in retail.

Proposes a variational autoencoder for long-term customer revenue forecasting.

Customized-GNN generates model-specific for each graph.