Machine Learning for Predicting and Optimizing User Behavior in Digital Marketing

Machine Learning Based Predictive Market for Sales Forecasting

Sales forecasting remains a cornerstone of effective retail management, guiding supply chain optimization, inventory planning, and revenue strategies. Traditional forecasting models often fall short in capturing the complexity of consumer behavior, seasonal fluctuations, and branch-level variations, resulting in ineffi...

Predicting Consumer Preferences for Online Versus Offline Shopping Channels Based on Machine Learning Models

With the advent of the omnichannel retail era, identifying consumer shopping channel preferences has become critical for retailers to achieve precision marketing and enhance market competitiveness. Based on a consumer dataset from the Kaggle platform, which contains 11,420 valid samples and 18 predictive features, this...

The Impact of Ensemble Stacking with Machine Learning and Statistical Models on Improving Sales Forecasting Accuracy

An ensemble stacking framework for machine learning and statistical models to improve forecasts in dynamic demand and shows that stacking can capture heterogeneous demand dynamics better than single models and, hence, provide a more robust structure.

Predicting User Purchase Behavior via Multidimensional Feature Engineering and Explainable Machine Learning

Accurately predicting user purchase intentions is a critical challenge in e-commerce operations. Existing methods often rely on simple statistical features, lacking systematic theoretical guidance and model interpretability. To address these limitations, this study proposes a predictive framework that integrates the e-...

Measurement and Optimization of TikTok Ads Effectiveness Using Bayesian Marketing Mix Modeling and Optimization

A Machine Learning Framework for S&P 500 Directional Classification and Kelly-Optimal Position Sizing

The findings suggest that while supervised learning offers a modest predictive signal, the Kelly Criterion is effective primarily as a risk-management tool rather than a return-enhancing strategy.